CN201000564Y - Edible fungus cultivation monitoring apparatus - Google Patents
Edible fungus cultivation monitoring apparatus Download PDFInfo
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- CN201000564Y CN201000564Y CNU2006200454664U CN200620045466U CN201000564Y CN 201000564 Y CN201000564 Y CN 201000564Y CN U2006200454664 U CNU2006200454664 U CN U2006200454664U CN 200620045466 U CN200620045466 U CN 200620045466U CN 201000564 Y CN201000564 Y CN 201000564Y
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Abstract
The utility model relates to a domestic fungus incubation monitoring device, which comprises a controlling component used to control each key control point, and a computer connected with the controlling component and is used to set a plurality group of different key control points data in the growing process of the domestic fungus, then transmits the data to the controlling component, and sets corresponding incubation condition in the controlling component, and then compares the data of each group of key control point according to the growing state of the domestic fungus in each group of incubation condition according to the set data of the key control point, to obtain the data threshold value of each key control point, wherein, the controlling component adopts an automatic material feeder, a temperature adjusting device which controls the temperature of the growing environment of the domestic fungus, and a moisture adjusting device which adjusts the moisture of the growing environment of the domestic fungus, thereby the automatic obtaining of the data threshold value of the key control point is realized, meanwhile, the relation between the data of each control point and the growing state of the domestic fungus can be obtained through analyzing the data, and the growing state of the domestic fungus can also be effectively controlled, thereby improving the economic benefit of the domestic fungus.
Description
Technical field
The utility model relates to a kind of breed of edible fungus supervising device.
Background technology
Along with international community is more and more higher to the requirement of agricultural product quality, the security monitoring of agricultural product has become the focus that various countries pay close attention to.Now, for guaranteeing food security, Codex Committee on Food of the United Nations has adopted hazard, and (hazardanalysis critical control point, HACCP) technical system are brought the hierarchy of control of HACCP into the robotization cultivation of edible fungi in the world especially.
Usually, the planting technique of edible fungi comprises that raw material mixes, bottling, sterilization, cooling, inoculation, cultivate, mycelium stimulation, urge flower bud, the sporophore growth and the packing multiple working procedure of gathering, China introduces the automation production flow line of edible fungi the end of the nineties, bring into use the robotization filling machine, sterilizing equipment decontaminating apparatus, inoculating facility and artificial climate equipment etc. carry out batch production and produce the edible fungi operation, owing to start late, numerous manufacturing enterprises fail to carry out automatically-monitored to each critical control point that may influence edible fungi safety, usually still adopt the mode of manual detection manual record, and critical control point relates to the materials requirement, water safety and production environment condition, for example temperature, humidity, CO
2The water cut of concentration, materials, potential of hydrogen etc., and each key point all needs periodic record, cause the manual work amount various, very easily fail in time to handle to influence follow-up regulation and control especially to each critical control point because of the data of each critical control point, when serious even can influence the quality of edible fungi, bring potential safety hazard to the consumer.
Moreover, critical control point related in the HACCP system is many, and because China is vast in territory, various places climatic environment and geographical conditions are different, each edible fungus culturing manufacturer looks for the threshold value of each critical control point and all is considered as trade secret and will not discloses for having spent a large amount of manpower and materials early stage, the work that causes each edible fungus culturing manufacturer when looking for the threshold value of each critical control point, all can repeat equally, totally unfavorable in improving industry efficient, and because each pass keying point data is by artificial collection, process is very complicated, and very easily make mistakes, so also be easy to reduce the precision of the data threshold of each critical control point, simultaneously manufacturer also is difficult to obtain the relation between the upgrowth situation of the data of each critical control point and edible fungi, therefore, if manufacturer wants to improve the edible fungi growth situation, only the data threshold with reference to resulting each critical control point is difficult to effectively regulate.
Therefore, how to solve the problems that exist in the existing breed of edible fungus and become the technical task that industry needs to be resolved hurrily in fact.
The utility model content
The purpose of this utility model is to provide a kind of breed of edible fungus supervising device and method for supervising, realize obtaining automatically the threshold value of each critical control point, can increase work efficiency by the data of automatic collection critical control point simultaneously, save human resources, and can improve Edible Fungi person's economic benefit by the analysis of data being realized effective control to the upgrowth situation of edible fungi.
Reach other purposes in order to achieve the above object, the utility model provides a kind of breed of edible fungus supervising device, and it comprises at least: one is used for being controlled at the control assembly of breed of edible fungus according to each critical control point of hazard technical system setting; One is used for setting the different critical control point data of many groups in the edible fungi growth process, and be sent to described control assembly, and behind the breeding condition of described control assembly according to the edible fungi of the data respective settings of the critical control point of setting, respectively organize the data of critical control point according to edible fungi at the upgrowth situation of each group under breeding condition, obtain the computing machine of the data threshold of each critical control point.
Wherein, also comprise the sensing part that is electrically connected with described computing machine, in order to the real data of corresponding each critical control point of sensing under many group breeding conditions; After described computing machine receives described real data, and replace corresponding data in the described data of respectively organizing critical control point with this real data, described sensing apparatus comprises infrared moisture meter, the PH meter, a plurality of or whole in electronic scales and the thermometer, described control assembly comprises automated packing equipment at least, temperature control equipment and Humidity regulating equipment, described computing machine also comprises and being used for according to obtaining many group critical control point data and corresponding edible fungi growth situation, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and edible fungi, and according to the critical control point analytic unit of the upgrowth situation of edible fungi under the different critical control point conditions of resulting Relationship Prediction.
In sum, breed of edible fungus supervising device of the present utility model is data of obtaining each critical control point of breed of edible fungus by sensing part, realize the data automatic collecting of critical control point, increase work efficiency, save human resources, and can obtain the relation of the upgrowth situation of the data threshold of each critical control point and each critical control point and edible fungi automatically by analysis to the edible fungi growth situation, realize effective prediction to the upgrowth situation of edible fungi.
Description of drawings
Fig. 1 is the structural representation of breed of edible fungus supervising device of the present utility model.
Embodiment
See also Fig. 1, the utility model provides a kind of breed of edible fungus supervising device 1, wherein, in breed of edible fungus, have a plurality of edible fungi safety that influence, the critical control point of health and quality, these critical control point are proposed by hazard analysis and critical control point technical system, for example, relate to the materials requirement, the critical control point of water safety and working condition etc. comprises raw material year censorship situation, the bottling water cut, the bottling pH value, the bottling average weight, whipping temp, sterilization time, cultivate anti-pollution rate, the mycelium stimulation water cut, the mycelium stimulation pH value, urge the 9th day sense organ sensing value of flower bud, knurl lid mushroom incidence, storage temperature, new wind filters situation, totally 15 critical control point (as shown in table 1 below) such as colony growth situation and regularity etc.
Table 1: critical control point signal table
| Sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| The critical control point title | Raw material year censorship situation | The bottling water cut | The bottling pH value | The bottling average weight | Whipping temp | Sterilization time | Cultivate the room pollution rate | The mycelium stimulation water cut |
| Sequence number | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
| The critical control point title | Mycelium stimulation PH value | Urge the 9th day sense organ sensing value of flower bud | Knurl lid mushroom incidence | Reserve temperature | New wind filters situation | The colony growth situation | Regularity (the 16th day whole fruiting situation of mycelium stimulation) |
Described breed of edible fungus supervising device l comprises at least: a control assembly 11, a computing machine 12 and a sensing part 13 (annexation of aforementioned each assembly is as shown in Figure 1), below will be described in detail aforementioned each assembly.
Described control assembly 11 is to be used for being controlled at each critical control point that breed of edible fungus is provided with according to the hazard technical system, described control assembly 11 comprises robotization filling machine, temperature control equipment and Humidity regulating equipment at least, in the present embodiment, temperature control equipment and Humidity regulating equipment are replaced by artificial climate equipment, it is noted that, described control assembly 11 is not to exceed with present embodiment, it can select distinct device as required, for example also can comprise sterilizing equipment decontaminating apparatus etc.
Described sensing part 13 is the real data in order to corresponding each critical control point of sensing edible fungi after respectively organizing breeding condition in described control assembly 11 settings, in the present embodiment, described sensing part 13 comprises gas chromatograph and the atomic fluorescence spectrophotometer that is used to test raw material year censorship situation, the infrared moisture meter that is used for sensing bottling water cut and mycelium stimulation water cut, be used to test the PH meter of bottling pH value, be used to test the electronic scales of bottling average weight, be used to test the automatically-monitored instrument of machine filial generation of sterilization time, be used to test the PH meter of mycelium stimulation pH value, the mercury temperature that is used to test storage temperature is taken into account and is used to test the machine open frequency proving installation that new wind filters situation, what must please note is, sensing part 13 set sensing apparatus are not to exceed with present embodiment, and the user can select the corresponding sensing device according to practical situation.
Described computing machine 12 is used for setting the different critical control point data of many groups in the edible fungi growth process, and be sent to described control assembly 11, and at described control assembly 11 according to the breeding condition of the edible fungi of the data respective settings of the critical control point of setting and after described sensing part 13 senses the real data of each critical control point, replace the corresponding data of the previous critical control point of setting with described real data, in the data of respectively organizing critical control point according to the upgrowth situation of edible fungi under each group breeding condition, obtain the data threshold of each critical control point, it also comprises a critical control point analytic unit 121, be used for according to obtaining many group critical control point data and corresponding edible fungi growth situation, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and edible fungi.In the present embodiment, computing machine 12 has been set the data of 8 groups of critical control point, after control assembly 11 is regulated robotization filling machine and artificial climate equipment according to the data of the critical control point of setting, edible fungi is promptly cultivated under corresponding crucial key control point condition, and obtained the upgrowth situation of edible fungi, crop yield (kilogram) and quality with per 75 kilograms of composts embody, and be as shown in table 2 below:
Output and the quality record table of table 2:1-8 group critical control point data and edible fungi
| Group | The critical control point value | Output | Quality |
| 1 | Bottling water cut 63%, 118 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45 | 1 grade of product |
| 2 | Bottling water cut 63%, 120 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.1 | 1 grade of product |
| 3 | Bottling water cut 65%, 121 ℃ of whipping temps, bottled general assembly (TW) 640, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 4 | Bottling water cut 64%, 120 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 5 | Bottling water cut 64%, 118 ℃ of whipping temps, bottling general assembly (TW) 640, sterilization time 9 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 6 | Bottling water cut 65%, 118 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45 | 1 grade of product |
| 7 | Bottling water cut 65%, 118 ℃ of whipping temps, bottling general assembly (TW) 620, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 42 | 1 grade of product |
| 8 | Bottling water cut 61%, 116 ℃ of whipping temps, bottling general assembly (TW) 630, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.1 | 1 grade of product |
Data according to the resulting edible fungi growth situation of last table 2, computer monitoring module 12 compares each group edible fungi output and qualitative data thinks that output is about 45 kilograms, quality is that the upgrowth situation of 1 edible fungi is preferable, so can obtain the data of each critical control point, for example, the threshold value of bottling water cut is 61%-65%, the whipping temp threshold value is 116 ℃-121 ℃, bottling general assembly (TW) threshold value is 625-630, the sterilization time threshold value is 9-10 minute or the like, it is noted that, because overabundance of data, be simplified illustration, omitted the partial data in the data of aforesaid 15 critical control point in the table 2, but these data all can obtain by corresponding apparatus, correspondingly, the data threshold of these critical control point also can obtain after comparing through computing machine 12, in addition, critical control point analytic unit 121 is according to resulting 8 groups of data, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and edible fungi, before analyzing with artificial neural network technology, computing machine 12 need carry out pre-service to 8 groups of data that obtained, be about to these data that can not quantize and carry out coded quantization, and make its data value [0 through handling each data, 1] within the scope, usually the coded quantization method is for to be divided into 3 grades respectively with corresponding data, each grade correspondence one numerical value, for example be 0.7,0.4,0.1, if variation has taken place in a certain data, then on its developing direction, increase by 0.01, if deep variation has taken place, then increase by 1 grade on the original basis, and the quality of product can be set respective level to the requirement of quality according to manufacturer, in the present embodiment, quality is divided into 3 grades, i.e. 1 grade of product, 2 grades of product and 3 grades of product, use numerical value 0 respectively, 0.5,1 represents, after computing machine 12 is with aforementioned 15 data process quantification treatment, can obtain the critical control point data set of 1-8 group:
In following formula: every group of 15 critical control point respective value of each row representative, each row is represented the respective value of certain reference mark in each group, and as input matrix, corresponding output matrix then is with following formula:
Wherein, Y
1Represent the output of edible fungi, Y
2Represent the quality of edible fungi.
Critical control point analytic unit 121 with the data sample of 1-7 group as learning sample, the 8th group data sample is as test samples, and the input layer of setting neural network is 15, output neuron is 2, and each training parameter of setting the BP network is: the number of hidden neuron is 16, and the margin of error is 0.001, initial weight between each layer of network and each layer threshold value are [1,1] Nei value immediately, the study step-length is 0.8, maximum study number of times is 2000 times.
At first import the 1st group of sample, begin training, training step is as follows:
(1) calculates j neuronic output a1 in the hidden layer
j, adopt logarithm saturability (Logsig) mapping function.
(2) calculate output layer output.Adopt linear (purelin) transforming function transformation function, the output of hidden neuron as the output layer nerve.The input of unit can get the neuronic output of output layer a2.
(3) error of calculation value is established y
(i)Through normalized is y
1,, this is to obtain square mean error amount:
If error amount less than the margin of error, changes (5) over to; If error amount greater than the margin of error, carries out next step, begin to adjust weights.
(4) adjust weights and threshold value, at first adjust connection weights and threshold value between output layer and the hidden layer, adjust then and be connected weights and threshold value between hidden layer and the input layer.
(5) train next group sample, import next group sample, change (1) over to
(6) recording learning number of times, if less than preset value 2000, training finishes, if greater than preset value, the signature failure to train is adjusted each adjustable parameter, changes (1) over to and trains again.
After with preceding 7 samples network being learnt, the connection weight value matrix between input layer and the hidden layer:
The threshold matrix of hidden neuron:
θ1
10×1＝[0.054 0.317....1.783]
T
Connection weight value matrix between hidden layer and the output layer:
W2
1×10＝[4.257 0.884....5.763]
The neuronic threshold value of output layer:
θ2＝1.097
Again the input quantity X in the 8th group of sample
(8)Be input in the network, obtain test value:
Y
(8) 1＝41.2，Y
(8) 2＝0.2
Close with the 8th group of edible fungi output and quality, the proof prediction accurately, at this moment, critical control point analytic unit 121 promptly generates each critical control point and agriculture upgrowth situation relational model, according to the relational model of being set up as can be known, if the data of each critical control point do not change, the output of then cultivating resulting corresponding edible fungi under similarity condition still can remain on about 45 kilograms, but if wish the output of edible fungi is improved 10 kilograms according to the demand in market, promptly reach 55 kilograms, then critical control point analytic unit 121 is by the analysis to each critical control point data, elder generation's trial general assembly (TW) of will bottling is arranged between the 660-665 and changes, then according to output of resulting edible fungi and the relational model between quality and each critical control point, measurable output (kilogram) and the quality that goes out corresponding 4 groups of edible fungis, as shown in table 3 below:
Table 3:4 group critical control point value variation prediction table
| Group | The critical control point value | Output | Quality |
| 9 | Bottling water cut 63%, 118 ℃ of whipping temps, bottling general assembly (TW) 660, sterilization time 10 minutes ... | 51 | 1 grade of product |
| 10 | Bottling water cut 64%, 120 ℃ of whipping temps, bottling general assembly (TW) 662, sterilization time 10 minutes ... | 51.1 | 1 grade of |
| 11 | Bottling water cut 64%, 121 ℃ of whipping temps, bottling general assembly (TW) 663, sterilization time 10 minutes ... | 51.3 | 1 grade of |
| 12 | Bottling water cut 65%, 120 ℃ of whipping temps, bottling general assembly (TW) 665, sterilization time 10 minutes ... | 50.2 | 1 grade of product |
As seen from the above table, only the bottling general assembly (TW) is arranged between the 650-655 and changes, then the output predicted of critical control point analytic unit 121 has only improved about 6 kilograms, is about 51 kilograms, and is therefore also undesirable, therefore, further adjust the value of bottling water cut again, it is changed between 58%-60%, with the value input artificial nerve network model of this bottling water cut, then can obtain the output of corresponding edible fungi and the value of quality, as shown in table 4 below:
Table 4:4 group critical control point value variation prediction table
| Group | The critical control point value | Output | Quality |
| 9 | Bottling water cut 60%, 118 ℃ of whipping temps, bottling general assembly (TW) 660, sterilization time 10 minutes ... | 55 | 1 grade of product |
| 10 | Bottling water cut 59%, 120 ℃ of whipping temps, bottling general assembly (TW) 662, sterilization time 10 minutes ... | 55.1 | 1 grade of |
| 11 | Bottling water cut 58%, 121 ℃ of whipping temps, bottling general assembly (TW) 663, sterilization time 10 minutes ... | 54.8 | 1 grade of |
| 12 | Bottling water cut 60%, 120 ℃ of whipping temps, bottling general assembly (TW) 665, sterilization time 10 minutes ... | 55.3 | 1 grade of product |
Thus, analyzing back acquisition bottling general assembly (TW) threshold value through critical control point analytic unit 121 is [660,665] and bottling water cut threshold value be [58%, 60%], other critical control point threshold values do not change, regulate data threshold that corresponding opertaing device makes each critical control point in aforementioned range when computing machine 12 makes control assembly 11, can improve the upgrowth situation of edible fungi, the output of edible fungi is improved 10 kilograms.
In sum, the breed of edible fungus supervising device of the utility model is analyzed to obtain the data threshold of each critical control point by setting many group critical control point data and obtaining the upgrowth situation of edible fungi under different critical control point conditions, realize the automatic acquisition of critical control point data threshold, can increase work efficiency by the data of automatic collection critical control point simultaneously, save human resources, moreover, can obtain the upgrowth situation of edible fungi and the relational model of critical control point by data analysis, can realize improvement by this relational model, improve the economic benefit of breed of edible fungus the edible fungi growth situation.
Claims (4)
1. breed of edible fungus supervising device, it is characterized in that comprising: receive the data of each critical control point and according to the control assembly of the data of each critical control point of data setting that receives and be connected with described control assembly, and be used for setting the different critical control point data of many groups in the edible fungi growth process, and respectively each group data is sent to described control assembly, respectively organize the data of critical control point again according to the upgrowth situation of edible fungi under each group breeding condition, obtain the computing machine of the data threshold of each critical control point.
2. breed of edible fungus supervising device as claimed in claim 1 is characterized in that also comprising: be electrically connected with described computing machine and in order to the sensing part of the real data of described each critical control point of sensing; Described computing machine receives after the described real data corresponding data of this real data being replaced in the described data of respectively organizing critical control point.
3. breed of edible fungus supervising device as claimed in claim 2 is characterized in that: described sensing part comprises the infrared moisture meter, the PH meter that is used to test bottling pH value and mycelium stimulation pH value that are used for sensing bottling water cut and mycelium stimulation water cut, be used to test the electronic scales of bottling average weight and be used to test thermometer a plurality of or whole of storage temperature.
4. breed of edible fungus supervising device as claimed in claim 1 is characterized in that: described control assembly comprises automatic filling machine at least, regulates the temperature control equipment of edible fungi growth environment temperature and the Humidity regulating equipment of adjusting edible fungi growth ambient humidity.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNU2006200454664U CN201000564Y (en) | 2006-09-01 | 2006-09-01 | Edible fungus cultivation monitoring apparatus |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNU2006200454664U CN201000564Y (en) | 2006-09-01 | 2006-09-01 | Edible fungus cultivation monitoring apparatus |
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| CN201000564Y true CN201000564Y (en) | 2008-01-02 |
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| CNU2006200454664U Expired - Lifetime CN201000564Y (en) | 2006-09-01 | 2006-09-01 | Edible fungus cultivation monitoring apparatus |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108739052A (en) * | 2018-06-01 | 2018-11-06 | 北京中环易达设施园艺科技有限公司 | A kind of system and method for edible fungi growth parameter optimization |
| CN113219871A (en) * | 2021-05-07 | 2021-08-06 | 淮阴工学院 | Curing room environmental parameter detecting system |
-
2006
- 2006-09-01 CN CNU2006200454664U patent/CN201000564Y/en not_active Expired - Lifetime
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108739052A (en) * | 2018-06-01 | 2018-11-06 | 北京中环易达设施园艺科技有限公司 | A kind of system and method for edible fungi growth parameter optimization |
| CN113711843A (en) * | 2018-06-01 | 2021-11-30 | 北京中环易达设施园艺科技有限公司 | System and method for optimizing growth parameters of edible fungi |
| CN113219871A (en) * | 2021-05-07 | 2021-08-06 | 淮阴工学院 | Curing room environmental parameter detecting system |
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