Dynamic formation of wireless power grids from natural resources

Abstract

Dynamic formation of wireless power grids from natural resources A computer implementation method for forming a wireless power grid in an environment is provided. The embodiment comprises the steps of determining activities to be performed by a set of data acquisition devices in the environment, acquiring geographical data relating to the environment, and identifying natural power sources in the environment based on the geographical data. The embodiment also comprises the steps of deploying a power generation device to the natural power source in the environment, deploying the set of data acquisition devices in the environment to perform the activities, and deploying a plurality of wireless power transmission devices in the environment. The embodiment further comprises the steps of commanding the power generation device to collect power from the natural power source and wirelessly transmit the power to one of the plurality of wireless power transmission devices, and commanding one of the plurality of wireless power transmission devices to wirelessly transmit the power to one of the set of data acquisition devices.

Description

Background Art

[0001] The present disclosure generally relates to power generation and distribution, and more particularly to dynamically forming a wireless power grid from natural resources to supply power in any activity zone.

[0002] In many cases, it is desirable to perform surveys or inspections using robotic electronic devices in environments that do not have an existing or functioning power grid. For example, in remote areas without a power grid, or in deployed areas where a power outage has occurred in the power grid. Since robotic electronic devices require electricity to operate and often have a limited battery capacity, it is required to charge the robotic electronic devices to complete the survey or inspection.

Summary of the Invention

[0003] Embodiments of the present disclosure are directed to a computer-implemented method for forming a wireless power grid in an environment. According to one aspect, the computer-implemented method includes determining an activity to be performed by a set of data collection devices in the environment, obtaining geographic data regarding the environment, and identifying natural power sources in the environment based on the geographic data. The computer-implemented method also includes deploying power generation devices to the natural power sources in the environment, deploying the set of data collection devices in the environment to perform the activity, and deploying a plurality of wireless power transmission devices in the environment. The computer-implemented method further includes instructing the power generation devices to collect power from the natural power sources and wirelessly transmit the power to one of the plurality of wireless power transmission devices, and instructing the one of the plurality of wireless power transmission devices to wirelessly transmit the power to one of the set of data collection devices.

[0004] Other embodiments described herein implement the features of the methods described above in computer systems and computer program products.

[0005] Additional technical features and advantages are realized through the techniques of this disclosure. Embodiments and aspects of this disclosure are described in detail herein and are considered to be part of the subject matter claimed. For a better understanding, please refer to the detailed description and drawings. [Brief explanation of the drawing]

[0006] Details of the exclusive rights described herein are specifically indicated and expressly claimed in the claims at the end of this specification. The aforementioned and other features and advantages of the embodiments of this disclosure will become apparent from the following detailed description when read in conjunction with the accompanying drawings.

[0007] [Figure 1] This is a block diagram of an exemplary computer system for use in combination with one or more embodiments of the present disclosure.

[0008] [Figure 2] This is a schematic diagram of an environment having a wireless power grid according to one or more embodiments of the present disclosure.

[0009] [Figure 3] This is a block diagram of a system for supplying power to a data acquisition device by a wireless power grid, according to one or more embodiments of the present disclosure.

[0010] [Figure 4] This is a flowchart of a method for forming a wireless power grid in an environment according to one or more embodiments of the present disclosure.

[0011] [Figure 5] This figure shows an environmentally friendly power generation grid according to one or more embodiments of the present disclosure.

[0012] [Figure 6] This figure shows an energy loss grid for the environment according to one or more embodiments of the present disclosure. [Modes for carrying out the invention]

[0013] As discussed above, it is often desirable to use robotic electronic devices to perform surveys or inspections in environments that do not have an existing or functioning power grid. However, since robotic electronic devices require electricity to operate and often have limited battery capacity, it is necessary to charge the robotic electronic devices to complete the survey or inspection. In exemplary embodiments, methods, systems, and computer program products are provided for dynamically forming a wireless power grid from natural resources to power robotic electronic devices in an activity zone. In exemplary embodiments, the wireless power grid is formed by deploying power generation devices in the environment to collect power from natural sources in the environment. The wireless power grid also includes wireless power transmission devices configured to receive power from the power generation devices and to supply power to data acquisition devices, i.e., robotic electronic devices. As a result, the data acquisition devices can operate in the environment for extended periods.

[0014] Various aspects of this disclosure are described by explanatory text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in computer program product (CPP) embodiments. With respect to any flowchart, depending on the technology involved, operations may be performed in a different order than those shown in a given flowchart. For example, again depending on the technology involved, two operations shown in consecutive flowchart blocks may be performed in reverse order, as a single integrated stage, simultaneously, or with at least partial time overlap.

[0015] Computer program product embodiment ("CPP embodiment" or "CPP") is a term used in this disclosure to describe any set of one or more storage media ("mediums") that collectively comprise a set of one or more storage devices that collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations specified in a given CPP claim. A "storage device" is any tangible device capable of holding and storing instructions for use by a computer processor. Computer-readable storage media may be, but are not limited to, electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, mechanical storage media, or any suitable combination of those described above. Some known types of storage devices, including these media, include diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices (such as pits / lands formed on the main surface of a punch card or disk), or any suitable combination of the foregoing. When the term "computer-readable storage medium" is used in this disclosure, it shall not be interpreted as storage in the form of a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, optical pulses passing through optical fiber cables, electrical signals communicated through wires, and / or other transmission media.As those skilled in the art will understand, data is typically moved at several intermittent points during the normal operation of a storage device, such as during access, defragmentation, or garbage collection; however, data is not transient while it is stored, and therefore, a storage device is not transient.

[0016] The computing environment 100 includes an example of an environment for executing at least a portion of the computer code involved in carrying out the methods of the present invention, such as forming a wireless power grid 150. In addition to block 150, the computing environment 100 includes, for example, a computer 101, a wide area network (WAN) 102, an end user device (EUD) 103, a remote server 104, a public cloud 105, and a private cloud 106. In this embodiment, the computer 101 includes a processor set 110 (including processing circuits 120 and a cache 121), a communication fabric 111, volatile memory 112, persistent storage 113 (including an operating system 122 and block 150 as identified above), a peripheral device set 114 (including a user interface (UI) device set 123, storage 124, and an Internet of Things (IoT) sensor set 125), and a network module 115. The remote server 104 includes a remote database 130. The public cloud 105 includes a gateway 140, a cloud orchestration module 141, a host physical machine set 142, a virtual machine set 143, and a container set 144.

[0017] Computer 101 may take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device currently known or to be developed in the future that is capable of running programs, accessing networks, or querying databases such as remote database 130. As is well understood in the field of computer technology, and depending on the technology, the execution of the computer implementation method may be distributed among multiple computers and / or across multiple locations. On the other hand, in this presentation of the computing environment 100, in order to keep the presentation as concise as possible, the detailed discussion focuses on a single computer, specifically computer 101. Computer 101 may be located in the cloud, although it is not shown in the cloud in Figure 1. On the other hand, computer 101 is not required to be located in the cloud, except to any extent that can be definitively shown.

[0018] The processor set 110 includes one or more computer processors of any type currently known or to be developed in the future. The processing circuitry 120 may be distributed across multiple packages, for example, multiple coordinated integrated circuit chips. The processing circuitry 120 may implement multiple processor threads and / or multiple processor cores. The cache 121 is memory located within the processor chip package and is typically used for data or code that should be available for high-speed access by threads or cores running on the processor set 110. The cache memory is typically organized into multiple levels, depending on its relative proximity to the processing circuitry. Alternatively, some or all of the cache for the processor set may be located "off-chip". In some computing environments, the processor set 110 may function using qubits and be designed to perform quantum computing.

[0019] Computer-readable program instructions typically cause a set of operational steps to be executed by the processor set 110 of computer 101, thereby loading them onto computer 101 to implement a computer implementation method, and thereby the instructions thus executed instantiate the methods specified in the flowcharts and / or descriptions of the computer implementation methods contained herein (collectively referred to as the "Methods of the Invention"). These computer-readable program instructions are stored in various types of computer-readable storage media, such as a cache 121 and other storage media discussed below. The program instructions and associated data are accessed by the processor set 110 to control and direct the execution of the Methods of the Invention. In the computing environment 100, at least some of the instructions for executing the Methods of the Invention may be stored in blocks 150 in persistent storage 113.

[0020] The communication fabric 111 is a signal conduction path that enables various components of the computer 101 to communicate with one another. Typically, this fabric is made up of switches and conductive paths, such as buses, bridges, physical input / output ports, and similar switches and conductive paths. Other types of signal communication paths, such as optical fiber communication paths and / or wireless communication paths, may be used.

[0021] The volatile memory 112 is any type of volatile memory currently known or to be developed in the future. Examples include dynamic random-access memory (RAM) or static RAM. Typically, volatile memory is characterized by random access, but this is not required unless explicitly stated. In computer 101, the volatile memory 112 is located in a single package and resides inside computer 101, but alternatively or in addition, the volatile memory may be distributed across multiple packages and / or located externally to computer 101.

[0022] The persistent storage 113 is any form of non-volatile storage for a computer, known currently or developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether direct power is supplied to the computer 101 and / or to the persistent storage 113. The persistent storage 113 may be read-only memory (ROM), but typically at least a portion of the persistent storage enables writing of data, deletion of data, and rewriting of data. Some well-known forms of persistent storage include magnetic disks and solid-state storage devices. The operating system 122 may take several forms, such as various known proprietary operating systems or an open-source Portable Operating System Interface (POSIX)-type operating system that employs a kernel. The code included in block 150 typically includes at least a portion of the computer code involved in the execution of the method of the present invention.

[0023] The peripheral device set 114 includes a set of peripheral devices of the computer 101. Data communication connections between the peripheral devices of the computer 101 and other components may be implemented in various ways, such as Bluetooth connections, near-field communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insert-type connections (e.g., secure digital (SD) cards), connections made via local area communication networks, and even connections made via wide area networks such as the internet. In various embodiments, the UI device set 123 may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smartwatches), keyboard, mouse, printer, touchpad, game controller, and haptic device. Storage 124 is external storage such as an external hard drive, or insertable storage such as an SD card. Storage 124 may be persistent and / or volatile. In some embodiments, storage 124 may take the form of a quantum computing memory device for storing data in the form of qubits. In embodiments where computer 101 is required to have a large amount of storage (for example, computer 101 locally stores and manages a large database), this storage may be provided by peripheral storage devices designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. The IoT sensor set 125 consists of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another may be a motion detector.

[0024] The network module 115 is an aggregate of computer software, hardware, and firmware that enables the computer 101 to communicate with other computers via the WAN 102. The network module 115 may include hardware such as a modem or a Wi-Fi transceiver, software for packetizing and / or depacketizing data for communication over a communication network, and / or web browser software for communicating data over the Internet. In some embodiments, the network control function and the network transfer function of the network module 115 are executed on the same physical hardware device. In other embodiments (e.g., embodiments that utilize software-defined networking (SDN)), the control function and the transfer function of the network module 115 are executed on physically separate devices such that the control function manages several different network hardware devices. The computer-readable program instructions for executing the method of the present invention can typically be downloaded to the computer 101 from an external computer or an external storage device through a network adapter card or a network interface included in the network module 115.

[0025] The WAN 102 is any wide area network (e.g., the Internet) capable of communicating computer data over a non-local distance by any technique for communicating computer data that is currently known or developed in the future. In some embodiments, the WAN may be replaced and / or supplemented by a local area network (LAN) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and / or LAN typically includes computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers.

[0026] The end-user device (EUD) 103 is any computer system used and controlled by an end-user (e.g., a customer of the company operating computer 101) and can take any of the forms discussed above in relation to computer 101. Typically, EUD 103 receives useful and valuable data from the operation of computer 101. For example, in a hypothetical case where computer 101 is designed to provide recommendations to the end-user, these recommendations would typically be communicated from the network module 115 of computer 101 to EUD 103 via WAN 102. In this way, EUD 103 can display or otherwise present the recommendations to the end-user. In some embodiments, EUD 103 may be a client device such as a thin client, heavy client, mainframe computer, or desktop computer.

[0027] The remote server 104 is any computer system that provides at least some data and / or functionality to computer 101. The remote server 104 may be controlled and used by the same entity that operates computer 101. The remote server 104 represents a machine that collects and stores useful and valuable data for use by other computers, such as computer 101. For example, in the hypothetical case where computer 101 is designed and programmed to provide recommendations based on historical data, this historical data may be provided to computer 101 from the remote database 130 of the remote server 104.

[0028] The public cloud 105 is any computer system available for use by multiple entities, providing on-demand availability of computer system resources and / or other computing capabilities, particularly data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages resource sharing to achieve coherence and economies of scale. Direct active management of the computing resources of the public cloud 105 is performed by the computer hardware and / or software of the cloud orchestration module 141. The computing resources provided by the public cloud 105 are typically implemented by virtual computing environments running on various computers that make up the computers of the host physical machine set 142, which is the universe of physical computers available in and / or to the public cloud 105. The virtual computing environment (VCE) typically takes the form of virtual machines from the virtual machine set 143 and / or containers from the container set 144. These VCEs may be stored as images and are understood to be transferable either as images or after instantiation of the VCE, among and between various physical machine hosts. The cloud orchestration module 141 manages the transfer and storage of images, deploys new VCE instances, and manages active instances of the VCE deployment. The gateway 140 is a collection of computer software, hardware, and firmware that enables the public cloud 105 to communicate over the WAN 102.

[0029] Here, some further explanation of virtualized computing environments (VCEs) is provided. A VCE can be stored as an "image." A new active instance of a VCE can be instantiated from an image. Two well-known types of VCEs are virtual machines and containers. A container is a VCE that uses operating system-level virtualization. This refers to an operating system feature in which the kernel allows for the existence of multiple isolated user-space instances called containers. These isolated user-space instances typically behave as actual computers from the perspective of the programs running in them. Computer programs running on a normal operating system can utilize all of that computer's resources, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and the devices allocated to the container; this feature is known as containerization.

[0030] The private cloud 106 is similar to the public cloud 105, except that its computing resources are available only for use by a single enterprise. While the private cloud 106 is shown as communicating with the WAN 102, in other embodiments, the private cloud may be completely isolated from the internet and accessible only via a local / private network. A hybrid cloud is a combination of multiple clouds of different types (e.g., private, community, or public cloud types), often implemented by different vendors. Each of the multiple clouds remains a separate discrete entity, but the larger hybrid cloud architecture is coupled by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple configuration clouds. In this embodiment, both the public cloud 105 and the private cloud 106 are part of a larger hybrid cloud.

[0031] One or more embodiments described herein may utilize machine learning techniques to perform, for example, prediction and / or classification tasks. In one or more embodiments, the machine learning function may be implemented using an artificial neural network (ANN) that has the ability to be trained to perform a function. In machine learning and cognitive science, ANNs are a group of statistical learning models inspired by the biological neural networks of animals, particularly the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs. Convolutional neural networks (CNNs) are a class of deep feedforward ANNs that are particularly useful in tasks such as visual image analysis and natural language processing (NLP). Recurrent neural networks (RNNs) are another class of deep feedforward ANNs that are particularly useful in tasks such as recognition of unsegmented connected handwritten characters and speech recognition. Other types of neural networks are also known and may be used according to one or more embodiments described herein.

[0032] An ANN (Analog Network) can be embodied as a so-called "neuromorphological" system of interconnected processor elements that function as simulated "neurons" and exchange "messages" with each other in the form of electronic signals. Similar to the so-called "plasticity" of synaptic neurotransmitter connections that carry messages between biological neurons, connections in an ANN that carry electronic messages between simulated neurons are given numerical weights corresponding to the strength or weakness of a given connection. These weights can be adjusted and tuned based on experience, allowing the ANN to become adaptive and learn to its inputs. For example, an ANN for handwritten character recognition is defined by a set of input neurons that can be activated by pixels in an input image. After being weighted and transformed by a function determined by the network designer, the activation of these input neurons is then passed on to other downstream neurons, often referred to as "hidden" neurons. This process is repeated until an output neuron is activated. The activated output neuron determines which character was input. It should be understood that these same techniques can be applied to the case of containers, which are VCEs that use operating system-level virtualization. This refers to an operating system feature where the kernel enables the existence of multiple isolated user-space instances called containers. These isolated user-space instances typically behave like actual computers from the perspective of the programs running within them. Computer programs running on a normal operating system can utilize all of that computer's resources, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and the devices allocated to the container; this feature is known as containerization.

[0033] Referring now to Figure 2, a schematic diagram of an environment 200 having a wireless power grid according to one or more embodiments of the present disclosure is shown. In one embodiment, the environment 200 includes a plurality of data acquisition devices 210, a plurality of power generation devices 230, and a plurality of wireless power transmission devices 220. As shown, each of the plurality of power generation devices 230 is located within the environment 200 such that the power generation device 230 can generate power from a natural power source 202. In an exemplary embodiment, the natural power source 202 includes one of photovoltaic, hydroelectric, wind, or chemical power generation. In an exemplary embodiment, depending on the location of the power generation devices 230 and the data acquisition devices 210, one or more wireless power transmission devices 220 are arranged between the power generation devices 230 and the data acquisition devices 210 to facilitate wireless power transfer from the power generation devices 230 to the data acquisition devices 210.

[0034] In exemplary embodiments, the data acquisition device 210 is a robotic electronic device configured to survey an environment 200 and / or collect data about the environment. In exemplary embodiments, the data acquisition device 210 is configured to survey the environment by traversing it to collect information about the terrain, topography, and other features of a particular area. The data acquisition device 210 may utilize one or more of the following to collect data about the environment: a global positioning system (GPS), a camera, LiDAR, and ground penetrating radar (GPR). Generally, GPS determines the location of a point on the ground. A GPS receiver can determine the latitude, longitude, and elevation of a point on the ground by receiving signals from GPS satellites. LiDAR uses a laser scanner to measure the distance between the scanner and the ground. The scanner emits laser pulses and measures the time it takes for the pulses to reflect back to the scanner, which can be used to calculate the distance between the scanner and the ground. GPR uses radar waves to penetrate the ground and detect subsurface features of embedded public facilities or geological formations.

[0035] In exemplary embodiments, the data acquisition device 210 is a robotic electronic device configured to collect environmental data. For example, in one embodiment, the data acquisition device 210 includes a Geiger counter used to measure radiation levels in the environment. In another embodiment, the data acquisition device 210 includes an air quality sensor used to measure various airborne pollutants such as particulate matter, volatile organic compounds (VOCs), carbon monoxide, and nitrogen dioxide. In exemplary embodiments, the data measured by the data acquisition device 210 is collected and stored for subsequent analysis.

[0036] Referring here to Figure 3, a block diagram of a system 300 for powering data acquisition devices by a wireless power grid according to one or more embodiments of the present disclosure is shown. As shown, the system 300 includes one or more data acquisition devices 310 configured to wirelessly receive power from one or more wireless power transmission devices 320 and / or power generation devices 330. In exemplary embodiments, electromagnetic waves are used to transmit power between the power generation devices 330 and one or more wireless power transmission devices 320 and data acquisition devices 310 over a distance without requiring physical wires or cables. The use of microwaves for wireless power transmission involves converting power into microwaves, which are then transmitted through the air using antennas. The microwaves are then received by another antenna and converted back into power, which can be used to power electronic devices or rechargeable batteries. In another embodiment, laser-based wireless power transmission can be used, which involves using a laser to transport power over a distance. Laser-based wireless power transmission works by converting electrical power into light, which is then directed through the air using a laser beam. The light is then received by a photovoltaic cell, which converts the light back into electrical power.

[0037] In an exemplary embodiment, the data acquisition device 310 is a robotic electronic device comprising a transceiver 312, one or more sensors 314, a processor 316, and a battery 318. In an exemplary embodiment, the sensor 314 is configured to collect data about the environment in which the data acquisition device 310 is deployed. Depending on the type of survey being conducted, the sensor 314 may collect air or soil samples, take photographs, perform a LiDAR or radar scan, or similar. The samples may be returned to a central location for further analysis, or the samples may be deployed after the data acquisition device 310 has obtained desired measurements from the samples. The transceiver 312 is configured to communicate wirelessly with other data acquisition devices 310, one or more wireless power transmission devices 320, and one or more power generation devices 330. In an exemplary embodiment, wireless communication between devices 310, 320, and 330 is used to exchange the operating status, battery level, and location of each device. In addition, the transceiver 312 is configured to receive wireless power from one or more wireless power transmission devices 320 and one or more power generation devices 330. In an exemplary embodiment, the data acquisition device 310 is configured to store the power received from the wireless power transmission device 320 in a battery 318. The processor 316 is configured to control the operation of the data acquisition device 310. The processor 316 includes a central processing unit, an application-specific integrated circuit (ASIC), a digital signal processor, a field-programmable gate array (FPGA), digital circuits, analog circuits, and combinations thereof.

[0038] In an exemplary embodiment, the wireless power transmission device 320 is a robotic electronic device comprising a transceiver 322, a processor 326, and a battery 328. The wireless power transmission device 320 may be embodied in a vehicle such as an aerial or ground vehicle, which may be configured to be remotely controlled by an operator or to operate autonomously. The transceiver 322 is configured to communicate wirelessly with other wireless power transmission devices 320, one or more data acquisition devices 310, and one or more power generation devices 330. In addition, the transceiver 322 is configured to receive wireless power from one or more wireless power transmission devices 320 and one or more power generation devices 330. In an exemplary embodiment, electromagnetic waves are used to transmit power between a power generation device 230 and one or more wireless power transmission devices 320 over a distance. The processor 326 is configured to control the operation of the wireless power transmission device 320. The processor 326 includes a central processing unit, an application-specific integrated circuit (ASIC), a digital signal processor, a field-programmable gate array (FPGA), digital circuits, analog circuits, and combinations thereof. In an exemplary embodiment, the wireless power transmission device 320 is configured to store power received from the power generation device 330 in a battery 328.

[0039] In an exemplary embodiment, the power generation device 230 is a robotic electronic device including a transceiver 332, a power recovery device 334, a processor 336, and a battery 338. In an exemplary embodiment, the power recovery device 334 is one of a hydroelectric power generation device, a solar power generation device, and a wind power generation device. The power recovery device 334 is configured to recover power from natural sources and store the recovered power in the battery 338. The processor 336 is configured to control the operation of the wireless power transmission device 320. The processor 326 includes a central processing unit, an application-specific integrated circuit (ASIC), a digital signal processor, a field-programmable gate array (FPGA), digital circuits, analog circuits, and combinations thereof. The transceiver 332 is configured to communicate wirelessly with one or more of the other power generation devices 230, one or more data acquisition devices 310, and one or more wireless power transmission devices 320. In addition, the transceiver 332 is configured to transmit wireless power to one or more wireless power transmission devices 320 and one or more power generation devices 330.

[0040] Referring here to Figure 4, a flowchart of Method 400 for forming a wireless power grid in an environment according to one or more embodiments of the present disclosure is shown. In exemplary embodiments, Method 400 is performed by a computer 101, such as the one shown in Figure 1, which controls the operation of data acquisition devices, wireless power transmission devices, and power generation devices. Method 400 includes a step of determining the activities to be performed by the set of data acquisition devices in the environment. In exemplary embodiments, the activity determination step includes a step of calculating the total power required by the set of data acquisition devices to complete the activities. In one example, the total power required is calculated based on the power consumption demand of the data acquisition devices to be deployed and the size of the environment to be surveyed. For example, the power required per square mile for the type of survey being conducted can be calculated, and the total power can be obtained by multiplying the power consumption per square mile of the data acquisition devices by the total area of ​​the environment to be surveyed.

[0041] Next, as shown in block 404, method 400 includes a step of acquiring geographical data about the environment. In exemplary embodiments, geographical data about the environment includes environmental images, environmental radar images, environmental maps, forecasts of solar, wind, and / or wave activity for the environment, and similar. Geographical data can be acquired via high-altitude aerial surveys, satellite-captured images, radar maps showing wind, solar, and cloud conditions for the environment, and environmental images captured from one or more of these combinations. In block 406, method 400 includes a step of identifying natural power sources in the environment based on the geographical data. In exemplary embodiments, natural power sources can be identified using a trained machine learning model that analyzes the acquired geographical data and for labeling potential natural power sources. In another embodiment, natural power sources are identified and labeled by a user who manually reviews the acquired geographical data. In exemplary embodiments, natural power sources may include one or more of solar, hydroelectric, wind, or chemical power sources. In an exemplary embodiment, each identified natural energy source is labeled with a type of natural energy source, which is used to determine the type of power generation device required to collect power from the natural energy source.

[0042] Method 400 also includes the step of deploying one or more power generation devices to one or more natural power sources in the environment, as shown in block 408. In exemplary embodiments, the number of power generation devices is determined on at least partly based on the total power required. In exemplary embodiments, the deployed power generation devices include several different types of power recovery devices and are deployed to identified locations of natural power sources. Method 400 then includes the step of deploying a set of data acquisition devices in the environment to perform activities, as shown in block 410.

[0043] Method 400 further includes the step of deploying a plurality of wireless power transmission devices into the environment, as shown in block 412. In an exemplary embodiment, the deployment of power generation devices, power transmission devices and data acquisition devices is initiated by the user after the number and deployment locations for each of the power generation devices, power transmission devices and data acquisition devices have been determined. Next, in block 414, Method 400 includes the step of instructing one or more power generation devices to collect power from natural sources and wirelessly transmit the power to one of the plurality of wireless power transmission devices. Method 400 concludes in block 416 by instructing one of the plurality of wireless power transmission devices to wirelessly transmit the power to one of the set of data acquisition devices.

[0044] In an exemplary embodiment, the deployment location of each data acquisition device is determined based on the data to be collected by the data acquisition device, and the location of the power generation device is determined based on the location of the natural power source. Once the locations of the data acquisition and power generation devices are determined, a plurality of wireless transmission lines from each natural power source to the deployment locations of the data acquisition devices are identified. In an exemplary embodiment, an estimated transmission loss is calculated for each of the plurality of wireless transmission lines, and one wireless transmission line is selected from the plurality of wireless transmission lines based at least in part on the estimated transmission loss. The location of the wireless transmission device is then determined based on the selected wireless transmission line. In an exemplary embodiment, at least one of the plurality of wireless transmission lines includes one of the plurality of wireless transmission devices.

[0045] In exemplary embodiments, acquired geographical data relating to the environment in which data acquisition devices, wireless power transmission devices, and power generation devices will be deployed is analyzed and divided into grids, for example, a matrix of cells, each representing a physical portion of the environment. In exemplary embodiments, power generation grids and energy loss grids are created and used to determine the deployment locations for data acquisition devices, wireless power transmission devices, and power generation devices. In exemplary embodiments, power generation grids are created by dividing the environment into fixed-size cells and labeling each cell with the estimated power generation capacity of the natural resources located in each cell. In exemplary embodiments, energy loss grids are created by dividing the environment into fixed-size cells and labeling each cell with the estimated transmission loss associated with wireless power transmission across the cell.

[0046] Referring now to Figure 5, a power generation grid 500 for an environment is shown according to one or more embodiments of the present disclosure. In exemplary embodiments, the power generation grid 500 is created by dividing the environment into fixed-size cells and labeling each cell with the estimated power generation capacity of the natural sources located in each cell. The estimated power generation capacity of the natural sources located in each cell is obtained based on geographical data acquired about the environment. In one embodiment, the power generation grid 500 is created and stored by a computing environment 100, such as the one shown in Figure 1. As shown, the power generation grid 500 includes a plurality of cells 502 arranged in a matrix. In exemplary embodiments, each cell represents a portion of the environment; for example, a cell may represent a 100-square-foot (9.2903 square meter) area of ​​the environment. Each cell 502 includes the estimated power generation capacity 504 of the natural sources located in the area represented by the cell 502.

[0047] In exemplary embodiments, the power grid 500 may be updated periodically or continuously based on changes in geographical data about the environment. For example, the estimated power generation capacity of natural sources such as solar and wind power may change frequently based on real-time environmental conditions, such as changes in cloud density or wind intensity in different parts of the environment. As a result of updating the power grid, the deployment locations of one or more devices among the power generation devices, wireless power transmission devices, and data collection devices may be updated.

[0048] Referring here to Figure 6, an energy loss grid 600 for an environment is shown according to one or more embodiments of the present disclosure. In exemplary embodiments, the energy loss grid 600 is created by dividing the environment into fixed-size cells and labeling each cell with an estimated transmission loss associated with wireless power transmission across the cell. In one embodiment, the energy loss grid 600 is created and stored by a computing environment 100, such as the one shown in Figure 1. In exemplary embodiments, the energy loss grid 600 may be updated periodically or continuously based on changes in geographical data about the environment. For example, the estimated transmission loss may change based on real-time conditions in the environment, such as changes in cloud density or rainfall in a portion of the environment. As a result of updating the energy loss grid, the deployment locations of one or more devices, including power generation devices, wireless power transmission devices, and data acquisition devices, may be updated.

[0049] In an exemplary embodiment, the energy loss grid 600 includes cells arranged in the same configuration as the power generation grid 500. In one embodiment, a data acquisition device 608 is deployed at a fixed location to collect data at that location. The location of the data acquisition device 608 is determined based on the data being collected. For example, the data acquisition device 608 may include a Geiger counter used to periodically measure radiation levels at a location over a specified duration.

[0050] Next, based on the power demand of the data acquisition device 608, deployment locations for the wireless power transmission device and power generation device are determined using the energy loss grid 600. In an exemplary embodiment, the energy loss grid 600 includes cell 610, which contains obstacles that would hinder wireless power transmission over an area represented by cell 610. In an exemplary embodiment, the location for power generation device 602 is evaluated based on the estimated power generation capacity of the location and the estimated transmission loss between that location and the data acquisition device 608. The energy loss grid 600 includes the cumulative estimated transmission loss 603 for the transmission line 606. For example, the transmission loss from power generation device 602 to wireless power transmission device (r1) 604 is 1.5. The cumulative estimated transmission loss 603 is obtained by adding the estimated transmission losses between adjacent cells.

[0051] In exemplary embodiments, the deployment location of each of the one or more power generation devices and wireless transmission devices is determined at least in part on the power generation grid and the energy loss grid. In one embodiment, the optimal locations of the power generation device (P) 602 and the wireless transmission device (r) 604 are determined using the following formula:

number

[0052] Various embodiments are described herein with reference to the relevant drawings. Alternative embodiments can be devised without departing from the scope of this disclosure. Various connections and positional relationships (e.g., above, below, adjacent, etc.) are described between elements in the following description and drawings. These connections and / or positional relationships may be direct or indirect unless otherwise specified, and this disclosure is not intended to limit them in this respect. Thus, connections between entities may refer to either direct or indirect connections, and positional relationships between entities may be direct or indirect positional relationships. Furthermore, various tasks and process steps described herein may be incorporated into broader procedures or processes having additional steps or functions not described in detail herein.

[0053] One or more of the methods described herein can be implemented using any of the following technologies, each well known in the art: discrete logic circuits having logic gates for implementing logic functions for data signals, application-specific integrated circuits (ASICs) having appropriate combinations of logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), or any combination thereof.

[0054] For the sake of brevity, prior art techniques relating to the creation and use of embodiments of this disclosure may or may not be described in detail herein. In particular, various embodiments of computing systems and specific computer programs for implementing the various technical features described herein are well known. Therefore, for the sake of brevity, many prior art implementation details are mentioned only briefly herein or are omitted entirely without providing details of well known systems and / or processes.

[0055] In some embodiments, various functions or operations may be performed at a given location and / or in connection with the operation of one or more devices or systems. In some embodiments, a portion of a given function or operation may be performed at a first device or location, and the remainder of that function or operation may be performed at one or more additional devices or locations.

[0056] The technical terms used herein are intended solely to describe specific embodiments and are not intended to limit them. Where used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless otherwise clearly indicated by the context. Where used herein, the terms "comprises" and / or "comprising" specify the presence of the mentioned features, integers, stages, actions, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, stages, actions, elemental components, and / or groups thereof.

[0057] All means or step-plus-function elements in the following claims are intended to include any structures, materials, or actions for performing a function in combination with other specifically claimed elements. While this disclosure is presented for illustrative and explanatory purposes, it is not intended to be exhaustive or to limit the disclosed forms. Many modifications and variations will become apparent to those skilled in the art without departing from the scope and spirit of this disclosure. The embodiments have been selected and described to best illustrate the principles and practical applications of this disclosure and to enable those skilled in the art to understand the disclosure in terms of various modifications to suit specific intended uses.

[0058] The figures shown herein are illustrative. Many variations can be made to the figures or the steps (or actions) described in the figures without departing from the spirit of this disclosure. For example, actions can be performed in a different order, or actions can be added, deleted, or modified. Also, the term “coupled” describes the presence of a signaling path between two elements and does not imply a direct connection between elements without an intervening element / connection between them. All of these variations are considered part of this disclosure.

[0059] The following definitions and abbreviations are used for the purposes of the claims and interpretation of this specification. Where used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains,” or “containing,” or any other variation thereof, are intended to cover non-exclusive inclusion. For example, a composition, mixture, process, method, article, or apparatus containing a list of elements is not necessarily limited to those elements alone, and may include other elements not expressly listed or that are inherent to such composition, mixture, process, method, article, or apparatus.

[0060] In addition, the term “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily construed to be preferable or advantageous to other embodiments or designs. The terms “at least one” and “one or more” are understood to include any integer greater than or equal to 1, i.e., 1, 2, 3, 4, etc. The term “multiple” is understood to include any integer greater than or equal to 2, i.e., 2, 3, 4, 5, etc. The term “connection” may include both indirect “connection” and direct “connection.”

[0061] The terms “about,” “substantially,” and “approximately,” and their variations, are intended to include the degree of error associated with measurements of specific quantities based on equipment available at the time of filing this application. For example, “about” may include a range of ±8%, 5%, or 2% of a given value.

[0062] The descriptions of the various embodiments of this disclosure are presented for illustrative purposes only and are not intended to be exhaustive or to limit oneself to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been selected to best describe the principles, practical applications, or technical improvements to the technologies available on the market of the embodiments, or to enable other persons skilled in the art to understand the embodiments described herein.

Claims

1. A method for forming a wireless power grid in an environment, wherein the method is A step of determining the activities to be performed by the set of data acquisition devices in the aforementioned environment; The step of acquiring geographical data relating to the aforementioned environment; A step of identifying natural power sources in the environment based on the aforementioned geographical data; A step of deploying one or more power generation devices to one or more of the natural power sources in the aforementioned environment; The step of deploying the set of data acquisition devices in the aforementioned environment and performing the aforementioned activity; The step of deploying multiple wireless power transmission devices in the aforementioned environment; A step of commanding one or more of the aforementioned power generation devices to collect power from the natural power source and to wirelessly transmit the power to one of the aforementioned wireless power transmission devices; and The step of commanding one of the plurality of wireless power transmission devices to wirelessly transmit power to one of the set of data acquisition devices. A method that includes [a certain feature].

2. The method according to claim 1, further comprising the step of calculating the total power required by the set of data acquisition devices to complete the activity, wherein the number of the one or more power generation devices is determined at least in part on the total power required.

3. The method according to claim 1 or 2, wherein the one or more power generation devices include one or more of a hydroelectric power generation device, a photovoltaic power generation device, and a wind power generation device.

4. A step of identifying the deployment location of the first data acquisition device of the set of data acquisition devices in the environment; A step of identifying multiple wireless transmission lines from each natural power source to the deployment location; A step of calculating the estimated transmission loss for each of the plurality of wireless transmission lines; and The step of selecting a wireless transmission path from the plurality of wireless transmission paths based at least in part on the estimated transmission loss. The method according to any one of the prior claims, further comprising:

5. The method according to claim 4, wherein at least one of the plurality of wireless power transmission lines includes one of the plurality of wireless power transmission devices.

6. The method according to any one of the preceding claims, further comprising the step of creating a power grid for the environment based on the acquired geographical data, wherein the power grid includes the location of each of the natural sources and the estimated power generation capacity of the natural sources.

7. The method according to claim 6, further comprising the step of creating an energy loss grid for the environment based on the acquired geographical data, wherein the energy loss grid includes a matrix of cells representing a portion of the environment, and the energy loss grid includes estimated power transmission losses between cells.

8. The method according to claim 7, wherein the deployment location of each of the one or more power generation devices and the wireless power transmission device is determined at least in part on the power generation grid and the energy loss grid.

9. A computing system comprising a memory having computer-readable instructions, and one or more processors for executing said computer-readable instructions, wherein the computer-readable instructions are A procedure for determining the activities that will be performed by a set of data collection devices in an environment; Procedure for obtaining geographical data related to the aforementioned environment; A procedure for identifying natural power sources in the environment based on the aforementioned geographical data; A procedure for deploying one or more power generation devices to one or more of the natural power sources in the aforementioned environment; A procedure for deploying the set of data acquisition devices in the aforementioned environment and performing the aforementioned activity; A procedure for deploying multiple wireless power transmission devices in the aforementioned environment; A procedure for commanding one or more of the aforementioned power generation devices to collect power from the natural power source and to wirelessly transmit the power to one of the aforementioned wireless power transmission devices; and A procedure for commanding one of the plurality of wireless power transmission devices to wirelessly transmit power to one of the set of data acquisition devices. A computing system that controls one or more processors to perform operations including the following.

10. The computing system according to claim 9, wherein the operation further includes a step of calculating the total power required by the set of data acquisition devices to complete the activity, the number of the one or more power generation devices is determined at least in part on the total power required.

11. The computing system according to claim 9 or 10, wherein the one or more power generation devices include one or more of a hydroelectric power generation device, a solar power generation device, and a wind power generation device.

12. The aforementioned operation is, A procedure for identifying the deployment location of the first data acquisition device of the set of data acquisition devices in the environment; A procedure for identifying multiple wireless transmission lines from each natural power source to the deployment location; A procedure for calculating the estimated transmission loss for each of the plurality of wireless transmission lines; and A procedure for selecting a wireless transmission line from a plurality of wireless transmission lines based at least in part on the estimated transmission loss. A computing system according to any one of claims 9 to 11, further comprising:

13. The computing system according to claim 12, wherein at least one of the plurality of wireless power transmission lines includes one of the plurality of wireless power transmission devices.

14. The computing system according to any one of claims 9 to 13, wherein the operation further includes the step of creating a power grid for the environment based on the acquired geographical data, the power grid including the location of each natural power source and the estimated power generation capacity of the natural power sources.

15. The computing system according to claim 14, wherein the operation further includes the step of creating an energy loss grid for the environment based on the acquired geographical data, the energy loss grid includes a matrix of cells representing a portion of the environment, and the energy loss grid includes estimated power transmission losses between cells.

16. The computing system according to claim 15, wherein the deployment location of each of the one or more power generation devices and the wireless power transmission device is determined at least in part on the power generation grid and the energy loss grid.

17. A computer program product comprising a computer-readable storage medium in which program instructions are embodied, wherein the program instructions are transmitted to a processor, A procedure for determining the activities that will be performed by a set of data collection devices in an environment; Procedure for obtaining geographical data related to the aforementioned environment; A procedure for identifying natural power sources in the environment based on the aforementioned geographical data; A procedure for deploying one or more power generation devices to one or more of the natural power sources in the aforementioned environment; A procedure for deploying the set of data acquisition devices in the aforementioned environment and performing the aforementioned activity; A procedure for deploying multiple wireless power transmission devices in the aforementioned environment; A procedure for commanding one or more of the aforementioned power generation devices to collect power from the natural power source and to wirelessly transmit the power to one of the aforementioned wireless power transmission devices; and A procedure for commanding one of the plurality of wireless power transmission devices to wirelessly transmit power to one of the set of data acquisition devices. A computer program product that is executable by the processor to perform an operation including the following.

18. The computer program product according to claim 17, wherein the operation further includes a step of calculating the total power required by the set of data acquisition devices to complete the activity, and the number of the one or more power generation devices is determined at least in part on the total power required.

19. The computer program product according to claim 17 or 18, wherein the one or more power generation devices include one or more of a hydroelectric power generation device, a solar power generation device, and a wind power generation device.

20. The aforementioned operation is, A procedure for identifying the deployment location of the first data acquisition device of the set of data acquisition devices in the environment; A procedure for identifying multiple wireless transmission lines from each natural power source to the deployment location; A procedure for calculating the estimated transmission loss for each of the plurality of wireless transmission lines; and A procedure for selecting a wireless transmission line from a plurality of wireless transmission lines based at least in part on the estimated transmission loss. A computer program product according to any one of claims 17 to 19, further comprising:

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