KR102642607B1 - Portable sterilization device with self-powering function - Google Patents

Abstract

A portable sterilizing device according to an embodiment includes: a solar module that generates power through self-generation using sunlight and charges a battery of an energy storage panel or a portable sterilizing device with the generated power; An illuminance sensor that senses ambient illuminance; and a charging control device that controls self-generation of solar modules and charging of batteries; It includes a charging control device; is a memory that stores at least one instruction; And a processor, wherein at least one instruction is executed by the processor to monitor the battery of the portable sterilizing device and collect battery status information including the amount of charge and state of health (SoH), and collect battery status information. Self-generation of the solar module is controlled based on at least one of status information, operation information of the portable sterilizing device, expected power usage, and sensed ambient illuminance.

Description

{PORTABLE STERILIZATION DEVICE WITH SELF-POWERING FUNCTION}

The present disclosure relates to a portable sterilizing device, and specifically, to a portable sterilizing device capable of self-powering through solar power generation.

Unless otherwise indicated herein, the material described in this section is not prior art to the claims of this application, and is not admitted to be prior art by inclusion in this section.

A portable sterilizing device is a device that uses light to remove or sterilize bacteria, viruses and other pathogens to clean hands or surfaces. Conventional portable sterilization devices use ultraviolet (UV-C) sterilization or morning sterilization methods.

UV-C ultraviolet rays are ultraviolet rays of very short wavelength and are effective in sterilizing bacteria and viruses by destroying their DNA and RNA. Portable UV-C sterilizing devices are mainly used to sterilize hands, phones, keyboards, problems, food, sleeping suits, mobile electronic devices, etc. The user performs the sterilization task by moving the device over the object to be sterilized or by directly irradiating the object. Additionally, ozone used for sterilization is effective in eliminating bacteria and viruses. Ozone breaks down oxygen molecules O2 and changes them into O3 molecules, creating ozone and destroying bacteria and viruses. Some portable ozone generators are used to purify air and water, and these devices can also be used as air purifiers or to clean water pipes.

After COVID-19, people's sense of hygiene has become stronger, and the use of portable sterilization devices is increasing. Portable sterilization devices must be mobile and readily available in emergency situations. However, the conventional portable sterilizing device requires charging, which limits its mobility, and there is a problem in that the portable sterilizing device cannot be used in emergency situations due to insufficient charging.

In addition, portable sterilization devices that sterilize through steam have better sterilization ability than devices that sterilize through light, but they use more power than sterilization devices that use light, and the temperature, amount, and temperature of the steam sprayed for sterilization are higher. There is a large difference in the amount of power required depending on the time. Accordingly, there is a great demand for portable sterilization devices that solve power supply problems through self-generation.

1. Korean Patent Registration No. 10-2161372 (2020.09.23) 2. Korean Patent Registration No. 10-2540628 (2023.06.01)

The portable sterilizing device equipped with a self-power generation function according to the embodiment adjusts whether or not to self-generate and the amount of charge by self-generating according to the battery charge amount, allowing the portable sterilizing device that sprays steam to be used at any time.

In addition, in the embodiment, a self-generation module using solar energy is provided, and self-generation and power generation amount are controlled according to ambient illumination and the remaining battery capacity of the portable sterilization device, thereby improving charging efficiency.

Additionally, in the embodiment, sterilizing water containing ozone is sprayed in a mist state to quickly sterilize indoor objects and areas exposed to the coronavirus (SARS-CoV-2).

A portable sterilizing device according to an embodiment includes: a solar module that generates power through self-generation using sunlight and charges a battery of an energy storage panel or a portable sterilizing device with the generated power; An illuminance sensor that senses ambient illuminance; and a charging control device that controls self-generation of solar modules and charging of batteries; It includes a charging control device; is a memory that stores at least one instruction; And a processor, wherein at least one instruction is executed by the processor to monitor the battery of the portable sterilizing device and collect battery status information including the amount of charge and state of health (SoH), and collect battery status information. Self-generation of the solar module is controlled based on at least one of status information, operation information of the portable sterilizing device, expected power usage, and sensed ambient illuminance.

The portable sterilizing device equipped with a self-power generation function according to the embodiment as described above generates solar energy according to the state of the battery such as the amount of charge and state of aging (SoH) of the battery provided in the portable sterilizing device, ambient illumination, and the amount of power of the solar module. By performing self-generation through , it is possible to extend the time for maintaining the battery of the portable sterilization device at an appropriate capacity. Through this, the portable sterilizing device can be used at any time regardless of the battery charge amount, and the lifespan of the battery provided in the portable sterilizing device can be improved.

In addition, the power production efficiency of the solar module of the portable sterilizing device according to the embodiment is improved, and the remaining energy depending on whether the battery is fully charged is stored in the energy storage panel of the solar module, thereby enabling efficient use of energy.

In addition, in the embodiment, the energy efficiency of the portable sterilizing device can be increased by calculating the charging time and charging amount according to the expected power consumption according to ambient illumination, battery status, charging amount, and operating time of the sterilizing device and performing self-power generation.

The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

1 to 3 are diagrams showing a portable sterilizer equipped with a self-power generation function according to an embodiment.
Figure 4 is a diagram showing a data processing configuration for self-power generation and charging control of a portable sterilizing device according to an embodiment.
Figure 5 is a block diagram of the charging control device 100 according to an embodiment.
Figure 6 is a diagram showing the configuration of commands stored in the memory of a charging control device according to an embodiment
Figure 7 is a diagram showing an example of a model that is stored in a learning unit and learns data according to an embodiment

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of the present invention are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

In this specification, 'part' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more pieces of hardware, and two or more units may be realized using one piece of hardware.

In this specification, some of the operations or functions described as being performed by a terminal, apparatus, or device may instead be performed on a server connected to the terminal, apparatus, or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

Hereinafter, the present invention will be described in detail with reference to the attached drawings.

Figures 1 to 3 are diagrams showing a portable sterilizer equipped with a self-power generation function according to an embodiment. Referring to FIGS. 1 to 3, the portable sterilizer according to the embodiment includes a solar module 15, a housing 10, an ozone gas supply unit 20, a compressed air supply unit 30, and a water supply unit 40. ), and a power supply unit 50.

In the embodiment, the solar power module 15 is configured outside the housing 10, which is the upper case, and converts light energy into power usable by the portable sterilizing device.

In the embodiment, the solar power generation module 15 is a module that performs self-generation, stores energy, and charges the battery of a portable sterilization device, and may be configured to include an illuminance sensor, a solar panel, a solar cell panel, and an inverter. there is.

The housing 10 is provided to form the appearance of a portable sterilizer and includes a water tank 12, a nozzle 14, and a Y-mixing pipe 16. The water tank 12 is installed inside the housing 10 and is formed to store a certain amount of water therein. A water inlet 13 exposed to the outside of the housing 10 is formed at the top of the water tank 12. As the water inlet is exposed to the outside of the housing, it is possible to conveniently replenish water in the water tank. A water outlet (not shown in the drawing) is formed in the lower part of the water tank 12, and the water outlet is connected to the water supply hose 42 provided in the water supply unit 40.

The nozzle 14 is installed on one side of the housing 10 to be exposed to the outside. This housing 10 is responsible for mixing ozone gas, compressed air, and water supplied from the ozone gas supply unit 20, compressed air supply unit 30, and water supply unit 40 and spraying them in a fog state. For this purpose, the nozzle 14 is formed as an atomized nozzle. Here, the atomizing nozzle mixes gas and liquid internally and sprays the liquid in a mist state with the power of compressed air, and the structure of this atomizing nozzle was determined by a person skilled in the art in the technical field of chemical sterilizers or chemical sprayers before filing the present invention. Since this is a known technology that is widely practiced, description thereof will be omitted.

The Y mixing pipe 16 is connected to one side of the nozzle 14. An ozone gas supply unit 20 is connected to one branched side of the Wiser mixing pipe 16, and a compressed air supply unit 30 is connected to the other branched side of the Wiser mixing pipe 16. This weiser mixing pipe 16 serves to mix the ozone gas supplied from the ozone gas supply unit 20 and the compressed air supplied from the compressed air supply unit 30 and supply them to the nozzle 14. That is, the ozone gas from the ozone gas supply unit 20 and the compressed air from the compressed air supply unit 30 are mixed inside the weiser mixing pipe 16 and then supplied to the nozzle.

The ozone gas supply unit 20 is provided to supply ozone gas to the nozzle 14 and includes an air pump 22, an ozone generator 24, and an ozone gas supply hose 26.

The air pump 22 sucks air and supplies it to the ozone generator 24. The ozone generator 24 generates ozone gas by mixing ozone with the air sucked in by the air pump 22. For this purpose, the ozone generator 24 is formed of a known ozone generator. The ozone gas supply hose 26 is connected to one branched side of the weiser mixing pipe 16 to supply the ozone gas generated by the ozone generator 24 to the nozzle 14.

The ozone gas supply unit 20 configured in this way supplies ozone gas generated by the air pump 22 and the ozone generator 24 to one branched side of the Wiser mixing pipe 16 through the ozone gas supply hose 26. It is structured to do so.

The compressed air supply unit 30 is provided to supply compressed air to the nozzle 14 and includes a compression pump 32 and a compressed air supply hose 34. The compression pump 32 generates compressed air and supplies it to the ozone generator 24. The compressed air supply hose 34 is connected to the other branched side of the weiser mixing pipe 16 to supply compressed air generated by the compression pump 32 to the nozzle 14.

The compressed air supply unit 30 configured in this way is structured to supply compressed air generated by the compression pump 32 to the other branched side of the Wiser mixing pipe 16 through the compressed air supply hose 34. The water supply unit 40 is provided to supply water stored in the water tank 12 to the nozzle and includes a water supply hose 42 and an electromagnetic valve 44. The water supply hose 42 is connected to a water outlet formed at the bottom of the water tank 12. Accordingly, the water in the water tank 12 is supplied to the nozzle 14 by a drop. The electromagnetic valve 44 is installed on the water supply hose 42. This electromagnetic valve 44 serves to regulate the flow of water supplied from the water tank 12 to the nozzle 14 through the water supply hose 42. For this purpose, the solenoid valve 44 is formed as a solenoid valve. That is, the electromagnetic valve 44 opens and closes under the control of the control unit 52 to regulate the flow of water.

Figure 4 is a diagram showing a data processing configuration for self-power generation and charging control of a portable sterilizing device according to an embodiment.

Referring to FIG. 4, the solar module 15 of the portable sterilizing device according to the embodiment may be connected to the battery 200 and the charging control device 100 of the portable sterilizing device.

In the embodiment, the charging control device 100 collects battery status information by monitoring the battery 200 of the portable sterilizing device. In an embodiment, the battery state information includes the battery's state of charge (SoC, State of Charge) and aging state (SoH, State of Health). In the embodiment, the state of aging (SoH) of the battery is an indicator of the performance of the battery or storage device, and in the embodiment, it may be measured through the internal resistance of the battery. In an embodiment, the charging control device 100 controls the solar module 15 according to at least one of collected battery status information, driving information including whether a portable sterilization device is used, power usage prediction information, and sensed ambient illuminance. Control self-generation. In the embodiment, the driving information is information on whether the portable sterilizing device is used and operation settings. For example, the driving information may include the amount of steam emitted from the portable sterilizing device, the injection speed, the temperature of the steam, etc.

Figure 5 is a block diagram of the charging control device 100 according to an embodiment.

The configuration of the charging control device 100 shown in FIG. 5 is only a simplified example.

The communication unit 110 can be configured regardless of the communication mode, such as wired or wireless, and can be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). In addition, the communication unit 110 can operate based on the known World Wide Web (WWW) and uses wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. You can also use it. For example, the communication unit 110 may be responsible for transmitting and receiving data necessary to perform a technique according to an embodiment of the present disclosure.

Memory 120 may refer to any type of storage medium. For example, memory 120 may be a flash memory type, hard disk type, or multimedia card type. micro type), card type memory (e.g. SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory) ), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. This memory 120 may form the database shown in FIG. 1.

Memory 120 may store at least one instruction that can be executed by processor 130. Additionally, the memory 120 may store any type of information generated or determined by the processor 130 and any type of information received by the server 200. For example, the memory 120 stores RM data and RM protocols according to users, as will be described later. Additionally, the memory 120 stores various types of modules, instruction sets, or models.

The processor 130 may perform technical features according to embodiments of the present disclosure, which will be described later, by executing at least one instruction stored in the memory 120. In one embodiment, the processor 130 may be comprised of at least one core, including a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and tensor processing of a computer device. It may include a processor for data analysis and/or processing, such as a tensor processing unit (TPU).

Figure 6 is a diagram showing the configuration of commands stored in the memory of a charging control device according to an embodiment.

Referring to FIG. 6, the memory 120 according to the embodiment may be configured to include a collection unit 121, a pre-processing unit 122, a learning unit 123, and a control unit 124. The term 'part' used in this specification should be interpreted to include software, hardware, or a combination thereof, depending on the context in which the term is used. For example, software may be machine language, firmware, embedded code, and application software. As another example, hardware may be a circuit, processor, computer, integrated circuit, integrated circuit core, sensor, Micro-Electro-Mechanical System (MEMS), passive device, or a combination thereof.

The collection unit 121 collects a series of data necessary for controlling solar modules that perform self-generation and battery charging. For example, the collection unit 121 collects battery status information that is a result of battery monitoring, operation information of a portable sterilizing device, operation information of a solar module, etc. In an embodiment, the operation information of the solar module may include whether the solar module is self-generated, power production, and accumulated power. Additionally, the collection unit 121 collects learning data for the model used in the charging control device 100. In an embodiment, the model may include a model for calculating the expected power usage of the portable sterilizing device and the optimal capacity of the battery, and a model for calculating the power generation amount of the solar module. In an embodiment, the model's learning data may include actual power consumption according to the time the portable sterilization device is used, and optimal charging capacity and discharge capacity according to the battery aging state (SoH). Additionally, the learning data may include power generation data according to the operating time and illuminance of the solar module.

The preprocessing unit 122 preprocesses the collected training data to remove data with bias or discrimination from the collected training data set. In an embodiment, the preprocessor 120 preprocesses the collected training data set and processes it into a form suitable for learning an artificial intelligence model. For example, the preprocessor 122 may perform processes such as noise removal, outlier removal, and missing value processing. In addition, the preprocessor 122 can prevent the model from learning unnecessary patterns by normalizing the data, removing outliers, or adjusting the scale of the data through data preprocessing.

The learning unit 123 implements a deep learning model by learning a deep learning neural network with the collected training data set. Figure 7 is a diagram showing an example of a model that is stored in a learning unit and learns data according to an embodiment. Referring to FIG. 7, the learning unit 123 of the memory may store a power consumption prediction model (1), an optimal capacity calculation model (2), and a self-power generation control model (3). Each module or model may be in the form of an application executable by the processor 130.

The power consumption prediction model (1) is an artificial neural network model that predicts the amount of power consumption according to the operating time, set functions, and battery status of the portable sterilizing device. In the embodiment, the power consumption prediction model 1 learns the operating time, set functions, and battery status of the actual portable sterilization device as input data, and learns the actual power consumption and battery discharge amount according to the input data as output data. Through this, it is possible to predict the portable sterilization device's operating time, set functions, and power usage according to battery status.

For example, the power consumption prediction model 1 learns from power consumption data for each usage time of the portable sterilizing device, inputs information on the point of use and usage time of the portable sterilizing device to the power consumption prediction model, and obtains power consumption prediction information. can do.

Additionally, in the embodiment, the power consumption prediction model 1 predicts power consumption according to driving information of the portable sterilizing device. For example, the power consumption prediction model (1) predicts power consumption according to driving information including the intensity of steam sprayed from a portable sterilizing device, steam temperature, speed, and spray duration.

The optimal capacity calculation model (2) calculates the optimal charging and discharging capacity according to the battery temperature and the battery status of the portable sterilizing device. In general, both the optimal charging capacity and discharging capacity of the battery are different depending on the battery aging condition (Soh). The optimal capacity calculation model (2) according to the embodiment learns the optimal charging capacity and discharging capacity for the battery aging state, receives the battery status of the portable sterilization device, and calculates the optimal charging capacity and discharging capacity. Through this, it is possible to perform self-generation and battery charge/discharge control according to the optimal capacity based on the state of the battery installed in the portable sterilization device.

The self-generation control model (3) is a model that controls the self-generation of solar modules according to ambient light and battery status, and adjusts the system to generate maximum power from sunlight. In the embodiment, the self-generation control model 3 maximizes the efficiency of the solar module and makes full use of the power generation capacity. In addition, by effectively managing the power stored in the energy storage panel of the solar module and the battery of the portable sterilization device, the power generated by self-generation can be stored and supplied when needed. In addition, the self-generating control model (3) produces and supplies power by considering the power usage patterns and requirements of the portable sterilizing device.

The power generation prediction model (4) is a model that predicts the power production of the solar module installed in the portable sterilization device. In an embodiment, the amount of power that can be generated can be predicted based on environmental information including illuminance, weather information, and self-generation time.

A model in this specification may refer to any type of computer program that operates based on a network function, artificial neural network, and/or neural network. Throughout this specification, model, neural network, network function, and neural network may be used interchangeably. In a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer.

A deep neural network (DNN) may refer to a neural network that includes a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), transformer, etc. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

A neural network can be learned in at least one of supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. You can. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of supervised learning, data in which the correct answer is labeled (labeled data) can be used in each learning data, and in the case of unsupervised learning, data in which the correct answer is not labeled in each learning data (unlabeled data) can be used. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. Additionally, to prevent overfitting, methods such as increasing learning data, regularization, dropout to disable some nodes, and batch normalization layer can be applied.

In one embodiment, the model may borrow at least a portion of a transformer. The transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. A transformer may have a structure that receives a series of data, goes through encoding and decoding steps, and outputs a series of data of different types. In one embodiment, a series of data can be processed into a form that can be operated by a transformer. The process of processing a series of data into a form that can be operated by a transformer may include an embedding process. Expressions such as data token, embedding vector, embedding token, etc. may refer to data embedded in a form that a transformer can process.

In order for the transformer to encode and decode a series of data, the encoders and decoders within the transformer can be processed using an attention algorithm. The attention algorithm calculates the similarity of one or more keys for a given query, reflects the given similarity to the value corresponding to each key, and returns the reflected similarity value ( It may refer to an algorithm that calculates the attention value by weighting the values.

Depending on how queries, keys, and values are set, various types of attention algorithms can be classified. For example, if attention is obtained by setting the query, key, and value all the same, this may mean a self-attention algorithm. In order to process a series of input data in parallel, when the dimension of the embedding vector is reduced and attention is obtained by obtaining individual attention heads for each divided embedding vector, this refers to a multi-head attention algorithm. can do.

In one embodiment, the transformer may be composed of modules that perform a plurality of multi-head self-attention algorithms or a multi-head encoder-decoder algorithm. In one embodiment, the transformer may also include additional components other than the attention algorithm, such as embedding, normalization, and softmax. A method of configuring a transformer using an attention algorithm may include the method disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

Transformers can be applied to various data domains such as embedded natural language, segmented image data, and audio waveforms to convert a series of input data into a series of output data. In order to convert data with various data domains into a series of data that can be input to the transformer, the transformer can embed the data. Transformers can process additional data expressing relative positional or phase relationships between sets of input data. Alternatively, a series of input data may be embedded by additionally reflecting vectors expressing relative positional relationships or phase relationships between the input data. In one example, the relative positional relationship between a series of input data may include, but is not limited to, word order within a natural language sentence, relative positional relationship of each segmented image, temporal order of segmented audio waveforms, etc. . The process of adding information expressing the relative positional relationship or phase relationship between a series of input data may be referred to as positional encoding.

In one embodiment, the model includes at least one of a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM) network, a Deep Neural Network (DNN), a Convolutional Neural Network (CNN), and a Bidirectional Recurrent Deep Neural Network (BRDNN). It can be done and is not limited to this.

In one embodiment, the model may be a model learned using transfer learning. Here, transfer learning obtains a pre-trained model with the first task by pre-training a large amount of unlabeled learning data using a semi-supervised learning or self-learning method, and obtains a pre-trained model with a first task. It represents a learning method that implements a target model by fine-tuning the model to suit the second task and learning the labeled learning data using a supervised learning method.

Referring again to FIG. 6, the control unit 124 monitors the battery of the portable sterilizing device to collect battery status information including charge amount and aging state (SoH, State of Health), and collects battery status information, the portable sterilizing device, and the collected battery status information. Self-generation of the solar module is controlled according to at least one of driving information, power usage prediction information, and sensed ambient illuminance.

In an embodiment, the control unit 124 may collect expected power usage information through user input, or calculate expected power usage information by analyzing usage patterns of a portable sterilizing device.

In the embodiment, the control unit 124 collects usage record data such as operating time, operating cycle, and operating mode of the portable sterilizing device in order to analyze usage patterns. Afterwards, the usage record data is analyzed to identify the user's usage patterns. For example, through analysis of the use record data of the control unit 124, the time when the portable sterilizing device is most frequently used and the time period when power use is improved are identified. Thereafter, the control unit 124 uses the power consumption prediction model to predict power usage by hour or day. In an embodiment, control 124 calculates how much power the portable sterilizing device is expected to use on a particular date and time. In an embodiment, the expected power usage information may be used by the control unit 124 to control self-generation. Specifically, during times when expected power usage is high, the control unit 124 may communicate with the inverter of the solar module to adjust the output to the maximum point or increase the battery charge through MPPT (Maximum Power Point Tracking) control.

In an embodiment, the controller 124 causes the solar module's inverter to adjust the output to its maximum point, thereby generating the energy needed to meet current energy demands and simultaneously charge the battery. The control unit 124 protects the battery of the portable sterilizing device from being overcharged or overdischarged, depending on the state of the battery. To this end, the control unit 124 measures the internal resistance of the battery and calculates the aging state (SoH) of the battery. Afterwards, the charging capacity of the battery is calculated using the calculated aging state (SoH) and the initial capacity of the battery, and the required charging amount, which is power to be used in the future, is calculated based on the calculated charging capacity and the charging amount of the battery. Afterwards, the control unit 124 calculates the difference between the charging amount and the battery charging amount as the amount of power generation required through the solar module.

In the embodiment, the control unit 124 measures the power generated from the solar module through a power generation measurement device and predicts the actual power generation of the solar module through a power generation prediction model in consideration of environmental conditions such as weather, sunlight, and temperature. . The power generation prediction model can collect environmental conditions through illuminance sensors and the Korea Meteorological Administration server.

Additionally, in the embodiment, the control unit 124 calculates the required charging amount, which is the amount of power that needs to be charged, in consideration of the battery state, such as the battery charge amount and aging state. Afterwards, the self-generation time of the solar module can be calculated by considering the predicted power generation amount and charging required amount.

For example, the control unit 124 may calculate the required charging amount, which is the amount of power required to maintain the battery in an optimal state or use energy at a specific time, depending on the current state and capacity of the battery and the user's request.

Specifically, the control unit 124 can calculate the required charging amount according to Equation 1.

Equation 1

Charge Required=

Target battery SOC (State of Charge) Current battery SOC + Self-Generation

In an embodiment, the target battery SOC (State of Charge) is a target charge amount that indicates how much the battery should be charged. For example, to charge the battery to 80%, the target SOC is 0.8. The current battery SOC is a SOC value that indicates the current battery charge. Self-generation is the amount of power generated through solar modules. Self-generated power value represents the amount of power generated during a specific time and may vary depending on the time period.

Additionally, the control unit 124 compares the predicted power generation amount with the amount of power that needs to be charged and determines whether the power generation amount is sufficient for each time period. If the amount of power generation exceeds the amount required for charging, the time period is set as a time when self-generation is possible. In the embodiment, after determining whether self-generation is possible for each time zone, the self-generation time of the solar module is calculated by calculating the sum of these time zones. Self-generation time refers to the time during which a solar module can generate power on its own.

Additionally, if the battery charge at the time of use is less than the expected power usage, the control unit 124 controls the self-generation of the solar module to charge the amount of power corresponding to the difference between the expected power usage and the battery charge.

To this end, the control unit 124 checks the current state of charge of the battery through a battery management system (BMS) or battery monitoring equipment. The battery state of charge indicates the current state of charge (SOC) of the battery and is used to calculate the amount of charging required. Thereafter, the control unit 124 calculates the expected power usage at the time of using the portable sterilizing device. Additionally, the control unit 124 compares the expected power usage and the current battery charge (SOC) to calculate the required charging amount, which is the amount of power that is insufficient. Charge requirement is the amount of additional energy that must be charged to the battery.

Thereafter, in the embodiment, the control unit 124 monitors the power generation amount of the solar module. At this time, the control unit 124 collects the amount of power generated from the solar module, and if the amount of power generated from the solar module, which is self-generated, does not meet the charging requirement, the control unit 124 uses the inverter of the solar module to output the maximum output. The signal is transmitted, and the inverter adjusts the output of the solar module to the maximum point through MPPT (Maximum Power Point Tracking) control. Additionally, in the embodiment, the battery can be charged with power stored in the energy storage panel of the solar module.

In an embodiment, the inverter of the solar module tracks the maximum output point through the MPPT algorithm. The MPPT algorithm is a model that tracks the operating point of the panel to obtain the maximum output of the solar module under current environmental conditions. The MPPT algorithm monitors the output power and detects the maximum output point by gradually adjusting the panel's voltage or current over a given time interval.

Additionally, in the embodiment, the control unit 124 controls self-generation of the solar module according to surrounding environment information. For example, when the ambient illuminance exceeds a certain value and the battery charge is less than the full capacity, the control unit 124 starts self-generation of the solar module. In the embodiment, when the battery is fully charged, but the ambient illuminance exceeds a certain value and exceeds a certain level of the solar module's power generation efficiency, self-generation starts, and the power generated by self-generation is stored in the battery of the solar module. do.

In addition, in the embodiment, the control unit 124 calculates the optimal capacity including the optimal charging capacity and discharging capacity according to the temperature and aging state (SoH) of the battery, and calculates the optimal capacity, charging amount, and expected power usage based on the calculated optimal capacity, charging amount, and expected power usage. Controls self-generation of optical modules.

To this end, the control unit 124 models the characteristics of the battery in use to calculate optimal capacity. In the embodiment, the control unit 124 models battery characteristics through a mathematical model that takes into account the temperature, state of aging (SoH), and capacity characteristics of the battery. Thereafter, the control unit 124 measures the internal temperature of the battery and obtains the temperature and aging state (SoH) of the battery through a sensor or algorithm that estimates the aging state (SoH). Thereafter, the control unit 124 calculates the optimal charging and discharging capacity through an optimal capacity calculation model based on the battery model and current temperature and aging state (SoH) information.

Thereafter, the control unit 124 collects the expected power usage according to the operation goal set by the system operator and monitors the amount of power generated from the solar panel. Afterwards, the control unit 124 determines whether the solar module self-generates and whether to charge and store energy. In the embodiment, the control unit 124 adjusts charging and discharging to maintain a balance between expected power usage and self-generated power. For example, if the charging amount exceeds the expected power usage, the control unit 124 stores the remaining energy in the energy storage panel of the solar module for later use.

In addition, the control unit 124 monitors the real-time power usage and change in power use of the portable sterilizing device and starts self-generation according to the power use and change in power use. For example, the control unit 124 is configured to operate the portable sterilization device if the amount of power consumption or change in power use exceeds a set threshold, such as by spraying steam at a constant rate above a certain level and continuing for a certain period of time or generating high-temperature steam. If the solar module is not running, the solar module is controlled to start self-generation. In the embodiment, when the real-time power usage or change in power usage exceeds a preset value, the control unit 124 starts self-generation regardless of the battery charge level (SoC) and controls it to generate power.

To this end, the control unit 124 monitors the power usage of the portable sterilizing device in real time and calculates the amount of change in power usage. Afterwards, the control unit 124 compares the set threshold (preset value) with the current power usage and the amount of change in power usage. If the current power usage or change exceeds the set threshold, this indicates a situation where excessive power supply is required. In other words, when the current power usage or change exceeds the set threshold, the battery is being discharged rapidly, so the control unit 124 allows self-power generation to start regardless of the battery's state of charge (SoC).

In an embodiment, the control unit 124 senses the ambient illuminance in a situation where battery discharge is rapidly progressing, and the ambient illuminance exceeds a certain level, and at least one of power usage, power usage change, and battery discharge exceeds a certain level. In this case, the maximum output signal is transmitted to the solar module's inverter, and the inverter adjusts the solar module's output to the maximum point through MPPT (Maximum Power Point Tracking) control. In the embodiment, in a situation where battery discharge progresses rapidly and the surrounding illumination or temperature is below a certain level, the control unit 124 predicts the amount of power to be used for self-generation through a solar module, and when the predicted amount of power is below a certain level, , the maximum output signal is transmitted to the inverter of the solar module, and the inverter adjusts the output of the solar module to the maximum point through MPPT (Maximum Power Point Tracking) control. Additionally, in the embodiment, when the ambient illumination and temperature are below a certain level and the amount of power to be used for self-generation exceeds a certain level, the control unit 124 starts charging the battery through the energy storage panel of the solar module.

Additionally, the control unit 124 calculates the power generation efficiency of the solar module according to environmental information including the ambient illuminance, and determines the driving amount and output amount of the solar module according to the calculated power generation efficiency. In embodiments, power generation efficiency may include the ratio of the power production of a solar module to the energy consumed to produce that power. Since power generation efficiency is high when illuminance and solar radiation are high, in the embodiment, the control unit 124 may calculate the driving time and output amount of the solar module according to the charging requirement and power generation efficiency.

To this end, the control unit 124 calculates the power generation efficiency of the solar module according to the current illuminance and solar radiation, and calculates the power generation estimate through a power generation prediction model, considering that the efficiency of the module is high under high illuminance and solar radiation conditions. . Afterwards, the operating time and output amount of the solar module are calculated based on the charging requirement and power generation efficiency. Thereafter, the control unit 124 manages the operation of the solar module based on the calculated operation time and output amount information. In the embodiment, the control unit 124 activates self-generation of the solar module at a time of high illuminance and solar radiation, and operates the solar module only until the required amount of output is produced, thereby enabling the solar module to be operated efficiently. .

The portable sterilizing device equipped with a self-power generation function according to the embodiment adjusts whether or not to self-generate and the amount of charge by self-generating according to the battery charge amount, allowing the portable sterilizing device that sprays steam to be used at any time.

In addition, in the embodiment, a self-generation module using solar energy is provided, and self-generation and power generation amount are controlled according to ambient illumination and the remaining battery capacity of the portable sterilization device, thereby improving charging efficiency. Additionally, in the embodiment, sterilizing water containing ozone is sprayed in a fog state to quickly sterilize indoor objects and areas exposed to the coronavirus (SARS-CoV-2).

The disclosed content is merely an example, and various modifications and implementations may be made by those skilled in the art without departing from the gist of the claims, so the scope of protection of the disclosed content is limited to the above-mentioned specific scope. It is not limited to the examples.

Claims (7)

In the portable sterilization device,
A solar module that generates power through self-generation using solar energy and charges an energy storage panel or a battery of the portable sterilization device with the generated power;
An illuminance sensor that senses ambient illuminance; and
A charging control device that controls self-generation of the solar module and charging of the battery; Including,
The charging control device; Is
a memory storing at least one instruction; and
Contains a processor,
As the at least one instruction is executed by the processor,
The battery of the portable sterilizing device is monitored to collect battery status information including charge amount and aging state (SoH, State of Health), and the collected battery status information, operation information of the portable sterilizing device, expected power usage, and sensed Control self-generation of the solar module according to at least one of the ambient illuminance levels,
The portable sterilizing device; Is
Measure the internal resistance of the battery to calculate the aging state (SoH) of the battery, calculate the charging capacity of the battery using the calculated aging state (SoH) and the initial capacity of the battery, and calculate the charging capacity of the battery and the calculated charging capacity and the battery Based on the charging amount, the charging required amount, which is the power to be used in the future, is calculated, and the difference between the charging required amount and the battery charging amount is calculated as the power generation required through the solar module.
The portable sterilizing device; Is
Train a power consumption prediction model with power consumption data for each usage time, enter driving information including the usage time of the portable sterilizing device into the power consumption prediction model, and obtain the expected power usage of the portable sterilizing device,
The portable sterilizing device; Is
If the battery charge at the time of use is less than the expected power usage, the self-generation of the solar module is controlled to charge the amount of power corresponding to the difference between the expected power usage and the battery charge.
The portable sterilizing device; Is
When the ambient illuminance exceeds a certain value and the battery charge is below the full capacity, the solar module starts self-generating power.
The portable sterilizing device; Is
Calculate the optimal capacity, including the optimal charging capacity and discharge capacity according to the temperature and aging state (SoH) of the battery, and control the self-generation of the solar module based on the calculated optimal capacity, charging amount, and expected power usage.
the charging control device; Is
Through analysis of usage record data, we identify when portable sterilizing devices are most frequently used and the times when power usage improves, and use a power consumption prediction model to predict power usage by hour or day.
Use predicted power usage information to control self-generation,
During times of high expected power usage, it communicates with the solar module's inverter to adjust the output to the maximum point or increase the battery charge through MPPT (Maximum Power Point Tracking) control.
the charging control device; Is
Monitors the real-time power usage and power usage changes of the portable sterilizing device, and if the power consumption or power usage change exceeds a set threshold and the solar module is not running, the solar module starts self-generation. control the module,
If the real-time power usage or change in power usage exceeds the preset value, self-generation starts and generates power regardless of the battery charge (SoC).
the charging control device; Is
When battery discharge progresses beyond a certain rate, the surrounding illumination is sensed, and when the surrounding illumination exceeds a certain level and at least one of power usage, power usage change, and battery discharge exceeds a certain level, solar power A portable sterilization device that transmits the maximum output signal to the module's inverter, and the inverter adjusts the output of the solar module to the maximum point through MPPT (Maximum Power Point Tracking) control.
delete delete delete delete delete The method of claim 1, further comprising: the portable sterilizing device; Is
A portable sterilizing device that stores power through self-generation in the energy storage panel of the solar module when the battery of the portable sterilizing device is fully charged and the surrounding illumination exceeds a certain level.