Spiral steel structure stair dynamic load self-adaption system based on sensor technology and big data

Abstract

The invention discloses a spiral steel structure stair dynamic load self-adaptive system based on a sensor technology and big data and an operation method thereof, the spiral steel structure stair dynamic load self-adaptive system comprises five levels of functional structures: the first level is a sensing layer which is composed of a pressure sensor array (precision + / -0.1%) and an acceleration sensor (measuring range + / -2g), and the pressure sensor array and the acceleration sensor are respectively installed below stair treads and on a main beam; the second stage is a data acquisition layer which adopts a high-speed data acquisition module (with the sampling rate of 100Hz) and is connected with each sensor; the third level is an edge computing layer, and data preprocessing is carried out by using an edge computing unit (the main frequency of a processor is 3GHz); the fourth level is a cloud analysis layer which is composed of a big data analysis platform and analyzes data by using a machine learning algorithm; the fifth stage is an execution layer and comprises an electric shock absorber (with the stroke being + / -50 mm) and a controller, and self-adaptive adjustment is achieved. All levels of structures are mutually connected through a high-speed data line, a wireless network and a special control interface to form a closed-loop system.

Description

Technical Field

[0001] The invention relates to a dynamic load adaptive system for spiral steel structure stairs based on sensor technology and big data, and belongs to the field of intelligent buildings and structural monitoring. Background Art

[0002] Spiral steel staircases are widely used in modern buildings due to their beautiful appearance and space-saving features. However, this structure faces problems such as large dynamic load changes and high structural stability requirements during use. Traditional spiral staircase design usually adopts static analysis methods, which is difficult to cope with dynamic load changes in actual use, and may lead to structural over-design or safety hazards.

[0003] At present, some intelligent monitoring systems have been applied to building structures, but most of them have the following problems: first, the perception accuracy is insufficient, and it is difficult to capture small structural deformations; second, the data processing capacity is limited, and it is impossible to analyze a large amount of sensor data in real time; third, there is a lack of effective adaptive adjustment mechanism, and it is difficult to adjust the structural performance in time according to the monitoring results. Therefore, it is of great engineering application value to develop an intelligent system that can monitor, analyze and adjust the dynamic load of spiral steel structure stairs in real time. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic load adaptive system for spiral steel structure stairs based on sensor technology and big data, aiming to overcome the defects existing in the prior art, realize intelligent monitoring and adaptive adjustment of spiral staircase structure, improve the safety and service life of the structure, and optimize material usage and structural performance.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A dynamic load adaptive system for spiral steel structure stairs based on sensor technology and big data, including a perception layer, a data acquisition layer, an edge computing layer, a cloud analysis layer and an execution layer, characterized by:

[0007] The sensing layer includes a pressure sensor array with an accuracy of ±0.1% and an acceleration sensor with a range of ±2g, wherein the pressure sensor array is installed under the stair treads, and the acceleration sensor is installed on the main beam;

[0008] The data acquisition layer includes a high-speed data acquisition module with a sampling rate of 100 Hz, which is used to connect various sensors and collect data;

[0009] The edge computing layer includes an edge computing unit with a processor main frequency of 3 GHz, which is used for data preprocessing;

[0010] The cloud analysis layer includes a big data analysis platform that uses machine learning algorithms to analyze data;

[0011] The execution layer includes an electric shock absorber with a stroke of ±50 mm and a matching controller for realizing adaptive adjustment;

[0012] The perception layer and the data acquisition layer are connected via a high-speed data line;

[0013] The data collection layer and the edge computing layer are connected via a high-speed data line;

[0014] The edge computing layer and the cloud analysis layer are connected via a wireless network;

[0015] The cloud analysis layer and the execution layer are connected via a dedicated control interface;

[0016] The system also includes an adaptive adjustment mechanism arranged at the execution layer, which is used to adjust the parameters of the electric shock absorber in real time according to the cloud analysis results.

[0017] Preferably, the specific structure of the perception layer is:

[0018] The pressure sensor array is composed of 16 thin-film pressure sensors of model XYZ-100, each sensor has a size of 30 mm × 30 mm × 5 mm, a measurement range of 0-1000 N, and an accuracy of ±0.1%;

[0019] The pressure sensor array is arranged in a 4×4 matrix and fixed on a high-strength aluminum alloy base plate with a size of 300mm×300mm×10mm. The base plate is fixed to the lower surface of the stair tread by four M8 anti-vibration bolts, and the bolt spacing is 260mm.

[0020] Each pressure sensor is certified by 3M TM DP460 epoxy resin adhesive is fixed on the base plate, and the thickness of the adhesive layer is 0.5mm to ensure close contact between the sensor and the base plate and effective transmission of force;

[0021] The acceleration sensor is a three-axis MEMS acceleration sensor of model ABC-2000, with a size of 20mm×20mm×10mm, a range of ±2g, and a sensitivity of 1000mV / g;

[0022] The acceleration sensor is fixed to the main beam through an adjustable stainless steel mounting bracket, which consists of a base and a rotatable sensor fixing arm;

[0023] The base size is 50mm×50mm×5mm and is fixed to the main beam with 4 M6 anti-loosening screws with a screw spacing of 40mm;

[0024] The sensor fixing arm is 100mm long and is connected to the base through a spherical joint at one end. It can rotate 360° in the horizontal plane and can be adjusted from 0-90° in the vertical direction.

[0025] The acceleration sensor is mounted on the other end of the sensor fixing arm through a 25mm×25mm×3mm stainless steel fixing plate, and a mounting hole matching the sensor housing is reserved on the fixing plate;

[0026] The pressure sensor array and acceleration sensor are connected to the data acquisition layer by a shielded 4-core signal cable. The cable is 2 meters long and 5 mm in outer diameter, and has waterproof and anti-interference properties.

[0027] Preferably, the specific structure of the data acquisition layer is:

[0028] The high-speed data acquisition module is a multi-channel data acquisition device of model DAQ-8000, with dimensions of 200mm×150mm×50mm, weight of 1.2kg, sampling rate of 100Hz and resolution of 16 bits;

[0029] The data acquisition device has 20 analog input channels, of which 16 channels are used to connect to the pressure sensor array, 3 channels are used to connect to the triaxial acceleration sensor, and the remaining channel is used as a backup;

[0030] The data collector adopts an aluminum alloy shell with a protection level of IP65, which can adapt to a working environment of -20℃ to 70℃;

[0031] The data collector is fixed under the main beam of the stairs through a customized mounting bracket, which is made of 2mm thick 304 stainless steel plate bent and has a size of 250mm×200mm×100mm;

[0032] The mounting bracket is fixed to the lower surface of the main beam by four M8 anti-vibration bolts, with a bolt spacing of 200mm×150mm to reduce the impact of vibration on data acquisition;

[0033] Four rubber shock-absorbing pads with a diameter of 20 mm and a thickness of 10 mm are provided between the data logger and the mounting bracket to further isolate environmental vibrations;

[0034] The input end of the data collector is connected to the sensor of the perception layer through 20 standard BNC interfaces, and each interface is equipped with a waterproof protective cover;

[0035] The output end of the data collector is connected to the edge computing layer using a 5-meter-long, 10-mm-diameter multi-core shielded cable. The cable is waterproof and anti-interference, and uses a military-grade quick connector.

[0036] The data collector is equipped with a 12V, 10Ah lithium battery pack as a backup power source. The battery pack has a size of 100mm×80mm×30mm and is fixed to the side of the mounting bracket;

[0037] The data logger is also equipped with an LED status indicator panel, which is 50mm×30mm in size and installed on the front of the device to display information such as working status, sampling rate, and battery power;

[0038] The data collector has a built-in 32GB solid-state hard drive for temporary storage of collected data to prevent data loss caused by network interruption;

[0039] The data collector establishes a wireless connection with the edge computing layer through a built-in Wi-Fi module (supporting 802.11ac standard and a maximum transmission rate of 1.3Gbps). The module antenna is installed on the top of the device and the antenna length is 50mm.

[0040] Preferably, the specific structure of the edge computing layer is:

[0041] The edge computing unit uses an industrial-grade edge server model EC-3000, with dimensions of 300mm×250mm×80mm and a weight of 3.5kg;

[0042] The edge server is equipped with an eight-core processor with a main frequency of 3GHz, using a 14nm process, supporting hyperthreading technology, and a maximum turbo frequency of up to 3.8GHz;

[0043] The edge server has 32GB DDR4-3200MHz memory, expandable to 64GB; it is equipped with a 512GB NVMe solid-state drive with read and write speeds of 3500MB / s and 2500MB / s respectively;

[0044] The edge server adopts an all-aluminum alloy body with a passive heat dissipation design, an operating temperature range of -20°C to 60°C, and a protection level of IP67;

[0045] The edge server is fixed to the stairwell wall through a customized hanging mounting bracket, which is laser cut from 5mm thick Q235 steel plate and measures 400mm×350mm×150mm.

[0046] The mounting frame is fixed to the wall with four M10 expansion bolts, with a bolt spacing of 350mm×300mm; four high-elastic rubber shock-absorbing pads with a diameter of 30mm and a thickness of 15mm are provided between the mounting frame and the edge server;

[0047] The edge server is equipped with multiple interfaces, including:

[0048] ·2 Gigabit Ethernet interfaces for communication with the data collection layer and cloud analysis layer;

[0049] ·4 USB 3.1 ports for connecting external storage devices or maintenance devices;

[0050] 1 HDMI interface, used to connect a monitor for on-site debugging;

[0051] 1 RS-485 interface for communication with the controller at the execution layer;

[0052] The edge server is connected to the data acquisition layer via a 5-meter-long CAT6A shielded network cable with an outer diameter of 7.5mm and a waterproof connector. The transmission rate can reach 10Gbps.

[0053] The edge server is equipped with a built-in 4G LTE module and an external omnidirectional antenna with a length of 150mm, which is installed on the top of the device to provide a backup communication link when the main network connection is interrupted;

[0054] The edge server is also equipped with a 7-inch touch screen with a resolution of 1024×600, installed on the front of the device, which is used to display system status, data processing progress and warning information;

[0055] The edge server runs customized edge computing software, including a data filtering module, a fast Fourier transform (FFT) analysis module, an anomaly detection module, and a data compression module;

[0056] The edge computing software has adaptive computing capabilities and can dynamically adjust the CPU frequency between 1.5GHz and 3.8GHz according to data traffic and processing needs to balance performance and energy consumption;

[0057] The edge server is equipped with a 1000W redundant power supply system, including two 500W hot-swappable power modules, supporting 220V AC input, and having overvoltage, overcurrent and short-circuit protection functions.

[0058] Preferably, the specific structure of the execution layer is:

[0059] The electric shock absorber is a magnetorheological fluid damper of model ED-5000, with a size of φ100mm×400mm, a maximum stroke of ±50mm, and a maximum output force of 5000N;

[0060] The magnetorheological fluid damper adopts a composite magnetic field excitation method of NdFeB permanent magnet and electromagnetic coil, and the magnetic field intensity can be continuously adjusted within the range of 0-1T;

[0061] The cylinder of the magnetorheological fluid damper is made of 7075 aviation aluminum alloy, and the piston rod is made of 42CrMo high-strength alloy steel with chrome plating on the surface, which has excellent corrosion resistance.

[0062] The magnetorheological fluid damper is filled with MRF-140CG magnetorheological fluid, with a shear yield stress range of 0-80 kPa and a response time of less than 10 ms;

[0063] Two magnetorheological fluid dampers are installed under each stair tread and fixed by a customized L-shaped steel mounting bracket. The bracket is 10mm thick and connected with 4 M12 high-strength bolts;

[0064] The controller adopts the intelligent controller of model MC-3000, with the size of 200mm×150mm×60mm, adopts 32-bit ARM Cortex-M7 core, and the main frequency is 240MHz;

[0065] The controller has 16 12-bit ADC input channels, 8 16-bit DAC output channels, and supports multiple communication interfaces such as CAN, RS485, and Ethernet;

[0066] The controller adopts a modular design, including a main control module, a power management module, a communication module and a drive module, which is easy to maintain and upgrade;

[0067] The controller is installed on the side of the stair main beam through a customized stainless steel waterproof chassis. The chassis protection level is IP66 and the size is 300mm×250mm×150mm;

[0068] The controller and the cloud analysis layer communicate with each other through an industrial-grade 4G LTE wireless module, supporting TCP / IP protocol with a maximum transmission rate of up to 150Mbps;

[0069] The controller uses the real-time operating system FreeRTOS, which supports concurrent execution of multiple tasks and ensures the real-time and reliability of control instructions;

[0070] The controller implements an adaptive PID control algorithm, which can dynamically adjust the PID parameters according to the real-time load of the stairs, and the control accuracy reaches ±0.1mm;

[0071] The controller is equipped with a 720W switching power supply, with an input voltage range of 85-264VAC, and has overvoltage, overcurrent, and overtemperature protection functions;

[0072] The adaptive adjustment mechanism includes the following functional modules:

[0073] Load identification module: Based on the pressure sensor and acceleration sensor data, it estimates the load distribution of the stairs in real time;

[0074] Vibration analysis module: uses wavelet transform algorithm to perform real-time analysis on the vibration characteristics of stairs;

[0075] State prediction module: Use Kalman filter to predict the state of the stairs in the short term;

[0076] Optimization control module: Calculates the optimal control strategy based on the model predictive control (MPC) algorithm;

[0077] The controller is connected to each magnetorheological fluid damper through a shielded multi-core control cable, which is 3 meters long, 8mm in outer diameter, and has oil-resistant, wear-resistant, and flame-retardant properties;

[0078] The controller has a self-diagnosis function, which can monitor the working status of each component of the system in real time and display system operation information through LED indicators and LCD display;

[0079] The execution layer also includes an emergency stop device, which consists of a φ60mm red mushroom head button and a corresponding safety relay, installed in an easily accessible position at the staircase entrance;

[0080] The system is equipped with a UPS uninterruptible power supply with a capacity of 3000VA / 2700W, which can maintain normal operation of the system for 30 minutes in the event of a city power outage, ensuring safe shutdown of the system.

[0081] Preferably, the connection structure between the layers of the system is:

[0082] The high-speed data line connection between the perception layer and the data acquisition layer adopts the following configuration:

[0083] Use multi-core shielded cable model PLTC-EB, with an outer diameter of 12mm and 20 twisted pairs;

[0084] Each twisted pair uses 24AWG tinned copper conductor, polyolefin insulation, aluminum foil shielding;

[0085] The cable is oil-resistant, wear-resistant and flame-retardant, and is suitable for temperatures ranging from -40°C to +80°C;

[0086] Both ends of the cable use military-grade MIL-DTL-38999III series circular connectors, providing IP67 protection level;

[0087] The cables are routed along the main beam of the stairs, fixed with stainless steel cable ties every 1 meter, and stress relief rings are set;

[0088] The high-speed data line connection between the data acquisition layer and the edge computing layer adopts the following configuration:

[0089] Use Cat 7a S / FTP double-shielded network cable with an outer diameter of 9mm, supporting a transmission rate of up to 10Gbps;

[0090] The network cable uses 4 pairs of 23AWG solid bare copper conductors, polyethylene insulation, aluminum foil and braid double shielding;

[0091] Both ends of the network cable use waterproof RJ45 connectors, providing IP68 protection level;

[0092] The total length of the network cable should not exceed 50 meters, and a signal repeater should be set every 15 meters to ensure signal quality;

[0093] The network cable is installed on the stairwell wall through a dedicated cable trough with a size of 40mm×25mm and made of flame-retardant ABS material;

[0094] The wireless network connection between the edge computing layer and the cloud analysis layer adopts the following configuration:

[0095] Mainly adopts 5G mobile communication technology and uses the embedded 5G module model MC7455;

[0096] The 5G module supports both SA and NSA networking modes, with a maximum downlink rate of 2Gbps;

[0097] The module is equipped with four omnidirectional antennas with an antenna gain of 5dBi and adopts MIMO 4×4 technology;

[0098] The antennas are installed on top of the edge computing unit and arranged at a 90° angle to obtain the best signal coverage;

[0099] The system is also equipped with a Wi-Fi 6 (802.11ax) module as a backup connection, with a maximum transmission rate of up to 1.2Gbps;

[0100] The dedicated control interface connection between the cloud analysis layer and the execution layer adopts the following configuration:

[0101] Mainly adopts industrial Ethernet communication, using industrial-grade switches of model IE-5000;

[0102] The switch has eight Gigabit Ethernet ports and supports IEEE 1588v2 precision time protocol;

[0103] ·Adopt DIN rail installation method, the size is 156mm×110mm×135mm;

[0104] The switch has a wide operating temperature range of -40°C to +70°C and a protection level of IP30;

[0105] The system uses PROFINET real-time industrial Ethernet protocol to achieve deterministic real-time communication;

[0106] The network topology adopts a redundant ring network structure and uses the RSTP protocol to achieve fast fault switching;

[0107] The connections between the various layers of the system also include the following security measures:

[0108] All data transmission uses AES-256 encryption algorithm to protect data transmission security;

[0109] Implement the secure transport layer protocol based on TLS1.3 to ensure end-to-end communication security;

[0110] Deploy intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor network traffic in real time;

[0111] Use two-factor authentication to ensure only authorized personnel can access the system;

[0112] Implement network segmentation and access control lists (ACLs) to restrict communication between different tiers;

[0113] The system is also equipped with a network management system with the following functions:

[0114] Real-time monitoring of the connection status and performance indicators between each layer;

[0115] Automatically generate network topology diagram to intuitively display system connection status;

[0116] Support SNMP v3 protocol to achieve remote configuration and fault diagnosis;

[0117] ·Network traffic analysis capabilities to identify abnormal traffic patterns;

[0118] Provide detailed logging and report generation functions to facilitate system maintenance and optimization.

[0119] Preferably, the specific structure of the adaptive adjustment mechanism of the execution layer is:

[0120] The adaptive adjustment mechanism includes an intelligent control unit model ACS-3000, with dimensions of 250mm×200mm×80mm and an industrial-grade IP67 protection grade housing;

[0121] The core of the intelligent control unit uses a dual-core ARM Cortex-A72 processor with a main frequency of 2.0GHz, equipped with 4GB LPDDR4 memory and 32GB eMMC storage;

[0122] The control unit is equipped with the following interfaces:

[0123] 8 16-bit ADC input channels for receiving sensor data;

[0124] 8 16-bit DAC output channels for controlling electric shock absorbers;

[0125] 2 Gigabit Ethernet interfaces for communication with the cloud analysis layer and local network;

[0126] · 4 CAN 2.0B interfaces for real-time communication with electric shock absorbers;

[0127] ·2 RS-485 interfaces for communication with auxiliary equipment;

[0128] The intelligent control unit uses the real-time operating system QNX 7.0 to ensure the real-time and reliability of control instructions;

[0129] The adaptive adjustment mechanism includes the following functional modules:

[0130] Data receiving module: receives analysis results and control strategies from the cloud analysis layer;

[0131] State estimation module: Based on the Kalman filter algorithm, it estimates the dynamic state of the stairs in real time;

[0132] Parameter optimization module: Adopts adaptive fuzzy neural network algorithm to dynamically optimize control parameters;

[0133] Control execution module: Generates precise control instructions based on optimized parameters;

[0134] The intelligent control unit adopts a model predictive control (MPC) algorithm with a prediction time domain of 100ms and a control period of 10ms;

[0135] The control unit has a built-in machine learning model and uses the TensorFlow Lite framework to achieve fast reasoning at the edge;

[0136] The adaptive adjustment mechanism also includes a programmable logic controller model PLC-200 as an auxiliary and backup for the control unit:

[0137] PLC adopts modular design, including CPU module, power module, I / O module and communication module;

[0138] The CPU module adopts a 32-bit RISC processor with a main frequency of 120MHz and built-in 512KB SRAM;

[0139] The I / O module includes 16 digital inputs, 16 digital outputs, 8 analog inputs and 8 analog outputs;

[0140] The communication module supports Modbus TCP / IP protocol and can exchange data with the intelligent control unit;

[0141] The intelligent control unit and PLC are installed in a 400mm×300mm×200mm waterproof junction box via DIN rails. The junction box is installed at an appropriate position on the main beam of the stairs.

[0142] The adaptive adjustment mechanism controls each electric shock absorber including the following parameters:

[0143] Damping coefficient: can be adjusted continuously within the range of 0-5000Ns / m;

[0144] Stiffness coefficient: can be continuously adjusted within the range of 0-100kN / m;

[0145] Response time: up to 5ms;

[0146] The control unit uses the CAN bus to communicate with each electric shock absorber at a communication rate of 1Mbps, using a combination of periodic transmission and event triggering;

[0147] The adaptive adjustment mechanism also includes a local human-machine interface (HMI), which uses a 7-inch capacitive touch screen with a resolution of 1024×600:

[0148] ·HMI is installed at a convenient location at the stair entrance;

[0149] Display the real-time operating status of the system, sensor data and control parameters;

[0150] Provide manual adjustment and emergency stop functions;

[0151] The system is equipped with an uninterruptible power supply model UPS-1000 with a capacity of 1000VA / 900W:

[0152] ·Adopt online double conversion technology to provide pure sine wave output;

[0153] The battery pack provides 15 minutes of full-load runtime;

[0154] Equipped with RS-232 and USB communication interfaces, supporting remote monitoring and management;

[0155] The adaptive regulation mechanism also includes the following safety features:

[0156] Watchdog timer: monitors the running status of the control program to prevent the system from freezing;

[0157] Redundant design: key control modules adopt dual redundant design to ensure system reliability;

[0158] Fault diagnosis: Real-time monitoring of the working status of each component, automatic detection and reporting of faults;

[0159] Safety limit: set software and hardware double limit to prevent the electric shock absorber from over-travel;

[0160] Data encryption: AES-256 encryption algorithm is used to protect control instructions and status data;

[0161] The system supports remote access and maintenance, uses VPN technology to ensure the security of remote connections, and supports remote firmware upgrades and parameter adjustments.

[0162] The beneficial effects of the present invention are: (1) It realizes intelligent dynamic load monitoring and adaptive adjustment of spiral steel structure stairs, greatly improving the safety and service life of the structure; (2) The use of high-precision sensors and advanced data processing technology can capture tiny structural deformations and load changes in real time; (3) The architecture that combines edge computing and cloud analysis realizes efficient processing and analysis of big data, providing an accurate basis for decision-making; (4) The adaptive adjustment mechanism can quickly adjust structural parameters according to real-time analysis results, effectively reducing vibration and deformation; (5) Multi-level safety design and redundancy mechanism ensure stable and reliable operation of the system under various working conditions; (6) Modular design facilitates installation, maintenance and upgrading, reducing the life cycle cost of the system; (7) Remote monitoring and management functions improve management efficiency and reduce the need for manual inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0163] Figure 1 It is a schematic diagram of the overall architecture of the dynamic load adaptive system of the spiral steel structure staircase of the present invention;

[0164] Figure 2 Schematic diagram of sensor layout for the perception layer;

[0165] Figure 3 It is a schematic diagram of the data acquisition layer structure;

[0166] Figure 4 This is a schematic diagram of the hardware composition of the edge computing layer;

[0167] Figure 5 This is the installation diagram of the electric shock absorber for the executive layer;

[0168] Figure 6 It is a schematic diagram of the connections between the various layers of the system;

[0169] Figure 7 This is the control flow chart of the adaptive adjustment mechanism. DETAILED DESCRIPTION

[0170] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0171] like Figure 1 As shown, the spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data provided by the present invention includes a perception layer, a data acquisition layer, an edge computing layer, a cloud analysis layer and an execution layer. These five layers are interconnected through high-speed data lines and wireless networks to form a complete intelligent monitoring and control system.

[0172] like Figure 2 As shown in the figure, the sensing layer consists of a pressure sensor array and an accelerometer. 16 thin-film pressure sensors are arranged in a 4×4 matrix, fixed on a high-strength aluminum alloy base plate, and installed under the stair treads. The triaxial MEMS accelerometer is installed on the main beam through an adjustable bracket to monitor the vibration state of the structure.

[0173] like Figure 3 As shown, the data acquisition layer uses a multi-channel data acquisition device with 20 analog input channels, a sampling rate of 100Hz, and a resolution of 16 bits. The data acquisition device is fixed under the main beam of the stairs through a customized mounting bracket, and an anti-vibration design is used to ensure the accuracy of data acquisition.

[0174] like Figure 4 As shown in the figure, the edge computing layer uses industrial-grade edge servers equipped with high-performance processors and large-capacity storage. The server is fixed to the wall of the stairwell through a hanging mounting frame. It adopts an all-aluminum alloy body and passive heat dissipation design to ensure stable operation in harsh environments. The edge server runs customized edge computing software, including modules such as data filtering, FFT analysis, anomaly detection and data compression, to achieve preliminary processing and analysis of data.

[0175] like Figure 5 As shown in the figure, the execution layer is mainly composed of electric shock absorbers and controllers. Two magnetorheological fluid dampers are installed under each stair tread and fixed by L-shaped steel mounting brackets. The controller adopts modular design, installed in a waterproof chassis and fixed on the side of the stair main beam. The controller implements an adaptive PID control algorithm, which can dynamically adjust the shock absorber parameters according to the real-time load conditions.

[0176] like Figure 6 As shown in the figure, various connection methods are used between the layers of the system to ensure the reliability and real-time performance of data transmission. Multi-core shielded cables are used between the perception layer and the data acquisition layer, Cat 7aS / FTP double-shielded network cables are used between the data acquisition layer and the edge computing layer, 5G mobile communication technology is mainly used between the edge computing layer and the cloud analysis layer, and industrial Ethernet communication is used between the cloud analysis layer and the execution layer. The system also implements multi-level security measures, including data encryption, intrusion detection, access control, etc.

[0177] like Figure 7 As shown in Figure 1, the control process of the adaptive adjustment mechanism includes four main steps: data reception, state estimation, parameter optimization, and control execution. The intelligent control unit adopts a model predictive control algorithm combined with a machine learning model to achieve rapid response and precise control of the dynamic load of the stairs. The system is also equipped with a programmable logic controller as a backup to further improve reliability.

[0178] In practical application, the working process of this system is as follows:

[0179] 1. The pressure sensors and acceleration sensors in the sensing layer collect the load distribution and vibration data of the stairs in real time;

[0180] 2. The data acquisition layer collects sensor data at a sampling rate of 100 Hz and performs preliminary signal conditioning;

[0181] 3. The edge computing layer receives the collected data and performs preprocessing such as fast Fourier transform and anomaly detection;

[0182] 4. The pre-processed data is transmitted to the cloud analysis layer via the 5G network;

[0183] 5. The cloud analysis layer uses the big data analysis platform and machine learning algorithms to conduct in-depth analysis of the data and generate control strategies;

[0184] 6. The control strategy is transmitted to the intelligent control unit of the execution layer through a dedicated interface;

[0185] 7. The intelligent control unit calculates the optimal control parameters based on the received strategy and local state estimation;

[0186] 8. The control parameters are sent to each electric shock absorber to adjust its damping coefficient and stiffness coefficient in real time;

[0187] 9. The system continuously monitors the status of the stairs, forms a closed-loop control, and continuously optimizes the structural performance.

[0188] The dynamic load adaptive system for spiral steel structure stairs of the present invention realizes real-time monitoring and adaptive adjustment of staircase structures through advanced sensing technology, edge computing, cloud analysis and intelligent control. The system has the characteristics of high precision, fast response and high reliability, which not only improves the safety and comfort of spiral stairs, but also extends the service life of the structure and reduces maintenance costs. The application of this system will bring significant technological progress and economic benefits to the fields of intelligent buildings and structural health monitoring.

[0189] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A dynamic load adaptive system for spiral steel structure stairs based on sensor technology and big data. It includes the perception layer, data collection layer, edge computing layer, cloud analysis layer and execution layer, and its characteristics are: The sensing layer includes a pressure sensor array with an accuracy of ±0.1% and an acceleration sensor with a range of ±2g, wherein the pressure sensor array is installed under the stair treads, and the acceleration sensor is installed on the main beam; The data acquisition layer includes a high-speed data acquisition module with a sampling rate of 100 Hz, which is used to connect various sensors and collect data; The edge computing layer includes an edge computing unit with a processor main frequency of 3 GHz, which is used for data preprocessing; The cloud analysis layer includes a big data analysis platform that uses machine learning algorithms to analyze data; The execution layer includes an electric shock absorber with a stroke of ±50 mm and a matching controller for realizing adaptive adjustment; The perception layer and the data acquisition layer are connected via a high-speed data line; The data collection layer and the edge computing layer are connected via a high-speed data line; The edge computing layer and the cloud analysis layer are connected via a wireless network; The cloud analysis layer and the execution layer are connected via a dedicated control interface; The system also includes an adaptive adjustment mechanism arranged at the execution layer, which is used to adjust the parameters of the electric shock absorber in real time according to the cloud analysis results.

2. The spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data according to claim 1 is characterized by: The specific structure of the perception layer is: The pressure sensor array is composed of 16 thin-film pressure sensors of model XYZ-100, each sensor has a size of 30 mm × 30 mm × 5 mm, a measurement range of 0-1000 N, and an accuracy of ±0.1%; The pressure sensor array is arranged in a 4×4 matrix and fixed on a high-strength aluminum alloy base plate with a size of 300mm×300mm×10mm. The base plate is fixed to the lower surface of the stair treads by four M8 anti-vibration bolts, and the bolt spacing is 260mm. Each pressure sensor is certified by 3M TM DP460 epoxy resin adhesive is fixed on the base plate, and the thickness of the adhesive layer is 0.5mm to ensure close contact between the sensor and the base plate and effective transmission of force; The acceleration sensor is a three-axis MEMS acceleration sensor of model ABC-2000, with a size of 20mm×20mm×10mm, a range of ±2g, and a sensitivity of 1000mV / g; The acceleration sensor is fixed to the main beam through an adjustable stainless steel mounting bracket, which consists of a base and a rotatable sensor fixing arm; The base size is 50mm×50mm×5mm and is fixed to the main beam with 4 M6 anti-loosening screws with a screw spacing of 40mm; The sensor fixing arm is 100mm long and is connected to the base through a spherical joint at one end. It can rotate 360° in the horizontal plane and can be adjusted from 0-90° in the vertical direction. The acceleration sensor is mounted on the other end of the sensor fixing arm through a 25mm×25mm×3mm stainless steel fixing plate, and a mounting hole matching the sensor housing is reserved on the fixing plate; The pressure sensor array and acceleration sensor are connected to the data acquisition layer by a shielded 4-core signal cable. The cable is 2 meters long and 5 mm in outer diameter, and has waterproof and anti-interference properties.

3. The dynamic load adaptive system for spiral steel structure stairs based on sensor technology and big data according to claim 1 is characterized by: The specific structure of the data acquisition layer is: The high-speed data acquisition module is a multi-channel data acquisition device of model DAQ-8000, with dimensions of 200mm×150mm×50mm, weight of 1.2kg, sampling rate of 100Hz and resolution of 16 bits; The data acquisition device has 20 analog input channels, of which 16 channels are used to connect to the pressure sensor array, 3 channels are used to connect to the triaxial acceleration sensor, and the remaining channel is used as a backup; The data collector adopts an aluminum alloy shell with a protection level of IP65, which can adapt to a working environment of -20℃ to 70℃; The data collector is fixed under the main beam of the stairs through a customized mounting bracket, which is made of 2mm thick 304 stainless steel plate bent and has a size of 250mm×200mm×100mm; The mounting bracket is fixed to the lower surface of the main beam by four M8 anti-vibration bolts, with a bolt spacing of 200mm×150mm to reduce the impact of vibration on data acquisition; Four rubber shock-absorbing pads with a diameter of 20 mm and a thickness of 10 mm are provided between the data logger and the mounting bracket to further isolate environmental vibrations; The input end of the data collector is connected to the sensor of the perception layer through 20 standard BNC interfaces, and each interface is equipped with a waterproof protective cover; The output end of the data collector is connected to the edge computing layer using a 5-meter-long, 10-mm-diameter multi-core shielded cable. The cable is waterproof and anti-interference, and uses a military-grade quick connector. The data collector is equipped with a 12V, 10Ah lithium battery pack as a backup power source. The battery pack has a size of 100mm×80mm×30mm and is fixed to the side of the mounting bracket; The data logger is also equipped with an LED status indicator panel, which is 50mm×30mm in size and installed on the front of the device to display information such as working status, sampling rate, and battery power; The data collector has a built-in 32GB solid-state hard drive for temporary storage of collected data to prevent data loss caused by network interruption; The data collector establishes a wireless connection with the edge computing layer through a built-in Wi-Fi module (supporting 802.11ac standard and a maximum transmission rate of 1.3Gbps). The module antenna is installed on the top of the device and the antenna length is 50mm.

4. The spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data according to claim 1 is characterized by: The specific structure of the edge computing layer is: The edge computing unit uses an industrial-grade edge server model EC-3000, with dimensions of 300mm×250mm×80mm and a weight of 3.5kg; The edge server is equipped with an eight-core processor with a main frequency of 3GHz, using a 14nm process, supporting hyperthreading technology, and a maximum turbo frequency of up to 3.8GHz; The edge server has 32GB DDR4-3200MHz memory, expandable to 64GB; it is equipped with a 512GB NVMe solid-state drive with read and write speeds of 3500MB / s and 2500MB / s respectively; The edge server adopts an all-aluminum alloy body with a passive heat dissipation design, an operating temperature range of -20°C to 60°C, and a protection level of IP67; The edge server is fixed to the stairwell wall through a customized hanging mounting bracket, which is laser cut from 5mm thick Q235 steel plate and measures 400mm×350mm×150mm. The mounting frame is fixed to the wall with four M10 expansion bolts, with a bolt spacing of 350mm×300mm; four high-elastic rubber shock-absorbing pads with a diameter of 30mm and a thickness of 15mm are provided between the mounting frame and the edge server; The edge server is equipped with multiple interfaces, including: ·2 Gigabit Ethernet interfaces for communication with the data collection layer and cloud analysis layer; ·4 USB 3.1 ports for connecting external storage devices or maintenance devices; 1 HDMI interface, used to connect a monitor for on-site debugging; 1 RS-485 interface for communication with the controller at the execution layer; The edge server is connected to the data acquisition layer via a 5-meter-long CAT6A shielded network cable with an outer diameter of 7.5mm and a waterproof connector. The transmission rate can reach 10Gbps. The edge server is equipped with a built-in 4G LTE module and an external omnidirectional antenna with a length of 150mm, which is installed on the top of the device to provide a backup communication link when the main network connection is interrupted; The edge server is also equipped with a 7-inch touch screen with a resolution of 1024×600, installed on the front of the device, which is used to display system status, data processing progress and warning information; The edge server runs customized edge computing software, including a data filtering module, a fast Fourier transform (FFT) analysis module, an anomaly detection module, and a data compression module; The edge computing software has adaptive computing capabilities and can dynamically adjust the CPU frequency between 1.5GHz and 3.8GHz according to data traffic and processing needs to balance performance and energy consumption; The edge server is equipped with a 1000W redundant power supply system, including two 500W hot-swappable power modules, supporting 220V AC input, and having overvoltage, overcurrent and short-circuit protection functions.

5. The spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data according to claim 1 is characterized by: The specific structure of the execution layer is: The electric shock absorber is a magnetorheological fluid damper of model ED-5000, with a size of φ100mm×400mm, a maximum stroke of ±50mm, and a maximum output force of 5000N; The magnetorheological fluid damper adopts a composite magnetic field excitation method of NdFeB permanent magnet and electromagnetic coil, and the magnetic field intensity can be continuously adjusted within the range of 0-1T; The cylinder of the magnetorheological fluid damper is made of 7075 aviation aluminum alloy, and the piston rod is made of 42CrMo high-strength alloy steel with chrome plating on the surface, which has excellent corrosion resistance. The magnetorheological fluid damper is filled with MRF-140CG magnetorheological fluid, with a shear yield stress range of 0-80 kPa and a response time of less than 10 ms; Two magnetorheological fluid dampers are installed under each stair tread and fixed by a customized L-shaped steel mounting bracket. The bracket is 10mm thick and connected with 4 M12 high-strength bolts; The controller adopts the intelligent controller of model MC-3000, with the size of 200mm×150mm×60mm, adopts 32-bit ARM Cortex-M7 core, and the main frequency is 240MHz; The controller has 16 12-bit ADC input channels, 8 16-bit DAC output channels, and supports multiple communication interfaces such as CAN, RS485, and Ethernet; The controller adopts a modular design, including a main control module, a power management module, a communication module and a drive module, which is easy to maintain and upgrade; The controller is installed on the side of the stair main beam through a customized stainless steel waterproof chassis. The chassis protection level is IP66 and the size is 300mm×250mm×150mm; The controller and the cloud analysis layer communicate with each other through an industrial-grade 4G LTE wireless module, supporting TCP / IP protocol with a maximum transmission rate of up to 150Mbps; The controller uses the real-time operating system FreeRTOS, which supports concurrent execution of multiple tasks and ensures the real-time and reliability of control instructions; The controller implements an adaptive PID control algorithm, which can dynamically adjust the PID parameters according to the real-time load of the stairs, and the control accuracy reaches ±0.1mm; The controller is equipped with a 720W switching power supply, with an input voltage range of 85-264VAC, and has overvoltage, overcurrent, and overtemperature protection functions; The adaptive adjustment mechanism includes the following functional modules: Load identification module: Based on the pressure sensor and acceleration sensor data, it estimates the load distribution of the stairs in real time; Vibration analysis module: uses wavelet transform algorithm to perform real-time analysis on the vibration characteristics of stairs; State prediction module: Use Kalman filter to predict the state of the stairs in the short term; Optimization control module: Calculates the optimal control strategy based on the model predictive control (MPC) algorithm; The controller is connected to each magnetorheological fluid damper through a shielded multi-core control cable, which is 3 meters long, 8mm in outer diameter, and has oil-resistant, wear-resistant, and flame-retardant properties; The controller has a self-diagnosis function, which can monitor the working status of each component of the system in real time and display system operation information through LED indicators and LCD display; The execution layer also includes an emergency stop device, which consists of a φ60mm red mushroom head button and a corresponding safety relay, installed in an easily accessible position at the staircase entrance; The system is equipped with a UPS uninterruptible power supply with a capacity of 3000VA / 2700W, which can maintain normal operation of the system for 30 minutes in the event of a city power outage, ensuring safe shutdown of the system.

6. The spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data according to claim 1 is characterized by: The connection structure between the various layers of the system is: The high-speed data line connection between the perception layer and the data acquisition layer adopts the following configuration: Use multi-core shielded cable model PLTC-EB, with an outer diameter of 12mm and 20 twisted pairs; Each twisted pair uses 24AWG tinned copper conductor, polyolefin insulation, aluminum foil shielding; The cable is oil-resistant, wear-resistant and flame-retardant, and is suitable for temperatures ranging from -40°C to +80°C; Both ends of the cable use military-grade MIL-DTL-38999III series circular connectors, providing IP67 protection level; The cables are routed along the main beam of the stairs, fixed with stainless steel cable ties every 1 meter, and stress relief rings are set; The high-speed data line connection between the data acquisition layer and the edge computing layer adopts the following configuration: Use Cat 7a S / FTP double-shielded network cable with an outer diameter of 9mm, supporting a transmission rate of up to 10Gbps; The network cable uses 4 pairs of 23AWG solid bare copper conductors, polyethylene insulation, aluminum foil and braid double shielding; Both ends of the network cable use waterproof RJ45 connectors, providing IP68 protection level; The total length of the network cable should not exceed 50 meters, and a signal repeater should be set every 15 meters to ensure signal quality; The network cable is installed on the stairwell wall through a dedicated cable trough with a size of 40mm×25mm and made of flame-retardant ABS material; The wireless network connection between the edge computing layer and the cloud analysis layer adopts the following configuration: Mainly adopts 5G mobile communication technology and uses the embedded 5G module model MC7455; The 5G module supports both SA and NSA networking modes, with a maximum downlink rate of 2Gbps; The module is equipped with four omnidirectional antennas with an antenna gain of 5dBi and adopts MIMO 4×4 technology; The antennas are installed on top of the edge computing unit and arranged at a 90° angle to obtain the best signal coverage; The system is also equipped with a Wi-Fi 6 (802.11ax) module as a backup connection, with a maximum transmission rate of up to 1.2Gbps; The dedicated control interface connection between the cloud analysis layer and the execution layer adopts the following configuration: Mainly adopts industrial Ethernet communication, using industrial-grade switches of model IE-5000; The switch has eight Gigabit Ethernet ports and supports IEEE 1588v2 precision time protocol; ·Adopt DIN rail installation method, the size is 156mm×110mm×135mm; The switch has a wide operating temperature range of -40°C to +70°C and a protection level of IP30; The system uses PROFINET real-time industrial Ethernet protocol to achieve deterministic real-time communication; The network topology adopts a redundant ring network structure and uses the RSTP protocol to achieve fast fault switching; the connection between the various layers of the system also includes the following security measures: All data transmission uses AES-256 encryption algorithm to protect data transmission security; Implement the secure transport layer protocol based on TLS1.3 to ensure end-to-end communication security; Deploy intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor network traffic in real time; Use two-factor authentication to ensure only authorized personnel can access the system; Implement network segmentation and access control lists (ACLs) to limit communications between different tiers; the system is also equipped with a network management system that has the following features: Real-time monitoring of the connection status and performance indicators between each layer; Automatically generate network topology diagram to intuitively display system connection status; Support SNMP v3 protocol to achieve remote configuration and fault diagnosis; ·Network traffic analysis capabilities to identify abnormal traffic patterns; Provide detailed logging and report generation functions to facilitate system maintenance and optimization.

7. The spiral steel structure staircase dynamic load adaptive system based on sensor technology and big data according to claim 1 is characterized by: The specific structure of the adaptive adjustment mechanism of the execution layer is: The adaptive adjustment mechanism includes an intelligent control unit model ACS-3000, with dimensions of 250mm×200mm×80mm and an industrial-grade IP67 protection grade housing; The core of the intelligent control unit uses a dual-core ARM Cortex-A72 processor with a main frequency of 2.0GHz, equipped with 4GB LPDDR4 memory and 32GB eMMC storage; The control unit is equipped with the following interfaces: 8 16-bit ADC input channels for receiving sensor data; 8 16-bit DAC output channels for controlling electric shock absorbers; 2 Gigabit Ethernet interfaces for communication with the cloud analysis layer and local network; · 4 CAN 2.0B interfaces for real-time communication with electric shock absorbers; ·2 RS-485 interfaces for communication with auxiliary equipment; The intelligent control unit uses the real-time operating system QNX 7.0 to ensure the real-time and reliability of control instructions; The adaptive adjustment mechanism includes the following functional modules: Data receiving module: receives analysis results and control strategies from the cloud analysis layer; State estimation module: Based on the Kalman filter algorithm, it estimates the dynamic state of the stairs in real time; Parameter optimization module: Adopts adaptive fuzzy neural network algorithm to dynamically optimize control parameters; Control execution module: Generates precise control instructions based on optimized parameters; The intelligent control unit adopts a model predictive control (MPC) algorithm with a prediction time domain of 100ms and a control period of 10ms; The control unit has a built-in machine learning model and uses the TensorFlow Lite framework to achieve fast reasoning at the edge; The adaptive adjustment mechanism also includes a programmable logic controller model PLC-200 as an auxiliary and backup for the control unit: PLC adopts modular design, including CPU module, power module, I / O module and communication module; The CPU module adopts a 32-bit RISC processor with a main frequency of 120MHz and built-in 512KB SRAM; The I / O module includes 16 digital inputs, 16 digital outputs, 8 analog inputs and 8 analog outputs; The communication module supports Modbus TCP / IP protocol and can exchange data with the intelligent control unit; The intelligent control unit and PLC are installed in a 400mm×300mm×200mm waterproof junction box via DIN rails. The junction box is installed at an appropriate position on the main beam of the stairs. The adaptive adjustment mechanism controls each electric shock absorber including the following parameters: Damping coefficient: can be adjusted continuously within the range of 0-5000Ns / m; Stiffness coefficient: can be continuously adjusted within the range of 0-100kN / m; Response time: up to 5ms; The control unit uses the CAN bus to communicate with each electric shock absorber at a communication rate of 1Mbps, using a combination of periodic transmission and event triggering; The adaptive adjustment mechanism also includes a local human-machine interface (HMI), which uses a 7-inch capacitive touch screen with a resolution of 1024×600: ·HMI is installed at a convenient location at the stair entrance; Display the real-time operating status of the system, sensor data and control parameters; Provide manual adjustment and emergency stop functions; The system is equipped with an uninterruptible power supply model UPS-1000 with a capacity of 1000VA / 900W: ·Adopt online double conversion technology to provide pure sine wave output; The battery pack provides 15 minutes of full-load runtime; Equipped with RS-232 and USB communication interfaces, supporting remote monitoring and management; The adaptive regulation mechanism also includes the following safety features: Watchdog timer: monitors the running status of the control program to prevent the system from freezing; Redundant design: key control modules adopt dual redundant design to ensure system reliability; Fault diagnosis: Real-time monitoring of the working status of each component, automatic detection and reporting of faults; Safety limit: set software and hardware double limit to prevent the electric shock absorber from over-travel; Data encryption: AES-256 encryption algorithm is used to protect control instructions and status data; The system supports remote access and maintenance, uses VPN technology to ensure the security of remote connections, and supports remote firmware upgrades and parameter adjustments.

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