CN104296846B - Silo and grain storage weight detection system based on optimal bottom surface pressure measurement point thereof - Google Patents

Abstract

The present invention relates to a kind of silo and grain storage weight detection system based on optimal bottom surface pressure measurement point thereof, according to the distribution of silo bottom surface pressure and pressure measurement Variation Features, proposing a kind of method for detecting quantity of stored grains in granary based on optimal bottom surface pressure measurement point, core technology includes the technology such as the consistency metric model of bottom surface pressure measurement point, the method for detecting position of optimal bottom surface pressure measurement point, silo weight detecting model based on optimal bottom surface pressure measurement point, system calibrating and modeling method.It is high that proposed method has accuracy of detection, and desirable pressure sensor is few, it is only necessary to 2～3, highly versatile, adapts to the reserves quantity detection of multiple barn structure type.Proposed detection method has huge using value, for ensureing that national food quantity has safely provided new technological means.

Description

Granary and stored grain weight detection system based on optimal bottom surface pressure intensity measuring point

Technical Field

The invention relates to a granary and a stored grain weight detection system based on an optimal bottom pressure measurement point.

Background

The grain safety includes quantity safety and quality safety. The online grain quantity detection technology and the system research application are important guarantee technologies for national grain quantity safety, and the development of the research and application on the aspect of national grain safety has important significance and can generate huge social and economic benefits.

Because of the important position of grains in national safety, the on-line detection of the quantity of grain piles is required to be accurate, rapid and reliable. Meanwhile, because the quantity of the grains is huge and the price is low, the cost of the grain pile quantity on-line detection equipment is low, and the detection equipment is simple and convenient. Therefore, the high precision of detection and the low cost of the detection system are key problems which must be solved for the development of the online detection system for the number of the granaries.

The patent 'grain depot grain storage quantity detection method based on a pressure sensor' (patent authorization number: ZL 201010240167.7) and the patent 'horizontal warehouse squat grain storage quantity detection method' (patent authorization number: ZL 201210148522) relate to grain storage quantity, namely grain storage weight detection. Specifically, ZL201010240167.7 relates to a calculation model and a specific system calibration method of granary grain storage quantity based on output average values of granary bottom surface and side surface pressure sensors. ZL201210148522 relates to novel methods such as compensation of side friction influence based on the square of the output mean value of a bottom surface pressure sensor, a grain pile weight prediction model based on the output mean value of the bottom surface pressure sensor, prediction model modeling based on a grain weight error ratio, rapid system calibration and the like.

Both of the above methods have their own features and advantages. However, the detection system itself needs a large number of sensors for detection, and the detection system is expensive. The construction and maintenance costs of the granary are correspondingly increased.

Disclosure of Invention

The invention aims to provide a granary and a grain storage weight detection system based on an optimal bottom pressure measurement point, and aims to solve the problems that the existing detection system needs a large number of sensors and is high in cost.

In order to achieve the above object, the scheme of the invention comprises:

a stored grain weight detection system based on an optimal bottom pressure measurement point comprises at least two bottom pressure sensors and the optimal bottom pressure measurement point arranged on a granary, wherein the optimal bottom pressure measurement point is a measurement point with high detection consistency.

The optimum bottom pressure measurement point is mainly located in the area with a certain distance from the side surface and the grain inlet.

The rule for selecting the measurement points with high consistency is as follows: is provided with n_WWeight W of seed grain_i，i＝1,...,n_wMeasuring the weight of each stored grain by n_MSecondly; for any given pressure measurement point s on the bottom surface of the granary, the weight W of the stored grain in the granary_iThe measured value of the measurement point s at the k-th measurement of (1) is Q_B(s,W_i,k)，k＝1,...,n_MDefinition of

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

A consistency measure for the bottom pressure measurement point s; for any measurement point s, the smaller the CM(s), the higher the measurement consistency, and vice versa.

A granary is provided with a grain storage weight detection system, wherein the grain storage weight detection system comprises at least two bottom surface pressure sensors and is arranged at an optimal bottom surface pressure measurement point of the granary, and the optimal bottom surface pressure measurement point is a measurement point with high detection consistency.

The optimum bottom pressure measurement point is mainly located in the area with a certain distance from the side surface and the grain inlet.

The rule for selecting the measurement points with high consistency is as follows: is provided with n_WWeight W of seed grain_i，i＝1,...,n_wMeasuring the weight of each stored grain by n_MSecondly; for any given pressure measurement point s on the bottom surface of the granary, the weight W of the stored grain in the granary_iThe measured value of the measurement point s at the k-th measurement of (1) is Q_B(s,W_i,k)，k＝1,...,n_MDefinition of

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

A consistency measure for the bottom pressure measurement point s; for any measurement point s, the smaller the CM(s), the higher the measurement consistency, and vice versa.

The granary stored grain weight detection system disclosed by the invention has the advantages that the number of required measuring points is small, only 2-3 measuring points are needed, the measuring precision is high, but the requirements on the performance of the sensor are high, particularly the repeatability error and the consistency of the performance of the sensor are high.

Furthermore, the invention provides a distribution rule of measurement points with high consistency and an experiment selection step to guide the actual operation.

Drawings

FIG. 1 a horizontal warehouse pressure sensor arrangement;

FIG. 2 a squat silo pressure sensor arrangement;

FIG. 3 shows the sensor arrangement and the feeding area for the best measurement point detection;

the CM(s) value at the 2 nd row measurement point of the horizontal warehouse of FIG. 4;

FIG. 5 CM(s) values for circle 2 measurement points of the squat silo;

FIG. 6 is a schematic diagram of the detection method.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings.

The invention provides a granary and a stored grain weight detection system thereof:

the detection system comprises at least two bottom surface pressure sensors which are arranged at the optimal bottom surface pressure intensity measurement point of the granary, and the optimal bottom surface pressure intensity measurement point is a measurement point with high detection consistency.

For given weight of granary and stored grain, how to select a limited number of bottom pressure measurement points to ensure that the average value of the measured values has high consistency in each grain feeding is a sufficient and necessary condition for ensuring the weight detection precision, if the consistency is high, the detection precision is certain high, otherwise, the opposite is true. According to the distribution characteristics of the pressure of the bottom surface of the granary, due to the influences of factors such as the nonuniformity of grain components in the grain stack and the volume mass distribution, the consistency of the contact degree of the grain stack and the stress surfaces of the pressure sensors, the randomness of the pressure of the side surfaces and the like, the measured values of the pressure measuring points of the bottom surface of the granary have randomness of different degrees, but the degrees are obviously different, and a large number of measuring points with small randomness exist, so that the measured values of the measuring points have strong robustness.

For consistency, there may be a variety of specific evaluation rules, according to the above description.

For example, for a given grain bin and grain type, assume that there is n_WWeight W of seed grain_i，i＝1,...,n_wMeasuring the weight of each stored grain by n_MNext, the process is carried out. For any given pressure measurement point s on the bottom surface of the granary, the weight W of the stored grain in the granary_iThe measured value of the measurement point s at the k-th measurement of (1) is Q_B(s,W_i,k)，k＝1,...,n_MDefinition of

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

Is a measure of the consistency of the bottom pressure measurement points s. As can be seen from equation (1), cm(s) represents the average percent dispersion error of the measured values of the measurement point s. Obviously, for any measurement point s, the smaller cm(s), the higher the measurement consistency, and vice versa. For a given granary, grain storage type and grain feeding mode, if CM(s) < Th, Th is a given threshold parameter, and 0 < Th, the measuring point s is an optimal bottom pressure measuring point.

As another example, variance is used to evaluate consistency.

The principle of selecting the optimal bottom pressure measurement point is introduced through experimental data, and the consistency is evaluated by adopting the formula (1).

For the horizontal warehouse and the shallow circular warehouse shown in fig. 1 and fig. 2, the horizontal warehouse pressure sensors are arranged in 6 rows, as shown by dots in the figure, 15 sensors are arranged in each row, the number of the measuring point in the 1 st row is 1# -15 #, the number of the measuring point in the 2 nd row is 16# -30 #, and the numbers of other rows are analogized in turn. The squat silo pressure sensor is arranged in 3 circles, 20 in the 1 st circle (the number is 1# -20 #), 20 in the 2 nd circle (the number is 21# -40 #), 15 in the 3 rd circle (the number is 41# -55 #), and 55 in total.

The length of the horizontal warehouse is 9m, the width is 4.2m, the area is 37.8m2, C_B/A_B0.35. The diameter of the squat silo is 6m, the area is 28.26m2, C_B/A_B0.67. Two kinds of granary belonging to small-sized granary C_B/A_BIs relatively large. A. the_BIs the area of the bottom of the grain heap C_BThe bottom surface circumference. The experimental grain type is wheat, 4 times of experiments are carried out in the horizontal warehouse, 6 times of grain feeding are divided in each experiment, and each time of grain feeding is about 1 meter and is flattened. The squat silo is used for 3 times of experiments, the experimental conditions are the same as the conditions of the first three times of the squat silo, 8 times of grain feeding are carried out in each experiment, and each time of grain feeding is about 1 meter and is flattened. From the 4 experimental data of the horizontal warehouse, Th is 8, the weight set of stored grain is {35,60,90,120,150,168}, the measurement estimation value of each detection point is obtained by interpolation, and the consistency measure of the bottom pressure measurement points of the horizontal warehouse is shown in table 1.

From the 3 experimental data of the squat silo, Th is 10, the set of grain weights is {30,50,70,90,110,130,150,170}, the calculated consistency measure calculation results of the squat silo bottom pressure measurement points are shown in table 2, the darkened measurement points are cm(s) small, and the value of 1E32 indicates that the sensor is faulty. The circled measuring points in fig. 1 and fig. 2 are schematic diagrams of the positions of small measuring points CM(s) on the bottom surface, and the grain outlet is also a grain inlet in the diagrams. It can be seen that:

(1) the pressure measurement values of the measurement points in different experiments have obvious inconsistency and randomness, but the inconsistency degrees are obviously different, and a large number of measurement points with small inconsistency and randomness exist. Such as horizontal silos 17#, 22#, 24#, 52# and 67# etc., and shallow silos 26#, 29#, 30#, 31#, 34#, 51# and 52# etc.

(2) For the horizontal compartment, cm(s) small measurement points are mainly located in row 2, and for the shallow compartment, cm(s) small measurement points are mainly located in circle 2. Therefore, the small points of measurement of CM(s) are mainly located in the area at a distance from the side and from the grain inlet. The side surface has a certain distance to reduce the influence of the friction force of the side surface, and the grain inlet has a certain distance to reduce the influence of the impact action of grain feeding. The small points of measurement of cm(s) are mainly related to the side friction and the feeding mode. For a large granary which is generally used, the grain feeding mode is generally unchanged, and a small MD(s) measuring point with stable position exists.

The technical difficulty of granary weight detection is randomness of pressure measurement values of the bottom surface and the side surface, and randomness of different degrees of pressure measurement values of bottom surface pressure measurement points, so how to overcome the randomness is always an urgent problem to be solved in the technical field of granary weight detection. The optimal bottom pressure measurement points are measurement points with small randomness of measurement values, which shows that the measurement values of the measurement points have strong robustness and are slightly influenced by the non-uniformity of grain components in the grain stack and the volume mass distribution, the consistency of the contact degree of the grain stack and the stress surfaces of the pressure sensors, the randomness of the side pressure and other factors, and the like.

TABLE 1 results of consistency measurement calculation of pressure measurement points at the bottom of a horizontal warehouse

TABLE 2 consistency measurement calculation results of pressure measurement points on the bottom surface of the squat silo

The respective rules of the optimal bottom pressure measurement points in different granaries are given, and according to the rules, the optimal measurement points can be selected manually or can be obtained through an experimental method.

The specific experimental method is as follows:

the position of the optimal bottom pressure measuring point is related to the specific structure of the granary, the grain feeding mode and the consistency of repeated measuring errors of the used pressure sensor. Therefore, for a given granary structure and size, if the consistency of repeated measurement errors of the pressure sensors is high, the optimal bottom pressure measurement point position is only related to the grain feeding mode. If the grain feeding modes are similar, and the structures and the sizes of the granaries are similar, the positions of the optimal bottom pressure measurement points are basically the same. If the consistency of repeated measurement errors of the pressure sensors is poor, indicating that the sensors are not compatible, it is necessary to detect the optimum measurement point position for each grain bin.

Since the small measurement points of cm(s) are mainly located in the area at a distance from the side and from the grain inlet. Therefore, according to the principle, the sensor arrangement and the grain feeding grain bulk position for the optimal measurement point position detection proposed herein are shown in fig. 3, where ab and bc are assumed sensor position candidate regions, and should be reasonably selected in actual detection according to the general grain feeding mode requirements and detection requirements of the grain bin; the color adding area is a grain feeding area so as to reduce the actual grain feeding amount of the experiment and reduce the experiment cost.

Fig. 4 shows the consistency metric cm(s) of the 15 measurement points in the 2 nd row of the horizontal warehouse as a function of the grain feeding height, and fig. 5 shows the consistency metric cm(s) of the 20 measurement points in the 2 nd circle of the squat warehouse as a function of the grain feeding height. As can be seen from the figure, the order of the points sorted according to the magnitude of the CM(s) value is related to the grain feeding height. When the grain feeding height is less than 3m, the sequence of each point is greatly changed along with the grain feeding height, and when the grain feeding height is more than 4 m, the sequence is basically fixed. Therefore, the experimental procedure for detecting the position of the optimal measurement point proposed herein is as follows:

(1) and preliminarily selecting detection point positions and arranging sensors. The grain feeding device has the advantages that the grain feeding device can conveniently feed grains and avoid damage of the sensors according to the principle that a certain distance is reserved between the grain feeding device and the side face and the grain feeding opening and the requirement of a common grain feeding mode, and the positions of the detection points are reasonably selected and the sensors are arranged. In fig. 3, the distance d between the sensors and the side surface (d is the distance between the sensor and the nearest side surface in the horizontal warehouse) is 2-3 m, and the distance between the sensors is about 1m (not less than 1 m).

(2) The height of the grain feeding is fixed every time, the grain feeding can be about 0.9 m, the top of the grain feeding is flattened, the width of the top surface of the grain pile above the sensor is not less than 1m, and the value of each sensor is collected.

(3) Repeating for 3-4 times, calculating CM(s) values of each point according to the formula (1), and sorting according to the value, wherein if CM(s) is small, the point is the best bottom pressure measurement point.

And after the detection value of the optimal measurement point is detected, bringing the detection value into a grain storage weight detection model based on the bottom pressure intensity to calculate, thus obtaining the grain storage weight. The method can be implemented according to the implementation mode shown in FIG. 6, and the specific steps are implemented as follows:

(1) system configuration

And selecting a specific pressure sensor, and configuring corresponding systems for data acquisition, data transmission and the like.

(2) Position detection of optimal bottom pressure measurement points

According to the principle that a certain distance is reserved between the sensor and the side face and the grain inlet, the requirement of a common grain inlet mode is met, grain can be conveniently discharged and fed, the sensor is prevented from being damaged, and the detection points are reasonably selected and the sensor is arranged. According to the graph shown in fig. 3, sensors and a grain feeding area for detecting the optimal measuring points are arranged, the distance d between the sensors and the side surface is 2-3 m, and the distance between the sensors is about 1 m. The top of the grain pile is flattened every time when the grain is fed by 0.9 m, the width of the top surface of the grain pile above the sensor is not less than 1m, and the value of each sensor is collected. Repeating for 3-4 times, calculating CM(s) values of each point according to the formula (1), and sorting according to the value, wherein if CM(s) is small, the point is the best bottom pressure measurement point.

(3) Bottom surface pressure sensor mounting

Selecting 2-5 CM (cm)(s) small optimal bottom pressure measurement points according to the detected optimal bottom pressure measurement points, and arranging sensors.

(4) System calibration and modeling

The sensors are arranged according to the position of the optimal bottom pressure measurement point, a calibration grain feeding area is established by utilizing the sandbags, the distance between each sandbag and each sensor is about 3-4 meters, and the height of each sandbag wall is 1.5-2 meters. And step-by-step grain feeding, flattening each batch of grain after 0.5 m, recording the grain feeding weight and each pressure sensor value, and calculating the grain feeding weight of the whole granary with the corresponding height according to the ratio of the area of the calibrated grain feeding area to the total area of the granary. Thus, 3-4 groups of experimental data can be obtained. And then, loading grains in a normal mode, and recording the total weight of the grains and the value of the pressure sensor on the bottom surface of the granary after the grains are loaded, so that 4-5 groups of data can be obtained. And (5) constructing a detection model by using the obtained 4-5 groups of data according to the formulas (2) to (5).

For multiple calibration experiments, a model shown in the formula (2) is respectively established by using data of each calibration experiment, and a is used₀、a₁、a₂And establishing a detection model by the mean value of the model coefficients.

(5) And (5) detecting the weight of the real bin.

And if the system is calibrated, detecting the output of the bottom surface pressure sensor and detecting the grain storage quantity of the granary by using the model shown in the formula (2).

Specifically, the granary weight detection model based on the optimal floor pressure measurement point is shown as formula (2).

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

Wherein,estimating the weight of the granary based on the optimal bottom pressure measurement point; AB is the area of the bottom surface of the granary;the pressure measurement value average of all the optimal bottom pressure measurement points,is the pressure measurement value of the ith optimum floor pressure measurement point, n_OPThe number of the optimal bottom pressure intensity measuring points; a is₀、a₁、a₂Are model coefficients.

For the weight detection model of the granary and grain pile shown in formula (2), it is assumed that n experimental sample points of weight and pressure have been obtainedWherein, W_iThe weight of the feed is the weight of the feed,the mean value of the measured values of the corresponding optimal bottom pressure measurement points can be obtained by deducing the formula of each coefficient in the formula (2)

a₀＝(b₀(c₂c₄-c₃₃)+b₁(c₂₃-c₁₄)+b₂(c₁₃-c₂₂))/Δ(3)

a₁＝(b₀(c₂₃-c₁₄)+b₁(c₀c₄-c₂₂)+b₂(c₁₂-c₀₃))/Δ(4)

a₂＝(b₀(c₁₃-c22)+b₁(c₁₂-c₀₃)+b₂(c₀₂-c₁₁) Is/delta (5) wherein delta is (c)₀₂-c₁₁)c₄+2.c₁₂c₃-c₂₂c₂-c₀ ^*c₃₃；c₁₁＝c₁c₁；c₂₂＝c₂c₂；c₃₃＝c₃c₃；c₃₃＝c₃c₃；c₃₃＝c₃c₃；c₀₂＝c₀c₂；c₀₃＝c₀c₃；c₁₂＝c₁c₂；c₁₃＝c₁c₃；c₁₄＝c₁c₄；c₂₃＝c₂c₃；

$c_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_i^2;c_3=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^3/W_i^2;c_4=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^4/W_i^2;c_1=\underset{i}{\mathrm{\&Sigma;}}1/W_i^2;b_0=\underset{i}{\mathrm{\&Sigma;}}1/W_i;$

$b_1\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}(s,W_i)/W_i;b_2=\underset{i}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{Q}}_{\mathrm{BOP}}{(s,W_i)}^2/W_{i.}$

The system calibration and detection model modeling are carried out according to the following steps:

(1) and establishing a calibration grain feeding area. The sensors are arranged according to the position of the selected optimal bottom pressure measurement point, the sand bag is used for establishing a calibration grain feeding area, the distance between the sand bag and the sensors is about 3-4 m, the influence of instability of a sand bag wall on pressure distribution of the calibration grain feeding area is reduced, the height of the sand bag wall is 1.5-2 m, and the pressure sensors are uniformly arranged.

(2) And obtaining calibration data. And for the calibrated grain feeding area, feeding grains step by step, flattening each batch of grains after feeding 0.5 m, recording the grain feeding weight and the values of all pressure sensors, and calculating the grain feeding weight of the whole granary with the corresponding height according to the ratio of the area of the calibrated grain feeding area to the total area of the granary. Thus, 3-4 groups of experimental data can be obtained. And then, loading grains in a normal mode, and recording the total weight of the grains and the value of the pressure sensor on the bottom surface of the granary after the grains are loaded, so that 4-5 groups of data can be obtained.

(3) And modeling a detection model.

The grain pile weight detection model shown in the formula (2) is simple, and less data are needed in modeling, so that an ideal detection effect can be obtained by directly utilizing the obtained 4-5 groups of data.

For multiple calibration experiments, the model shown in the formula (2) is built by using the data of each calibration experiment, and a0 and a are used₁、a₂And establishing a detection model by the mean value of the model coefficients.

Experimental data are given below to demonstrate the practical effects of the present invention.

According to the pressure measurement value mean value data of 3 CM(s) in the 2 nd row of the horizontal warehouse and small measurement points (17 #, 22# and 24 #), the modeling is carried out by utilizing the data of 2 experiments (experiments 2 and 3), a prediction model established by the formula (2) is shown as a formula (6), and the calculation results of the grain storage weight of the granary of each experiment are shown in tables 3 to 6.

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

According to the pressure measurement value mean value data of 2 CM(s) small measurement points (21 #, 29 #) in the 2 nd circle of the squat silo, 2 times of experiments (experiment 2 and experiment 3) are utilized for modeling, a prediction model established by the formula (2) is shown as a formula (7), and the grain storage weight calculation results of the experiments are shown in tables 7 to 9.

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

Table 3 horizontal warehouse experiment 1 grain weight calculation result 4 horizontal warehouse experiment 2 grain weight calculation result

Table 5 horizontal warehouse experiment 1 grain weight calculation results table 6 horizontal warehouse experiment 2 grain weight calculation results

Table 7 results of calculation of stored grain weight in squat silo experiment 1 table 8 results of calculation of stored grain weight in squat silo experiment 2

TABLE 9 squat silo experiment 3 stored grain weight calculation results

As can be seen from the calculation results of the grain storage weight of the granary based on the average value of the measurement points of the optimal bottom pressure measurement point, except for the condition of small grain storage weight, the detection results of other detection points are ideal. Therefore, the grain warehousing grain weight monitoring method needs few measuring points, only 2-3 measuring points and high measuring precision, but has high requirements on the performance of the sensor, particularly the repeatability error and the consistency of the performance of the sensor.

For the horizontal warehouse shown in fig. 1, the test results of the test model constructed using the previous method and all the 90 measurement point data are shown in tables 10 to 13. For the squat silo shown in fig. 2, the test results of the test model constructed using the previous method and all 55 measurement point data are shown in tables 14 to 16.

Table 10 horizontal warehouse experiment 1 stored grain weight calculation results table 11 horizontal warehouse experiment 2 stored grain weight calculation results

Table 12 horizontal warehouse experiment 3 stored grain weight calculation results table 13 horizontal warehouse experiment 4 stored grain weight calculation results

Table 14 results of calculation of stored grain weight in squat silo experiment 1 table 15 results of calculation of stored grain weight in squat silo experiment 2

Table 16 squat silo experiment 3 stored grain weight calculation results

From the results of the calculation of the grain weight in the granary by using the previous method, although the number of the measurement points is large, the prediction result of partial points exceeds 3% under the condition of large grain weight. This shows that the method proposed by this patent has a higher measurement accuracy and requires fewer floor pressure sensors.

The method selects the best measuring point for measurement, instead of being dedicated to arranging more and more dense sensors like the prior art, so that more and more environmental parameters are reflected more comprehensively by the detection result, the method is quite unknown, although the sensors are continuously added, the data volume seems to be rich, but due to the particularity of the granary, the grain feeding and storing processes have different influences on the measuring results of different positions, the rich quantity cannot improve the detection accuracy, and valuable measuring data can be diluted due to the possibility of mixing more random information, so that the measurement accuracy is reduced. Therefore, the invention is carried out against the way, the detection scheme not only greatly reduces the number of sensors, but also can improve the measurement accuracy.

A specific embodiment is given above, but the invention is not limited to the described embodiment. The basic idea of the present invention lies in the above solution, and it is obvious to those skilled in the art that it is not necessary to spend creative efforts to design various modified models, formulas and parameters according to the teaching of the present invention. Variations, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, and still fall within the scope of the invention.

Claims (6)

1. A stored grain weight detection system based on an optimal bottom pressure measurement point is characterized by comprising at least two bottom pressure sensors and the optimal bottom pressure measurement point arranged on a granary, wherein the optimal bottom pressure measurement point is a measurement point with high detection consistency.

2. The stored grain weight detection system based on the optimal floor pressure measurement point of claim 1, wherein the optimal floor pressure measurement point is mainly located in an area having a distance from the side surface and the grain inlet.

3. The stored grain weight detection system based on the optimal bottom pressure measurement point as claimed in claim 1, wherein the measurement point selection rule with high consistency is as follows: is provided with n_WWeight W of seed grain_i，i＝1,...,n_wMeasuring the weight of each stored grain by n_MSecondly; for any given pressure measurement point s on the bottom surface of the granary, the weight W of the stored grain in the granary_iThe measured value of the measurement point s at the k-th measurement of (1) is Q_B(s,W_i,k)，k＝1,...,n_MDefinition of

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

A consistency measure for the bottom pressure measurement point s; for any measurement point s, the smaller the CM(s), the higher the measurement consistency, and vice versa.

4. A granary is provided with a stored grain weight detection system and is characterized in that the stored grain weight detection system comprises at least two bottom surface pressure sensors and is arranged at an optimal bottom surface pressure measurement point of the granary, and the optimal bottom surface pressure measurement point is a measurement point with high detection consistency.

5. The grain bin according to claim 4, wherein the optimal floor pressure measurement point is located primarily in a region spaced from the side and the grain inlet.

6. The grain bin according to claim 5, wherein the measurement point selection rule with high consistency is as follows: is provided with n_WWeight W of seed grain_i，i＝1,...,n_wMeasuring the weight of each stored grain by n_MSecondly; for any given onePressure measuring point s on the bottom surface of the granary, and weight W of stored grains in the granary_iThe measured value of the measurement point s at the k-th measurement of (1) is Q_B(s,W_i,k)，k＝1,...,n_MDefinition of

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

A consistency measure for the bottom pressure measurement point s; for any measurement point s, the smaller the CM(s), the higher the measurement consistency, and vice versa.