CN112866644A - Fishery aquaculture online intelligent monitoring platform based on big data and Internet of things synergistic effect and cloud server - Google Patents
Fishery aquaculture online intelligent monitoring platform based on big data and Internet of things synergistic effect and cloud server Download PDFInfo
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Abstract
The invention discloses a fishery aquaculture online intelligent monitoring platform and a cloud server based on big data and internet of things synergistic effect, and the fishery aquaculture online intelligent monitoring platform and the cloud server comprise an aquaculture area dividing module, an image acquisition module, a feature extraction module, a database, an analysis cloud platform and a display terminal, wherein floaters are monitored on the lake surface of an aquaculture area through the aquaculture area dividing module, the pattern acquisition module and the feature extraction module, meanwhile, the volume of aquaculture fishes in an aquaculture water area is monitored, the floaters type contrast coincidence coefficient and the proportion of aquaculture fishes of each grade are counted by combining the analysis cloud platform, managers can conveniently and visually know the influence effect of the aquaculture water area on the aquaculture fish culture, the monitoring and analysis efficiency of the environment of the aquaculture water area where the aquaculture fishes are located is improved, and the fishery aquaculture online intelligent monitoring platform and the cloud server have the characteristics of high reliability and.
Description
Technical Field
The invention belongs to the technical field of fishery breeding monitoring, and particularly relates to a fishery breeding online intelligent monitoring platform and a cloud server based on the synergistic effect of big data and the Internet of things.
Background
With the development of economy and fishery, the traditional breeding mode plays an important role in the rapid increase of the yield of aquatic products in China, but with the enhancement of the consumption level and environmental awareness of people, the eating habits and structures of the masses have changed greatly, and green aquatic products are more and more favored by consumers. The traditional culture mode has various defects in production practice, and the produced aquatic products are difficult to meet the market demand.
The environmental conditions of the existing aquaculture water areas are continuously worsened, as domestic garbage, industrial garbage and waste water enter a water body, algae such as blue-green algae, green algae and diatom are greatly propagated, a layer of blue-green floating foam with fishy smell is formed on the water surface, the floating foam is called as water bloom, the water bloom can cause water quality deterioration, and oxygen in water can be exhausted to cause fish death in severe cases. Therefore, the monitoring of the aquaculture water area needs to be enhanced, but the current aquaculture mode cannot monitor the fishery aquaculture environment in real time, the aquaculture personnel need to adopt a visual inspection mode, the problems of low monitoring level and unscientific management exist, the aquaculture fish needs to be salvaged to know the aquaculture condition, and the development condition of the aquaculture fish cannot be clearly mastered at any time.
Disclosure of Invention
Aiming at the problems, the invention provides a fishery aquaculture online intelligent monitoring platform and a cloud server based on the synergistic effect of big data and the Internet of things, floaters on the lake surface of an aquaculture area are monitored through an aquaculture area dividing module, a pattern collecting module and a feature extracting module, the volume of aquaculture fish in an aquaculture water area is monitored, the floaters type contrast coincidence coefficient and the proportion of aquaculture fish of each grade are counted by combining an analysis cloud platform, and the problems in the prior art are solved.
The purpose of the invention can be realized by the following technical scheme:
the fishery breeding online intelligent monitoring platform based on the big data and the internet of things synergistic effect comprises a breeding area dividing module, an image acquisition module, a feature extraction module, a database, an analysis cloud platform and a display terminal;
the image acquisition module is respectively connected with the culture region division module, the feature extraction module and the analysis cloud platform, the database is respectively connected with the feature extraction module and the analysis cloud platform, and the display terminal is connected with the analysis cloud platform;
the culture area dividing module is used for dividing a culture area, dividing the culture area into a plurality of detection subareas which are identical in area and are connected with each other according to the lake water from a water inlet to a water outlet, numbering the divided detection subareas according to the distance from each detection subarea to the water inlet in sequence from near to far, and marking the detection subareas as 1,2,. once, i,. once, g;
the image acquisition module comprises a plurality of first image acquisition units and a plurality of second image acquisition units, the first image acquisition units are a plurality of high-definition cameras and are respectively installed on the lake surfaces of the detection sub-regions and used for acquiring image information of floats on the lake surfaces of the detection sub-regions and sending the acquired image information of the floats on the lake surfaces of the detection sub-regions to the feature extraction module, the second image acquisition units are a plurality of high-definition cameras and are respectively installed in the lake water of the detection sub-regions and used for acquiring the image information of the cultured fishes in the lake water of the detection sub-regions and sending the acquired image information of the cultured fishes in the lake water of the detection sub-regions to the analysis cloud platform.
The characteristic extraction module receives the image information of the floaters on the lake surface of each detection subarea sent by the image acquisition module, amplifies the received image information, extracts the characteristics corresponding to different floaters stored in the database, compares the characteristics of the floaters in the image information of each detection subarea with the characteristics corresponding to different floaters stored in the database to obtain the characteristics of the floaters in the image information of the detection subarea, further obtains the characteristics of the floaters in the image information of each detection subarea, and forms a floaters type characteristic set Ai(ai1,ai2,...,aij,...,aik)，aij represents the jth floater type characteristic in the ith detection subarea image information, and sends the formed floater type characteristic set to an analysis cloud platform;
the database is used for storing characteristics corresponding to different floater types, storing standard volumes corresponding to cultured fish in different culture stages, storing volume contrast value ranges corresponding to cultured fish of different grades, and storing the total number of cultured fish in a culture area;
the analysis cloud platform receives the floater kind characteristic set sent by the characteristic extraction module, extracts each floater kind characteristic in each detection subarea image information in the floater kind characteristic set, compares each floater kind characteristic in each detection subarea image information in the floater kind characteristic set with different floater kind characteristics stored in the database to form a floater kind characteristic comparison set Ai′(ai′1,ai′2,...,ai′j,...,ai′k)，ai'j' is a contrast value of the jth floating object type characteristic in the ith detection subarea image information and the characteristic corresponding to different floating object types stored in the database, and if the characteristics corresponding to the different floating object types stored in the database have the jth floating object type characteristic in the ith detection subarea image information, ai' j is equal to a fixed value R, R>0, if the characteristics corresponding to different floater types stored in the database do not have the jth floater type characteristic in the ith detection subarea image information, aiWhen j is equal to 0, the analysis cloud platform counts the floater type comparison coincidence coefficient according to the floater type characteristic comparison set, and respectively sends the number of the detection subarea containing the floater type characteristics and the floater type comparison coincidence coefficient to the display terminal;
the analysis cloud platform receives the image information of each cultured fish in each detection sub-region lake water sent by the image acquisition module, extracts the volume of each cultured fish from the image information of each cultured fish in each detection sub-region lake water, inputs the volume of each cultured fish in the lake water into the culture stage where the cultured fish is located, compares the volume of each cultured fish in each detection sub-region lake water with the standard volume corresponding to the culture stage where the cultured fish is located and stored in the database, and forms a cultured fish volume comparison set Di(di1,di2,...,dip,...,diq)，dip is expressed as a contrast value of the volume of the p-th farmed fish in the ith detection sub-area and the standard volume corresponding to the breeding stage of the farmed fish, and the obtained volume isComparing the volume contrast value of each cultured fish in the lake water of each detection subarea with the volume contrast value range corresponding to the cultured fish of different grades, if the volume contrast value of the cultured fish is in the volume contrast range corresponding to the cultured fish of excellent grade, the fish is excellent-grade fish, if the volume contrast value of the fish is in the volume contrast range corresponding to the good-grade fish, the strip of cultured fish is a good-grade fish, if the volume contrast value of the strip of cultured fish is in the volume contrast range corresponding to the medium-grade cultured fish, the fish is a medium-grade fish, if the volume contrast value of the fish is in the volume contrast range corresponding to the unqualified-grade fish, and the cultured fish is unqualified, and the times of the cultured fish of each grade appearing in lake water of each detection subarea are accumulated and counted to form a cultured fish grade frequency set lambda.w(λw1,λw2,...,λwi,...,λwg)，λwThe method comprises the following steps that i represents the number of times of appearance of the w-th level of cultured fish in the ith detection sub-area, w represents the cultured fish level, w represents the excellent cultured fish level, the good cultured fish level, the medium cultured fish level and the unqualified cultured fish level respectively, p2, p3, p4, p1, p2, p3 and p4 respectively represent the excellent cultured fish level, the good cultured fish level, the medium cultured fish level and the unqualified cultured fish level, an analysis cloud platform counts the proportion of all cultured fish in each level of cultured fish in the culture area according to a cultured fish level frequency set, and the proportion of all cultured fish in each level of cultured fish in the culture area is sent to a display terminal;
the display terminal is used for receiving and displaying the numbers of the detection subareas containing the floating object type characteristics, the matching coefficients of the floating object type comparison and all the cultured fish proportion of each level in the culture area, which are sent by the analysis cloud platform.
Furthermore, the number of the first acquisition units and the number of the second acquisition units are respectively consistent with the number of the detection sub-regions.
Further, the floating material types include domestic garbage, industrial garbage, plant debris and algae.
Further, said aij is more than or equal to 0 in j, and if j is 0, the image information of the ith detection subarea does not existFloat species characteristics.
Further, the calculation formula of the floater kind contrast coincidence coefficient is as follows
αiThe flotage species contrast coincidence coefficient, a, expressed as the ith detector regioni' j is expressed as a contrast value of the j-th floating object type characteristic in the ith detection subarea image information and the characteristic corresponding to the different floating object types stored in the database.
Furthermore, the proportion calculation formula of the cultured fish of each grade in all the cultured fish in the culture area is as follows
λwi is expressed as the number of times of appearance of the w-th level of cultured fish in the ith detection sub-area, muGeneral assemblyExpressed as the total number of all farmed fish in the farming area.
A cloud server comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one fishery cultivation online intelligent monitoring device, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the fishery cultivation online intelligent monitoring platform based on big data and internet of things synergy.
Has the advantages that:
(1) according to the invention, the culture area division module, the pattern acquisition module and the feature extraction module are used for monitoring floaters on the lake surface of the culture area, the volume of cultured fish in the culture water area is monitored, the comparison and coincidence coefficient of floaters and the proportion of cultured fish of each grade are counted by combining the analysis cloud platform, so that managers can conveniently and visually know the influence effect of the culture water area on the culture of the cultured fish, the monitoring and analysis efficiency of the environment of the culture water area where the cultured fish is located is improved, the characteristics of high reliability and high accuracy are provided, and a beneficial growth environment is provided for the cultured fish.
(2) According to the invention, in the image acquisition module, by monitoring the floaters on the lake surface of the detection sub-area and the volume of the cultured fish in the lake water, reliable early-stage data preparation is provided for later-stage statistics of floaters type comparison coincidence coefficients and cultured fish proportions of all levels, and the method has the characteristics of high authenticity and high reliability.
(3) According to the invention, at the display terminal, through displaying the number of the detection subarea containing the floater species, the comparison coincidence coefficient of the floater species and the proportion of the cultured fishes of all grades, managers can directly clean the floater conveniently, the working efficiency is improved, the growth condition of the fishes can be clearly and visually known, so that different measures can be taken to culture the fishes, and the aquatic products meeting the market demands can be provided.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the fishery aquaculture online intelligent monitoring platform based on big data and internet of things synergistic effect comprises an aquaculture area dividing module, an image acquisition module, a feature extraction module, a database, an analysis cloud platform and a display terminal;
the image acquisition module is respectively connected with the culture region division module, the feature extraction module and the analysis cloud platform, the database is respectively connected with the feature extraction module and the analysis cloud platform, and the display terminal is connected with the analysis cloud platform;
the culture area dividing module is used for dividing a culture area, dividing the culture area into a plurality of detection subareas which are identical in area and are connected with each other according to the lake water from a water inlet to a water outlet, numbering the divided detection subareas according to the distance from each detection subarea to the water inlet in sequence from near to far, and marking the detection subareas as 1,2,. once, i,. once, g;
the image acquisition module comprises a plurality of first image acquisition units and a plurality of second image acquisition units, the number of the first image acquisition units and the number of the second image acquisition units are respectively consistent with the number of the detection subareas, the first image acquisition units are a plurality of high-definition cameras and are respectively installed on the lake surfaces of the detection subareas and are used for acquiring the image information of the floating objects on the lake surfaces of the detection subareas and sending the acquired image information of the floating objects on the lake surfaces of the detection subareas to the feature extraction module, and the second image acquisition units are a plurality of high-definition cameras and are respectively installed in the lake water of the detection subareas and are used for acquiring the image information of each cultured fish in the lake water of the detection subareas and sending the acquired image information of each cultured fish in the lake water of the detection subareas to the analysis cloud platform.
The embodiment monitors the floaters on the lake surface of the detection sub-area and the volume of the cultured fish in the lake water, provides reliable early-stage data preparation for later-stage statistics of floaters type contrast coincidence coefficients and fish culture proportions of all levels, and has the characteristics of high authenticity and high reliability.
The characteristic extraction module receives the image information of the floaters on the lake surface of each detection subarea sent by the image acquisition module, amplifies the received image information, extracts the characteristics corresponding to different floaters stored in the database, compares the characteristics of the floaters in the image information of each detection subarea with the characteristics corresponding to different floaters stored in the database to obtain the characteristics of the floaters in the image information of the detection subarea, and further obtains the characteristics of each detection subareaMeasuring the characteristics of the floating object types in the region image information to form a floating object type characteristic set Ai(ai1,ai2,...,aij,...,aik)，aij is expressed as the jth floating object kind characteristic in the ith detection subarea image information, aij is not less than 0 in j, if j is 0, it is indicated that no floater type feature exists in the image information of the ith detection subarea, and the formed floater type feature set is sent to the analysis cloud platform;
the database is used for storing characteristics corresponding to different floater types, the floater types comprise domestic garbage, industrial garbage, plant debris and algae, standard volumes corresponding to cultured fishes in different culture stages are stored, volume contrast value ranges corresponding to cultured fishes of different grades are stored, and the total number of the cultured fishes in a culture area is stored;
the analysis cloud platform receives the floater kind characteristic set sent by the characteristic extraction module, extracts each floater kind characteristic in each detection subarea image information in the floater kind characteristic set, compares each floater kind characteristic in each detection subarea image information in the floater kind characteristic set with different floater kind characteristics stored in the database to form a floater kind characteristic comparison set Ai′(ai′1,ai′2,...,ai′j,...,ai′k)，ai'j' is a contrast value of the jth floating object type characteristic in the ith detection subarea image information and the characteristic corresponding to different floating object types stored in the database, and if the characteristics corresponding to the different floating object types stored in the database have the jth floating object type characteristic in the ith detection subarea image information, ai' j is equal to a fixed value R, R>0, if the characteristics corresponding to different floater types stored in the database do not have the jth floater type characteristic in the ith detection subarea image information, aiJ is equal to 0, the analysis cloud platform counts the flotage type comparison coincidence coefficient according to the flotage type characteristic comparison set, and the calculation formula of the flotage type comparison coincidence coefficient is
αiThe flotage species contrast coincidence coefficient, a, expressed as the ith detector regioni' j is expressed as a contrast value of the jth floating object type characteristic in the ith detection subarea image information and the characteristics corresponding to different floating object types stored in the database, and the numbers of the detection subareas containing the floating object type characteristics and the contrast matching coefficients of the floating object types are respectively sent to the display terminal;
the analysis cloud platform receives the image information of each cultured fish in each detection sub-region lake water sent by the image acquisition module, extracts the volume of each cultured fish from the image information of each cultured fish in each detection sub-region lake water, inputs the volume of each cultured fish in the lake water into the culture stage where the cultured fish is located, compares the volume of each cultured fish in each detection sub-region lake water with the standard volume corresponding to the culture stage where the cultured fish is located and stored in the database, and forms a cultured fish volume comparison set Di(di1,di2,...,dip,...,diq)，dip is expressed as a contrast value of the volume of the p-th farmed fish in the ith detection subarea and a standard volume corresponding to the farming stage of the farmed fish, the obtained volume contrast value of each farmed fish in lake water of each detection subarea is compared with the volume contrast value range corresponding to the farmed fish of different grades, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the good-grade farmed fish, the farmed fish is the good-grade fish, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the good-grade farmed fish, the farmed fish is the medium-grade fish, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the medium-grade farmed fish, the farmed fish is the unqualified-grade fish, and accumulatively counting the occurrence frequency of the cultured fish of each grade in lake water of each detection subarea to form a cultured fish grade frequency set lambdaw(λw1,λw2,...,λwi,...,λwg)，λwi is expressed as the ith testThe method comprises the steps of measuring the number of times of appearance of the w-th level of cultured fish in a sub-area, wherein w is the cultured fish level, w is p1, p2, p3, p4, p1, p2, p3 and p4 are respectively expressed as an excellent cultured fish level, a good cultured fish level, a medium cultured fish level and a unqualified cultured fish level, analyzing the proportion of the cultured fish in each level in all cultured fish in the culture area according to a cultured fish level frequency set by a cloud platform, and calculating the proportion of the cultured fish in each level in all the cultured fish in the culture area by a formula
λwi is expressed as the number of times of appearance of the w-th level of cultured fish in the ith detection sub-area, muGeneral assemblyThe total number of all cultured fish in the culture area is represented, and the proportion of all cultured fish in the culture area in each grade is sent to a display terminal;
the display terminal is used for receiving and analyzing the number of the detection subarea containing the floater type characteristics, the matching coefficient of the floater type contrast and the proportion of all the cultured fishes in the culture area in each grade sent by the cloud platform, displaying the number of the detection subarea containing the floater type, the matching coefficient of the floater type contrast and the proportion of the cultured fishes in each grade, and facilitating managers to directly clear the floater, thereby improving the working efficiency, clearly and visually knowing the growth condition of the fishes, and providing aquatic products meeting the market demands by adopting different measures to culture the fishes.
A cloud server comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one fishery cultivation online intelligent monitoring device, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the fishery cultivation online intelligent monitoring platform based on big data and internet of things synergy.
According to the invention, the culture area division module, the pattern acquisition module and the feature extraction module are used for monitoring floaters on the lake surface of the culture area, the volume of cultured fish in the culture water area is monitored, the comparison and coincidence coefficient of floaters and the proportion of cultured fish of each grade are counted by combining the analysis cloud platform, so that managers can conveniently and visually know the influence effect of the culture water area on the culture of the cultured fish, the monitoring and analysis efficiency of the environment of the culture water area where the cultured fish is located is improved, the characteristics of high reliability and high accuracy are provided, and a beneficial growth environment is provided for the cultured fish.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. Fishery aquaculture online intelligent monitoring platform based on big data and thing networking synergism, its characterized in that: the system comprises a culture area dividing module, an image acquisition module, a feature extraction module, a database, an analysis cloud platform and a display terminal;
the image acquisition module is respectively connected with the culture region division module, the feature extraction module and the analysis cloud platform, the database is respectively connected with the feature extraction module and the analysis cloud platform, and the display terminal is connected with the analysis cloud platform;
the culture area dividing module is used for dividing a culture area, dividing the culture area into a plurality of detection subareas which are identical in area and are connected with each other according to the lake water from a water inlet to a water outlet, numbering the divided detection subareas according to the distance from each detection subarea to the water inlet in sequence from near to far, and marking the detection subareas as 1,2,. once, i,. once, g;
the image acquisition module comprises a plurality of first image acquisition units and a plurality of second image acquisition units, the first image acquisition units are a plurality of high-definition cameras and are respectively installed on the lake surfaces of the detection subareas and used for acquiring the image information of the floating objects on the lake surfaces of the detection subareas and sending the acquired image information of the floating objects on the lake surfaces of the detection subareas to the characteristic extraction module, the second image acquisition units are a plurality of high-definition cameras and are respectively installed in the lake water of the detection subareas and used for acquiring the image information of each cultured fish in the lake water of the detection subareas and sending the acquired image information of each cultured fish in the lake water of the detection subareas to the analysis cloud platform;
the characteristic extraction module receives the image information of the floaters on the lake surface of each detection subarea sent by the image acquisition module, amplifies the received image information, extracts the characteristics corresponding to different floaters stored in the database, compares the characteristics of the floaters in the image information of each detection subarea with the characteristics corresponding to different floaters stored in the database to obtain the characteristics of the floaters in the image information of the detection subarea, further obtains the characteristics of the floaters in the image information of each detection subarea, and forms a floaters type characteristic set Ai(ai1,ai2,...,aij,...,aik)，aij represents the jth floater type characteristic in the ith detection subarea image information, and sends the formed floater type characteristic set to an analysis cloud platform;
the database is used for storing characteristics corresponding to different floater types, storing standard volumes corresponding to cultured fish in different culture stages, storing volume contrast value ranges corresponding to cultured fish of different grades, and storing the total number of cultured fish in a culture area;
the analysis cloud platform receives the floating object type feature set sent by the feature extraction module, extracts each floating object type feature in each detection subarea image information in the floating object type feature set, compares each floating object type feature in each detection subarea image information in the floating object type feature set with different floating object type features stored in the database to form a floating object type feature comparison set A'i(a′i1,a′i2,...,a′ij,...,a′ik)，a′ij is expressed as the jth float in the ith detector area image informationA 'if the characteristics corresponding to the ith floater type characteristic in the image information of the detection subarea exist in the characteristics corresponding to the different floater types stored in the database, the species characteristic and the characteristics corresponding to the different floater types stored in the database have a contrast value'ij is equal to a fixed value R, R>0, if the jth floating object type feature in the ith detection subarea image information does not exist in the features corresponding to the different floating object types stored in the database, a'ij is equal to 0, the analysis cloud platform counts the floater type comparison coincidence coefficient according to the floater type characteristic comparison set, and respectively sends the number of the detection subarea containing the floater type characteristics and the floater type comparison coincidence coefficient to the display terminal;
the analysis cloud platform receives the image information of each cultured fish in each detection sub-region lake water sent by the image acquisition module, extracts the volume of each cultured fish from the image information of each cultured fish in each detection sub-region lake water, inputs the volume of each cultured fish in the lake water into the culture stage where the cultured fish is located, compares the volume of each cultured fish in each detection sub-region lake water with the standard volume corresponding to the culture stage where the cultured fish is located and stored in the database, and forms a cultured fish volume comparison set Di(di1,di2,...,dip,...,diq)，dip is expressed as a contrast value of the volume of the p-th farmed fish in the ith detection subarea and a standard volume corresponding to the farming stage of the farmed fish, the obtained volume contrast value of each farmed fish in lake water of each detection subarea is compared with the volume contrast value range corresponding to the farmed fish of different grades, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the good-grade farmed fish, the farmed fish is the good-grade fish, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the good-grade farmed fish, the farmed fish is the medium-grade fish, if the volume contrast value of each farmed fish is in the volume contrast range corresponding to the medium-grade farmed fish, the farmed fish is the unqualified-grade fish, and areAccumulating and counting the occurrence frequency of the cultured fish of each grade in lake water of each detection subarea to form a cultured fish grade frequency set lambdaw(λw1,λw2,...,λwi,...,λwg)，λwThe method comprises the following steps that i represents the number of times of appearance of the w-th level of cultured fish in the ith detection sub-area, w represents the cultured fish level, w represents the excellent cultured fish level, the good cultured fish level, the medium cultured fish level and the unqualified cultured fish level respectively, p2, p3, p4, p1, p2, p3 and p4 respectively represent the excellent cultured fish level, the good cultured fish level, the medium cultured fish level and the unqualified cultured fish level, an analysis cloud platform counts the proportion of all cultured fish in each level of cultured fish in the culture area according to a cultured fish level frequency set, and the proportion of all cultured fish in each level of cultured fish in the culture area is sent to a display terminal;
the display terminal is used for receiving and displaying the numbers of the detection subareas containing the floating object type characteristics, the matching coefficients of the floating object type comparison and all the cultured fish proportion of each level in the culture area, which are sent by the analysis cloud platform.
2. The fishery aquaculture online intelligent monitoring platform based on big data and internet of things synergy according to claim 1, characterized in that: the number of the first acquisition units and the number of the second acquisition units are respectively consistent with the number of the detection subareas.
3. The fishery aquaculture online intelligent monitoring platform based on big data and internet of things synergy according to claim 1, characterized in that: the floating material types comprise domestic garbage, industrial garbage, plant remains and algae.
4. The fishery aquaculture online intelligent monitoring platform based on big data and internet of things synergy according to claim 1, characterized in that: a is aij is more than or equal to 0 in j, and if j is 0, the image information of the ith detection subarea has no floating object type characteristic.
5. Big data and internet of things based synergy according to claim 1The fishery culture on-line intelligent monitoring platform is characterized in that: the calculation formula of the floater type contrast coincidence coefficient is as follows
αiThe contrast coincidence coefficient of the flotage species expressed as the ith detection subarea, a'ij is expressed as a contrast value of the jth floating object type characteristic in the ith detection subarea image information and the characteristic corresponding to different floating object types stored in the database.
6. The fishery aquaculture online intelligent monitoring platform based on big data and internet of things synergy according to claim 1, characterized in that: the proportion of the cultured fish in each grade to all the cultured fish in the culture area is calculated by the formula
λwi is expressed as the number of times of appearance of the w-th level of cultured fish in the ith detection sub-area, muGeneral assemblyExpressed as the total number of all farmed fish in the farming area.
7. A cloud server, characterized by: the server comprises a processor, a machine-readable storage medium and a network interface, wherein the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one fishery cultivation online intelligent monitoring device, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the fishery cultivation online intelligent monitoring platform based on the synergy of big data and the internet of things according to any one of claims 1 to 6.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114997536A (en) * | 2022-08-01 | 2022-09-02 | 中国水产科学研究院南海水产研究所 | Culture tail water pollution detection method, system and medium |
| CN115067243A (en) * | 2022-07-07 | 2022-09-20 | 武汉很多鱼钓具有限公司 | Fishery monitoring and analyzing method and system based on Internet of things technology and storage medium |
| CN115396754A (en) * | 2022-10-27 | 2022-11-25 | 江西省水生生物保护救助中心 | Fishery water quality remote Internet of things environment monitoring system |
| CN117132417A (en) * | 2023-08-10 | 2023-11-28 | 山东科翔智能科技有限公司 | Plant epidemic disease monitoring system based on intelligent AI (advanced technology attachment) cultivation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001407A (en) * | 2018-09-12 | 2018-12-14 | 叶福达 | A kind of lake water water quality analytical system |
| CN111881864A (en) * | 2020-08-03 | 2020-11-03 | 王美华 | Wisdom farming crops growth full-period full-dynamic monitoring management system |
-
2021
- 2021-01-09 CN CN202110027060.2A patent/CN112866644A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001407A (en) * | 2018-09-12 | 2018-12-14 | 叶福达 | A kind of lake water water quality analytical system |
| CN111881864A (en) * | 2020-08-03 | 2020-11-03 | 王美华 | Wisdom farming crops growth full-period full-dynamic monitoring management system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115067243A (en) * | 2022-07-07 | 2022-09-20 | 武汉很多鱼钓具有限公司 | Fishery monitoring and analyzing method and system based on Internet of things technology and storage medium |
| CN115067243B (en) * | 2022-07-07 | 2024-02-13 | 武汉很多鱼钓具有限公司 | Fishery monitoring and analyzing method, system and storage medium based on Internet of things technology |
| CN114997536A (en) * | 2022-08-01 | 2022-09-02 | 中国水产科学研究院南海水产研究所 | Culture tail water pollution detection method, system and medium |
| CN115396754A (en) * | 2022-10-27 | 2022-11-25 | 江西省水生生物保护救助中心 | Fishery water quality remote Internet of things environment monitoring system |
| CN117132417A (en) * | 2023-08-10 | 2023-11-28 | 山东科翔智能科技有限公司 | Plant epidemic disease monitoring system based on intelligent AI (advanced technology attachment) cultivation |
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