Methods for processing information related to waste oil recovery.

Abstract

The invention relates to a method for processing information concerning the recovery of waste oil, performed by a computing device. This method comprises the following steps: obtaining information on the weight of the container in which the waste oil is contained; obtaining information on the means of transport used to transport the container in which the waste oil is contained; and calculating the carbon emissions from the container based on the information on the weight of the container in which the waste oil is contained and the information on the means of transport.

Description

How to handle information related to waste oil recovery

[0001] The present invention relates to a method for processing information related to waste oil recovery, and more particularly, to a technique for calculating carbon emissions based on information related to waste oil recovery.

[0002] Recently, global warming and the resulting climate change have become increasingly serious. Countries around the world, including Korea, are focusing on alternative energy sources, such as biofuels, to combat global warming, the primary cause of climate change, and to address energy resource depletion. The most representative examples are Sustainable Aviation Fuel (SAF) and biodiesel. Sustainable aviation fuel (SAF) can be produced by refining waste oil (e.g., waste cooking oil).

[0003] Meanwhile, when collecting waste oil, collection information is manually recorded or randomly signed and stored. This makes it difficult to ensure the reliability of data collection on the origin of waste oil. Furthermore, data collection during the transport and collection process of waste oil is not conducted, making it impossible to understand the entire process of waste oil (e.g., waste cooking oil), a key raw material for bioenergy, from generation to transport and utilization.

[0004] Korean Patent Publication No. 10-2004-0106877 (December 18, 2004) discloses a method for voluntary collection, collection, and management of waste cooking oil or animal fat nationwide.

[0005] The present disclosure aims to provide a method for processing information related to waste oil recovery, which enables monitoring of the entire process for raw materials by recording all data from the initial generation of waste oil to movement and collection.

[0006] In addition, the present disclosure aims to provide a method for calculating carbon emissions based on information related to waste oil recovery and processing information related to waste oil recovery that can be utilized to generate carbon emission rights information in the process of waste oil being converted into bio-raw materials.

[0007] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0008] According to one embodiment of the present disclosure, a method for processing information related to waste oil recovery performed by a computing device to achieve the aforementioned task is disclosed. The method may include the steps of: obtaining weight information of the waste oil; obtaining information regarding a means of transporting the waste oil; and calculating a carbon emission amount for the waste oil based on the weight information of the waste oil and the information regarding the means of transport.

[0009] In one embodiment, the information about the means of transportation may include at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation.

[0010] In one embodiment, the step of calculating the carbon emissions for the waste oil based on the weight information of the waste oil and the information about the moving means may include the steps of: calculating the carbon emissions of the moving means based on the information about the moving means for a section moved after the waste oil stored in the container was loaded onto the moving means; calculating the contribution rate of the waste oil for the section based on the weight information of the waste oil; and calculating the carbon emissions of the waste oil based on the carbon emissions of the moving means for the section and the contribution rate of the waste oil for the section.

[0011] In one embodiment, the step of calculating the contribution rate of the waste oil for the section may include the step of calculating the contribution rate of the waste oil based on weight information of the waste oil and weight information of other waste oils loaded together on the moving means during the section.

[0012] In one embodiment, the number of other waste oils loaded together with the moving means during the section may vary by additional loading, and the contribution rate of the waste oil may dynamically vary within the section according to the variation.

[0013] In one embodiment, the method may further include a step of generating carbon credit information related to the recovery of the waste oil based on information about the waste oil and carbon emission information for the waste oil.

[0014] In one embodiment, the method may further include the step of obtaining weight information for each point where the waste oil is collected; and the step of calculating carbon emissions for each point based on the weight information for each point and information about the means of transportation.

[0015] In one embodiment, the step of calculating the carbon emissions per point based on the weight information per point and the information about the means of transportation may include the steps of: calculating the carbon emissions of the means of transportation for a section moved after waste oils at a specific point are loaded onto the means of transportation; calculating a contribution rate of the specific point to the section based on the weight information of the specific point; and calculating the carbon emissions of the specific point based on the carbon emissions of the means of transportation for the section and the contribution rate of the specific point to the section.

[0016] In one embodiment, the step of calculating the contribution rate of the specific point to the section may include the step of calculating the contribution rate of the specific point based on weight information of the specific point and weight information of other waste oils loaded together on the moving means during the section.

[0017] In one embodiment, the number of other waste oils loaded together on the moving means during the section varies by loading at additional points, and the contribution rate of the specific point can be dynamically changed within the section according to the variation.

[0018] In one embodiment, the method may further include a step of generating carbon emission rights information for each point based on information about the waste oil and information about carbon emissions for each point.

[0019] In one embodiment, the step of obtaining weight information of the waste oil may include a step of obtaining weight information for each unit of the waste oil stored in the container by utilizing at least one of a weight measuring device or vision analysis.

[0020] In one embodiment, the step of obtaining weight information for each unit of waste oil stored in the container by utilizing at least one of the weight measuring device or vision analysis may include, when the container is made of an opaque material, the step of measuring the weight information using the weight measuring device; and, when the container is made of a transparent material, the step of estimating the weight information using the vision analysis.

[0021] In one embodiment, the method may further include the step of obtaining quality information of the waste oil by utilizing a quality measuring device; and the step of generating at least one of authentication information of the waste oil and information on a processing method of the waste oil based on the quality information of the waste oil.

[0022] In one embodiment, the quality information of the waste oil can be utilized to generate carbon emission information.

[0023] According to one embodiment of the present disclosure for achieving the above-described task, a computer program stored in a computer-readable storage medium is disclosed. When the computer program is executed on one or more processors, the computer program causes the one or more processors to perform the following operations for processing information related to waste oil recovery, wherein the operations may include: an operation for obtaining weight information of the waste oil; an operation for obtaining information regarding a means of transporting the waste oil; and an operation for calculating a carbon emission amount for the waste oil based on the weight information of the waste oil and the information regarding the means of transport.

[0024] In one embodiment, the information about the means of transportation may include at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation.

[0025] In one embodiment, the operation may further include generating carbon credit information related to the recovery of the waste oil based on information about the waste oil and carbon emission information for the waste oil.

[0026] In one embodiment, the operation may further include: obtaining weight information for each point where the waste oil is collected; and calculating carbon emissions for each point based on the weight information for each point and information about the means of transportation.

[0027] In one embodiment, the operation of obtaining weight information of the waste oil may include an operation of obtaining weight information for each unit of the waste oil stored in the container by utilizing at least one of a weight measuring device or vision analysis.

[0028] In one embodiment, the operation may further include: obtaining quality information of the waste oil by utilizing a quality measuring device; and generating at least one of authentication information of the waste oil or information on a method of processing the waste oil based on the quality information of the waste oil.

[0029] A computing device according to one embodiment of the present disclosure for achieving the aforementioned task is disclosed. The device comprises at least one processor and a memory, and is configured to acquire weight information of waste oil; acquire information regarding a means of transporting the waste oil; and calculate a carbon emission amount for the waste oil based on the weight information of the waste oil and the information regarding the means of transport.

[0030] In one embodiment, the information about the means of transportation may include at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation.

[0031] In one embodiment, the device may be configured to generate carbon credit information related to the recovery of the waste oil based on information about the waste oil and carbon emission information for the waste oil.

[0032] In one embodiment, the device may be further configured to obtain weight information for each point where the waste oil is collected; and calculate carbon emissions for each point based on the weight information for each point and information about the means of transportation.

[0033] In one embodiment, the at least one processor may be configured to obtain weight information for a unit of waste oil stored in a container by utilizing at least one of a weight measuring device or vision analysis.

[0034] In one embodiment, the device may be further configured to obtain quality information of the waste oil by utilizing a quality measuring device; and, based on the quality information of the waste oil, generate at least one of authentication information of the waste oil and information on a processing method of the waste oil.

[0035] This disclosure enables monitoring of all processes related to waste oil, since all data from the initial generation of waste oil to its movement and collection are recorded as information related to waste oil recovery.

[0036] In addition, the present disclosure can prevent errors resulting from manual entry by collectors or food businesses by transparently collecting and managing information related to waste oil recovery.

[0037] In addition, the present disclosure can calculate the carbon emissions generated during the process of converting waste oil into a bio-raw material based on information related to waste oil recovery, and can generate information on future carbon emission rights.

[0038] Meanwhile, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents described below.

[0039] FIG. 1 is a block diagram of a computing device for processing information related to waste oil recovery according to one embodiment of the present disclosure.

[0040] FIG. 2 is a conceptual diagram illustrating a neural network according to one embodiment of the present disclosure.

[0041] FIG. 3 is a flowchart illustrating a method for processing information related to waste oil recovery according to one embodiment of the present disclosure.

[0042] FIG. 4 is a drawing for explaining an operation of obtaining weight information of waste oil stored in a container according to one embodiment of the present disclosure.

[0043] FIG. 5 is a diagram for explaining vision analysis for estimating weight information of waste oil stored in a container according to one embodiment of the present disclosure.

[0044] FIG. 6 is a drawing for explaining an operation of obtaining information regarding a moving means for moving waste oil according to one embodiment of the present disclosure.

[0045] FIG. 7 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0046] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0047] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0048] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0049] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0050] And, the term "at least one of A or B" should be interpreted to mean "if it includes only A", "if it includes only B", or "if it is combined in the composition of A and B".

[0051] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0052] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments disclosed herein. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0053] In the present disclosure, network function, artificial neural network and neural network can be used interchangeably.

[0054]

[0055] FIG. 1 is a block diagram of a computing device for processing information related to waste oil recovery according to one embodiment of the present disclosure.

[0056] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0057] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0058] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) to perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of a network function. For example, a CPU and a GPGPU can jointly process network function learning and data classification using network functions. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be jointly used to process network function learning and data classification using network functions. Furthermore, a computer program executed on a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0059] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0060] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0061] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0062] In addition, the network unit (150) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0063] In the present disclosure, the network unit (150) may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks, such as a local area network (LAN), a personal area network (PAN), and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0064] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0065]

[0066] FIG. 2 is a conceptual diagram illustrating a neural network according to one embodiment of the present disclosure.

[0067] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be comprised of a set of interconnected computational units, generally referred to as nodes. These nodes may also be referred to as neurons. A neural network comprises at least one node. The nodes (or neurons) comprising a neural network may be interconnected by one or more links.

[0068] Within a neural network, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, meaning that any node that is in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0069] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by the user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set on the link corresponding to each input node.

[0070] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0071] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0072] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0073] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

[0074] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using DNNs, one can identify latent structures in data. For example, one can identify the latent structures of images, text, videos, audio, and music (e.g., what objects are in the image, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). DNNs can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siamese networks, and generative adversarial networks (GANs). The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

[0075] In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the dimensionality after preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure in which the number of nodes decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and decoder) may be kept above a certain number (e.g., more than half of the input layer), as too few nodes may not transmit enough information.

[0076] Neural networks can learn through at least one of the following methods: supervised learning, self-supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Learning a neural network can be the process of applying knowledge to the neural network to perform a specific action.

[0077] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. Supervised learning uses training data with the correct answer labeled for each training data (i.e., labeled training data). Unsupervised learning, on the other hand, may not have the correct answer labeled for each training data. For example, in the case of supervised learning for data classification, the training data may be data with each category labeled. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the training data labels. Alternatively, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of neural network training to quickly achieve a certain level of performance, thereby increasing efficiency. A lower learning rate can be used in the later stages of training to increase accuracy.

[0078] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout, which disables some nodes in the network during the learning process, and the use of batch normalization layers.

[0079]

[0080] FIG. 3 is a flowchart illustrating a method for processing information related to waste oil recovery according to one embodiment of the present disclosure, FIG. 4 is a diagram for explaining an operation for obtaining weight information of waste oil stored in a container according to one embodiment of the present disclosure, FIG. 5 is a diagram for explaining vision analysis for obtaining weight information of waste oil stored in a container according to one embodiment of the present disclosure, and FIG. 6 is a diagram for explaining an operation for obtaining information on a moving means for moving waste oil according to one embodiment of the present disclosure. For reference, the method for processing information related to waste oil recovery illustrated in FIG. 3 may be performed by a computing device (100).

[0081] For reference, waste oil is a substance that is no longer necessary for human life or business activities, such as garbage, combustion ash, sludge, waste oil, waste acid, waste alkali, and animal carcasses, and among them, waste oil containing 5% or more of an oil component is called waste oil and can be classified as a designated waste. Meanwhile, in the following, "waste oil" is limited to "waste cooking oil (animal fat)" which is cooking oil used for cooking, and the present disclosure is described focusing on an example of calculating carbon emissions for waste oil (waste cooking oil) based on weight information and information on a means of transportation of the waste oil (waste cooking oil).

[0082]

[0083] According to one embodiment of the present disclosure, the computing device (100) can obtain weight information of the container (10) in which the waste oil is stored (S110). For example, the computing device (100) can obtain weight information for each unit of waste oil stored in the container (10) by utilizing at least one of a weight measuring device (20) or vision analysis (30). For example, the weight measuring device (20) may include tools used to measure the mass and weight of an object (e.g., a scale, a portable scale, etc.). In addition, the vision analysis (30) may include a technique for analyzing an image of waste oil stored in a container obtained from a portable terminal (e.g., a smartphone, etc.) by utilizing an artificial intelligence-based model, etc. For reference, computer vision is a field of computer science in which a computer extracts and interprets information from digital images or videos to perform specific tasks. Computer vision can detect and recognize objects, patterns, features, structures, etc. In addition, vision analysis can be used in conjunction with technologies such as artificial intelligence, deep learning, and machine learning to deeply analyze visual data (e.g., images and videos) and extract meaningful information. For example, vision analysis can perform object classification, object detection and localization, object segmentation, image captioning, object tracking, action classification, etc. For example, vision analysis (30) can extract features (e.g., texture, shape, color, boundary, etc.) of images of waste oil stored in acquired containers by utilizing edge detection, corner detection, and histogram-based feature extraction techniques.In addition, the vision analysis (30) can recognize patterns or objects in images of waste oil stored in containers by performing feature-based pattern recognition using at least one of algorithms such as statistical analysis, machine learning, or neural networks. In addition, the vision analysis (30) can utilize object detection and segmentation to find the location of an object (e.g., a container storing waste oil) in an image of waste oil stored in a container and perform object segmentation. In addition, the vision analysis (30) can assign a specific label and classify images of waste oil stored in containers by utilizing supervised learning. However, the vision analysis is not limited thereto, and various analysis methods that have been developed or will be developed in the future can be applied. In addition, the computing device (100) can also obtain weight information of the waste oil by utilizing ultrasonic equipment.

[0084] More specifically, referring to FIG. 4, the computing device (100) can obtain the weight information measured by the weight measuring device (20) when the container is made of an opaque material. For example, the container made of an opaque material may include an aluminum can, etc. The computing device (100) can obtain the weight information of waste oil stored in an opaque (e.g., aluminum can) container, and obtain the unique weight information of the waste oil by subtracting the weight of an empty opaque container from the weight information of the waste oil stored in the opaque container.

[0085] In addition, the computing device (100) can estimate the weight information of the waste oil through the vision analysis (30) when the container is made of a transparent material. For example, the container made of a transparent material may include a transparent plastic container, etc. For example, referring to FIG. 5, the computing device (100) can obtain a container image in which a transparent container containing waste oil is photographed from a portable terminal (e.g., a smartphone, etc.), and can estimate the weight information of the waste oil through the vision analysis (30) by applying the container image to an artificial intelligence-based model. For example, the computing device (100) can estimate the weight information of the waste oil stored in the container through the vision analysis (30) that analyzes the size and shape of the container using the artificial intelligence model and approximates the weight based on the average density of the container. In addition, the computing device (100) can estimate the volume of the container and the weight of the waste oil through the vision analysis (30) that creates a three-dimensional model of the container through the obtained container image and predicts the volume. According to one embodiment, the computing device (100) may collect an image of a container and weight-related data including weight information of the container to estimate weight information through vision analysis (30). In addition, the computing device (100) may analyze a correlation between the weight of the container and the image of the container from the collected weight-related data using an artificial intelligence model or the like. In addition, the computing device (100) may extract features (e.g., size, shape, density, color, etc.) related to the weight of waste oil from the image of the container using an artificial intelligence model or the like. In addition, the computing device (100) may build a model (e.g., a machine learning or deep learning-based model) capable of predicting the weight of waste oil based on the features related to the weight of the waste oil. In addition, the computing device (100) may estimate the weight information of the waste oil through vision analysis (30) by applying the acquired container image to the model capable of predicting the weight of the waste oil.However, the method of measuring (estimating) the weight of the waste oil is not limited to this and various embodiments may exist.

[0086] For example, referring to FIG. 4, the computing device (100) can obtain identification information of the container (10) based on information obtained by scanning a digital code (e.g., QR, NFC, RFID, etc.) inserted into the container (10) in which the waste oil is stored. In conjunction with the identification information of the container, the computing device (100) can store weight information obtained by utilizing at least one of a weight measuring device (20) or vision analysis (30), the time of collection of the container (10), the location (GPS) of the collection of the container (10), information on the collector (App ID) who collected the container (10), and quality information. At this time, the obtained information can be stored in a blockchain format. The computing device (100) can increase the collection rate of waste oil and contribute to improving the quality of raw materials by storing and utilizing data in which the entire process from the generation of waste oil to the final collection is recorded in a blockchain format. Additionally, the computing device (100) can manage data blocks and connect them to the subsequent process to enable first-in, first-out input to the refining process.

[0087]

[0088] According to one embodiment of the present disclosure, the computing device (100) can obtain quality information of the waste oil by utilizing a quality measuring device. The quality information of the waste oil may include information on the acidity (acidity) of the waste oil. For example, the quality information of the waste oil may be obtained together with information on the weight of the waste oil stored in the container (10). In addition, the quality information of the waste oil may be obtained once more after it arrives at the final destination, that is, a refinery. The quality information of the waste oil may be stored in conjunction with the identification information of the container (10), which is obtained based on information obtained by scanning a digital code (e.g., QR, NFC, RFID, etc.) inserted in the container (10) in which the waste oil is stored.

[0089] In addition, the computing device (100) may generate at least one of authentication information for the waste oil and information on the processing method of the waste oil based on the quality information of the waste oil. For example, the authentication information for the waste oil may be information for authenticating waste cooking oil. For example, the authentication information for the waste oil may be information for authenticating whether the obtained waste oil is suitable for waste cooking oil, which is an important raw material for bioenergy. In addition, the information on the processing method of the waste oil may include information on the amount of a neutralizing agent added in a process for producing a bio-raw material later based on the quality information of the waste oil. For example, since the amount of a neutralizing agent added varies depending on the acidity of the waste oil, the computing device (100) may generate information on the processing method of the waste oil based on the quality information of the waste oil.

[0090] Meanwhile, waste oil quality information can be used to generate carbon credit information. Carbon credits are a regulatory tool used to regulate carbon emissions, maintain a sustainable environment, and mitigate climate change. Companies or countries are allocated a certain amount of carbon credits, which represent the maximum amount of greenhouse gases a company or country can emit. For example, when converting waste oil into a biofuel (e.g., biodiesel), the lower the quality of the waste oil, the more greenhouse gases it generates, resulting in fewer carbon credits generated. Conversely, the higher the quality of the waste oil, the less greenhouse gases it generates, resulting in more carbon credits generated. Furthermore, when converting waste oil into a biofuel (e.g., biodiesel), the greater the amount of waste oil used, the more greenhouse gases it generates, resulting in fewer carbon credits generated. Conversely, the lower the amount of waste oil used, the less greenhouse gases it generates, resulting in more carbon credits generated.

[0091]

[0092] According to one embodiment of the present disclosure, the computing device (100) may obtain information regarding a means of transporting the waste oil (S120). For example, the information regarding the means of transport may include at least one of location information of the means of transport, fuel efficiency information of the means of transport, type information of the means of transport, or weight information of the means of transport. For reference, the means of transport may refer to a vehicle capable of transporting a container containing the waste oil. For example, the location information of the means of transport may include a Global Positioning System (GPS), Wi-Fi-based location information, cellular signal-based location information, IP address-based location information, Bluetooth-based location information, geographic database location information, and the like. In addition, the fuel efficiency information of the means of transport may be information measuring the amount of fuel consumed when traveling a specific distance. For example, the fuel efficiency information of the means of transport may be information measuring the amount of fuel consumed for a distance traveled after the container containing the waste oil was loaded onto the means of transport. In addition, the type information of the transportation means may include type information that can be classified by appearance, such as motorcycles, passenger cars, vans, trucks, special vehicles, etc., and type information that can be classified by fuel, such as gasoline vehicles, diesel vehicles, and hybrid vehicles. In addition, the weight information of the transportation means may include weight information on waste oil stored in a container, weight information on the transportation means itself, etc. In addition, the computing device (100) may calculate the carbon emissions of the transportation means based on the information on the transportation means. For reference, the carbon emissions may be calculated by considering the fuel consumption of the transportation means, the carbon content of the fuel, the driving distance, the fuel efficiency of the vehicle, etc., but is not limited thereto, and various embodiments may exist.

[0093]

[0094] Hereinafter, we will explain i) an operation of calculating carbon emissions for waste oil based on weight information of waste oil and information on means of transportation, and ii) an operation of calculating carbon emissions at a point level based on weight information at a point level where waste oil is collected and information on means of transportation.

[0095]

[0096] According to one embodiment of the present disclosure, i) the computing device (100) can calculate the carbon emissions for the waste oil based on the weight information of the waste oil and the information about the means of transportation (S130).

[0097] Additionally, the computing device (100) can calculate the carbon emissions of the moving means based on information about the moving means, for the section moved after the waste oil stored in the container was loaded onto the moving means. For reference, a section may include sub-sections corresponding to A->B(1), B->C(1'), and C->final(1"). Here, the final section may be a refinery, which is the final destination. In addition, carbon emissions may be calculated by considering the fuel consumption of the vehicle, the carbon content of the fuel, the driving distance, the fuel efficiency of the vehicle, etc. For example, referring to FIG. 6, the computing device (100) may calculate the carbon emissions of the vehicle for a section 1 in which the first waste oil stored in the first container in section A is loaded onto the vehicle and then moved to section B, based on information about the vehicle. In addition, the computing device may calculate the carbon emissions of the vehicle for a section 1 in which the first waste oil stored in the first container in section A is loaded onto the vehicle, based on information about the vehicle. In addition, the computing device may calculate the carbon emissions of the vehicle for a section 1 in which the first waste oil is moved from section B to section C, based on information about the vehicle. In addition, the computing device may calculate the carbon emissions of the vehicle for a section 1 in which the first waste oil is moved to the final section from section C, based on information about the vehicle. In other words, the computing device (100) can calculate the carbon emissions generated by the moving means based on at least one of the location information of the moving means, the fuel efficiency information of the moving means, or the weight information of the moving means, for the section moved from the departure point to the arrival point after the first waste oil stored in the first container is loaded onto the moving means.Alternatively, when the first waste oil stored in the first container in section A is collected into a collection container separately provided in the transportation means, the computing device (100) can calculate the carbon emissions generated by the transportation means based on at least one of the location information of the transportation means, the fuel efficiency information of the transportation means, or the weight information of the transportation means for the section moved from the departure point to the arrival point after the first waste oil is collected into the collection container included in the transportation means.

[0098]

[0099] Additionally, the computing device (100) may calculate the contribution rate of the waste oil for the section based on the weight information of the waste oil. For example, the computing device (100) may calculate the contribution rate of the waste oil based on the weight information of the waste oil and the weight information of other waste oils loaded together on the moving means during the section. For example, if the first waste oil stored in the first container having a weight of 10 kg is loaded in section A, the second waste oil stored in the second container having a weight of 5 kg is loaded in section B, and the third waste oil stored in the third container having a weight of 5 kg is loaded in section C, the computing device (100) may calculate the contribution rate of the first waste oil based on the weight information of the first waste oil and the weight information of other waste oils (e.g., the second waste oil and the third waste oil) loaded together on the moving means during the sub-sections corresponding to A->B(1), B->C(1'), and C->final(1"). At this time, the number of other waste oils loaded together on the moving means during the section changes due to additional loading, and the contribution rate of the waste oil may dynamically change within the section according to the change. For example, if the first waste oil stored in the first container having a weight of 10 kg is loaded in section A, When the second waste oil stored in the second container of 5 kg is loaded in section B, and the third waste oil stored in the third container of 5 kg is loaded in section C, the contribution rate of the first waste oil can dynamically change in the order of 1 / 10 in section A->B(1), 10 / 15 in section B->C(1'), and 10 / 20 in section C->Final(1").

[0100] In addition, the computing device (100) can calculate the carbon emissions of the waste oil based on the carbon emissions of the transportation means for the section and the contribution rate of the waste oil for the section. For example, the computing device (100) can calculate the carbon emissions of the transportation means for the section corresponding to A->B(1), B->C(1'), C->Final(1") and the contribution rate of the waste oil for the section corresponding to A->B(1), B->C(1'), C->Final(1"), respectively, for the first waste oil stored in the first container loaded on the transportation means, the second waste oil stored in the second container, and the third waste oil stored in the third container. For example, when a first waste oil stored in a first container having a mass of 10 kg is loaded in section A, a second waste oil stored in a second container having a mass of 5 kg is loaded in section B, and a third waste oil stored in a third container having a mass of 5 kg is loaded in section C, the computing device (100) can calculate the carbon emissions of the first waste oil based on the carbon emissions of the moving means for each section in which the first waste oil moves from A->B(1), B->C(1'), and C->final (1"), and the contribution rate of the first waste oil for the sections that dynamically change in the order of 1 / 10 in the section A->B(1), 10 / 15 in the section B->C(1'), and 10 / 20 in the section C->final (1"). In the same way, the computing device (100) can calculate the carbon emissions of the second waste oil based on the carbon emissions of the moving means for each section in which the second waste oil moves from B->C(1'), C->final (1"), and the contribution rate of the second waste oil for sections that dynamically change in the order of 5 / 15 in the section B->C(1'), and 5 / 20 in the section C->final (1").In addition, the computing device (100) can calculate the carbon emissions of the third waste oil based on the carbon emissions of the moving means for the section in which the third waste oil moves from C to the final (1") and the 5 / 20 contribution rate of the third waste oil in the section in which the third waste oil moves from C to the final (1"). Alternatively, even when the first waste oil stored in the first container, the second waste oil stored in the second container, and the third waste oil stored in the third container in section A are collected into a collection container separately provided in the moving means, the computing device (100) can calculate the carbon emissions of each of the plurality of waste oils based on the carbon emissions of the moving means for the section and the contribution rate of the waste oil for the section after the first to third waste oils are collected into the collection container included in the moving means.

[0101]

[0102] According to one embodiment of the present disclosure, the computing device (100) may generate carbon credit information related to the recovery of the "waste oil" based on information about the waste oil and carbon emission information for the waste oil. Carbon credits are a regulatory tool used to regulate carbon emissions, maintain a sustainable environment, and mitigate climate change. A company or a country is allocated a certain amount of carbon credits, and the amount represents the maximum amount of greenhouse gases that the company or country can emit. For example, in the process of converting waste oil into a bio-based feedstock (e.g., biodiesel), the lower the quality of the waste oil, the more greenhouse gases are generated, and thus fewer carbon credits are generated. On the other hand, the better the quality of the waste oil, the less greenhouse gases are generated, and thus more carbon credits can be generated. In addition, in the process of converting waste oil into a bio-raw material (e.g., biodiesel), the greater the amount of waste oil, the greater the amount of greenhouse gases generated, and thus, fewer carbon credits are generated. On the other hand, the less the amount of waste oil, the less greenhouse gases are generated, and thus, more carbon credits can be generated. For example, the computing device (100) can generate carbon credit information related to the recovery of the "waste oil" based on information about the waste oil, including weight information of the waste oil, quality information of the waste oil, etc., and the amount of carbon emissions generated by the means of transporting the waste oil.

[0103]

[0104] According to one embodiment of the present disclosure, the computing device (100) may calculate the carbon emissions per point based on ii) the weight information per point and information about the moving means. For example, referring to FIG. 6, the point is a location where the moving means stops to collect waste oil stored in a container, and may include Point A, Point B, Point C, a final point, etc. For example, the final point may be a refinery, which is the final destination. As an example, the computing device (100) may obtain weight information per point where the waste oil stored in the container is collected. For example, if at Point A, the first waste oil stored in a first container weighing 10 kg and the second waste oil stored in a second container weighing 5 kg are collected, the computing device (100) may obtain the weight information of Point A as 15 kg. In addition, at point B, when the third waste oil stored in the third container weighing 100 kg is collected, the computing device (100) can obtain the weight information of point B as 100 kg. In addition, at point C, when the fourth to tenth waste oils stored in the fourth to tenth containers weighing 10 kg are collected, the computing device (100) can obtain the weight information of point C as 70 kg.

[0105] In addition, the computing device (100) can calculate the carbon emissions of the moving means based on information about the moving means, with respect to a section moved after waste oils at a specific point are loaded onto the moving means. Referring again to FIG. 6 as an example, the computing device (100) can calculate the carbon emissions of the moving means for section 1, which is moved to point B after the first waste oil stored in the first container at point A and the second waste oil stored in the second container are loaded, based on information about the moving means. In addition, the computing device (100) can calculate the carbon emissions of the moving means for section 1', which is moved to point C after the third waste oil stored in the third container at point B is loaded, accumulated in multiple containers loaded at point A. In addition, the computing device (100) can calculate the carbon emissions of the moving means based on the information about the moving means for a section 1" that is moved to the final point after the 4th to 10th waste oils stored in the 4th to 10th containers at point C are loaded by accumulating the plurality of containers loaded at points A and B. In other words, the computing device (100) can calculate the carbon emissions generated by the moving means based on at least one of the location information of the moving means, the fuel efficiency information of the moving means, or the weight information of the moving means for a section moved from the departure point to the arrival point after the plurality of waste oils are loaded on the moving means at points A, B, and C.

[0106] Additionally, the computing device (100) may calculate the contribution rate of the specific point to the section based on the weight information of the waste oil at the specific point. For example, the computing device (100) may calculate the contribution rate of the specific point based on the weight information of the specific point and the weight information of other waste oils loaded together on the moving means during the section. For example, at point A, if the first waste oil stored in the first container of 10 kg and the second waste oil stored in the second container of 5 kg are loaded, at point B, the third waste oil stored in the third container of 100 kg are loaded, and at point C, the fourth to tenth waste oils stored in the fourth to tenth containers of 10 kg are loaded, the computing device (100) may calculate the contribution rate of point A based on the weight information of 15 kg in section A and the weight information of other waste oils (e.g., the third to tenth waste oils) loaded together on the moving means during sections corresponding to A->B(1), B->C(1'), and C->final(1"). At this time, the number of other waste oils loaded together on the moving means during the section changes by loading at additional points, and the contribution rate of the specific point may dynamically change within the section according to the change. There is. For example, at point A, if the first waste oil stored in the first container of 10 kg, the second waste oil stored in the second container of 5 kg, are loaded, at point B, the third waste oil stored in the third container of 100 kg are loaded, and at point C, the fourth to tenth waste oils stored in the fourth to tenth containers of 10 kg are loaded, the contribution rate of point A can dynamically change in the order of 1 / 15 in the A->B(1) section, 15 / 115 in the B->C(1') section, and 15 / 185 in the C->final (1") section.

[0107] In addition, the computing device (100) can calculate the carbon emissions of the specific point based on the carbon emissions of the means of transportation for the section and the contribution rate of the specific point for the section. For example, the computing device (100) can calculate the carbon emissions of points A, B, and C based on the carbon emissions of the means of transportation for the section corresponding to A->B(1), B->C(1'), and C->Final(1") and the contribution rate of the specific point for the section corresponding to A->B(1), B->C(1'), and C->Final(1"). For example, when, at point A, first waste oil stored in a first container of 10 kg, second waste oil stored in a second container of 5 kg are loaded, at point B, third waste oil stored in a third container of 100 kg are loaded, and at point C, fourth to tenth waste oils stored in fourth to tenth containers of 10 kg are loaded, the computing device (100) can calculate the carbon emissions of point A based on the carbon emissions of the transportation means for the sections in which the waste oil loaded at point A moves from A->B(1), B->C(1'), and C->final (1"), and the contribution rate of a specific point that dynamically changes in the order of 1 / 15 in the section A->B(1), 15 / 115 in the section B->C(1'), and 15 / 185 in the section C->final (1"). In the same way, the carbon emissions at points B and C can also be calculated.

[0108]

[0109] According to one embodiment of the present disclosure, the computing device (100) may generate carbon credit information for each "point" based on the information regarding the waste oil and the information regarding the carbon emissions at each point. Carbon credits are a regulatory tool used to regulate carbon emissions, maintain a sustainable environment, and mitigate climate change. A company or a country is allocated a certain amount of carbon credits, and the amount represents the maximum amount of greenhouse gases that the company or country can emit. For example, in the process of converting waste oil into a biofuel (e.g., biodiesel), the lower the quality of the waste oil, the more greenhouse gases are generated, resulting in fewer carbon credits being generated. Conversely, in the process of converting waste oil into a biofuel (e.g., biodiesel), the higher the quality of the waste oil, the less greenhouse gases are generated, resulting in more carbon credits being generated. Furthermore, in the process of converting waste oil into a biofuel (e.g., biodiesel), the greater the amount of waste oil, the more greenhouse gases are generated, resulting in fewer carbon credits being generated. Conversely, in the process of converting waste oil into a biofuel (e.g., biodiesel), the lower the amount of waste oil, the less greenhouse gases are generated, resulting in more carbon credits being generated. For example, the computing device (100) can generate carbon emission rights information for the “point unit” based on information about the waste oil, including weight information of the waste oil, quality information of the waste oil, etc., and information about carbon emissions for the point unit.

[0110]

[0111] In the above description, steps S110 to S130 may be further divided into additional steps or combined into fewer steps, depending on the implementation example of the present invention. Furthermore, some steps may be omitted as needed, and the order of steps may be changed.

[0112]

[0113] According to one embodiment of the present disclosure, a computer-readable medium storing a data structure is disclosed.

[0114] A data structure can refer to the organization, management, and storage of data to enable efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include connections between user-defined data elements. Physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0115] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one data item is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each item having a pointer. In a linked list, a pointer can contain information about the next or previous item. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0116] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structures can include tree data structures. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0117] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they are collectively referred to as neural networks. The data structure may include a neural network. And the data structure including the neural network may be stored on a computer-readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. The data structure including the neural network may include any of the components disclosed above. That is, the data structure including the neural network may be configured to include all or any combination of preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. In addition to the aforementioned configurations, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0118] The data structure may include data input to a neural network. The data structure including the data input to the neural network may be stored on a computer-readable medium. The data input to the neural network may include training data input during the neural network training process and / or input data input to the neural network after training has been completed. The data input to the neural network may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to the neural network. Accordingly, the data structure may include data that is the target of preprocessing and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0119] The data structure may include weights of the neural network. (In this specification, the terms "weight" and "parameter" may be used interchangeably.) The data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set for links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0120] By way of example and not limitation, the weights may include weights that vary during the neural network training process and / or weights that have completed neural network training. The weights that vary during the neural network training process may include weights at the start of the training cycle and / or weights that vary during the training cycle. The weights that have completed neural network training may include weights that have completed the training cycle. Accordingly, a data structure including the weights of a neural network may include a data structure including weights that vary during the neural network training process and / or weights that have completed neural network training. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of a neural network. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0121] A data structure including neural network weights can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The serialized data structure including neural network weights can be reconstructed on the same or different computing devices through deserialization. The data structure including neural network weights is not limited to serialization. Furthermore, the data structure including neural network weights can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0122] The data structure may include hyperparameters of a neural network. Furthermore, the data structure including the hyperparameters of the neural network may be stored on a computer-readable medium. The hyperparameters may be variables that can be varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle repetitions, weight initialization (e.g., setting a range of weight values ​​to be subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0123]

[0124] FIG. 7 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0125] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0126] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0127] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0128] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0129] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0130] An exemplary environment (1100) implementing various aspects of the present disclosure is illustrated, including a computer (1102) comprising a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0131] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0132] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0133] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of media readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0134] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0135] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0136] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0137] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0138] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0139] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0140] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0141] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0142] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0143] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0144] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0145] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0146]

[0147] As described above, the relevant contents have been described in the best form for carrying out the invention.

Claims

1. A method for processing information related to waste oil recovery, performed by a computing device, A step of obtaining weight information of the waste oil; A step of obtaining information about a moving means for moving the waste oil; and A step of calculating carbon emissions for the waste oil based on weight information of the waste oil and information about the means of transportation; including, method.

2. In paragraph 1, Information about the above means of transportation is: Including at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation. method.

3. In paragraph 2, Based on the weight information of the waste oil and the information about the means of transportation, the step of calculating the carbon emissions for the waste oil is as follows: A step of calculating the carbon emissions of the moving means based on information about the moving means, for a section moved after the waste oil stored in the container is loaded onto the moving means; A step of calculating the contribution rate of the waste oil to the section based on the weight information of the waste oil; and A step of calculating the carbon emissions of the waste oil based on the carbon emissions of the means of transportation for the section and the contribution rate of the waste oil for the section. including, method.

4. In paragraph 3, The step of calculating the contribution rate of the waste oil to the above section is: A step of calculating the contribution rate of the waste oil based on the weight information of the waste oil and the weight information of other waste oils loaded together on the moving means during the section, method.

5. In paragraph 4, The number of other waste oils loaded together on the means of transportation during the above section changes due to additional loading, and the contribution rate of the waste oil dynamically changes within the section according to the change. method.

6. In paragraph 1, The above method, A step of generating carbon emission rights information related to the recovery of the waste oil based on the information on the waste oil and the carbon emission information for the waste oil. including more, method.

7. In paragraph 1, The above method, A step of obtaining weight information at each point where the waste oil is collected; and A step of calculating carbon emissions for each point based on weight information for each point and information about the means of transportation. including more, method.

8. In paragraph 7, The step of calculating the carbon emissions of the point unit based on the weight information of the point unit and the information about the means of transportation is as follows: A step of calculating the carbon emissions of the moving means based on information about the moving means, for a section moved after waste oil at a specific point is loaded onto the moving means; A step of calculating the contribution rate of the specific point to the section based on the weight information of the specific point; and A step of calculating the carbon emissions of the specific point based on the carbon emissions of the means of transportation for the section and the contribution rate of the specific point for the section. including, method.

9. In paragraph 8, The step of calculating the contribution rate of the specific point to the above section is: A step of calculating the contribution rate of the specific point based on the weight information of the specific point and the weight information of other waste oils loaded together on the moving means during the section, method.

10. In paragraph 9, The number of other waste oils loaded together on the means of transportation during the above section changes by loading at additional points, and the contribution rate of the specific point dynamically changes within the section according to the change. method.

11. In paragraph 7, The above method, A step of generating carbon emission rights information for each branch based on the information on the waste oil and the information on carbon emissions for each branch. including more, method.

12. In paragraph 1, The step of obtaining the weight information of the above waste oil is: A step of obtaining weight information for each unit of waste oil stored in a container by utilizing at least one of a weight measuring device or vision analysis. including, method.

13. In paragraph 12, The step of obtaining weight information for each unit of waste oil stored in the container by utilizing at least one of the above weight measuring device or vision analysis is as follows: If the container is made of an opaque material, a step of measuring the weight information through the weight measuring device; and If the above container is made of a transparent material, a step of estimating the weight information through the vision analysis including, method.

14. In paragraph 1, The above method, A step of obtaining quality information of the waste oil by using a quality measuring device; and A step of generating at least one of authentication information of the waste oil or processing method information of the waste oil based on the quality information of the waste oil. including more, method.

15. In paragraph 14, The quality information of the above waste oil is used to generate carbon emission information. method.

16. A computer program stored in a computer-readable storage medium, wherein when the computer program is executed on one or more processors, the computer program causes the one or more processors to perform the following operations for processing information related to waste oil recovery, wherein the operations are: An operation of obtaining weight information of the above waste oil; An operation of obtaining information about a means of transport for moving the waste oil; and An operation of calculating carbon emissions for the waste oil based on weight information of the waste oil and information about the means of transportation; including, A computer program stored on a computer-readable storage medium.

17. In paragraph 16, Information about the above means of transportation is: Including at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation. A computer program stored on a computer-readable storage medium.

18. In paragraph 16, The above action is, An operation of generating carbon emission rights information related to the recovery of the waste oil based on the information on the waste oil and the carbon emission information for the waste oil. including more, A computer program stored on a computer-readable storage medium.

19. In paragraph 16, The above action is, An operation of obtaining weight information at each point where the waste oil is collected; and An operation of calculating carbon emissions for each point based on weight information for each point and information about the means of transportation. including more, A computer program stored on a computer-readable storage medium.

20. In paragraph 16, The operation of obtaining the weight information of the above waste oil is as follows: An operation of obtaining weight information for each unit of waste oil stored in a container by utilizing at least one of a weight measuring device or vision analysis. including, A computer program stored on a computer-readable storage medium.

21. In paragraph 16, The above action is, An operation of obtaining quality information of the waste oil by utilizing a quality measuring device; and An operation of generating at least one of authentication information of the waste oil or processing method information of the waste oil based on the quality information of the waste oil. including more, A computer program stored on a computer-readable storage medium.

22. As a computing device, at least one processor; and memory; Including, Obtain weight information of waste oil; Obtain information about the means of transport for moving the waste oil; and Based on the weight information of the waste oil and the information about the means of transportation, it is configured to calculate the carbon emissions for the waste oil. device.

23. In paragraph 22, Information about the above means of transportation is: Including at least one of location information of the means of transportation, fuel efficiency information of the means of transportation, type information of the means of transportation, or weight information of the means of transportation. device.

24. In paragraph 22, The above device, Based on the information about the waste oil and the carbon emission information for the waste oil, it is configured to generate carbon emission information related to the recovery of the waste oil. device.

25. In paragraph 22, The above device, Obtain weight information at each point where the waste oil is collected; and It is further configured to calculate the carbon emissions of the point unit based on the weight information of the point unit and the information about the means of transportation. device.

26. In paragraph 22, At least one processor, configured to obtain weight information for each unit of waste oil stored in a container by utilizing at least one of a weight measuring device or vision analysis, device.

27. In paragraph 22, The above device, By using a quality measuring device, quality information of the waste oil is obtained; and Based on the quality information of the waste oil, it is further configured to generate at least one of authentication information of the waste oil or processing method information of the waste oil. device.

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