CN116559385A - Cereal detection data processor - Google Patents
Cereal detection data processor Download PDFInfo
- Publication number
- CN116559385A CN116559385A CN202310495585.8A CN202310495585A CN116559385A CN 116559385 A CN116559385 A CN 116559385A CN 202310495585 A CN202310495585 A CN 202310495585A CN 116559385 A CN116559385 A CN 116559385A
- Authority
- CN
- China
- Prior art keywords
- mass
- rate
- grain
- sample
- impurity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000013339 cereals Nutrition 0.000 title claims abstract description 200
- 238000001514 detection method Methods 0.000 title claims abstract description 173
- 238000005303 weighing Methods 0.000 claims abstract description 102
- 239000012535 impurity Substances 0.000 claims description 250
- 241000209094 Oryza Species 0.000 claims description 124
- 235000007164 Oryza sativa Nutrition 0.000 claims description 124
- 235000009566 rice Nutrition 0.000 claims description 124
- 108010068370 Glutens Proteins 0.000 claims description 30
- 235000021312 gluten Nutrition 0.000 claims description 30
- 238000002156 mixing Methods 0.000 claims description 25
- 235000021329 brown rice Nutrition 0.000 claims description 24
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 21
- 239000011707 mineral Substances 0.000 claims description 21
- 239000002245 particle Substances 0.000 claims description 21
- 230000002950 deficient Effects 0.000 claims description 18
- 206010039509 Scab Diseases 0.000 claims description 15
- 238000010521 absorption reaction Methods 0.000 claims description 15
- 230000006378 damage Effects 0.000 claims description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 241000209140 Triticum Species 0.000 claims description 10
- 235000021307 Triticum Nutrition 0.000 claims description 10
- 240000008042 Zea mays Species 0.000 claims description 10
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 claims description 10
- 235000002017 Zea mays subsp mays Nutrition 0.000 claims description 10
- 235000005822 corn Nutrition 0.000 claims description 10
- 208000027418 Wounds and injury Diseases 0.000 claims description 9
- 239000008187 granular material Substances 0.000 claims description 9
- 208000014674 injury Diseases 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 6
- 201000010099 disease Diseases 0.000 claims description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 6
- 230000003685 thermal hair damage Effects 0.000 claims description 6
- 239000008188 pellet Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 21
- 238000011156 evaluation Methods 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000000034 method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
Landscapes
- Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Medicinal Chemistry (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Adjustment And Processing Of Grains (AREA)
Abstract
The invention belongs to the technical field of grain detection equipment, in particular to a grain detection data processor, which comprises an instrument main body, wherein a control panel, an electronic scale and a control assembly electrically connected with the control panel and the electronic scale are arranged on the instrument main body, more than one grain item option is arranged on the control panel, each grain item option comprises more than one detection item option, each detection item option comprises more than one weighing item, a processing formula corresponding to the detection item options one by one is arranged in the control assembly, and each processing formula comprises weighing items in the corresponding detection item options. The invention integrates weighing and calculating, determines cereal items to be detected and detection items thereof by means of the control panel, scales corresponding cereal samples one by the electronic scale, and automatically converts the cereal samples according to the corresponding processing formula by the control assembly to obtain detection data, thereby effectively improving the working efficiency of detection personnel.
Description
Technical field:
the invention belongs to the technical field of grain detection equipment, and particularly relates to a grain detection data processor.
The background technology is as follows:
the quality of the grains is measured by performing professional detection and analysis on the grains to obtain evaluation indexes capable of reflecting the quality of the grains, wherein the evaluation indexes comprise impurity rate, imperfect grain rate, mildew grain rate, broken grain rate, blending rate and the like. The evaluation indexes are more in types, each evaluation index is required to be manually weighed, recorded and converted by a detector one by one, and the conversion of formulas corresponding to each evaluation index is different, so that the detector is relatively laborious, easy to make mistakes and lower in efficiency in the data processing process.
The invention comprises the following steps:
the invention aims to provide a grain detection data processor, which is used for weighing corresponding grain samples one by an electronic scale according to selected detection items, obtaining detection data through automatic conversion of a control assembly and effectively improving the working efficiency of detection personnel.
The invention is realized in the following way:
the grain detection data processor comprises an instrument main body, wherein the instrument main body is provided with a control panel, an electronic scale for weighing grain samples and a control assembly electrically connected with the control panel and the electronic scale, the control panel is provided with more than one grain item option, each grain item option comprises more than one detection item option, each detection item option comprises more than one weighing item, a processing formula corresponding to the detection item option one by one is arranged in the control assembly, each processing formula comprises weighing items in the corresponding detection item option,
the detection processing steps are as follows:
step A, selecting cereal item options to be detected and detection item options on a control panel;
b, weighing grain samples corresponding to the weighing items one by an electronic scale according to the weighing items of the detection item options selected in the step A so as to obtain corresponding weighing data, and transmitting the weighing data to a control assembly;
and C, processing the received weighing data by the control assembly according to a processing formula corresponding to the detection item option selected in the step A so as to obtain detection data.
In the grain detection data processor, one grain item is rice, the detection item of rice comprises impurities, brown rice rate outside the grain, coarse rice yield, whole polished rice rate, broken rice rate, yellow grain rice rate and mixing rate,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the brown rice rate outside the grains comprise sample mass, brown rice mass, large sample mass and large sample impurities, and the processing formula corresponding to the brown rice rate outside the grains is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; off-cereal brown rice rate = brown rice mass/sample mass (100-bulk impurity rate); the detection data obtained by the control assembly in the step C is the brown rice rate outside the grain;
the weighing items of the coarse yield comprise sample mass, all brown rice and imperfect grain mass, and the processing formula corresponding to the coarse yield is as follows: yield = (total brown rice-0.5 imperfect grain mass)/sample mass; the detection data obtained by the control assembly in the step C is the roughness rate;
the weighing items of the whole polished rice rate comprise sample mass and whole polished rice mass, and the processing formula corresponding to the whole polished rice rate is as follows: whole polished rice rate = whole polished rice mass/sample mass; the detection data obtained by the control assembly in the step C is the whole polished rice rate;
the weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows: broken rice rate = broken rice mass/sample mass; small broken rice rate = small broken rice mass/sample mass; the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows: huang Limi rate = yellow rice mass/sample mass; the detection data obtained by the control assembly in the step C is yellow grain rate;
the weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows: miscibility = heterogeneous grain mass/sample mass; and C, the detection data obtained by the control assembly in the step is the mixing rate.
In the grain detection data processor, one grain item option is wheat, the detection item option of the wheat comprises impurities, imperfect grain rate, mineral content, scab grain and gluten water absorption,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate); the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the mineral content comprise mineral mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mineral content is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; mineral content= (100-bulk impurity rate) = (mineral mass/bulk mass); the detection data obtained by the control assembly in the step C are the mineral content;
the weighing items of the scab granule comprise sample mass, scab granule mass, large sample mass and large sample impurities, and the treatment formula corresponding to the scab granule is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; gibberellic disease grain= (gibberellic disease grain mass/sample mass) (100-bulk impurity rate); the detection data obtained by the control assembly in the step C are scab particles;
the weighing items of the gluten water absorption comprise wet gluten quality and dry gluten quality, and the processing formula corresponding to the gluten water absorption is as follows: gluten water absorption= ((wet gluten mass-dry gluten mass)/dry gluten mass) 100; and C, obtaining detection data of the gluten water absorption rate by the control assembly.
In the grain detection data processor, one grain item is corn, the corn detection item comprises impurities, imperfect grain rate, heat damage grain and mildew grain,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate); the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the thermal injury particle comprise the mass of the thermal injury particle, the mass of a small sample, the mass of a large sample and the impurities of the large sample, and the treatment formula corresponding to the thermal injury particle is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; thermal damage rate= (100-bulk impurity rate) × (thermal damage pellet mass/bulk mass); the detection data obtained by the control assembly in the step C is the heat damage rate;
the weighing items of the mildew particles comprise mildew particle mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mildew particles is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; mildew grain rate= (100-bulk impurity rate) × (mildew grain mass/bulk mass); and C, the detection data obtained by the control assembly in the step is mildew grain rate.
In the grain detection data processor, one grain item is rice, the detection item of rice comprises impurities, imperfect grain rate, broken grain rate, yellow grain rate and mixing rate,
the weighing items of the impurities comprise sample mass and the impurities, and the processing formula corresponding to the impurities is as follows: impurity rate = impurity/sample mass; the detection data obtained by the control assembly in the step C is impurity rate;
the weighing items of the imperfect grain rate comprise sample quality and imperfect grain quality, and the processing formula corresponding to the imperfect grain rate is as follows: defective grain rate = defective grain mass/sample mass; the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows: broken rice rate = broken rice mass/sample mass; small broken rice rate = small broken rice mass/sample mass; the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows: huang Limi rate = yellow rice mass/sample mass; the detection data obtained by the control assembly in the step C is yellow grain rate;
the weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows: miscibility = heterogeneous grain mass/sample mass; and C, the detection data obtained by the control assembly in the step is the mixing rate.
In the above grain detection data processor, the control panel is a touch display screen, and the touch display screen can display grain item options, detection item options, weighing items, and detection data obtained by the control assembly in step C.
In the grain detection data processor, the grain detection data processor further comprises a plurality of correction weights with different weights, the control assembly performs zero resetting treatment on the electronic scale, the correction weights are sequentially placed on the electronic scale, and the control assembly performs correction treatment on the electronic scale.
Compared with the prior art, the invention has the outstanding advantages that:
the invention integrates weighing and calculating, determines cereal items to be detected and detection items thereof by means of the control panel, scales corresponding cereal samples one by the electronic scale, and automatically converts the cereal samples according to the corresponding processing formula by the control assembly to obtain detection data, thereby effectively improving the working efficiency of detection personnel.
Description of the drawings:
fig. 1 is a perspective view of the whole machine of the present invention.
In the figure: 1. an instrument body; 2. a control panel; 3. an electronic scale.
The specific embodiment is as follows:
the invention is further described below with reference to fig. 1 by way of specific examples:
the grain detection data processor comprises an instrument main body 1, wherein the instrument main body 1 is provided with a control panel 2, an electronic scale 3 for weighing grain samples and a control assembly electrically connected with the control panel 2 and the electronic scale 3, the control panel 2 is provided with more than one grain item option, each grain item option comprises more than one detection item option, each detection item option comprises more than one weighing item, the control assembly is internally provided with a processing formula corresponding to the detection item option one by one, and each processing formula comprises weighing items in the corresponding detection item options,
the detection processing steps are as follows:
step A, selecting cereal item options to be detected and detection item options on a control panel 2;
step B, weighing grain samples corresponding to the weighing items one by the electronic scale 3 according to the weighing items of the detection item options selected in the step A so as to obtain corresponding weighing data, and transmitting the weighing data to the control assembly;
and C, processing the received weighing data by the control assembly according to a processing formula corresponding to the detection item option selected in the step A so as to obtain detection data.
The invention further comprises a printer electrically connected with the control assembly, and the control assembly prints the detection data obtained in the step C into detection paper through the printer so as to facilitate the use of detection personnel.
The invention integrates weighing and calculating, the grain items to be detected and the detection items thereof are determined by means of the control panel 2, the corresponding grain samples are weighed one by the electronic scale 3, and detection data are obtained by automatic conversion of the control assembly according to the corresponding processing formula, so that the working efficiency of detection personnel is effectively improved.
Further, in the present embodiment, the grain item options include rice, wheat, corn, and rice, and the detection item options required for detecting the rice, wheat, corn, and rice are different because the evaluation indexes of the rice, wheat, corn, and rice are different.
Therefore, the following detection items and processing formulas are respectively needed for rice, wheat, corn and rice:
the grain item options are rice:
the detection item options required to be detected by the rice comprise impurity, brown rice rate outside the rice, coarse rice yield, polished rice rate, broken rice rate, yellow rice rate and mixing rate,
1. the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate);
total amount of impurities = large sample impurity rate + small sample impurity rate;
the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
2. The weighing items of the brown rice rate outside the grains comprise sample mass, brown rice mass, large sample mass and large sample impurities, and the processing formula corresponding to the brown rice rate outside the grains is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
off-cereal brown rice rate = brown rice mass/sample mass (100-bulk impurity rate);
the detection data obtained by the control assembly in the step C is the brown rice rate outside the grain;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
3. The weighing items of the coarse yield comprise sample mass, all brown rice and imperfect grain mass, and the processing formula corresponding to the coarse yield is as follows:
yield = (total brown rice-0.5 imperfect grain mass)/sample mass;
the detection data obtained by the control assembly in the step C is the roughness rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
4. The weighing items of the whole polished rice rate comprise sample mass and whole polished rice mass, and the processing formula corresponding to the whole polished rice rate is as follows:
whole polished rice rate = whole polished rice mass/sample mass;
the detection data obtained by the control assembly in the step C is the whole polished rice rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
5. The weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows:
broken rice rate = broken rice mass/sample mass;
small broken rice rate = small broken rice mass/sample mass;
the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
6. The weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows:
huang Limi rate = yellow rice mass/sample mass;
the detection data obtained by the control assembly in the step C is yellow grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
7. The weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows:
miscibility = heterogeneous grain mass/sample mass;
the detection data obtained by the control assembly in the step C is the mixing rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
The grain item is selected from wheat:
the detection item options required to be detected for wheat comprise impurities, imperfect grain rate, mineral content, scab grain and gluten water absorption,
1. the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate);
total amount of impurities = large sample impurity rate + small sample impurity rate;
the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
2. The weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate);
the detection data obtained by the control assembly in the step C are imperfect grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
3. The weighing items of the mineral content comprise mineral mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mineral content is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
mineral content= (100-bulk impurity rate) = (mineral mass/bulk mass);
the detection data obtained by the control assembly in the step C are the mineral content;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
4. The weighing items of the scab granule comprise sample mass, scab granule mass, large sample mass and large sample impurities, and the treatment formula corresponding to the scab granule is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
gibberellic disease grain= (gibberellic disease grain mass/sample mass) (100-bulk impurity rate);
the detection data obtained by the control assembly in the step C are scab particles;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
5. The weighing items of the gluten water absorption comprise wet gluten quality and dry gluten quality, and the processing formula corresponding to the gluten water absorption is as follows:
gluten water absorption= ((wet gluten mass-dry gluten mass)/dry gluten mass) 100;
the detection data obtained by the control assembly in the step C is the gluten water absorption rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
The grain item is selected from corn:
the detection item options required to be detected for the corn comprise impurities, imperfect grain rate, heat damage grains and mildew grains,
1. the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate);
total amount of impurities = large sample impurity rate + small sample impurity rate;
the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
2. The weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate);
the detection data obtained by the control assembly in the step C are imperfect grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
3. The weighing items of the thermal injury particle comprise the mass of the thermal injury particle, the mass of a small sample, the mass of a large sample and the impurities of the large sample, and the treatment formula corresponding to the thermal injury particle is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
thermal damage rate= (100-bulk impurity rate) × (thermal damage pellet mass/bulk mass);
the detection data obtained by the control assembly in the step C is the heat damage rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
4. The weighing items of the mildew particles comprise mildew particle mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mildew particles is as follows:
bulk impurity rate= (bulk impurity/bulk mass) ×100;
mildew grain rate= (100-bulk impurity rate) × (mildew grain mass/bulk mass);
c, the detection data obtained by the control assembly are mildew grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
The grain item is selected from rice:
the detection item options required to be detected by the rice comprise impurity, imperfect grain rate, broken rice rate, yellow grain rate and mixing rate,
1. the weighing items of the impurities comprise sample mass and the impurities, and the processing formula corresponding to the impurities is as follows:
impurity rate = impurity/sample mass;
the detection data obtained by the control assembly in the step C is impurity rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
2. The weighing items of the imperfect grain rate comprise sample quality and imperfect grain quality, and the processing formula corresponding to the imperfect grain rate is as follows:
defective grain rate = defective grain mass/sample mass;
the detection data obtained by the control assembly in the step C are imperfect grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
3. The weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows:
broken rice rate = broken rice mass/sample mass;
small broken rice rate = small broken rice mass/sample mass;
the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
4. The weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows:
huang Limi rate = yellow rice mass/sample mass;
the detection data obtained by the control assembly in the step C is yellow grain rate;
the detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
5. The weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows:
miscibility = heterogeneous grain mass/sample mass;
and C, the detection data obtained by the control assembly in the step is the mixing rate.
The detection result allows the difference to be not more than 0.3%, the average is taken as a result, and the calculation result takes two bits after the decimal point.
Meanwhile, in order to facilitate the human-computer interaction, in this embodiment, the control panel 2 is a touch display screen, and the touch display screen can display cereal item options, detection item options, weighing items, and detection data obtained by the control assembly in step C. In addition, basic information of some system operation, such as time and date, usage records, system settings, balance corrections, etc., can be displayed on the touch display screen.
In order to ensure that the electronic scale 3 can accurately weigh to obtain weighing data corresponding to weighing items, the weighing device further comprises a plurality of correcting weights with different weights, the control assembly performs zeroing treatment on the electronic scale 3, the correcting weights are sequentially placed on the electronic scale 3, and the control assembly performs correction treatment on the electronic scale 3.
The above embodiment is only one of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, therefore: all equivalent changes in shape, structure and principle of the invention should be covered in the scope of protection of the invention.
Claims (7)
1. A cereal detection data processor, its characterized in that: comprises an instrument main body (1), wherein a control panel (2), an electronic scale (3) for weighing grain samples and a control assembly electrically connected with the control panel (2) and the electronic scale (3) are arranged on the instrument main body (1), more than one grain item option is arranged on the control panel (2), each grain item option comprises more than one detection item option, each detection item option comprises more than one weighing item, a processing formula corresponding to the detection item options one by one is arranged in the control assembly, each processing formula comprises weighing items in the corresponding detection item options,
the detection processing steps are as follows:
step A, selecting cereal item options to be detected and detecting item options on a control panel (2);
b, weighing grain samples corresponding to the weighing items one by an electronic scale (3) according to the weighing items of the detection item options selected in the step A so as to obtain corresponding weighing data, and transmitting the weighing data to a control assembly;
and C, processing the received weighing data by the control assembly according to a processing formula corresponding to the detection item option selected in the step A so as to obtain detection data.
2. A grain detection data processor as in claim 1, wherein: wherein one grain item option is rice, the detection item option of the rice comprises impurity, brown rice rate outside the grain, coarse rice yield, whole polished rice rate, broken rice rate, yellow grain rice rate and mixing rate,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the brown rice rate outside the grains comprise sample mass, brown rice mass, large sample mass and large sample impurities, and the processing formula corresponding to the brown rice rate outside the grains is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; off-cereal brown rice rate = brown rice mass/sample mass (100-bulk impurity rate); the detection data obtained by the control assembly in the step C is the brown rice rate outside the grain;
the weighing items of the coarse yield comprise sample mass, all brown rice and imperfect grain mass, and the processing formula corresponding to the coarse yield is as follows: yield = (total brown rice-0.5 imperfect grain mass)/sample mass; the detection data obtained by the control assembly in the step C is the roughness rate;
the weighing items of the whole polished rice rate comprise sample mass and whole polished rice mass, and the processing formula corresponding to the whole polished rice rate is as follows: whole polished rice rate = whole polished rice mass/sample mass; the detection data obtained by the control assembly in the step C is the whole polished rice rate;
the weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows: broken rice rate = broken rice mass/sample mass; small broken rice rate = small broken rice mass/sample mass; the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows: huang Limi rate = yellow rice mass/sample mass; the detection data obtained by the control assembly in the step C is yellow grain rate;
the weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows: miscibility = heterogeneous grain mass/sample mass; and C, the detection data obtained by the control assembly in the step is the mixing rate.
3. A grain detection data processor as in claim 1, wherein: wherein one grain item option is wheat, the detection item option of the wheat comprises impurities, imperfect grain rate, mineral content, scab grain and gluten water absorption,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate); the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the mineral content comprise mineral mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mineral content is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; mineral content= (100-bulk impurity rate) = (mineral mass/bulk mass); the detection data obtained by the control assembly in the step C are the mineral content;
the weighing items of the scab granule comprise sample mass, scab granule mass, large sample mass and large sample impurities, and the treatment formula corresponding to the scab granule is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; gibberellic disease grain= (gibberellic disease grain mass/sample mass) (100-bulk impurity rate); the detection data obtained by the control assembly in the step C are scab particles;
the weighing items of the gluten water absorption comprise wet gluten quality and dry gluten quality, and the processing formula corresponding to the gluten water absorption is as follows: gluten water absorption= ((wet gluten mass-dry gluten mass)/dry gluten mass) 100; and C, obtaining detection data of the gluten water absorption rate by the control assembly.
4. A grain detection data processor as in claim 1, wherein: one of the grain item options is corn, the corn detection item options comprise impurity, imperfect grain rate, heat damage grain and mildew grain,
the weighing items of the impurities comprise large sample mass, large sample impurities, small sample mass and small sample impurities, and the processing formula corresponding to the impurities is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; small sample impurity rate= (small sample impurity/small sample mass) × (100-large sample impurity rate); total amount of impurities = large sample impurity rate + small sample impurity rate; the detection data obtained by the control assembly in the step C are the large sample impurity rate, the small sample impurity rate and the total impurity amount;
the weighing items of the imperfect grain rate comprise small sample mass, imperfect grain mass, large sample mass and large sample impurities, and the processing formula corresponding to the imperfect grain rate is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; defective grain rate = defective grain mass/small sample mass (100-large sample impurity rate); the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the thermal injury particle comprise the mass of the thermal injury particle, the mass of a small sample, the mass of a large sample and the impurities of the large sample, and the treatment formula corresponding to the thermal injury particle is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; thermal damage rate= (100-bulk impurity rate) × (thermal damage pellet mass/bulk mass); the detection data obtained by the control assembly in the step C is the heat damage rate;
the weighing items of the mildew particles comprise mildew particle mass, small sample mass, large sample mass and large sample impurities, and the processing formula corresponding to the mildew particles is as follows: bulk impurity rate= (bulk impurity/bulk mass) ×100; mildew grain rate= (100-bulk impurity rate) × (mildew grain mass/bulk mass); and C, the detection data obtained by the control assembly in the step is mildew grain rate.
5. A grain detection data processor as in claim 1, wherein: wherein one grain item option is rice, the detection item option of the rice comprises impurities, imperfect grain rate, broken grain rate, yellow grain rate and mixing rate,
the weighing items of the impurities comprise sample mass and the impurities, and the processing formula corresponding to the impurities is as follows: impurity rate = impurity/sample mass; the detection data obtained by the control assembly in the step C is impurity rate;
the weighing items of the imperfect grain rate comprise sample quality and imperfect grain quality, and the processing formula corresponding to the imperfect grain rate is as follows: defective grain rate = defective grain mass/sample mass; the detection data obtained by the control assembly in the step C are imperfect grain rate;
the weighing items of the broken rice rate comprise sample mass, broken rice mass and small broken rice mass, and the processing formula corresponding to the broken rice rate is as follows: broken rice rate = broken rice mass/sample mass; small broken rice rate = small broken rice mass/sample mass; the detection data obtained by the control assembly in the step C are the broken rice rate and the small broken rice rate;
the weighing item of Huang Limi rate comprises the mass of a sample and the mass of yellow rice, and the processing formula corresponding to Huang Limi rate is as follows: huang Limi rate = yellow rice mass/sample mass; the detection data obtained by the control assembly in the step C is yellow grain rate;
the weighing items of the blending ratio comprise sample mass and heterogeneous grain mass, and the processing formula corresponding to the blending ratio is as follows: miscibility = heterogeneous grain mass/sample mass; and C, the detection data obtained by the control assembly in the step is the mixing rate.
6. A grain detection data processor according to any one of claims 1 to 5, wherein: the control panel (2) is a touch control display screen, and the touch control display screen can display grain item options, detection item options, weighing items and detection data obtained by the control assembly in the step C.
7. A grain detection data processor according to any one of claims 1 to 5, wherein: the electronic balance (3) is zeroed by the control assembly, the correction weights are sequentially placed on the electronic balance (3), and the electronic balance (3) is corrected by the control assembly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310495585.8A CN116559385A (en) | 2023-04-28 | 2023-04-28 | Cereal detection data processor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310495585.8A CN116559385A (en) | 2023-04-28 | 2023-04-28 | Cereal detection data processor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116559385A true CN116559385A (en) | 2023-08-08 |
Family
ID=87489228
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310495585.8A Pending CN116559385A (en) | 2023-04-28 | 2023-04-28 | Cereal detection data processor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116559385A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116893127A (en) * | 2023-09-11 | 2023-10-17 | 中储粮成都储藏研究院有限公司 | Grain appearance quality index detector |
-
2023
- 2023-04-28 CN CN202310495585.8A patent/CN116559385A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116893127A (en) * | 2023-09-11 | 2023-10-17 | 中储粮成都储藏研究院有限公司 | Grain appearance quality index detector |
| CN116893127B (en) * | 2023-09-11 | 2023-12-08 | 中储粮成都储藏研究院有限公司 | Grain appearance quality index detector |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116559385A (en) | Cereal detection data processor | |
| CN105741180B (en) | Grain yield graph drawing system of combined harvester | |
| CN103808591B (en) | A kind of continous way Grain Drying Process moisture on-line detection method | |
| CN103615881A (en) | On-line measuring and control method and system of grain circulation drying moisture based on total-weight detection | |
| CN102445408A (en) | Automatic detector for size grading of filler | |
| CN102090207A (en) | Method and device for detecting cleaning loss of grain combine | |
| CN1009762B (en) | Multi-range load cell weighing instrument | |
| CN203597317U (en) | Grain circulating drying system based on gross weight detection | |
| CN109566064B (en) | Loss detection device and detection method for grain harvester | |
| CN209156467U (en) | A kind of finished product processing quality of rice detection device | |
| CN106228876A (en) | A kind of high-precision pressure sensor experimental model and experimental technique thereof | |
| CN101929938B (en) | Method for measuring volume weight of grain | |
| CN107328681B (en) | Thousand-grain weight and water content detection system for grains and beans based on machine vision | |
| US8184274B2 (en) | Measuring instrument and method for the measuring of properties of a particulate sample | |
| CN202305462U (en) | Device for online measurement of grain moisture | |
| CN1896716A (en) | Vibration cereal weight | |
| CN205246472U (en) | Tobacco material moisture content detecting system | |
| CN206573191U (en) | Electronic platform scale | |
| CN1061096A (en) | Multi-purpose quick detector for grain | |
| CN202256107U (en) | Automatic detector for filler size grading | |
| Bolton et al. | A comparison of X‐ray fluorescence spectrometry and chemical methods for determining sulphur in plant material | |
| CN210803187U (en) | Automatic detection device for grain components and volume weight | |
| CN202442776U (en) | Device for automatically calculating output rates of grain and oil raw materials | |
| CN209597247U (en) | A kind of rice mill Ji Kang mechanism and its rice mill | |
| CN110823343B (en) | Granary detection method and system based on bottom surface single-circle large-small value polynomial model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |