CN110199945B - Management device for intelligent breeding of wood frogs in multiple places through remote monitoring - Google Patents
Management device for intelligent breeding of wood frogs in multiple places through remote monitoring Download PDFInfo
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- 241000191896 Rana sylvatica Species 0.000 title claims abstract description 65
- 238000012544 monitoring process Methods 0.000 title claims abstract description 36
- 238000009395 breeding Methods 0.000 title claims description 6
- 230000001488 breeding effect Effects 0.000 title claims description 6
- 239000003102 growth factor Substances 0.000 claims abstract description 64
- 238000004458 analytical method Methods 0.000 claims abstract description 46
- 238000007726 management method Methods 0.000 claims abstract description 23
- 230000000694 effects Effects 0.000 claims description 11
- 241001336827 Rana chensinensis Species 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 8
- 229910052760 oxygen Inorganic materials 0.000 claims description 8
- 239000001301 oxygen Substances 0.000 claims description 8
- 125000004122 cyclic group Chemical group 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 230000001932 seasonal effect Effects 0.000 claims description 6
- 238000012300 Sequence Analysis Methods 0.000 claims description 4
- 238000010224 classification analysis Methods 0.000 claims description 4
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- 238000010191 image analysis Methods 0.000 description 2
- 238000013433 optimization analysis Methods 0.000 description 2
- 238000012731 temporal analysis Methods 0.000 description 2
- 238000000700 time series analysis Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 1
- 241000269350 Anura Species 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
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- 238000007796 conventional method Methods 0.000 description 1
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- 210000003128 head Anatomy 0.000 description 1
- 230000006266 hibernation Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
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Abstract
The utility model provides a management device that multi-site wood frog intelligence was bred with remote monitoring, can manage a plurality of wood frog farms simultaneously and breed the wood frog, management device contains high in the clouds service subsystem and a plurality of real time monitoring subsystem, the real time monitoring subsystem is located in the wood frog farm, produce growth image state information and growth factor state information respectively with monitor and growth factor sensor, carry out the data exploration by the exploration analysis module in the high in the clouds service subsystem again in order to produce growth condition information, finally, combine growth condition information and environmental planning information can become growth factor and control the information, carry out automatic and intelligent breed to the wood frog in order to provide the wood frog farm.
Description
Technical Field
The invention relates to the technical field of intelligent control devices, in particular to a management device for remotely monitoring intelligent breeding of wood frogs in multiple places.
Background
The wood frog belongs to the China subspecies of the European wood frog. Trade name is forest frog. Widely distributed in northern China. Siberia, korean, etc. are also distributed. The individual variation in each region is large. The head body and the limbs are slender, the action is agile, the jumping force is strong, and the drum membrane part has triangular black and brown color spots. The back of the body is mostly earthy yellow, and dark spots are scattered on the warts. The dorsal pleats curve laterally over the tympanic membrane. Mainly inhabitation on land, and people often live in a moist and cool environment without strong light. Feed on a variety of insects. Moving from the mountain forest area to the vicinity of the river channel from the beginning of the last ten days of 9 months to the beginning of 10 months, and continuously entering the water bottom cluster for hibernation. A minority overwinter in mud or under the roots, and resuscitating before and after the next clear period. The middle of 4 months to the beginning of 5 months is the breeding season. During the propagation period, tadpoles can hatch from ditches and river edges of the fields for 8-20 days, and the tadpoles completely become frogs in 1 month, and the body length is about 19 mm.
The existing wood frog cultivation is mostly carried out in the field, the cultivation mode has the defect of inconvenient management, and the expected effect cannot be achieved when the wood frog returns, and the survival rate is lower, so the wood frog cultivation device and the method are provided.
Disclosure of Invention
In order to overcome the defects in the background art, the invention provides a management device for remotely monitoring the intelligent cultivation of the wood frogs in multiple places, which can simultaneously manage a plurality of wood frog cultivation places, accumulate the cultivation related data along with time, continuously optimize growth condition information and further generate ideal growth factor control information.
In order to achieve the purpose, the invention adopts the following technical scheme:
a management device for remotely monitoring intelligent cultivation of wood frogs in multiple places is a management system for remotely monitoring intelligent cultivation of wood frogs in multiple places and is used for managing at least one wood frog cultivation field, and the wood frog cultivation field is used for cultivating at least one wood frog. The management device comprises at least one real-time monitoring subsystem and a cloud service subsystem; the real-time monitoring subsystem is arranged in a wood frog culture farm and is provided with at least one monitor and at least one growth factor sensor, the monitor collects wood frog images to form growth image state information, and the growth factor sensor monitors growth factors of the chain surrounding environment to generate growth factor state information; the cloud service subsystem is in information connection with the real-time monitoring subsystem and comprises a growth state database, a tracking analysis module, a prospecting analysis module and a cultivation knowledge base.
The growth state database is used for receiving and storing growth image state information and growth factor state information from the real-time monitoring subsystem.
The tracking analysis module is linked with the growth state database, performs growth factor time sequence analysis to generate time sequence model parameters according to growth factor state information in the growth state database and a corresponding time sequence, and performs image variation analysis to generate growth variation according to growth image state information in the growth state database and a corresponding time sequence.
The exploration analysis module is linked with the tracking analysis module and is used for carrying out data exploration according to the time sequence model parameters and the growth variation degree so as to generate growth condition information.
The cultivation knowledge base link exploration analysis module is used for storing growth condition information; wherein, the growth condition information in the cultivation knowledge base is transmitted to the wood frog cultivation farm, and the wood frog in the wood frog cultivation farm can be cultivated.
The real-time monitoring subsystem further comprises at least one growth environment control device, environment planning information is generated according to the at least one growth environment control device and stored in the cultivation knowledge base, and growth condition information and the environment planning information are combined into growth factor control information.
And the growth condition information in the cultivation knowledge base is transmitted to the wood frog cultivation farm, and further the growth factor control information is transmitted to the wood frog cultivation farm so as to control the growth environment control equipment to cultivate the wood frogs in the wood frog cultivation farm.
The tracking analysis module can further perform growth factor difference analysis to generate difference model parameters according to the variation of the growth factor state information in the growth state database and the corresponding time sequence, and the exploration analysis module can further perform data exploration according to the time sequence model parameters, the difference model parameters and the growth variation, so that the effect is more accurate.
The data exploration can be association rule analysis, classification analysis or grouping analysis; the growth factors refer to cell temperature, room temperature, illumination, and oxygen concentration.
The growth factor time sequence analysis further constructs a time sequence model by a component decomposition method, and the time sequence model has the time sequence model parameters; the component decomposition method decomposes the growth factor state information corresponding to the time sequence into trend components, cyclic components, seasonal components and random components, then eliminates the effect of the seasonal components, the effect of the trend components and the effect of the cyclic components in sequence to calculate residual errors, and constructs a time sequence model according to the residual errors.
Compared with the prior art, the invention has the beneficial effects that:
the invention can simultaneously manage a plurality of wood frog farms, accumulate the relevant data of cultivation along with time, and continuously optimize the growth condition information so as to generate ideal growth factor control information.
Drawings
FIG. 1 is a schematic diagram of the structure of the management device of the present invention;
FIG. 2 is a schematic diagram of a real-time monitoring subsystem;
fig. 3 is a schematic diagram of a cloud service subsystem.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
Referring to fig. 1, a management system 10 of the present invention relates to a remotely monitored management device 10 for intelligent cultivation of wood frogs in multiple locations, which is configured to manage at least one wood frog farm 12 under a remote configuration, wherein the wood frog farm 12 is configured to cultivate at least one wood frog. The management device 10 includes a plurality of real-time monitoring subsystems 20 and a cloud service subsystem 22, wherein the real-time monitoring subsystems 20 are disposed in the wood frog farm 12, and each real-time monitoring subsystem 20 is communicatively linked to the cloud service subsystem 22 through a network.
Fig. 2 is a schematic diagram of the real-time monitoring subsystem 20. The real-time monitoring subsystem 20 has at least one monitor 44, a plurality of growth factor monitors 40 and a plurality of growth environment control devices 42 on the spot near the wood frog, the monitor 44 collects the wood frog image to become growth image state information 5002, the growth state of the mushroom can be known from the growth image state information 5002 of the wood frog image at different time through image analysis and comparison, and practically, each wood frog has its corresponding monitor 44.
The growth environment control device 42 corresponds to the above-described various growth factors, and includes a water temperature heater 4202, an air conditioner 4204, an LED lamp 4206, an exhaust fan 4208, and the like, and changes the pool temperature, the indoor temperature, the illuminance, and the oxygen concentration around the wood frog.
The real-time monitoring subsystem 20 has a management host 30, a manipulation database 32, a growth state memory 34, and a network communication device 36 at a control center relatively far from the wood frog. The management host 30 is a processing and control core in the wood frog farm 12, and is respectively linked with the control database 32, the growth state memory 34, and the network communication device 36, and further communicatively connected with the monitor 44, the growth factor sensors 40, and the growth environment control devices 42 through the network.
The growth status memory 34 stores the growth factor status information 5004 of the pool temperature, the indoor temperature, the illuminance, and the oxygen concentration, which are distributed in time series and monitored by the various growth factor sensors 40, and the growth image status information 5002 of the wood frog images, which are distributed in time series and collected by the monitor 44, in a temporary storage, and then transmits the information to the cloud service subsystem 22 through the management host 30 and the network communication device 36. The control database 32 stores the growth factor control information 60 received from the cloud service subsystem 22 via the network communication device 36 and the management host 30, so that the management host 30 can subsequently control the growth environment control device 42 in the wood frog farm 12, and further control various growth factors.
Fig. 3 is a schematic diagram of cloud services subsystem 22. The cloud service subsystem 22 is in communication with the real-time monitoring subsystem 20, and includes a growth state database 50, a tracking analysis module 52, a prospecting analysis module 54, and a breeding knowledge base 56.
The growth status database 50 is used to receive and store growth image status information 5002 and growth factor status information 5004 from the real-time monitoring subsystem 20. The growth factor status information 5004 includes growth factor status information 5004 such as pond temperature, indoor temperature, illuminance, and oxygen concentration, the growth image status information 5002 is an image of the wood frog collected by the monitor 44, the growth factor status information 5004 or the growth image status information 5002 corresponds to the growth image status information 5002 in a time sequence, and the growth image status information 5002 also corresponds to the specific monitor 44 to determine which footwear of which wood frog farm 12 is, and the growth factor status information 5004 corresponds to the specific growth factor monitor 40 to which wood frog farm 12 is to be cultivated.
The tracking analysis module 52 is linked to the growth status database 50, and the tracking analysis module 52 performs growth factor time series analysis 5202 to generate time series model parameters according to the growth factor status information 5004 in the growth status database 50 and the corresponding time series. The tracking analysis module 52 performs an image variation analysis 5204 to generate a growth variation according to the growth image status information in the growth status database 50 and the corresponding time series, that is, performs an image analysis to find the variation, and determines the growth status of the wood frog according to the variation.
The exploration analysis module 54 is linked to the tracking analysis module 52, and performs data exploration according to the time-series model parameters and the growth variation to generate growth condition information 5604. The data exploration may be association rule analysis 5402, classification analysis 5404, or clustering analysis 5406, among others. For example, taking the neural analysis of the classification analysis 5404 as an example, the time sequence model parameters and the growth variation degree are taken as data input, the growth condition information 5604 is taken as data output, and after a plurality of training, the optimized weight is generated, so that after inputting new growth factor state information and growth image state information in the future, the optimized growth condition information 5604 can be generated to be used as the control basis for the subsequent cultivation of wood frogs.
In the exploration analysis module 54, the time sequence model parameters and the growth variation are used as data input, and the difference model parameters are also used as data input. The tracking analysis module 52 further performs growth factor difference analysis 5206 to generate difference model parameters according to the variation of the growth factor status information 5004 in the growth status database 50 and the corresponding time series, so that the final exploration analysis module 54 can perform data exploration according to the time sequence model parameters, the difference model parameters and the growth variation, that is, perform optimization analysis by using the time sequence model parameters, the difference model parameters and the growth variation as data inputs, and the output data of the growth condition information 5604 is more ideal.
The cultivation knowledge base 56 is linked to the exploration analysis module 54 for storing the continuously optimized growth condition information 5604. Wherein, the growth condition information 5604 in the cultivation knowledge base 56 is transmitted to the wood frog cultivation farm 12, so that the wood frog in the wood frog cultivation farm 12 can be cultivated.
However, the real-time monitoring subsystems 20 in different wood frog farms 12 may have different growth environment control devices 42, and a corresponding environment planning information 5602 may be analyzed and generated according to the configuration combination of the unique growth environment control device 42 in each real-time monitoring subsystem 20, and the environment planning information 5602 may be stored in the cultivation knowledge base 56. Subsequently, the growth condition information 5604 and the environment planning information 5602 should be combined into the growth factor control information 60, which is the information to be controlled by each growth environment control device 42 in the real-time monitoring subsystem 20, so that the growth condition information 5604 in the cultivation knowledge base 56 should be transmitted to the junctional farm 12, and the growth factor control information 60 should be further transmitted to the wood frog farm 12 to control the growth environment control device 42 to cultivate the wood frogs in the wood frog farm 12.
In the exploration analysis module 54, the time sequence model parameters and the growth variation are used as data input, and the difference model parameters are also used as data input. The tracking analysis module 52 further performs growth factor difference analysis 5206 to generate difference model parameters according to the variation of the growth factor status information 5004 in the growth status database 50 and the corresponding time series, so that the final exploration analysis module 54 can perform data exploration according to the time sequence model parameters, the difference model parameters and the growth variation, that is, perform optimization analysis by using the time sequence model parameters, the difference model parameters and the growth variation as data inputs, and the output data of the growth condition information 5604 is more ideal.
The time series analysis 5202 of the growth factors can be used to construct a time series model using a component decomposition method, where the time series model has the time series model parameters. Further, the component decomposition method can decompose the growth factor status information 5004 corresponding to the time sequence into trend components, cyclic components, seasonal components, and random components, and then sequentially eliminate the effect of the seasonal components, the effect of the trend components, and the effect of the cyclic components to calculate residual errors, and construct a time sequence model according to the residual errors.
Therefore, with the management device 10 for remotely monitoring and intelligently culturing rana chensinensis in multiple places, the remote architecture and exploration analysis module 54 can simultaneously manage a plurality of rana chensinensis farms 12, and the relevant culture data is accumulated over time, so that the growth condition information 5604 can be continuously optimized, and the environment planning information 5602 can be generated, thereby generating the ideal growth factor control information 60.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (4)
1. A management device for remotely monitoring intelligent breeding of wood frogs in multiple places is characterized by comprising at least one real-time monitoring subsystem and a cloud service subsystem;
the real-time monitoring subsystem is arranged in a wood frog culture farm and is provided with at least one monitor and at least one growth factor sensor, the monitor collects wood frog images to form growth image state information, and the growth factor sensor monitors growth factors of the chain surrounding environment to generate growth factor state information;
the cloud service subsystem is in information connection with the real-time monitoring subsystem and comprises a growth state database, a tracking analysis module, a prospecting analysis module and a cultivation knowledge base;
the tracking analysis module is linked with the growth state database, performs growth factor time sequence analysis to generate time sequence model parameters according to growth factor state information in the growth state database and a time sequence corresponding to the growth factor state information, and performs image variation analysis to generate growth variation according to growth image state information in the growth state database and a time sequence corresponding to the growth image state information;
the exploration analysis module is linked with the tracking analysis module and is used for carrying out data exploration according to the time sequence model parameters and the growth variation degree so as to generate growth condition information;
the cultivation knowledge base link exploration analysis module is used for storing growth condition information; wherein, the growth condition information in the cultivation knowledge base is transmitted to the wood frog cultivation farm, so that the wood frogs in the wood frog cultivation farm can be cultivated;
the tracking analysis module performs growth factor difference analysis to generate difference model parameters according to the variation of the growth factor state information in the growth state database and the corresponding time sequence of the variation, and the exploration analysis module performs data exploration according to the time sequence model parameters, the difference model parameters and the growth variation, so that the effect is more accurate;
the data exploration is association rule analysis, classification analysis or grouping analysis; the growth factor refers to the temperature of the pool, the indoor temperature, the illumination and the oxygen concentration;
the growth factor time sequence analysis further constructs a time sequence model by a component decomposition method, and the time sequence model has the time sequence model parameters; the component decomposition method decomposes the growth factor state information corresponding to the time sequence into trend components, cyclic components, seasonal components and random components, then eliminates the effect of the seasonal components, the effect of the trend components and the effect of the cyclic components in sequence to calculate residual errors, and constructs a time sequence model according to the residual errors.
2. The management device for the intelligent cultivation of rana chensinensis with remote monitoring as claimed in claim 1, wherein the growth status database is used for receiving and storing the growth image status information and growth factor status information from the real-time monitoring subsystem.
3. The management device for the intelligent cultivation of rana chensinensis with remote monitoring as claimed in claim 1, wherein the real-time monitoring subsystem further comprises at least one growth environment control device, the environment planning information is generated by the at least one growth environment control device and stored in the cultivation knowledge base, and the growth condition information and the environment planning information are combined into growth factor control information.
4. The management device for the intelligent cultivation of rana chensinensis with remote monitoring and control functions as claimed in claim 3, wherein the information of the growth conditions in the cultivation knowledge base is transmitted to the rana chensinensis cultivation farm, and the information of the growth factor control is further transmitted to the rana chensinensis cultivation farm to control the growth environment control device to cultivate the rana chensinensis in the rana chensinensis cultivation farm.
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