Method and system for monitoring and diagnosing hydrogen fusion charging

Abstract

A dynamic risk prediction method and system are disclosed. A dynamic risk prediction method according to one embodiment may include the step of predicting a dynamic risk level of a task using task information and outdoor environment information; and the step of providing the dynamic risk level of the task to a user terminal through a service platform.

Description

Technology Field

[0001] The following embodiments relate to a method and system for monitoring and diagnosing hydrogen fusion charging. Background Technology

[0003] Globally, interest in the depletion of energy resources and the reduction of carbon emissions is intensifying, and hydrogen vehicles are a good means of transportation to address this. Hyundai Motor Company began mass production of the world's first Tuscon ix Fuel Cell in 2013, followed by Toyota's Mirai in 2014 and Honda's Clarity Fuel Cell in 2016.

[0004] As the establishment of hydrogen charging infrastructure is essential for the widespread adoption of hydrogen vehicles, the deployment of hydrogen charging stations is actively underway, and furthermore, hydrogen convergence charging stations that integrate hydrogen production and charging are also becoming widely distributed.

[0005] In such hydrogen convergence charging stations, if a leakage accident occurs in the process equipment, flames and explosions may occur, potentially resulting in severe casualties.

[0006] Accordingly, there is a need to propose a technology for evaluating the risks associated with the process of hydrogen convergence charging stations. The problem to be solved

[0008] The technical problem that the embodiments aim to solve is to predict and diagnose the status of the processes and evaluate risks by proposing a method and system for monitoring and diagnosing each of the processes of a hydrogen convergence charging station.

[0009] In addition, some embodiments propose a method and system for evaluating economic feasibility or profitability considering hydrogen productivity, hydrogen shipment volume, and maintenance of hydrogen convergence charging.

[0010] The technical problems that the embodiments aim to solve are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art to which the embodiments belong from the description below. means of solving the problem

[0012] To achieve the above technical objective, a hydrogen convergence charging monitoring and diagnostic method performed by a computer device linked to a hydrogen convergence charging station according to one embodiment may include: a step of monitoring each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for supplying the stored hydrogen; and a step of diagnosing each of the production process, the compression process, the storage process, and the charging process based on the results of the monitoring.

[0013] According to one aspect, the diagnosing step may be characterized by including a step of predicting and diagnosing the state of each of the production process, the compression process, the storage process, and the charging process by analyzing sensing data collected from at least one sensor using a multivariate regression analysis ensemble algorithm based on Partial Least Squares (PLS) regression analysis, Dynamic Principal Component Analysis (DPCA) technique, and Nearest Neighbor (k-NN) model.

[0014] According to another aspect, the prediction and diagnosis step may be characterized by using the multivariate regression analysis ensemble algorithm based on the season, weather, time, the operating mode and component information of each of the production process equipment, the compression process equipment, the storage process equipment, and the filling process equipment.

[0015] According to another aspect, the step of predicting and diagnosing may be characterized by including a step of calculating the causal relationship and contribution of the sensing data to the state of each of the predicted and diagnosed production process, compression process, storage process, and charging process.

[0016] According to another aspect, the step of predicting and diagnosing may be characterized by including a step of providing information related to the quality and recommended process execution patterns of each of the production process, the compression process, the storage process, and the charging process, based on the state of each of the production process, the compression process, the storage process, and the charging process that were predicted and diagnosed.

[0017] According to another aspect, the diagnostic step may be characterized by including a step of evaluating the risk of each of the production process, the compression process, the storage process, and the filling process.

[0018] According to another aspect, the evaluation step may be characterized by including: a step of predicting the accident frequency and the number of fatalities in each of the production process, the compression process, the storage process, and the charging process; and a step of evaluating the risk of each of the production process, the compression process, the storage process, and the charging process based on the predicted accident frequency and the number of fatalities in each of the production process, the compression process, the storage process, and the charging process.

[0019] According to another aspect, the predicting step is characterized by including: a step of selecting at least one process showing an abnormal state among the production process, the compression process, the storage process, and the charging process based on sensing data collected from at least one sensor; and a step of predicting the accident frequency and the number of fatalities in the selected at least one process, and the evaluating step may be characterized by evaluating the risk in the selected at least one process based on the accident frequency and the number of fatalities in the predicted at least one process.

[0020] According to another aspect, the evaluation step may be characterized by including the step of classifying the compression process and the storage process into a plurality of compression processes and a plurality of storage processes based on at least one preset threshold pressure value, and evaluating the risk for each of the classified compression processes and storage processes.

[0021] According to another aspect, the diagnostic step may be characterized by including a step of evaluating hydrogen productivity, hydrogen shipment volume, and economic feasibility or profitability considering maintenance of the hydrogen fusion charging based on sensing data collected from at least one sensor.

[0022] According to another aspect, the evaluation step may include a step of deriving the hardware cost of the hydrogen composite charging based on the state of each of the production process, the compression process, the storage process, and the charging process predicted and diagnosed based on the sensing data, and a step of evaluating the economic feasibility by calculating the Benefit Cost Ratio (B / C Ratio), Net Present Value (NPV), or Internal Rate of Return (IRR) using a Cost-Benefit Analysis method based on the derived hardware cost; or a step of evaluating the profitability using a Net Present Value Method, an Internal Rate of Return Method, or a Profitability Index Method based on the derived hardware cost.

[0023] A computer-readable recording medium having a computer program for executing a hydrogen convergence charging monitoring and diagnostic method according to one embodiment on a computer device linked to a hydrogen convergence charging station, wherein the hydrogen convergence charging monitoring and diagnostic method may include: a step of monitoring each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, compression process facility for compressing the produced hydrogen, storage process facility for storing the compressed hydrogen, and charging process facility for charging and supplying the stored hydrogen; and a step of diagnosing each of the production process, compression process, storage process, and charging process based on the results of the monitoring.

[0024] A computer device for performing a hydrogen convergence charging monitoring and diagnostic method in conjunction with a hydrogen convergence charging station according to one embodiment includes at least one processor configured to execute computer-readable commands, and the at least one processor may include: a monitoring unit that monitors each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for charging and supplying the stored hydrogen; and a diagnostic unit that diagnoses each of the production process, the compression process, the storage process, and the charging process based on the results of the monitoring.

[0025] A hydrogen combined charging station according to one embodiment comprises: a production process facility for producing hydrogen using biomethane as a raw material; a compression process facility for compressing the produced hydrogen; a storage process facility for storing the compressed hydrogen; and a charging process facility for charging and supplying the stored hydrogen; and a computer device linked with the production process facility, the compression process facility, the storage process facility, and the charging process facility, wherein at least one processor configured to execute computer-readable commands included in the computer device may include: a monitoring unit that monitors the production process, the compression process, the storage process, and the charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for charging and supplying the stored hydrogen; and a diagnostic unit that diagnoses the production process, the compression process, the storage process, and the charging process based on the results of the monitoring. Effects of the invention

[0027] One embodiment can achieve the technical effect of predicting and diagnosing the state of the process and evaluating the risk by proposing a method and system for monitoring and diagnosing each of the processes of a hydrogen convergence charging station.

[0028] In addition, some embodiments may propose a method and system for evaluating economic feasibility or profitability considering hydrogen productivity, hydrogen shipment volume, and maintenance of hydrogen combined charging.

[0029] The technical effects described are not limited to those stated above and should be understood to include all effects that can be inferred from the composition of the invention described in the detailed description below or the claims. Brief explanation of the drawing

[0031] FIG. 1 is a drawing illustrating a hydrogen composite charging station according to one embodiment. FIG. 2 is a block diagram illustrating an example of a computer device according to one embodiment. FIG. 3 is a block diagram illustrating examples of components that may be included in the processor illustrated in FIG. 2. FIG. 4 is a flowchart illustrating a hydrogen fusion charging monitoring and diagnostic method that can be performed by the computer device shown in FIG. 2. Specific details for implementing the invention

[0032] The present invention will be described below with reference to the attached drawings. However, the present invention may be implemented in various different forms and is therefore not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification have been given similar reference numerals.

[0033] Throughout the specification, when it is stated that a part is "connected (connected, in contact, combined)" with another part, this includes not only cases where they are "directly connected," but also cases where they are "indirectly connected" with other members interposed between them. Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but rather allows for the inclusion of additional components.

[0034] The terms used herein are merely for describing specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0035] In the following embodiments, a method (hereinafter referred to as the hydrogen convergence charging monitoring and diagnosis method) and a system (hereinafter referred to as the hydrogen convergence charging monitoring and diagnosis system) for monitoring hydrogen convergence charging at a hydrogen convergence charging station and evaluating and diagnosing the economic feasibility and profitability of hydrogen convergence charging considering its status, risk, productivity, shipment volume, and maintenance are described.

[0036] The hydrogen convergence charging monitoring and diagnostic method may be performed by a hydrogen convergence charging monitoring and diagnostic system implemented in the processor of at least one computer device linked to a hydrogen convergence charging station comprising production process equipment, compression process equipment, storage process equipment, and charging process equipment, and the hydrogen convergence charging monitoring and diagnostic system may be operated under the control of a computer program. The aforementioned computer program may be combined with the computer device and stored on a computer-readable recording medium to execute the hydrogen convergence charging monitoring and diagnostic method on the computer device. The computer program described herein may take the form of an independent single program package, or it may take the form of an independent single program package already installed on the computer device and linked with an operating system or other program packages.

[0037] In addition, as the hydrogen convergence charging monitoring and diagnosis method described below is performed, the status, risk, evaluated productivity, shipment volume, economic feasibility, and profitability of the diagnosed hydrogen convergence charging may be provided to an administrator or user through the hydrogen convergence charging monitoring and diagnosis platform (hereinafter referred to as the monitoring and diagnosis platform). That is, the monitoring and diagnosis platform may be implemented in the form of a dedicated application or web page to function as an interface for providing and displaying information and data to an administrator or user, and for receiving input from an administrator or user.

[0039] FIG. 1 is a drawing illustrating a hydrogen combined charging station according to one embodiment. The hydrogen combined charging station (100) of FIG. 1 illustrates an example including a production process facility (110), a compression process facility (120), a storage process facility (130), a charging process facility (140), a computer device (150), and a network (160).

[0040] Figure 1 is an example for explaining the invention, and the number of process equipment (110, 120, 130, 140) or computer devices (150) is not limited to that shown in Figure 1.

[0041] The computer device (150) may be configured to communicate with process equipment (110, 120, 130, 140) through the network (150) to receive data from process equipment (110, 120, 130, 140) or to transmit commands for control to process equipment (110, 120, 130, 140), and to provide a monitoring and diagnostic platform to a terminal (not shown) of an administrator or user.

[0042] The computer device (150) communicating with process equipment (110, 120, 130, 140) through the network (150) means that the computer device (150) communicates with a control processor performing a process at each of the process equipment (110, 120, 130, 140), and further means communicating with at least one sensor (111, 121, 131, 141) equipped at each of the process equipment (110, 120, 130, 140). For example, the computer device (150) can receive sensing data from at least one sensor (111, 121, 131, 141) provided in each of the process equipment (110, 120, 130, 140) and can transmit a command for control to a control processor of each of the process equipment (110, 120, 130, 140).

[0043] The communication method between the computer device (150) and the process equipment (110, 120, 130, 140) (or at least one sensor (111, 121, 131, 141) provided in each of the process equipment (110, 120, 130, 140)) is not limited and may include a communication method utilizing a communication network (e.g., mobile communication network, wired internet, wireless internet, broadcasting network) that the network (160) may include, as well as short-range wireless communication between the devices. For example, the network (160) may include any one or more networks such as a PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet. Additionally, the network (160) may include any one or more network topologies including a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or hierarchical network, but is not limited thereto.

[0044] A hydrogen convergence charging monitoring and diagnostic system, which is the entity performing the hydrogen convergence charging monitoring and diagnostic method, may be implemented in such a computer device (150). A detailed description thereof will be provided below.

[0045] The production process equipment (110) is a facility that performs a production process to produce hydrogen using biomethane as a raw material, and may be, for example, a hydrogen extractor that produces hydrogen by electrochemical decomposition or thermal catalytic decomposition of biomethane.

[0046] The compression process equipment (120) is equipment that performs a compression process for compressing hydrogen produced in the production process equipment (110), and may be, for example, a piston-type compressor that compresses hydrogen gas. At this time, the compression process equipment (120) may be implemented with a plurality of compression process equipment (e.g., high-pressure compression process equipment and medium-pressure compression process equipment) to compress hydrogen to different pressures based on a preset critical pressure value.

[0047] The storage process facility (130) is a facility that performs a storage process for storing hydrogen compressed in the compression process facility (120), and may be, for example, a storage container for storing compressed hydrogen gas. As described above, if the compression process facility (120) is implemented as a plurality of compression process facilities (e.g., high-pressure compression process facility and medium-pressure compression process facility), the storage process facility (130) may also be implemented as a plurality of storage process facilities (e.g., high-pressure storage process facility and medium-pressure storage process facility) to correspond to the plurality of compression process facilities.

[0048] The charging process facility (140) is a facility that performs a charging process for supplying hydrogen stored in the storage process facility (130), and may be, for example, a dispenser or injection panel that supplies the stored hydrogen gas to a vehicle requiring charging.

[0049] Although the hydrogen convergence charging station has been described as an example including a production process facility (110), it is not limited to or restricted thereto and may include a tube trailer (not shown) that receives hydrogen gas produced elsewhere via a hydrogen tank vehicle instead of the production process facility (110). In such a case, the hydrogen convergence charging monitoring and diagnostic method performed at the hydrogen convergence charging station may include a transport and supply process of the tube trailer (a process of transporting and supplying hydrogen gas) instead of the production process of the production process facility (110).

[0051] FIG. 2 is a block diagram illustrating an example of a computer device according to one embodiment.

[0052] As illustrated in FIG. 2, the computer device (150) may include memory (210), a processor (220), a communication interface (230), and an input / output interface (240). The memory (210) is a computer-readable recording medium and may include a non-perishable mass storage device such as RAM (random access memory), ROM (read only memory), and a disk drive. Here, the non-perishable mass storage device such as ROM and the disk drive may be included in the computer device (150) as a separate permanent storage device distinct from the memory (210). Additionally, an operating system and at least one program code may be stored in the memory (210). These software components may be loaded into the memory (210) from a computer-readable recording medium separate from the memory (210). This separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, or a memory card. In another embodiment, software components may be loaded into memory (210) via a communication interface (230) rather than a computer-readable recording medium. For example, software components may be loaded into memory (210) of a computer device (150) based on a computer program installed by files received through a network (160).

[0053] The processor (220) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (220) via memory (210) or a communication interface (230). For example, the processor (220) may be configured to execute instructions received according to program code stored in a recording device such as memory (210).

[0054] The communication interface (230) may provide a function for the computer device (150) to communicate with other devices (e.g., storage devices described above) through the network (160). For example, requests, commands, data, files, etc. generated by the processor (220) of the computer device (150) according to program code stored in a recording device such as memory (210) may be transmitted to other devices through the network (160) under the control of the communication interface (230). Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device (150) through the communication interface (230) of the computer device (150) via the network (160). Signals, commands, data, etc. received through the communication interface (230) may be transmitted to the processor (220) or memory (210), and files, etc. may be stored in a storage medium (the permanent storage device described above) that the computer device (150) may further include.

[0055] The input / output interface (240) may be a means for interfacing with an input / output device (250). For example, the input device may include a device such as a microphone, keyboard, or mouse, and the output device may include a device such as a display or speaker. As another example, the input / output interface (240) may be a means for interfacing with a device in which the functions for input and output are integrated into one, such as a touchscreen. The input / output device (250) may be composed of a computer device (150) and a single device.

[0056] Additionally, in other embodiments, the computer device (150) may include fewer or more components than the components of FIG. 2. However, it is not necessary to clearly illustrate most of the prior art components. For example, the computer device (150) may be implemented to include at least some of the input / output devices (250) described above, or may include other components such as a transceiver, a database, etc.

[0057] In the following, specific embodiments of the hydrogen convergence charging monitoring and diagnosis method and system, and the monitoring and diagnosis platform will be described.

[0059] FIG. 3 is a block diagram illustrating examples of components that may be included in the processor shown in FIG. 2, and FIG. 4 is a flowchart illustrating a hydrogen fusion charging monitoring and diagnostic method that the computer device shown in FIG. 2 can perform.

[0060] In the embodiments, the computer device (150) performs a hydrogen convergence charging monitoring and diagnosis method, thereby monitoring each of the production process, compression process, storage process, and charging process in real time, diagnosing each of the production process, compression process, storage process, and charging process based on the results, and then providing the diagnosis results to a manager or user's terminal through a monitoring and diagnosis platform. To this end, the computer device (150) may be configured with a hydrogen convergence charging monitoring and diagnosis system that is the entity performing the hydrogen convergence charging monitoring and diagnosis method. For example, the hydrogen convergence charging monitoring and diagnosis system may be implemented in the form of an independently operating program, or configured as an in-app of a dedicated application so that it can operate on the dedicated application.

[0061] The processor (220) of the computer device (150) may be implemented as a component for performing the dynamic risk prediction method according to FIGS. 4 and 5. For example, the processor (220) may include a monitoring unit (310) and a diagnostic unit (320) as shown in FIG. 3 so as to be able to perform the steps (S410 to S420) shown in FIG. 4. Depending on the embodiment, the components of the processor (220) may be optionally included in or excluded from the processor (220). Additionally, depending on the embodiment, the components of the processor (220) may be separated or merged to represent the function of the processor (220).

[0062] These processors (220) and components of the processor (220) can control a computer device (150) to perform steps (S410 to S420) included in the hydrogen fusion charging monitoring and diagnostic method of FIG. 4. For example, the processor (220) and components of the processor (220) may be implemented to execute instructions according to the code of an operating system included in the memory (210) and the code of at least one program.

[0063] Here, the components of the processor (220) may be representations of different functions performed by the processor (220) according to instructions provided by program code stored in the computer device (150). For example, a diagnostic unit (320) may be used as a functional representation of the processor (220) that controls the computer device (150) to diagnose each of the production process, compression process, storage process, and charging process based on the results of monitoring.

[0064] The processor (220) can read necessary commands from memory (210) in which commands related to the control of the computer device (150) are loaded. In this case, the read commands may include commands to control the processor (220) to execute steps (S410 to S420) to be described later.

[0065] The steps (S410 to S420) to be described later may be performed in a different order than the order shown in FIG. 4, and some of the steps (S410 to S420) may be omitted or additional processes may be included.

[0066] In step (S410), the processor (220) (more precisely, the monitoring unit (310) included in the processor (220)) can monitor the production process, compression process, storage process, and charging process in real time using at least one sensor (111, 121, 131, 141) provided in each of the production process equipment (110), compression process equipment (120), storage process equipment (130), and charging process equipment (140).

[0067] More specifically, the processor (220) (more precisely, the monitoring unit (310) included in the processor (220)) can monitor the sensing data of each of the production process, compression process, storage process, and charging process in real time by collecting sensing data from at least one sensor (111, 121, 131, 141) provided in each of the production process equipment (110), compression process equipment (120), storage process equipment (130), and charging process equipment (140).

[0068] In step (S420), the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can diagnose each of the production process, compression process, storage process, and charging process in real time based on the results monitored through step (S410). The diagnostic results can be provided to a manager or user's terminal through a monitoring and diagnostic platform.

[0069] In the following, diagnosing each of the production process, compression process, storage process, and charging process may mean predicting and diagnosing the status of each of the production process, compression process, storage process, and charging process, evaluating the risks of each of the production process, compression process, storage process, and charging process, or evaluating the hydrogen productivity, hydrogen shipment volume, economic feasibility, or profitability of the hydrogen convergence charging including the production process, compression process, storage process, and charging process.

[0070] To explain the prediction and diagnosis of the state of each of the production process, compression process, storage process, and charging process in step (S420), the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can predict and diagnose the state of each of the production process, compression process, storage process, and charging process by analyzing the sensing data collected from at least one sensor (111, 121, 131, 141) equipped in each of the production process facility (110), compression process facility (120), storage process facility (130), and charging process facility (140) using a multivariate regression analysis ensemble algorithm based on Partial Least Squares (PLS) regression analysis, Dynamic Principal Component Analysis (DPCA) technique, and Nearest Neighbor (k-NN) model.

[0071] For example, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) may apply a PLS regression analysis technique to improve the modeling accuracy of sensing data collected from at least one sensor (111, 121, 131, 141) equipped in each of the production process equipment (110), compression process equipment (120), storage process equipment (130), and charging process equipment (140), by analyzing the principal components of the sensing data, estimating the dependent variable value through the independent variable value, and performing regression analysis from the dependent variable value to check the prediction error. In addition, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) may apply a DPCA technique for data analysis considering time and space, and may apply a k-NN model to ensure the reliability of the sensing data.

[0072] At this time, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) may use a multivariate regression ensemble algorithm by considering the season, weather, time, and the operating mode and component information of each of the production process equipment, compression process equipment, storage process equipment, and charging process equipment in order to improve prediction accuracy. To this end, the multivariate regression ensemble algorithm may be pre-learned using the sensing data of each of the production process equipment (110), compression process equipment (120), storage process equipment (130), and charging process equipment (140) as input data in a learning environment of various seasons, weather, time, operating modes, and component information.

[0073] Additionally, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can calculate the causal relationships and contributions of sensing data for each of the predicted and diagnosed production process, compression process, storage process, and charging process in order to improve prediction accuracy. The calculated causal relationships and contributions can be used in the process of predicting and diagnosing the next production process, compression process, storage process, and charging process.

[0074] The status of each of the predicted and diagnosed production, compression, storage, and charging processes can be visualized through a monitoring and diagnostic platform and provided to a manager's or user's terminal.

[0075] Additionally, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can further provide information related to the quality and recommended process execution patterns of each of the production process, compression process, storage process, and charging process, based on the predicted and diagnosed state of each of the production process, compression process, storage process, and charging process, to the terminal of the manager or user through the monitoring and diagnostic platform.

[0076] To explain the evaluation of the risk of each of the production process, compression process, storage process, and charging process in step (S420), the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can evaluate the risk of each of the production process, compression process, storage process, and charging process based on the predicted accident frequency and death number in each of the production process, compression process, storage process, and charging process by predicting the accident frequency and death number in each of the production process, compression process, storage process, and charging process.

[0077] For example, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can evaluate the risk of the AIR (Average Individual Ratio) for each of the production process, compression process, storage process, and charging process through the following Equations 1 to 3 based on the accident frequency and number of fatalities in each of the predicted production process, compression process, storage process, and charging process.

[0079] <Equation 1>

[0080]

[0081] <Equation 2>

[0082]

[0083] <Equation 3>

[0084]

[0086] In Equations 1 to 3 represents the accident frequency for the nth process equipment, and represents the predicted number of deaths at the nth process facility, and represents the process execution time of the nth process equipment, and represents the amount of time an individual spends at a hydrogen convergence charging station annually.

[0087] In hydrogen fusion charging, there exists a process with a relatively higher risk due to the characteristics of each process. Therefore, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can evaluate the risk by further subdividing the compression process and storage process, which have a higher risk due to process characteristics or statistically. For example, as described above, the compression process equipment (120) and storage process equipment (130) are each equipped with multiple compression process equipment (e.g., medium-pressure compression process equipment and high-pressure compression process equipment) and storage process equipment (e.g., medium-pressure storage process equipment and high-pressure storage process equipment) to compress and store at different pressures based on at least one preset threshold pressure value. Accordingly, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can classify the compression process and storage process into multiple compression processes and multiple storage processes based on at least one preset threshold pressure value, and evaluate the risk for each of the classified compression processes and storage processes.

[0088] Additionally, risk assessment may be performed for only selected parts instead of all production, compression, storage, and charging processes. For example, a processor (220) (specifically, a diagnostic unit (320) included in the processor (220)) can select at least one process showing an abnormal state among the production, compression, storage, and charging processes based on sensing data collected from at least one sensor (111, 121, 131, 141) provided in each of the production process facility (110), compression process facility (120), storage process facility (130), and charging process facility (140), and then predict the accident frequency and number of deaths in the selected at least one process, thereby assessing the risk in the selected at least one process based on the predicted accident frequency and number of deaths in the selected at least one process.

[0089] In step (S420), the hydrogen productivity, hydrogen shipment volume, economic feasibility, or profitability of a hydrogen composite charging system including a production process, a compression process, a storage process, and a charging process is evaluated. The processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) can evaluate the hydrogen productivity, which is the amount of hydrogen produced, the hydrogen shipment volume, and the economic feasibility or profitability based on sensing data collected from at least one sensor (111, 121, 131, 141) provided in each of the production process facility (110), the compression process facility (120), the storage process facility (130), and the charging process facility (140).

[0090] Here, economic feasibility or profitability is evaluated based on sensing data collected from at least one sensor (111, 121, 131, 141) equipped in each of the production process facility (110), compression process facility (120), storage process facility (130), and charging process facility (140), because economic feasibility or profitability is calculated by taking into account the maintenance of the hydrogen composite charging. More specifically, the processor (220) (more precisely, the diagnostic unit (320) included in the processor (220)) derives the hardware cost of hydrogen fusion charging based on the state of each of the production process, compression process, storage process, and charging process, which is predicted and diagnosed based on sensing data collected from at least one sensor (111, 121, 131, 141) equipped in each of the production process facility (110), compression process facility (120), storage process facility (130), and charging process facility (140), and then evaluates economic feasibility by calculating the Benefit Cost Ratio (B / C Ratio), Net Present Value (NPV), or Internal Rate of Return (IRR) using a Cost-Benefit Analysis method based on the derived hardware cost, or evaluates economic feasibility by calculating the Net Present Value Method, Internal Rate of Return Method, or profitability index based on the derived hardware cost Profitability can be evaluated using the Profitability Index Method.

[0092] The device described above may be implemented as a hardware component, a software component, and / or a combination of a hardware component and a software component. For example, the device and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. In addition, other processing configurations, such as parallel processors, are also possible.

[0093] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or instruct the processing unit independently or collectively. Software and / or data may be embodied in any type of machine, component, physical device, computer storage medium, or device so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0094] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. In this case, the medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or several hardware combined, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Additionally, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0095] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0096] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

[0097] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0098] The scope of the present invention is defined by the claims set forth below, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention. Explanation of the symbols

[0100] 100: Hydrogen Convergence Charging Station 110: Production Process Equipment 120: Compression process equipment 130: Storage process equipment 140: Charging process equipment 111, 121, 131, 141: At least one sensor 150: Computer device 160: Network

Claims

Claim 1 A method for monitoring and diagnosing hydrogen convergence charging, performed by a computer device linked to a hydrogen convergence charging station, comprising: a step of monitoring each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for supplying the stored hydrogen; and a step of diagnosing each of the production process, the compression process, the storage process, and the charging process based on the results of the monitoring, wherein The above diagnostic step is, A step of predicting and diagnosing the state of each of the production process, the compression process, the storage process, and the charging process by analyzing sensing data collected from at least one sensor using a multivariate regression analysis ensemble algorithm based on Partial Least Squares (PLS) regression analysis, Dynamic Principal Component Analysis (DPCA) technique, and Nearest Neighbor (k-NN) model. Includes, The above-mentioned prediction and diagnosis steps are, Characterized by using the multivariate regression analysis ensemble algorithm based on season, weather, time, and the operating mode and component information of each of the production process equipment, compression process equipment, storage process equipment, and filling process equipment. Hydrogen Convergence Charging Monitoring and Diagnosis Method. Claim 2 delete Claim 3 delete Claim 4 A hydrogen convergence charging monitoring and diagnosis method according to claim 1, wherein the step of predicting and diagnosing includes the step of calculating the causal relationship and contribution of the sensing data for each of the predicted and diagnosed states of the production process, the compression process, the storage process, and the charging process. Claim 5 A hydrogen convergence charging monitoring and diagnosis method according to claim 1, wherein the predicting and diagnosing step comprises providing information related to the quality and recommended process execution patterns of each of the production process, the compression process, the storage process, and the charging process based on the state of each of the predicted and diagnosed production process, the compression process, the storage process, and the charging process. Claim 6 A hydrogen hybrid charging monitoring and diagnosis method according to claim 1, wherein the diagnosing step comprises a step of evaluating the risk of each of the production process, the compression process, the storage process, and the charging process. Claim 7 A hydrogen composite charging monitoring and diagnosis method according to claim 6, wherein the evaluation step comprises: a step of predicting the accident frequency and the number of fatalities in each of the production process, the compression process, the storage process, and the charging process; and a step of evaluating the risk of each of the production process, the compression process, the storage process, and the charging process based on the predicted accident frequency and the number of fatalities in each of the production process, the compression process, the storage process, and the charging process. Claim 8 A hydrogen fusion charging monitoring and diagnosis method according to claim 7, wherein the predicting step comprises: a step of selecting at least one process showing an abnormal state among the production process, the compression process, the storage process and the charging process based on sensing data collected from at least one sensor; and a step of predicting the accident frequency and the number of fatalities in the selected at least one process, and wherein the evaluating step comprises a step of evaluating the risk in the selected at least one process based on the predicted accident frequency and the number of fatalities in the at least one process. Claim 9 A hydrogen composite charging monitoring and diagnostic method according to claim 6, wherein the evaluation step comprises classifying the compression process and the storage process into a plurality of compression processes and a plurality of storage processes based on at least one preset threshold pressure value, and evaluating the risk for each of the classified compression processes and storage processes. Claim 10 A hydrogen convergence charging monitoring and diagnosis method according to claim 1, wherein the diagnosing step comprises a step of evaluating hydrogen productivity, hydrogen shipment volume, and economic feasibility or profitability considering maintenance of the hydrogen convergence charging based on sensing data collected from at least one sensor. Claim 11 A hydrogen convergence charging monitoring and diagnosis method according to claim 10, wherein the evaluation step comprises a step of deriving the hardware cost of the hydrogen convergence charging based on the state of each of the production process, the compression process, the storage process, and the charging process predicted and diagnosed based on the sensing data, and a step of evaluating the economic feasibility by calculating the Benefit Cost Ratio (B / C Ratio), Net Present Value (NPV), or Internal Rate of Return (IRR) using a Cost-Benefit Analysis method based on the derived hardware cost; or a step of evaluating the profitability using a Net Present Value Method, an Internal Rate of Return Method, or a Profitability Index Method based on the derived hardware cost. Claim 12 A computer-readable recording medium having a computer program recorded thereon for executing a hydrogen convergence charging monitoring and diagnostic method on a computer device linked to a hydrogen convergence charging station, wherein the hydrogen convergence charging monitoring and diagnostic method comprises: a step of monitoring each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for charging and supplying the stored hydrogen; and a step of diagnosing each of the production process, the compression process, the storage process, and the charging process based on the results of the monitoring. The above diagnostic step is, A step of predicting and diagnosing the state of each of the production process, the compression process, the storage process, and the charging process by analyzing sensing data collected from at least one sensor using a multivariate regression analysis ensemble algorithm based on Partial Least Squares (PLS) regression analysis, Dynamic Principal Component Analysis (DPCA) technique, and Nearest Neighbor (k-NN) model. Includes, The above-mentioned prediction and diagnosis steps are, Characterized by using the multivariate regression analysis ensemble algorithm based on season, weather, time, and the operating mode and component information of each of the production process equipment, compression process equipment, storage process equipment, and filling process equipment. is a computer-readable recording medium. Claim 13 A computer device for performing a hydrogen convergence charging monitoring and diagnostic method in conjunction with a hydrogen convergence charging station, comprising at least one processor configured to execute computer-readable commands, wherein the at least one processor comprises: a monitoring unit that monitors each of the production process, compression process, storage process, and charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for charging and supplying the stored hydrogen; and a diagnostic unit that diagnoses each of the production process, the compression process, the storage process, and the charging process based on the results of the monitoring. The above diagnostic unit is, Characterized by using a multivariate regression ensemble algorithm considering season, weather, time, and the operating mode and component information of each of the production process equipment, compression process equipment, storage process equipment, and filling process equipment. Computer device. Claim 14 In a hydrogen convergence charging station, the system comprises: a production process facility for producing hydrogen using biomethane as a raw material; a compression process facility for compressing the produced hydrogen; a storage process facility for storing the compressed hydrogen; and a charging process facility for charging and supplying the stored hydrogen; and a computer device linked with the production process facility, the compression process facility, the storage process facility, and the charging process facility, wherein at least one processor configured to execute computer-readable commands included in the computer device comprises: a monitoring unit that monitors the production process, the compression process, the storage process, and the charging process in real time using at least one sensor provided in each of the production process facility for producing hydrogen using biomethane as a raw material, the compression process facility for compressing the produced hydrogen, the storage process facility for storing the compressed hydrogen, and the charging process facility for charging and supplying the stored hydrogen; and a diagnostic unit that diagnoses the production process, the compression process, the storage process, and the charging process based on the results of the monitoring. Includes, The above diagnostic unit is, Characterized by using a multivariate regression ensemble algorithm considering season, weather, time, and the operating mode and component information of each of the production process equipment, compression process equipment, storage process equipment, and filling process equipment. Hydrogen Convergence Charging Station.

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