CN107368135B - Fish farm environment detection control method - Google Patents

Fish farm environment detection control method Download PDF

Info

Publication number
CN107368135B
CN107368135B CN201710739052.4A CN201710739052A CN107368135B CN 107368135 B CN107368135 B CN 107368135B CN 201710739052 A CN201710739052 A CN 201710739052A CN 107368135 B CN107368135 B CN 107368135B
Authority
CN
China
Prior art keywords
parameter
data
rfgps
pwtmp
prtopt
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201710739052.4A
Other languages
Chinese (zh)
Other versions
CN107368135A (en
Inventor
吴世贵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sanmingshuo Information Technology Co ltd
Original Assignee
Sanmingshuo Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sanmingshuo Information Technology Co Ltd filed Critical Sanmingshuo Information Technology Co Ltd
Priority to CN201710739052.4A priority Critical patent/CN107368135B/en
Priority to CN202010792654.8A priority patent/CN111879362A/en
Priority to CN202010792655.2A priority patent/CN111928896A/en
Publication of CN107368135A publication Critical patent/CN107368135A/en
Application granted granted Critical
Publication of CN107368135B publication Critical patent/CN107368135B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/38—Information transfer, e.g. on bus
    • G06F13/42—Bus transfer protocol, e.g. handshake; Synchronisation
    • G06F13/4282—Bus transfer protocol, e.g. handshake; Synchronisation on a serial bus, e.g. I2C bus, SPI bus
    • G06F13/4286—Bus transfer protocol, e.g. handshake; Synchronisation on a serial bus, e.g. I2C bus, SPI bus using a handshaking protocol, e.g. RS232C link
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/251—Fusion techniques of input or preprocessed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a fish farm environment detection control method, which belongs to the technical field of environment detection. The technical problems to be solved are that the existing fish farm Internet of things technology has the problems of unstable system, incomplete functions, unreliable performance, short power supply time, low system data acquisition precision, old and lagging visualization technology and the like.

Description

Fish farm environment detection control method
Technical Field
The invention relates to the technical field of fish farm environment detection, in particular to a fish farm environment detection control method.
Background
The traditional planting management mode of the fish farm is mainly based on personal experience management, when water is required to be discharged, feed is required to be fed, and how the water is supplied according to needs, fish citizens can generate different breeding results according to experience and feeling. Therefore, the information acquisition, analysis and processing of fish people are different from person to person, the waste of manpower and material resources is easily caused, the quality of the produced fish is different, and the quantity and the quality cannot be guaranteed. The internet of things collects various required information such as any object or process needing monitoring, connection and interaction in real time through various information sensing devices, and is combined with the internet to form a huge network. With the advent of the internet of things era, the management and management mode of the traditional fish farm in China can not meet the requirements of consumers gradually. The new fish farm management mode continues to be developed and innovated. The fish farm internet of things technology can acquire the information of the fish more accurately and comprehensively. Through installing equipment such as various sensors, cameras at plant etc. can more accurately and in time gather, collect and analyze various information such as animal, air temperature, humidity, soil moisture to in time inform the peasant household through intelligent platform with information, the peasant household just so can in time take corresponding action according to the information of gathering. Although the internet of things technology of the current fish farm is widely applied, the problems of unstable system, incomplete functions, unreliable performance, short power supply time, low system data acquisition precision and the like still exist.
Disclosure of Invention
The invention aims to solve the technical problems that the existing fish farm Internet of things technology has the problems of unstable system, incomplete functions, unreliable performance, short power supply time, low system data acquisition precision, old and laggard visualization technology and the like, and provides a fish farm environment detection control method.
The invention solves the problems through the following technical scheme:
a fish farm environment detection control method comprises a detection device including a GPS module, a temperature and humidity sensor, an illumination sensor, a data processing module, a video acquisition module and a data transmission module, wherein the output ends of the GPS module, the temperature and humidity sensor and the illumination sensor are all connected with the data processing module, the output ends of the data processing module and the video acquisition module are connected with an external server or a client through the data transmission module, the temperature and humidity sensor is installed in water in a fish pond, the method comprises the following steps,
step 1: the GPS module, the temperature and humidity sensor and the illumination sensor in the device are used for respectively acquiring multi-parameter characteristic parameters including a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd an illumination parameter UPRTopt；
Step 2: calculating comprehensive parameter observation information Z for collected multi-parameter characteristic parametersj(K) The calculation process is as follows:
Zj(K)＝func[Zj(K-1)，URFGPS,j(K-1)，UPWtmp,j(K+1),UPRTopt,j(K+1)T]
where K is the sampling sequence number, j is the sequence number of the parallel sequence, func is the algorithm for calculating the observation information of the integrated parameter, URFGPS,jExpressed as the j-th position parameter URFGPS，UPWtmp,jExpressed as j temperature and humidity parameter UPWtmp，UPRTopt,jExpressed as the jth illumination parameter UPRTopt；URFGPS,j(K-1) the jth position parameter U of the sampling sequence number K-1RFGPS，UPWtmp,j(K +1) th temperature and humidity parameter U of sampling sequence number K +1PWtmp，UPRTopt,j(K +1) th illumination parameter U of sampling sequence number K +1PRTopt；
And step 3: the data processing module utilizes the comprehensive parameter observation information and the multi-parameter characteristic parameters to perform system information fusion, and the specific steps are as follows:
multiple parameters of each acquired systemThe numerical characteristic parameters are constructed into N tracks, the data acquisition is synchronous, the N tracks are converted into the same coordinate system in space, and the mean value of the observation information of the comprehensive parameters is calculated
Figure GDA0002623728010000021
Wherein K is a sampling serial number, j is a serial number of the parallel sequence, and N is a flight path;
and 4, step 4: the data processing module calculates the motion distance of the sampling point parallel sequence of each characteristic parameter of the multi-parameter characteristic parameters, and the specific process is that the distance of N parallel sequences of the sampling points K is calculated, the minimum distance is reserved, and the rest is eliminated;
and 5: the data processing module converts the position parameter U obtained in the step 4 into a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd a position parameter UPRToptInputting the data into a multi-parameter fusion MIMO filtering algorithm for calculation and outputting processed data;
step 6: the data processing module transmits the processing data to the data transmission module through a 485 bus, a DSP (digital signal processor) of the data transmission module processes the processing data to obtain secondary processing data, and the secondary processing data and video image signals acquired by the video acquisition module are processed in real time by the DSP and then transmitted to a cloud server or a user side through a WiFi (wireless fidelity) module or a wired network.
In the foregoing scheme, it is preferable that the detailed process of the func algorithm in step 2 is as follows:
step 2.1: calculating location component parameters
Figure GDA0002623728010000031
In particular to
Figure GDA0002623728010000032
Step 2.2: calculating temperature and humidity component parameters
Figure GDA0002623728010000033
In particular to
Figure GDA0002623728010000034
Step 2.3: calculating an illumination component parameter
Figure GDA0002623728010000035
In particular to
Figure GDA0002623728010000036
Step 2.4: calculating the comprehensive parameter observation information Zj(K) Is concretely provided with
Figure GDA0002623728010000037
In the above scheme, it is preferable that the specific algorithm in step 4 is:
step 4.1: calculating a position parameter URFGPSA distance of DRFGPS,j＝|URFGPS,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000038
Then leave U behindRFGPS,i(K) As URFGPSK, at sampling point K, except for the number i of the parallel sequence (the corresponding data is U)RFGPS,i(K) The rest of data U)RFGPS,j(K)|j≠iAre all deleted;
step 4.2: calculate humiture parameter UPWtmpDistance D ofPWtmp,j＝|UPWtmp,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000039
Then leave U behindPWtmp,i(K) As UPWtmpAt sampling point K, except the number i of the parallel sequence (the corresponding data is U)PWtmp,i(K) The rest of data U)PWtmp,j(K)|j≠iAre all deleted;
step 4.3: calculating an illumination parameter UPRToptDistance D ofPRTopt,j＝|UPRTopt,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000041
Then leave U behindPRTopt,i(K) As UPRToptAt sampling point K, except the number i of the parallel sequence (the corresponding data is U)PRTopt,i(K) The rest of data U)PRTopt,j(K)|j≠iAre all deleted.
The invention has the advantages and effects that:
according to the invention, through the MIMO filtering algorithm, the system precision is higher, the calculation complexity is low, and the real-time performance of the system can be improved; a 485 bus-based multiprocessor hybrid processing system is constructed in the system, a distributed computing mode is adopted, the processing capacity of the system is greatly improved, a high-end multi-core processor is avoided, the cost is effectively reduced, and the heat dissipation capacity is improved; the illumination sensing data acquired by the multi-parameter fusion MIMO filtering algorithm are applied to video acquisition processing parameters of an adjustment system, the quality of a video image is improved, and meanwhile, an illumination big data interface at the position where a solar wireless camera device is located can be provided, so that a user can configure a solar photovoltaic panel, and the future development requirements of the Internet of things are met; the whole system is supported by a novel algorithm, so that the power consumption is lower, the efficiency is higher, more optimized energy utilization can be realized, and better cruising ability can be obtained.
Drawings
FIG. 1 is a flow chart of a control method of the present invention.
Detailed Description
The present invention is further illustrated by the following examples.
A fish farm environment detection control method is characterized in that as shown in figure 1, a detection device of the fish farm environment detection control method comprises a GPS module, a temperature and humidity sensor, an illumination sensor, a data processing module, a video acquisition module and a data transmission module, wherein output ends of the GPS module, the temperature and humidity sensor and the illumination sensor are all connected with the data processing module, and the temperature and humidity sensor is installed in water in a fish pond to obtain the temperature of water. And the output ends of the data processing module and the video acquisition module are connected with an external server or a client through a data transmission module. The data transmission module comprises a DSP processor, a WIFI module and a network interface module. The data processing module comprises an FPGA processor.
The control method comprises the following steps of,
step 1: the GPS module, the temperature and humidity sensor and the illumination sensor in the device are used for respectively acquiring multi-parameter characteristic parameters including a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd an illumination parameter UPRTopt. The GPS module acquires the position parameter URFGPSThe temperature and humidity sensor collects temperature and humidity parameters UPWtmpThe illumination sensor collects the illumination parameter UPRToptAnd the data is acquired according to the designed acquisition period, and the sampling serial number is K.
Step 2: calculating comprehensive parameter observation information Z for collected multi-parameter characteristic parametersj(K) The calculation process is as follows:
Zj(K)＝func[Zj(K-1)，URFGPS,j(K-1)，UPWtmp,j(K+1),UPRTopt,j(K+1)T]
where K is the sampling sequence number, j is the sequence number of the parallel sequence, func is the algorithm for calculating the observation information of the integrated parameter, URFGPS,jExpressed as the j-th position parameter URFGPS，UPWtmp,jExpressed as j temperature and humidity parameter UPWtmp，UPRTopt,jExpressed as the jth illumination parameter UPRTopt；URFGPS,j(K-1) the jth position parameter U of the sampling sequence number K-1RFGPS，UPWtmp,j(K +1) th temperature and humidity parameter U of sampling sequence number K +1PWtmp，UPRTopt,j(K +1) th illumination parameter U of sampling sequence number K +1PRTopt。
The concrete process of the func algorithm is as follows:
step 2.1: calculating location component parameters
Figure GDA0002623728010000051
In particular to
Figure GDA0002623728010000052
Step 2.2: calculating temperature and humidity component parameters
Figure GDA0002623728010000053
In particular to
Figure GDA0002623728010000054
Step 2.3: calculating an illumination component parameter
Figure GDA0002623728010000055
In particular to
Figure GDA0002623728010000056
Step 2.4: calculating the comprehensive parameter observation information Zj(K) Is concretely provided with
Figure GDA0002623728010000057
And step 3: the data processing module utilizes the comprehensive parameter observation information and the multi-parameter characteristic parameters to perform system information fusion, and the specific steps are as follows:
constructing each multi-parameter characteristic parameter acquired by the system into N tracks, synchronizing the acquired data, converting the acquired data into the same coordinate system in space, and calculating the mean value of the observation information of the comprehensive parameters
Figure GDA0002623728010000061
Where K is the sample number, j is the number of the parallel sequence, and N is the track.
And 4, step 4: the data processing module calculates the motion distance of the sampling point parallel sequence of each characteristic parameter of the multi-parameter characteristic parameters, and the specific process is that the distance of N parallel sequences of the sampling points K is calculated, the minimum distance is reserved, and the rest is eliminated.
The specific algorithm is as follows:
step 4.1: calculating a position parameter URFGPSA distance of DRFGPS,j＝|URFGPS,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000062
Then leave U behindRFGPS,i(K) As URFGPSK, at sampling point K, except for the number i of the parallel sequence (the corresponding data is U)RFGPS,i(K) The rest of data U)RFGPS,j(K)|j≠iAre all deleted;
step 4.2: calculate humiture parameter UPWtmpDistance D ofPWtmp,j＝|UPWtmp,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000063
Then leave U behindPWtmp,i(K) As UPWtmpAt sampling point K, except the number i of the parallel sequence (the corresponding data is U)PWtmp,i(K) The rest of data U)PWtmp,j(K)|j≠iAre all deleted;
step 4.3: calculating an illumination parameter UPRToptDistance D ofPRTopt,j＝|UPRTopt,j(K) l-Z (K), minimum distance
Figure GDA0002623728010000064
Then leave U behindPRTopt,i(K) As UPRToptAt sampling point K, except the number i of the parallel sequence (the corresponding data is U)PRTopt,i(K) The rest of data U)PRTopt,j(K)|j≠iAre all deleted.
And 5: the data processing module converts the position parameter U obtained in the step 4 into a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd a position parameter UPRToptAnd inputting the data into a multi-parameter fusion MIMO filtering algorithm for calculation and outputting processed data.
Step 6: the data processing module transmits the processing data to the data transmission module through a 485 bus, a DSP (digital signal processor) of the data transmission module processes the processing data to obtain secondary processing data, and the secondary processing data and video image signals acquired by the video acquisition module are processed in real time by the DSP and then transmitted to a cloud server or a user side through a WiFi (wireless fidelity) module or a wired network.
While the preferred embodiments of the present invention have been described in detail, it is to be understood that the invention is not limited thereto, and that various equivalent modifications and substitutions may be made by those skilled in the art without departing from the spirit of the present invention and are intended to be included within the scope of the present application.

Claims (3)

1. A fish farm environment detection control method is characterized by comprising the following steps: the detection device comprises a GPS module, a temperature and humidity sensor, an illumination sensor, a data processing module, a video acquisition module and a data transmission module, wherein the output ends of the GPS module, the temperature and humidity sensor and the illumination sensor are all connected with the data processing module, the output ends of the data processing module and the video acquisition module are connected with an external server or a client through the data transmission module, the temperature and humidity sensor is arranged in water in the fish pond, and the method comprises the following steps,
step 1: the GPS module, the temperature and humidity sensor and the illumination sensor in the device are used for respectively acquiring multi-parameter characteristic parameters including a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd an illumination parameter UPRTopt；
Step 2: calculating comprehensive parameter observation information Z for collected multi-parameter characteristic parametersj(K) The calculation process is as follows:
Zj(K)＝func[Zj(K-1)，URFGPS,j(K-1)，UPWtmp,j(K+1),UPRTopt,j(K+1)T]
where K is the sampling sequence number, j is the sequence number of the parallel sequence, func is the algorithm for calculating the observation information of the integrated parameter, URFGPS,jExpressed as the j-th position parameter URFGPS，UPWtmp,jIs denoted as the j-thTemperature and humidity parameter UPWtmp，UPRTopt,jExpressed as the jth illumination parameter UPRTopt；URFGPS,j(K-1) the jth position parameter U of the sampling sequence number K-1RFGPS，UPWtmp,j(K +1) th temperature and humidity parameter U of sampling sequence number K +1PWtmp，UPRTopt,j(K +1) th illumination parameter U of sampling sequence number K +1PRTopt；
And step 3: the data processing module utilizes the comprehensive parameter observation information and the multi-parameter characteristic parameters to perform system information fusion, and the specific steps are as follows:
constructing each multi-parameter characteristic parameter acquired by the system into N tracks, synchronizing the acquired data, converting the acquired data into the same coordinate system in space, and calculating the mean value of the observation information of the comprehensive parameters
Figure FDA0002623727000000011
Wherein K is a sampling serial number, j is a serial number of the parallel sequence, and N is a flight path;
and 4, step 4: the data processing module calculates the motion distance of the sampling point parallel sequence of each characteristic parameter of the multi-parameter characteristic parameters, and the specific process is that the distance of N parallel sequences of the sampling points K is calculated, the minimum distance is reserved, and the rest is eliminated;
and 5: the data processing module converts the position parameter U obtained in the step 4 into a position parameter URFGPSTemperature and humidity parameter UPWtmpAnd a position parameter UPRToptInputting the data into a multi-parameter fusion MIMO filtering algorithm for calculation and outputting processed data;
step 6: the data processing module transmits the processing data to the data transmission module through a 485 bus, a DSP (digital signal processor) of the data transmission module processes the processing data to obtain secondary processing data, and the secondary processing data and video image signals acquired by the video acquisition module are processed in real time by the DSP and then transmitted to a cloud server or a user side through a WiFi (wireless fidelity) module or a wired network.
2. The fish farm environment detection control method according to claim 1, characterized in that: the specific process of the func algorithm in the step 2 is as follows:
step 2.1: calculating location component parameters
Figure FDA0002623727000000021
In particular to
Figure FDA0002623727000000022
Step 2.2: calculating temperature and humidity component parameters
Figure FDA0002623727000000023
In particular to
Figure FDA0002623727000000024
Step 2.3: calculating an illumination component parameter
Figure FDA0002623727000000025
In particular to
Figure FDA0002623727000000026
Step 2.4: calculating the comprehensive parameter observation information Zj(K) Is concretely provided with
Figure FDA0002623727000000027
3. The fish farm environment detection control method according to claim 1, characterized in that: the specific algorithm in the step 4 is as follows:
step 4.1: calculating a position parameter URFGPSA distance of DRFGPS,j＝|URFGPS,j(K) l-Z (K), minimum distance
Figure FDA0002623727000000028
Then leave U behindRFGPS,i(K) As URFGPSThe effective value of the sampling value of the K point, at the sampling point K, except the serial number i of the parallel sequence, the corresponding data is URFGPS,i(K) The rest of data URFGPS,j(K)|j≠iAre all deleted;
step 4.2: calculate humiture parameter UPWtmpDistance D ofPWtmp,j＝|UPWtmp,j(K) l-Z (K), minimum distance
Figure FDA0002623727000000031
Then leave U behindPWtmp,i(K) As UPWtmpThe effective value of the sampling value of the K point is that at the sampling point K, except the serial number i of the parallel sequence, the corresponding data is UPWtmp,i(K) The rest of data UPWtmp,j(K)|j≠iAre all deleted;
step 4.3: calculating an illumination parameter UPRToptDistance D ofPRTopt,j＝|UPRTopt,j(K) l-Z (K), minimum distance
Figure FDA0002623727000000032
Then leave U behindPRTopt,i(K) As UPRToptThe effective value of the sampling value of the K point is that at the sampling point K, except the serial number i of the parallel sequence, the corresponding data is UPRTopt,i(K) The rest of data UPRTopt,j(K)|j≠iAre all deleted.
CN201710739052.4A 2017-08-25 2017-08-25 Fish farm environment detection control method Active CN107368135B (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201710739052.4A CN107368135B (en) 2017-08-25 2017-08-25 Fish farm environment detection control method
CN202010792654.8A CN111879362A (en) 2017-08-25 2017-08-25 Environment detection method
CN202010792655.2A CN111928896A (en) 2017-08-25 2017-08-25 Fish farm environment detection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710739052.4A CN107368135B (en) 2017-08-25 2017-08-25 Fish farm environment detection control method

Related Child Applications (2)

Application Number Title Priority Date Filing Date
CN202010792654.8A Division CN111879362A (en) 2017-08-25 2017-08-25 Environment detection method
CN202010792655.2A Division CN111928896A (en) 2017-08-25 2017-08-25 Fish farm environment detection method

Publications (2)

Publication Number Publication Date
CN107368135A CN107368135A (en) 2017-11-21
CN107368135B true CN107368135B (en) 2020-12-22

Family

ID=60310944

Family Applications (3)

Application Number Title Priority Date Filing Date
CN202010792654.8A Pending CN111879362A (en) 2017-08-25 2017-08-25 Environment detection method
CN202010792655.2A Pending CN111928896A (en) 2017-08-25 2017-08-25 Fish farm environment detection method
CN201710739052.4A Active CN107368135B (en) 2017-08-25 2017-08-25 Fish farm environment detection control method

Family Applications Before (2)

Application Number Title Priority Date Filing Date
CN202010792654.8A Pending CN111879362A (en) 2017-08-25 2017-08-25 Environment detection method
CN202010792655.2A Pending CN111928896A (en) 2017-08-25 2017-08-25 Fish farm environment detection method

Country Status (1)

Country Link
CN (3) CN111879362A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101846656A (en) * 2010-03-31 2010-09-29 天津科技大学 Crop disease on-condition control simulation model system based on crop acoustic emission mechanism
CN101965807A (en) * 2010-08-25 2011-02-09 河北农业大学 System for individually recording production performance of layers and automatically monitoring environment of layer house
CN102271422A (en) * 2011-04-11 2011-12-07 江苏大学 WSN-based photovoltaic greenhouse monitoring system and construction method thereof
CN102445933A (en) * 2011-10-14 2012-05-09 兰泽华 System for monitoring, alarming and managing farmland greenhouses based on Internet of things
US20140265927A1 (en) * 2013-03-15 2014-09-18 Enlighted, Inc. Configuration free and device behavior unaware wireless switch

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101846656A (en) * 2010-03-31 2010-09-29 天津科技大学 Crop disease on-condition control simulation model system based on crop acoustic emission mechanism
CN101965807A (en) * 2010-08-25 2011-02-09 河北农业大学 System for individually recording production performance of layers and automatically monitoring environment of layer house
CN102271422A (en) * 2011-04-11 2011-12-07 江苏大学 WSN-based photovoltaic greenhouse monitoring system and construction method thereof
CN102445933A (en) * 2011-10-14 2012-05-09 兰泽华 System for monitoring, alarming and managing farmland greenhouses based on Internet of things
US20140265927A1 (en) * 2013-03-15 2014-09-18 Enlighted, Inc. Configuration free and device behavior unaware wireless switch

Also Published As

Publication number Publication date
CN111879362A (en) 2020-11-03
CN107368135A (en) 2017-11-21
CN111928896A (en) 2020-11-13

Similar Documents

Publication Publication Date Title
CN106525132B (en) Water resource gridding supervisory systems and its implementation method based on NB-IoT
CN101709954B (en) Overhead wire icing on-line monitoring device based on machine vision
CN111600297B (en) Online analysis method and device for operation data of power grid equipment under cloud-edge cooperation
CN104089590A (en) Automatic measuring device for acquiring vegetation canopy structure parameters
CN113589096A (en) Edge calculation system and method for multi-state-quantity configurable power transformation equipment
Xian Internet of things online monitoring system based on cloud computing.
CN107368135B (en) Fish farm environment detection control method
CN107396060B (en) Agricultural Internet of things solar wireless camera device and control method
Proietti et al. Edge Intelligence with Deep Learning in Greenhouse Management.
Mushtaq SMART AGRICULTURE SYSTEM+ AND IMAGE PROCESSING.
CN105373088A (en) Grassland resource intelligence monitoring system
CN116189076A (en) Observation and identification system and method for bird observation station
CN203340191U (en) Multi-source image information acquisition and processing system
Balaji et al. IoT Enabled Smart Irrigation System
CN205610824U (en) First image acquisition of city surveillance camera and environmental parameter mark system
CN202075598U (en) Agroforestry zoology remote real-time monitoring system
Wu et al. An RFID-assisted smart livestock and poultry farming system on the cloud
CN206323472U (en) A kind of data acquisition network video camera
CN210270550U (en) Unmanned aerial vehicle sensing system for monitoring river channel
CN204730880U (en) Based on Zigbee intelligent network numeric type environmental detection set
CN108876787A (en) A kind of sick monitoring master control borad of crops based on deep learning
CN107621281A (en) A kind of paddy environment detects control method
Lu Remote Monitoring System of Digital Agricultural Greenhouse Based on Internet of Things
CN104122175A (en) Imaging method for visualized photoelectric tracking of flocs
CN104319883B (en) Dynamic panoramic monitoring system for power grid net rack

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20201202

Address after: No.7, Huanan Road, Shuinan Town, Jiangle County, Sanming City, Fujian Province

Applicant after: Sanmingshuo Information Technology Co.,Ltd.

Address before: 530003 the Guangxi Zhuang Autonomous Region Nanning XiXiangTang District Road No. 188 Guangxi University For Nationalities University

Applicant before: Wu Shigui

GR01 Patent grant
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20171121

Assignee: Datian County Dingnong Agriculture Co.,Ltd.

Assignor: Sanmingshuo Information Technology Co.,Ltd.

Contract record no.: X2023350000117

Denomination of invention: A Method for Environmental Detection and Control in Fish Farms

Granted publication date: 20201222

License type: Common License

Record date: 20230323