CN114471798A - On-line detection method for rice processing reduction rate and rice milling pressure control method - Google Patents
On-line detection method for rice processing reduction rate and rice milling pressure control method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02B—PREPARING GRAIN FOR MILLING; REFINING GRANULAR FRUIT TO COMMERCIAL PRODUCTS BY WORKING THE SURFACE
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- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
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Abstract
The invention discloses an online detection method of rice processing reduction rate and a rice milling pressure control method, wherein a machine vision detection device is used for obtaining the average volume of a sample and further obtaining a coefficient eta, and the reduction rate of each rice milling procedure is calculated on the basis of the coefficient eta. Meanwhile, the invention can detect the reduction rate of each rice milling procedure, and the detection result can be used not only as a post evaluation basis, but also as an evaluation basis in the rice milling process, thereby being convenient for improving the control precision of rice processing.
Description
Technical Field
The invention relates to a rice processing and detecting technology, in particular to an online detecting method for the milling reduction rate of rice processing and a rice milling pressure control method.
Background
The components of the rice are distributed in a layered mode, according to the hierarchical change from outside to inside, the outermost rice layer is a fruit peel, and the outermost rice layer is a seed coat and a bead core layer in sequence from inside to inside, the residue of the pesticide used in the planting process is homopolymerized and collected, meanwhile, the three layers are rich in crude fibers, so that the rice is rough in taste when being eaten, and therefore the three layers are usually called rice 'outer skin layers', and the rice needs to be removed in a grinding mode in the processing process.
The outer layer is inward, and the aleurone layer, the sub-aleurone layer and the endosperm are sequentially arranged, and different rice layers contain different important nutrient components.
In the rice processing process, if the grinding is reduced more, the loss of nutrient components is more, and the rice yield is reduced; a reduction in grind indicates that the ingredients that should be removed are not removed, not only reducing the quality of the finished product, but also leading to food safety hazards.
Therefore, the reduction rate, which is a measure of the degree of reduction, has an extremely important guiding role in the actual production of rice processing.
However, the current evaluation of the reduction can only be manually measured by sending samples randomly obtained from a production line to a laboratory for weighing the thousand-grain weight, so that the significance of the actual production guidance is basically lost:
(1) the manual detection time is too long, the detection frequency is too low, the current most rigorous detection is only once for 2 hours, the detection time is not less than 10 minutes each time, the rice processing is promoted at the flow rate of 5-25 tons each hour, and the detection speed is far beyond the processing production speed;
(2) the manual detection sample size is small, and the data has great randomness;
(3) the detection result can only be used as a post evaluation basis, and cannot be used as an evaluation basis in the processing process.
The grinding reduction rate of rice milling is detected on line, and real-time and reliable control parameters can be provided for an intelligent control system: the rice milling pressure is increased, and the milling reduction rate is increased; the rice milling pressure is reduced, and the milling reduction rate is reduced. Therefore, a method for detecting the reduction rate of rice milling on line is needed, which can detect the reduction rate of rice milling in real time and improve the control precision of rice processing.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is insufficient, and provides an online detection method and a rice milling pressure control method for the rice milling reduction rate, so that the rice milling reduction rate of each rice mill can be detected online in real time.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an on-line detection method for the rice milling reduction rate utilizes the following formula to calculate the reduction rate and the total reduction rate n of each rice milling procedureTotal roller：
nj roller= {（ VGrinding j-1 - VGrind j）/VCoarse rice}*η*100%；
nTotal roller= {（ VCoarse rice - VGrind L）/VCoarse rice}*η*100%；
Wherein, the calculation process of the coefficient eta comprises the following steps:
1) obtaining K brown rice samples;
2) inputting the first brown rice sample into a machine vision detection device to obtain an average volume value V1 of each brown rice, weighing the brown rice sample to obtain a weight W1, inputting the brown rice sample into a rice mill to obtain processed rice, weighing the processed rice to obtain a weight W2, and inputting the processed rice into the machine vision device to obtain an average volume value V2 of each milled rice;
3) calculate n1= (V1-V2)/V1 × 100%; n2= (W1-W2)/W1 × 100%;
4) introducing a parameter η 1 such that equation n2= n1 ×. η 1 holds, calculating the value of η 1;
5) repeating the steps 1) to 3) for the rest K-1 parts of brown rice samples to obtain the rest K-1 eta 1 values;
6) taking the average value of the obtained K eta 1 to obtain eta;
nj rollerThe grinding reduction rate of the jth rice milling procedure is obtained; vGrinding j-1The average volume of the detected sample in the j-1 st rice milling procedure; vGrind jThe average volume of the detected sample in the jth rice milling procedure is obtained; vCoarse riceThe average volume of the detected sample in the brown rice is obtained; vGrind LThe average volume of the detected sample in the L-th rice milling process is obtained; when j =1, VGrinding j-1= VBrown rice。
The method utilizes the machine vision detection device to obtain the average volume of the sample, further obtains the coefficient eta, and calculates the reduction rate of each rice milling process on the basis, the calculation process is simple, the automatic detection of the reduction rate is realized, and compared with manual detection, the method greatly shortens the detection time, saves the manual detection cost and improves the detection precision. Meanwhile, the invention can detect the reduction rate of each rice milling procedure, and the detection result can be used not only as a post evaluation basis, but also as an evaluation basis in the rice milling process, thereby being convenient for improving the control precision of rice processing.
The calculation process of the average volume of the detected sample comprises the following steps: and obtaining samples of the brown rice feeding port and the rice milling machine discharging ports, inputting the selected samples into a machine vision detection device, removing broken rice in the samples, and calculating the volume of the samples after the broken rice is removed to obtain the average volume of the detected samples in each rice milling process.
The invention utilizes the machine vision device to detect the average volume of the selected sample, the machine vision device can detect a large amount of data, and compared with the randomness problem of manual detection, the detection method greatly improves the detection precision.
The invention also provides a rice milling pressure control method based on reduction rate detection, which comprises the following steps:
for each rice milling procedure, the current rice milling pressure required to be adjusted in each rice milling procedure is calculated by the following formula:
∆Fji = [R1M*(nj grind 0 - nj grind i）+ R2N*(Li-L0)]* R1M (n1 roller i-n1 roller i-1) Δ Fji-1;
wherein, the Δ Fji is the rice milling pressure currently needing to be adjusted; the margin Fji-1 is the milling pressure to be adjusted for the previous milling; n isj grind 0The target reduction rate n of the jth rice millingj grind iThe reduction rate n of the jth rice milling currently detectedj grinding i-1The reduction rate of the jth rice milling detected in the previous rice milling process is obtained; li is the final rolling skin retention rate of the current detection, and L0 is the final rolling target allowed skin retention rate; r1、R2Is the set weight coefficient.
The reduction rate of the jth rice milling is obtained by detection of the detection method.
In the invention, in order to improve the control precision, the weight coefficient R1、R2The setting rule is as follows: for the rice milling from the 1 st rice milling to the last rice milling, R1、R2And decreases in turn.
If the rice milling process is six times, the weight coefficient R corresponding to the six rice milling processes1、R2Respectively setting as follows:
first pass millRice: r1=100，R2=20；
Rice milling for the second time: r1=80，R2=10；
Rice milling in the third step: r1=60，R2=8；
Fourth rice milling: r1=30，R2=5；
Fifth rice milling: r1=20，R2=3；
Sixth rice milling: r1=10，R2=1。
In the rice milling process of 'multi-machine light milling', the influence of 1, 2, 3 and 4. Therefore, in the invention, R is used for the rice milling from the 1 st rice milling to the last rice milling1、R2And decreases in turn.
In the invention, when the milling process is more than six times, the weight coefficient R from the seventh milling to the last milling1、R2The weight coefficient is the same as that of the sixth rice milling.
As an inventive concept, the present invention also provides a computer arrangement comprising a memory, a processor and a computer program stored on the memory; the processor executes the computer program to implement the steps of the method of the present invention.
As an inventive concept, the present invention also provides a computer program product comprising computer programs/instructions; which when executed by a processor implement the steps of the method of the present invention.
As an inventive concept, the present invention also provides a computer-readable storage medium having stored thereon a computer program/instructions; which when executed by a processor implement the steps of the method of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention utilizes the machine vision detection device to realize the on-line rapid detection of the rice nutrient composition, greatly shortens the detection time and improves the detection precision and the detection efficiency compared with a manual detection method, and can carry out reduction rate detection on each rice milling procedure;
2) based on the online detection, the rice milling pressure control system based on the reduction rate detection is constructed, and the rice milling pressure is accurately controlled according to the reduction rate by taking the online detection data of the reduction rate as the basis of the rice milling pressure control;
3) the invention provides an online control basis for closed-loop intelligent rice milling control, and an algorithm is constructed on the basis of the online control basis, so that the rice milling unmanned intelligent control based on the reduction rate is realized.
Drawings
FIG. 1 is a flow chart of a detection method according to an embodiment of the present invention;
FIG. 2 is a machine vision result diagram of brown rice in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of a rice pattern with several layers of rice removed by milling with a rice mill in accordance with an embodiment of the present invention;
FIG. 4 shows rice particles of different volumes according to examples of the present invention.
Detailed Description
The embodiment of the invention uses the corresponding relation between the volume and the actual weight of the rice under the machine vision, measures the volume change of particles in the previous and subsequent processes on a rice milling production line through the machine vision, and detects and calculates the reduction rate of the rice in the rice milling process on line; the intelligent manufacturing system for rice processing realizes refined process control on rice processing according to the change of the online detection reduction rate.
The rice milling rate detection process of embodiment 1 of the invention is shown in fig. 1, and specifically comprises the following steps:
definition of "reduction rate"
Taking the brown rice as a reference, the weight percentage of the milled part of the rice in the rice milling process to the brown rice is the milling reduction rate, and the weight percentage is expressed by percentage.
The weight of the brown rice before milling is W brown, the weight of the rice after milling is W roller, and n is the reduction rate, the calculation formula is shown as follows:
n (%) = { (W coarse-W grind)/W coarse }. 100%
The machine vision results of the brown rice are shown in fig. 1.
The machine vision results after the milling of the brown rice are shown in fig. 2.
Fig. 2 shows a rice sample from which several layers of rice have been removed by milling with a rice mill, and from fig. 2 to fig. 3, the reduced portion of rice, called "de-grind", can be calculated by machine vision. In actual production, the rice which is also milled cleanly is not completely milled with the same 'reduction', the reduction degree is different, and the volume size of the processed rice particles is different. As shown in fig. 4.
Therefore, on the actual production line, the detection of the reduction rate is beneficial to the control in the processing process, so that the rice can remove the residual pesticide and the outer skin layer with rough taste, and the natural nutrition of the rice can be retained to the maximum extent.
Two, calibration
Using a device with a stable machine vision inspection environment (e.g., 201720785155. X);
20 parts of 50 g/part brown rice sample is prepared, one small rice mill (50-100 g sample amount) is used in a laboratory, and 1 electronic balance (the precision is 0.001 g) is arranged;
inputting a first brown rice sample before a rice milling experiment into a machine vision device for detection to obtain an average volume value (V1) of each brown rice, wherein the volume of the rice is expressed by taking a machine vision pixel as a unit; weighing the brown rice sample (W1), putting into an experimental rice mill to obtain processed rice, and weighing (W2); inputting the rice grains into a machine vision device for detection to obtain the average volume value (V2) of each milled rice grain;
respectively calculating:
n1(%) = (V1-V2)/V1 *100%；
n2(%) = (W1-W2)/W1 *100%；
a parameter η 1 is introduced such that equation n2= n1 ×. η 1 holds, and the value of η 1 is found.
Repeating the rest 19 samples in turn to obtain the eta 2 to eta 20 values, and taking the average value of eta 1 to eta 20 to calculate the eta.
Third, detection
Constructing online intelligent material taking for a brown rice feeding port, a rice mill discharging port 1, a rice mill discharging port 2, a rice mill discharging port 3 and a rice mill discharging port 4 through an online intelligent material taking system (if 5 rice milling processes are performed before and after a production line, a material taking port is expanded, if 3 rice milling processes are performed before and after the production line, a material taking port is reduced, and the like), and inputting the materials into a machine vision detection device through a centralized material distributor;
setting a control rule of the intelligent material taking system:
the material taking sequence comprises a brown rice feeding port, a rice mill discharging port 1, a rice mill discharging port 2, a rice mill discharging port 3 and a rice mill discharging port 4;
the detection time of the detection system for each material taking port is set to be 2 minutes, and the time interval between material taking is 15s (or can be set to be 20-30 s) each time;
the detection system performs online detection on brown rice before rice milling, rice after 1-roller milling, rice after 2-roller milling, rice after 3-roller milling and rice after 4-roller milling according to the material taking rule of the intelligent material taking system;
in the detection, the system eliminates broken rice in the obtained samples (the samples obtained by each detection port cannot be absolutely equal; particularly, on a production line, the mass characteristics of rice processing which is the same processing object in the previous and subsequent processes cannot be obtained forever; meanwhile, the broken rice content in different processes in the production process has different proportions, and the broken rice can influence the volume detection accuracy of the system on the particles), and the volume average of each round (namely each detection) is carried out on all the whole rice particles.
Fourth, make statistics of
The average volume of the detected particles of brown rice, 1-roller (namely, the first roller milling), 2-roller (namely, the second roller milling), 3-roller (namely, the third roller milling) and 4-roller (namely, the fourth roller milling) is respectively calculated as follows: vCoarse rice、VGrinding 1、VGrinding 2、VGrind 3、VGrinding mill 4；
Obtaining the reduction rate of each rice milling sub procedure:
reduction ratio of 1 grind, n1 grinding（%）= {（ VCoarse rice - VGrinding 1）/VCoarse rice}*η*100%；
2 reduction rate of grinding, n2 grind（%）= {（ VGrinding 1- VGrind 2）/VCoarse rice}*η*100%；
3 reduction rate of grinding, n3 grind（%）= {（ VGrinding 2- VGrind 3）/VCoarse rice}*η*100%；
4 reduction ratio of grind, n4 grind（%）= {（ VGrind 3- VGrinding mill 4）/VCoarse rice}*η*100%；
Total reduction rate, nTotal roller（%）= {（ VCoarse rice - VGrinding mill 4）/VCoarse rice}*η*100%。
Obtaining the grinding of the round 1-4 and the reduction rate of the whole rice grinding process:
n1 grinding (%)
n2 grinding (%)
n3 grinding (%)
n4 grinding (%)
n total mill (%)
And adds a detection index for the final grinding (the last rice milling): skin retention rate.
And each round lasts for 10 minutes, after the detection is finished, the intelligent material taking system starts the next round of detection, and the process is circulated continuously, the intelligent detection system continuously detects the current machining reduction, and control decision data are continuously provided for the intelligent manufacturing system.
In the embodiment 2 of the invention, an intelligent control model (namely an intelligent control model) of the reduction rate-rice milling pressure is constructed.
Each rice milling sub-process has a process task with own attribute, each rice milling sub-process also has the characteristics of the process, and the total reduction rate is formed by integrating the reduction rates of the sub-processes, therefore, in the actual production, the total reduction rate is distributed to each sub-process according to the actual situation, which is beneficial to simplifying an intelligent control model, and meanwhile, in an intelligent control system, the target reduction rate of each sub-process is taken as a setting parameter, which is further beneficial to the optimization of the control model in the actual production according to the actual situation of a production line.
The total reduction rate nTotal rollerDecomposition into n1 grinding，n2 grind，n3 grind，n4 grind(more than 4 times of rice milling, sequential expansion, less than 4 times of rice milling, sequential reduction), and simultaneously introducing final milling 'skin retention rate' as an auxiliary control parameter (in actual production, different production lines and rice milling machine combination modes generally produce different rice milling effects, the same reduction rate can lead to different 'skin retention rates' on different production lines, when residual outer skin layers exist on the surfaces of rice grains, the rice grains are called 'skin retention rice', the number of the skin particles left in each 100 rice grains is 'skin retention rate', expressed in percentage), and an 'reduction rate-rice milling pressure' intelligent control model is established for each rice milling process:
the first rice milling intelligent control model:
∆F1i = [100M*(n1 grinding 0 - n1 grinding i）+ 20N*(Li-L0)]*100M(n1 grinding i-n1 grinding i-1）/∆F1i-1；
The margin F1i is 1 rice milling pressure (gram) needing to be adjusted before milling; the distance F1i-1 is the adjustment amount in the previous round;
n1 grinding 0Is 1 grind target reduction rate, n1 grinding iFor the currently detected 1 grind reduction, n1 grinding i-1The 1-grinding reduction rate detected in the previous round;
the final skin retention rate currently detected by Li, L0 is the final target allowed skin retention rate.
The second rice milling intelligent control model:
∆F2i = [80M*(n2 grinding 0- n2 grind i）+ 10N*(Li-L0)]*80M(n2 grind i-n2 grinding i-1）/∆F2i-1；
The Δ F2i is 2 to grind the current rice milling pressure (gram) needing to be adjusted; the dosage is adjusted in the last round if the patient is at the dosage of F2 i-1;
n2 grinding 0Is 2 target reduction rate, n2 grind iFor the currently detected 2-roll reduction, n2 grinding i-1The 2-roll reduction rate detected in the previous round;
the final skin retention rate currently detected by Li, L0 is the final target allowed skin retention rate.
The third rice milling intelligent control model:
∆F3i = [60M*(n3 grinding 0 - n3 grind i）+ 8N*(Li-L0)]*60M(n3 grind i-n3 grinding of i-1）/∆F3i-1；
The margin F3i is the rice milling pressure (gram) which needs to be adjusted before 3 times of milling; the distance F3i-1 is the adjustment amount in the previous round;
n3 grinding 0Is a target reduction rate of 3 rolls, n3 grind iFor the currently detected 3 grind reduction, n3 grinding of i-1The 3-roll reduction rate detected in the previous round;
the final skin retention rate currently detected by Li, L0 is the final target allowed skin retention rate.
The fourth rice milling intelligent control model:
∆F4i = [30M*(n4 grind 0 - n4 grind i）+ 5N*(Li-L0)]*30M(n4 grind i-n4 grinding i-1）/∆F4i-1；
The margin F4i is the rice milling pressure (gram) which needs to be adjusted before 4 times of milling; the distance F4i-1 is the adjustment amount in the previous round;
n4 grind 0Is 4-roll target reduction rate, n4 grind iFor the currently detected 4-roll reduction, n4 grind i-1The 4-grinding reduction rate detected in the previous round;
the final skin retention rate currently detected by Li, L0 is the final target allowed skin retention rate.
In all formulas, M, N is a parameter and is configured according to the actual conditions of the configuration, model, sand mold and rice milling principle of a production line rice mill. In the embodiment of the invention, the value range of M is 0.5-0.9, and the value range of N is 0.1-0.45.
If the rice milling is less than 4 times, selecting in sequence, if the rice milling is more than 4 times, then:
the fifth rice milling intelligent control model:
∆F5i = [20M*(n5 grinding 0 - n5 grind i）+ 3N*(Li-L0)]*20M(n5 grind i-n5 grinding i-1）/∆F5i-1
The sixth rice milling intelligent control model:
∆F6i = [10M*(n6 grinding 0- n6 grind i）+ N*(Li-L0)]*10M(n6 grind i-n6 grinding of i-1）/∆F6i-1
If the number of the rice husks is more than six, each rice husking control model is the same as the six rice husking control models.
Examples of applications are:
the production line is 4 rice husks, and the rice machine configuration:
1. 2 rice milling is a sand roller rice mill, and 3 and 4 rice milling is an iron roller rice mill:
the rice machine is characterized in that:
1. the 2, 3 and 4 rice mills are vertical rice mills;
a discharging and feeding mode:
the rice milling in the 1-step is upper inlet and lower outlet, and the rice milling in the 2-step, 3-step and 4-step is lower inlet and upper outlet;
the processed rice has deeper back furrows, the target total reduction rate is set to be 12 percent, and the final grinding allowable skin retention rate is set to be 10 percent;
according to the big data of intelligent manufacturing system the same type produces line characteristic, preset the reduction rate of 1, 2, 3, 4 lines of husk rice respectively:
firstly, grinding: 5.5 percent;
grinding: 3.0 percent;
and (3) grinding: 2.0 percent;
and (4) grinding: 1.5 percent;
m, N parameters are configured according to the characteristics of the same type of production line of the big data of the intelligent manufacturing system;
detecting the reduction rate and the total reduction rate of each rice milling on line, namely detecting the reduction rate of the first rice milling, namely executing the rice milling pressure control of the first rice milling, wherein the execution amount is determined by the algorithm of the embodiment 2; thus finishing the rice milling pressure modulation of 2, 3 and 4 times.
When the reduction rate and the total reduction rate of each rice milling reach the set values, the final milling and husk retaining rate is always greater than 12% (exceeding the allowable husk retaining rate), which indicates that the distribution of the milling rate of each rice milling is slightly unreasonable, the characteristics are presented according to the big data of the intelligent manufacturing system, and the reduction rates of 1, 2, 3 and 4 rice milling are adjusted as follows:
firstly, grinding: 5.8 percent;
grinding: 3.0 percent;
and (3) grinding: 2.1 percent;
and (4) grinding: 1.1 percent;
the total reduction rate is still 12% and the final reduction rate is still 10%.
And repeating the previous step.
The control result meets the target.
Under the rice milling target condition, the rice milling reduction rate control scheme of each path is obtained.
The detection-intelligent control process is repeatedly executed, and the stability of the processing quality is guaranteed under the actual condition that the supplied materials are constantly changed.
Through closed-loop control of the reduction rate and the rice milling pressure, the highest rice yield can be obtained on the premise of ensuring the cleanness of the processed rice on the grain surface.
In the application example of the present invention, a total reduction rate of 12% means a rice rate of: 80% (brown rice yield) × (1-12%) =70.4%, under the rice milling effect that the peel retention rate is less than 10%, the rice obtaining rate is very considerable, and especially under the condition of the variety of raw grains with deeper back ditches, the rice obtaining rate is difficult and expensive, so that the method has great practical significance for grain saving, loss reduction and efficiency improvement, the economic benefit of processing enterprises can be improved, and the method is more favorable for guaranteeing the grain safety.
Claims (10)
1. An on-line detection method for the rice milling reduction rate is characterized in that the reduction rate and the total reduction rate n of each rice milling procedure are calculated by the following formulaTotal roller：
nj roller= {（ VGrinding j-1 - VGrind j）/VCoarse rice}*η*100%；
nTotal roller= {（ VCoarse rice - VGrind L）/VCoarse rice}*η*100%；
Wherein, the calculation process of the coefficient eta comprises the following steps:
obtaining K brown rice samples;
inputting the first brown rice sample into a machine vision detection device to obtain an average volume value V1 of each brown rice, weighing the brown rice sample to obtain a weight W1, inputting the brown rice sample into a rice mill to obtain processed rice, weighing the processed rice to obtain a weight W2, and inputting the processed rice into the machine vision device to obtain an average volume value V2 of each milled rice;
calculate n1= (V1-V2)/V1 × 100%; n2= (W1-W2)/W1 × 100%;
introducing a parameter η 1 such that equation n2= n1 ×. η 1 holds, calculating the value of η 1;
repeating the steps 1) to 3) for the rest K-1 parts of brown rice samples to obtain the rest K-1 eta 1 values;
taking the average value of the obtained K eta 1 to obtain eta;
nj rollerThe reduction rate of the jth rice milling procedure is obtained; vGrinding j-1The average volume of the detected sample in the j-1 st rice milling procedure; vGrind jThe average volume of the detected sample in the jth rice milling procedure is obtained; vCoarse riceThe average volume of the detected sample in the brown rice is obtained; vGrind LThe average volume of the detected sample in the L-th rice milling process is obtained; when j =1, VGrinding j-1= VCoarse rice。
2. The method for detecting a rice processing reduction rate in accordance with claim 1, wherein the calculation of the average volume of the detected samples comprises: and obtaining samples of the brown rice feeding port and the rice milling machine discharging ports, inputting the selected samples into a machine vision detection device, removing broken rice in the samples, and calculating the volume of the samples after the broken rice is removed to obtain the average volume of the detected samples in each rice milling process.
3. A rice milling pressure control method based on reduction rate detection is characterized by comprising the following steps:
for each rice milling procedure, the current rice milling pressure required to be adjusted in each rice milling procedure is calculated by the following formula:
∆Fji = [R1M*(nj grind 0 - nj grind i）+ R2N*(Li-L0)]* R1M (n1 roller i-n1 roller i-1) Δ Fji-1;
wherein, the Δ Fji is the rice milling pressure currently needing to be adjusted; the margin Fji-1 is the milling pressure to be adjusted for the previous milling; n isj grind 0The target reduction rate n of the jth rice millingj grind iThe reduction rate n of the jth rice milling currently detectedj grinding i-1The reduction rate of the jth rice milling detected in the previous rice milling process is obtained; li is the final rolling skin retention rate of the current detection, and L0 is the final rolling target allowed skin retention rate; r1、R2To set upThe weight coefficient of (a); m, N is a set parameter.
4. The reduction rate of the jth rice milling is detected by the method of claim 1 or 2.
5. A rice milling pressure control method based on reduction rate detection as claimed in claim 3, characterized in that the weight coefficient R1、R2The setting rule is as follows: for the rice milling from the 1 st rice milling to the last rice milling, R1、R2And decreases in turn.
6. A rice milling pressure control method based on reduction rate detection as claimed in claim 4, wherein if the rice milling process is six, the weighting factor R corresponding to six rice milling processes is set to six1、R2Respectively setting as follows:
first rice milling: r1=100，R2=20；
Rice milling for the second time: r1=80，R2=10；
Rice milling in the third step: r1=60，R2=8；
Fourth rice milling: r1=30，R2=5；
Fifth rice milling: r1=20，R2=3；
Sixth rice milling: r1=10，R2=1。
7. A rice milling pressure control method based on reduction rate detection as claimed in claim 5, characterized in that when the rice milling process is more than six times, the weighting factor R from the seventh rice milling to the last rice milling is1、R2The weight coefficient is the same as that of the sixth rice milling.
8. A computer apparatus comprising a memory, a processor and a computer program stored on the memory; characterized in that the processor executes the computer program to carry out the steps of the method according to one of claims 1 to 6.
9. A computer program product comprising a computer program/instructions; characterized in that the computer program/instructions, when executed by a processor, performs the steps of the method according to one of claims 1 to 6.
10. A computer readable storage medium having stored thereon a computer program/instructions; characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of one of claims 1 to 6.
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