CN108628266A - Intelligent cultivation greenhouse based on big data analysis monitors system - Google Patents
Intelligent cultivation greenhouse based on big data analysis monitors system Download PDFInfo
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- CN108628266A CN108628266A CN201810380547.7A CN201810380547A CN108628266A CN 108628266 A CN108628266 A CN 108628266A CN 201810380547 A CN201810380547 A CN 201810380547A CN 108628266 A CN108628266 A CN 108628266A
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- 238000004891 communication Methods 0.000 claims abstract description 13
- 230000007613 environmental effect Effects 0.000 claims abstract description 11
- 238000013480 data collection Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 12
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- 230000000153 supplemental effect Effects 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 11
- 241001269238 Data Species 0.000 claims description 7
- 230000005856 abnormality Effects 0.000 claims description 6
- 238000005286 illumination Methods 0.000 claims description 6
- HUTDUHSNJYTCAR-UHFFFAOYSA-N ancymidol Chemical compound C1=CC(OC)=CC=C1C(O)(C=1C=NC=NC=1)C1CC1 HUTDUHSNJYTCAR-UHFFFAOYSA-N 0.000 claims description 5
- 238000010438 heat treatment Methods 0.000 claims description 4
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- 239000002689 soil Substances 0.000 claims description 3
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- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
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- 235000008434 ginseng Nutrition 0.000 description 1
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The present invention provides the intelligent cultivation greenhouses based on big data analysis to monitor system, including greenhouse monitoring center, network communication module, control module, information acquisition module, greenhouse monitoring center is communicated by network communication module with control module, and control module is electrically connected the multiple equipment in agricultural greenhouse；Information acquisition module is for being monitored the environment of agricultural greenhouse by wireless sensor network, acquiring ambient parameter data and ambient parameter data being sent to greenhouse monitoring center；Greenhouse monitoring center includes data prediction device, data analysis set-up, data prediction device is for pre-processing the ambient parameter data of reception, data analysis set-up is for judging whether pretreated ambient parameter data meets preset environmental parameter condition, when a certain ambient parameter data is unsatisfactory for preset environmental parameter condition, control instruction is sent to the controller by network communication module, controls corresponding equipment running.
Description
Technical field
The present invention relates to agricultural technology fields, and in particular to the intelligent cultivation greenhouse based on big data analysis monitors system.
Background technology
In the related technology, agricultural greenhouse is mainly managed by way of artificial detection and maintenance.Greenhouse administrative staff
Want to know in canopy the information such as humiture, illumination, the humiture of soil of air must by check in person canopy temperature meter,
Humidity display instrument, illumination detection device etc. obtain the growing environment information of crop in current canopy, and to the growth ring of crops
Border is artificially adjusted.Its production efficiency is low, and intelligence degree is not high, wastes a large amount of human and material resources.
Invention content
In view of the above-mentioned problems, the present invention, which provides the intelligent cultivation greenhouse based on big data analysis, monitors system.
The purpose of the present invention is realized using following technical scheme：
Provide the intelligent cultivation greenhouse monitoring system based on big data analysis, including greenhouse monitoring center, network communication
Module, control module, information acquisition module, the greenhouse monitoring center are communicated by network communication module with control module,
Control module is electrically connected the multiple equipment in agricultural greenhouse；The information acquisition module is for passing through wireless sensor network
The environment of agricultural greenhouse is monitored, ambient parameter data is acquired and ambient parameter data is sent to greenhouse monitoring center；
The greenhouse monitoring center includes data prediction device, data analysis set-up, and data prediction device is used for reception
Ambient parameter data is pre-processed, and pretreatment includes carrying out clustering processing, abnormality detection processing, data to ambient parameter data
Analytical equipment is for judging whether pretreated ambient parameter data meets preset environmental parameter condition, when a certain environment is joined
When number data are unsatisfactory for preset environmental parameter condition, control instruction, control are sent to the controller by network communication module
Make corresponding equipment running.
Beneficial effects of the present invention are：The production environment data in agricultural greenhouse can be obtained in real time, and intelligentized control method is big
The running of equipment in canopy, provides precision agricultural production and visualized management, and intelligence degree is high.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the structure diagram of the intelligent cultivation greenhouse monitoring system of an illustrative embodiment of the invention；
Fig. 2 is the structure diagram of the greenhouse monitoring center of an illustrative embodiment of the invention.
Reference numeral：
Greenhouse monitoring center 1, network communication module 2, control module 3, information acquisition module 4, data prediction device
100, data analysis set-up 200.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, Fig. 2, the intelligent cultivation greenhouse provided in this embodiment based on big data analysis monitors system, including big
Canopy monitoring center 1, network communication module 2, control module 3, information acquisition module 4, the greenhouse monitoring center 1 pass through network
Communication module 2 is communicated with control module 3, and control module 3 is electrically connected the multiple equipment in agricultural greenhouse；The information collection
Module 4 is for being monitored the environment of agricultural greenhouse by wireless sensor network, acquisition ambient parameter data and by environment
Supplemental characteristic is sent to greenhouse monitoring center 1；The greenhouse monitoring center 1 includes data prediction device 100, data analysis
Device 200, for data prediction device 100 for being pre-processed to the ambient parameter data of reception, pretreatment includes to environment
Supplemental characteristic carries out clustering processing, abnormality detection processing, and data analysis set-up 200 is for judging pretreated environmental parameter number
According to whether preset environmental parameter condition is met, when a certain ambient parameter data is unsatisfactory for preset environmental parameter condition, lead to
It crosses network communication module 2 and sends control instruction to the controller, control corresponding equipment running.
Preferably, the ambient parameter data includes the CO of soil temperature and humidity in agricultural greenhouse, air2Concentration and illumination
Intensity；The multiple equipment includes watering device, roller shutter equipment, heating equipment, Fan Equipment, is preset when the humiture is less than
Minimum humiture when, control module 3 controls the heating equipment and watering device and opens, as the CO2Concentration is more than default
Highest CO2The Fan Equipment is controlled when concentration to open, when the intensity of illumination is more than preset maximum light intensity, control
Make the roller shutter opening of device.
In one embodiment, the data analysis set-up 200 includes display module and instruction sending module, the display
Module is connect with described information acquisition module 4, the ambient parameter data acquired for showing described information acquisition module 4, described
Instruction sending module is connect with 3 wireless telecommunications of the control module, for sending control instruction to the control module 3.
The above embodiment of the present invention can obtain the production environment data in agricultural greenhouse in real time, in intelligentized control method greenhouse
Equipment running, provide precision agricultural production and visualized management, intelligence degree is high.
In one embodiment, data prediction device 100 carries out clustering processing to ambient parameter data, specifically includes：
(1) to there are the ambient parameter datas of 0 value or negative value to pre-process, 0 value or negative value is replaced with and preset
Substitution value, extract the ambient parameter data of set period of time as an ambient parameter data collection, Z be set as, wherein each ring
Border supplemental characteristic includes W dimensional features；
(2) in first time iteration, first unlabelled ambient parameter data in ambient parameter data collection Z is selected to make
For first cluster central point O1, calculate remaining ambient parameter data and cluster central point O1Between similarity distance, according to it is similar away from
From distribution principle to ambient parameter data ziIt is allocated operation；
Wherein, similarity distance distribution principle is：If ambient parameter data ziIt is similar between the cluster central point newly selected
Similarity distance threshold value L of the distance no more than settingT, not to ambient parameter data ziIt is allocated operation；If ambient parameter data zi
It is more than the similarity distance threshold value L of setting with the similarity distance between the cluster central point that newly selectsT, continue computing environment supplemental characteristic
ziWith the similarity distance between the ambient parameter data in the arest neighbors set of the cluster central point, if ambient parameter data ziWith this
Between an ambient parameter data in the arest neighbors set of cluster central point, meet the similarity distance threshold that similarity distance is more than setting
Value LT, then by ambient parameter data ziIt is assigned to the cluster central point, and is marked；
(3) it enables iterations λ add 1, first unlabelled ambient parameter data in ambient parameter data collection Z is selected to make
For another cluster central point Oλ+1, calculate remaining ambient parameter data and cluster central point Oλ+1Between similarity distance, if environment
Supplemental characteristic zjIt is unmarked, according to similarity distance distribution principle to ambient parameter data zjIt is allocated operation；If environmental parameter number
According to zjIt is marked and can be assigned to cluster central point O according to similarity distance distribution principleλ+1, compare its with original distribution cluster central point,
Cluster central point Oλ+1Between similarity distance, select similarity distance bigger cluster central point be added cluster；
(4) (3) are repeated until iterations λ reaches the threshold value of setting or all ambient parameter datas are all marked
Note.
It, need not be pre- when the present embodiment carries out clustering processing to the pretreated ambient parameter data of data pre-processing unit
The number for first specifying cluster, wherein innovatively setting similarity distance distribution principle so that only when in ambient parameter data and cluster
When heart point is similar and one or more of arest neighbors set to cluster central point ambient parameter data is similar, ambient parameter data
The same cluster could be located at cluster central point so that the clustering processing of the present embodiment is gathered more suitable for detecting any shape cluster
Class is efficient and quality is high.
In one embodiment, set the calculation formula of the similarity distance between two ambient parameter datas as：
In formula, L (zi,zp) indicate ambient parameter data ziWith zpBetween similarity distance, HjFor the jth of ambient parameter data
The weighted value of dimensional feature value, zijIndicate ambient parameter data ziJth dimensional feature value, zpjFor ambient parameter data zpJth dimension
Characteristic value, W are the dimension of ambient parameter data.
Weigh the otherness between two ambient parameter datas frequently with absolute distance in the prior art, such as it is European away from
From, manhatton distance etc., that is to say, that the similitude that the distance between two ambient parameter datas both show more greatly is smaller, instead
Then similitude it is bigger, but this distance metric mode generally involves all properties of object, and thinks these attributes pair
In the importance of distance metric be identical.The present embodiment innovatively sets a kind of new similarity distance measure formulas, should
Formula considers the attribute factor of ambient parameter data, is subject to different weighted values for the feature in different dimensions, allows difference
Dimension serves according to the size of weighted value in cluster different, enables to the ambient parameter data in certain characteristic dimensions
Difference is distinguished, and is solved the problems, such as that the Clustering Effect caused by the dimension difference in different characteristic dimension is bad, is improved
The precision and efficiency of clustering.
In one embodiment, the corresponding weighted value of characteristic value of each dimension of ambient parameter data is set by expert
It is fixed.In a further advantageous embodiment, if ambient parameter data collection Z={ z1,z2,…,zs, set environment according to the following formula
The corresponding weighted value of characteristic value of each dimension of supplemental characteristic：
In formula, HjIndicate the weighted value of the jth dimensional feature value of ambient parameter data, zrjFor in ambient parameter data collection Z
The jth dimensional feature value of r-th of ambient parameter data, zr+For the b of r-th of ambient parameter data in ambient parameter data collection Z
Dimensional feature value, S are the ambient parameter data number that ambient parameter data collection Z includes, and W is the dimension of ambient parameter data.
The corresponding weighted value of characteristic value that the present embodiment creatively sets each dimension of ambient parameter data calculates
Formula, the characteristic value weight smaller which makes the departure degree of property distribution smaller, and the deviation journey of property distribution
Degree is bigger, and characteristic value weight is bigger, advantageously accounts for caused by ambient parameter data density difference that Clustering Effect is bad to ask
Topic sets the mode of weight, more practical property and science relative to expert, improves the precision of cluster.
Due to comparatively loose between the ambient parameter data in the smaller cluster of scale, and relative to other environment
Supplemental characteristic is more isolated, therefore the data in the cluster of scale is smaller are usually considered as abnormal data in the prior art.At one
In embodiment, data prediction device 100 carries out abnormality detection processing to ambient parameter data, specifically includes：
(1) if there are the numerical lower limits that the ambient parameter data number of a cluster is less than setting after cluster, which is regarded
For abnormal clusters, the average value z of ambient parameter data in abnormal clusters is soughte；
(2) similarity distance between the cluster central point of other normal clusters and the cluster central point of abnormal clusters is calculated；
(3) similarity distance between the cluster central point of cluster and the cluster central point of abnormal clusters is not more than the cluster phase set if normal
Like distance threshold, then using the normal clusters as cluster to be detected, and z is utilizedeDetect the ambient parameter data in cluster to be detected, when
Ambient parameter data in cluster to be detectedWhen meeting following exceptional condition, by ambient parameter dataIt is considered as abnormal environment ginseng
Number data：
In formula, HjIndicate the weighted value of the jth dimensional feature value of ambient parameter data,For ambient parameter dataJth
Dimensional feature value, zejFor the average value z of ambient parameter data in the abnormal clusterseJth dimensional feature value, W is ambient parameter data
Dimension, LtFor the abnormality detection distance threshold of setting.
The present embodiment carries out abnormality detection the ambient parameter data after clustering processing, therefrom innovatively proposes and is used for
Whether detection ambient parameter data is abnormal exceptional condition, and the exceptional condition is according to environment in ambient parameter data and abnormal clusters
The distance between average value of supplemental characteristic judges whether the ambient parameter data is abnormal environment supplemental characteristic, has certain
Accuracy of detection, detection mode is simple and effective.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (6)
1. the intelligent cultivation greenhouse based on big data analysis monitors system, characterized in that including greenhouse monitoring center, network communication
Module, control module, information acquisition module, the greenhouse monitoring center are communicated by network communication module with control module,
Control module is electrically connected the multiple equipment in agricultural greenhouse；The information acquisition module is for passing through wireless sensor network
The environment of agricultural greenhouse is monitored, ambient parameter data is acquired and ambient parameter data is sent to greenhouse monitoring center；
The greenhouse monitoring center includes data prediction device, data analysis set-up, and data prediction device is used for reception
Ambient parameter data is pre-processed, and pretreatment includes carrying out clustering processing, abnormality detection processing, data to ambient parameter data
Analytical equipment is for judging whether pretreated ambient parameter data meets preset environmental parameter condition, when a certain environment is joined
When number data are unsatisfactory for preset environmental parameter condition, control instruction, control are sent to the controller by network communication module
Make corresponding equipment running.
2. the intelligent cultivation greenhouse according to claim 1 based on big data analysis monitors system, characterized in that the ring
Border supplemental characteristic includes the CO of soil temperature and humidity in agricultural greenhouse, air2Concentration and intensity of illumination；The multiple equipment includes
Watering device, roller shutter equipment, heating equipment, Fan Equipment control mould when the humiture is less than preset minimum humiture
Block controls the heating equipment and watering device is opened, as the CO2Concentration is more than preset highest CO2When concentration described in control
Fan Equipment is opened, and when the intensity of illumination is more than preset maximum light intensity, controls the roller shutter opening of device.
3. the intelligent cultivation greenhouse according to claim 2 based on big data analysis monitors system, characterized in that the number
Include display module and instruction sending module according to analytical equipment, the display module connect with described information acquisition module, is used for
Show the ambient parameter data of described information acquisition module acquisition, described instruction sending module and the control module wireless telecommunications
Connection, for sending control instruction to the control module.
4. monitoring system, feature according to intelligent cultivation greenhouse of the claim 1-3 any one of them based on big data analysis
It is that data prediction device carries out clustering processing to ambient parameter data, specifically includes：
(1) to there are the ambient parameter datas of 0 value or negative value to pre-process, 0 value or negative value are replaced with into preset replace
Generation value, extracts the ambient parameter data of set period of time as an ambient parameter data collection, is set as Z, wherein each environment is joined
Number data include W dimensional features；
(2) in first time iteration, select first unlabelled ambient parameter data in ambient parameter data collection Z as
One cluster central point O1, calculate remaining ambient parameter data and cluster central point O1Between similarity distance, according to similarity distance point
With principle to ambient parameter data ziIt is allocated operation；
Wherein, similarity distance distribution principle is：If ambient parameter data ziNot with the similarity distance between the cluster central point that newly selects
More than the similarity distance threshold value L of settingT, not to ambient parameter data ziIt is allocated operation；If ambient parameter data ziWith new choosing
Similarity distance between the cluster central point selected is more than the similarity distance threshold value L of settingT, continue computing environment supplemental characteristic ziWith this
The similarity distance between ambient parameter data in the arest neighbors set of cluster central point, if ambient parameter data ziWith the cluster center
Between an ambient parameter data in the arest neighbors set of point, meet the similarity distance threshold value L that similarity distance is more than settingT,
Then by ambient parameter data ziIt is assigned to the cluster central point, and is marked；
(3) it enables iterations λ add 1, selects first unlabelled ambient parameter data in ambient parameter data collection Z as another
One cluster central point Oλ+1, calculate remaining ambient parameter data and cluster central point Oλ+1Between similarity distance, if environmental parameter
Data zjIt is unmarked, according to similarity distance distribution principle to ambient parameter data zjIt is allocated operation；If ambient parameter data zj
It is marked and can be assigned to cluster central point O according to similarity distance distribution principleλ+1, compare it with the cluster central point of original distribution, in cluster
Heart point Oλ+1Between similarity distance, select similarity distance bigger cluster central point be added cluster；
(4) (3) are repeated until iterations λ reaches the threshold value of setting or all ambient parameter datas have all been labeled.
5. the intelligent cultivation greenhouse according to claim 4 based on big data analysis monitors system, characterized in that setting two
The calculation formula of similarity distance between a ambient parameter data is：
In formula, L (zi,zp) indicate ambient parameter data ziWith zpBetween similarity distance, HjFor the jth Wei Te of ambient parameter data
The weighted value of value indicative, zijIndicate ambient parameter data ziJth dimensional feature value, zpjFor ambient parameter data zpJth dimensional feature
Value, W are the dimension of ambient parameter data.
6. the intelligent cultivation greenhouse according to claim 4 based on big data analysis monitors system, characterized in that set environment
Parameter data set Z={ z1,z2,…,zS, the characteristic value of each dimension of set environment supplemental characteristic is corresponding according to the following formula
Weighted value：
In formula, HjIndicate the weighted value of the jth dimensional feature value of ambient parameter data, zrjFor r-th in ambient parameter data collection Z
The jth dimensional feature value of ambient parameter data, zrbFor the b dimensional features of r-th of ambient parameter data in ambient parameter data collection Z
Value, S are the ambient parameter data number that ambient parameter data collection Z includes, and W is the dimension of ambient parameter data.
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Cited By (3)
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| CN111198549A (en) * | 2020-02-18 | 2020-05-26 | 陈文翔 | Poultry breeding monitoring management system based on big data |
| CN117829381A (en) * | 2024-03-05 | 2024-04-05 | 成都农业科技职业学院 | Agricultural greenhouse data optimization acquisition system based on Internet of things |
| CN117992894A (en) * | 2024-04-03 | 2024-05-07 | 山东济宁丰泽农业科技有限公司 | Agricultural greenhouse environment abnormal data monitoring method based on Internet of things |
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