WO2016048966A1

WO2016048966A1 - Risk management for bio-production

Info

Publication number: WO2016048966A1
Application number: PCT/US2015/051361
Authority: WO
Inventors: Evensen ØYSTEIN; Onken RALF
Original assignee: Benchmark Holdings Plc.; Fvg Inc.
Priority date: 2014-09-24
Filing date: 2015-09-22
Publication date: 2016-03-31

Abstract

Processes and systems described herein enable a computing device to risk management of bio-production of livestock and/or aquatic animals. Various embodiments of this disclosure include obtaining, by a computing device, previous bio-production data. The computing device may establish baseline data based on the bio-production data. Then, the computing device may receive additional bio-production data (e.g., real-time data), and calculate a deviation from the baseline data based on the additional bio-production data. The computing device may also provide a recommendation in response to the deviation to a user device.

Description

RISK MANAGEMENT FOR BIO-PRODUCTION

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 62/054,462, filed on September 24, 2014, entitled "Risk Management for Bio- Production," which is hereby incorporated by reference in its entirety.

BACKGROUND

Bio-production in an aquatic environment (e.g., fish farming) has great prospects for supplying high value proteins to the growing world population, and is considered to be the next revolution in protein production and supply. Compared to fish in a wild/free environment, bio-produced fish have different susceptibilities/resistance to stress, pathogen exposure, ability to resist pathogen proliferation, etc. Since bio-produced fish are cultivated in a pond, a cage, or a farm site, various factors may cause stress and even diseases to fish. Given the variation of these factors, it is difficult to identify clinical signs in an early stage among the bio- produced fish. This may increase the risk of pathogen proliferation among the bio- produced fish.

SUMMARY

Described herein are techniques and systems for risk management for bio- production. Embodiments of this disclosure relate to risk management of bio- production of livestock and/or aquatic animals. Various embodiments of this disclosure include obtaining, by a computing device, previous bio-production data associated with aquatic animals. The computing device may establish baseline data based on the bio-production data. Then, the computing device may receive additional bio-production data (e.g., real-time data) from, for example, a recording device, and calculate a deviation from the baseline data based on the additional bio-production data. The computing device may also provide a report in response to the deviation.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same reference numbers in different figures indicate similar or identical items.

FIG. 1 is a diagram of an illustrative computing system that includes a computing architecture for managing bio-production of aquatic animals.

FIG. 2 is a schematic diagram of an illustrative computing architecture to management of bio-production of aquatic animals.

FIG. 3 is a flow diagram of an illustrative process for managing bio-production of aquatic animals.

FIG. 4 is another flow diagram of an illustrative process for establishing baseline data associated with risk management of bio-production of aquatic animals.

DETAILED DESCRIPTION

Overview

Processes and systems described in the present disclosure enable risk management of bio-production of aquatic animals (e.g., fin-fish, shell-fish, or crustaceans). There are various factors that cause stress to aquatic animals and increase likelihood of disease occurrence to the fish. Earlier intervention may significantly avoid undesired situations for bio-production of fish. Embodiments of the present disclosure provide a system to collect real-time data from multiple on-site devices or probes that record environmental data (e.g., values associated with temperature, salinity, light intensity, 0₂, C0₂, current, ammonia, etc.) and transmit the data to a server. The onsite recording device and/or the server may compile and analyze the data to prepare a recommendation using, for example, a reasoning algorithm approach.

Illustrative Environment

FIG. 1 is a diagram of an illustrative computing system 100 that includes a computing architecture for managing bio-production of livestock and/or aquatic animals. The computing system 100 includes a server 102, which may include a server or a collection of servers in a distributed configuration (e.g., cloud computing service, server farm, etc.) or non-distributed configuration. The articles "a" and "an" are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, "a server" means one server or more than one server.

The server 102 may be associated with a service provider 104, which provide and/or maintain a service 106. The service 106 may include a set of related software and/or hardware functionalities, together with the policies that, for example, managing risk of bio-production of aquatic animals.

As defined herein, bio-production refers to various forms of livestock production and/or aquaculture production. For example, the bio-production may include aquaculture production and/or mariculture production. The aquaculture production may include aquafarming production and mariculture production. The aquafarming production may include cultivating freshwater and saltwater populations under confined conditions. The mariculture production may refer to the aquaculture production that is practiced in marine environments and in underwater habitats. For example, the mariculture production may include the cultivation of marine organisms for food and other products in the open ocean, an enclosed section of the ocean, or in tanks, ponds or raceways which are filled with seawater.

The risk management of bio-production may include implementations to reduce the cost of risk, which is the chance of an undesired situation happening. The undesired situation may include an event or outcome that is adverse to the bio- production (e.g., aquaculture production). In some embodiments, the risk may be divided into various categories, such as a production risk and marketing risk that are associated with bio-produced animals (e.g., fish). The production risk may be associated with the aquaculture production and may include, for example, pathogen exposure, disease, predation, natural events, water quality, power outages, equipment failure, etc. The marketing risk may be associated with market information of bio- produced animals that includes, for example, item pricing, demand/supply, competition from other producers, etc.

In some embodiments, the server 102 may receive bio-production data 108 from various sources, such as a bio-production facility 110 and online resources 112 via a network 116, and store the bio-production data 108 in, for example, a database 114. In some embodiments, the database 114 may be configured to store the bio- production data 108 and/or analyzed data (e.g., baseline data for risk management). In these instances, the database 114 may also cause updates of the analyzed data based on real-time data of the bio-production data 108.

The network 116 enables the server 102 to exchange information with various computing devices, such as a computing device associated with the bio-production facility 110, a computing device associated with the online resources 112, a computing device 118 associated with a user 120, etc. The network 116 may include wired and/or wireless networks that enable communications between the various computing devices described in the computing system 100. In some embodiments, the network 116 may include local area networks (LANs), wide area networks (WAN), mobile telephone networks (MTNs), and other types of networks, possibly used in conjunction with one another, to facilitate communication between the various computing devices (e.g., the server 102, the computing device 118, etc.).

The computing device 118 may be a mobile device, a desktop computer, a personal data assistant (PDA), an internet appliance, an internet enabled mobile phone, a server or any other computing device configured with a network connection. The mobile device may include a wide variety of telecommunications devices or components that are capable of initiating, receiving or facilitating communications over the network 116. The mobile device may include personal computing devices, electronic book readers (e.g., e-book readers), hand held computing devices, integrated components for inclusion in computing devices, home electronics, appliances, vehicles, machinery, landline telephones, network-based telephones (e.g., voice over IP ("VoIP"), cordless telephones, cellular telephones, smart phones, modems, personal digital assistants, laptop computers, gaming devices, media devices, and the like.

Via the network 116, the server 102 may receive the bio-production data 108 from the bio-production facility 110, which may include a cultivating system for bio- production of aquatic animals 124. The aquatic animals 124 may include an animal that spends all or some of the life in marine, brackish or fresh water. The terms "marine", "brackish" or "freshwater" refer to the natural environment of an aquatic animal. The term "marine" refers to any environment relating to the oceans or seas wherein the water is saline. The term "freshwater" refers to, but is not limited to, lakes, ponds, rivers, streams, brooks or any other low salinity water. The term "brackish" refers to any environment relating to the zone between marine and freshwater environments and with intermediate salinity water, including for example 10-20 ppm of salt content.

An aquatic animal may include, but is not limited to, a mammal, such as a seal, sea lion, walrus, manatee, dugong, porpoise, dolphin, cetaceous or non-cetaceous whale, otter, or beaver; a bird, such as, but not limited to, a web-footed bird, such as a duck, goose, swan, gull, cormorant, penguin, a wading bird, such as a coot, moor hen, flamingo, stork, heron; an aquatic reptile, such as, but not limited to, an alligator, cayman, crocodile, turtle, snake or lizard; an amphibian, such as, but not limited to, frogs, toads, newts and salamanders, neotenous larva or larvae thereof; fish and aquatic invertebrates, such as, but not limited to, Crustacea, insects, or molluscs.

A fish may include a marine or freshwater fish species maintained in a tank, aquarium, pool, pond, net-pen, hapas, aquaculture facility, fish farm, or any means other than the natural environment of the fish species. The term "fish" also refers to species and individuals thereof captured, rescued or taken from their native habitat and which may require treatment for microbial infestations. Fish species to which the methods of the present disclosure may be applied include, but are not limited to, ornamental fish, zebrafish, goldfish, koi, oscar, cichlids, tropical fish and fish for human or animal food, such as, but not limited to, catfish, and salmonids, such as trout, or salmon. In addition, examples of fish may include a brown trout, an Atlantic salmon, a rainbow trout, a coho salmon, a channel catfish, a pike, an arctic char, an eel, a roach, a carp, a sturgeon, a kissing gourami, a guppy, a swordfish, a pangasius, a tilapia, or a platyfish. Fish also refers to a fish of different stages between birth and adulthood, for example, eggs, fry, fingerlings, juvenile fish, growing fish or a mature fish.

The bio-production data 108 may include information associated with bio- production of the aquatic animals 124. In some embodiments, the bio-production data 108 may include production data and market data associated with the aquatic animals 124. For example, the production data may include environmental information 126, production information 128, pathogen information 130, tool information 132, etc. The market data may include online data associated with market information of the aquatic animals 124 that includes a price, demand/supply, information of competition from a third party, etc.

In some embodiments, the market data may include insurance data associated with an aquaculture commodity insurance, which may provide the bio-production facility 110 with certain protection from risk. For example, the aquaculture commodity insurance may protect revenues of the bio-production facility 110 against natural disasters or market fluctuations. As for another example, the aquaculture commodity insurance may safeguard the bio-production facility 110 against low-yields, loss of inventory, or the inability to stock, for example, as results of a result of natural disaster.

In some embodiments, the market data may further include logistics data associated with the bio-produced aquatic animals 124. For example, the logistics data may include data of the management of the flow of the bio-produced aquatic animals 124 between the bio-production facility 110 and the point of consumption, such as customers of the bio-production facility 110.

The environmental information 126 may include data of various environmental parameters, such as water quality parameters (e.g., concentrations of dissolved oxygen and/or carbon oxygen in the facility, pH, a water temperature, salinity, concentrations of ammonia and other nitrogen-compounds, conductivity, concentration of pathogenic bacteria, viruses, parasites or fungal components in the water, etc.), weather data associated with the bio-production facility 110, hazard information associated with the bio-production facility 110, information of predation associated with the aquatic animals 124, etc. For example, the weather data may include various parameters, such as temperature, current, precipitation, wind speed, solar radiation, cloud covering, cooling rate, growing degree days, rate of evaporation, weather alerts, etc. In some embodiments, the environmental information 126 may include light parameters associated with a culturing area of the bio-production facility 110. For example, the light parameters may indicate the light intensity and the wavelength spectrum of the culturing area.

The production information 128 may include data of various production parameters, such as information of the aquatic animals 124 (e.g., species, sizes, ages or other categories of animals), information of animal behavior (e.g., size, quantity, total produced animal biomass in the facility), feeding information, feed conversion rates, etc. In some embodiments, the information of animal size and behavior may be collected from under-water camera devices that allow identification of unique identity size and weight estimates and that can be used for production control and health monitoring surveillance. For example, an on-site device may collect the information of animal behavior like feeding behavior, schooling, swimming rate, dispersion, crowding, etc. and transmit the information to the server 102, which may then determine whether the aquatic animals 124 are in a healthy condition.

The pathogen information 130 may include data of various pathogen parameters, such as information of diseases associated with the aquatic animals 124. The pathogen information may be obtained from on-site data associated with on-site tests and diagnosis, and/or the online resources. The diseases may include a pathological condition recognizable as an abnormal condition of an animal. For example, a subclinical or clinical fish disease may be a pathological condition of fish that may be fatal or benign, such as, but not limited to, systemic infections of bacterial, viral, fungal, oomycotic, or parasitic origin/etiology, skin/fin ulcers, fin rot, dropsy, gill disease and columnaris, Saprolegnia infections, see louse infections, or saddlepatch disease.

The tool information 132 may include data of various tool parameters, such as information of production systems (e.g., ponds, tanks, cages, raceway, flow-through or recirculating systems) including the multiple on-site devices 122.

In some embodiments, the production data may include on-site information of the bio-production facility 110 and/or online data that are associated with the bio- production of the aquatic animals 124. The production data may be updated in various methods, such as updating within a predetermined time period or updating continuously.

The on-site information may include various information associated with bio- production of the aquatic animals in the bio-production facility 110. In some embodiments, the on-side information may be divided into various categories, such as environmental information 126, the production information 128, the pathogen information 130, the tool information 132, etc. The on-site information may be collected using multiple on-site devices 122 (e.g., device 122(1), device 122(2), device 122(n) associated with the bio-production facility 110.

The multiple on-site devices 122 may include an electrical and/or mechanical device that accomplishes one or more functions associated with the bio-production of the aquatic animals, such as pumping, harvesting, aeration, transportation, grading, elevating, feeding, monitoring, measuring/probing, heating, cooling, etc. The multiple on-site devices 122 may also collect associated with the bio-production system and communicate the on-side information to the server 102 and/or other on-site devices (e.g., device 122(2)). The energy consumption of the on-site device 122(2) may be tracked.

For example, the on-site device 122(1) may include a feeding device (e.g., a blower). Feeding in bio-production facility 110 may be performed using blowers that transports dry pellets from a central storage unit and into a cage that cultivates aquatic animals (e.g., fish). The cage may be equipped with a polyvinyl hose (e.g., a 6-8 cm in diameter) connected to the on-site device 122(1) and the central storage unit. The on- site device 122(1) may also include or be associated with a monitoring device to monitor the amount of the feed blown out into the cage in a predetermined time (e.g., a day, a week, etc.) or a predetermined number of feeding. The monitoring device may include an underwater camera that monitors the amount of the feed given to the cage. The underwater camera may be placed at the bottom of the cage such that feed pellets may be observed and/or measured while falling through a water column associated with the cage. The monitoring device may include a noise recording device that is installed, for example, near a feeding hose such that the feeding may be monitored by sound. For example, the amount of the feed (e.g., per time and accumulated in a predetermined time period) may be determined based on the cumulated noise generated in the feeding hose during the blowing.

In some embodiments, at least one of the environmental information 126, the production information 128, the pathogen information 130, or the tool information 132 may be obtained from other resources (e.g., the online resources 112). For example, historic data of the environmental information 126 (e.g., weather data) may be obtained from databases (e.g., a weather database). In some embodiments, the on-site information may be generated based on analysis and/or diagnosis (e.g., a realtime PCR) of samples from the bio-production facility 110. The online resources 112 may include, for example, databases (e.g., Aquatic Sciences and Fisheries Abstracts (ASFA)) and/or social media that are associated with the bio-production (e.g., animal diseases). Social media is the social interaction among people in which they create, share or exchange information and ideas in virtual communities and networks. For example, social media may include social interaction from at least one of collaborative projects (e.g., Wikipedia), blogs and microblogs (e.g., Twitter and Tumblr), content communities (e.g., YouTube and Youku), social networking sites (e.g., Facebook, Wechat), virtual game-worlds, or virtual social worlds. Social media technologies take on many different forms including magazines, Internet forums, weblogs, social blogs, microblogging, wikis, social networks, podcasts, photographs or pictures, video, rating and social bookmarking.

The online data may be obtained from the online resources 112. In some embodiments, the online data may include historic data, pattern data, forecast data, etc. For example, weather online data include historical weather data (e.g., the temperature last May), the pattern data (e.g., the average temperature in May), and forecast weather data (e.g., a prediction as to what the temperature will be next May) that correspond to the bio-production facility 110. In some embodiments, the current weather data (e.g., the current temperature in the bio-production facility 110) may be obtained from multiple on-site devices 122 and/or from the online resources (e.g., weather.com).

In some embodiments, the bio-production facility 110 may include a central fuse box 134, which is configured to detect changes in energy usage associated the multiple on-site devices 122 or to monitor performance of the multiple on-site devices 122. The central fuse box 134 may transmit information of the energy usage or the performance to the server 102. For example, the multiple on-site devices may be installed in or associated with the central fuse boxes 134, which may allow profiling of the energy usage of an individual device of the multiple on-site devices (e.g., the device 122(1)). Changes in energy usages of the multiple on-site devices 122 may also be detected, and these changes may indicate needs for services or repairs. In some embodiments, multiple load cells may be installed on moorings to detect a load during periods of, for example, bad weather conditions (e.g., windy weather).

After receiving the bio-production data 108, the server 102 may perform analysis to combine various information of the bio-production data 108 and cause generation of a report 136. In some embodiments, a first portion of the on-site information (e.g., data of water quality parameters) may be combined with a second portion of the on-site information (e.g., the pathogen information 130) to conduct an early-pathogen detection (e.g., pre-clinical). In these instances, the first portion of the on-site information may be collected by the multiple on-site devices 122, and the second portion of the on-site information may be generated by an on-site test/diagnosis and/or from the online resources. For example, the on-site test/diagnosis may be associated with health monitoring surveillance, which may be conducted by the bio-production facility 110 or a third party.

In some embodiments, the on-site test/diagnosis may be conducted using high-throughput and/or sensitive methods (e.g., real-time PCR) to generate the pathogen information 130. For example, the pathogen information 130 may include statics data (e.g., occurrence) of clinical diseases and mortality figures. The statics data may include historical data, such as occurrence of disease in the bio-production facility 110 in one or more previous time periods (e.g., years).

In some embodiments, the bio-production data 108 may include real-time monitoring data, which may be transfer to the server 102. The server 102 may implement an analysis tool to conduct an early detection of abnormal conditions. For example, a deviation from baseline data (e.g., a delta-value) may be measured based on the real-time monitoring data and/or data from the online resources 112. This enables the risk management associated with the bio-production of aquatic animals in the bio-production facility 110 in an early stage.

In some embodiments, a reasoning algorithm may be implemented to analyze historic data of the bio-production data 108 and real-time data of the bio-production data 108. In these instances, the historic data of the bio-production data 108 may be updated in a predetermined time (e.g., 60 or 90 days). For example, the historic data may be updated more frequently in a first predetermined time. In some embodiments, the real-time data of the bio-production data 108 may include an event occurring in the bio-production facility 110, such as diseases that may be detected by various screening methods (e.g., on-site diagnosis). When the bio-production data 108 is received, the server 102 may establish association between potentially occurring events and observations. For example, a deviation from baseline data (e.g., a delta- value) may be used to establish and provide early warning signals/alerts to the user 120 and/or the bio-production facility 110. The early warning signals/alerts may be included in the report 136.

In some embodiments, the report 136 may also include a recommendation 140 in response to the early warning signals/alerts. In some embodiments, the server 102 may generate a command 138 based on analyzed data (e.g., the deviation) and transmit the command 138 to at least a portion of the multiple on-site devices 122 to perform a certain task to reduce the production risk and/or the marketing risk of the bio-production facility 110. For example, the server 102 may transmit the command 138 to the multiple on-site devices 122 to cause one or more on-site actions including changing frequency of sampling associated with early detections of environmental changes in the bio-production facility 110. The one or more on-site actions may further include performing analysis of bacterial or parasitic sensitivity for available antimicrobials or parasiticides, such that early implementation of treatment, altered or reduced feeding regimes, and/or introduction of functional feed for alleviation of viral or parasitic exposures may be performed. In some embodiments, the one or more on-site actions may include manipulating the light intensity and/or wavelength spectrum associated with the bio-production facility 110, such that the behavior of the aquatic animals 124 is similar or substantially similar to those living in natural light environments.

In some embodiments, the server 102 and/or the multiple on-site devices 122 may facilitate the bio-production facility 110 to transport fish away from areas of exposure to pathogens or toxics (e.g., algae) in the bio-production facility 110 or to cause early slaughter of fish aligned with a market need and a market price that are held against costs associated with the treatment of undesirable situations (e.g., diseases).

Via the network 116, the server 102 may also exchange information with the computing device 118. For example, the computing device 118 may implement a mobile app to transmit a request 142 for information (e.g., the recommendation 140, the bio-production data 108, etc.) to the server 102. The server 102 may provide return information (e.g., the report 136) to the computing device 118 for the user 120 review.

Illustrative Architecture

FIG. 2 is a schematic diagram of an illustrative computing architecture 200 to management of bio-production of the aquatic animals 124. The computing architecture 200 shows additional details of the server 102, which may include additional modules, kernels, data, and/or hardware.

The computing architecture 200 may include processor(s) 202 and memory 204. The memory 204 may store various modules, applications, programs, or other data. The memory 204 may include instructions that, when executed by the processor(s) 202, cause the processor(s) 202 to perform the operations described herein for the server 102. The processors 202 may include one or more graphics processing units (GPU) and one or more central processing units (CPU).

The server 102 may have additional features and/or functionality. For example, the server 102 may also include additional data storage devices (removable and/or non-removable). Computer-readable media may include, at least, two types of computer-readable media, namely computer storage media and communication media. Computer storage media may include volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, program data, or other data. The system memory, the removable storage and the non-removable storage are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be accessed by the server 102. Any such computer storage media may be part of the server 102. Moreover, the computer-readable media may include computer-executable instructions that, when executed by the processor(s), perform various functions and/or operations described herein.

In contrast, communication media may embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other mechanism. As defined herein, computer storage media does not include communication media.

The memory 204 may store an operating system 206 as well as various components, such as a communication module 208, an analysis module 210, a reporting module 212, program data 214, etc.

The communication module 208 may be configured to receive the bio- production data 108. For example, the communication module 208 may receive data of multiple environmental parameters (e.g., values of water quality parameters and weather data) for a predetermined time. For example, the data of multiple environmental parameters may be previously generated by the multiple on-site devices 122 associated with the bio-production of the aquatic animals 124, and may include data of water quality parameters and data of weather parameters.

In some embodiments, the water quality parameters may include at least one of a concentration of dissolved oxygen of a culturing environment of the bio- production facility 110, a pH value the culturing environment, a salinity of the culturing environment, or a concentration of ammonia of the culturing environment.

In some embodiments, the weather parameters are associated with at least one of a temperature of the bio-production facility 110, a precipitation associated with the bio-production facility 110, a wind speed associated with the bio-production facility 110, a solar radiation associated with the bio-production facility 110, or a rate of evaporation associated with the bio-production facility 110.

In some embodiments, the bio-production data 108 may include data of multiple production parameters and data of multiple pathogen parameters. In these instances, the data of the multiple production parameters may include at least one of feeding information of the bio-production, a size of the aquatic animals 124, a quantity of the aquatic animals in the bio-production facility 110, or a total biomass of the aquatic animals in the bio-production facility 110. The data of the multiple pathogen parameters may include information of presence and quantity of pathogens in the water environment, diseases associated with the aquatic animals 124, and the information of diseases may be confirmed by an on-site test.

In some embodiments, the multiple on-site devices 122 may include at least one of a pump, a feeding device, or a monitor. The communication module 208 may receive signals from the multiple on-site devices. In these instances, the signals may include working status associated with multiple on-site devices 122, the report may include the recommendation 140 in response to the work status. In some embodiments, the aquatic animals 124 may include a fish.

The analysis module 210 may be configured to establish baseline data using the bio-production data 108. For example, the analysis module 210 may implement a reasoning a lgorithm to establish dateline data corresponding to one or more parameters of the bio-production data 108. I n some embodiments, the reasoning algorithm may be implemented in a recurrent analysis schedule (e.g., 90 days) with partial or full time (e.g., start-to-end) analysis in a predetermined time (e.g., 6 months) such that the analysis module 210 may detect at an early time a deviation from baseline data (e.g., a delta-value). I n these instances, the deviation may indicate a potential abnormal conditions of the bio-production of the aquatic animals 124 in the bio-production facility 110. For example, after the communication module 208 receives additional data of the multiple environmental parameters, the analysis module 210 may calculate the deviation from the baseline data using the additional data of the multiple environmental parameters.

The reporting module 212 may be configured to generate the 136 report based on the deviation. I n some embodiments, the report 136 may include a recommendation in response to the deviation. In some embodiments, the communication module 208 may receive the request 142 from a mobile device (e.g., the computing device 118), and transmit the report 136 to the computing device 118 for display.

I n some embodiments, the communication module 208 may retrieve market data associated with the aquatic animals 124. I n these instances, the analysis module 210 may perform analysis and the reporting module 222 may generate the report 136 based on the deviation and the market data.

Illustrative Processes

The FIGS. 3 and 4 include processes that are described with reference to the computing system 100 and computing architecture 200. However, the processes may be implemented using other schemes, environments, and/or computing architecture. Each of the processes 300 and 400 are illustrated as a collection of blocks in a logical flow graph, which represent a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or in parallel to implement the processes.

FIG. 3 is a flow diagram of an illustrative process 300 for managing bio- production of aquatic animals 124. At 302, one or more processors of the server 102 may receive the bio-production data 108. For example, the communication module 208 may receive data of multiple environmental parameters (e.g., values of water quality parameters and weather data) for a predetermined time. For example, the data of multiple environmental parameters may be previously generated by the multiple on-site devices 122 associated with the bio-production of the aquatic animals 124, and may include data of water quality parameters and data of weather parameters.

At 304, the one or more processors of the server 102 may establish baseline data using the bio-production data 108. For example, the analysis module 210 may implement a reasoning algorithm to establish dateline data corresponding to one or more parameters of the bio-production data 108. In some embodiments, the reasoning algorithm may be implemented in a recurrent analysis schedule (e.g., 90 days) with partial or full time (e.g., start-to-end) analysis in a predetermined time (e.g., 6 months) such that the analysis module 210 may detect at early time a deviation from baseline data (e.g., a delta-value).

At 306, the one or more processors of the server 102 may receive additional data of the multiple environmental parameters, and then calculate a deviation from the baseline data using the additional data of the multiple environmental parameters. In some embodiments, the communication module 208 may retrieve market data associated with the aquatic animals 124. In these instances, the analysis module 210 may perform analysis and the reporting module 222 may generate the report 136 based on the deviation and the market data.

At 308, the one or more processors of the server 102 may cause generation of the 136 report based on the deviation and provide the report 136 to the computing device 118. In some embodiments, the report 136 may include a recommendation in response to the deviation. In some embodiments, the communication module 208 may receive the request 142 from a computing device 118, and transmit the report 136 to the computing device 118 for display.

FIG. 4 is another flow diagram of an illustrative process 400 for establishing baseline data associated with risk management of bio-production of aquatic animals 124. At 402, one or more processors of the server 102 may receive the bio-production data 108 associated with the aquatic animals 124 for a predetermined time. For example, the bio-production data 108 may include data of multiple environmental parameters that are previously generated by multiple on-site devices, and include data of water quality parameters and data of weather parameters.

At 404, the one or more processors of the server 102 may store the data of multiple environmental parameters in the database 114. For example, the data of water quality parameters may include at least one of a concentration of dissolved oxygen of a culturing environment associated with the bio-production facility 110, a pH value the culturing environment, a salinity of the culturing environment, or a concentration of ammonia of the culturing environment. The data of weather parameters are associated with at least one of a temperature of associated with the bio-production facility 110, a precipitation associated with the bio-production facility 110, a wind speed of associated with the bio-production facility 110, a solar radiation associated with the bio-production facility 110, or a rate of evaporation associated with the bio-production facility 110.

At 406, the one or more processors of the server 102 may establish baseline data corresponding to one or more parameters of the data of water quality parameters and the data of weather parameters using the stored data of the multiple environmental parameters.

In some embodiments, the one or more processors of the server 102 may receive additional data of the multiple environmental parameters in a predetermined time period, and update the baseline data using the additional data. In some embodiments, the one or more processors of server 102 may receive online data including historic data, pattern data, and forecast data that are associated with the multiple environmental parameters, and modify the baseline data using the online data.

I n some embodiments, the one or more processors of the server 102 may assign a weight to the baseline data corresponding to an individual parameter of the one or more parameters. In these instances, the weight may indicate a strength of correlation between the bio-production of the aquatic animals 124 and a value of the individual parameter. In some embodiments, the one or more processors of the server 102 may establish the baseline data corresponding to the one or more parameters of the data of water quality parameters and the data of weather parameters using the stored data of the multiple environmental parameters and one or more weights corresponding to the one or more parameters.

I n some embodiments, the bio-production data 108 may include data of multiple production parameters and multiple pathogen parameters. I n these instances, the data of the multiple production parameters may include at least one of feeding information of the bio-production, a size of the aquatic animals, a quantity of the aquatic animals in the bio-production facility 110, or a total biomass of the aquatic animals in the bio-production facility 110. The data of the multiple pathogen parameters may include information of diseases associated with the aquatic animals 124, and the information of diseases may be confirmed by an on-site test/diagnosis. Conclusion

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts are disclosed as example forms of implementing the claims.

Claims

CLAIMS What is claimed is:

1. A method for managing bio-production of aquatic animals, the method comprising:

establishing, by one or more processors of a server, baseline data using data of bio-production, the data of bio-production comprising data of a plurality of environmental parameters that:

is previously generated by a plurality of on-site devices associated with the bio-production of the aquatic animals, and

comprise data of a plurality of water quality parameters and data of a plurality of weather parameters;

receiving, by the one or more processors, additional data of the plurality of environmental parameters;

calculating, by the one or more processors, deviation from the baseline data using the additional data of the plurality of environmental parameters; and

generating, by the one or more processors, a report based on the deviation, the report comprising a recommendation in response to the deviation.

2. The method of claim 1, wherein the data of the plurality of water quality parameters comprise at least one of a concentration of dissolved oxygen of a culturing environment, a water temperature, a pH value of the culturing environment, a salinity of the culturing environment, a water current, or a concentration of ammonia of the culturing environment.

3. The method of claim 1, wherein the data of the plurality of weather parameters are associated with at least one of a temperature of a facility of the bio- production, a precipitation of the facility, a wind speed of the facility, a solar radiation of the facility, or a rate of evaporation of the facility.

4. The method of claim 1, wherein the data of bio-production comprises data of a plurality of production parameters and a plurality of pathogen parameters.

5. The method of claim 4, wherein the data of the plurality of production parameters comprise at least one of feeding information of the bio-production, a size of the aquatic animals, a quantity of the aquatic animals in a facility of the bio- production, or a total biomass of the aquatic animals in the facility.

6. The method of claim 4, wherein the data of the plurality of pathogen parameters comprise information of diseases associated with the aquatic animals, and wherein the information of diseases is confirmed by local sampling, on-site tests, or off-site laboratory examination.

7. The method of claim 1, wherein the plurality of on-site devices comprise at least one of a pump, a feeding device, or a monitor.

8. The method of claim 1, further comprising:

receiving signals from the plurality of on-site devices, the signals comprising working status associated with the plurality of on-site devices, wherein the report comprises a recommendation in response to the work status.

9. The method of claim 1, further comprising:

retrieving market data of the aquatic animals, wherein the generating the report based on the deviation comprises generating the report based on the deviation and the market data.

10. The method of claim 1, wherein the aquatic animals comprise a fish.

11. The method of claim 1, further comprising:

receiving a request from a mobile device; and

transmitting the report to the mobile device for display.

12. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, causes the one or more processors to perform acts comprising: receiving data of bio-production of aquatic animals for a predetermined time, the data of bio-production comprising data of a plurality of environmental parameters:

previously generated by a plurality of on-site devices, and comprising data of a plurality of water quality parameters and data of a plurality of weather parameters;

storing the data of a plurality of environmental parameters; and

establishing baseline data corresponding to one or more parameters of the data of the plurality of water quality parameters and the data of the plurality of weather parameters using the stored data of the plurality of environmental parameters.

13. The one or more computer-readable media of claim 12 wherein the aquatic animals comprise a fish.

14. The one or more computer-readable media of claim 12, wherein the acts further comprise:

receiving additional data of the plurality of environmental parameters; and updating the baseline data using the additional data.

15. The one or more computer-readable media of claim 12, wherein the acts further comprise:

receiving online data comprising historic data, pattern data, and forecast data that are associated with the plurality of environmental parameters; and

modifying the baseline data using the online data.

16. The one or more computer-readable media of claim 12, wherein the acts further comprise:

assigning a weight to the baseline data corresponding to an individual parameter of the one or more parameters, the weight indicating a strength of correlation between the bio-production of the aquatic animals and values of the individual parameter.

17. The one or more computer-readable media of claim 16, wherein the establishing baseline data corresponding to one or more parameters of the data of the plurality of water quality parameters and data of the plurality of weather parameters using the stored data of the plurality of environmental parameters comprises establishing the baseline data corresponding to the one or more parameters of the data of the plurality of water quality parameters and the data of the plurality of weather parameters using the stored data of the plurality of environmental parameters and one or more weights corresponding to the one or more parameters.

18. The one or more computer-readable media of claim 12, wherein the data of water quality parameters comprise at least one of a concentration of dissolved oxygen of a culturing environment, a pH value of the culturing environment, a salinity of the culturing environment, a water current, or a concentration of ammonia of the culturing environment.

19. The one or more computer-readable media of claim 12, wherein the data of weather parameters are associated with at least one of a temperature of a facility of the bio-production, a precipitation of the facility, a wind speed of the facility, a solar radiation of the facility, or a rate of evaporation of the facility.

20. The one or more computer-readable media of claim 12, wherein the data of bio-production comprises data of a plurality of production parameters and a plurality of pathogen parameters.

21. The one or more computer-readable media of claim 20, wherein the data of the plurality of production parameters comprise at least one of feeding information of the bio-production, a size of the aquatic animals, a quantity of the aquatic animals in a facility of the bio-production, or a total biomass of the aquatic animals in the facility.

22. The one or more computer-readable media of claim 20, wherein the data of the plurality of pathogen parameters comprise information of diseases associated with the aquatic animals, and wherein the information of diseases is confirmed by an on-site test.

23. The one or more computer-readable media of claim 12, wherein the plurality of on-site devices comprise at least one of a pump, a feeding device, or a monitor.

24. A risk management system comprising:

one or more processors; and

memory to maintain a plurality of components executable by the one or more processors, the plurality of components comprising:

a baseline module configured to establish baseline data using data of bio-production of aquatic animals that comprises data of a plurality of environmental parameters that:

is previously generated by a plurality of on-site devices, comprise data of a plurality of water quality parameters and data of a plurality of weather parameters;

a communication module configured to receive additional data of the plurality of environmental parameters;

a recommendation module configured to:

calculate deviation from the baseline data using the additional data; and

cause generation of a report based on the deviation, the report comprising a recommendation in response to the deviation.

25. The risk management system of claim 24, further comprising:

the plurality of on-site devices configured to perform a plurality of tasks associated with the bio-production.

26. The risk management system of claim 24, wherein the plurality of on-site devices comprise at least one of a pump, a feeding device, or a monitor.

27. The risk management system of claim 24, further comprising:

a database configured to:

store the baseline data;

cause updates of the baseline data based on additional data of the plurality of environmental parameters.

28. The risk management system of claim 24, further comprising:

a central fuse box configured to:

detect changes in energy usage associated the plurality of on-site devices or monitor performance of the plurality of on-site devices, and

transmit information of the energy usage or the performance to the communication module.

29. The risk management system of claim 24, wherein the data of the plurality of water quality parameters comprise at least one of a concentration of dissolved oxygen of a culturing environment, a pH value of the culturing environment, a salinity of the culturing environment, a water current, or a concentration of ammonia of the culturing environment.

30. The risk management system of claim 24, wherein the data of the plurality of weather parameters are associated with at least one of a temperature of a facility of the bio-production, a precipitation of the facility, a wind speed of the facility, a solar radiation of the facility, or a rate of evaporation of the facility.

31. The risk management system of claim 24, wherein the data of bio- production comprises data of a plurality of production parameters and a plurality of pathogen parameters.

32. The risk management system of claim 31„ wherein the data of the plurality of production parameters comprise at least one of feeding information of the bio- production, a size of the aquatic animals, a quantity of the aquatic animals in a facility of the bio-production, or a total biomass of the aquatic animals in the facility.

33. The risk management system of claim 31, wherein the data of the plurality of pathogen parameters comprise information of diseases associated with the aquatic animals, and wherein the information of diseases is confirmed by an on-site test.