Field of the Invention
This present invention relates to an intelligent Flood Management System with deep learning technology and LoRa network.
Background of the Invention
In states like Assam, Maharashtra, Karnataka, Uttarakhand, etc. A large number of people die every year due to flood or overflowing river. Every year due to rainfall the speed of water increases as well as its depth and hence people don’t even have enough time to sense the danger and as a result loses their life.
KR102277997B1 says that the present invention relates to a hydraulic lift water gate remote control system for smart flood management. More specifically, the present invention relates to a hydraulic lift water gate remote control system for smart flood management, which is able to configure a PLC on a water gate, configure a control room with a PC installed to exchange information with the PLC at a random position which is near or far away from the water gate for remotely controlling the PLC, make one or more external terminals access the PLC, allow the PLC to manually and automatically control the water gate along with the PC of the control room, maintain the original purpose to remotely control the water gate, receive and monitor each information in real time in controlling the water gate through a smart unit, immediately respond to various situations which may occur, have a high management efficiency, allow another camera to work when a camera is out of order, continuously check the status of the mainframe of the water gate, store the PLC, which provides and controls a variety of information of the water gate and controls the mainframe and the water gate, and a hydraulic unit in a stable manner, have a high stability, and grant convenience to a worker when the worker maintains the remote control process through a separate control means.
Research Gap:
• Automation is missing in this invention.
• AI can be implemented for monitoring rate of flow to river.
• Lora WAN can be used for transmission of information.
• Direct connection with cloud server.
KR102159620B1 says that the present invention relates to an AI and deep learning-based automated smart flood management and sluice control system which is configured to control a sluice installed in the river or stream by means of an AI and deep learning-based system and integrally manage an operation of the sluice, abnormality of the sluice and the like, thereby promptly responding to a disaster. The system comprises: a CCTV camera (100) provided between inside water and outside water to record an image between the inside water and the outside water; a central server (200) receiving and storing an image photographed by the CCTV camera (100); an image preprocessing module (300) data-preprocessing the image transmitted to the central server (200); an AI extraction engine module (400) creating a water level rise or water level fall prediction model based on an image in which data preprocessing is completed in the image preprocessing module (300); and a water level control module (500) determining whether to open or close the sluice based on the prediction model created by the AI extraction engine module (400).
Research Gap:
• Direct connection with cloud server for future flood predictions.
• Gates can be set up for the safety of people.
• On the spot calculation of data.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
In states like Assam, Maharashtra, Karnataka, Uttarakhand, etc. A large number of people die every year due to flood or overflowing river. Every year due to rainfall the speed of water increases as well as its depth and hence people don’t even have enough time to sense the danger and as a result loses their life.
The present invention provides a system which provides the facility of real-time monitoring of rate of flow of water as well as its speed, depth, etc. for flood management by the use of deep learning (DL), LoRaWAN, Wi-Fi and Cloud (102). This model is basically divided into three parts. Figure 1. illustrates the first part of the invention. A system of CCTV cameras (100) is set up at a place where flow of river begins to rise along with AI deep learning module, connection with cloud (102), power source (105), co-processor (109), control unit (101), etc. The CCTV camera (100) will continuously monitor the rate of flow of river and sends all the information to control unit (101) where it is analyzed, and the information is converted in the form of graphical representation as well as the information is uploaded to the cloud server (102). The information is sent whenever there is an increase in the speed as well as the depth of river. The information is sent to the control center through control unit (101) by Lora communication and then from control center the information is sent to other parts of the invention. The information stored in the cloud (102) is used for future predictions of flood. The control unit consists of AI software which will compile and analyze the data received by CCTV Cameras (100). Then the information is sent to the control unit (101) in the form of graph only when there is a change (increase) in the obtained data. And all the data received is sent to the cloud server (102). With the help of Deep Learning and the stored data in the cloud server (102) the prediction of flood in the future can be done more efficiently.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: The system of CCTV Cameras (100) along with control unit
Figure 2. The system of Gates (108)
Figure 3. System of Sluice Gates (107)
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
The present invention provides a system which provides the facility of real-time monitoring of rate of flow of water as well as its speed, depth, etc. for flood management by the use of deep learning (DL), LoRaWAN, Wi-Fi and Cloud (102). This model is basically divided into three parts. Figure 1. illustrates the first part of the invention. A system of CCTV cameras (100) is set up at a place where flow of river begins to rise along with AI deep learning module, connection with cloud (102), power source (105), co-processor (109), control unit (101), etc. The CCTV camera (100) will continuously monitor the rate of flow of river and sends all the information to control unit (101) where it is analyzed, and the information is converted in the form of graphical representation as well as the information is uploaded to the cloud server (102). The information is sent whenever there is an increase in the speed as well as the depth of river. The information is sent to the control center through control unit (101) by Lora communication and then from control center the information is sent to other parts of the invention. The information stored in the cloud (102) is used for future predictions of flood. The control unit consists of AI software which will compile and analyze the data received by CCTV Cameras (100). Then the information is sent to the control unit (101) in the form of graph only when there is a change (increase) in the obtained data. And all the data received is sent to the cloud server (102). With the help of Deep Learning and the stored data in the cloud server (102) the prediction of flood in the future can be done more efficiently.
Figure 2. Illustrates the second part of the invention. A system of Gates (108) including CCTV cameras (100), actuators (103), siren (104) and a Dual Power source (105) is set up which are all interconnected. Upon receiving the signal from the control unit through LoRaWAN the actuators (103) firstly send signal to the siren (104) so that people present in that area can evacuate. The CCTV cameras (100) will be scouting the area thoroughly which is directly in control of the Control Center through PLC (106). After it is ensured that the place is empty the signal is sent to the gates (108) and they close so that no one can entre.
Figure 3. Illustrates the third part of the invention. A system of sluice gates (107) at river including actuator (103), direct connection with Control Center (101) by LoRaWAN, etc.When the information about the flood is received by the control center (101) through Lora communication then this information is instantly sent to the sluice gates (107) present at different locations and are opened to divide the water present in the river resulting in decrease the water level. All the sluice gates (107) are connected with each other through lora communication and having their own actuator (103) and are connected to the control center (101). All the mechanism is powered by the Dual power source (105).
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
BEST METHOD OF WORKING
In this invention we have proposed a system provides the facility of real-time monitoring of rate of flow of water as well as its speed, depth, etc. for flood management by the use of deep learning (DL), LoRaWAN, Wi-Fi and Cloud (102). This model is basically divided into three parts. Figure 1. illustrates the first part of the invention. A system of CCTV cameras (100) is set up at a place where flow of river begins to rise along with AI deep learning module, connection with cloud (102), power source (105), co-processor (109), control unit (101), etc. The CCTV camera (100) will continuously monitor the rate of flow of river and sends all the information to control unit (101) where it is analyzed, and the information is converted in the form of graphical representation as well as the information is uploaded to the cloud server (102). The information is sent whenever there is an increase in the speed as well as the depth of river. The information is sent to the control center through control unit (101) by Lora communication and then from control center the information is sent to other parts of the invention. The information stored in the cloud (102) is used for future predictions of flood. The control unit consists of AI software which will compile and analyze the data received by CCTV Cameras (100). Then the information is sent to the control unit (101) in the form of graph only when there is a change (increase) in the obtained data. And all the data received is sent to the cloud server (102). With the help of Deep Learning and the stored data in the cloud server (102) the prediction of flood in the future can be done more efficiently.
Figure 2. Illustrates the second part of the invention. A system of Gates (108) including CCTV cameras (100), actuators (103), siren (104) and a Dual Power source (105) is set up which are all interconnected. Upon receiving the signal from the control unit through LoRaWAN the actuators (103) firstly send signal to the siren (104) so that people present in that area can evacuate. The CCTV cameras (100) will be scouting the area thoroughly which is directly in control of the Control Center through PLC (106). After it is ensured that the place is empty the signal is sent to the gates (108) and they close so that no one can entre.
Figure 3. Illustrates the third part of the invention. A system of sluice gates (107) at river including actuator (103), direct connection with Control Centre (101) by LoRaWAN, etc. When the information about the flood is received by the control center (101) through Lora communication then this information is instantly sent to the sluice gates (107) present at different locations and are opened to divide the water present in the river resulting in decrease the water level. All the sluice gates (107) are connected with each other through lora communication and having their own actuator (103) and are connected to the control centre (101). All the mechanism is powered by the Dual power source (105).
ADVANTAGES OF THE INVENTION
Real time monitoring of rate of flow of river and all the information instantly sent to control unit through lora communication.
AI & DL technology is used for efficient flood management.
Negligible amount of man power needed.
Through deep learning module future prediction of flood is achieved
LoRaWAN with AI and Deep learning module based efficient flood management.
Deep learning module and cloud computing assisted architecture for future perdition of flood.
Hybrid architecture for controlling system of gates and sluice gates with the assimilation of vision and wireless technology.

We Claims:

1. An intelligent flood management system with deep learning technology and LORA network, comprising, a LoRaWAN module, a Wi-Fi module, a Cloud (102), CCTV module (100), AI deep learning module, a power source (105), a co-processor (109), and a control unit (101).
2. The system as claimed in claim 1, wherein, the CCTV camera (100) will continuously monitor the rate of flow of river and sends all the information to control unit (101) where it is analyzed, and the information is converted in the form of graphical representation as well as the information is uploaded to the cloud server (102).
3. The system as claimed in claim 1, wherein, the information is sent; whenever there is an increase in the speed as well as the depth of river and to the control center through control unit (101) by Lora communication.
4. The system as claimed in claim 1, wherein, the control unit consists of AI software which will compile and analyze the data received by CCTV Cameras (100).
5. The system as claimed in claim 1, wherein, With the help of Deep Learning and the stored data in the cloud server (102) the prediction of flood in the future can be done more efficiently.