Smart elevator system with hybrid standby power control based on vision recognition congestion prediction

Abstract

The present invention relates to a smart elevator power saving control system, and more specifically, to an intelligent group control system for maximizing elevator operation efficiency and minimizing standby power, comprising: an image acquisition unit installed at an elevator landing to capture images of a waiting space in real time; an edge computing analysis unit that identifies passenger objects through an object recognition algorithm in image data received from the image acquisition unit and calculates passenger count and density information; and a dual power supply unit configured such that a main power line supplying high voltage to the elevator's drive motor and inverter and an auxiliary power line supplying low voltage to the control circuit and sensor are physically separated. and a power saving control unit that controls the dual power supply unit based on the output data of the edge computing analysis unit; wherein the power saving control unit performs a 'Deep Sleep mode' that cuts off the main power line and maintains only the auxiliary power line in a standby state where there is no input signal from the elevator button, and performs a 'Pre-wakeup' that immediately connects the main power line to preheat the inverter when it is confirmed that a passenger object detected by the edge computing analysis unit has entered a preset waiting area (ROI), even before the input signal from the elevator button is generated.

Description

Technology Field

[0001] The present invention relates to a smart elevator power saving control system, and more specifically, to an intelligent group control system for maximizing elevator operation efficiency and minimizing standby power.

[0002] In particular, the present invention includes a vision AI-based demand forecasting technology that analyzes in real time whether a passenger has entered the boarding waiting area (ROI), the number of waiting passengers, and population density before the passenger physically operates the boarding button, by utilizing an edge computing module equipped with an object detection algorithm and a video collection device (such as CCTV) installed within the platform.

[0003] Furthermore, the present invention relates to a 'pre-wakeup' control technology that fuses the analyzed vision data with external environmental data (weather information, traffic information, etc.) to preemptively predict potential passenger demand, and thereby preheats and starts the inverter and drive unit before passengers arrive, even in a 'deep sleep' state where the elevator's main power is cut off.

[0004] Furthermore, the present invention relates to a hybrid standby power management system that minimizes passenger waiting time and maximizes energy consumption efficiency by physically separating a high-voltage main power line for elevator operation and a low-voltage auxiliary power line for sensors and communication, and actively controlling the power supply path in multiple stages (Deep Sleep, Idle, Active) according to predicted demand intensity. Background Technology

[0006] Elevators have established themselves as an essential means of transportation in modern urbanized environments and have become an indispensable element in contemporary buildings. As buildings become increasingly taller and user demand grows, elevator technology continues to advance. However, a problem is emerging regarding how this technological progress is not limited to mere improvements in speed and convenience, but is also increasing system complexity and consequently energy consumption. In particular, as elevators are one of the major energy-consuming facilities within a building, there is a high likelihood of significant energy waste if efficient operational measures are not established.

[0007] Recently, energy conservation and environmental protection have emerged as major global topics. Efforts to reduce carbon emissions and prevent global warming are being made across various industries, and the elevator industry is no exception to this trend. Elevators with low energy efficiency lead to increased carbon emissions due to excessive power consumption, which has a negative impact on the environment. To address this issue, a technological approach capable of reducing elevator energy consumption is essential.

[0008] Various technical approaches exist for energy conservation. For example, utilizing regenerative braking technology allows for the recovery and recycling of energy generated when an elevator descends or decelerates. Additionally, the introduction of smart control systems can optimize elevator operation by analyzing user movement patterns. These technologies not only reduce energy consumption but also provide additional benefits, such as extending system lifespan and reducing maintenance costs.

[0009] In conclusion, rather than focusing simply on manufacturing faster and larger elevators, the elevator industry must actively research and adopt energy-saving technologies to align with the global trend of environmental protection. This is not merely about ensuring the sustainability of the industry, but also a crucial responsibility to contribute to preserving the global environment. We look forward to further advancements in energy-saving technologies that will have a positive impact on both the industry and the environment.

[0010] Therefore, moving away from passive control that relies solely on passenger button input or past statistics, using CCTV, etc. Vision AI technology At this time, there is an urgent need to develop a new level of smart elevator control system that can simultaneously achieve two conflicting goals of energy saving and rapid service provision by recognizing passenger approach in advance of button input and actively predicting potential demand by linking with external data (API).

[0011] The aforementioned background technology is one that the inventor possessed or acquired in the process of deriving the content of the disclosure of the present application, and it cannot be considered as prior art disclosed to the general public prior to the filing of this application. The problem to be solved

[0012] The first objective of the present invention to solve the above problems is to accurately identify the actual number of people waiting in the platform using vision recognition technology before inputting the elevator button, thereby dramatically improving the accuracy of demand forecasting.

[0013] The second objective of the present invention is to resolve the uncertainty of external events by replacing real-time weather and traffic conditions with quantitative data and reflecting them in the prediction formula.

[0014] The third objective of the present invention is to simultaneously achieve the conflicting goals of reducing standby power and ensuring passenger service quality (QoS) by pre-wake-up the elevator the moment a passenger enters the lobby, even when the main power is off (Deep Sleep), thereby securing inverter preheating time.

[0015] The fourth objective of the present invention is to clearly present a dual power control circuit that separates the driving load and the control load of an elevator, thereby increasing the technical feasibility. means of solving the problem

[0017] A smart elevator system equipped with a hybrid standby power control function through vision recognition-based congestion prediction according to one embodiment comprises: an image acquisition unit installed at the elevator landing to capture images of the waiting space in real time; an edge computing analysis unit that identifies passenger objects through an object recognition algorithm in image data received from the image acquisition unit and calculates passenger count and density information; and a dual power supply unit configured such that a main power line supplying high voltage to the elevator's drive motor and inverter and an auxiliary power line supplying low voltage to the control circuit and sensor are physically separated. and a power saving control unit that controls the dual power supply unit based on the output data of the edge computing analysis unit; wherein the power saving control unit performs a 'Deep Sleep mode' that cuts off the main power line and maintains only the auxiliary power line in a standby state where there is no input signal of the elevator button, and can perform a 'Pre-wakeup' that immediately connects the main power line and preheats the inverter even before the input signal of the elevator button is generated if it is confirmed that a passenger object detected by the edge computing analysis unit has entered a preset waiting area (ROI).

[0018] According to one embodiment of the present invention, it further includes an external data receiving unit that receives real-time weather information and nearby public transportation arrival information by linking with an external weather server and a traffic information server; and the power saving control unit uses the following [Equation 1] to determine the number of operating elevators required at the current time (t) You can adjust the number of operating elevators in standby by determining ).

[0019] [Mathematical Formula 1]

[0020]

[0021] (Here, is the real-time number of people waiting at the platform calculated by the edge computing analysis unit above, is a psychological correction coefficient based on waiting crowd density (1.0 ~ 1.5), is the historical statistical average number of passengers for the corresponding day and time, is a weighting factor based on weather information from the external data receiver (greater than 1.0 in case of rain or snow), is the inflow weighting based on traffic information of the external data receiver above, is the appropriate passenger capacity of one elevator.

[0022] According to one embodiment of the present invention, the edge computing analysis unit sets the area corresponding to the waiting space for boarding an elevator among the entire area of ​​the image data as a Region of Interest (ROI), and can distinguish a passenger from a simple passerby by counting it as a valid waiting passenger only when the centroid coordinates of the identified passenger object are located within the ROI and the dwell time of the coordinates is maintained for a preset threshold time (0.5 seconds to 2 seconds).

[0023] According to one embodiment of the present invention, a magnetic contactor that opens and closes physical contacts according to a signal from a power saving control unit is connected in series to the main power line of the dual power supply unit, and a Switching Mode Power Supply (SMPS) that supplies power at all times regardless of whether the main power line is cut off is connected to the auxiliary power line, and the power saving control unit can complete the preparation for operation before a passenger reaches the elevator button by applying an excitation current to the coil of the magnetic contactor to close the contacts and simultaneously transmitting an initialization command to the inverter when the pre-startup is performed.

[0024] According to one embodiment of the present invention, the power saving control unit controls the standby mode in stages according to the passenger density change rate (D) calculated by the edge computing analysis unit, wherein if the change rate is less than a first threshold, it maintains a first standby mode (Deep Sleep) in which the main power line is cut off, and if the change rate is greater than or equal to the first threshold and less than a second threshold, it switches to a second standby mode (Idle Standby) in which the main power line is connected but the motor drive is stopped, and if the change rate is greater than or equal to the second threshold, it switches to a third standby mode (Active Standby) in which the elevator door is immediately put on standby to open. Effects of the invention

[0026] A smart elevator system equipped with a hybrid standby power control function based on vision recognition and congestion prediction according to one embodiment minimizes passenger inconvenience by shortening the elevator's response time after a call to near 0 seconds, as the inverter's preheating and self-diagnosis are already completed before a passenger presses the elevator button.

[0027] The present invention can reduce standby power consumption by more than 15% compared to the existing method by operating a deep sleep mode that completely cuts off the main power supply (inverter) during late-night hours when demand is extremely low.

[0028] The present invention uses actual personnel data based on CCTV instead of ambiguous 'events' ( ) and API-based quantitative weights( By applying a hybrid formula that combines ), the failure rate of forecasting sudden changes in demand is reduced. Brief explanation of the drawing

[0031] Figure 1 is an overall block diagram of a smart elevator power saving control system according to the present invention. FIG. 2 is a conceptual configuration diagram of an image acquisition unit and an edge computing analysis unit within a platform according to the present invention. FIG. 3 is a detailed block diagram of a dual power supply and power saving control circuit according to the present invention. FIG. 4 is a flowchart showing the operating mode (Deep Sleep, Idle, Active) switching logic of the power saving control unit according to the present invention. FIG. 5 is a block diagram of a demand calculation algorithm according to the present invention. Specific details for implementing the invention

[0032] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, various modifications may be made to the embodiments, and thus the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, and substitutions to the embodiments are included within the scope of the rights.

[0033] The terms used in the embodiments are for illustrative purposes only and should not be interpreted as intended to be limiting. 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 existence 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.

[0034] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0035] In addition, when describing with reference to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the embodiments, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the embodiments, such detailed description is omitted.

[0036] In addition, terms such as first, second, A, B, (a), (b), etc., may be used when describing the components of the embodiments. These terms are intended only to distinguish the components from other components, and the nature, order, or sequence of the components is not limited by the terms. Where it is stated that a component is "connected," "combined," or "connected" to another component, it should be understood that the component may be directly connected or connected to the other component, but that another component may also be "connected," "combined," or "connected" between each component.

[0037] Components included in any one embodiment and components having common functions shall be described using the same names in other embodiments. Unless otherwise stated, the description in any one embodiment may also apply to other embodiments, and specific descriptions shall be omitted to the extent of overlap.

[0039] FIG. 1 is an overall block diagram of a smart elevator power saving control system according to the present invention, FIG. 2 is a conceptual block diagram of an image acquisition unit and an edge computing analysis unit within a landing according to the present invention, FIG. 3 is a detailed block diagram of a dual power supply unit and a power saving control circuit according to the present invention, FIG. 4 is a flowchart showing the operation mode (Deep Sleep, Idle, Active) switching logic of the power saving control unit according to the present invention, and FIG. 5 is a block diagram of a demand calculation algorithm according to the present invention.

[0040] Referring to FIGS. 1 to 4, the smart elevator system (100) having a hybrid standby power control function through vision recognition-based congestion prediction according to the present invention may include an image acquisition unit (110), an edge computing analysis unit (120), an external data receiving unit (130), a dual power supply unit (200), a power saving control unit (300), and an elevator driving unit (400) that drives an actual elevator. The power saving control unit (300) may be embedded in a central server or inside an individual elevator control panel.

[0041] The above image acquisition unit (110) may be installed on the ceiling or in a corner of the platform and may be equipment capable of acquiring images in the form of an IP camera. The above image acquisition unit (110) may transmit the acquired image stream to an edge computing unit (120) placed near the platform. The above edge computing analysis unit (120) performs the following algorithm to determine the number of waiting personnel ( Produces ).

[0042] The edge computing analysis unit (120) can perform object recognition and filtering. The edge computing analysis unit (120) identifies 'person' objects within the frame by utilizing a lightweight YOLO model installed therein.

[0043] The edge computing analysis unit (120) can set an area of ​​interest (ROI). The edge computing analysis unit (120) sets the actual waiting space within approximately 2m in front of the elevator door in the platform video as the ROI.

[0044] The edge computing analysis unit (120) can check the dwell time. The edge computing analysis unit (120) identifies the object as a valid waiting passenger ( It is finally counted as ) to clearly distinguish it from simple passersby. This data is transmitted in real time to the power saving control unit (300) at 1-second intervals.

[0045] An image acquisition unit (110) according to one embodiment of the present invention is a visual sensor module for detecting the situation of an elevator lobby in real time, and is configured to generate optimal image data for object recognition beyond the function of a simple surveillance camera.

[0046] The above image acquisition unit (110) includes a wide-angle lens with a horizontal field of view (FOV) of 120 degrees or more or a fisheye lens with a field of view of 180 degrees in order to capture the entire waiting area in front of the elevator door without blind spots. Through this, not only the area right in front of the elevator but also passengers approaching from the lobby entrance can be captured early.

[0047] The above image acquisition unit (110) adopts a CMOS image sensor with excellent low-light characteristics (e.g., Sony Starvis grade or higher) so that clear object identification is possible even in various lighting environments (natural light during the day, indoor lights at night, emergency lights at night, etc.). The resolution is recommended to be Full HD (1920x1080) or higher to consider the efficiency of edge computing analysis, and the frame rate is set to at least 30fps or higher so that the movement speed vector of the passenger can be tracked without interruption.

[0048] The above image acquisition unit (110) incorporates an Image Signal Processor (ISP) that performs internal pre-processing to improve recognition rate before transmitting the original image to the edge computing analysis unit (120).

[0049] The above image acquisition unit (110) corrects the gradation of bright and dark areas by activating a WDR (Wide Dynamic Range) function of 120dB or more to prevent object silhouette phenomena caused by backlighting at the lobby entrance or differences in lighting inside the elevator.

[0050] The above image acquisition unit (110) performs 3D DNR (Digital Noise Reduction) to digitally remove image noise in environments where noise is likely to occur due to low illumination, such as late night hours, thereby maintaining the sharp edges of objects.

[0051] The above image acquisition unit (110) can transmit and receive data using the PoE (Power over Ethernet) method. The above image acquisition unit (110) supports the PoE (IEEE 802.3af / at) method, which transmits power and data simultaneously with a single LAN cable without separate power construction, thereby ensuring ease of installation and maintenance.

[0052] The above image acquisition unit (110) can perform a streaming protocol. The image acquired by the above image acquisition unit (110) is compressed into an H.264 or H.265 codec via the RTSP (Real Time Streaming Protocol) or ONVIF profile S / T standard protocol and streamed in real time to the edge computing analysis unit (120).

[0053] The above image acquisition unit (110) can provide a masking function that distinguishes between a 'non-ROI' and an 'ROI' within the camera's field of view in the initial setting mode after installation. For example, simple passageways (corridors) next to the platform or movements outside the glass windows are set as non-ROIs to prevent misrecognition, and only the area where passengers actually stay to board is designated as the ROI to reduce the data processing load.

[0054] The edge computing analysis unit (120) is an independent computing device that immediately analyzes high-definition video data received from the image acquisition unit (110) at the platform local level and generates control signals, without sending the data to a central server. Through this, network latency is minimized to enable 'real-time pre-activation' that responds to the fast movement speed of passengers.

[0055] The edge computing analysis unit (120) may have an AI accelerator built in. To perform high-speed deep learning inference, the edge computing analysis unit (120) may be configured as an embedded board based on a System on Chip (SoC) (e.g., NVIDIA Jetson series, Google Coral, etc.) that integrates a Graphics Processing Unit (GPU) or Neural Processing Unit (NPU) specialized for parallel processing in addition to a CPU.

[0056] The edge computing analysis unit (120) may include a communication interface. For delay-free communication with the power saving control unit (300), the edge computing analysis unit (120) may be equipped with a physically secure RS-485 or CAN (Controller Area Network) communication port, or include a Gigabit Ethernet port for high-speed data transmission.

[0057] The following software algorithms are loaded into the memory of the edge computing analysis unit (120) and executed sequentially.

[0058] The edge computing analysis unit (120) can perform object detection. The edge computing analysis unit (120) runs a lightweight CNN (Convolutional Neural Network) model, such as YOLO (You Only Look Once) v8-Nano or EfficientDet-Lite algorithm, for each input frame. Through this, it selects only the classes of 'Person', 'Wheelchair', and 'Stroller' from among numerous objects in the image and outputs bounding boxes and confidence scores.

[0059] The edge computing analysis unit (120) can perform object tracking. The edge computing analysis unit (120) applies the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm to determine the movement path of passengers beyond simple detection. Through this, a unique ID is assigned to each object, and movement vectors between frames are analyzed to determine whether the object is approaching the elevator or simply passing through.

[0060] The edge computing analysis unit (120) performs the following filtering logic to prevent unnecessary elevator operation (energy waste).

[0061] The edge computing analysis unit (120) can perform ROI (Region of Interest) filtering. An area of ​​2 to 3 meters in front of the elevator door is set as the ROI based on the floor surface of the landing, and the object is determined to be a valid object only if its foot coordinates or centroid are located inside the ROI.

[0062] The edge computing analysis unit (120) above can analyze the dwell time. An object that has entered the ROI for a preset time (e.g., T th A person is confirmed and counted as a 'waiting to board' only when they stay for more than 0.5 seconds or exhibit a pattern of significantly reduced movement speed (deceleration) within the ROI. This prevents malfunctions caused by people running quickly through the hallway.

[0063] The edge computing analysis unit (120) transmits the following structured data to the power saving control unit (300) every second (Sec) or whenever an event occurs.

[0064] The above silver Currently, the number of available waiting personnel within the ROI, and the above This represents the increase in personnel at the current time (t) compared to the previous time (t-1). (Used as a trigger to immediately switch to 'Active Standby' mode in case of a rapid increase in personnel.)

[0065] When the edge computing analysis unit (120) is in deep sleep mode, it transmits a Trigger Signal (start signal), a 'High (1)' signal when the valid object 1 is first identified, thereby physically closing the MC (magnetic contactor).

[0066] In order to resolve personal information protection issues arising from filming in public places, the edge computing analysis unit (120) performs the function of automatically detecting a person's face area after object recognition is complete, blurring or mosaicing it, extracting only metadata (numerical information), and not storing the original video or storing it in an encrypted form.

[0067] The above external data receiving unit (130) communicates with an external server through an internet communication module (Wi-Fi, LTE, etc.).

[0068] The above external data receiving unit (130) receives weather data, such as current precipitation probability, precipitation type (snow / rain), wind speed, etc., from the Korea Meteorological Administration Open API, and weather weights ( Determine ) within the range of 1.0 to 1.5.

[0069] The above external data receiving unit (130) receives traffic data, including arrival and expected disembarkation data for nearby public transportation (subway / bus) stops, and traffic weights ( Calculates ). A weight of 1.5 or higher can be applied when a sudden inflow is expected.

[0070] The above external data receiving unit (130) is connected to the internet network and performs the role of an IoT gateway that collects environmental variables outside the building, converts them into quantitative values ​​(weights) required for elevator control, and transmits them to the power saving control unit (300).

[0071] The external data receiving unit (130) may include a communication module. The external data receiving unit (130) includes an LTE / 5G modem or a Gigabit Wi-Fi (Wi-Fi 6) module for stable connection with an external server.

[0072] The above external data receiving unit (130) can be network-secured. To prevent hacking threats from the external internet network from propagating to the elevator internal control network (CAN, RS-485), the external data receiving unit (130) is placed in a DMZ (Demilitarized Zone) section or has a dual Ethernet port structure with a physical / logical network separation solution applied.

[0073] The above external data receiving unit (130) periodically (e.g., every 10 minutes) connects to the Open API server of the Korea Meteorological Administration or a weather service provider to update data.

[0074] The external data receiving unit (130) can perform data parsing. The external data receiving unit (130) extracts precipitation type (PTY), hourly precipitation amount (RN1), and temperature (T1H) data from the received JSON or XML format data.

[0075] The above external data receiving unit (130) can determine weights based on the following weight mapping table.

[0076] o Clear (PTY=0): = 1.0 (default)

[0077] o Rain / Snow (PTY=1, 2, 5): Reflecting umbrella organization, reduced walking speed to prevent slipping, and increased preference for using elevators instead of stairs in the event of rain or snow Set to = 1.2.

[0078] o Heavy Rain / Heavy Snow (RN1 ≥ 10mm or PTY=3): To prepare for a sudden concentration of people entering indoors during severe weather. Set to an upward value of 1.5.

[0079] o Heatwave / Extreme Cold: Reflecting the psychology of avoiding walking when the temperature is 30 degrees or higher or minus 10 degrees or lower Add 1.1.

[0080] The above external data receiving unit (130) monitors the public transportation (subway, bus) conditions near the building and predicts 'pulse-type demand' where crowds gather at a specific time.

[0081] The above external data receiving unit (130) is connected via a traffic API. It can be determined. The external data receiving unit (130) utilizes the 'real-time subway arrival information API' or 'bus arrival information API' of the public data portal (e.g., Seoul Open Data Plaza).

[0082] The above external data receiving unit (130) calculates the arrival time delay (Time Delay) It can determine the average walking time (T) from the nearby subway station exit to the platform of the main building. walk Set the time in advance (e.g., 5 minutes).

[0083] The above external data receiving unit (130) uses the following prediction logic through It can produce.

[0084] When the above external data receiving unit (130) receives information that a train of the 'Congestion High' class has arrived at a nearby station, from the current time (t) T walk time later, t + T walk Predicting a surge in elevator demand at a certain point in time, and at that point in time The value is temporarily adjusted upward from 1.0 to 1.3–1.5. This is based on existing statistical data (H avg It reflects real-time changes in floating population that cannot be predicted by ).

[0085] Since the collected weather and traffic data have different scales, a normalization process is performed in the microcontroller (MCU) inside the external data receiving unit (130). The finally calculated and The value is converted into a packet in the form of a simple floating-point number and transmitted to the power saving control unit (300).

[0086] In the event that the latest data cannot be received due to an internet disconnection or API server failure (timeout occurs), the external data receiving unit (130) immediately enters 'safe mode' to initialize all weights ($W$) to the default value of 1.0, or uses the 'average value of the same month and time of the previous year' stored in internal memory as replacement data to prevent system malfunction.

[0087] The above dual power supply unit (200) consists of a main power line (L1) for the inverter and drive motor, which are the main power consumers of the elevator, and an auxiliary power line (L2) for the control panel (300), sensor (110), and communication module.

[0088] The above dual power supply unit (200) is composed of two physically separated main power lines (L1) and auxiliary power lines (L2) to selectively supply power according to the elevator's operating mode (Deep Sleep, Idle, Active) to maximize energy efficiency and ensure the stability of the control system.

[0089] The above main power line (L1) is a high-voltage / high-current line for driving the elevator's drive motor (Traction Machine) and inverter (VVVF Inverter), and typically AC 380V 3-phase or 440V power is applied.

[0090] On the above L1 line, a large-capacity magnetic contactor (MC; Magnetic Contactor, 210) that opens and closes physical contacts according to a digital control signal of the power saving control unit (300) is connected in series. The reason for using a mechanical MC instead of a semiconductor switch (SSR) is to achieve perfect power cutoff by making the leakage current '0' during deep sleep mode.

[0091] To protect the internal components of the inverter from arcs and surge voltages generated when the contacts of the above MC are opened or closed, an RC snubber circuit or a varistor is connected in parallel to the input and output terminals of the MC.

[0092] The above auxiliary power line (L2) is a low-voltage line for supplying power to the power saving control unit (300), image acquisition unit (110), edge computing analysis unit (120), and emergency communication device. It is branched from the main power (L1) or receives a separate single-phase AC 220V input.

[0093] A high-efficiency SMPS (Switching Mode Power Supply) (220) is connected to the above L2 line to convert the input AC power into DC 24V and DC 5V / 12V constant voltages.

[0094] The SMPS (220) is connected to the front side (Grid Side) of the MC (210) and supplies power to the control unit and sensor without interruption even in deep sleep mode where the main power line (L1) is cut off. Through this, the system maintains a state of readiness to 'pre-start' at any time by external stimuli (vision recognition, API data).

[0095] The above dual power supply (200) includes a safety circuit to prevent malfunctions beyond simply turning the power on and off, including power status feedback and a safety interlock.

[0096] The above MC (210) includes an auxiliary contact, so that when the control unit (300) sends a start signal, it checks whether the MC is actually closed using a feedback signal. If no feedback is received within 0.5 seconds after the start signal is sent, the control unit diagnoses the MC as 'MC failure' and switches to safety mode.

[0097] The above dual power supply unit (200) can perform zero-crossing control. The power saving control unit (300) controls the turn-on time of the MC to be near the zero-crossing point of the AC voltage waveform to minimize the inrush current and extend the lifespan of the inverter capacitor.

[0098] The dual power supply unit (200) described above can perform an emergency power switching function (UPS interlocking). To prevent elevator entrapment accidents and maintain emergency communication even in the event of a building power outage, the auxiliary power line (L2) of the dual power supply unit (200) is connected to a UPS (Uninterruptible Power Supply) or emergency battery within the building via an Automatic Transfer Switch (ATS). This enables the edge computing analysis unit and control unit to operate for at least 30 minutes to propagate the situation, even if the main power is cut off.

[0099] The above power saving control unit (300) is a central processing unit that collects various sensor data and external information and issues optimal control commands, and is implemented as an industrial embedded system that guarantees real-time and stability.

[0100] The power saving control unit (300) may include a microcontroller unit (MCU). The power saving control unit (300) is equipped with a high-performance MCU of 32-bit ARM Cortex-M7 class or higher capable of high-speed floating-point operations (FPU). This is to process data packets received from the edge computing analysis unit (120) tens of times per second and external API data without delay (Zero Latency).

[0101] The above power saving control unit (300) is equipped with RTOS (Real-Time Operating System) based firmware to ensure the system's response speed and to stably perform multiple tasks (communication, computation, I / O control).

[0102] The power saving control unit (300) above The communication port is connected to the edge computing analysis unit (120) and the external data receiving unit (130) via Ethernet (TCP / IP) or RS-485, and receives information on the current status of the elevator (floor number, door open, error code) via CAN (Controller Area Network) communication with the elevator main controller.

[0103] The control output port of the power saving control unit (300) is equipped with a digital output (DO) port with a photocoupler isolation method that has strong noise resistance to drive the magnetic contactor (MC) of the dual power supply unit (200).

[0104] The above power saving control unit (300) can utilize a watchdog timer and current feedback detection as a safety monitoring module (Watchdog & Feedback). The watchdog timer (WDT) forcibly resets the system within 100ms in the event of an operation error or infinite loop in the MCU to prevent an uncontrollable state. Current feedback detection detects the current flowing through the main power line (L1) using a CT (Current Transformer) sensor after the MC control signal is transmitted to cross-check whether power is actually supplied.

[0105] The above power saving control unit (300) controls the state transition between the following three modes as shown in Table 1 below in order to balance energy efficiency and response speed.

[0106] Driving mode Main power (L1) status Auxiliary Power (L2) Status Features and Target Power Consumption Mode 1 (Deep Sleep) Block (MC OFF) Maintain (SMPS ON) Inverter power cut off. Communication / sensors only operate. Maximum power saving. Mode 2 (Idle Standby) Connect (MC ON) Maintain (SMPS ON) Inverter preheating complete. Waiting for motor drive. Pre-start state. 3rd Mode (Active Operation) Connect (MC ON) Maintain (SMPS ON) Motor drive and normal operation.

[0107] (1) Deep Sleep to Idle Standby (Pre-wakeup) Transition Logic. This is the 'pre-start' step, which is the core of the present invention.

[0108] Trigger condition: A signal "Entering ROI with 1 or more valid passengers" is received from the edge computing analysis unit (120). (No button input)

[0109] Operation: The power saving control unit (300) immediately closes (turns ON) the MC and sends a 'Wake-up' command to the inverter.

[0110] Effect: Completes inverter initialization during the time it takes for a passenger to walk from the lobby entrance to the elevator button (approx. 3 to 5 seconds).

[0111] (2) Idle Standby to Active Run Transition Logic

[0112] Trigger Condition: Passenger physically presses the "Call Button".

[0113] Operation: Immediately drives the door motor to open the door using the preheated inverter.

[0114] Special note: If the preset time (e.g., $T) is set while in Idle state wait If there is no button input for 10 seconds (e.g., passenger exiting again), it returns to Deep Sleep mode to cut off wasted standby power.

[0115] (3) Active Run to Deep Sleep return logic (Hysteresis control)

[0116] After operation is completed, it is not turned off immediately, and delay logic is performed to protect the mechanical life.

[0117] Trigger condition: Elevator operation ends and door closing completed.

[0118] movement:

[0119] 1. First, switch to Idle Standby mode and wait for the next call.

[0120] 2. Demand forecasting algorithm during waiting time (N req Performs ).

[0121] 3. The forecast demand at the current time is low, and T idle There are no additional calls for (e.g., 5 minutes), and P cam Open (OFF) MC only when =0 to switch to Deep Sleep.

[0122] Prevention of chattering: To prevent frequent ON / OFF of the MC, a 'Minimum Run Timer' logic is applied to forcibly maintain power for at least 3 minutes once the MC is turned on.

[0123] The above power saving control unit (300) can control elevators based on a group control algorithm. This is a control strategy in an environment where multiple elevators (e.g., 4) are installed.

[0124] Sequential Wake-up

[0125] o P cam If the number of people waiting is 1 to 3: Pre-wakeup only for Unit 1.

[0126] o P cam If this number surges to 4 or more, When (traffic weight) is high: Start Unit 1 and Unit 2 simultaneously, or sequentially at 2-second intervals.

[0127] Rotation Control

[0128] o To prevent parts wear caused by a specific unit (e.g., Unit 1) running continuously, the wake-up priority from Deep Sleep is periodically changed. (e.g., Unit 1 -> Unit 2 -> Unit 3)

[0129] Pre-wakeup Detailed Scenario:

[0130] 1. When the system is in the first mode (deep sleep) state, P from the edge computing analysis unit (120) cam A passenger entry event occurs where}(t) increases from 0 to 1 or more.

[0131] 2. The power saving control unit (300) above determines the estimated time (T) when a passenger reaches the elevator button. reach Calculate ) and generate a signal to turn on the MC (210) before the corresponding time.

[0132] 3. The moment the above MC (210) is connected, the inverter receives power, starts booting, and switches to the second mode (idle standby).

[0133] 4. When a passenger presses the boarding / alighting button (call occurs), the inverter is already booted up, so it starts operating immediately without delay and switches to the third mode (active operation).

[0134] The above power saving control unit (300) performs the following hybrid demand calculation algorithm to combine the advantages of real-time response based on real-time sensing and proactive response based on external data.

[0135] In the present invention, the following mathematical formula 1 is calculated to obtain a physical sensing value (N) without relying on a single prediction model. real ) and statistical predictions (N pred The 'Max Gating' method is adopted to determine the final number of vehicles in operation as the larger value among the two. This is intended to mutually compensate for sensor blind spots or statistical errors.

[0136] [Mathematical Formula 1]

[0137]

[0138] The above mathematical formula 1 can be expanded as shown in the following mathematical formula 2.

[0139] [Mathematical Formula 2]

[0140]

[0141] Here, is the real-time number of people waiting at the platform calculated by the edge computing analysis unit above, is a psychological correction coefficient based on waiting crowd density (1.0 ~ 1.5), is the historical statistical average number of passengers for the corresponding day and time, is a weighting factor based on weather information from the external data receiver (greater than 1.0 in case of rain or snow), is the inflow weighting based on traffic information of the external data receiver above, This is the appropriate passenger capacity for one elevator.

[0142] The above is the number of passengers staying within the ROI (region of interest) received from the edge computing analysis unit (120). It is calculated as the sum of objects (IDs) with a dwell time of 1 second or more within the ROI, excluding simple passersby. The above Since it is a value obtained by physically counting the 'potential demand' when the button is not pressed, it is a first-order control variable with the highest reliability.

[0143] The above is a weighting factor designed to relieve the psychological pressure felt by passengers due to platform congestion, and is determined according to the following criteria.

[0144] · ≤ 5 (Quiet): = 1.0 (default)

[0145] · 5 < ≤ 15 (usually): = 1.1

[0146] · > 15 (Very Congested): = 1.3

[0147] The above As the number of passengers increases, boarding and alighting times become longer and the probability of failing to board increases, so dispatching vehicles with more space than the physical capacity has the effect of quickly relieving crowd density.

[0148] The above This refers to the 'effective capacity' that allows for a comfortable ride, rather than the elevator's specified maximum capacity. The above It is typically set to 80% of the rated capacity. (e.g., 20-passenger -> = 16) The above It prevents full detection errors and prevents door opening delay accidents caused by excessive boarding.

[0149] The above is data on the average number of calls or passengers over the past four weeks for the corresponding day and time period (in 10-minute intervals). The power saving control unit (300) accumulates daily operation data and the above It performs a self-learning function that updates the table.

[0150] The above is a value that quantifies the impact of weather on elevator usage patterns.

[0151] The above It is calculated according to the following criteria.

[0152] Default value: 1.0

[0153] During rainfall / snowfall: 1.2 ~ 1.3 applied.

[0154] reason: When it rains, the boarding time per passenger increases by an average of 3 to 5 seconds due to the action of folding or shaking umbrellas, and the tendency for passengers moving to lower floors (2nd and 3rd floors) to use elevators instead of stairs increases rapidly, so the number of dispatched vehicles is increased to compensate for this.

[0155] The above is a value that reflects 'pulse-type demand' based on nearby public transportation arrival information.

[0156] The above It is calculated based on the following criteria.

[0157] When the "subway arrival complete" signal is received by the external data receiving unit (130), from that point in time T walk 5 minutes after (walking time) Set to = 1.5.

[0158] reason: Statistical data ( Since ) is an average value, it tends to underestimate instantaneous crowding (Burst) at a specific point in time by smoothing it out. It compensates for these statistical blind spots.

[0159] The above power saving control unit (300) is N calculated by a formula req To prevent the 'chattering' phenomenon where the elevator frequently turns on and off due to value fluctuations every second, the following stabilization logic is additionally applied.

[0160] 1. Immediate reflection upon increase (Fast Attack):

[0161] o Calculated N req The number of elevators currently in operation (N current )see If it gets bigger , immediately wake up the waiting elevator N current Increases. (Service quality priority)

[0162] 2. Slow Decay:

[0163] o Calculated N req Current operating count (N current )see Even if it gets smaller , without turning off immediately, pre-set retention time (T hold Wait for , e.g., 3 minutes.

[0164] o No additional calls during the retention time or N req Only when the level is maintained at a low level, the elevator is sequentially switched to deep sleep mode.

[0165] Although the embodiments described above have been explained with reference to limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, appropriate 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.

[0166] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 An image acquisition unit installed at the elevator landing to capture images of the waiting area in real time; an edge computing analysis unit that identifies passenger objects through an object recognition algorithm in image data received from the image acquisition unit and calculates passenger count and density information; and a dual power supply unit configured such that a main power line supplying high voltage to the elevator's drive motor and inverter, and an auxiliary power line supplying low voltage to the control circuit and sensor are physically separated. and a power saving control unit that controls the dual power supply unit based on the output data of the edge computing analysis unit; wherein the power saving control unit performs a 'Deep Sleep mode' that cuts off the main power line and maintains only the auxiliary power line in a standby state where there is no input signal from the elevator button, and performs a 'Pre-wakeup' that immediately connects the main power line to preheat the inverter when it is confirmed that a passenger object detected by the edge computing analysis unit has entered a preset waiting area (ROI) even before the input signal from the elevator button occurs, and further includes an external data receiving unit that receives real-time weather information and nearby public transportation arrival information by linking with an external weather server and a traffic information server; and the power saving control unit uses the following [Equation 1] to determine the number of operating elevators required at the current time point (t) A smart elevator power saving control system characterized by controlling the number of operating elevators in standby by determining ).[Mathematical Formula 1] (Here, is the real-time number of people waiting at the platform calculated by the edge computing analysis unit above, is a psychological correction factor (1.0 ~ 1.5) based on waiting crowd density, is the historical statistical average number of passengers for the corresponding day and time, is a weighting factor based on weather information from the external data receiver (greater than 1.0 in case of rain or snow), is the inflow weighting based on traffic information of the external data receiver above, is the appropriate passenger capacity of one elevator. Claim 2 delete Claim 3 A smart elevator power saving control system according to claim 1, wherein the edge computing analysis unit sets the area corresponding to the waiting space for boarding the elevator within the entire area of ​​the image data as a Region of Interest (ROI), and counts the identified passenger object as a valid waiting passenger only when the centroid coordinates of the passenger object are located within the ROI and the dwell time of the said coordinates is maintained for a preset threshold time (0.5 seconds to 2 seconds) or longer, thereby distinguishing it from a simple passerby. Claim 4 A smart elevator power saving control system according to claim 1, wherein a magnetic contactor that opens and closes physical contacts according to a signal from a power saving control unit is connected in series to the main power line of the dual power supply unit, and a Switching Mode Power Supply (SMPS) that supplies power at all times regardless of whether the main power line is cut off is connected to the auxiliary power line, and wherein the power saving control unit applies an excitation current to the coil of the magnetic contactor to close the contacts when the pre-start is performed, and simultaneously transmits an initialization command to the inverter to complete the preparation for operation before the passenger reaches the elevator button. Claim 5 A smart elevator power saving control system according to claim 1, wherein the power saving control unit controls the standby mode in stages according to the passenger density change rate (D) calculated by the edge computing analysis unit, wherein if the change rate is less than a first threshold, it maintains a first standby mode (Deep Sleep) in which the main power line is cut off, if the change rate is greater than or equal to the first threshold and less than a second threshold, it switches to a second standby mode (Idle Standby) in which the main power line is connected but motor operation is stopped, and if the change rate is greater than or equal to the second threshold, it switches to a third standby mode (Active Standby) in which the elevator door is immediately put on standby to open.

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