CN103645107A - Bombyx mori silkworm cocoon optimization detection method and system - Google Patents

Bombyx mori silkworm cocoon optimization detection method and system Download PDF

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CN103645107A
CN103645107A CN201310712181.6A CN201310712181A CN103645107A CN 103645107 A CN103645107 A CN 103645107A CN 201310712181 A CN201310712181 A CN 201310712181A CN 103645107 A CN103645107 A CN 103645107A
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cocoon
weight
average
grain
silk
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刘文烽
龙华敏
罗承孝
陈瑛
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LIUZHOU INSTITUTE OF AUTOMATION SCIENCE
Liuzhou Zb Science & Technology Co Ltd
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LIUZHOU INSTITUTE OF AUTOMATION SCIENCE
Liuzhou Zb Science & Technology Co Ltd
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Abstract

The invention discloses a bombyx mori silkworm cocoon optimization detection method which comprises the following steps: (1) grouping female or male silkworm cocoons, detecting the whole cocoon weight and cocoon shell weight of each cocoon in groups, and calculating the cocoon shell rate; (2) with the data as the basis, calculating the average whole cocoon weight, the average cocoon shell weight and the average cocoon shell rate of each group of silkworm cocoons; (3) calculating the whole cocoon weight standard deviation and cocoon shell weight standard deviation of each group of silkworm cocoons according to the previous data; and (4) screening variety, namely screening the silkworm cocoons according to an upper optimization method and a lower optimization method, thus optimizing the first-class variety. The bombyx mori silkworm cocoon optimization detection system is formed by sequentially connecting a weighing module, a recording module, a data storage module, a statistic analysis module and a display module with one another. The method is mainly used for optimizing the quality of the bombyx mori silkworm cocoons, the breeding optimization method is advanced, rapid and convenient, and data analysis and sharing can be realized.

Description

Silkworm silk cocoon preferred detection method and system
Technical field
The invention belongs to silkworm silk cocoon seed selection field, specifically a kind of silkworm cocoon quality is carried out to preferred method and system.
Background technology
Silk cocoon is often referred to mulberry cocoon.The scrotiform protective seam in silkworm pupa time, includes pupal cell.Protective seam comprises the parts such as husks, cocoon layer and pelettes.Cocoon layer can filature, and the waste silk after husks and reel silk from cocoons system can be made silk flosssilk wadding and silk spinning raw material.
Cocoon weight, cocoon shell weight and the cocoon layer rate of measuring cocoon in silkworm breeding process is to weigh an important indicator of cocoon quality.Adopt traditional crossbreeding technology selection and breeding of silkworm new varieties be one technical strong, the research work that human input is larger, in Cocoon Quality Traits investigation selection course, first cocoon weight that will be to female silk cocoon and male silk cocoon, cocoon shell weights etc. carry out a large amount of cluster samplings and weigh and individual weighing investigation, and by hand computation cocoon layer rate, then according to breeding objective, require to select, the system of selection of producing at present upper tradition employing is limit value concentration, according to the weighing data of sample cocoon, obtain respectively cocoon weight, the average of cocoon shell weight, with this, formulate and select and remain and superseded boundary, owing to there being higher correlativity between cocoon weight and cocoon shell weight, adopt limit value concentration respectively cocoon weight and cocoon shell weight to be selected, sometimes be difficult to reach requirement.
Summary of the invention
The object of the invention is in order to overcome the deficiencies in the prior art, for a kind of silkworm silk cocoon preferred detection method and system are provided in silkworm breeding production of units management process, be mainly used in silkworm cocoon quality to carry out preferably, this breeding method for optimizing is first and then efficient and convenient, can also realize data analysis and share, effectively improve workman's work efficiency and work quality, to solve the weak point of silk cocoon preferred mass detection method in silkworm breeding.
To achieve these goals, the present invention is achieved by the following technical solutions:
A silkworm silk cocoon preferred detection method, comprises the following steps:
(1) to female or male silk cocoon grouping, Yi Zuwei unit, detects cocoon weight, the cocoon shell weight of each cocoon grain, and calculates cocoon layer rate;
(2) take above-mentioned data as basis, calculate the average cocoon weight of every group of silk cocoon, average cocoon shell weight and average cocoon layer rate;
(3), according to as above drawn data, calculate cocoon weight standard deviation and the cocoon shell weight standard deviation of every group of silk cocoon;
(4) screening varieties: according in upper optimum seeking method and in lower optimum seeking method, silk cocoon is screened, optimize one-level kind.Complete after every a collection of cocoon grain preferred detection work, can the quality of the silk cocoon of all detections in the past be added up and be calculated, thereby form the quality table of each kind silk cocoon and the quality form of male and female cocoon, for breeding production management provides in time Data support reliably.
As further illustrating, in the above, upper optimum seeking method screening step is:
(1) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is not more than 0.2g, select cocoon layer rate to be greater than average cocoon layer rate, and the cocoon grain that cocoon weight is greater than average cocoon weight is as preferred object;
(2) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is greater than 0.2g, can be according to the cocoon weight standard deviation calculating and cocoon shell weight standard deviation, because the genetic force of cocoon layer rate is large compared with cocoon weight, if ignore the individual choice of cocoon layer rate, previous generation's cocoon layer rate has the individuality of selecting and remain of larger difference certainly will cause the variation that offspring is larger, add the impact of genetic drift, in colony, gene frequency changes, thereby breediness is changed, therefore to select cocoon layer rate to be slightly larger than and equal average cocoon layer rate, and cocoon weight is slightly larger than the cocoon grain of average cocoon weight as preferred object.
As further illustrating, at the above, select cocoon weight to be slightly larger than in average cocoon weight, require the scope of cocoon weight for being more than or equal to average cocoon weight, and be less than the maximum cocoon weight of single silk cocoon.
As further illustrating, lower optimum seeking method in lower optimum seeking method one-level kind employing in the above, screening step is:
(1) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is not more than 0.2g, select cocoon layer rate to be slightly less than average cocoon layer rate, and the cocoon grain that cocoon weight is slightly less than average cocoon weight is as preferred object;
(2) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is greater than 0.2g, selects cocoon layer rate to be slightly less than and equal average cocoon layer rate, and cocoon weight be slightly less than equal average cocoon weight cocoon grain as preferred object.
As further illustrating, at the above, select cocoon weight to be slightly less than in average cocoon weight, require the scope of cocoon weight for being less than or equal to average cocoon weight, be greater than the minimum cocoon weight of single silk cocoon.
The silkworm silk cocoon preferred detection system that silkworm silk cocoon preferred detection method as above is used, is connected to form successively by Weighing module, typing module, data memory module, statistical analysis module and display module; Described Weighing module, for weighing cocoon weight and the cocoon shell weight of cocoon grain; Described typing module, for the data message of typing cocoon grain; Described data memory module, for storing the data message of cocoon grain; Described statistical analysis module, calculates and analyzes silk cocoon preferred mass according to the data message of cocoon grain; Described display module, for showing the quality form of each kind silk cocoon and the quality form of male and female cocoon.
As further illustrating, the data message of the above cocoon grain, comprises silk cocoon numbering, affiliated regional area code, Sex in Silkworm Cocoons, cocoon weight, cocoon shell weight and cocoon grain number information.
Data memory module of the present invention comprises database management module---SQL Server2000, according to the flow process of raw silk size test and its middle relation being associated, use the relational database management system of SQL Server2000 to set up the table being associated with system, then data are entered in table, form the database of silk cocoon regional information, silk cocoon individual information etc.
Native system can be realized data sharing by LAN (Local Area Network)/Internet, for breeding management personnel, calls and consults at any time, facilitates breeding management personnel to understand and grasp in time silkworm breeding procedural information, improves breeding management statistical study efficiency.
System of the present invention can be moved on active computer.
Compared with prior art, the invention has the beneficial effects as follows:
1. in adopting preferably upper and in lower preferred method more reasonable in individual choice than limit value concentration, although its amount of calculation is more much bigger than limit value concentration, to traditional weighing system of selection, be inapplicable.Along with computer application, the problem that calculated amount is large will be readily solved.During concrete enforcement if calculate during by kind of cocoon investigation cocoon weight, the average of cocoon shell weight, standard deviation input computing machine, can select preferred individuality, thereby improve operator's work quality, shorten workman's working time, increase work efficiency;
2. cocoon layer rate is also relevant to light folding, basicly stable at reelability percentage, the sericin within cocoon layer dissolve-loss ratio length amount of telling, the substantially constant certain percentage point of the every lifting of situation dried cocoon cocoon layer rate of grain cocoon pupa clothing amount, light folding just rises or declines, according to generally, prediction reel silk from cocoons is rolled over available 1.18 the coefficient (ratio of dried cocoon cocoon shell weight and silk amount 1.18: 1, so because of the sample standard of each producer different and different, each producer can be applicable to according to summary of experience separately the coefficient of oneself), the dried cocoon cocoon layer rate while drying fresh cocoon purchase dried cocoon of receiving in real production and operation differs one or two, even several percentage points is all common occurrence and be exactly these points, several thousand yuan of silk cocoon costs per ton have been increased widely, units even up to ten thousand as can be seen here, understand exactly judgement dried cocoon cocoon layer rate, estimate reel silk from cocoons folding cocoon originally, reduce business risk, increase the benefit and seem extremely important.
Accompanying drawing explanation
Fig. 1 is system architecture schematic diagram of the present invention.
Embodiment
Below in conjunction with embodiment, the present invention is described in further detail, but embodiments of the present invention are not limited to the scope that embodiment represents.
Embodiment 1:
Silkworm silk cocoon preferred detection method and system, carry out in accordance with the following steps:
(1) to female or male silk cocoon grouping, in Weighing module, Yi Zuwei unit, detects cocoon weight D, the cocoon shell weight w of each cocoon grain, and calculates cocoon layer rate, every group of 60 cocoon grains.Statistical information is as shown in table 1, then by regional area code, Sex in Silkworm Cocoons and cocoon grain number Message Entry System under the information of table 1 and silk cocoon;
Table 1
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%)
1 0.28 1.48 18.91
2 0.27 1.4 19.29
3 0.26 1.38 18.84
4 0.26 1.47 17.69
5 0.26 1.39 18.71
6 0.24 1.31 18.32
7 0.29 1.55 18.71
8 0.27 1.44 18.75
9 0.22 1.26 17.46
10 0.25 1.34 18.66
11 0.27 1.42 19.01
12 0.27 1.42 19.01
13 0.27 1.54 17.53
14 0.23 1.29 17.83
15 0.29 1.5 19.33
16 0.27 1.48 18.24
17 0.22 1.26 17.46
18 0.31 1.65 18.79
19 0.24 1.32 18.18
20 0.25 1.34 18.66
21 0.26 1.41 18.44
22 0.25 1.37 18.25
23 0.30 1.6 18.75
24 0.29 1.54 18.83
25 0.30 1.62 18.52
26 0.26 1.29 20.16
27 0.29 1.55 18.71
28 0.30 1.53 19.61
29 0.28 1.48 18.92
30 0.26 1.4 18.57
31 0.25 1.41 17.73
32 0.28 1.4 20.00
33 0.24 1.29 18.60
34 0.27 1.48 18.24
35 0.28 1.44 19.44
36 0.22 1.42 15.49
37 0.31 1.52 20.39
38 0.24 1.48 16.22
39 0.25 1.41 17.73
40 0.26 1.55 16.77
41 0.25 1.5 16.67
42 0.30 1.47 20.41
43 0.29 1.53 18.95
44 0.30 1.46 20.55
45 0.26 1.44 18.06
46 0.29 1.34 21.64
47 0.30 1.41 21.28
48 0.28 1.37 20.44
49 0.26 1.6 16.25
50 0.25 1.54 16.23
51 0.28 1.62 17.28
52 0.24 1.29 18.60
53 0.27 1.55 17.42
54 0.28 1.53 18.30
55 0.30 1.48 20.27
56 0.26 1.4 18.57
57 0.29 1.41 20.57
58 0.30 1.45 20.69
59 0.28 1.48 18.92
60 0.26 1.49 17.45
By data memory module, the information of typing is stored, then utilizes statistical analysis module to carry out the calculating of step:
(2) take table 1 information as basis, calculate the average cocoon weight Do of this group silk cocoon, average cocoon shell weight Wo and average cocoon layer rate, wherein, D o = D 1 + D 2 + . . . + D ( m + 1 ) + D m m , W o = W 1 + W 2 + . . . + W ( m - 1 ) + W m m , Tight all cocoon layer rates
Figure BDA0000443188910000052
in formula, m is this group cocoon grain sum, 60, obtain Do=0.27g, Wo=i.45g, average cocoon layer rate=18.6%.
(3), according to as above drawn data, calculate cocoon weight standard deviation and the cocoon shell weight standard deviation of every group of silk cocoon;
Figure BDA0000443188910000053
(4) screening varieties: according in upper optimum seeking method and in lower optimum seeking method silk cocoon is screened, optimize one-level kind.
In upper optimum seeking method screening step be:
In this group cocoon grain, when cocoon weight standard deviation is less than 0.1, cocoon shell weight standard deviation is less than 0.028, select cocoon layer rate to be greater than average cocoon layer rate, and cocoon weight is greater than the cocoon grain of average cocoon weight as preferred object, the scope that is cocoon weight is 1.46g～1.5g, and the scope of cocoon layer rate is 18.61%～18.99%;
In lower optimum seeking method screening step be:
In this group cocoon grain, when cocoon weight standard deviation is less than 0.1, cocoon shell weight standard deviation is less than 0.028, select cocoon weight to be slightly less than average cocoon weight, and the cocoon grain that cocoon layer rate is slightly less than average cocoon layer rate is as preferred object; The scope of cocoon weight is 1.45～1.4g, and the scope of cocoon layer rate is 18.01%～18.6%.
Through above-mentioned preferably after, obtain one-level kind as shown in table 2 (in upper optimum seeking method), table 3 (in lower optimum seeking method):
Table 2
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%)
1 0.28 1.48 18.91
7 0.29 1.55 18.71
15 0.29 1.5 19.33
18 0.31 1.65 18.79
23 0.30 1.6 18.75
24 0.29 1.54 18.83
27 0.29 1.55 18.71
29 0.28 1.48 18.92
43 0.29 1.53 18.95
59 0.28 1.48 18.92
Table 3
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%)
6 0.24 1.31 18.32
19 0.24 1.32 18.18
33 0.24 1.29 18.60
21 0.26 1.41 18.44
22 0.25 1.37 18.25
30 0.26 1.4 18.57
31 0.25 1.41 17.73
52 0.24 1.29 18.60
56 0.26 1.4 18.57
45 0.26 1.44 18.06
Embodiment 2:
Silkworm silk cocoon preferred detection method and system, carry out in accordance with the following steps:
(1) to female or male silk cocoon grouping, in Weighing module, Yi Zuwei unit, detects cocoon weight D, the cocoon shell weight W of each cocoon grain, and calculates cocoon layer rate,
Figure BDA0000443188910000071
every group of 60 cocoon grains.Statistical information is as shown in table 4, then by regional area code, Sex in Silkworm Cocoons and cocoon grain number Message Entry System under the information of table 3 and silk cocoon;
Table 4
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%)
1 0.25 1.34 18.66
2 0.26 1.41 18.44
3 0.25 1.37 18.25
4 0.30 1.6 18.75
5 0.29 1.54 18.83
6 0.30 1.62 18.52
7 0.26 1.29 20.16
8 0.29 1.55 18.71
9 0.30 1.53 19.61
10 0.28 1.4 20.00
11 0.26 1.25 20.80
12 0.25 1.6 15.63
13 0.28 1.23 22.76
14 0.24 1.64 14.63
15 0.27 1.32 20.45
16 0.28 1.34 20.90
17 0.30 1.41 21.28
18 0.26 1.37 18.98
19 0.29 1.6 18.13
20 0.25 1.54 16.23
21 0.30 1.62 18.52
22 0.29 1.29 22.48
23 0.30 1.55 19.35
24 0.26 1.53 16.99
25 0.29 1.58 18.35
26 0.30 1.54 19.48
27 0.28 1.41 19.86
28 0.26 1.26 20.63
29 0.25 1.55 16.13
30 0.28 1.2 23.33
31 0.24 1.62 14.81
32 0.25 1.53 16.34
33 0.28 1.22 22.95
34 0.24 1.26 19.05
35 0.27 1.29 20.93
36 0.28 1.55 18.06
37 0.30 1.53 19.61
38 0.26 1.58 16.46
39 0.29 1.54 18.83
40 0.25 1.41 17.73
41 0.30 1.26 23.81
42 0.29 1.55 18.71
43 0.30 1.2 25.00
44 0.26 1.54 16.88
45 0.29 1.62 17.90
46 0.26 1.29 20.16
47 0.25 1.55 16.13
48 0.30 1.53 19.61
49 0.29 1.4 20.71
50 0.30 1.25 24.00
51 0.26 1.6 16.25
52 0.29 1.43 20.28
53 0.30 1.56 19.23
54 0.28 1.44 19.44
55 0.26 1.52 17.11
56 0.25 1.66 15.06
57 0.28 1.65 16.97
58 0.24 1.61 14.91
59 0.27 1.28 21.09
60 0.28 1.33 21.05
By data memory module, the information of typing is stored, then utilize statistical analysis module to carry out the calculating of step: (2) take table 3 information as basis, calculate the average cocoon weight Do of this group silk cocoon, average cocoon shell weight Wo and average cocoon layer rate, wherein, D o = D 1 + D 2 + . . . + D ( m + 1 ) + D m m , W o = W 1 + W 2 + . . . + W ( m - 1 ) + W m m , Average cocoon layer rate
Figure BDA0000443188910000092
in formula, m is this group cocoon grain sum, 60, obtain Do=1.45g, Wo=0.27g, average cocoon layer rate=18.62%.
(3), according to as above drawn data, calculate cocoon weight standard deviation and the cocoon shell weight standard deviation of every group of silk cocoon;
Figure BDA0000443188910000093
(4) screening varieties: according in upper optimum seeking method and in lower optimum seeking method, silk cocoon is screened, optimize one-level kind.
The screening step of one-level kind is:
In this group cocoon grain, because cocoon weight standard deviation is greater than, select cocoon layer rate to be greater than average cocoon layer rate, and cocoon weight is greater than the cocoon grain of average cocoon weight at 0.1 o'clock, select cocoon layer rate to be less than average cocoon layer rate, and the cocoon grain that cocoon weight is less than average cocoon weight is as preferred object simultaneously.
Through above-mentioned preferably after, obtain one-level kind as shown in table 5:
Table 5
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%)
2 0.26 1.41 18.44
3 0.25 1.37 18.25
4 0.30 1.6 18.75
5 0.29 1.54 18.83
39 0.29 1.54 18.83
40 0.25 1.41 17.73
37 0.30 1.53 19.61
19 0.29 1.6 18.13
8 0.29 1.55 18.71
21 0.30 1.62 18.52
Complete after every a collection of cocoon grain preferred detection work, can the quality of the silk cocoon of all detections in the past be added up and be calculated, thereby the quality form of formation male and female cocoon be for breeding production management provides in time Data support reliably, as shown in table 6.
Table 6
Numbering Cocoon shell weight W (g) Cocoon weight D (g) Cocoon layer rate (%) Remarks
1 0.25 1.34 18.66 ?
2 0.26 1.41 18.44 Preferably
3 0.25 1.37 18.25 Preferably
4 0.30 1.6 18.75 Preferably
5 0.29 1.54 18.83 Preferably
6 0.30 1.62 18.52 ?
7 0.26 1.29 20.16 ?
8 0.29 1.55 18.71 Preferably
9 0.30 1.53 19.61 ?
10 0.28 1.4 20.00 ?
11 0.26 1.25 20.80 ?
12 0.25 1.6 15.63 ?
13 0.28 1.23 22.76 ?
14 0.24 1.64 14.63 ?
15 0.27 1.32 20.45 ?
16 0.28 1.34 20.90 ?
17 0.30 1.41 21.28 ?
18 0.26 1.37 18.98 ?
19 0.29 1.6 18.13 Preferably
20 0.25 1.54 16.23 ?
21 0.30 1.62 18.52 Preferably
22 0.29 1.29 22.48 ?
23 0.30 1.55 19.35 ?
24 0.26 1.53 16.99 ?
25 0.29 1.58 18.35 ?
26 0.30 1.54 19.48 ?
27 0.28 1.41 19.86 ?
28 0.26 1.26 20.63 ?
29 0.25 1.55 16.13 ?
30 0.28 1.2 23.33 ?
31 0.24 1.62 14.81 ?
32 0.25 1.53 16.34 ?
33 0.28 1.22 22.95 ?
34 0.24 1.26 19.05 ?
35 0.27 1.29 20.93 ?
36 0.28 1.55 18.06 ?
37 0.30 1.53 19.61 Preferably
38 0.26 1.58 16.46 Preferably
39 0.29 1.54 18.83 Preferably
40 0.25 1.41 17.73 Preferably
41 0.30 1.26 23.81 ?
42 0.29 1.55 18.71 ?
43 0.30 1.2 25.00 ?
44 0.26 1.54 16.88 ?
45 0.29 1.62 17.90 ?
46 0.26 1.29 20.16 ?
47 0.25 1.55 16.13 ?
48 0.30 1.53 19.61 ?
49 0.29 1.4 20.71 ?
50 0.30 1.25 24.00 ?
51 0.26 1.6 16.25 ?
52 0.29 1.43 20.28 ?
53 0.30 1.56 19.23 ?
54 0.28 1.44 19.44 ?
55 0.26 1.52 17.11 ?
56 0.25 1.66 15.06 ?
57 0.28 1.65 16.97 ?
58 0.24 1.61 14.91 ?
59 0.27 1.28 21.09 ?
60 0.28 1.33 21.05 ?

Claims (7)

1. a silkworm silk cocoon preferred detection method, is characterized in that, comprises the following steps:
(1) to female or male silk cocoon grouping, Yi Zuwei unit, detects cocoon weight, the cocoon shell weight of each cocoon grain, and calculates cocoon layer rate;
(2) take above-mentioned data as basis, calculate the average cocoon weight of every group of silk cocoon, average cocoon shell weight and average cocoon layer rate;
(3), according to as above drawn data, calculate cocoon weight standard deviation and the cocoon shell weight standard deviation of every group of silk cocoon;
(4) screening varieties: according in upper optimum seeking method and in lower optimum seeking method, silk cocoon is screened, optimize one-level kind.
2. silkworm silk cocoon preferred detection method according to claim 1, is characterized in that: in described, upper optimum seeking method is:
(1) if in this group cocoon grain, when the difference of the cocoon weight between each cocoon grain is not more than 0.2g, select cocoon layer rate to be greater than average cocoon layer rate, and the cocoon grain that cocoon weight is greater than average cocoon weight is as preferred object;
(2) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is greater than 0.2g, select cocoon layer rate to be more than or equal to average cocoon layer rate, and the cocoon grain that cocoon weight is more than or equal to average cocoon weight is as preferred object.
3. silkworm silk cocoon preferred detection method according to claim 2, is characterized in that: at described selection cocoon weight, be greater than in average cocoon weight, require the scope of cocoon weight for being more than or equal to average cocoon weight, and be less than the maximum cocoon weight of single silk cocoon.
4. silkworm silk cocoon preferred detection method according to claim 1, is characterized in that: in described, the second of lower optimum seeking method one-level kind screening step is:
(1) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is not more than 0.2g, select cocoon layer rate to be less than slightly average cocoon layer rate, and the cocoon grain that cocoon weight is slightly less than average cocoon weight is as preferred object;
(2) if in this group cocoon grain, when the cocoon weight between every two cocoon grains is greater than 0.2g, select cocoon layer rate to be less than or equal to average cocoon layer rate, and the cocoon grain that cocoon weight is less than or equal to average cocoon weight is as initial option object.
5. silkworm silk cocoon preferred detection method according to claim 4, is characterized in that: at described selection cocoon weight, be less than in average cocoon weight, require the scope of cocoon weight for being less than or equal to average cocoon weight, be greater than the minimum cocoon weight of single silk cocoon.
6. the silkworm silk cocoon preferred detection system that the silkworm silk cocoon preferred detection method as described in any one in claim 1-5 is used, it is characterized in that, by Weighing module, typing module, data memory module, statistical analysis module and display module, connected to form successively; Described Weighing module, for weighing cocoon weight and the cocoon shell weight of cocoon grain; Described typing module, for the data message of typing cocoon grain; Described data memory module, for storing the data message of cocoon grain; Described statistical analysis module, calculates and analyzes silk cocoon preferred mass according to the data message of cocoon grain; Described display module, for showing the quality form of each kind silk cocoon and the quality form of male and female cocoon.
7. silkworm silk cocoon preferred detection system according to claim 6, is characterized in that: the data message of described cocoon grain, comprises silk cocoon numbering, affiliated regional area code, Sex in Silkworm Cocoons, cocoon weight, cocoon shell weight and cocoon grain number information.
CN201310712181.6A 2013-12-20 2013-12-20 Bombyx mori silkworm cocoon optimization detection method and system Pending CN103645107A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104614277A (en) * 2015-01-26 2015-05-13 浙江大学 Method for utilizing sericin dissolution property to distinguish fresh cocoon raw silk from dried cocoon raw silk
CN110063306A (en) * 2019-05-15 2019-07-30 四川省农业科学院蚕业研究所 The quantifiable silk cocoon individual choice method for maintaining silkworm mother seeds of original silkworm egg intrinsic properties

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN2465151Y (en) * 2001-01-17 2001-12-12 重庆市丰都县丝绸公司 Quality tester by electronic weighing
KR20020058156A (en) * 2000-12-29 2002-07-12 안효헌 charcoal aromatic manufacture method and manufacture method of cocoon pillow
CN2599564Y (en) * 2003-02-21 2004-01-14 四川省农业科学院蚕业研究所 Intelligent type cocoon evaluation apparatus
CN203310763U (en) * 2013-06-22 2013-11-27 重庆市纤维检验局 Rapid grading instrument for silkworm fresh cocoons

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20020058156A (en) * 2000-12-29 2002-07-12 안효헌 charcoal aromatic manufacture method and manufacture method of cocoon pillow
CN2465151Y (en) * 2001-01-17 2001-12-12 重庆市丰都县丝绸公司 Quality tester by electronic weighing
CN2599564Y (en) * 2003-02-21 2004-01-14 四川省农业科学院蚕业研究所 Intelligent type cocoon evaluation apparatus
CN203310763U (en) * 2013-06-22 2013-11-27 重庆市纤维检验局 Rapid grading instrument for silkworm fresh cocoons

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张大成 等: "家蚕育种中茧层率自动测定与信息化管理系统的实现", 《广西计算机学会2013年学术年会论文集》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104614277A (en) * 2015-01-26 2015-05-13 浙江大学 Method for utilizing sericin dissolution property to distinguish fresh cocoon raw silk from dried cocoon raw silk
CN110063306A (en) * 2019-05-15 2019-07-30 四川省农业科学院蚕业研究所 The quantifiable silk cocoon individual choice method for maintaining silkworm mother seeds of original silkworm egg intrinsic properties
CN110063306B (en) * 2019-05-15 2021-03-23 四川省农业科学院蚕业研究所 Quantifiable silkworm cocoon individual selection method for maintaining inherent characters of silkworm stock seeds

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Application publication date: 20140319