CN110823347B - Granary detection method and system based on bottom-side surface two-circle standard deviation polynomial model - Google Patents
Granary detection method and system based on bottom-side surface two-circle standard deviation polynomial model Download PDFInfo
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Abstract
The invention relates to a granary detection method and system based on a bottom surface and side surface two-circle standard deviation polynomial model. The core technology comprises the following steps: the granary grain storage quantity detection system comprises three parts, namely a granary bottom surface and side surface two-circle pressure sensor arrangement model, a model item structure based on the bottom surface and side surface two-circle pressure sensors, and a granary grain storage quantity detection model based on the bottom surface and side surface two-circle pressure sensors. The invention has the characteristics of high detection precision, suitability for various granary structure types, convenience for remote online granary quantity detection and the like.
Description
Technical Field
The invention belongs to the technical field of granary detection, and particularly relates to a granary detection method and system based on a bottom-side surface two-circle standard deviation polynomial model.
Background
The grain safety includes quantity safety and raw grain safety. The online grain quantity detection technology and the system research application are important guarantee technologies for national grain quantity safety, and the development of the research and application on the aspect of national grain safety has important significance and can generate huge social and economic benefits.
Due to the important position of the grain in national safety, the grain quantity online detection is required to be accurate, rapid and reliable. Meanwhile, the grain quantity is huge, the price is low, and the grain quantity online detection equipment is required to be low in cost, simple and convenient. Therefore, the high precision of detection and the low cost of the detection system are key problems which need to be solved for the development of the online grain quantity detection system.
The patent document of the invention in China with the publication number of CN105403294B discloses a grain storage weight detection method and a device thereof based on polynomial expansion. The invention relates to a grain storage weight detection method and device based on polynomial expansion. According to a theoretical detection model of the grain storage weight of the granary, a granary grain storage weight detection model based on polynomial expansion is established, and model parameters are optimized by a polynomial maximum order optimization method based on regression and polynomial maximum order selection sample sets.
The scheme improves the detection accuracy of the stored grain weight (namely the storage quantity), and also has stronger adaptability and robustness. However, due to the limitations of the storage properties of the grain and the accuracy of the sensor, the detection accuracy of the amount of stored grain is yet to be further improved.
Disclosure of Invention
The invention aims to provide a granary detection method and a granary detection system based on a bottom-side surface two-circle standard deviation polynomial model, which are used for solving the problem of how to further improve the detection accuracy on the basis of the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the invention provides a granary stored grain detection method based on two circles of pressure sensors on the bottom surface and the side surface, which comprises the following steps:
1) detecting output values of two circles of pressure sensors of a bottom circle pressure sensor arranged on the bottom surface of the granary and a side circle pressure sensor arranged on the side surface of the granary respectively;
2) average value of output values using two-turn pressure sensor
Estimating the pressure mean at the bottom of a grain heap
Construction of
And
the relationship of (1);
and
the relationship of (1) is:
wherein,
is composed of
Estimation of (b)B(m) is
Coefficient of the estimated term, NBIs composed of
Estimate polynomial coefficients of terms, m 0B；
3) Average value of output values using two-turn pressure sensor
Estimating the height H of the grain pile and constructing
The relationship to H;
the relationship to H is:
wherein,
is an estimate of H, bH(j) Coefficient of the estimated term of H, NHPolynomial order estimated for H, j 0H；
4) Using estimation terms IDBF(s) estimating average friction per unit area of the side of the grain bulk
Construction of the mean value of the output values of the bottom surface ring pressure sensor
Mean value of output values of side ring pressure sensor
Output value standard deviation SD(s) of bottom surface ring pressure sensorBottom) Side ring pressure sensor output value standard deviation SD(s)Side) And IDBF(s) relationship:
wherein, KXIs a set coefficient; when the scattering property of the corresponding grain pile is smaller than the set standard, the corresponding IDBF(s) is:
when the scattering property of the corresponding grain pile is more than or equal to the set standard, the corresponding IDBF(s) is:
furthermore, it is possible to provide a liquid crystal display device,
and IDBFThe relationship of(s) is:
wherein,
is composed of
Estimation of (b)F(n) is
Coefficient of the estimated term, NFIs composed of
Estimate a polynomial order of the term, N0F；
5) Substituting the relations obtained in the steps 2), 3) and 4) into a theoretical detection model of the grain storage quantity of the granary
Obtaining the grain storage quantity of the granary
And
SD(sBottom)、SD(sSide) Detection model of relationship:
wherein, aB(m)、aF(n, m) are coefficients of the estimation term; further obtaining the grain storage quantity of the granary according to the output values of the two circles of pressure sensors detected in the step 1)
Wherein, Kc＝CB/AB，ABIs the area of the bottom of the grain heap CBIs the perimeter of the bottom surface of the grain pile.
The invention has the beneficial effects that:
the invention provides a granary grain storage weight detection method adopting a granary grain storage quantity detection model based on the standard deviation of output values of two circles of pressure sensors on the bottom surface and the side surface according to the pressure distribution characteristics of the granary.
Further, the output value of the pressure sensor is screened in the step 1), and the screening method comprises the following steps: only the output value with the difference of the average value of the output values of the ring of pressure sensors within a set range is reserved; the average value of the output values of the pressure sensors is the average value of the median value of the output values of the sensors and the output values of the adjacent set number of the sensor output values.
Further, if the output value of the bottom surface ring pressure sensor meets the following requirements:
removing the output value of the sensor to obtain the output value sequence Q of the removed bottom surface ring pressure sensorBS(sBottom(i) ); wherein Q isB(sBottom(i) Is) the ith base ring pressure sensor output value,
the mean value of the output values of the pressure sensors of the bottom surface ring and the mean value, SD, of the output values of the adjacent set numberMed(sBottom) Is a standard value of output value of the bottom surface ring pressure sensor, TSDThe threshold coefficient is removed for the bottom bezel pressure sensor points.
Further, if the output value of the side ring pressure sensor meets the following requirements:
removing the output value of the sensor to obtain a removed output value sequence Q of the side ring pressure sensorBS(sSide(i) ); wherein Q isF(sSide(i) Is) the ith side ring pressure sensor output value,
for the median value of the output values of the side-ring pressure sensors and the mean value, SD, of the output values of a set number of adjacent side-ring pressure sensorsMed(sSide) Is a standard value of the output value of the side ring pressure sensor, CTSDThe threshold coefficients are removed for the side ring pressure sensor points.
Further, the average value of the output values of the two circles of pressure sensors
The calculation method comprises the following steps:
wherein,
is QBS(sBottom(i) ) of the average value of the average values,
is QBS(sSide(i) ) average value of the measured values.
Further, the method also comprises a step 6), wherein the step 6) comprises the step of arranging the detection model in the step 5) and limiting
Maximum order of the term being NBLimit of IDBFThe maximum order of the(s) term being NFTo obtain:
wherein, aB(m)、aF(n, m) are coefficients of the estimation terms.
Further, the detection model in the step 6) is arranged, and the second item is pressed
And IDBF(s) the order of the product term and Nn+mAscending sort of Nn+mAccording to IDBF(s) the order of the orders is from low to high, to obtain：
Wherein N isn+mIn the second term of the detection model
And IDBFThe sum of the order of the(s) product term is in the value range of [1, NB+NF]；
Further, in step 4):
when in use
When, corresponding to KXIs composed of
When in use
When, corresponding to KXIs composed of
Wherein, KSDIs a preset adjustment factor.
The invention also provides a granary grain storage detection system based on the bottom surface and the side surface two-ring pressure sensor, which comprises a processor, wherein the processor is used for executing instructions to realize the method.
Drawings
FIG. 1-1 is a schematic view of a granary side pressure sensor arrangement;
fig. 1-2 are schematic views of a granary floor pressure sensor arrangement;
FIG. 2 is a graph of errors in the calculation of grain weights for wheat grain bins when samples Nos. 7 to 12 were used as test samples;
FIG. 3 is a graph of the error in the calculation of the quantity of grain stored in a rice grain bin modeled using all samples;
fig. 4 is a flow chart of a method of the present invention.
Detailed Description
The invention provides a granary stored grain detection system based on two circles of pressure sensors on the bottom surface and the side surface, which comprises a processor, wherein the processor is used for executing instructions to realize the granary stored grain detection method based on the two circles of pressure sensors on the bottom surface and the side surface.
1. Detection theoretical model
The system can be pushed out by grain pile stress analysis, and a theoretical detection model of the grain storage quantity of the granary is as follows:
wherein A isBIs the area of the bottom of the grain heap, KCAs a model parameter, Kc＝CB/AB，CBIs the perimeter of the bottom of the grain pile, H is the height of the grain pile, fFIs the average friction coefficient between the side of the grain pile and the side of the grain bin,
in order to obtain a mean value of the pressure on the bottom surface,
is the average value of the pressure intensity of the side surface of the grain pile,
order:
wherein,
the average friction force per unit area of the side surface of the grain pile is shown. Then there are:
as can be seen from the formula (3), the weight of the grain pile and the pressure intensity mean value of the bottom surface of the grain pile are only
Average friction force per unit area of side surface
And the grain bulk height H. Therefore, the core of the granary grain storage quantity detection based on the pressure sensor lies in
And H, detecting and estimating three parameters.
2. Sensor arrangement model
Without loss of generality, for a commonly used horizontal warehouse, a model of the arrangement of the granary floor and side pressure sensors is shown in fig. 1-1 and 1-2. The distance d between each pressure sensor of the side ring and the bottom surface can be about 1 meter generally; under the condition of guaranteeing convenient grain loading and unloading, the distance D between each pressure sensor of the bottom surface ring and the side wall can be about 2 meters generally. In order to ensure the universality of the detection model, D and D of each granary should be the same. The number of the pressure sensors on the bottom surface and the two circles on the side surface is 6-10, and the distance between the sensors is larger than 1 m. Circular granaries such as squat silos, silos and the like can be arranged in a similar manner.
3. Sensor selection and standard deviation calculation
3.1 bottom surface Ring pressure sensor selection and Standard deviation calculation
For the output value sequence Q of the bottom surface ring pressure sensorB(sBottom(i))，i＝1,2,...,NSB，NSBThe number of the pressure sensors of the bottom surface ring is. And sorting the output value sequence according to the size to obtain a median point. Taking the left adjacent N of the median pointLMAn output value point, taking the adjacent N on the right side of the middle value pointRMOutput value points forming a sensor output value sequence Q of the median neighborhood pointsMed(sBottom(i) ). Taking N in generalLM＝2-3，NRM2-3. Determining a sequence Q of selected sensor output valuesMed(sBottom(i) Mean value of)
Namely:
output value sequence Q of bottom surface ring pressure sensorMed(sBottom(i) ) and mean value
Calculating standard deviation SD of output value of bottom surface ring pressure sensorMed(sBottom) Namely:
wherein,
the average value of the adjacent output value points at the two sides of the middle value point of the bottom surface circle is shown.
The rule for removing the output value points of the bottom surface ring pressure sensor is as follows:
if it is
Then Q is removedB(sBottom(i) Point (6) wherein TSDThreshold coefficients are removed from the bottom surface ring pressure sensor points, and the threshold coefficients can be reasonably adjusted according to the error change of the grain storage quantity detection model of the granary.
The rule for removing the output value points of the bottom surface ring pressure sensor shown in the formula (6) adopts the mean value of the adjacent output value points on two sides of the median adjacent point
Standard Deviation of (SD)Med(sBottom) To eliminate the influence of the randomness of the output value of the sensor and realize the self-adaptive adjustment of the point removal threshold of the output value of the pressure sensor of the bottom surface ring, and the standard deviation SD of the output value of the pressure sensor of the bottom surface ringMed(sBottom) If the output value point is larger than the threshold, the output value point removal threshold is increased, and vice versa; simultaneously, a bottom surface ring pressure sensor point removing threshold coefficient T based on error change of a granary grain storage quantity detection model is introducedSDAnd the reasonable adjustment and optimization of the threshold for removing the output value point of the bottom surface ring pressure sensor are realized.
For the output value sequence Q of the bottom surface ring pressure sensorB(sBottom(i) According to the bottom surface ring pressure sensor output value point removal rule shown in the formula (6), after the sensor output value points meeting the rule are removed, a removed bottom surface ring pressure sensor output value sequence Q is formedBS(sBottom(i))，i＝1,2,...,NBS，NBSThe number of the sequence data of the output value of the pressure sensor of the bottom surface ring after the removal. Mean value of output values of pressure sensor of bottom surface ring
Comprises the following steps:
and the formula (7) is a calculation formula of the output average value of the bottom surface ring pressure sensor. The calculation method is mainly characterized in that the influence of the randomness of the output value of the sensor on the calculation of the mean value and the standard deviation of the output value of the pressure sensor of the bottom ring is reduced by removing the region output value points with smaller and larger values.
3.2 side Ring pressure sensor selection and Standard deviation calculation
By using the sameMethod of sampling, for a sequence of side-ring pressure sensor output values QF(sSide(i))，i＝1,2,...,NSF，NSFThe number of the pressure sensors of the side ring is. And sorting the output value sequence according to the size to obtain a median point. Taking the left adjacent N of the median pointLMAn output value point, taking the adjacent N on the right side of the middle value pointRMOutput value points forming a sensor output value sequence Q of the median neighborhood pointsMed(sSide(i) ). Determining a sequence Q of selected sensor output valuesMed(sSide(i) Mean value of)
Namely:
output value sequence Q of side ring pressure sensorF(sSide(i) ) and mean value
Calculating standard deviation SD of output value of side ring pressure sensorMed(sSide) Namely:
wherein,
the average value of adjacent output value points at two sides of the middle value point of the side circle is shown.
The rule for removing the output value points of the side ring pressure sensor is as follows:
if it is
Then Q is removedF(sSide(i) Point (10) wherein CTSDRemoving threshold coefficient for pressure sensor points on side ring, and storing grain according to grain storage quantityAnd detecting the error change of the model to reasonably adjust. Here, C is usedTSDTSDRemoving the threshold coefficient as the output point of the side ring pressure sensor so as to facilitate coefficient CTSDSelection and optimization.
For a sequence of side ring pressure sensor output values QF(sSide(i) According to the rule for removing the output value points of the side ring pressure sensor shown in the formula (10), after the sensor output value points meeting the rule are removed, a removed side ring sensor output value sequence Q is formedFS(sSide(i))，i＝1,2,...,NFS，NFSThe number of the sequence data of the output values of the rear side ring pressure sensor is removed. Average value of output values of the side ring pressure sensor
Comprises the following steps:
equation (11) is a calculation equation of the output average value of the side ring pressure sensor.
4. Model item construction
For the granary bottom and side pressure sensor arrangement models shown in figures 1-1 and 1-2, the output value mean value of the bottom ring pressure sensor is determined according to the mechanical characteristics of the granary bulk
The size of the pressure sensor represents the average value of the pressure on the bottom surface of the grain pile
And the height H of the grain pile. At the same time, the two also have the average friction force with the unit area of the side surface of the grain pile
It is related. Average friction force per unit area of side surface of grain pile
Increase of the pressure of the bottom of the grain pile
Average value of output values of pressure sensors of bottom surface ring
And decreases. Average value of output values of pressure sensor due to side ring
The size of the grain pile represents the average friction force of the side surface of the grain pile in unit area
So that the average value of the output values of the bottom surface ring pressure sensor can be utilized
And average value of output values of side ring pressure sensor
To estimate the pressure mean value of the bottom of the grain pile
And a grain bulk height H. Order:
wherein,
is QBS(sBottom(i) ) of the average value of the average values,
is QBS(sSide(i) ) average value of the measured values.
Obviously, there are:
therefore, the output average value of the bottom surface ring pressure sensor shown in the formula (12) is used
And average value of output values of side ring pressure sensor
Mean value of (1) to describe the pressure at the bottom of the grain pile
And the height H of the grain pile not only reflects the output average value of the bottom ring pressure sensor
Also embodies
Average friction force with unit area of side surface
Negative correlation of (c).
From the above experimental results, it is found that the average friction force per unit area of the side surface is caused
The effect of the pressure sensors on the bottom surface and the side surface ring is to cause the changes of the average value and the standard deviation of the output values of the pressure sensors,
the increase of the pressure will lead to the pressure transmission of the bottom surface and the side surface ringThe difference degree of the average value and the standard deviation of the output values of the sensors is increased. Therefore, the difference and standard deviation of the average values of the output values of the pressure sensors of the bottom surface and the side surface ring can be reflected
Can be used to construct the average friction force per unit area of the side surface
Is estimated. Therefore, the following steps are performed:
wherein, IDBF(s) mean friction per unit area of side surface of grain pile based on standard deviation difference between output values of pressure sensors at bottom surface and side surface ring
Estimate of (I)MBF(s) mean friction force per unit area of side surface of grain pile based on standard deviation mean value of output values of pressure sensors at bottom surface and side surface ring
The estimated term of (2).
To make the preset adjustment coefficient K in the formula (15) and the formula (16)SDThe value is close to 1, so that K is convenientSDValue selection, incorporating constant terms
It is clear that the first terms of the formulae (15) and (16) represent the average friction per unit area of the lateral surface of the grain bulk
To the bottom surface circleThe second term represents the influence of the mean value of the output values of the pressure sensor
Influence on the standard deviation of the output value of the pressure sensor of the bottom surface ring.
The practical modeling result shows that generally speaking, for the grain pile such as the paddy with lower fluidity, the pressure of the side surface of the grain pile
Relatively small, high linear correlation between standard deviation of each circle and grain bulk weight, and preferably adopts formula I shown in formula (15)DBF(s) Structure
The estimated term of (2); on the contrary, for the grain pile of wheat and the like with stronger fluidity, the pressure intensity of the side surface of the grain pile
Relatively large, low linear correlation between standard deviation of each circle and weight of grain pile, and preferably adopts formula I shown in formula (16)MBF(s) Structure
The estimated term of (2).
The fluidity of grains is also called as the scattering property of grains, and the scattering property of grains mainly comprises scattering property, automatic grading, porosity and the like, which are inherent physical properties of granular grains. When the grains naturally form grain piles, the grains flow to four sides to form a cone, and the property of the cone is called the scattering property of the grains. The size, shape, surface smoothness, volume and impurity content of grains all influence the scattering property of grains. Grains with large, full and round grains, large specific gravity, smooth surface and less impurities have good scattering property, otherwise the scattering property is poor. The above appearance characteristics are significantly different from grain to grain, and thus, have different scattering characteristics.
The good and bad of the grain scattering property is generally expressed by a static angle. The static angle refers to the angle between the inclined plane of the cone and the horizontal line of the bottom surface formed naturally when the grains fall from the high point. The static angle is in inverse proportion to the scattering property, namely the scattering property is good (equivalent to the scattering property is more than or equal to a set standard), and the static angle is small; the scattering property is poor (equivalent to the scattering property is less than the set standard), and the angle of repose is large. The magnitude of the angle of repose for the major grain species is given in table a.
TABLE A angle of repose of several common grains (unit: degree)
When the grain pile angle of repose is less than 40 degrees, the formula (16) is adopted to calculate IMBF(s) when the grain angle of repose is 40 degrees or more, calculating I by using the formula (15)DBF(s), the angle of repose refers to the maximum angle of repose corresponding to the grain variety (i.e., the angle of repose in table a).
5. Detection model
For the theoretical detection model of the grain quantity in the granary shown in the formula (3), the method adopts
IDBF(s) polynomial construction
And H is estimated as:
wherein, bB(m) is
Coefficient of the estimated term, bH(j) Coefficient of the estimated term of H, bF(n) is
Estimate coefficients of the term, m 0B，j＝0,...,NH，n＝0,...,NF，NBIs composed of
Polynomial coefficient of estimated term, NHPolynomial order estimated for H, NFIs composed of
The polynomial order of the term is estimated.
When formula (17) to formula (19) is substituted for formula (3), there are:
arranging (20) and restricting
Maximum order of the term being NBLimit of IDBFThe maximum order of the(s) term being NFIt can be derived that:
wherein, aB(m)、aF(n, m) are coefficients of the estimation terms.
Obviously, the total number of terms in the first term of the formula (21) is NB+1, maximum order number NB(ii) a The second total number of terms is (N)B+1)NF，
And IDBF(s) the sum of the maximum orders of the product terms is NB+NF. In order to limit the degree of nonlinearity of the detection model represented by equation (21), the sum of the maximum orders of the product terms in the second term should be controlled. Therefore, in order to facilitate the optimization of the total number of terms of the model, the formula (21) is arranged according to the second term
And IDBF(s) the order of the product term and Nn+mAscending sort of Nn+mAt the same time press IDBF(s) the order of the orders is from low to high, then:
wherein N isn+mIn the second term of the detection model
And IDBFThe sum of the order of the(s) product term is in the value range of [1, NB+NF]，mb、meThe value of (A) is shown as follows:
obviously, the total number of product terms of the second term of equation (24) is (N)B+1)NFTotal number of model terms NItemMaximum value of (1) is NB+(NB+1)NF+1. To limit the degree of non-linearity of the model, the model can be followed by the model tail (Nth)B+(NB+1)NF+1 product terms) terms, removing several product terms to reduce the total number of model terms NItem。
Formula (24) is based on
IDBF(s) a polynomial grain bin stored grain quantity detection model. According to IDBFThe characteristic of the item(s) is that the model shown in the formula (24) is suitable for detecting the grain storage quantity of grain warehouses such as paddy and the like with low fluidity.
For detecting the grain storage quantity of grain granaries with stronger fluidity such as wheat and the like, the similar method can be adopted to construct the structure based on
IMBFOf(s) polynomials
And estimation of H. Can be derived based on two circles of pressure sensors on the bottom surface and the side surface and
IMBF(s) the polynomial grain bin stored grain quantity detection model is shown as the following formula:
wherein N isn+mIn the second term of the detection model
And IDBFThe sum of the order of the(s) product term is in the value range of [1, NB+NF]，mb、meThe values of (A) are shown in formulas (23) and (24).
Obviously, the total number of product terms of the second term of equation (25) is (N)B+1)NFTotal number of model terms NItemMaximum value of (1) is NB+(NB+1)NF+1. To limit the degree of non-linearity of the model, the model can be followed by the model tail (Nth)B+(NB+1)NF+1 product term) term startRemoving several product terms to reduce the total number of model terms NItem。
Formula (25) is based on
IMBF(s) a polynomial grain bin stored grain quantity detection model. According to IMBFThe model is suitable for detecting the grain storage quantity of grain warehouses such as wheat and the like with strong fluidity.
6. Test examples and results analysis
6.1 testing example 1
The length of the horizontal warehouse adopted by the experiment is 9m, the width is 4.2m, and the area is 37.8m2，CB/AB0.698. The granaries all belong to small-sized granaries CB/ABIs relatively large. According to the pressure sensor arrangement model shown in fig. 1-1 and 1-2, 8 pressure sensors are arranged on the side surface, and 16 pressure sensors are arranged on the bottom surface, so that 24 pressure sensors are arranged. The distance D between each pressure sensor of the side surface ring and the bottom surface is 1 meter, and the distance D between each pressure sensor of the bottom surface ring and the side wall is 2 meters. The height of the wheat grain pile is about 6 meters, data is taken every 1 meter when the grains are fed, and 5 times of experiments are repeated to obtain 30 samples.
For the base shown in formula (25)
IMBFIn the polynomial granary stored grain quantity detection model of(s), samples 7 to 12 of experiment 2 are used as test samples, samples 13 to 18 of experiment 3 are used as parameter optimization samples, and the rest 18 samples are used as modeling samples. The optimized modeling parameters are shown in table 1, and the obtained parameters are shown in tables 2 and 3. The error of the calculation of the grain weight in the granary is shown in figure 2, and the maximum error of the test percentage is 1.75%. Since the maximum test error is large due to too few modeling samples, the prediction error can be further reduced if the number of modeling samples is increased.
TABLE 1 optimized modeling parameters
TABLE 2 model coefficients aB(m)
TABLE 3 model coefficients aF(n,m)
6.2 test example 2
The diameter of the silo is 6m, and the area is 28.26m2，CB/ABIs 0.67. 6 side pressure sensors are uniformly arranged on the side wall of the silo, and the height between the sensors and the bottom surface is 1 m. 12 side pressure sensors are uniformly arranged on the bottom surface. The height of the rice grain pile is about 8 meters, data is taken every 1 meter when the grains are fed, and 4 times of experiments are repeated to obtain 32 samples.
For the base shown in formula (22)
IDBFAnd(s) using all 32 samples as modeling samples in the polynomial granary stored grain quantity detection model. The optimized modeling parameters are shown in Table 4, and the obtained parameters are shown in tables 5, 6-1 and 6-2. The error of the calculation of the grain weight in the granary is shown in figure 3, and the maximum percentage error is 0.628%.
TABLE 4 optimized modeling parameters
TABLE 5 model coefficients aB(m)
TABLE 6-1 model coefficients aF(n,m)
TABLE 6-2 model coefficients aF(n,m)
The method provided by the invention can be implemented according to the implementation mode shown in fig. 4, and the specific steps are implemented as follows:
1) system configuration
And selecting a specific pressure sensor, and configuring corresponding systems for data acquisition, data transmission and the like.
2) Bottom ring pressure sensor and side ring pressure sensor mounting
The sensors are arranged as shown in figures 1-1 and 1-2, the pressure sensors are arranged according to a bottom surface circle and a side surface circle, the distance D between each pressure sensor of the side surface circle and the bottom surface is about 1 meter, and the distance D between each pressure sensor of the bottom surface circle and a side wall is about 2 meters. In order to ensure the universality of the detection model, D and D of each granary should be the same. The number of the two circles of pressure sensors is 6-10, and the distance between the sensors is not less than 1 m.
3) System calibration and model modeling
For given sensors, grain types and bin types, if the system is not calibrated, arranging pressure sensors in more than 6 bins, feeding grains to full bins, collecting the output values of the pressure sensors in the bins after the output values of the pressure sensors are stable, and forming a sample set
Wherein k is a sample point number, k is 1,2, and M is the number of samples;
the sequence of output values of the bottom loop pressure sensor for the kth sample point, i ═ 1,2SB，NSBThe number of the pressure sensors of the bottom surface ring is;
sequence of output values of the side-loop pressure sensor for k sample points, j ═ 1,2SF，NSFThe number of the pressure sensors of the side ring is; wkIs the actual grain feed weight at sample point k,
is the corresponding area of the bottom surface of the granary.
When the number of samples is large, the sample set S is divided into three parts which are respectively used as a multiple regression sample set SMParameter optimization sample set SOAnd test sample set ST. By multivariate regression of the sample set SMSample and parameter optimization sample set SOAnd the samples are different so as to avoid over-learning of the model and improve the generalization capability of the model. When the number of samples is small, the sample set S is divided into two parts, and one part is simultaneously used as a multiple regression sample set SMSum-parameter optimized sample set SOAnd the other part is used as a test sample set ST。
For a given sample set S, without loss of generality, the base shown in equation (22)
IDBF(s) in the polynomial grain storage quantity detection model, it can be seen that the modeling parameters of the grain storage quantity detection model shown in the formula (22) comprise
Maximum order of termNumber NB、IDBFMaximum order N of(s) termFTotal number of model items NItem、IDBFTerm(s) preset adjustment coefficient KSDBottom surface ring pressure sensor point removal threshold coefficient TSDAnd a side ring pressure sensor point removal threshold coefficient CTSDAnd polynomial coefficient aB(m) and aF(n, m), etc. Order:
CR＝((NB,NF,NItem.KSD,TSD,CTSD)) (26)
wherein, CRIs a parameter set.
As can be seen from equation (22), given the parameter set CRIs aB(m) and aF(n, m) can be obtained using a multiple linear regression method. Thus, parameter set C can be employedRThe method of the combination of parameter optimization and regression is used to realize the method based on the formula (22)
IDBF(s) for the polynomial grain bin stored grain quantity detection model based on equation (25)
IMBFAnd(s) the polynomial grain bin grain storage quantity detection model can be modeled by adopting a similar method.
4) Real bin weight detection
And if the system is calibrated, detecting the output value of the pressure sensor of the bottom ring and the output value of the pressure sensor of the side ring and detecting the grain storage quantity of the granary by using a model shown in the formula (22) or the formula (25).
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (9)
1. A granary grain storage detection method based on two circles of pressure sensors on the bottom surface and the side surface is characterized by comprising the following steps:
1) detecting output values of two circles of pressure sensors of a bottom circle pressure sensor arranged on the bottom surface of the granary and a side circle pressure sensor arranged on the side surface of the granary respectively;
2) average value of output values using two-turn pressure sensor
Estimating the pressure mean at the bottom of a grain heap
Construction of
And
the relationship of (1);
and
the relationship of (1) is:
wherein,
is composed of
Estimation of (b)B(m) is
Coefficient of the estimated term, NBIs composed of
Estimate polynomial coefficients of terms, m 0B；
3) Average value of output values using two-turn pressure sensor
Estimating the height H of the grain pile and constructing
The relationship to H;
the relationship to H is:
wherein,
is an estimate of H, bH(j) Coefficient of the estimated term of H, NHPolynomial order estimated for H, j 0H；
4) Using estimation terms IDBF(s) estimating average friction per unit area of the side of the grain bulk
Construction of the mean value of the output values of the bottom surface ring pressure sensor
Mean value of output values of side ring pressure sensor
Output value standard deviation SD(s) of bottom surface ring pressure sensorBottom) Side ring pressure sensor output value standard deviation SD(s)Side) And IDBF(s) relationship:
wherein, KXIs a set coefficient; when the scattering property of the corresponding grain pile is smaller than the set standard, the corresponding IDBF(s) is:
when the scattering property of the corresponding grain pile is more than or equal to the set standard, the corresponding IDBF(s) is:
furthermore, it is possible to provide a liquid crystal display device,
and IDBFThe relationship of(s) is:
wherein,
is composed of
Estimation of (b)F(n) is
Coefficient of the estimated term, NFIs composed of
Estimate a polynomial order of the term, N0F；
5) Substituting the relations obtained in the steps 2), 3) and 4) into a theoretical detection model of the grain storage quantity of the granary
Obtaining the grain storage quantity of the granary
And
SD(sBottom)、SD(sSide) Detection model of relationship:
wherein, aB(m)、aF(n, m) are coefficients of the estimation term; further obtaining the grain storage quantity of the granary according to the output values of the two circles of pressure sensors detected in the step 1)
Wherein, Kc＝CB/AB，ABIs the area of the bottom of the grain heap CBIs the perimeter of the bottom surface of the grain pile.
2. The grain storage detection method of the granary based on the bottom surface and the side surface two-ring pressure sensor according to claim 1, wherein the output value of the pressure sensor is further screened in the step 1), and the screening method comprises the following steps: only the output value with the difference of the average value of the output values of the ring of pressure sensors within a set range is reserved; the average value of the output values of the pressure sensors is the average value of the median value of the output values of the sensors and the output values of the adjacent set number of the sensor output values.
3. A base according to claim 2, based on both the bottom and the sideThe grain bin grain storage detection method of the ring pressure sensor is characterized in that if the output value of the bottom ring pressure sensor meets the following requirements:
removing the output value of the sensor to obtain the output value sequence Q of the removed bottom surface ring pressure sensorBS(sBottom(i) ); wherein Q isB(sBottom(i) Is) the ith base ring pressure sensor output value,
the mean value of the output values of the pressure sensors of the bottom surface ring and the mean value, SD, of the output values of the adjacent set numberMed(sBottom) Is a standard value of output value of the bottom surface ring pressure sensor, TSDThe threshold coefficient is removed for the bottom bezel pressure sensor points.
4. The grain storage detection method of claim 3, wherein if the output value of the side ring pressure sensor satisfies the following condition:
removing the output value of the sensor to obtain a removed output value sequence Q of the side ring pressure sensorBS(sSide(i) ); wherein Q isF(sSide(i) Is) the ith side ring pressure sensor output value,
for the median value of the output values of the side-ring pressure sensors and the mean value, SD, of the output values of a set number of adjacent side-ring pressure sensorsMed(sSide) Is a standard value of the output value of the side ring pressure sensor, CTSDThe threshold coefficient is removed for the side pressure sensor points.
5. Granary according to claim 4, based on two rings of pressure sensors on the bottom and sideThe stored grain detection method is characterized in that the average value of the output values of the two circles of pressure sensors
The calculation method comprises the following steps:
wherein,
is QBS(sBottom(i) ) of the average value of the average values,
is QBS(sSide(i) ) average value of the measured values.
6. The grain storage detection method of the granary based on the bottom surface and the side surface two-ring pressure sensor as claimed in claim 1, further comprising the step 6), wherein the step 6) comprises arranging the detection model in the step 5) to limit
Maximum order of the term being NBLimit of IDBFThe maximum order of the(s) term being NFTo obtain:
wherein, aB(m)、aF(n, m) are coefficients of the estimation terms.
7. The grain storage detection method of claim 6, wherein the detection model in the step 6) is arranged to perform the second item pressing
And IDBF(s) the order of the product term and Nn+mAscending sort of Nn+mAccording to IDBF(s) the orders are ordered from low to high, giving:
wherein N isn+mIn the second term of the detection model
And IDBFThe sum of the order of the(s) product term is in the value range of [1, NB+NF]；
8. The granary stored grain detection method based on the bottom surface and side surface two-circle pressure sensor according to claim 1, wherein in the step 4):
when in use
When, corresponding to KXIs composed of
When in use
When, corresponding to KXIs composed of
Wherein, KSDIs a preset adjustment factor.
9. A grain storage detection system for a granary based on two rings of pressure sensors on the bottom surface and the side surface, comprising a processor for executing instructions for implementing the method according to any one of claims 1 to 8.
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