AU2020101190A4 - Intelligent Management System For Multi Site Rana Chensinensis Monitored By Internet Of Fhings - Google Patents
Intelligent Management System For Multi Site Rana Chensinensis Monitored By Internet Of Fhings Download PDFInfo
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- 241001336827 Rana chensinensis Species 0.000 title abstract description 22
- 239000003102 growth factor Substances 0.000 abstract description 57
- 238000009395 breeding Methods 0.000 abstract description 31
- 230000001488 breeding effect Effects 0.000 abstract description 30
- 238000007726 management method Methods 0.000 abstract description 26
- 238000012544 monitoring process Methods 0.000 abstract description 25
- 241000269435 Rana <genus> Species 0.000 abstract description 9
- 230000007613 environmental effect Effects 0.000 abstract description 8
- 238000009360 aquaculture Methods 0.000 abstract description 6
- 244000144974 aquaculture Species 0.000 abstract description 6
- 238000004458 analytical method Methods 0.000 description 39
- 238000007418 data mining Methods 0.000 description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 8
- 229910052760 oxygen Inorganic materials 0.000 description 8
- 239000001301 oxygen Substances 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 238000005286 illumination Methods 0.000 description 7
- 238000000034 method Methods 0.000 description 6
- 238000000354 decomposition reaction Methods 0.000 description 5
- 238000012731 temporal analysis Methods 0.000 description 5
- 238000000700 time series analysis Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000010224 classification analysis Methods 0.000 description 3
- 230000001932 seasonal effect Effects 0.000 description 3
- 230000004083 survival effect Effects 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 210000003454 tympanic membrane Anatomy 0.000 description 2
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003631 expected effect Effects 0.000 description 1
- 238000009313 farming Methods 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000013433 optimization analysis Methods 0.000 description 1
- 235000015096 spirit Nutrition 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
-
- 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
- A01K2227/00—Animals characterised by species
- A01K2227/50—Amphibians, e.g. Xenopus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
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- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A multi site Rana chensinensis intelligent breeding
management system monitored by the Internet of things can
manage multiple Rana frog farms at the same time to breed Rana
chensinensis. The management system includes cloud service
subsystem and multiple real-time monitoring subsystems. The
real-time monitoring subsystem is located in the Rana Rana
Rana breeding field, and the monitor information and growth
factor sensors are respectively used to generate the growth
image status information and growth factor status
information.Finally, the information ofgrowthconditions and
environmental planning information can be used as
manipulation information of growth factors to provide
automatic and intelligent aquaculture for Rana chensinensis.
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20 20 20
Fig. 1
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40 42
. 4002 - 4202
40 04 - 42 04
4006 4206
4008 - 42 08
Fig. 2
Description
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12 12 12
20 20 20
Fig. 1
22
20
32
34
40 42 . 4002 - 4202 40 04 - 42 04 4006 4206 4008 - 42 08
Fig. 2
Editorial Note 2020101190 There is only seven pages of the description
Intelligent management system for multi site Rana chensinensis monitored by Internet of things
Technical field The invention relates to a management system for forest frog breeding, especially the management system of multi site Rana chensinensis intelligent breeding monitored by Internet of things. Background technology Rana chensinensis, a subspecies of Rana chensinensis, is widely distributed in northern China. Siberia, North Korea and so on are also distributed. Individual variation is greater in each region. The head and limbs are slender, agile and strong in jumping, and there are triangular dark brown spots in the tympanic membrane. The back of the body is mostly yellow. There are usually dark spots on the warts. The dorsal fold is curved outside the direction of the tympanic membrane. It is mainly terrestrial.They usually live in the absence of strong light and humid and cool environment. They feed on a variety ofinsects. Fromlate.9 to early October, they migrate from the hillside forest to the river gully. They gradually enter the bottom of the river to hibernate. A few of them are wintering in the mud or under the roots of trees. After the next year, they recover from.4 to early May. They are breeding season. During the breeding season, they will hatch tadpoles in the fields of ditches and river banks for 8-20 days.In 1 months, it was completely transformed into a frog with a body length of about 19 millimeters. Most of the current Rana chensinensis farming is carried outin the wild. This kindofcultivationmethodhas the defects of inconvenient management. At the same time, when the Rana chensinensis returns, it often fails to achieve the expected effect and the survival rate is low. Therefore, the present inventionproposes amethod for cultivating forest frog, which uses the existing greenhouse to simulate the outdoor survival environment for Rana chensinensis breeding, and abandons the defect of inconvenient management of traditional breeding methods.At the same time, the survival rate of Rana chensinensis was increased. Summary of the invention The aim of the invention is to provide a multi site Rana chensinensis intelligent breeding management system based on the Internet of things monitoring, which can simultaneously manage multiple Rana Rana breeding farms, and can continuously optimize the growth condition information as time accumulates the relevant data of breeding, thus producing ideal growth factor manipulation information. The invention relates to a multi site Rana chensinensis intelligent breeding management system monitored by the Internet of things, which is used to manage at least one forest frog farm, and the Rana breeding farm is used to breed at least one frog. The management system comprises at least one real-time monitoring subsystem and a cloud service subsystem. The real-time monitoring subsystem is located in the Rana Rana chensinensis farm. It has at least one monitor and at least one growth factor sensor. The monitor collects the frog image to form a growth image state information, and the growth factor sensor monitors the growth factors in the surrounding environment of the chain to generate the growth factor status information. Cloud service subsystem real-time monitoring subsystem, including growth status database, tracking and analysis module, exploration and analysis module, and breeding knowledge base. The growth state database is used to receive and store the growth image status information and growth factor status information from the real-time monitoring subsystem. The tracking analysis module links the growth state database, and the tracking analysismodule analyzes the growth factor time series according to the growth factor status information and corresponding time series in the growth state database to generate the time series model parameters, track the analysis module, and according to the growing image state information and corresponding time series in the growing state database.Image variation analysis is used to generate growth variability. The link analysis module ofexploration and analysis module is used for data mining based on time series model parameters and growth variability to produce growth condition information. The aquaculture knowledge base links the exploration and analysis module to store the growth condition information. In this way, the growth condition information in the breeding knowledge base can be transmitted to the Rana Rana Rana, so that the Rana frog can be cultured. Further, the real-time monitoring subsystem has at least one growth environment control device. According to the at least one growth environment control device, environmental planning information will be generated and stored in the breeding knowledge base, and growth condition information and environmental planning information will be combined into growth factor manipulation information.It is to further transfer the growth factor control information to the Rana Rana Rana, so as to control the frog in the Rana Rana breeding farmby controlling the growthenvironment controlequipment. It is added that the tracking analysis module can make the difference analysis of growth factors based on the variation ofgrowth factor information and the corresponding time series in the growth state database to produce the difference model parameters. The exploration analysis module can further carry out data mining based on time series model parameters, differentialmodelparameters and growthvariability, and the effect will be more accurate. Data mining can be used for association rule analysis, classification analysis, or clustering analysis. Growth factors refer to pool temperature, indoor temperature, illumination and oxygen concentration. The time series analysis of growth factor can further construct the time series model by component decomposition method. The time series model has the parameters of the time series model. The component decomposition method decomposes the growth factor state information of the time series into trend components, cyclical components, seasonal components and random components.Then, in order to eliminate the effects of seasonal components, the effects of trend components and the effects of cyclic components, we can calculate the residuals and construct a time series model based on residuals. Therefore, the invention provides a multi site forest frog intelligent breeding management system monitored by the Internet of things, which is able to carry out the management of several forest frog farms simultaneously by means of the Internet of things architecture and exploration analysis module, and can continuously optimize the growth condition information with the accumulation of relevant data in time, and is generated by environmental planning information.And then produce ideal growth factor manipulation information. The advantages and spirits of the invention can be further understood by the following inventions and the accompanying drawings. Description of drawings Fig. 1 is a schematic diagram of the management system of the invention. Fig. 2 is a schematic diagram of the real-time monitoring subsystem of the invention. Fig. 3 is a schematicdiagram of the cloud service subsystem of the invention. Specific implementation methods Fig. 1 is a schematic diagram of the management system 10 of the invention. The invention relates to a management system for intelligent breeding of multi site Rana chensinensis, which is monitored by the Internet of things. Under the framework of the Internet of things, it is used to manage at least one Rana frog farm 12, and the forest frog farm 12 is used to breed at least one frog. The management system 10 comprises a plurality of real-time monitoring subsystem 20 and a cloud service subsystem 22.The real-time monitoring subsystem 20 is located in the Rana Rana 12, and each real-time monitoring subsystem20 can communicate with the cloud service subsystem 22 through the network. Fig. 2 is a schematic diagram of the real-time monitoring subsystem 20 of the invention. The real-time monitoring subsystem 20 has at least one monitor 44, a plurality of growth factor monitor 40, and a plurality of growth environment control equipment 42 at the near end of the frog, and the monitor 44 collects the frog image to become the growth image status information 5002, and the growth image status information 5002 of the frog image is 5002 at different times.After image analysis and comparison, we can understand the growth of mushrooms. Practically every Rana frog has its corresponding monitor 44.. The growth factor sensor 40 monitors the growth factors of the environment around the Rana chensinensis to produce the growth factor status information 5004.. The growth factor contains pool temperature, indoor temperature, illumination and oxygen concentration, so the growth factor sensor 40 can have a pool temperature sensor 4002, an indoor temperature sensor 4004, an illuminance monitor 4006, and an oxygen sensor 4008.The pond temperature, indoor temperature, illumination and oxygen concentration were monitored separately. The growth environment control device 42 corresponds to the foregoing growth factors, including water temperature heater 4202, air conditioner 4204, LED lamp 4206, and exhaust fan 4208, and changes the pool temperature, indoor temperature, illumination and oxygen concentration around the Rana chensinensis respectively.
The real-time monitoring subsystem 20 is located at the control center far away from the forest frog, and has the management host 30, the control database 32, the growth state memory library 34, and the network communication equipment 36. management host 30 as the treatment and control core of the forest frog farm 12, and is respectively linked with the control database 32, the growth state memory library 34, and the network communication equipment 36.The network is connected with monitor 44, the growth factor sensor 40, and the growth environment control device 42 through the network. The growth state memory bank 34 will temporarily store various growth factor sensor 40 to monitor the growth factor status information 5004, such as pool temperature, indoor temperature, illumination and oxygen concentration, whichare distributed in time series, and the growing image status information 5002 of the frog frog image collected by the monitor 44 according to the time sequence.The follow-up will be transmitted to the cloud service subsystem 22. through the management host 30 and the network communication equipment 36. The database 32 will store the growth factor manipulation information 60, which is received from the cloud service subsystem 22 through the network communication equipment 36 and the management host 30, so as to control the growth environment control equipment 42 of the forest frog farm 12 through the management host 30, and then control various growth factors. Fig. 3 is a schematicdiagram of the cloud service subsystem 22 of the invention. The cloud service subsystem 22, the information connection real-time monitoring subsystem 20, comprises the growth state database 50, the tracking analysis module 52, the exploration analysis module 54, and the aquaculture knowledge base 56.. The growth state database 50 is used to receive and store the growth image status information 5002 from the real-time monitoring subsystem 20, and the growth factor status information 5004.. These growth factor state information 5004 includes the growth factor status information 5004, such as pool temperature, indoor temperature, illumination and oxygen concentration, etc. the growth image status information 5002 is the image of a forest frog collected by monitor 44.Regardless of the growth factor status information 5004 or the growth image status information 5002, there will be corresponding time series. The growth image status information 5002 will correspond to the specific monitor 44, to confirm which kind of footwear of which forest frog farm 12, and the growth factor status information 5004 corresponds to the specific growth factor monitor 40.What kind of Rana chensinensis can be cultivated in 12 forest frog farms? The tracking analysis module 52 links the growth state database 50, and the tracking analysis module 52 carries out the growth factor time series analysis 5202 according to the growth factor state information 5004 and corresponding time series in the growth state database 50, so as to generate the time series model parameters.Image variation analysis 5204 to generate growth variability, that is, image analysis found variability, the degree of variation to determine the growth status of Rana chensinensis. Next, datamining, exploration and analysismodule 54, link tracking analysis module 52, data mining based on time series model parameters and growth variability to generate growth condition information 5604., data mining can be used for association rule analysis 5402, classification analysis 5404, or group analysis 5406., for example, taking classification analysis 5404 as a case of neural analysis.Using time series model parameters and growth variability as data input, growth condition information 5604 is used as data output. After multiple training, the optimized weights are generated. Thus, the optimalgrowthconditioninformation 5604 canbe generated after the new growth factor status information and the growing image status information are used as the control basis for subsequent breeding frog. The breeding knowledge base 56 links the exploration and analysismodule 54, whichisused to store the optimizedgrowth condition information 5604., which transfers the growth condition information 5604 of the breeding knowledge base 56 to the Rana breeding farm 12, that is, the frog can be cultured in the forest frog farm 12. However, the 20 real-time monitoring subsystems of different Rana Rana 12 will have different growth environment control devices 42. According to the configuration combination of the unique growth environment control equipment 42 in each real-time monitoring subsystem 20, a corresponding environmental planning information 5602 can be analyzed, and the environmentalplanning information 5602 can be stored in the 56. of the aquaculture knowledge base.It is necessary to combine the growth condition information 5604 and the environmental planning information 5602 into the growth factor manipulation information 60, which will be the information needed to control the growth environment control equipment 42 in the real-time monitoring subsystem20, so that the growth condition information 5604 in the 56 56 of the aquaculture knowledge base can be transmitted to the class farm 12. The Rana chensinensis was cultured by controlling the growth environment control equipment 42. It is added that in the exploration analysis module 54, besides the time series model parameters and growth variability as input data, the difference model parameters can also be input as data input. Among them, the tracking analysis module 52 is based on the variation of the growth factor information 5004 in the growth state database 50 and its corresponding time series.The growth factor difference analysis 5206 is used to generate the difference model parameters, so the final exploration analysis module 54 can carry out data mining based on time series model parameters, differential model parameters and growth variability, that is, the timing model parameters, the difference model parameters and the growth variability as input data for optimization analysis.The growth condition information 5604 output data will be more ideal. The growth factor time series analysis 5202 can be used to construct a time seriesmodelusing component decomposition method. The time series model has the parameters of the time series model. Further, it is shown that the component decomposition method can decompose the growth factor state information 5004 of the time series into trend components, cyclical components, seasonal components, and random components.Then, in order to eliminate the effects ofseasonal components, the effects of trend components and the effects of cyclic components, we can calculate the residuals and construct a time series model based on residuals. Therefore, the invention provides a multi site forest frog intelligent management system 10 monitored by the Internet of things, which is capable of carrying out the management of multiple forest frog farms 12 at the same time by the Internet of things architecture and the exploration and analysis module 54. With the accumulation of relevant data in time, the growth condition information 5604 can be continuously optimized and generated by the environmental planning information 5602.And then produce ideal growth factor manipulation information 60.
Editorial Note 2020101190 There is only two pages of the claim
Right-claiming document 1. amultisite frog Rancho intelligent breedingmanagement system based on Internet of things is used to manage at least one Rana frog farm, which is used to breed at least one frog. At least one real-time monitoring subsystem is located in the Rana Rana aquaculture field, and has at least one monitor and at least one growth factor sensor. The monitor is a collection of the frog image to become a life long image state information. The growth factor sensor is a growth factor monitoring the surrounding environment of the Rana chensinensis to generate life long factor status information.And a cloud service subsystem, which is a real-time monitoring subsystem connected by information, contains a lifetime long status database, a tracking analysis module, an exploration analysis module, and a breeding knowledge base. The growth state database is used to receive and store the growing image status information and the growth factor status information from the real-time monitoring subsystem.The tracking analysis module is linked to the growth state database, which is based on the growth factor status information and its corresponding time series in the growth state database, and carries out a growth factor time series analysis to produce a time series modelparameter.The tracking analysis module performs an image variation analysis based on the growing image status information in the growth state database to generate a growth variability. The exploration analysis module is linked with the tracking analysis module, and carries out data mining according to the time series model parameters and the growth variability, so as to produce a growth condition information, which is linked to the exploration and analysis module.In order to store the growth condition information, the growth condition information in the breeding knowledge base is transmitted to the Rana Rana breeding farm to breed the knots in the Suzhou breeding farm. 2. if there are first management systems mentioned in the patent application scope, the real-time monitoring subsystem has at least one growth environment control device. According to the at least one growth environment control device, one environment planning information willbe generated and stored in the breeding knowledge base, and the growth condition information will be combined with the environmental planning informationinto a growth factor manipulationinformation.The growth condition information in the breeding knowledge base is transmitted to the Rana breeding farm, and the control information of the growth factor is transmitted to the Rana breeding farm to control the growth environment control device to breed the frog in the Rana Rana Rana. 3. if the management system mentioned in the patent application scope is first, the tracking analysis module will make a growth factor difference analysis based on the variation of the growth factor state information and its corresponding time series in the growth state database to produce a difference model parameter.The exploration and analysis module performs data mining according to the parameters of the time series model, the difference model parameter and the growth variability. 4.if there is amanagement systemmentionedin first patent applications, the growth factor refers to pool temperature, indoor temperature, illumination and oxygen concentration. 5 if the management system mentioned in the fourth patent application scope is mentioned, the time series analysis of the growth factor is based on one component decomposition method to construct a time series model, and the time series model has the parameters of the time series model.
Fig. 2 Fig. 1
Fig. 3
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112568183A (en) * | 2020-12-08 | 2021-03-30 | 贵州省种畜禽种质测定中心 | Poultry breeding and seed selection system and method based on Internet of things |
| CN112970673A (en) * | 2021-04-20 | 2021-06-18 | 通威股份有限公司 | Greenhouse type bullfrog breeding equipment and use method |
| CN115167579A (en) * | 2022-06-24 | 2022-10-11 | 国网山东省电力公司梁山县供电公司 | Substation box monitoring system and method |
| CN116138209A (en) * | 2021-11-23 | 2023-05-23 | 江西省龙泰水产养殖有限公司 | Feeding method and feeding equipment for young black-spot frogs |
-
2020
- 2020-06-30 AU AU2020101190A patent/AU2020101190A4/en not_active Ceased
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112568183A (en) * | 2020-12-08 | 2021-03-30 | 贵州省种畜禽种质测定中心 | Poultry breeding and seed selection system and method based on Internet of things |
| CN112970673A (en) * | 2021-04-20 | 2021-06-18 | 通威股份有限公司 | Greenhouse type bullfrog breeding equipment and use method |
| CN116138209A (en) * | 2021-11-23 | 2023-05-23 | 江西省龙泰水产养殖有限公司 | Feeding method and feeding equipment for young black-spot frogs |
| CN116138209B (en) * | 2021-11-23 | 2024-04-26 | 江西省龙泰水产养殖有限公司 | Feeding method and feeding equipment for young black-spot frogs |
| CN115167579A (en) * | 2022-06-24 | 2022-10-11 | 国网山东省电力公司梁山县供电公司 | Substation box monitoring system and method |
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