DISASTER PREDICTION AND PROACTIVE MITIGATION

According to one embodiment, a method, computer system, and computer program product for disaster prediction and mitigation is provided. The embodiment may include generating a knowledge corpus of a grazing pattern of a livestock group in a preconfigured area. The embodiment may also include generating a knowledge corpus of a growing pattern of flora for each of a plurality of sections in the preconfigured area. The embodiment may further include generating a prediction based on the knowledge corpuses. The embodiment may also include performing an action based on the prediction.

BACKGROUND

The present invention relates generally to the field of computing, and more particularly to the Internet of Things (IoT).

IoT relates to an interrelated system of objects that are capable of transferring data across a network without requiring human participation. Currently, many devices available in the consumer marketplace are equipped with “smart” capabilities which include the capability to connect to a network through wired or wireless connections. These devices include many items from smartphones and wearables to refrigerators, lightbulbs, and vehicles. Despite many known uses in the commercial sphere, IoT can also be utilized industrially to improve efficiency and reduce consumable resources. For example, implementing IoT technology throughout a city transportation or electrical grid may assist in reduction of traffic or inefficient energy usage.

SUMMARY

According to one embodiment, a method, computer system, and computer program product for disaster prediction and mitigation is provided. The embodiment may include generating a knowledge corpus of a grazing pattern of a livestock group in a preconfigured area. The embodiment may also include generating a knowledge corpus of a growing pattern of flora for each of a plurality of sections in the preconfigured area. The embodiment may further include generating a prediction based on the knowledge corpuses. The embodiment may also include performing an action based on the prediction.

DETAILED DESCRIPTION

Embodiments of the present invention relate to the field of computing, and more particularly to the Internet of Things (IoT). The following described exemplary embodiments provide a system, method, and program product to, among other things, track and manipulate a livestock grazing pattern in order to prevent and mitigate risk of a natural disaster. Therefore, the present embodiment has the capacity to improve the technical field of IoT by reducing natural disaster risk through integration of IoT-enabled devices and sensors throughout a preconfigured area.

As previously described, IoT relates to an interrelated system of objects that are capable of transferring data across a network without requiring human participation. Currently, many devices available in the consumer marketplace are equipped with “smart” capabilities which include the capability to connect to a network through wired or wireless connections. These devices include many items from smartphones and wearables to refrigerators, lightbulbs, and vehicles. Despite many known uses in the commercial sphere, IoT can also be utilized industrially to improve efficiency and reduce consumable resources. For example, implementing IoT technology throughout a city transportation or electrical grid may assist in reduction of traffic or inefficient energy usage.

IoT technology may also have beneficial implementation throughout industrial farming areas, such as livestock management. In forested, mountainous, or grassland environments, grazing livestock may freely roam. In such areas, there may be an increased risk of natural disasters, such as wildfires and landslides, due to a delicate balance between the natural landscape and the livestock’s necessary food and water consumption. For example, if grasses, brush, and other shrubbery become overgrown due livestock favoring other landscape areas, the risk of a quickly spreading brushfire, for example, due to a nearby lighting strike, may result. Similarly, overgrazing by livestock may result in a barren landscape devoid of natural flora and susceptible to landslides in the event of excessive rains. The risk of natural disasters such as these is made even more significant due to the increasing frequency of extreme weather events, such as droughts, hurricanes, severe thunderstorms, and heat waves, caused by climate change. As such, it may be advantageous to, among other things, utilize IoT technology to monitor and influence the grazing patterns of livestock so as to predict and mitigate the risk of natural disasters over a preconfigured area.

According to at least one embodiment, IoT sensors may monitor the grazing pattern of livestock in a preconfigured area, such as a grazing pasture, to determine areas of the preconfigured area that the livestock favor or disfavor. When a particular segment of the preconfigured area is determined to be at an increased risk for a natural disaster, such as a wildfire or a landslide, due to the growing pattern of the flora in the segment, IoT devices, such as robotic shepherding devices, may be implemented to herd the livestock away from or toward the segment of that is at an increased natural disaster risk.

The following described exemplary embodiments provide a system, method, and program product to identify areas of a preconfigured area at an increased risk for the occurrence of a natural disaster and shepherd livestock around the preconfigured area in order to prevent or mitigate the increased risk.

Referring toFIG.1, an exemplary networked computer environment100is depicted, according to at least one embodiment. The networked computer environment100may include client computing device102, a server112, one or more autonomous shepherding devices118, and one or more sensors120interconnected via a communication network114. According to at least one implementation, the networked computer environment100may include a plurality of client computing devices102, servers112, autonomous shepherding devices118and sensors120, of which only one of each is shown for illustrative brevity. Additionally, in one or more embodiments, the client computing device102and server112may each individually host a disaster prediction and mitigation program110A,110B. In one or more other embodiments, the disaster prediction and mitigation program110A,110B may be partially hosted on both the client computing device102and the server112so that functionality may be separated between the devices.

Client computing device102may include a processor104and a data storage device106that is enabled to host and run a software program108and a disaster prediction and mitigation program110A, receive data from one or more sensors, such as sensor120, transmit instructions to the autonomous shepherding device118, and communicate with the server112via the communication network114, in accordance with one embodiment of the invention. In one or more other embodiments, client computing device102may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing device capable of running a program and accessing a network. As previously described, one client computing device102is depicted inFIG.1for illustrative purposes, however, any number of client computing devices102may be utilized. As will be discussed with reference toFIG.3, the client computing device102may include internal components302aand external components304a, respectively.

The server computer112may be a laptop computer, netbook computer, personal computer (PC), a desktop computer, or any programmable electronic device or any network of programmable electronic devices capable of hosting and running a disaster prediction and mitigation program110B and a database116and communicating with the client computing device102via the communication network114, in accordance with embodiments of the invention. As will be discussed with reference toFIG.3, the server computer112may include internal components302band external components304b, respectively. The server112may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The server112may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

According to the present embodiment, sensor120may include location tracking devices capable of identifying the location of one or more members in a herd of livestock. Sensor120may include a global positioning system (GPS) device, a Bluetooth-enabled device, Wifi-enabled device, a cellular communication device, or any other location tracking device capable of being affixed to (e.g., an ear tag or collar-like device) or embedded within (e.g., microchipping) livestock. In at least one embodiment, the sensor120may be capable of transmitting captured location tracking information to the client computing device102and the server112via communication network114. A single sensor120is depicted inFIG.1for illustrative purposes, however, any number of sensors120may be utilized.

According to the present embodiment, one or more sensors120, which may be separate entities from the location tracking devices previously described, may also be capable of image capture capabilities, such as a video capture. When representing a video capture device (e.g., a camera), the one or more sensors120may be affixed to stationary objects, such as a fence post or tree, within or nearby an enclosure within which the livestock reside and capable of capturing images of the livestock. In at least one embodiment, the sensor120may be capable of changing the angle and position at which images are captured. Furthermore, the sensor120may also be capable of capturing micromovements of each member of a group of livestock to which the sensor120is affixed, such as capable by an accelerometer or a gyroscope. In at least one embodiment, the sensor120may be capable of transmitting captured images or videos to the client computing device102and the server112via communication network114.

According to the present embodiment, the autonomous shepherding device118may include any robotic device capable of autonomous or remotely-controlled shepherding of livestock. For example, the autonomous shepherding device118may be a drone device or a dynamic highly-mobile robotic device. The autonomous shepherding device118may be capable of communicating with client computing device102and server120via communication network114to receive livestock location data and shepherding instructions. In at least one embodiment, the autonomous shepherding device118may be capable of capturing and transmitting a video feed or still images of the activities of each livestock animal to the client computing device102or server112that may then be used by the disaster prediction and mitigation program110A,110B described below and in any of the method steps inFIG.2.

According to the present embodiment, the disaster prediction and mitigation program110A,110B may be capable of monitoring a grazing pattern of a group of livestock through analysis of received location data and image data from one or more sensors, such as sensor120. The disaster prediction and mitigation program110A,110B may also be capable of determining whether risk of a natural disaster to a preconfigured area is elevated based on the captured image data and the grazing pattern. In the event the risk is elevated, the disaster prediction and mitigation program110A,110B may utilize a shepherding device, such as autonomous shepherding device118, to move the livestock away from or toward a specific segment of the preconfigured area according to the specific determined risk. The disaster prediction and mitigation method is explained in further detail below with respect toFIG.2.

Referring now toFIG.2, an operational flowchart illustrating a disaster prediction and mitigation process200is depicted according to at least one embodiment. At202, the disaster prediction and mitigation program110A,110B monitors a group of livestock around a preconfigured area. By definition, the group of livestock may include more than one herbivorous animal and, as naturally occurs, each animal in the group may be present in various locations around a pasture, field, or other grazing area. In order to monitor the location and behavior of each member of the group, a location tracking sensor, such as sensor120, may be affixed to one or more members of the group of livestock. For example, each location tracking sensor may be affixed, such as an ear tag or a collar, or embedded under skin, such as a microchip. Each sensor120may transmit real-time location data of each member of the group of livestock to the client computing device102or the server112via network114. In at least one embodiment, the disaster prediction and mitigation program110A,110B may uniquely identify each member of the group of livestock so as to monitor the movements and activity of each member separately from each other member.

In at least one embodiment, the sensor120may be one or more video capture devices capable of identifying livestock through image recognition technology and transmitting the presence of the livestock near the sensor120to the client computing device102or the server112via network114. In such an example, the livestock location may be tracked without location tracking sensors being affixed or embedded as described above.

Then, at204, the disaster prediction and mitigation program110A,110B determines whether the livestock remained in a location for a preconfigured time. Using the location data captured from the sensor120, the disaster prediction and mitigation program110A,110B may determine whether the livestock being tracked have remained in a specific location for a preconfigured time. As such, the disaster prediction and mitigation program110A,110B may be capable of identifying whether the group of livestock have overgrazed a specific segment of the preconfigured area. In at least one embodiment, a determination that the group of livestock has remained in the preconfigured area for the preconfigured period of time may also indicate to the disaster prediction and mitigation program110A,110B that one or more other sections of the preconfigured area are under-grazed and/or overgrown and may be in need of grazing by the group of livestock in order to reduce an risk of occurrence of a natural disaster. If the disaster prediction and mitigation program110A,110B determines the livestock have remained in the location for a preconfigured period of time (step204, “Yes” branch), then the disaster prediction and mitigation process200may proceed to step206to identify an activity of each livestock animal. If the disaster prediction and mitigation program110A,110B determines the livestock have not remained in the location for the preconfigured period of time (step204, “No” branch), then the disaster prediction and mitigation process200may return to step202to monitor the group of livestock in the preconfigured area.

Then, at206, the disaster prediction and mitigation program110A,110B identifies an activity of each livestock animal. Using various feature capabilities of sensors120, the disaster prediction and mitigation program110A,110B may identify the current activities of the group of livestock. Identification of the current activities is necessary for the disaster prediction and mitigation program110A,110B to determine if the current location of the group of livestock or each individual member has been overgrazed. For example, using an embedded gyroscope and/or accelerometer in a sensor120affixed as an ear tag, the disaster prediction and mitigation program110A,110B may be capable of determining that a member of the group of livestock is eating due to regular and consistent head movements. Similarly, through image recognition of a video feed, the disaster prediction and mitigation program110A,110B may be capable of determining that a group of livestock are sleeping. In at least one embodiment, the autonomous shepherding device118may be capable of capturing and transmitting a video feed or still images of the activities of each livestock animal to the client computing device102or server112.

Next, at208, the disaster prediction and mitigation program110A,110B generates a knowledge corpus of the livestock grazing pattern. Through collection of the identified activities around the preconfigured area, the disaster prediction and mitigation program110A,110B may generate a knowledge corpus that tracks the grazing pattern of the group of livestock. The grazing pattern may be calculated using the location of each member of the group of livestock, the length of time each member of the group of livestock remained in a specific location, and the activity each member of the group of livestock engaged in while present at a specific location in the preconfigured area. Furthermore, the disaster prediction and mitigation program110A,110B may be capable of determining an average grazing rate for each member of the group of livestock based on the species of the member. For example, a cow may graze at a different rate than a sheep. Since a group of livestock may consist of a variety of species, the disaster prediction and mitigation program110A,110B may identify a grazing rate for each member of the group of livestock.

Then, at210, the disaster prediction and mitigation program110A,110B generates a knowledge corpus of the landscape growing pattern. Each pasture containing a group of livestock may have a different growing rate based on the flora growing within the preconfigured area. For example, a quackgrass may grow at a different rate that an annual ryegrass depending on environmental conditions. The disaster prediction and mitigation program110A,110B may generate the knowledge corpus to identify a growing pattern of one or more sections of the preconfigured area in order to determine a frequency and amount of grazing necessary by the group of livestock to maintain the preconfigured area and reduce or eliminate any natural disaster risk presented by overgrazing or overgrowth. The disaster prediction and mitigation program110A,110B may identify each flora type through image recognition of a video feed, or images captured, by one or more image capture devices, such as sensor120. In at least one other embodiment, manual user input of flora type and location of each flora type may be implemented. The disaster prediction and mitigation program110A,110B may be capable of identifying the growing rates, soil requirements, water requirements, root depth, flammability, and other characteristics of various flora types through user preconfiguration or through a database search, such as utilizing an internet-based search engine. Furthermore, the disaster prediction and mitigation program110A,110B may consider the landscape characteristics, such as soil characteristics, terrain characteristics, plant coverage, tree coverage, etc. when generating the knowledge corpus.

When generating the knowledge corpus, the disaster prediction and mitigation program110A,110B may also consider recent weather conditions that may affect, even temporarily, the growing pattern. For example, if an inch of rain was received in the previous 24 hours, the disaster prediction and mitigation program110A,110B may determine that the growing pattern of the preconfigured area may be increased for the next two or three days until wet conditions subside. Similarly, the disaster prediction and mitigation program110A,110B may consider recent weather characteristics when determining whether flora within the preconfigured area are presently more susceptible to a natural disaster. For example, flora that received rain recently may be less likely to foster and spread a wildfire than flora that is currently experiencing a drought.

Next, at212, the disaster prediction and mitigation program110A,110B generates a prediction based on the knowledge corpuses. The disaster prediction and mitigation program110A,110B may make a prediction as to the status of the flora of sections of the preconfigured area using the knowledge corpuses. For example, the disaster prediction and mitigation program110A,110B may determine that a specific section of the preconfigured area has not been frequented by the group of livestock in some time and, due to overgrowth as estimated by a calculated growth pattern in that section, the specific section may be at an elevated risk for a wildfire and should be grazed by the livestock to reduce the elevated risk. Similarly, the disaster prediction and mitigation program110A,110B may determine that the current location of a group of livestock has been overgrazed due to the group’s extended presence in that location and should be moved to a less grazed area so as to reduce an increased risk of a landslide in the event of increased rainfall. In this sense, the disaster prediction and mitigation program110A,110B may identify a location as to where the group of livestock should be shepherded by a shepherding device, such as the autonomous shepherding device118, when making the prediction.

Then, at214, the disaster prediction and mitigation program110A,110B performs an action based on the prediction. The disaster prediction and mitigation program110A,110B may perform a variety of actions based on the determination that the group of livestock should be relocated to a different segment of the preconfigured area. In at least one embodiment, the disaster prediction and mitigation program110A,110B may utilize an autonomous device, such as autonomous shepherding device118, to direct the group of livestock from one location to another based on the location identified in the prediction. The autonomous device may shepherd the livestock using one or more shepherding methods, such as recorded audio cues or haptic sensations. For example, the disaster prediction and mitigation program110A,110B may use one or more autonomous shepherding devices118to play a prerecorded cue of a dog while the one or more autonomous shepherding devices118maneuver around the group of livestock in order to direct the group to a location identified in the prediction. Similarly, the haptic sensations utilized by the disaster prediction and mitigation program110A,110B may relate to causing tactile sensations to be felt on the skin of one or more animals in the group of livestock through focused pressure fields created in mid-air by an array of ultrasound transducers. The autonomous device may produce the pressure fields through hosted technology and focus created waves towards one or more members of the group of livestock. In at least one other embodiment, the disaster prediction and mitigation program110A,110B may utilize a plurality of sound cue devices installed around a preconfigured area to direct the group of livestock to the location identified in the prediction. For example, the disaster prediction and mitigation program110A,110B may play a sound cue on one or more speakers affixed to poles or rods around the enclosed area that may provoke the livestock to move away from the speaker and toward the location identified in the prediction.

It may be appreciated thatFIG.2provides only an illustration of one implementation and does not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. In at least one embodiment, any information captured by the sensor120and/or the autonomous shepherding device118as described above (e.g., a video feed, still images, location data, etc.) may also be utilized to monitor a status of the livestock. For example, the information captured by the sensor120and/or the autonomous shepherding device118may be utilized to identify when one or more members of the group of livestock are ill, dehydrated, or otherwise in need of attention. When such a determination is made, the action performed by the disaster prediction and mitigation program110A,110B in step214may include notifying a user (e.g., in the situation when an animal is identified as ill, in danger, or otherwise in need of individual attention) or shepherding the group of livestock to a location of need for the group (e.g., directing the group of livestock to water)

The data processing system302,304is representative of any electronic device capable of executing machine-readable program instructions. The data processing system302,304may be representative of a smart phone, a computer system, PDA, or other electronic devices. Examples of computing systems, environments, and/or configurations that may represented by the data processing system302,304include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, and distributed cloud computing environments that include any of the above systems or devices.

The client computing device102and the server112may include respective sets of internal components302a,band external components304a,billustrated inFIG.3. Each of the sets of internal components302include one or more processors320, one or more computer-readable RAMs322, and one or more computer-readable ROMs324on one or more buses326, and one or more operating systems328and one or more computer-readable tangible storage devices330. The one or more operating systems328, the software program108and the disaster prediction and mitigation program110A in the client computing device102and the disaster prediction and mitigation program110B in the server112are stored on one or more of the respective computer-readable tangible storage devices330for execution by one or more of the respective processors320via one or more of the respective RAMs322(which typically include cache memory). In the embodiment illustrated inFIG.3, each of the computer-readable tangible storage devices330is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices330is a semiconductor storage device such as ROM324, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

Each set of internal components302a,balso includes a R/W drive or interface332to read from and write to one or more portable computer-readable tangible storage devices338such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the disaster prediction and mitigation program110A,110B, can be stored on one or more of the respective portable computer-readable tangible storage devices338, read via the respective R/W drive or interface332, and loaded into the respective hard drive330.

Each set of internal components302a,balso includes network adapters or interfaces336such as a TCP/IP adapter cards, wireless Wi-Fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The software program108and the disaster prediction and mitigation program110A in the client computing device102and the disaster prediction and mitigation program110B in the server112can be downloaded to the client computing device102and the server112from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces336. From the network adapters or interfaces336, the software program108and the disaster prediction and mitigation program110A in the client computing device102and the disaster prediction and mitigation program110B in the server112are loaded into the respective hard drive330. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components304a,bcan include a computer display monitor344, a keyboard342, and a computer mouse334. External components304a,b can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components302a,balso includes device drivers340to interface to computer display monitor344, keyboard342, and computer mouse334. The device drivers340, R/W drive or interface332, and network adapter or interface336comprise hardware and software (stored in storage device330and/or ROM324).

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and disaster prediction and mitigation96. Disaster prediction and mitigation96may relate monitoring a grazing pattern of a group of livestock and shepherding the livestock to identified portions of a preconfigured area to prevent and/or mitigate the occurrence risk of a natural disaster.