CN104296845A - Granary stored grain weight detection method and device based on optimum bottom pressure intensity measurement point - Google Patents

Abstract

The invention relates to a granary stored grain weight detection method and device based on an optimum bottom pressure intensity measurement point, and provides the granary stored grain quantity detection method based on the optimum bottom pressure intensity measurement point according to the change characteristics of the granary bottom pressure intensity distribution and pressure intensity measurement value. The core technology comprises a consistency measurement model of the bottom pressure intensity measurement points, a position detection method of the optimum bottom pressure intensity measurement point, a granary weight detection model based on the optimum bottom pressure intensity measurement point, and a system calibration and modeling method and the like. The method is high in detection precision; the needed pressure sensors are few, and only 2-3 pressure sensors are needed; the method is strong in versatility, and is suitable for quantity detection of stored grains in a plurality of granary structures; and the method has huge application value, and provides a new technological means for guaranteeing nation grain quantity safety.

Description

Based on granary storage gravimetric analysis sensing method and the device of best bottom surface pressure measurement point

Technical field

The present invention relates to the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point and device.

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, price is low, requires grain 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 " the grain reserve in grain depot quantity measuring method based on pressure transducer " (license number: ZL201010240167.7), patent " horizontal warehouse silo grain storage quantity detection method " (license number: ZL201210148522) all relates to grain storage quantity, i.e. grain storage weight detecting.Specifically, ZL201010240167.7 relates to and exports the computation model of quantity of stored grains in granary of average and concrete system calibrating method based on silo bottom surface, side pressure sensor.ZL201210148522 relate to 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.

Above-mentioned two kinds of methods have self characteristic and advantage.But all need a large amount of sensors, detection system cost is higher.

Summary of the invention

The object of this invention is to provide the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point and device, many in order to the number of sensors solving existing detection method needs, the problem that detection system cost is high.

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

Based on the granary storage gravimetric analysis sensing method of best bottom surface pressure measurement point, step is as follows:

Step 1) chooses best bottom surface pressure measurement point, and best bottom surface pressure measurement point detects the high measurement point of consistance;

Step 2) ask the average measurement value of repetitive measurement, bring the grain storage weight detecting model based on bottom surface pressure into, calculate grain storage weight.

Best bottom surface pressure measurement point is mainly positioned at the region having certain distance with side and grain-entering mouth.

The selection of measuring point rule that consistance is high is: be provided with n _wplant grain storage weight W _i, i=1 ..., n _w, often kind of grain storage weight all measures n _msecondary; For arbitrary given silo bottom surface pressure measurement point s, granary storage weight W _ithe measured value of kth time measurement point s when measuring be Q _b(s, W _i, k), k=1 ..., n _m, definition

$\mathrm{CM}\left(s\right)=\frac{100}{n_w}\underset{i}{\mathrm{\Σ}}\left(\frac{\underset{k}{\max }\left(Q_B(s,W_i,k)\right)-\underset{k}{\min \left(Q_B(s,W_i,k)\right)}}{\frac{1}{n_k}\underset{k}{\mathrm{\Σ}}{Q}_B(s,W_i,k)}\right)---\left(1\right)$

For the consistency metric of bottom surface pressure measurement point s; For arbitrary measurement point s, CM (s) is less, then its measurement consistance is higher, otherwise then contrary.

The described grain storage weight detecting model based on bottom surface pressure is:

$\hat{W}=A_B(a_0+a_1{\stackrel{\‾}{Q}}_{\mathrm{BPP}}\left(s\right)+a_2{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{\left(s\right)}^2)---\left(2\right)$

Wherein, for the silo weight based on best bottom surface pressure measurement point is estimated; A _bfor silo base area; for the pressure measurement average of all best bottom surfaces pressure measurement point, be the pressure measurement of i-th best bottom surface pressure measurement point, n _oPfor the number of best bottom surface pressure measurement point; a ₀, a ₁, a ₂for model coefficient.

N weight and pressure intensity testing sample point are obtained wherein, W _ifor entering grain weight, for the average of corresponding best bottom surface pressure measurement point measured value, in described (2), each coefficient formulas is

a ₀＝(b ₀(c ₂c ₄-c ₃₃)+b ₁(c ₂₃-c ₁₄)+b ₂(c ₁₃-c ₂₂))/Δ (3)

a ₁＝(b ₀(c ₂₃-c ₁₄)+b ₁(c ₀c ₄-c ₂₂)+b ₂(c ₁₂-c ₀₃))/Δ (4)

A ₂=(b ₀(c ₁₃-c22)+b ₁(c ₁₂-c ₀₃)+b ₂(c ₀₂-c ₁₁))/Δ (5) wherein, Δ=(c ₀₂-c ₁₁) c ₄+ 2.c ₁₂c ₃-c ₂₂c ₂-c ₀* c ₃₃; c ₁₁=c ₁c ₁; c ₂₂=c ₂c ₂; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₀₂=c ₀c ₂; c ₀₃=c ₀c ₃; c ₁₂=c ₁c ₂; c ₁₃=c ₁c ₃; c ₁₄=c ₁c ₄; c ₂₃=c ₂c ₃; $c_2=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/{W_i}^2;c_3=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/{W_i}^2;c_4=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/{W_i}^2;c_1=\underset{i}{\mathrm{\Σ}}1/{W_i}^2;b_0=\underset{i}{\mathrm{\Σ}}1/W_i;$ $b_1\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i.$

It is as follows that a kind of optimum measuring point experiment detection method comprises step,

(1) initial option check point position placement sensor: have the principle of certain distance according to side and grain-entering mouth, and take into account the requirement of usually entering grain mode, conveniently go out into grain and avoid sensor degradation, selecting check point position and placement sensor; Sensor and lateral distance d are taken as 2 ~ 3m, and each transducer spacing is not less than 1m.

(2) enter grain: enter grain height at every turn and fix, can be taken as about 0.9 meter, top is shakeout, ensure that the grain heap end face width above sensor is not less than 1 meter, gather each sensor values.

(3) enter grain 3 ~ 4 times, calculate consistance, calculated CM (s) value of each point by formula (1), choose CM (s) be less than setting threshold value for best bottom surface pressure measurement point.

A kind of granary storage Weight detecting device, comprises as lower module:

Module 1), choose best bottom surface pressure measurement point, best bottom surface pressure measurement point detects the high measurement point of consistance, relevant with measuring position;

Module 2), ask the average measurement value of repetitive measurement, bring the grain storage weight detecting model based on bottom surface pressure into, calculate grain storage weight.

The selection of measuring point rule that consistance is high is: be provided with n _wplant grain storage weight W _i, i=1 ..., n _w, often kind of grain storage weight all measures n _msecondary; For arbitrary given silo bottom surface pressure measurement point s, granary storage weight W _ithe measured value of kth time measurement point s when measuring be Q _b(s, W _i, k), k=1 ..., n _m, definition

$\mathrm{CM}\left(s\right)=\frac{100}{n_w}\underset{i}{\mathrm{\Σ}}\left(\frac{\underset{k}{\max }\left(Q_B(s,W_i,k)\right)-\underset{k}{\min \left(Q_B(s,W_i,k)\right)}}{\frac{1}{n_k}\underset{k}{\mathrm{\Σ}}{Q}_B(s,W_i,k)}\right)---\left(1\right)$

For the consistency metric of bottom surface pressure measurement point s; For arbitrary measurement point s, CM (s) is less, then its measurement consistance is higher, otherwise then contrary.

The described grain storage weight detecting model based on bottom surface pressure is:

$\hat{W}=A_B(a_0+a_1{\stackrel{\‾}{Q}}_{\mathrm{BPP}}\left(s\right)+a_2{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{\left(s\right)}^2)---\left(2\right)$

Wherein, for the silo weight based on best bottom surface pressure measurement point is estimated; A _bfor silo base area; for the pressure measurement average of all best bottom surfaces pressure measurement point, be the pressure measurement of i-th best bottom surface pressure measurement point, n _oPfor the number of best bottom surface pressure measurement point; a ₀, a ₁, a ₂for model coefficient.

N weight and pressure intensity testing sample point are obtained wherein, W _ifor entering grain weight, for the average of corresponding best bottom surface pressure measurement point measured value, in described (2), each coefficient formulas is

a ₀＝(b ₀(c ₂c ₄-c ₃₃)+b ₁(c ₂₃-c ₁₄)+b ₂(c ₁₃-c ₂₂))/Δ (3)

a ₁＝(b ₀(c ₂₃-c ₁₄)+b ₁(c ₀c ₄-c ₂₂)+b ₂(c ₁₂-c ₀₃))/Δ (4)

A ₂=(b ₀(c ₁₃-c22)+b ₁(c ₁₂-c ₀₃)+b ₂(c ₀₂-c ₁₁))/Δ (5) wherein, Δ=(c ₀₂-c ₁₁) c ₄+ 2.c ₁₂c ₃-c ₂₂c ₂-c ₀* c ₃₃; c ₁₁=c ₁c ₁; c ₂₂=c ₂c ₂; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₀₂=c ₀c ₂; c ₀₃=c ₀c ₃; c ₁₂=c ₁c ₂; c ₁₃=c ₁c ₃; c ₁₄=c ₁c ₄; c ₂₃=c ₂c ₃; $c_2=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/{W_i}^2;c_3=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/{W_i}^2;c_4=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/{W_i}^2;c_1=\underset{i}{\mathrm{\Σ}}1/{W_i}^2;b_0=\underset{i}{\mathrm{\Σ}}1/W_i;$ $b_1\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\Σ}}{\stackrel{\‾}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i.$

Granary storage gravimetric analysis sensing method of the present invention, required measurement point is few, and only need 2 ~ 3, measuring accuracy is high, but high to the performance requirement of sensor, particularly the consistance of reproducibility error and sensor performance.

Further, The present invention gives the high measurement point distribution rule of consistance and experiment selecting step, to instruct practical operation.

Further, The present invention gives a kind of silo weight detecting model based on best bottom surface pressure measurement point, rapidly, accurately can calculate grain storage weight after demarcating.

Accompanying drawing explanation

Fig. 1 horizontal warehouse pressure transducer is arranged;

Fig. 2 silo pressure transducer is arranged;

The sensor that Fig. 3 optimum measuring point detects is arranged and Jin Liang district;

Fig. 4 horizontal warehouse the 2nd arranges CM (s) value of measurement point;

Fig. 5 silo the 2nd encloses CM (s) value of measurement point;

Fig. 6 detection method implementation step schematic diagram.

Embodiment

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

The solution of the present invention is: a kind of grain storage gravimetric analysis sensing method, and step is as follows:

Step 1) chooses best bottom surface pressure measurement point, and best bottom surface pressure measurement point detects the high measurement point of consistance;

Step 2) ask the average measurement value of repetitive measurement, bring the grain storage weight detecting model based on bottom surface pressure into, calculate grain storage weight.

For given silo and grain storage weight, how to select the bottom surface pressure measurement point of limited quantity, making its measured value average enter in grain to have very high consistance at each time is the sufficient and necessary condition ensureing weight detecting precision, and consistance height then accuracy of detection is necessarily high, otherwise then contrary.According to silo bottom surface pressure distribution character, the unevenness distributed due to grain composition and volume mass in grain heap, grain piles and the factor such as each consistance of pressure transducer stress surface exposure level and the randomness of side pressure affects, all there is randomness in various degree in the measured value of silo bottom surface pressure measurement point, but degree is obviously different, there is the measurement point that the randomness of a greater number is little, show that the measured value of these measurement points has very strong robustness.

For consistance, according to foregoing description, multiple concrete evaluation rule can be had.

Such as, for given silo and grain storage kind, suppose there is n _wplant grain storage weight W _i, i=1 ..., n _w, often kind of grain storage weight all measures n _msecondary.For arbitrary given silo bottom surface pressure measurement point s, granary storage weight W _ithe measured value of kth time measurement point s when measuring be Q _b(s, W _i, k), k=1 ..., n _m, definition

$\mathrm{CM}\left(s\right)=\frac{100}{n_w}\underset{i}{\mathrm{\Σ}}\left(\frac{\underset{k}{\max }\left(Q_B(s,W_i,k)\right)-\underset{k}{\min \left(Q_B(s,W_i,k)\right)}}{\frac{1}{n_k}\underset{k}{\mathrm{\Σ}}{Q}_B(s,W_i,k)}\right)---\left(1\right)$

For the consistency metric of bottom surface pressure measurement point s.As can be seen from formula (1), CM (s) represents the average percent discretization error of the measured value of measurement point s.Obviously, for arbitrary measurement point s, CM (s) is less, then its measurement consistance is higher, otherwise then contrary.For given silo, grain storage kind with enter grain mode, if CM (s) < is Th, Th is given threshold parameter, 0 < Th, then measurement point s is best bottom surface pressure measurement point.

For another example, variance is adopted to evaluate consistance.

Below, by experiment data introduction best bottom surface pressure measurement point choose principle, adopt formula (1) to evaluate consistance.

For the horizontal warehouse shown in Fig. 1 and Fig. 2 and silo, horizontal warehouse pressure transducer divides 6 arrangements to put, as shown in figure orbicular spot, often arrange 15, totally 90, the 1st row's measurement point is numbered 1# ~ 15#, 2nd row's measurement point is numbered 16# ~ 30#, other each scheduling number the like.Silo pressure transducer divides 3 circles to arrange, the 1st circle 20 (numbering 1# ~ 20#), the 2nd circle 20 (numbering 21# ~ 40#), the 3rd circle 15 (numbering 41# ~ 55#), totally 55.

The long 9m of horizontal warehouse, wide 4.2m, area is 37.8m2, C _b/ A _b≈ 0.698.Silo diameter is 6m, and area is 28.26m2, C _b/ A _b≈ 0.67.Two kinds of silos all belong to Minitype granary, C _b/ A _brelatively large.A _bfor grain heap base area, C _bfor bottom surface girth.Experiment types of food is wheat, and horizontal warehouse carries out 4 experiments altogether, divides and enters grain 6 times, enter about 1 meter, grain at every turn and shakeout in each experiment.Silo carries out 3 experiments altogether, and experiment condition and horizontal warehouse first three situation is identical, divides and enters grain 8 times, enter about 1 meter, grain at every turn and shakeout in each experiment.According to 4 experimental datas of horizontal warehouse, get Th=8, get the set of grain storage weight for { 35,60,90,120,150,168}, utilize interpolation to obtain the measurement estimated value of each check point, the consistency metric of the bottom surface pressure measurement point of calculated horizontal warehouse is as shown in table 1.

According to 3 experimental datas of silo, get Th=10, get the set of grain storage weight for 30,50,70,90,110,130,150,

170}, the consistency metric result of calculation of calculated silo bottom surface pressure measurement point is as shown in table 2, and add the black measurement point little for CM (s), the expression sensor be worth for 1E32 has fault.The measurement point that Fig. 1, Fig. 2 add circle is the little measurement point position view in bottom surface CM (s), and in figure, grain outlet is also grain-entering mouth.Can find out:

(1) pressure measurement of each measurement point of different experiments has significant inconsistency and randomness, but inconsistent degree is obviously different, there is the inconsistency of a greater number and the very little measurement point of randomness.Such as horizontal warehouse 17#, 22#, 24#, 52# and 67# etc., silo 26#, 29#, 30#, 31#, 34#, 51# and 52# etc.

(2) for horizontal warehouse, the measurement point that CM (s) is little is mainly positioned at the 2nd row, and for silo, the measurement point that CM (s) is little is mainly positioned at the 2nd circle.Therefore, the measurement point that CM (s) is little is mainly positioned at the region having certain distance with side and grain-entering mouth.There is certain distance can reduce the impact of side friction power with side, have certain distance can reduce impact into grain percussive action with grain-entering mouth.The little measurement point of CM (s) main with side friction power with to enter grain mode relevant.For normally used large granary, it is generally constant that it enters grain mode, the measurement point that the stable MD (s) in location is little.

The technological difficulties of silo weight detecting are the randomness of bottom surface and side pressure measurement, and the measured value of bottom surface pressure measurement point all exists randomness in various degree, and how to overcome this randomness is silo weight detecting technical field problem urgently to be resolved hurrily always.Best bottom surface pressure measurement point is the measurement point that measured value randomness is little, show that the measured value of these measurement points has very strong robustness, unevenness distribute by grain composition and volume mass in grain heap, grain pile affects little with the factor such as each consistance of pressure transducer stress surface exposure level and the randomness of side pressure, the pressure detection of being put by these and realize silo weight detecting and can be the detection of silo quantity and provide new approach.

The consistency metric result of calculation of table 1 horizontal warehouse bottom surface pressure measurement point

The consistency metric result of calculation of table 2 silo bottom surface pressure measurement point

Be presented above the regularity of distribution of best bottom surface pressure measurement point at different silo, according to this rule, can people for choosing optimum measuring point, also can obtain concrete best bottom surface pressure measurement point by experimental technique.

Specific experiment method is as follows:

Best bottom surface pressure measurement select position and silo concrete structure, to enter grain mode relevant, while also relevant with the duplicate measurements error consistency of used pressure transducer.Therefore, for the silo of given barn structure and size, if the duplicate measurements error consistency of pressure transducer is high, then to select position only relevant with entering grain mode for best bottom surface pressure measurement.If it is close to enter grain mode, barn structure is close with size, then best bottom surface pressure measurement point position is also substantially identical.If the duplicate measurements error consistency of pressure transducer is poor, represents that sensor does not have interchangeability, then must carry out the detection of optimum measuring point position to each silo.

Because the measurement point that CM (s) is little is mainly positioned at the region having certain distance with side and grain-entering mouth.Therefore according to this principle, the sensor that optimum measuring point position in this paper is detected is arranged and is entered grain grain heap position as shown in Figure 3, wherein ab, bc are the sensing station candidate region of supposition, actual detect in should usually enter grain mode according to silo and require and detection demand and choose reasonable; Additive color region is Jin Liang district, with reduce experiment actual enter flow vector, reduce experimental cost.

Fig. 4 is that horizontal warehouse the 2nd is arranged consistency metric CM (s) of 15 measurement points and enters the relation of grain height, and Fig. 5 is that silo the 2nd encloses consistency metric CM (s) of 20 measurement points and enters the relation of grain height.As can be seen from the figure, each point is relevant with entering grain height according to the order of CM (s) value size sequence.When entering grain height and being less than 3 meters, each dot sequency is large with entering grain height change, and when entering after grain height is greater than 4 meters, order is then substantially fixing.Therefore, optimum measuring point position in this paper test experience step is as follows:

(1) initial option check point position placement sensor.The principle of certain distance should be had according to side and grain-entering mouth, and take into account the requirement of usually entering grain mode, conveniently go out into grain and avoid sensor degradation, and choose reasonable check point position placement sensor.In Fig. 3, sensor and lateral distance d(sensor and lateral distance d, for horizontal warehouse, refer to nearest lateral distance) be taken as 2 ~ 3m, each transducer spacing is taken as about 1m.

(2) enter grain height to fix, can be taken as about 0.9 meter, top is shakeout at every turn, ensures that the grain heap end face width above sensor is not less than 1 meter, gathers each sensor values.

(3) repeat 3 ~ 4 times, calculated CM (s) value of each point by formula (1), according to value size sequence, what CM (s) was little is then best bottom surface pressure measurement point.

Step 2 in the present invention program) in, relate to the grain storage weight detecting model based on bottom surface pressure, the grain storage weight detecting model based on bottom surface pressure of the prior art can be chosen, a kind of preferred model is provided below.

Best bottom surface pressure measurement point is the measurement point that in measuring at each time, measured value randomness is little, shows that the measured value of these measurement points has very strong robustness.According to the pressure measurement of best bottom surface pressure measurement point and the relation of granary storage capacity weight, the silo weight detecting model based on best bottom surface pressure measurement point in this paper as the formula (2).

$\hat{W}=A_B(a_0+a_1{\stackrel{\&OverBar;}{Q}}_{\mathrm{BPP}}\left(s\right)+a_2{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{\left(s\right)}^2)---\left(2\right)$

Wherein, for the silo weight based on best bottom surface pressure measurement point is estimated; A _bfor silo base area; for the pressure measurement average of all best bottom surfaces pressure measurement point, be the pressure measurement of i-th best bottom surface pressure measurement point, n _oPfor the number of best bottom surface pressure measurement point; a ₀, a ₁, a ₂for model coefficient.

For the silo grain heap weight detecting model shown in formula (2), suppose to obtain n weight and pressure intensity testing sample point wherein, W _ifor entering grain weight, for the average of corresponding best bottom surface pressure measurement point measured value, can in push type (2) each coefficient formulas be then

a ₀＝(b ₀(c ₂c ₄-c ₃₃)+b ₁(c ₂₃-c ₁₄)+b ₂(c ₁₃-c ₂₂))/Δ (3)

a ₁＝(b ₀(c ₂₃-c ₁₄)+b ₁(c ₀c ₄-c ₂₂)+b ₂(c ₁₂-c ₀₃))/Δ (4)

A ₂=(b ₀(c ₁₃-c22)+b ₁(c ₁₂-c ₀₃)+b ₂(c ₀₂-c ₁₁))/Δ (5) wherein, Δ=(c ₀₂-c ₁₁) c ₄+ 2.c ₁₂c ₃-c ₂₂c ₂-c ₀* c ₃₃; c ₁₁=c ₁c ₁; c ₂₂=c ₂c ₂; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₀₂=c ₀c ₂; c ₀₃=c ₀c ₃; c ₁₂=c ₁c ₂; c ₁₃=c ₁c ₃; c ₁₄=c ₁c ₄; c ₂₃=c ₂c ₃; $c_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/{W_i}^2;c_3=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/{W_i}^2;c_4=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/{W_i}^2;c_1=\underset{i}{\mathrm{\&Sigma;}}1/{W_i}^2;b_0=\underset{i}{\mathrm{\&Sigma;}}1/W_i;$ $b_1\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i.$

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

(1) demarcation Jin Liang district is set up.According to the position of selected best bottom surface pressure measurement point and placement sensor, and utilize sandbag to set up demarcation Jin Liang district, the distance of sandbag and sensor is about 3 ~ 4 meters, to reduce the instability of wall of sandbags to the impact of demarcating Jin Liang district pressure distribution, wall of sandbags height 1.5 ~ 2 meters, pressure transducer is evenly arranged.

(2) nominal data obtains.For demarcation Jin Liang district, progressively enter grain, often criticize and shakeout after 0.5 meter, grain, record into grain weight and each pressure sensor value, and according to demarcating the ratio of Jin Liang district area and the silo total area, the whole silo calculating corresponding height enters grain weight.3 ~ 4 groups of experimental datas can be obtained like this.Then by normal mode loading, after to be done, record into grain general assembly (TW) and silo base pressure sensor values, obtainable like this 4 ~ 5 groups of data.

(3) detection model modeling.

Because the grain heap weight detecting model shown in formula (2) is fairly simple, during modeling, need less data, therefore, directly utilize obtain 4 ~ 5 groups of data and can obtain desirable Detection results.

For repeatedly calibration experiment, utilize each calibration experiment data to set up the model shown in formula (2) respectively, and utilize a ₀, a ₁, a ₂the average of model coefficient sets up detection model.

Provide experimental data below to prove actual effect of the present invention.

According to the pressure measurement mean data of the little measurement point (17#, 22# and 24#) of 3 CM (s) that horizontal warehouse the 2nd is arranged, utilize 2 experiments (experiment 2 and and experiment 3) data modeling, as the formula (6), the granary storage Weight computation result of each experiment is as shown in table 3 to table 6 for the forecast model set up by formula (2).

$\hat{W}=A_B(0.05945+0.08099{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}\left(s\right)+0.0002528{\left({\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}\left(s\right)\right)}^2)---\left(6\right)$

According to the pressure measurement mean data of the little measurement point (21#, 29#) of 2 CM (s) that silo the 2nd encloses, utilize 2 experiments (experiment 2 and and experiment 3) data modeling, as the formula (7), the granary storage Weight computation result of each experiment is as shown in table 7 to table 9 for the forecast model set up by formula (2).

$\hat{W}=A_B(0.16002+0.06602{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}\left(s\right)+0.000592725{\left({\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}\left(s\right)\right)}^2)---\left(7\right)$

As can be seen from the granary storage Weight computation result of the above measurement point average based on best bottom surface pressure measurement point, except the little situation of grain storage weight, the testing result of other check point is more satisfactory.Therefore, this granary storage monitoring weight method, required measurement point is few, and only need 2 ~ 3, measuring accuracy is high, but high to the performance requirement of sensor, particularly the consistance of reproducibility error and sensor performance.

For the horizontal warehouse shown in Fig. 1, method before utilization, all 90 measurement point data construct the testing result of detection model as shown in table 10 to table 13.For the silo shown in Fig. 2, before utilization method and all 55 measurement point data construct the testing result of detection model as shown in table 14 to table 16.

As can be seen from the granary storage Weight computation result of method before above utilization, count a lot although measure, when the larger situation of grain storage weight, still there is predicting the outcome of part point and exceed 3%.This shows that this patent put forward the methods has higher measurement accuracy and needs little base pressure sensor.

Quantity of stored grains in granary testing process based on best bottom surface pressure measurement point proposed by the invention can be implemented by Fig. 6 illustrated embodiment, and 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) position of best bottom surface pressure measurement point is detected

There is the principle of certain distance according to side and grain-entering mouth, and take into account the requirement of usually entering grain mode, conveniently go out into grain and avoid sensor degradation, choose reasonable check point placement sensor.According to Fig. 3, arrange the sensor that optimum measuring point detects and Jin Liang district, in figure, sensor and lateral distance d are taken as 2 ~ 3m, and each transducer spacing is taken as about 1m.Enter 0.9 meter, grain, top is shakeout at every turn, ensures that the grain heap end face width above sensor is not less than 1 meter, gathers each sensor values.Repeat 3 ~ 4 times, calculated CM (s) value of each point by formula (1), according to value size sequence, what CM (s) was little is then best bottom surface pressure measurement point.

(3) base pressure sensor is installed

According to detected best bottom surface pressure measurement point, select the best bottom surface pressure measurement point that 2 ~ 5 CM (s) are little, and placement sensor.

(4) system calibrating and modeling

According to the location arrangements sensor of best bottom surface pressure measurement point, and utilize sandbag to set up demarcation Jin Liang district, the distance of sandbag and sensor is about 3 ~ 4 meters, wall of sandbags height 1.5 ~ 2 meters.Progressively enter grain, often criticize and shakeout after 0.5 meter, grain, record into grain weight and each pressure sensor value, and according to demarcating the ratio of Jin Liang district area and the silo total area, the whole silo calculating corresponding height enters grain weight.3 ~ 4 groups of experimental datas can be obtained like this.Then by normal mode loading, after to be done, record into grain general assembly (TW) and silo base pressure sensor values, obtainable like this 4 ~ 5 groups of data.Utilize institute obtain 4 ~ 5 groups of data, by formula (2) to formula (5) structure detection model.

For repeatedly calibration experiment, utilize each calibration experiment data to set up the model shown in formula (2) respectively, and utilize a ₀, a ₁, a ₂the average of model coefficient sets up detection model.

(5) real storehouse weight detecting.

If system is demarcated, detect base pressure sensor and export and utilize formula (2) institute representation model to carry out quantity of stored grains in granary detection.

The present invention chooses optimum measuring point and measures, instead of need to arrange more, more intensive sensor as prior art, testing result is made to reflect more, more fully environmental parameter more comprehensively, hardly realize, although it is very abundant that continuous increase sensor seems the data volume made, but due to the singularity of silo, enter grain different with the measurement result impact of grain storage process on diverse location, abundant quantity not only can not improve accuracy in detection, may be mixed into more random information on the contrary and water down valuable measurement data measurement accuracy is reduced.So the present invention acts in a diametrically opposite way, the detection scheme of employing not only greatly reduces number of sensors, can also improve measurement accuracy.

Be presented above a kind of concrete embodiment, but the present invention is not limited to described embodiment.Basic ideas of the present invention are such 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 (10)

1., based on the granary storage gravimetric analysis sensing method of best bottom surface pressure measurement point, it is characterized in that, step is as follows:

Step 1) chooses best bottom surface pressure measurement point, and best bottom surface pressure measurement point detects the high measurement point of consistance;

Step 2) ask the average measurement value of repetitive measurement, bring the grain storage weight detecting model based on bottom surface pressure into, calculate grain storage weight.

2. the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point according to claim 1, is characterized in that, best bottom surface pressure measurement point is mainly positioned at the region having certain distance with side and grain-entering mouth.

3. the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point according to claim 1, is characterized in that, the selection of measuring point rule that consistance is high is: be provided with n _wplant grain storage weight W _i, i=1 ..., n _w, often kind of grain storage weight all measures n _msecondary; For arbitrary given silo bottom surface pressure measurement point s, granary storage weight W _ithe measured value of kth time measurement point s when measuring be Q _b(s, W _i, k), k=1 ..., n _m, definition

$\mathrm{CM}\left(s\right)=\frac{100}{n_w}\underset{i}{\mathrm{\&Sigma;}}\left(\frac{\underset{k}{\max }\left(Q_B(s,W_i,k)\right)-\underset{k}{\min \left(Q_B(s,W_i,k)\right)}}{\frac{1}{n_k}\underset{k}{\mathrm{\&Sigma;}}{Q}_B(s,W_i,k)}\right)---\left(1\right)$

For the consistency metric of bottom surface pressure measurement point s; For arbitrary measurement point s, CM (s) is less, then its measurement consistance is higher, otherwise then contrary.

4. the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point according to claim 1, is characterized in that, the described grain storage weight detecting model based on bottom surface pressure is:

$\hat{W}=A_B(a_0+a_1{\stackrel{\&OverBar;}{Q}}_{\mathrm{BPP}}\left(s\right)+a_2{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{\left(s\right)}^2)---\left(2\right)$ Wherein, for the silo weight based on best bottom surface pressure measurement point is estimated; A _bfor silo base area; for the pressure measurement average of all best bottom surfaces pressure measurement point, be the pressure measurement of i-th best bottom surface pressure measurement point, n _oPfor the number of best bottom surface pressure measurement point; a ₀, a ₁, a ₂for model coefficient.

5. the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point according to claim 4, is characterized in that, has obtained n weight and pressure intensity testing sample point wherein, W _ifor entering grain weight, for the average of corresponding best bottom surface pressure measurement point measured value, in described (2), each coefficient formulas is

a ₀＝(b ₀(c ₂c ₄-c ₃₃)+b ₁(c ₂₃-c ₁₄)+b ₂(c ₁₃-c ₂₂))/Δ (3)

a ₁＝(b ₀(c ₂₃-c ₁₄)+b ₁(c ₀c ₄-c ₂₂)+b ₂(c ₁₂-c ₀₃))/Δ (4)

A ₂=(b ₀(c ₁₃-c22)+b ₁(c ₁₂-c ₀₃)+b ₂(c ₀₂-c ₁₁))/Δ (5) wherein, Δ=(c ₀₂-c ₁₁) c ₄+ 2.c ₁₂c ₃-c ₂₂c ₂-c ₀* c ₃₃; c ₁₁=c ₁c ₁; c ₂₂=c ₂c ₂; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₀₂=c ₀c ₂; c ₀₃=c ₀c ₃; c ₁₂=c ₁c ₂; c ₁₃=c ₁c ₃; c ₁₄=c ₁c ₄; c ₂₃=c ₂c ₃; $c_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/{W_i}^2;c_3=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/{W_i}^2;c_4=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/{W_i}^2;c_1=\underset{i}{\mathrm{\&Sigma;}}1/{W_i}^2;b_0=\underset{i}{\mathrm{\&Sigma;}}1/W_i;$ $b_1\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i.$

6. the granary storage gravimetric analysis sensing method based on best bottom surface pressure measurement point according to claim 4, is characterized in that, it is as follows that a kind of optimum measuring point experiment detection method comprises step,

(1) initial option check point position placement sensor: have the principle of certain distance according to side and grain-entering mouth, and take into account the requirement of usually entering grain mode, conveniently go out into grain and avoid sensor degradation, selecting check point position and placement sensor; Sensor and lateral distance d are taken as 2 ~ 3m, and each transducer spacing is not less than 1m;

(2) enter grain: enter grain height at every turn and fix, can be taken as about 0.9 meter, top is shakeout, ensure that the grain heap end face width above sensor is not less than 1 meter, gather each sensor values;

(3) enter grain 3 ~ 4 times, calculate consistance, calculated CM (s) value of each point by formula (1), choose CM (s) be less than setting threshold value for best bottom surface pressure measurement point.

7. a granary storage Weight detecting device, is characterized in that, comprises as lower module:

Module 1), choose best bottom surface pressure measurement point, best bottom surface pressure measurement point detects the high measurement point of consistance, relevant with measuring position;

Module 2), ask the average measurement value of repetitive measurement, bring the grain storage weight detecting model based on bottom surface pressure into, calculate grain storage weight.

8. a kind of granary storage Weight detecting device according to claim 7, is characterized in that, the selection of measuring point rule that consistance is high is: be provided with n _wplant grain storage weight W _i, i=1 ..., n _w, often kind of grain storage weight all measures n _msecondary; For arbitrary given silo bottom surface pressure measurement point s, granary storage weight W _ithe measured value of kth time measurement point s when measuring be Q _b(s, W _i, k), k=1 ..., n _m, definition

$\mathrm{CM}\left(s\right)=\frac{100}{n_w}\underset{i}{\mathrm{\&Sigma;}}\left(\frac{\underset{k}{\max }\left(Q_B(s,W_i,k)\right)-\underset{k}{\min \left(Q_B(s,W_i,k)\right)}}{\frac{1}{n_k}\underset{k}{\mathrm{\&Sigma;}}{Q}_B(s,W_i,k)}\right)---\left(1\right)$

For the consistency metric of bottom surface pressure measurement point s; For arbitrary measurement point s, CM (s) is less, then its measurement consistance is higher, otherwise then contrary.

9. a kind of granary storage Weight detecting device according to claim 7, is characterized in that, the described grain storage weight detecting model based on bottom surface pressure is:

$\hat{W}=A_B(a_0+a_1{\stackrel{\&OverBar;}{Q}}_{\mathrm{BPP}}\left(s\right)+a_2{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{\left(s\right)}^2)---\left(2\right)$ Wherein, for the silo weight based on best bottom surface pressure measurement point is estimated; AB is silo base area; for the pressure measurement average of all best bottom surfaces pressure measurement point, be the pressure measurement of i-th best bottom surface pressure measurement point, n _oPfor the number of best bottom surface pressure measurement point; a ₀, a ₁, a ₂for model coefficient.

10. a kind of granary storage Weight detecting device according to claim 9, is characterized in that, has obtained n weight and pressure intensity testing sample point wherein, W _ifor entering grain weight, for the average of corresponding best bottom surface pressure measurement point measured value, in described (2), each coefficient formulas is

a ₀＝(b ₀(c ₂c ₄-c ₃₃)+b ₁(c ₂₃-c ₁₄)+b ₂(c ₁₃-c ₂₂))/Δ (3)

a ₁＝(b ₀(c ₂₃-c ₁₄)+b ₁(c ₀c ₄-c ₂₂)+b ₂(c ₁₂-c ₀₃))/Δ (4)

A ₂=(b ₀(c ₁₃-c22)+b ₁(c ₁₂-c ₀₃)+b ₂(c ₀₂-c ₁₁))/Δ (5) wherein, Δ=(c ₀₂-c ₁₁) c ₄+ 2.c ₁₂c ₃-c ₂₂c ₂-c ₀* c ₃₃; c ₁₁=c ₁c ₁; c ₂₂=c ₂c ₂; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₃₃=c ₃c ₃; c ₀₂=c ₀c ₂; c ₀₃=c ₀c ₃; c ₁₂=c ₁c ₂; c ₁₃=c ₁c ₃; c ₁₄=c ₁c ₄; c ₂₃=c ₂c ₃; $c_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/{W_i}^2;c_3=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/{W_i}^2;c_4=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/{W_i}^2;c_1=\underset{i}{\mathrm{\&Sigma;}}1/{W_i}^2;b_0=\underset{i}{\mathrm{\&Sigma;}}1/W_i;$ $b_1\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i.$