CN105424147B - Silo gravimetric analysis sensing method and device based on grain bulk height Yu bottom surface pressure relation - Google Patents

Abstract

The present invention relates to a kind of silo gravimetric analysis sensing method and device based on grain bulk height Yu bottom surface pressure relation, belong to grain weight detecting technical field.The present invention establishes silo weight detecting model by arranging two groups of pressure sensors on silo bottom surface $\hat{W}=A_B\{\underset{m=0}{\overset{N_B}{\Σ}}{b}_B\left(m\right){\stackrel{\‾}{Q}}_B{\left(s_{Inner}\right)}^m+K_c^{2}ln\[1-K_p{\stackrel{\‾}{Q}}_{BF}\left(s\right)\]\underset{n=0}{\overset{N_F}{\Σ}}{b}_F\left(n\right){\stackrel{\‾}{Q}}_B{\left(s_{Outer}\right)}^n\},$ Each sensor output value is detected, according to the detection model established, realizes the detection to silo weight.Detection method proposed by the invention has accuracy of detection height, require sensor performance low, adaptability and strong robustness, be easy to the features such as detection of remote online silo quantity and silo status monitoring, the needs usually using the detection of quantity of stored grains in granary remote online can be met, the detection method of the present invention is adapted to the grain storage quantity detection of a variety of barn structure types, with huge application value, to ensure that national food quantity provides safely new technological means.

Description

Silo gravimetric analysis sensing method and device based on grain bulk height Yu bottom surface pressure relation

Technical field

The present invention relates to a kind of silo gravimetric analysis sensing method and device based on grain bulk height Yu bottom surface pressure relation, belong to Grain weight detecting technical field.

Background technology

Grain security includes quantity safety and quality safety.Grain Quantity online measuring technique and system research application are states The important leverage technology of family's Grain Quantity safety, carry out the research of this respect with using concerning national food security, having important Meaning, and huge economic results in society will be produced.Due to critical role of the grain in national security, it is desirable to grain heap quantity On-line checking is accurate, quick and reliable.Simultaneously because Grain Quantity is huge, price is low, it is desirable to grain heap quantity online detection instrument Cost is low, simple and convenient.Therefore the high accuracy of detection and the low cost of detecting system are the exploitations of silo quantity online test method The key issue that must be solved.

A kind of patent application of Application No. 201410101693.5, there is provided grain based on structure adaptive detection model Storage index of flow quantity measuring method, the detection method arrange two circle pressure sensors on silo bottom surface, detect the defeated of each sensor Go out value, calculate the estimation of silo weight according to the detection model established, the detection model established isThe detection model is by the way that side pressure, bottom surface pressure to be estimated as passing on outer ring pressure respectively Sensor, the multinomial of inner ring pressure sensor output average obtain.

The content of the invention

It is an object of the invention to provide a kind of silo gravimetric analysis sensing method based on grain bulk height and bottom surface pressure relation and Device, it is a kind of new reserves detection thinking.

The present invention provides a kind of silo weight based on grain bulk height and bottom surface pressure relation and examined to achieve the above object Survey method, the detection method comprise the following steps：

1) two groups of pressure sensors are arranged on silo bottom surface, one group is interior coil sensor, and one group is outer coil sensor, outside Coil sensor is close to flank wall arranged for interval, inner ring sensor distance flank wall setpoint distance and arranged for interval；

2) arrangement according to sensor in step 1), establishes silo weight detecting model：

Wherein A_BFor grain heap base area,C_BFor bottom surface girth,It is defeated for interior coil sensor Go out average,Average, b are exported for outer coil sensor_BAnd b (m)_F(n) it is respectivelyWith Estimate the coefficient of item, m=0 ..., N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThat estimates is more Item formula exponent number,

3) detecting step 1) in each sensor output valve, calculated according to the detection model in step 2) and be detected silo weight The estimate of amount

The demarcation of each parameter is as follows in silo weight detecting model in the step 2)：

A. pressure sensor is arranged in the way of step 1) in more than 6 silos, enters grain to buying securities with all one's capital, treat pressure sensing After device output value stabilization, the pressure sensor output valve in each storehouse is gathered, forms sample setWherein i is sample period, i=1,2,3 ..., M, M be number of samples；Respectively i-th sample pointWithValue；W_iFor sample point i Actually enter grain weight,For corresponding silo area；

B. it is three parts sample set S to be divided, optimization and multiple regression sample set S_M、With Item maximum order selection sample S_OAnd test sample S_T；

C. a K is given_P, utilize optimization and multiple regression sample set S_M, regression parameter b is determined by multiple regression procedure_B And b (m)_F(n)；

D. according to optimization and multiple regression sample set S_M, using following Optimized model Optimal Parameters K_P,

Constraints：K_P＞ 0

E. sample set S is calculated according to percentage error model_OAnd S_MPrediction error E (N_B,N_F)

Set N_BRange of choice [1, MaxN_B],N_FRange of choice [1, MaxN_F], if

ThenAs detection modelWithOptimal maximum order required by.

MaxN in the step E_BAnd MaxN_FValue be 4-10.

Described detection model obtains on the basis of silo weight theory detection model, the theoretical detection of silo weight Model is：

Wherein,Estimate for grain heap weight,A_BFor grain heap base area, C_BFor bottom surface Girth,Q_B(s)、Q_F(s) it is respectively s in grain heap bottom surface and side The pressure of point,It is far above bottom surface pressure saturation value during certain altitude for grain heap.

The outer ring sensor distance flank wall distance d is more than 0 and is less than 1 meter, and inner ring sensor distance flank wall distance D is big In 2 meters.

Present invention also offers a kind of silo Weight detecting device based on grain bulk height Yu bottom surface pressure relation, the detection Device includes detection unit and is connected with detection unit and is arranged on the pressure sensor of silo bottom surface, the pressure sensor It is divided to two groups of arrangements, one group is interior coil sensor, and one group is outer coil sensor, and outer coil sensor is close to silo flank wall interval cloth Put, inner ring sensor distance silo flank wall setpoint distance and arranged for interval, being performed in the detection unit has one or more Module, one or more of modules are used to perform following steps：

1) silo weight detecting model is established：

Wherein A_BFor grain heap base area,C_BFor bottom surface girth,Exported for interior coil sensor Average,Average, b are exported for outer coil sensor_BAnd b (m)_F(n) it is respectivelyWithEstimation The coefficient of item, m=0 ..., N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThe multinomial of estimation Exponent number,

2) output valve of each sensor is detected, is calculated according to the silo weight detecting model established and is detected silo weight Estimate

The demarcation of each parameter is as follows in described silo weight detecting model：

A. pressure sensor is arranged in the way of claim 6 in more than 6 silos, enters grain to buying securities with all one's capital, treat pressure After sensor output value is stable, the pressure sensor output valve in each storehouse is gathered, forms sample setWherein i is sample period, i=1,2,3 ..., M, M be number of samples；Respectively i-th sample pointWithValue；W_iFor sample point i Actually enter grain weight,For corresponding silo area；

B. it is three parts sample set S to be divided, optimization and multiple regression sample set S_M、With Maximum order selection sample S_OAnd test sample S_T；

C. a K is given_P, utilize optimization and multiple regression sample set S_M, regression parameter b is determined by multiple regression procedure_B And b (m)_F(n)；

D. according to optimization and multiple regression sample set S_M, using following Optimized model Optimal Parameters K_P,

Constraints：K_P＞ 0

E. sample set S is calculated according to percentage error model_OAnd S_MPrediction error E (N_B,N_F ⁾

Set N_BRange of choice [1, MaxN_B],N_FRange of choice [1, MaxN_F], if

ThenAs detection modelWithOptimal maximum order required by.

MaxN in the step E_BAnd MaxN_FValue be 4-10.

Described detection model obtains on the basis of silo weight theory detection model, the theoretical detection of silo weight Model is：

Wherein,Estimate for grain heap weight,A_BFor grain heap base area, C_BThe bottom of for Face girth,Q_B(s)、Q_F(s) it is respectively grain heap bottom surface and side The pressure of middle s points,It is far above bottom surface pressure saturation value during certain altitude for grain heap.

The outer ring sensor distance flank wall distance d is more than 0 and is less than 1 meter, and inner ring sensor distance flank wall distance D is big In 2 meters.

The beneficial effects of the invention are as follows:The present invention establishes silo by arranging two groups of pressure sensors on silo bottom surface Weight detecting modelDetection is each Sensor output value, according to the detection model established, realize the detection to silo weight.Detection method proposed by the invention It is high with accuracy of detection, sensor performance is required low, adaptability and strong robustness, be easy to remote online silo quantity detect and The features such as silo status monitoring, the needs usually using the detection of quantity of stored grains in granary remote online, detection of the invention can be met Method is adapted to the grain storage quantity detection of a variety of barn structure types, has huge application value, to ensure national grain eclipse number Amount safety provides new technological means.

Brief description of the drawings

Fig. 1 is horizontal warehouse base pressure sensor arrangement model schematic；

Fig. 2 is silo base pressure sensor arrangement model schematic；

Fig. 3 is that modeling sample weight predicts error schematic diagram in detection example 2 of the present invention；

Fig. 4 is all sample weight prediction error schematic diagrames in detection example 2 of the present invention；

Fig. 5 is the implementing procedure figure of silo gravimetric analysis sensing method of the present invention.

Embodiment

The embodiment of the present invention is described further below in conjunction with the accompanying drawings.

First, the embodiment based on grain bulk height Yu the silo gravimetric analysis sensing method of bottom surface pressure relation

The present invention based on grain bulk height with the silo gravimetric analysis sensing method of bottom surface pressure relation by establishing corresponding grain Storehouse weight detecting model, silo weight is calculated according to the detection model established, the theoretical premise that is obtained on the model, correspondingly Silo sensor arrangement, model inference and parameter calibration, it is specific in turn below to introduce.

1. silo weight theory detection model

Usually used silo has a types such as horizontal warehouse, silo, silo, requires to shakeout after conveying grain into storehouse, at the top of grain heap, The substantially various sizes of cube of grain pile in horizontal warehouse shape, silo, silo grain heap shap are substantially various sizes of Cylinder.It can be drawn by grain heap force analysis, silo grain heap weight is with the distribution of silo pressure with the relation shown in following formula.

Wherein,Estimate for grain heap weight, A_BFor grain heap base area, C_BFor bottom surface girth, H is grain bulk height, f_FFor Average friction coefficient between grain heap side and silo side； Q_B (s)、Q_F(s) it is respectively pressure at grain heap bottom surface and side midpoints s.

According to Janssen models, can release silo bottom surface pressure and grain bulk height has the approximation relation being shown below.

Wherein,For silo grain heap feature height, K is pressure steer coefficient；It is far above for grain heap Bottom surface pressure saturation value during feature height.It can be released by formula (2)

Wherein,

Formula (3) is substituted into formula (1), then had

Wherein,Estimate for grain heap weight,A_BFor grain heap base area, C_B For bottom surface girth,Q_B(s)、Q_F(s) it is respectively grain heap bottom surface and side The pressure of s points in face,It is far above bottom surface pressure saturation value during certain altitude for grain heap.

2. silo pressure sensor is arranged

For usually used horizontal warehouse and silo, pressure sensor is arranged by outer ring and the circle of inner ring two in silo bottom surface, As depicted in figs. 1 and 2, circle is pressure sensor position, and outer ring pressure sensor is d with flank wall distance, inner ring Sensor is D with flank wall distance.Obviously as d=0, bottom surface pressure is also the pressure of side bottom at outer ring.Therefore can Described using outer ring pressure sensor output valveSize, described using inner ring pressure sensor output valveSize.

Actual experiment shows, when outer ring pressure sensor is with flank wall distance d=0, the description of its pressure sensor output valveAccuracy improve, but the fluctuation of output valve also significantly increases, so as to influence detection model precision, therefore to ensure mould Type precision, it can use 1 meter of 0 meter of d ＞ and d ＜.Interior coil sensor and flank wall distance D are bigger, the description of pressure sensor output valveValidity improve, therefore it is convenient load and unload grain under conditions of, should suitably increase D, therefore desirable D>2 meters, typically take 3 Rice or so.In order to ensure the versatility of detection model, Internal and external cycle pressure sensor and flank wall the distance d and D of each silo answer phase Together, two coil sensor numbers are 6-10, and sensor spacing should be not less than 1m.

3. silo weight detecting model inference

Using the coil sensor placement model of silo bottom surface two shown in Fig. 1, Fig. 2, outer ring sensor output value average is utilizedThe pressure estimation of silo side is built, utilizes inner ring sensor output value averageStructure silo bottom surface pressure is estimated Meter.For the silo weight detecting theoretical model shown in formula (4), order

OrderIn structure formula (5)Be estimated as

Then H_∞It is estimated as

Wherein K_P=K_∞b_BF.Formula (7) is substituted into formula (4) to obtain

For formula (5), useMultinomial is builtIt is estimated as

Wherein, b_BAnd b (m)_F(n) it is respectivelyWithEstimate the coefficient of item, m=0 ..., N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThe polynomial order of estimation.Formula (9) and formula (10) are substituted into formula (8), then had

Formula (8) is the silo weight detecting model proposed by the invention based on grain bulk height Yu bottom surface pressure relation.

4. the demarcation of each parameter in detection model

For given sensor, types of food and storehouse type, it is necessary to be carried out to the silo weight detecting model established Demarcation, that is, solve the parameters in formula (11), and detailed process is as follows：

A. pressure sensor is arranged in the way of step 1) in more than 6 silos, enters grain to buying securities with all one's capital, treat pressure sensing After device output value stabilization, the pressure sensor output valve in each storehouse is gathered, forms sample setWherein i is sample period, i=1,2,3 ..., M, M be number of samples；Respectively i-th sample pointWithValue；W_iFor actually entering for sample point i Grain weight,For corresponding silo area；

B. it is three parts sample set S to be divided, optimization and multiple regression sample set S_M、WithItem is maximum Exponent number selection sample S_OAnd test sample S_T；

C. a K is given_P, utilize optimization and multiple regression sample set S_M, regression parameter b is determined by multiple regression procedure_B And b (m)_F(n)；

D. according to optimization and multiple regression sample set S_M, using following Optimized model Optimal Parameters K_P,

Constraints：K_P＞ 0

E. according to percentage error model sample collection S_OAnd S_MPrediction error E (N_B,N_F)

Set N_BRange of choice [1, MaxN_B],N_FRange of choice [1, MaxN_F], MaxN_BAnd MaxN_FFor 4-10, if

ThenAs detection modelWithOptimal maximum order required by.

2nd, the embodiment based on grain bulk height Yu the silo Weight detecting device of bottom surface pressure relation

Detection means provided by the present invention includes detection unit and is connected with detection unit and is arranged on silo bottom surface Pressure sensor, wherein pressure sensor is divided to two groups of arrangements, and one group is interior coil sensor, and one group is outer coil sensor, outer ring Sensor is such as schemed close to silo flank wall arranged for interval, inner ring sensor distance silo flank wall setpoint distance and arranged for interval 1st, shown in Fig. 2.Detection unit can use single-chip microcomputer, DSP, PLC or MCU etc., and being performed in detection unit has one or more moulds Block, module here can be located at RAM memory, flash memory, ROM memory, eprom memory, eeprom memory, deposit The storage, can be situated between by device, hard disk, mobile disk, the storage medium of CD-ROM or any other form known in the art Matter is coupled to detection unit, enables detection unit from the read information, or the storage medium can be detection The part of unit.One or more modules are used to perform following steps：

1) silo weight detecting model is established：

Wherein A_BFor grain heap base area,C_BFor bottom surface girth,Average is exported for interior coil sensor,Average, b are exported for outer coil sensor_BAnd b (m)_F(n) it is respectivelyWithEstimate the coefficient of item, m= 0,...,N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThe polynomial order of estimation,

2) output valve of each sensor is detected, is calculated according to the silo weight detecting model established and is detected silo weight Estimate

Wherein the calibration process of the derivation of silo weight detecting model and model parameter is carried out in the embodiment of method Describe in detail, repeat no more here.

3rd, detection example and interpretation of result

Detection example 1

The long 9m of horizontal warehouse used by this detection example, wide 4.2m, area 37.8m², C_B/A_B≈0.698.Silo belongs to In Minitype granary, C_B/A_BIt is relatively large.Pressure sensor placement model according to Fig. 1, for horizontal warehouse, pressure sensor Divide 2 to enclose to arrange, inner ring 8, outer ring 10, totally 18 pressure sensors.

Experiment types of food is corn, about 160 tons of weight, carries out 4 experiments altogether.Take MaxN_B=10 and MaxN_F=10.By It is very few in sample, with 1-3 experiment to return sample, sample and test sample are selected using experiment 4 as item most high order exponent number.Root Shape parameter is modeled as shown in table 1 to table 2 according to formula (11), and weight prediction result is as shown in table 3 to table 6, and wherein table 3 is experiment 1 Reserves weight result of calculation, table 4 are 2 reserves weight result of calculations of experiment, and table 5 is 3 reserves weight result of calculations of experiment, and table 6 is 4 reserves weight result of calculations are tested, total prediction error of 4 experiments is 21.7668.

Table 1

Table 2

With 1-3 experiment to return sample, using experiment 4 as test sample, sample, root are selected without item most high order exponent number Shape parameter is modeled as shown in table 7 and table 8 according to formula (11), and weight prediction result is as shown in table 9 to table 12, and wherein table 9 is experiment 1 Reserves weight result of calculation, table 10 are 2 reserves weight result of calculations of experiment, and table 11 is to test 3 reserves weight result of calculations, table 12 To test 4 reserves weight result of calculations, the total prediction error for participating in 1-3 experiment of modeling is the total pre- of 10.715,4 experiments It is 27.0091 to survey error.

Table 7

Table 8

It can be seen from table 3 and table 6 present invention given by silo weight detecting model have preferable modeling accuracy and Test sample weight result of calculation shown in precision of prediction, comparison sheet 6 and table 12, which can be seen that, passes through introducingWithItem maximum order selection sample S_OThe predictive ability of model can be significantly improved.

Detection example 2

Below to be illustrated exemplified by 3 silos of Hongze and Qihe, the species of putting out cereal of these three silos is wheat and rice Paddy, reserves weight are respectively 2455.6 tons, 2009.98 tons and 2100 tons, using the sensor different from detection example 1, through inspection Survey and obtain 501, sample of detection.Wherein choose 297 and be used as modeling sample, 197 as multiple regression sample S_M, 100 works ForWithItem maximum order selection sample S_O, remaining is as test sample.According to formula (11) finding model parameter As shown in table 13 to 14, weight prediction result is as shown in Figure 3 and Figure 4.Wherein Fig. 3 is to carry out weight prediction using modeling sample Error schematic diagram, Fig. 4 are the error schematic diagram that weight prediction is carried out using all samples.

Table 13

Table 14

From figs. 3 and 4 it can be seen that the prediction error of all test points is respectively less than 0.4%, granary storage weight can be met The requirement of detection is measured, this also demonstrates the validity of proposed formula (11) model and modeling method.

Specifically, silo gravimetric analysis sensing method and dress proposed by the invention based on grain bulk height Yu bottom surface pressure relation Put embodiment to implement as shown in Figure 5, and specific steps are implemented as follows：

(1) system configuration

Specific pressure sensor is selected, and configures the systems such as corresponding data acquisition, data transfer.

(2) base pressure sensor is installed

Horizontal warehouse sensor arrangement as shown in figure 1, silo as shown in Fig. 2 base pressure sensor presses outer ring and inner ring two Circle arrangement, outer ring pressure sensor are 1 meter of d ＞ 0 and d ＜ with flank wall distance, interior coil sensor with flank wall distance D>2 Rice.Two coil sensor numbers are 6-10, and sensor spacing should be not less than 1m.

(3) system calibrating and model modeling

For given sensor, types of food and storehouse type, if system there has been no demarcation, in more than 6 silos Middle arrangement pressure sensor, enter grain to buying securities with all one's capital, after pressure sensor output value stabilization, gather the pressure sensor output in each storehouse Value, form sample setWherein i is sample period, i=1,2,3 ..., M, M be Number of samples；Respectively i-th sample pointWithValue；W_iFor sample Point i's actually enters grain weight,For corresponding silo area.By sample set, S points are three parts, optimization and multiple regression sample This collection S_M、WithItem maximum order selection sample S_OAnd test sample S_T.According to optimization and polynary time Return sample set S_M, using optimized algorithm, determine Optimal Parameters K_P.According to optimization and multiple regression sample set S_M, utilize homing method Determine the regression parameter b in formula (11)_BAnd b (m)_F(n), and according to the regression model and maximum order established sample set is selected S_O, optimizationPolynomial maximum order N_BAnd N_F, so as to construct the silo weight shown in formula (11) Detection model.

(4) real storehouse weight detecting.

If system has been demarcated, detection base pressure sensor exports and carries out granary storage using formula (11) institute representation model Quantity detects.

Specific embodiment is presented above, but the present invention is not limited to described embodiment, base of the invention This thinking is basic modeling and scaling scheme, for those of ordinary skill in the art, according to the teachings of the present invention, design Go out the models of various modifications, formula, parameter and creative work need not be spent, do not depart from the principle of the present invention and spirit In the case of to embodiment carry out change, modification, replacement and modification be still within the scope of the present invention.

Claims (8)

1. the silo gravimetric analysis sensing method based on grain bulk height Yu bottom surface pressure relation, it is characterised in that the detection method includes Following steps：

1) two groups of pressure sensors are arranged on silo bottom surface, one group is interior coil sensor, and one group is outer coil sensor, and outer ring passes Sensor is close to flank wall arranged for interval, inner ring sensor distance flank wall setpoint distance and arranged for interval；

2) arrangement according to sensor in step 1), establishes silo weight detecting model：

Wherein A_BFor grain heap base area,C_BFor bottom surface girth,Average is exported for interior coil sensor,Average, b are exported for outer coil sensor_BAnd b (m)_F(n) it is respectivelyWithEstimate the coefficient of item, m= 0,...,N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThe polynomial order of estimation,K_pFor Optimized model parameter；

3) detecting step 1) in each sensor output valve, calculated according to the detection model in step 2) and be detected silo weight Estimate

The demarcation of each parameter is as follows in silo weight detecting model in the step 2)：

A. pressure sensor is arranged in the way of step 1) in more than 6 silos, enters grain to buying securities with all one's capital, treat that pressure sensor is defeated After going out value stabilization, the pressure sensor output valve in each storehouse is gathered, forms sample setIts Middle i is sample period, i=1,2,3 ..., M, M be number of samples；Respectively i-th of sample point 'sWithValue；W_iActually enter grain weight for sample point i,For corresponding silo area；

B. it is three parts sample set S to be divided, optimization and multiple regression sample set S_M、WithItem maximum order Select sample S_OAnd test sample S_T；

C. a K is given_P, utilize optimization and multiple regression sample set S_M, regression parameter b is determined by multiple regression procedure_B(m) And b_F(n)；

D. according to optimization and multiple regression sample set S_M, using following Optimized model Optimal Parameters K_P,

Constraints：K_P＞ 0

E. sample set S is calculated according to percentage error model_OAnd S_MPrediction error E (N_B,N_F)

Set N_BRange of choice [1, MaxN_B],N_FRange of choice [1, MaxN_F], if

ThenAs detection modelWithOptimal maximum order required by.

2. the silo gravimetric analysis sensing method according to claim 1 based on grain bulk height Yu bottom surface pressure relation, its feature It is, the MaxN in the step E_BAnd MaxN_FValue be 4-10.

3. the silo gravimetric analysis sensing method according to claim 2 based on grain bulk height Yu bottom surface pressure relation, its feature It is, described detection model obtains on the basis of silo weight theory detection model, the theoretical detection mould of silo weight Type is：

Wherein,Estimate for grain heap weight,A_BFor grain heap base area, C_BFor bottom surface girth,Q_B(s)、Q_F(s) be respectively s points in grain heap bottom surface and side pressure,It is far above bottom surface pressure saturation value during certain altitude for grain heap；K is pressure steer coefficient, N_BAnd N_FRespectivelyWithThe polynomial order of estimation.

4. the silo gravimetric analysis sensing method according to claim 1 based on grain bulk height Yu bottom surface pressure relation, its feature It is, the outer ring sensor distance flank wall distance d is more than 0 and is less than 1 meter, and inner ring sensor distance flank wall distance D is more than 2 Rice.

A kind of 5. silo Weight detecting device based on grain bulk height Yu bottom surface pressure relation, it is characterised in that the detection means It is connected including detection unit and with detection unit and is arranged on the pressure sensor of silo bottom surface, the pressure sensor point Two groups of arrangements, one group is interior coil sensor, and one group is outer coil sensor, outer coil sensor close to silo flank wall arranged for interval, Inner ring sensor distance silo flank wall setpoint distance and arranged for interval, performing in the detection unit has one or more moulds Block, one or more of modules are used to perform following steps：

1) silo weight detecting model is established：

Wherein A_BFor grain heap base area,C_BFor bottom surface girth,Average is exported for interior coil sensor,Average, b are exported for outer coil sensor_BAnd b (m)_F(n) it is respectivelyWithEstimate the coefficient of item, m= 0,...,N_B, n=0 ..., N_F, N_BAnd N_FRespectivelyWithThe polynomial order of estimation,K_pFor Optimized model parameter；

2) output valve of each sensor is detected, is calculated according to the silo weight detecting model established and is detected estimating for silo weight Evaluation

The demarcation of each parameter is as follows in described silo weight detecting model：

A. pressure sensor is arranged in more than 6 silos, enters grain to buying securities with all one's capital, after pressure sensor exports value stabilization, gather The pressure sensor output valve in each storehouse, form sample setWherein i is sample period, i =1,2,3 ..., M, M be number of samples；Respectively i-th sample pointWithValue；W_iActually enter grain weight for sample point i,For corresponding silo area；

B. it is three parts sample set S to be divided, optimization and multiple regression sample set S_M、WithItem maximum order Select sample S_OAnd test sample S_T；

C. a K is given_P, utilize optimization and multiple regression sample set S_M, regression parameter b is determined by multiple regression procedure_B(m) And b_F(n)；

D. according to optimization and multiple regression sample set S_M, using following Optimized model Optimal Parameters K_P,

Constraints：K_P＞ 0

E. sample set S is calculated according to percentage error model_OAnd S_MPrediction error E (N_B,N_F)

Set N_BRange of choice [1, MaxN_B],N_FRange of choice [1, MaxN_F], if

ThenAs detection modelWithOptimal maximum order required by.

6. the silo Weight detecting device according to claim 5 based on grain bulk height Yu bottom surface pressure relation, its feature It is, the MaxN in the step E_BAnd MaxN_FValue be 4-10.

7. the silo Weight detecting device according to claim 6 based on grain bulk height Yu bottom surface pressure relation, its feature It is, described detection model obtains on the basis of silo weight theory detection model, the theoretical detection mould of silo weight Type is：

Wherein,Estimate for grain heap weight,A_BFor grain heap base area, C_BFor bottom surface girth,Q_B(s)、Q_F(s) be respectively s points in grain heap bottom surface and side pressure,It is far above bottom surface pressure saturation value during certain altitude for grain heap；K is pressure steer coefficient, N_BAnd N_FRespectivelyWithThe polynomial order of estimation.

8. the silo Weight detecting device according to claim 5 based on grain bulk height Yu bottom surface pressure relation, its feature It is, the outer ring sensor distance flank wall distance d is more than 0 and is less than 1 meter, and inner ring sensor distance flank wall distance D is more than 2 Rice.