An elevator remote monitoring platform based on intelligent internet of things and embedded equipment

Abstract

This invention relates to the field of elevator intelligent IoT technology, specifically disclosing an elevator remote monitoring platform based on intelligent IoT and embedded equipment. The platform consists of a sensing terminal layer, an edge computing gateway layer, a cloud-based big data analysis layer, and a mobile interactive application layer. The sensing terminal layer independently collects elevator physical signals through a multimodal sensor array. The edge computing gateway layer utilizes an embedded gateway with an integrated neural processing unit for local feature extraction and fault pre-diagnosis. The cloud-based big data analysis layer constructs a digital twin model based on the received feature data, realizing a virtual mapping of the elevator's operating status. The mobile interactive application layer is responsible for predictive work order push and on-site auxiliary diagnosis. This invention achieves health prediction for the entire lifecycle of the elevator through edge intelligence and multidimensional data fusion logic. It reduces unplanned elevator downtime, improves rescue reliability, and utilizes digital twin technology to achieve visualization and refined management of the maintenance process.

Description

Technical Field

[0001] This invention belongs to the field of elevator intelligent Internet of Things technology, specifically relating to an elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment. Background Technology

[0002] With the acceleration of urbanization, high-rise buildings are increasingly reliant on elevators. Traditional elevator monitoring systems mostly rely on the communication protocol output of the elevator main controller, which has obvious limitations. First, there is a lag in perception; traditional sensors can often only record the elevator's on / off information, making it difficult to capture unstructured physical characteristics such as guide rail wear, minor deformation of the wire rope, or early abnormal noises from the traction machine.

[0003] Secondly, most existing IoT solutions adopt a data pass-through mode, directly uploading massive amounts of raw sampling data to the cloud. In high-frequency operating equipment like elevators, this not only generates enormous network bandwidth pressure but also leads to delays in real-time response, making it impossible to instantly identify sudden safety hazards at the edge. Furthermore, when extreme conditions such as power outages or signal blockages occur in buildings, existing monitoring systems often become paralyzed, delaying the crucial rescue window for trapped individuals. Therefore, developing a remote elevator monitoring platform with independent sensing capabilities, edge intelligent analysis, and digital twin interaction functions has become an urgent need to improve the level of vertical transportation safety management. Summary of the Invention

[0004] To overcome the above-mentioned technical problems, the present invention provides an elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment.

[0005] The present invention adopts the following technical solution: An elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment includes: The sensing terminal layer includes a multimodal sensor array deployed in key parts of the elevator, used to collect raw physical signals independently of the elevator's main control system. The edge computing gateway layer contains embedded gateways with neural processing units for local feature extraction and fault pre-diagnosis of raw physical signals. The cloud-based big data analytics layer receives feature data uploaded by the edge computing gateway and uses a digital twin model to virtually map the elevator's operational status. The mobile interactive application layer is used to push forwarded work orders and provide auxiliary diagnosis at the maintenance site.

[0006] Preferably, the sensing terminal layer includes a triaxial accelerometer installed on the car roof, a temperature and vibration integrated sensor installed on the traction machine, a time-of-flight lidar installed inside the car, and strain gauges installed on the wire rope.

[0007] Preferably, the embedded gateway in the edge computing gateway layer has a built-in hardware encryption chip and a unique device identification identifier, and the gateway has local fast Fourier transform processing capability to identify guide rail wear, guide shoe loosening or traction machine bearing abnormal noise by analyzing vibration frequency characteristics.

[0008] Preferably, the sensing terminal layer also includes a non-contact wire rope monitoring device, which consists of a high-definition high-speed camera and a strip structured light projector. The device analyzes the continuity of the light spot and the brightness distribution through image processing algorithms to calculate the diameter reduction rate of the wire rope.

[0009] Preferably, the cloud-based big data analysis layer has multi-dimensional data fusion logic. By associating the pressure sensor data at the bottom of the car with the leveling accuracy data of the time-of-flight lidar on the top of the car, it automatically determines the leveling error trend under a specific load, so as to warn of abnormal brake gap or controller parameter drift.

[0010] Preferably, the sensing terminal layer includes a high-angle camera with a human skeleton recognition algorithm, used to identify violent jumping, prolonged door blocking behavior, and the entry and exit status of special groups during the elevator ride, and the edge computing gateway triggers the voice broadcast system to provide reminders.

[0011] Preferably, the digital twin model constructed by the cloud big data analysis layer is implemented using WebGL technology. This model supports synchronous mapping with the physical elevator operating parameters and has a history reproduction function. It uses color depth changes to mark the wear degree and health status of each mechanical component of the elevator in real time.

[0012] Preferably, the platform has a closed-loop maintenance processing logic. The cloud AI engine automatically generates predictive work orders based on abnormal current curves of the gantry motor and pushes them to the mobile interactive application layer. This allows maintenance personnel to obtain high-frequency raw waveform diagrams via Bluetooth connection to the embedded gateway for fine-tuning diagnosis.

[0013] Preferably, the edge computing gateway layer is equipped with an emergency communication support module, which integrates an emergency power supply system and a satellite and low-frequency dual-mode communication module to ensure that the location of trapped people and elevator status data can still be transmitted in the event of external power outage or conventional network signal blockage.

[0014] Preferably, the edge computing gateway layer communicates with the elevator motherboard protocol through a multi-channel isolated CAN interface, and uses narrowband IoT communication to report feature packets to the cloud only when abnormal features are detected or a preset period is reached.

[0015] Compared with the prior art, the beneficial effects of the present invention are: It has achieved a leap from passive maintenance to predictive maintenance: the platform can extract high-frequency characteristic frequencies during elevator operation through independently deployed multimodal sensor arrays and local NPU algorithms of embedded gateways, and identify the fatigue and wear trends of mechanical parts before a fault actually occurs.

[0016] It improves the real-time processing efficiency at the edge: the gateway has the ability to extract local feature codes and uploads feature packets only when an anomaly is detected or a specific period is met, which greatly reduces the communication load of narrowband IoT and ensures millisecond-level response to sudden failures.

[0017] A transparent digital twin mapping system was constructed: using WebGL technology to recreate the physical operating status of the elevator in the cloud, managers can intuitively view the health distribution of each component, and accurately trace the cause of failure through the historical reproduction function, thus eliminating information asymmetry in the maintenance process.

[0018] It enhances emergency communication support in extreme environments: The unique dual-mode communication architecture of satellite and low frequency, combined with the emergency power supply system, ensures that even in the most unfavorable conditions such as elevator power outages and signal jamming, the rescue center can still obtain real-time physiological status and location information of passengers in the elevator car.

[0019] Improved ride comfort and leveling accuracy: Through the dynamic correlation between ToF lidar and load data, the platform can automatically compensate for leveling errors caused by mechanical wear, providing passengers with a consistently high-quality travel experience. Attached Figure Description

[0020] Figure 1 This is the system architecture diagram of the present invention. Detailed Implementation

[0021] This embodiment describes an elevator remote monitoring platform that integrates an embedded gateway, a multimodal sensor array, and cloud-based AI analysis capabilities. The core of this platform lies in changing the traditional passive mode of elevator monitoring that relies solely on switch alarms. Instead, it achieves health prediction throughout the entire lifecycle of the elevator through high-frequency sampling, edge feature extraction, and digital twin mapping.

[0022] I. Overall System Architecture The physical architecture of this platform is divided into four layers: the sensing terminal layer, the edge computing gateway layer, the cloud big data analysis layer, and the mobile interactive application layer.

[0023] At the sensing terminal layer, the system no longer relies solely on the protocol output of the elevator mainboard, but instead deploys an independent embedded sensor network. This network includes a three-axis high-precision accelerometer mounted on the car roof, a temperature and vibration integrated sensor mounted on the traction machine, a time-of-flight lidar located inside the car, and strain gauges monitoring the tension of the steel wire rope. These sensors aggregate the raw signals to the edge computing gateway via industrial-grade RS485 bus or LoRa wireless communication technology.

[0024] II. Hardware Implementation of Embedded Edge Computing Gateway The edge computing gateway is the "frontline brain" of the entire system. In this embodiment, the gateway uses a dual-core processor based on the ARM Cortex-A7 architecture, running at a clock speed of 1.2GHz. The hardware has 512MB of DDR3 memory and 8GB of eMMC flash storage, ensuring that it can store at least 30 days of rolling raw data locally.

[0025] In terms of hardware interface design, the gateway integrates multiple isolated CAN interfaces, which are compatible with the motherboard protocols of most mainstream elevator brands on the market. Simultaneously, the gateway has a built-in hardware encryption chip, and each gateway has a unique device identification identifier at the factory, ensuring that data is not tampered with or hijacked during IoT transmission.

[0026] The gateway integrates a lightweight neural processing unit. Instead of uploading all vibration data to the cloud in real time, the system performs Fast Fourier Transform locally. By analyzing changes in vibration frequency, the gateway can instantly identify characteristic frequencies such as guide rail wear, loose guide shoes, or abnormal noises from traction machine bearings. The gateway only sends the feature packet to the cloud when an abnormal feature is detected or a preset reporting cycle is reached, significantly reducing bandwidth overhead in narrowband IoT.

[0027] III. Multidimensional Data Fusion and Anomaly Diagnosis Logic The core competitiveness of this platform lies in its "multi-dimensional mutual verification" at the algorithm level.

[0028] 1. Coordinated monitoring of load and leveling accuracy Using pressure sensors installed at the bottom of the elevator car and a lidar sensor on the top, the system can acquire real-time information about the elevator's load. When the elevator stops at a floor, the lidar sensor scans the height difference between the door sill and the car floor sill. The system automatically correlates the current load with the leveling error. If the leveling error continues to increase under a specific load, the algorithm will automatically determine that the brake clearance is too large or the controller parameters are drifting, and will issue a warning before a fault actually occurs.

[0029] 2. Non-contact assessment of wire rope fatigue Traditional manual inspection often relies on visual observation of broken wires in the wire rope. This embodiment utilizes a high-definition, high-speed camera mounted above the traction sheave, combined with a strip-shaped structured light projection. When the elevator is running, the camera captures reflected light spots on the wire rope surface. An embedded gateway uses image processing algorithms to analyze the continuity and brightness distribution of the light spots, thereby calculating the wire rope's diameter thinning rate. This non-contact monitoring method effectively prevents the risk of rope breakage.

[0030] 3. Analysis of Elevator Environment and Passenger Behavior The high-angle cameras deployed inside the elevator car are not only used for safety monitoring, but also incorporate human skeleton recognition algorithms. The system can identify dangerous behaviors by passengers, such as violent jumping inside the car, obstructing the elevator doors from closing for extended periods, or wheelchairs and strollers entering or exiting. When abnormal behavior is detected, the edge gateway immediately provides a gentle alert via the voice broadcast system and prioritizes the elevator's operation based on the urgency level.

[0031] IV. Construction of a Cloud-Based Digital Twin Platform The cloud platform is a virtual representation of the physical elevators. Using WebGL technology, the platform builds a 1:1 dynamic three-axis model for each monitored elevator.

[0032] When the physical elevator moves, real-time parameters such as height, speed, and door lock status uploaded by the embedded gateway drive the virtual model to move synchronously. Maintenance personnel can visually observe the elevator's operating posture through the virtual model in the central control room.

[0033] A deeper application lies in the "history replay" function. When a complaint or malfunction occurs, maintenance personnel can trace back all the elevator's operating parameters before and after the malfunction, much like dragging a progress bar. The digital twin model uses color to indicate the wear level of each component; for example, red indicates excessive traction machine temperature, and yellow indicates excessive guide rail vibration. This visualization method makes complex mechanical systems transparent and traceable.

[0034] V. Intelligent Dispatch and Closed-Loop Maintenance Process The monitoring platform is not an isolated display board; it is deeply integrated into the business processes of maintenance companies.

[0035] When the cloud-based AI engine determines through big data analysis that the current curve of a door operator motor in an elevator is abnormal, the system will not generate a simple alarm message. Instead, it will generate a proactive work order containing fault diagnosis suggestions. The work order will be automatically pushed to the mobile phone of the maintenance engineer closest to the elevator via a mobile app.

[0036] Once engineers arrive on site, they can establish a local Bluetooth connection with the elevator's embedded gateway via an app. The app then acts as an auxiliary diagnostic tool, displaying real-time high-frequency raw waveforms collected by the gateway. This "remote early warning + on-site precision repair" model transforms traditional emergency repairs into planned preventative maintenance, significantly reducing elevator downtime.

[0037] VI. Emergency Communication Support under Special Operating Conditions Considering that elevator entrapment is often accompanied by power outages or signal jamming in the shaft, this embodiment integrates an ultra-long standby emergency power supply system and a satellite / low-frequency dual-mode communication module into the embedded gateway.

[0038] In the event of a complete power outage, the emergency power supply can support the gateway, elevator emergency lights, and voice communication system for at least 4 hours. If the regular 4G / 5G signal is interrupted, the gateway will automatically switch to a low-frequency long-range wireless protocol to send critical data such as the location of trapped passengers, elevator status, and passenger status to repeaters deployed on the top floor of the building, which will then forward the data to the rescue center, ensuring uninterrupted rescue information flow.

[0039] This platform is not merely a stack of technologies, but a reconstruction of the logic of vertical traffic management. It transforms the originally cold, mechanical structure into an intelligent entity capable of real-time dialogue through embedded equipment.

[0040] To more intuitively demonstrate the actual effectiveness of this platform, we selected a representative application case: a large office building in the core area of ​​a city's CBD—an international center. This building is 280 meters tall, has 42 high-speed elevators, and handles over 30,000 passengers daily. Due to the extremely high frequency of elevator operation, traditional manual inspections are no longer sufficient to meet its stringent requirements for safety and timeliness.

[0041] I. On-site deployment and environment construction At the beginning of the project, the technical team carried out a deep intelligent transformation of the elevator group in the high-rise area of ​​Tower A of the office building (with an operating speed of 8m / s). We deployed an edge computing gateway with an integrated Neural Processing Unit (NPU) in the traction machine room of each elevator.

[0042] Because high-speed elevators generate subtle aerodynamic vibrations during operation, traditional sensors are prone to false alarms. We specifically installed a three-axis accelerometer with a high sampling rate on the car top and matched it with the feature extraction algorithm in the gateway to specifically filter out normal vibrations caused by wind resistance, while accurately locking onto abnormal high-frequency harmonics generated by minor deformations of the guide rail in the 120-meter to 150-meter range.

[0043] II. Real-world application scenarios 1. Proactive maintenance: Turning downtime into "invisibility" In the third month of system operation, the cloud-based digital twin platform issued an orange alert. Monitoring data showed that when elevator No. 3 descended to the vicinity of the 15th floor without load, the temperature rise curve of the traction machine was 4.2°C higher than usual, and it was accompanied by bearing spalling characteristic frequencies in a specific frequency band.

[0044] Traditionally, such subtle changes would go undetected by the naked eye or a simple thermometer. The edge gateway, through a Fast Fourier Transform of the original vibration waveform, determined that the gears inside the elevator's gearbox were at risk of pitting corrosion. Taking advantage of a lull in the office building at 2 AM, the maintenance team conducted a targeted disassembly based on a system-generated "predictive work order," discovering a minor blockage in the lubrication system. This intervention prevented a potential emergency entrapment during the morning rush hour, reducing the potential downtime for repairs from two days to two hours.

[0045] 2. Optimized elevator experience: Dynamic compensation for leveling accuracy Tenants on the upper floors of the International Center have extremely high requirements for elevator comfort. Through real-time monitoring of the sill height using Time-of-Flight (ToF) lidar, the platform discovered that elevator No. 8, when fully loaded with 15 people, had a leveling error exceeding 5 millimeters on multiple occasions, which could cause passengers to experience a slight tripping sensation when entering and exiting.

[0046] Instead of directly triggering an alarm, the system interacted with the elevator's mainboard via an edge computing gateway, feeding the error data back to the control algorithm. Based on the load and displacement correlation data provided by the platform, the elevator controller automatically fine-tuned the braking deceleration curve. Without replacing any hardware, the software-defined leveling compensation logic brought the elevator's leveling accuracy back to within 2 millimeters, a top-tier level in the industry.

[0047] 3. Lifeline protection under extreme operating conditions During a sudden, localized power outage in the summer, the entire building's power supply was cut off. At this moment, the emergency communication module installed in the embedded gateway immediately activated. Although the 5G base station inside the building failed due to the power outage, the gateway still transmitted the status monitoring (identified by the algorithm of the car camera) and location coordinates of the four trapped passengers in elevator No. 5 to the property control room via a low-frequency long-range wireless protocol.

[0048] Rescuers used the emergency power supply on the gateway to power the car's voice system, enabling clear real-time communication with the trapped passengers, greatly alleviating their anxiety. The entire process, from the power outage to the successful rescue of the passengers, took only 12 minutes.

[0049] III. Analysis of Project Implementation Effectiveness After a year of operation, the International Center has achieved significant digital transformation benefits through this platform: Unplanned elevator downtime decreased by 42%: the vast majority of mechanical hazards were identified and eliminated by edge-side algorithms at the nascent stage.

[0050] Maintenance costs have been reduced by 25%: the shift from "monthly inspections" to "on-demand maintenance" based on equipment health has reduced unnecessary parts replacements.

[0051] Passenger complaint rate is close to zero: Through continuous optimization of leveling accuracy and vibration curves using digital twins, the comfort of riding the elevator has been substantially improved.

[0052] This case study fully demonstrates the closed-loop capability of "embedded equipment + edge intelligence" in complex business scenarios. It is no longer just a cold, impersonal monitoring backend, but a digital assistant capable of proactively sensing, thinking autonomously, and making collaborative decisions.

[0053] Although embodiments of the present invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to the above embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment, characterized in that, include: The sensing terminal layer includes a multimodal sensor array deployed in key parts of the elevator, used to collect raw physical signals independently of the elevator's main control system. The edge computing gateway layer contains embedded gateways with neural processing units for local feature extraction and fault pre-diagnosis of raw physical signals. The cloud-based big data analytics layer receives feature data uploaded by the edge computing gateway and uses a digital twin model to virtually map the elevator's operational status. The mobile interactive application layer is used to push forwarded work orders and provide auxiliary diagnosis at the maintenance site.

2. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The sensing terminal layer includes a triaxial accelerometer installed on the car roof, a temperature and vibration integrated sensor installed on the traction machine, a time-of-flight lidar installed inside the car, and strain gauges installed on the steel wire rope.

3. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The embedded gateway in the edge computing gateway layer has a built-in hardware encryption chip and a unique device identity identifier. The gateway also has local fast Fourier transform processing capability, which can identify guide rail wear, guide shoe loosening or abnormal noise of traction machine bearings by analyzing vibration frequency characteristics.

4. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The sensing terminal layer also includes a non-contact steel wire rope monitoring device, which consists of a high-definition high-speed camera and a strip structured light projector. The device analyzes the continuity of the light spot and the brightness distribution through image processing algorithms to calculate the diameter reduction rate of the steel wire rope.

5. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The cloud-based big data analysis layer has multi-dimensional data fusion logic. By linking the pressure sensor data at the bottom of the car with the leveling accuracy data of the time-of-flight lidar on the top of the car, it automatically determines the leveling error trend under a specific load, which can be used to warn of abnormal brake gap or controller parameter drift.

6. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The sensing terminal layer includes a high-angle camera with a human skeleton recognition algorithm, used to identify violent jumping, prolonged door blocking behavior, and the entry and exit status of special groups during the elevator ride, and the edge computing gateway triggers the voice broadcast system to provide reminders.

7. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The digital twin model constructed by the cloud-based big data analysis layer is implemented using WebGL technology. This model supports synchronous mapping with the physical elevator operating parameters and has a history reproduction function. It uses color depth changes to mark the wear and health status of each mechanical component of the elevator in real time.

8. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The platform has a closed-loop maintenance processing logic. The cloud-based AI engine automatically generates predictive work orders based on abnormal current curves of the gantry motor and pushes them to the mobile interactive application layer. It supports maintenance personnel to obtain high-frequency raw waveform diagrams for fine-tuning diagnosis by connecting to the embedded gateway via Bluetooth.

9. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The edge computing gateway layer is equipped with an emergency communication support module, which integrates an emergency power supply system and a satellite and low-frequency dual-mode communication module to ensure that the location of trapped people and elevator status data can still be transmitted in the event of external power outage or conventional network signal blockage.

10. The elevator remote monitoring platform based on intelligent Internet of Things and embedded equipment according to claim 1, characterized in that, The edge computing gateway layer communicates with the elevator motherboard protocol through a multi-channel isolated CAN interface and uses narrowband IoT communication to send feature packets to the cloud only when abnormal features are detected or a preset period is reached.

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