CN104330137A - Grain bin stored-grain quantity detection method based on detection point pressure intensity value sequence - Google Patents

Abstract

The invention relates to a grain bin stored-grain quantity detection method based on a detection point pressure intensity value sequence. A circle of pressure sensors are arranged on the bottom face of a grain bin and the vertical distance of each pressure sensor to a closest outer wall is d; and the output value of each sensor is detected and according to a detection model (as is shown in the specification), the stored-grain weight estimation W<^> of the grain bin is calculated. According to characteristics of pressure intensity distribution of the bottom face of the grain bin and change of pressure intensity measurement values, the invention proposes the grain bin stored-grain quantity detection method of a support vector regression model, based on the detection point pressure intensity value sequence. According to a corresponding sensor arrangement method, the core technology of the method includes two parts: the support vector regression grain bin stored-grain quantity detection model based on the detection point pressure intensity value sequence and a system calibration and modeling method. The grain bin stored-grain quantity detection method has the characteristics of being high in detection precision, high in universality and adaptive to stored-grain quantity detection of a plurality of kinds of grain bin structure types and the like.

Description

Based on the method for detecting quantity of stored grains in granary of check point pressure values sequence

Technical field

The present invention relates to the method for detecting quantity of stored grains in granary of a kind of employing based on the support vector regression model of check point pressure values sequence.Belong to sensor network technique field.

Background technology

Grain security comprises Quantity Security and quality safety.It is the important leverage technology of national food Quantity Security that Grain Quantity online measuring technique and systematic study are applied, and the research and apply carrying out this respect, concerning national food security, has great importance, and will produce huge economic results in society.

Due to the critical role of grain in national security, require that grain piles quantity on-line checkingi accurately, fast and reliably.Simultaneously because Grain Quantity is huge, at the bottom of price, grain is required to pile quantity online detection instrument cost low, simple and convenient.Therefore the high precision detected and the low cost of detection system are that silo quantity on-line detecting system develops the key issue that must solve.

Patent related to the present invention comprises:

(1) patent " the grain reserve in grain depot quantity measuring method based on pressure transducer " (license number: ZL201010240167.7), the core technology of this patent of invention comprises and exports the computation model of the quantity of stored grains in granary of average and concrete system calibrating method based on silo bottom surface, side pressure sensor.Its distinguishing feature uses side pressure sensor, and need pressure transducer many, detection system cost is higher.

(2) patent " horizontal warehouse silo grain storage quantity detection method " (license number: ZL201210148522), the core technology of this patent of invention comprise propose based on base pressure sensor export the side friction power impact of mean square compensation, export the grain heap weight forecast model of average, the new method such as forecast model modeling, rapid system demarcation based on grain weight error ratio based on base pressure sensor.The method feature is that model is simple, only utilizes base pressure sensor to export average and carries out grain weight detecting.Because this model does not take into full account the mutual branch problem of side pressure and bottom surface pressure, be only applicable to large granary.

Summary of the invention

The object of this invention is to provide a kind of method for detecting quantity of stored grains in granary of the support vector regression model based on check point pressure values sequence, is a kind of detection method of uniqueness.

For achieving the above object, the solution of the present invention comprises:

Based on the method for detecting quantity of stored grains in granary of check point pressure values sequence, step is as follows:

1) on silo bottom surface, arrange a circle pressure transducer, each pressure transducer is d with the vertical range closest to exterior wall;

2) detect the output valve of each sensor, suppose s _ithe pressure sensor measurements of point is Q _bL(s _i), i=1 ..., n _bL, n _bLfor arranged pressure transducer number, to arranged n _bLindividual silo base pressure sensor is numbered, and according to numbering composition sensor sequence Q _bL, $Q_{\mathrm{BL}}=(Q_{\mathrm{BL}}\left(s_1\right),Q_{\mathrm{BL}}\left(s_2\right),...,Q_{\mathrm{BL}}\left(s_{n_{\mathrm{BL}}}\right));$ According to detection model (9)

$\hat{W}=A_B(\underset{j=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_j\exp (-\mathrm{\&gamma;}{\left|\right|Q_{\mathrm{BL}}-Q_{\mathrm{BL}}^j\left|\right|}^2)+b)---\left(9\right)$

Calculate detected granary storage weight to estimate wherein A _bfor detected silo base area, γ be greater than 0 parameter; α _j, b is for training the parameter obtained, α by training sample and SVM _j≠ 0; for corresponding support vector point, j=1 ..., l, l are support vector number, and model parameter is determined by calibration process.

Described scaling method comprises pressure sensor calibrating: the pass of pressure transducer output valve and pressure is

Q＝k ₀+k ₁s(Q) (10)

Wherein, Q is strong by being exerted pressure; S (Q) is sensor output value; k ₀, k ₁for the calibration coefficient of sensor.

By above-mentioned steps 1) described arrangement, in more than 6 silos, arrange the sensor installed and demarcated, enter grain to buying securities with all one's capital and shakeouing, after pressure transducer output valve is stable, gather the pressure transducer output valve in each storehouse, and according to the calibration coefficient k of each pressure transducer ₀, k ₁, calculate the pressure values of each sensor, according to pressure transducer numbering, for any given detected quantity of stored grains in granary W _mwith detected silo base area its corresponding base pressure sensor output value sequence is expressed as for silo bottom surface s _ithe pressure values of point, then corresponding sample is for the multiple weight of silo, then forming sample set is wherein M is sample number; For given sample set support vector regression learning algorithm is adopted to realize model modeling shown in formula (9).

Described d=2 ~ 4 meter.

The present invention is according to the distribution of silo bottom surface pressure and pressure measurement Variation Features, propose a kind of method for detecting quantity of stored grains in granary of the support vector regression model based on check point pressure values sequence, the present invention is according to the sensor arrangement of correspondence, and its core technology comprises based on the support vector regression quantity of stored grains in granary detection model of check point pressure values sequence, system calibrating and modeling method two parts.It is high that proposed method has accuracy of detection, highly versatile, adapts to the features such as the grain storage quantity detection of multiple barn structure type.

Accompanying drawing explanation

Fig. 1 is horizontal warehouse base pressure sensor placement model;

Fig. 2 is silo base pressure sensor placement model;

Fig. 3 is detection model schematic diagram;

Fig. 4 is concrete implementation step schematic diagram.

Embodiment

Below in conjunction with accompanying drawing, the present invention will be further described in detail.

Grain Quantity of the present invention detects detection method, relate to a kind of support vector regression model based on check point pressure values sequence of uniqueness, the theoretical premise obtained about this model, corresponding silo sensor layout, model inference, system calibrating and modeling and some data, specifically introduce below successively.

1. the theoretical detection model of quantity of stored grains in granary

Normally used silo has the types such as horizontal warehouse, silo, silo, and after conveying grain into storehouse, grain heap top requires to shakeout, and grain pile in horizontal warehouse shape is the cube of different size haply, and silo, silo grain heap shape is the right cylinder of different size haply.For given silo and types of food, suppose that the pressure of s point in grain heap bottom surface and side is respectively Q _b(s), Q _fs (), then grain heap bottom surface and side pressure average are as shown in for formula (1) and formula (2).

${\stackrel{\&OverBar;}{Q}}_B\left(s\right)=\frac{1}{n_B}\underset{i=0}{\overset{n_B}{\mathrm{\&Sigma;}}}{Q}_B\left(s_i\right)---\left(1\right)$

${\stackrel{\&OverBar;}{Q}}_F\left(s\right)=\frac{1}{n_F}\underset{j=0}{\overset{n_F}{\mathrm{\&Sigma;}}}{Q}_F\left(s_j\right)---\left(2\right)$

Wherein, Q _b(s _i) be silo bottom surface s _ithe pressure transducer output valve of point, i=1 ..., n _b, n _bfor the pressure transducer number that silo bottom surface is arranged; Q _f(s _j) be silo side s _jthe pressure transducer output valve of point, j=1 ..., n _f, n _ffor the pressure transducer number that silo side is arranged.Can be drawn by grain heap force analysis, silo grain heap weight and silo pressure distribute the relation had shown in following formula.

$\hat{W}=A_B({\stackrel{\&OverBar;}{Q}}_B\left(s\right)+\frac{C_B}{A_B}{\mathrm{Hf}}_F{\stackrel{\&OverBar;}{Q}}_F\left(s\right))---\left(3\right)$

Wherein, for grain heap weight is estimated, A _bfor grain heap base area, C _bfor bottom surface girth, H is grain bulk height, f _fs () is the average friction coefficient between grain heap side and silo side; q _b(s), Q _fs () is respectively the pressure of s point in grain heap bottom surface and side.

For given silo and types of food, the average friction coefficient f between grain heap side and silo side _ffor constant, formula (3) can be expressed as

$\hat{W}/A_B=f({\stackrel{\&OverBar;}{Q}}_B\left(s\right),{\stackrel{\&OverBar;}{Q}}_F\left(s\right),H)---\left(4\right)$

As can be seen from the above equation, grain is piled weight and and is only piled bottom surface pressure average with grain side pressure average and grain bulk height H is relevant.Therefore the core detected based on the quantity of stored grains in granary of pressure transducer is with the Detection and estimation of H tri-parameter, as long as accurately Detection and estimation goes out this three parameters, then necessarily accurately quantity of stored grains in granary can be estimated.

According to grain heap side pressure average bottom surface pressure average is piled with grain bulk height H and grain relation, can build based on 's with the estimator of H be

${\widehat{\stackrel{\&OverBar;}{Q}}}_F\left(s\right)=f_F\left({\stackrel{\&OverBar;}{Q}}_B\left(s\right)\right)---\left(5\right)$

$\hat{H}=f_H\left({\stackrel{\&OverBar;}{Q}}_B\left(s\right)\right)---\left(6\right)$

Wherein, be respectively the estimation of H.

By formula (1) to formula (6), then granary storage weight can be estimated be expressed as

$\hat{W}/A_B=f(Q_B\left(s_1\right),Q_B\left(s_2\right),...,Q_B\left(s_{n_B}\right))---\left(7\right)$

2. silo pressure transducer is arranged

According to each area pressure characteristic distributions in silo bottom surface, silo base pressure sensor arrangement form as depicted in figs. 1 and 2 can be adopted, pressure transducer number is 6-10, transducer spacing should be not less than 1m, ensureing under the condition facilitating grain to load and unload, each pressure transducer and flank wall distance d should be large as far as possible, generally can be taken as about 3 meters.In order to ensure the versatility of detection model, the pressure transducer of each silo should be identical with flank wall distance d, and pressure transducer number is identical.

3. quantity of stored grains in granary detection model

According to the relation of each area pressure distribution in silo bottom surface, the pressure average of the pressure mean approximation estimation grain heap bottom surface of the some check points in bottom surface can be piled with grain.Therefore, the silo base pressure sensor according to Fig. 1 and Fig. 2 is arranged, supposes s _ithe pressure sensor measurements of point is Q _bL(s _i), i=1 ..., n _bL, n _bLfor arranged pressure transducer number, then formula (7) can approximate representation be

$\hat{W}/A_B=f(Q_{\mathrm{BL}}\left(s_1\right),Q_{\mathrm{BL}}\left(s_2\right),...,Q_{\mathrm{BL}}\left(s_{n_{\mathrm{BL}}}\right))---\left(8\right)$

According to certain order to arranged n _bLindividual silo base pressure sensor number, then can form a sensor sequence Q _bL, for any given detected granary storage weight W _mwith detected silo base area its corresponding base pressure sensor output value can be expressed as for silo bottom surface s _ithe pressure values of point, then corresponding sample can be expressed as then multiple sample can form sample set wherein M is sample number.

Relational model due to the quantity of stored grains in granary shown in formula (8) and pressure transducer detected value has very high non-linear, and pressure transducer detected value also has certain randomness, for this reason, according to sample set simultaneously support vector regression method modeling can be adopted.

For given silo, types of food and sample set S, respectively will value and each component value specification to [-Δ, Δ], wherein Δ is constant, 0 < Δ≤2, utilize common supporting vector machine model and training algorithm, then the quantity of stored grains in granary detection model that can construct based on support vector regression is following form.

$\hat{W}=A_B(\underset{j=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_j\exp (-\mathrm{\&gamma;}{\left|\right|Q_{\mathrm{BL}}-Q_{\mathrm{BL}}^j\left|\right|}^2)+b)---\left(9\right)$

Wherein, A _bfor silo base area, γ be greater than 0 parameter; α _j, b is usual supporting vector machine model parameter, support vector machine training algorithm can be utilized to obtain, α _j≠ 0; for corresponding support vector point, j=1 ..., l, l are support vector number.Fig. 2 is model schematic shown in formula (9).

The support vector regression quantity of stored grains in granary detection model based on check point pressure values sequence that formula (9) proposes for the present invention.Can find out, the quantity of stored grains in granary predicted value of this detection model depends on for sensor detected value and support vector point distance, support vector point for typical sample point, therefore this detection model has the pattern-recognition feature based on template, has good predictive ability.

4. system calibrating and modeling method

System calibrating and detection model modeling are carried out according to the following steps:

(1) pressure sensor calibrating

In order to ensure the interchangeability of pressure transducer, then needing to carry out pressure sensor calibrating for different types of food, obtaining the relation of pressure transducer output valve and the pressure be shown below.

Q＝k ₀+k ₁s(Q) (10)

Wherein, Q is strong by being exerted pressure; S (Q) is sensor output value; k ₀, k ₁for the calibration coefficient of sensor.

(2) system calibrating data acquisition.Utilize the placement model of sensor shown in Fig. 1 and Fig. 2, in more than 6 silos, arrange the sensor demarcated, enter grain to buying securities with all one's capital and shakeouing, after pressure transducer output valve is stable, gather the pressure transducer output valve in each storehouse, and according to the calibration coefficient k of each pressure transducer ₀, k ₁, calculate the pressure values of each sensor, and form sample set wherein, W _mfor detected silo enters grain weight, for detected silo base area, M is sample number.

(3) detection model modeling.

For given sample set common supporting vector machine model and training algorithm can be utilized, realize model modeling shown in formula (9).Supporting vector machine model and training algorithm belong to routine techniques means, do not repeat them here.

5. test experience and result

Test the long 9m of horizontal warehouse adopted, wide 4.2m, area is 37.8m ².Silo diameter is 6m, and area is 28.26m ².Pressure transducer placement model according to Fig. 1 and Fig. 2, for horizontal warehouse, arranges 15 pressure transducers along its length, and silo is by circle layout 15 pressure transducers.For tested often kind grain (wheat, corn and paddy), horizontal warehouse point enters grain 6 times in testing at every turn, enters about 1 meter, grain at every turn and shakeouts.Silo point enters grain 8 times in testing at every turn, enters about 1 meter, grain at every turn and shakeouts.

Utilize wheat horizontal warehouse 4 experimental datas, to test 2,3 and 4 data as modeling sample, utilize interpolation to form 180 samples, to test 1 data as test sample book, get support vector training parameter C=3, γ=0.02, obtain 92 support vectors after training and count.According to obtained computation model, the granary storage Weight computation result of each experiment is as shown in table 1 to table 4.

Utilize corn silo 4 experimental datas, to test 1,2 and 3 data as modeling sample, utilize interpolation to form 240 samples, to test 4 data as test sample book, get support vector training parameter C=3, γ=0.02, obtain 125 support vectors after training and count.According to obtained computation model, the granary storage Weight computation result of each experiment is as shown in table 5 to table 8.

Utilize corn horizontal warehouse 4 experimental datas, to test 1,2 and 3 data as modeling sample, utilize interpolation to form 180 samples, to test 4 data as test sample book, get support vector training parameter C=3, γ=0.02, obtain 93 support vectors after training and count.According to obtained computation model, the granary storage Weight computation result of each experiment is as shown in table 9 to table 12.

As can be seen from above granary storage Weight computation result, except the little situation of grain storage weight, the testing result of other check point is more satisfactory.Therefore, this granary storage gravimetric analysis sensing method measuring accuracy is high, also relatively low to the performance requirement of sensor, is applicable to the detection of various structures type quantity of stored grains in granary.

An actual testing process is as follows:

Can implement by embodiment as shown in Figure 4, concrete steps are implemented as follows:

(1) system configuration

Selected concrete pressure transducer, and configure the system such as corresponding data acquisition, data transmission.

(2) base pressure transducer calibration and installation

Carry out pressure sensor calibrating for different types of food, obtain the calibration coefficient k of pressure transducer ₀, k ₁.Horizontal warehouse sensor is arranged as shown in Figure 1, and silo as shown in Figure 2, is ensureing under the condition facilitating grain to load and unload, and each pressure transducer and flank wall distance d should be large as far as possible, generally can be taken as about 3 meters.In order to ensure the versatility of detection model, the pressure transducer of each silo should be identical with flank wall distance d.

(3) system calibrating and modeling

For given sensor, types of food and storehouse type, if system not yet has demarcation, then in more than 6 silos, arrange the sensor demarcated, enter grain to buying securities with all one's capital and shakeouing, after pressure transducer output valve is stable, gather the pressure transducer output valve in each storehouse, and according to the calibration coefficient k of each pressure transducer ₀, k ₁, calculate the pressure values of each sensor, and form sample set wherein, W _mfor detected silo enters grain weight, for detected silo base area, M is sample number.For given sample set S, by W _mvalue and each component value all specification is to [-Δ, Δ], wherein Δ is constant, 0 < Δ≤2, and adopts common support vector regression learning algorithm to realize model modeling shown in formula (9).

(4) real storehouse weight detecting.

If system is demarcated, detect base pressure sensor and export, according to the calibration coefficient k of each pressure transducer ₀, k ₁, calculate the pressure values of each sensor, and utilize formula (9) institute representation model to carry out quantity of stored grains in granary detection.

Be presented above concrete embodiment, but the present invention is not limited to described embodiment.Basic ideas of the present invention are above-mentioned basic scheme, and for those of ordinary skill in the art, according to instruction of the present invention, designing the model of various distortion, formula, parameter does not need to spend creative work.The change carried out embodiment without departing from the principles and spirit of the present invention, amendment, replacement and modification still fall within the scope of protection of the present invention.

Claims (4)

1. based on the method for detecting quantity of stored grains in granary of check point pressure values sequence, it is characterized in that, step is as follows:

1) on silo bottom surface, arrange a circle pressure transducer, each pressure transducer is d with the vertical range closest to exterior wall;

2) detect the output valve of each sensor, suppose s _ithe pressure sensor measurements of point is QBL (s _i), i=1 ..., n _bL, n _bLfor arranged pressure transducer number, to arranged n _bLindividual silo base pressure sensor is numbered, and according to numbering composition sensor sequence Q _bL, $Q_{\mathrm{BL}}=(Q_{\mathrm{BL}}\left(s_1\right),Q_{\mathrm{BL}}\left(s_2\right),...,Q_{\mathrm{BL}}\left(s_{n_{\mathrm{BL}}}\right));$ According to detection model (9)

$\hat{W}=A_B(\underset{j=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_j\exp (-\mathrm{\&gamma;}{\left|\right|Q_{\mathrm{BL}}-Q_{\mathrm{BL}}^j\left|\right|}^2)+b)---\left(9\right)$

Calculate detected granary storage weight to estimate wherein A _bfor detected silo base area, γ be greater than 0 parameter; α _j, b is for training the parameter obtained, α by training sample and SVM _j≠ 0; for corresponding support vector point, j=1 ..., l, l are support vector number, and model parameter is determined by calibration process.

2. method for detecting quantity of stored grains in granary according to claim 1, is characterized in that, described scaling method comprises pressure sensor calibrating: the pass of pressure transducer output valve and pressure is

Q＝k ₀+k ₁s(Q) (10)

Wherein, Q is strong by being exerted pressure; S (Q) is sensor output value; k ₀, k ₁for the calibration coefficient of sensor.

3. method for detecting quantity of stored grains in granary according to claim 2, it is characterized in that, by above-mentioned steps 1) described arrangement, the sensor installed and demarcated is arranged in more than 6 silos, enter grain to buying securities with all one's capital and shakeouing, after pressure transducer output valve is stable, gather the pressure transducer output valve in each storehouse, and according to the calibration coefficient k of each pressure transducer ₀, k ₁, calculate the pressure values of each sensor, according to pressure transducer numbering, for any given detected quantity of stored grains in granary W _mwith detected silo base area its corresponding base pressure sensor output value sequence is expressed as $Q_{\mathrm{BL}}^m,Q_{\mathrm{BL}}^m=({Q_{\mathrm{BL}}}^m\left(s_1\right),{Q_{\mathrm{BL}}}^m\left(s_2\right),...,{Q_{\mathrm{BL}}}^m\left(s_{n_{\mathrm{BL}}}\right)),{Q_{\mathrm{BL}}}^m\left(s_i\right)$ For silo bottom surface s _ithe pressure values of point, then corresponding sample is for the multiple weight of silo, then forming sample set is wherein M is sample number; For given sample set support vector regression learning algorithm is adopted to realize model modeling shown in formula (9).

4. method for detecting quantity of stored grains in granary according to claim 1, is characterized in that, described d=2 ~ 4 meter.