Intelligent isolation bearing on-line monitoring system, installation process and monitoring method

Abstract

The application relates to an intelligent isolation bearing online monitoring system, an installation process and a monitoring method. The system integrates force value, angular acceleration and displacement monitoring functions on the basis of an isolation bearing, is composed of an upper sealing plate, a rubber body, a lower sealing plate and a monitoring system, is used for simultaneously monitoring vertical loads transmitted from an upper building to a lower foundation and angular acceleration and horizontal displacement of the isolation bearing, and is composed of a ring-shaped pressure sensor, an inertial measurement unit (IMU), a displacement sensor, a data acquisition and transmission module, a cloud server and a monitoring platform. The system adopts a full-sensor external modular structure, is matched with a six-order Kalman fusion algorithm, a multi-parameter coupling early warning criterion, an IMU dynamic drift calibration model, and a special pre-tightening and anti-loosening installation process, is suitable for various working conditions, realizes synchronous acquisition of multi-dimensional parameters of a bearing, data noise reduction fusion and fault grading early warning, and can realize real-time monitoring of stress, angular acceleration and displacement states of a building isolation bearing.

Description

Technical Field

[0001] This invention relates to a seismic isolation bearing monitoring system, and in particular to an intelligent online monitoring system for seismic isolation bearings with multi-parameter fusion, installation process and monitoring method, belonging to the field of seismic isolation bearing health monitoring technology. Background Technology

[0002] Seismic isolation rubber bearings are key components connecting the superstructure and substructure, bearing the core functions of load transfer and structural deformation adaptation. Located at the load center of the structural system, the bearing's stress state, deformation behavior, and dynamic response directly affect the overall structural safety and service life. Traditional bearings, after service, can only be assessed through factory testing and manual inspection, failing to capture real-time core operating parameters such as vertical load, horizontal deformation, and torsional overturning. Furthermore, seismic isolation rubber bearings are concentrated in the building's isolation layer, which is typically lower than the inter-story height and also houses various pipelines such as water supply, drainage, fire protection, and cable trays, making manual inspection difficult, creating blind spots, and resulting in long inspection cycles. Bearing failure is usually only discovered after an earthquake or structural anomalies, easily missing the optimal time for intervention.

[0003] Existing patent documents do not provide corresponding solutions. For example, CN114481820A discloses a convenient height-adjustable intelligent monitoring support, including: a support body with an upper steel plate at the top and a lower steel plate at the bottom; an adjusting component located below the support body; the adjusting component includes an upper adjusting steel plate and an lower adjusting steel plate spaced apart, and a wedge-shaped component located between the upper and lower adjusting steel plates; the wedge-shaped component includes two symmetrically arranged wedge-shaped adjusting blocks, an adjusting rod, and a locking nut; and an intelligent monitoring component including a force measuring device, a data acquisition instrument, and a computer terminal. CN118065529A discloses a seismic isolation system and construction method for seismically isolated buildings. The system includes a building foundation and a lower support pier. A lower adjustable support, a seismic isolation bearing, an upper adjustable bearing, and an upper column are sequentially installed on the lower support pier. The lower adjustable support includes a lower support plate, a lower connecting sleeve, a lower support rod, and a lower connecting plate. Several dynamic monitoring points are arranged on the building structure, and these points are electrically connected to a centralized online monitoring system. The system also includes an online health management platform. The method includes lower adjustable support installation, preliminary scanning, precise scanning, seismic isolation bearing installation and adjustment, and dynamic monitoring. However, the aforementioned prior art documents all address the technical problem of precise monitoring of seismic isolation bearing failure.

[0004] Therefore, those skilled in the art urgently need to study a solution that can comprehensively monitor the condition of the bearings. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention proposes an intelligent online monitoring system for seismic isolation bearings, along with its installation process and monitoring method, incorporating multi-parameter fusion. It employs a modular structure with all sensors externally mounted, coupled with a sixth-order Kalman fusion algorithm, multi-parameter coupled early warning criteria, and an IMU dynamic drift calibration model, along with a dedicated pre-tightening and anti-loosening installation process. Adaptable to various working conditions, it achieves synchronous acquisition of multi-dimensional bearing parameters, data noise reduction and fusion, and fault classification early warning, enabling real-time monitoring of the stress, angular acceleration, and displacement status of building seismic isolation bearings.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An intelligent online monitoring system for seismic isolation bearings is a multi-parameter fusion intelligent online monitoring system for seismic isolation bearings. It integrates force, angular acceleration, and displacement monitoring functions on the basis of seismic isolation bearings. It consists of an upper sealing plate, a rubber body, a lower sealing plate, and a monitoring system. It is used to simultaneously monitor the vertical load transmitted from the superstructure to the lower foundation, as well as the angular acceleration and horizontal displacement of the seismic isolation bearings. The monitoring system consists of a ring pressure sensor, an inertial measurement unit (IMU), a displacement sensor, a data acquisition and transmission module, a cloud server, and a monitoring platform. The top surface of the upper sealing plate has at least one threaded hole for installing an annular pressure sensor. The annular pressure sensor is subjected to force by being in contact with the rubber body through a sensor cap. Simultaneously, the top surface of the upper sealing plate also has a wire groove for laying the sensor transmission line at the tail of the annular pressure sensor. The annular pressure sensor is arranged in concentric radial layers with staggered spacing along the rubber layers of the seismic isolation bearing, and measuring points are evenly distributed on the circumference of different radii to fully cover the vertical force distribution area of ​​the seismic isolation bearing. The annular pressure sensor is embedded in a pre-set threaded hole in the upper sealing plate, and is fixed and preload adjusted by a fine-tooth height-adjusting threaded shim. The fine-tooth height-adjusting threaded shim and the threaded hole are machined to ensure fitting accuracy. The preload of the fine-tooth height-adjusting threaded shim and the annular pressure sensor is stable within the range of 0.5±0.05MPa. An inertial measurement unit (IMU) is installed on the bottom surface of the upper sealing plate near the rubber body; the displacement sensor is arranged on one side of the rubber body; the output terminals of the annular pressure sensor, the inertial measurement unit (IMU), and the displacement sensor are all connected to the data acquisition and transmission module, which is then connected to the cloud server and the monitoring platform in sequence.

[0008] Furthermore, the length of each spot weld between the fine-tooth height-adjusting threaded shim and the edge of the threaded hole on the upper sealing plate is 3~5mm, which is used to prevent the fine-tooth height-adjusting threaded shim from loosening and shifting.

[0009] Furthermore, in cold regions with ambient temperatures ≤-20℃, the sensor cap and fine-pitch height adjustment thread gasket are made of Q355E grade cold-resistant steel; in coastal and high-salt-spray areas, the sensor cap and fine-pitch height adjustment thread gasket are made of Q355NH weathering steel or 304 stainless steel.

[0010] Furthermore, the inertial measurement unit (IMU) is integrated using industrial-grade MEMS (Micro-Electro-Mechanical Systems) technology, and incorporates a 3-axis accelerometer, a 3-axis gyroscope, a microcontroller (MCU), and a communication unit. It uses a 6th-order Kalman filter algorithm to perform noise reduction and fusion processing on the raw data, which is used to accurately measure the acceleration and angular velocity parameters of the moving vehicle.

[0011] Furthermore, the horizontality error of the mounting plane of the inertial measurement unit (IMU) is ≤0.5°; the IMU collects the three-dimensional angular acceleration and angular velocity signals of the support in real time, transmits them to the cloud server through the data acquisition and transmission module, and the monitoring platform calculates the data and displays the angular acceleration of the support in real time, thereby realizing the monitoring of the angular acceleration status of the support.

[0012] Furthermore, the displacement sensor adopts the MPS high-precision wire-type displacement sensor, which converts the mechanical relative displacement of the seismic isolation support into the extension and contraction of the wire, and then converts it into an electrical signal that can be measured, recorded or transmitted through an internal encoder.

[0013] Furthermore, the displacement sensor is fixed to the side of the lower sealing plate by a connecting plate, and one end of the displacement sensor pull wire is fixed to the corresponding position on the bottom surface of the upper sealing plate by a connector. During installation, ensure that the pull wire is perpendicular to the horizontal displacement direction of the seismic isolation support, and control the initial tension of the pull wire within the range of 5~10N to avoid the pull wire being too loose or too tight, which would affect the measurement accuracy.

[0014] Furthermore, when the seismic isolation bearing undergoes horizontal displacement, the displacement sensor collects displacement data in real time, which is then uploaded to the cloud server via the data acquisition and transmission module. The monitoring platform processes the data and displays it on the front-end page, showing the horizontal displacement of the seismic isolation bearing in real time, thereby enabling the monitoring of the horizontal deformation state of the bearing.

[0015] The specific steps for installing the above-mentioned intelligent seismic isolation bearing online monitoring system are as follows: First, the ring pressure sensor, inertial measurement unit (IMU), and displacement sensor in the monitoring system can be either pre-installed in the factory or installed on-site, depending on project requirements. Before installation, the appearance of each sensor should be checked for integrity. For factory pre-installation, the sensor installation, pre-tightening force adjustment, and initial calibration are completed during the production of the seismic isolation bearing. For on-site installation, the sensor is installed and debugged after the seismic isolation bearing is hoisted into place, and on-site calibration must be performed after installation. During installation, the annular pressure sensor needs to be gradually tightened with fine-tooth height adjustment shims to adjust the preload of the annular pressure sensor to 0.5 MPa. Allow it to stand for 15-30 minutes to release the installation stress. After stress release, re-measure the preload. If it is lower than 0.5 MPa, repeat the adjustment steps until the preload stabilizes within the range of 0.5 ± 0.05 MPa after stress release. Once the preload meets the standard, symmetrical spot welding is performed between the annular pressure sensor and the edge of the threaded hole on the upper sealing plate, with each spot weld being 3-5 mm long, to prevent the shims from loosening or shifting. When installing the inertial measurement unit (IMU), it is fixed to the bottom surface of the upper sealing plate near the rubber body with bolts. During installation, it is ensured that the coordinate system of the IMU is strictly aligned with the coordinate system of the seismic isolation support, and the horizontality error of the installation plane is ≤0.5°. When installing the displacement sensor, it is first fixed to the side of the lower sealing plate by a connecting plate. Then, one end of the displacement sensor pull wire is fixed to the corresponding position on the bottom surface of the upper sealing plate by a connector. During installation, ensure that the pull wire is perpendicular to the horizontal displacement direction of the seismic isolation support. The initial tension of the pull wire is controlled within the range of 5~10N to avoid the pull wire being too loose or too tight, which would affect the measurement accuracy. Secondly, avoid violent impacts and pulling on the cables connecting the sensors during installation; all cables should be waterproof and flame-retardant, neatly routed and securely fixed, and the joints should be waterproofed and sealed. Finally, the outputs of the ring pressure sensor, inertial measurement unit (IMU), and displacement sensor are sequentially connected to the data acquisition and transmission module, the cloud server, and the monitoring platform. The ring pressure sensor collects vertical pressure data from the seismic isolation bearing in real time. After preprocessing by the data acquisition module in the data acquisition and transmission module, the data is uploaded to the cloud server via a wireless transmission module. The monitoring platform dynamically displays the real-time stress status and historical data, enabling continuous monitoring of the vertical stress state of the seismic isolation bearing. The IMU collects the three-dimensional angular acceleration and angular velocity signals of the bearing in real time. This data is transmitted to the cloud server via the data acquisition and transmission module. The monitoring platform calculates the data and displays the angular acceleration of the bearing in real time, enabling monitoring of the bearing's angular acceleration state. The displacement sensor collects displacement data in real time, which is then uploaded to the cloud server via the data acquisition and transmission module. The monitoring platform processes the data and displays it on the front-end page, showing the horizontal displacement of the seismic isolation bearing in real time, thereby monitoring the horizontal deformation state of the bearing. In addition, the online monitoring system should be fully integrated and debugged before being put into use to ensure that the data acquisition, transmission and display of each sensor are normal. This online monitoring system adopts 2.4GHz / Sub-1GHz dual-band adaptive frequency hopping transmission technology, which can detect the intensity of surrounding channel interference in real time and automatically switch to the optimal communication frequency band to avoid electromagnetic interference and metal shielding problems. It ensures that multi-source monitoring data is uploaded to the cloud server simultaneously, and finally, the data storage, visualization display, data analysis and early warning push are completed on the intelligent platform.

[0016] The monitoring method of the above-mentioned intelligent seismic isolation bearing online monitoring system includes three linked modules: a sixth-order Kalman filter spatiotemporal synchronous fusion algorithm, a multi-parameter coupled slip early warning algorithm, and an IMU displacement online drift calibration algorithm. (I) Sixth-order Kalman filter spatiotemporal synchronization fusion algorithm The sixth-order Kalman filter spatiotemporal synchronization fusion algorithm constructs a six-dimensional state equation to synchronously reduce noise, synchronize and weightedly fuse triaxial angular acceleration, vertical pressure and bidirectional horizontal displacement, unify data timing, improve overall measurement accuracy, and solve the problems of timing misalignment, environmental noise interference and incompatibility of dimensions in multi-source data. The calculation formula is as follows: State prediction equation:

[0017] Observational update equation:

[0018] Optimal estimation equation for filtering:

[0019] in, The state vector consists of six parameters that correspond sequentially to the angular acceleration along the X-axis of the support. α x Y-axis angular acceleration α y Z-axis angular acceleration α z Vertical bearing pressure F v X-axis horizontal displacement D x Y-axis horizontal displacement D y ; This is the theoretical prediction value for the current moment, calculated based on the previous sampling period; This is the optimal true state value for this sampling period, output after correction based on measured data. k The sampling period; A It is a 6×6 order state transition matrix, used to characterize the parameter iteration law of the seismic isolation bearing under low-speed vibration conditions; For the control matrix, This is an external active control input; since this online monitoring system has no active control components, it is set to zero by default. This represents the six-dimensional raw observation vector acquired in real time by the sensor. H This is the observation mapping matrix, used to establish the correspondence between theoretical state values ​​and sensor measured data; K kThe dynamic Kalman gain matrix adaptively assigns weights to predicted and measured values. W, V These are process noise and observation noise, respectively, used to compensate for random errors caused by temperature disturbances, electromagnetic interference, and structural micro-vibrations. The sixth-order Kalman filter spatiotemporal synchronization fusion algorithm runs iteratively with a fixed sampling period. First, it predicts the current theoretical operating parameters of the seismic isolation bearing based on historical state data. Then, it compares the measured data collected in real time by each sensor and automatically corrects the error through a dynamic Kalman gain matrix. Finally, it outputs standardized six-dimensional data with noise reduction and time synchronization, providing accurate data support for damage early warning, displacement calibration, and condition rating. (II) Multi-parameter coupled slip early warning algorithm The multi-parameter coupled slip early warning algorithm combines the dual characteristics of vertical force change and three-dimensional torsional vibration to construct a dual-condition coupled judgment mechanism, avoiding the problems of false alarms and missed alarms in single-parameter early warning; The core calculation formula is as follows: Formula for calculating triaxial resultant angular acceleration:

[0020] Critical slip comprehensive judgment criteria:

[0021] in, α peak The peak value of the overall composite angular acceleration of the support is used to characterize the overall torsional vibration intensity and micro-shear deformation of the support. The instantaneous rate of change of vertical force is used to characterize the amplitude of sudden changes in vertical force on the support. T f The safety threshold for the rate of change of vertical force. The threshold values ​​are the synthetic angular acceleration warning thresholds; both threshold values ​​can be adaptively calibrated according to the bearing specifications, building seismic fortification intensity, and site seismic characteristics to adapt to various engineering scenarios. The multi-parameter coupled slip early warning algorithm reads the standardized parameters after Kalman filtering fusion in the above-mentioned sixth-order Kalman filter spatiotemporal synchronous fusion algorithm, and calculates the instantaneous rate of change of vertical force and the peak value of the composite angular acceleration respectively. The system sets dual judgment conditions. Only when both parameters exceed the preset safety threshold at the same time is the seismic isolation bearing determined to have entered a slip critical danger state and a graded early warning is triggered. Exceeding the limit of a single parameter will not trigger an early warning, effectively reducing the probability of false alarms caused by environmental interference. (III) Online IMU Displacement Drift Calibration Algorithm IMUs rely on quadratic integral acceleration to calculate horizontal displacement. However, during long-term service, they are susceptible to temperature drift and component bias errors, which can lead to distortion and shift in displacement data. The IMU online displacement drift calibration algorithm uses high-precision measured data from displacement sensors as the true value to build a dynamic iterative correction model, enabling all-weather online calibration of displacement parameters without the need for downtime for disassembly and calibration. The core calculation formula is as follows: Accurate displacement formula after calibration:

[0022] Iterative update formula for drift coefficient:

[0023] in, The final high-precision calibration displacement value output by the algorithm; The original displacement value is obtained from the second integral calculation of the IMU acceleration. The true value of the measured displacement acquired by the displacement sensor; The dynamic drift correction coefficient is updated iteratively based on changes in error. This is the iteration step size, used to control the convergence speed of the correction coefficients; The displacement error difference between two adjacent sampling periods serves as the core basis for coefficient iterative updates; When the IMU displacement online drift calibration algorithm is running, the system compares the error difference between the IMU integral displacement and the displacement sensor wire measured displacement in real time, and dynamically updates the drift correction coefficient. The offset error caused by temperature drift and zero drift is offset by a linear correction formula. As the number of iterations increases, the correction coefficient gradually converges, taking into account both short-term instantaneous measurement accuracy and long-term service stability of the equipment.

[0024] By adopting the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art: This invention addresses the problems of traditional seismic isolation bearings, such as the difficulty of manual inspection, inability to monitor core parameters in real time, easy damage to sensors, low data accuracy, and frequent false alarms and missed alarms. It adopts an external modular sensor architecture, integrating a ring pressure sensor, an IMU inertial measurement unit, and a wire-type displacement sensor, which can simultaneously monitor the vertical load, three-dimensional angular acceleration, and horizontal displacement of the bearing.

[0025] It features a dedicated pre-tightening and anti-loosening installation process, distinguishing between factory pre-installation and on-site installation modes. Through precise pre-tightening, stress release, spot welding to prevent loosening, and regional material selection, it takes into account both standardized construction and adaptability to complex environments such as high and low temperatures and salt spray. Moreover, the external design does not damage the original mechanical properties of the support, and it is convenient to disassemble and maintain.

[0026] The system is equipped with three core algorithms: a sixth-order Kalman filter for multi-source data noise reduction, time-series synchronization, and fusion calculation; a multi-parameter coupling early warning mechanism that combines force change and torsional vibration indicators to significantly reduce the probability of false alarms and missed alarms; and an IMU online drift calibration algorithm that automatically corrects long-term measurement errors without system downtime. Simultaneously, dual-band adaptive frequency hopping transmission technology is employed to effectively resist electromagnetic interference and metal shielding, ensuring stable data upload.

[0027] The entire system combines cloud servers and monitoring platforms to achieve data visualization, full-cycle storage, remote monitoring and hierarchical early warning. It can predict safety hazards such as bearing slippage, off-center loading and aging in real time, transforming traditional post-event investigation into proactive prevention and control, comprehensively improving the operational safety of building seismic isolation systems, reducing the construction and operation and maintenance costs throughout the entire life cycle, and is applicable to all types of new and existing seismic isolation buildings.

[0028] All sensors in this invention are externally mounted, which will not damage the rubber body and mechanical properties of the support. They also support independent disassembly and quick replacement, making maintenance more convenient. Attached Figure Description

[0029] Figure 1 This is a three-dimensional schematic diagram of the sensor assembly of the intelligent seismic isolation bearing online monitoring system of the present invention; Figure 2 This is a schematic diagram of the connection of the various components of the monitoring system of the present invention. Detailed Implementation

[0030] The following is in conjunction with the appendix Figure 1-2 The present invention will be further described in detail below to facilitate a clear understanding of the invention, but these descriptions do not constitute a limitation thereof.

[0031] In the description of this invention, it should be noted that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0032] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0033] Example 1 like Figure 1 As shown in this embodiment, an intelligent online monitoring system for seismic isolation bearings is a multi-parameter fusion intelligent online monitoring system for seismic isolation bearings. It integrates force, angular acceleration, and displacement monitoring functions on the basis of seismic isolation bearings. It consists of an upper sealing plate 1, a rubber body 2, a lower sealing plate 3, and a monitoring system 4. It is used to simultaneously monitor the vertical load transmitted from the superstructure to the lower foundation, as well as the angular acceleration and horizontal displacement of the seismic isolation bearings. The monitoring system 4 consists of a ring pressure sensor 41, an inertial measurement unit (IMU) 42, a displacement sensor 43, a data acquisition and transmission module 44, a cloud server 45, and a monitoring platform 46.

[0034] like Figure 1 As shown, the top surface of the upper sealing plate 1 has at least one threaded hole for installing the annular pressure sensor 41. The annular pressure sensor 41 is in contact with the rubber body 2 through the sensor cap 411 and bears the force. Simultaneously, the top surface of the upper sealing plate 1 also has a wire groove for laying the sensor transmission line 412 at the tail of the annular pressure sensor 41. The annular pressure sensor 41 is arranged in concentric radial layers with staggered spacing along the rubber layer of the seismic isolation bearing rubber body 2, and measuring points are evenly distributed on the circumference of different radii, fully covering the vertical force distribution area of ​​the seismic isolation bearing. The annular pressure sensor 41 is embedded in the pre-set threaded hole of the upper sealing plate 1, and is fixed and the preload is adjusted by a fine-tooth height-adjusting threaded shim 413. The fine-tooth height-adjusting threaded shim 413 and the threaded hole are machined to ensure fitting accuracy. The preload of the fine-tooth height-adjusting threaded shim 413 and the annular pressure sensor 41 is stable within the range of 0.5±0.05MPa. In this embodiment, the length of each spot weld between the fine-pitch height-adjusting threaded washer 413 and the edge of the threaded hole in the upper sealing plate 1 is 3-5 mm, which is used to prevent the fine-pitch height-adjusting threaded washer 413 from loosening or shifting. Furthermore, in cold regions with ambient temperatures ≤-20℃, both the sensor cap 411 and the fine-pitch height-adjusting threaded washer 413 are made of Q355E grade cold-resistant steel. In coastal and high-salt-spray areas, the sensor cap 411 and the fine-pitch height-adjusting threaded washer 413 are made of Q355NH weathering steel or 304 stainless steel.

[0035] like Figure 1As shown, an inertial measurement unit (IMU) 42 is also installed on the bottom surface of the upper sealing plate 1 near the rubber body 2. The IMU 42 is integrated using industrial-grade MEMS (Micro-Electro-Mechanical Systems) technology, incorporating a 3-axis accelerometer, a 3-axis gyroscope, a microcontroller (MCU), and a communication unit. It uses a 6th-order Kalman filter algorithm to reduce noise and fuse the raw data, enabling accurate measurement of the acceleration and angular velocity parameters of the moving carrier. The horizontality error of the IMU 42's mounting plane is ≤0.5°. The IMU 42 acquires the three-dimensional angular acceleration and angular velocity signals of the support in real time, transmitting them to the cloud server 45 via the data acquisition and transmission module 44. The monitoring platform 46 processes the data and displays the angular acceleration of the support in real time, thus realizing the monitoring of the support's angular acceleration status.

[0036] like Figure 1 As shown, displacement sensor 43 is arranged on one side of rubber body 2. In this embodiment, displacement sensor 43 is an MPS high-precision wire-type displacement sensor, which converts the mechanical relative displacement of the seismic isolation bearing into the extension and contraction of the wire, and then converts it into a measurable, recordable or transmittable electrical signal through an internal encoder. Displacement sensor 43 is fixed to the side of lower sealing plate 3 by a connecting plate, and one end of the wire 431 of displacement sensor 43 is fixed to the corresponding position on the bottom surface of upper sealing plate 1 by a connector. During installation, ensure that the wire 431 is perpendicular to the horizontal displacement direction of the seismic isolation bearing, and control the initial tension of the wire 431 within the range of 5~10N to avoid the wire being too loose or too tight and affecting the measurement accuracy. When the seismic isolation bearing undergoes horizontal displacement, displacement sensor 43 collects displacement data in real time, uploads it to cloud server 45 through data acquisition and transmission module 44, and the monitoring platform 46 processes the data and displays it on the front-end page, showing the horizontal displacement of the seismic isolation bearing in real time, thereby realizing the monitoring of the horizontal deformation state of the bearing.

[0037] like Figure 1 As shown, the output terminals of the ring pressure sensor 41, the inertial measurement unit (IMU) 42, and the displacement sensor 43 are all connected to the data acquisition and transmission module 44, which in turn is connected to the cloud server 45 and the monitoring platform 46.

[0038] Example 2 The installation method of the intelligent seismic isolation bearing online monitoring system in Embodiment 1 above includes the following specific steps: First, the ring pressure sensor 41, inertial measurement unit (IMU) 42, and displacement sensor 43 in monitoring system 4 can be either pre-installed in the factory or installed on-site, depending on project requirements. Before installation, the appearance of each sensor should be checked for integrity. For factory pre-installation, sensor installation, pre-tightening adjustment, and initial calibration are completed during the production of the seismic isolation bearing. For on-site installation, the sensors are installed and debugged after the seismic isolation bearing is hoisted into place, and on-site calibration must be performed after installation.

[0039] During installation of the annular pressure sensor 41, the fine-tooth height adjustment shim 413 needs to be gradually tightened to adjust the preload of the annular pressure sensor 41 to 0.5MPa. Allow it to stand for 15-30 minutes to release the installation stress. After stress release, re-measure the preload. If it is lower than 0.5MPa, repeat the adjustment steps until the preload stabilizes within the range of 0.5±0.05MPa after stress release. Once the preload meets the standard, symmetrical spot welding is performed between the annular pressure sensor 41 and the edge of the threaded hole in the upper sealing plate 1. Each spot weld is 3-5mm long to prevent the shim from loosening or shifting.

[0040] When installing the inertial measurement unit (IMU42), it is fixed to the bottom surface of the upper sealing plate 1 near the rubber body 2 with bolts. During installation, ensure that the coordinate system of the IMU42 is strictly aligned with the coordinate system of the seismic isolation support, and the horizontality error of the installation plane is ≤0.5°.

[0041] When installing the displacement sensor 43, it is first fixed to the side of the lower sealing plate 3 using a connecting plate. Then, one end of the pull wire 431 of the displacement sensor 43 is fixed to the corresponding position on the bottom surface of the upper sealing plate 1 using a connector. During installation, ensure that the pull wire 431 is perpendicular to the horizontal displacement direction of the seismic isolation support. The initial tension of the pull wire 431 should be controlled within the range of 5~10N to avoid the pull wire being too loose or too tight, which would affect the measurement accuracy.

[0042] Secondly, avoid violent impacts and pulling on the cables connecting the sensors during installation. All cables should be waterproof and flame-retardant, neatly routed and securely fixed, with waterproof seals applied to the joints.

[0043] Finally, the outputs of the ring pressure sensor 41, inertial measurement unit (IMU) 42, and displacement sensor 43 are sequentially connected to the data acquisition and transmission module 44, the cloud server 45, and the monitoring platform 46. The ring pressure sensor 41 collects vertical pressure data from the seismic isolation bearing in real time. After preprocessing by the data acquisition module in the data acquisition and transmission module 44, the data is uploaded to the cloud server 45 via a wireless transmission module. The monitoring platform 46 then dynamically displays the real-time stress status and historical data, enabling continuous monitoring of the vertical stress state of the seismic isolation bearing. The IMU 42 collects the three-dimensional angular acceleration and angular velocity signals of the bearing in real time. This data is transmitted to the cloud server 45 via the data acquisition and transmission module 44. The monitoring platform 46 calculates the data and displays the angular acceleration of the bearing in real time, enabling monitoring of the bearing's angular acceleration status. The displacement sensor 43 collects displacement data in real time. This data is uploaded to the cloud server 45 via the data acquisition and transmission module 44. The monitoring platform 46 processes the data and displays it on the front-end page, showing the real-time horizontal displacement of the seismic isolation bearing, thus enabling monitoring of the bearing's horizontal deformation status. In addition, a comprehensive commissioning should be conducted before the online monitoring system is put into use to ensure that the data acquisition, transmission, and display of each sensor are normal. This online monitoring system adopts 2.4GHz / Sub-1GHz dual-band adaptive frequency hopping transmission technology, which can detect the intensity of surrounding channel interference in real time and automatically switch to the optimal communication frequency band to avoid electromagnetic interference and metal shielding problems. It ensures that multi-source monitoring data is uploaded to the cloud server synchronously, and finally, the data storage, visualization, data analysis, and early warning push are completed on the intelligent platform 46.

[0044] Example 3 The monitoring method of the intelligent seismic isolation bearing online monitoring system in Embodiment 1 above includes three major linkage modules for data fusion and calibration: a sixth-order Kalman filter spatiotemporal synchronous fusion algorithm, a multi-parameter coupled slip early warning algorithm, and an IMU displacement online drift calibration algorithm.

[0045] (I) Sixth-order Kalman filter spatiotemporal synchronization fusion algorithm The sixth-order Kalman filter spatiotemporal synchronization fusion algorithm constructs a six-dimensional state equation to synchronously reduce noise, synchronize and weightedly fuse triaxial angular acceleration, vertical pressure and bidirectional horizontal displacement, unify data timing, improve overall measurement accuracy and solve the problems of timing misalignment, environmental noise interference and incompatibility of dimensions in multi-source data.

[0046] The calculation formula is as follows: State prediction equation:

[0047] Observational update equation:

[0048] Optimal estimation equation for filtering:

[0049] in, The state vector consists of six parameters that correspond sequentially to the angular acceleration along the X-axis of the support. α x Y-axis angular acceleration α y Z-axis angular acceleration α z Vertical bearing pressure F v X-axis horizontal displacement D x Y-axis horizontal displacement D y . This is the theoretical prediction value for the current moment calculated based on the previous sampling period. This is the optimal true state value for this sampling period, output after correction based on measured data. k The sampling period.

[0050] A It is a 6×6 order state transition matrix, used to characterize the parameter iteration law of seismic isolation bearings under low-speed vibration conditions. For the control matrix, This is an external active control input. This online monitoring system has no active control components and is set to zero by default. This is the six-dimensional raw observation vector acquired in real time by the sensor.

[0051] H This is the observation mapping matrix, used to establish the correspondence between theoretical state values ​​and sensor measured data. K k It is a dynamic Kalman gain matrix that adaptively assigns weights to predicted and measured values. W, V These are process noise and observation noise, respectively, used to compensate for random errors caused by temperature disturbances, electromagnetic interference, and structural micro-vibrations.

[0052] The sixth-order Kalman filter spatiotemporal synchronous fusion algorithm runs iteratively with a fixed sampling period. First, it predicts the current theoretical operating parameters of the seismic isolation bearings based on historical state data. Then, it compares the data with the real-time measured data collected by various sensors and automatically corrects errors through a dynamic Kalman gain matrix. Finally, it outputs standardized six-dimensional data with noise reduction and time synchronization, providing accurate data support for damage early warning, displacement calibration, and condition rating.

[0053] (II) Multi-parameter coupled slip early warning algorithm The multi-parameter coupled slip early warning algorithm combines the dual characteristics of vertical force change and three-dimensional torsional vibration to construct a dual-condition coupled judgment mechanism, avoiding the problems of false alarms and missed alarms in single-parameter early warning.

[0054] The core calculation formula is as follows: Formula for calculating triaxial resultant angular acceleration:

[0055] Critical slip comprehensive judgment criteria:

[0056] in, α peak The peak value of the overall composite angular acceleration of the support is used to characterize the overall torsional vibration intensity and micro-shear deformation of the support. It represents the instantaneous rate of change of vertical force, used to characterize the amplitude of sudden changes in vertical force on the support. T f The safety threshold for the rate of change of vertical force. The threshold values ​​are for synthetic angular acceleration warning. Both threshold values ​​can be adaptively calibrated based on the bearing specifications, building seismic fortification intensity, and site seismic characteristics to suit various engineering scenarios.

[0057] The multi-parameter coupled slip early warning algorithm reads the standardized parameters after Kalman filtering fusion in the aforementioned sixth-order Kalman filter spatiotemporal synchronous fusion algorithm, and calculates the instantaneous rate of change of vertical force and the peak value of the synthesized angular acceleration, respectively. The system sets dual judgment conditions: only when both parameters simultaneously exceed the preset safety threshold is the seismic isolation bearing determined to have entered a critical slip danger state and a graded early warning is triggered. Exceeding the limit of a single parameter will not trigger an early warning, effectively reducing the probability of false alarms caused by environmental interference.

[0058] (III) Online IMU Displacement Drift Calibration Algorithm The IMU relies on quadratic integral acceleration to calculate horizontal displacement. However, long-term operation makes it susceptible to temperature drift and component bias errors, leading to distortion and shifts in displacement data. The IMU online displacement drift calibration algorithm uses high-precision measured data from the displacement sensor 43 as the true value to build a dynamic iterative correction model, enabling 24 / 7 online calibration of displacement parameters without requiring downtime for disassembly and calibration.

[0059] The core calculation formula is as follows: Accurate displacement formula after calibration:

[0060] Iterative update formula for drift coefficient:

[0061] in, This is the high-precision calibration displacement value output by the algorithm. The original displacement value is obtained from the second integral of the IMU acceleration. The true value of the measured displacement is collected by displacement sensor 43. It is a dynamic drift correction coefficient that is updated iteratively based on changes in error. The iteration step size is used to control the convergence speed of the correction coefficients. The displacement error difference between two adjacent sampling periods serves as the core basis for coefficient iterative updates.

[0062] During the operation of the IMU displacement online drift calibration algorithm, the system compares the error difference between the IMU integral displacement and the displacement measured by the displacement sensor 43 wire in real time, and dynamically updates the drift correction coefficient iteratively. The offset error caused by temperature drift and zero drift is offset by a linear correction formula. As the number of iterations increases, the correction coefficient gradually converges, balancing short-term instantaneous measurement accuracy with long-term service stability of the equipment.

[0063] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the structure of the present invention. The arrangement and quantity of the present invention are not limited to this example and can be optimized according to actual engineering conditions. Any modifications, equivalent changes, and decorations made to the above embodiments based on the technical principles of the present invention, without departing from the scope of the present invention, are still within the scope of the present invention.

Claims

1. An intelligent online monitoring system for seismic isolation bearings, which is a multi-parameter integrated intelligent online monitoring system for seismic isolation bearings, integrating force, angular acceleration, and displacement monitoring functions on the basis of seismic isolation bearings, and consisting of an upper sealing plate (1), a rubber body (2), a lower sealing plate (3), and a monitoring system (4), used to simultaneously monitor the vertical load transmitted from the superstructure to the lower foundation, as well as the angular acceleration and horizontal displacement of the seismic isolation bearings, characterized in that: The monitoring system (4) consists of a ring pressure sensor (41), an inertial measurement unit (IMU) (42), a displacement sensor (43), a data acquisition and transmission module (44), a cloud server (45), and a monitoring platform (46); The top surface of the upper sealing plate (1) has at least one threaded hole for installing an annular pressure sensor (41). The annular pressure sensor (41) is attached to the rubber body (2) and subjected to force through a sensor cap (411). At the same time, the top surface of the upper sealing plate (1) also has a wire groove for laying the sensor transmission line (412) at the tail of the annular pressure sensor (41). The annular pressure sensor (41) is arranged in a concentric radial layered staggered manner along the rubber layer of the seismic isolation bearing rubber body (2), and measuring points are evenly arranged on the circumference of different radii to fully cover the vertical force distribution area of ​​the seismic isolation bearing. The annular pressure sensor (41) is embedded in the threaded hole of the upper sealing plate (1), and the fixing and preload adjustment are achieved by a fine-tooth height-adjusting threaded washer (413). The fine-tooth height-adjusting threaded washer (413) and the threaded hole are machined to ensure the fitting accuracy. The preload of the fine-tooth height-adjusting threaded washer (413) and the annular pressure sensor (41) is stable at 0.5±0.05MPa. Within the range; An inertial measurement unit (IMU) (42) is installed on the bottom surface of the upper sealing plate (1) near the rubber body (2); the displacement sensor (43) is arranged on one side of the rubber body (2); the output ends of the annular pressure sensor (41), the inertial measurement unit (IMU) (42), and the displacement sensor (43) are all connected to the data acquisition and transmission module (44), and the data acquisition and transmission module (44) is then connected to the cloud server (45) and the monitoring platform (46) in sequence.

2. The intelligent seismic isolation bearing online monitoring system according to claim 1, characterized in that: The length of each spot weld between the fine-tooth height-adjusting threaded gasket (413) and the edge of the threaded hole of the upper sealing plate (1) is 3~5mm, which is used to prevent the fine-tooth height-adjusting threaded gasket (413) from loosening and shifting.

3. The intelligent online monitoring system for seismic isolation bearings according to claim 2, characterized in that: In cold regions with ambient temperatures ≤-20℃, the sensor cap (411) and fine-pitch height adjustment threaded gasket (413) are made of Q355E grade cold-resistant steel; in coastal and high-salt-spray areas, the sensor cap (411) and fine-pitch height adjustment threaded gasket (413) are made of Q355NH weathering steel or 304 stainless steel.

4. The intelligent online monitoring system for seismic isolation bearings according to claim 1, characterized in that: The inertial measurement unit (IMU) (42) is integrated using industrial-grade MEMS microelectromechanical technology. It has a built-in 3-axis accelerometer, 3-axis gyroscope, microcontroller (MCU) and communication unit. The original data is denoised and fused using a 6th-order Kalman filter algorithm to accurately measure the acceleration and angular velocity parameters of the moving vehicle.

5. The intelligent seismic isolation bearing online monitoring system according to claim 4, characterized in that: The inertial measurement unit (IMU) (42) has a horizontality error of ≤0.5° on the mounting plane. The inertial measurement unit (IMU) (42) collects the three-dimensional angular acceleration and angular velocity signals of the support in real time, and transmits them to the cloud server (45) through the data acquisition and transmission module (44). The monitoring platform (46) calculates the data and displays the angular acceleration of the support in real time, thereby realizing the monitoring of the angular acceleration status of the support.

6. The intelligent online monitoring system for seismic isolation bearings according to claim 1, characterized in that: The displacement sensor (43) adopts the MPS high-precision wire-type displacement sensor, which converts the mechanical relative displacement of the seismic isolation support into the extension and contraction of the wire, and then converts it into an electrical signal that can be measured, recorded or transmitted through an internal encoder.

7. The intelligent online monitoring system for seismic isolation bearings according to claim 1, characterized in that: The displacement sensor (43) is fixed to the side of the lower sealing plate (3) by a connecting plate. One end of the pull wire (431) of the displacement sensor (43) is fixed to the corresponding position on the bottom surface of the upper sealing plate (1) by a connector. During installation, ensure that the pull wire (431) is perpendicular to the horizontal displacement direction of the seismic isolation support. The initial tension of the pull wire (431) is controlled within the range of 5~10N to avoid the pull wire from being too loose or too tight and affecting the measurement accuracy.

8. The intelligent seismic isolation bearing online monitoring system according to claim 7, characterized in that: When the seismic isolation bearing undergoes horizontal displacement, the displacement sensor (43) collects displacement data in real time, and uploads it to the cloud server (45) through the data acquisition and transmission module (44). The monitoring platform (46) processes the data and displays it on the front-end page, showing the horizontal displacement of the seismic isolation bearing in real time, thereby realizing the monitoring of the horizontal deformation state of the bearing.

9. An installation method for an intelligent online monitoring system for seismic isolation bearings according to any one of claims 1-8, characterized in that, The specific steps are as follows: First, the ring pressure sensor (41), inertial measurement unit (IMU) (42), and displacement sensor (43) in the monitoring system (4) can be either pre-installed in the factory or installed on-site according to the project requirements. Before installation, the appearance of each sensor should be checked for integrity. For factory pre-installation, the sensor installation, pre-tightening force adjustment, and initial calibration are completed during the production process of the seismic isolation bearing. For on-site installation, the sensor is installed and debugged after the seismic isolation bearing is hoisted into place. On-site calibration must be performed after installation. When installing the annular pressure sensor (41), the fine-tooth height adjustment shim (413) needs to be tightened gradually to adjust the preload of the annular pressure sensor (41) to 0.5MPa. After standing for 15 to 30 minutes to release the installation stress, the preload is retested after the stress is released. If it is lower than 0.5MPa, the adjustment steps are repeated until the preload stabilizes within the range of 0.5±0.05MPa after the stress is released. After the preload meets the standard, symmetrical spot welding is performed on the edge of the threaded hole of the annular pressure sensor (41) and the upper sealing plate (1). The length of each spot welding is 3 to 5mm to prevent the shim from loosening and shifting. When the inertial measurement unit (IMU) (42) is installed, it is fixed to the bottom surface of the upper sealing plate (1) near the rubber body (2) by bolts. During installation, the coordinate system of the inertial measurement unit (IMU) (42) is strictly aligned with the coordinate system of the seismic isolation support, and the horizontality error of the installation plane is ≤0.5°. When installing the displacement sensor (43), it is first fixed to the side of the lower sealing plate (3) by the connecting plate. Then, one end of the pull wire (431) of the displacement sensor (43) is fixed to the corresponding position on the bottom surface of the upper sealing plate (1) by the connector. During installation, ensure that the pull wire (431) is perpendicular to the horizontal displacement direction of the seismic isolation support. The initial tension of the pull wire (431) is controlled within the range of 5~10N to avoid the pull wire from being too loose or too tight and affecting the measurement accuracy. Secondly, avoid violent impacts and pulling on the cables connecting the sensors during installation; all cables should be waterproof and flame-retardant, neatly routed and securely fixed, and the joints should be waterproofed and sealed. Finally, the outputs of the ring pressure sensor (41), inertial measurement unit (IMU) (42), and displacement sensor (43) are sequentially connected to the data acquisition and transmission module (44), the cloud server (45), and the monitoring platform (46). The ring pressure sensor (41) collects vertical pressure data of the seismic isolation bearing in real time. After preprocessing by the data acquisition module in the data acquisition and transmission module (44), the data is uploaded to the cloud server (45) via a wireless transmission module. The real-time force condition and historical data are then dynamically displayed on the monitoring platform (46), enabling continuous monitoring of the vertical force state of the seismic isolation bearing. The inertial measurement unit (IMU) (42) collects the three-dimensional angular acceleration and angular velocity signals of the bearing in real time. These signals are transmitted to the cloud server (45) via the data acquisition and transmission module (44). The monitoring platform (46) calculates the data and displays the bearing's... Angular acceleration is used to monitor the angular acceleration state of the support; the displacement sensor (43) collects displacement data in real time, and uploads it to the cloud server (45) through the data acquisition and transmission module (44). The monitoring platform (46) processes the data and displays it on the front-end page, showing the horizontal displacement of the seismic isolation support in real time, thereby realizing the monitoring of the horizontal deformation state of the support; in addition, the online monitoring system should be fully integrated before it is put into use to ensure that the data acquisition, transmission and display of each sensor are normal; this online monitoring system adopts 2.4GHz / Sub-1GHz dual-band adaptive frequency hopping transmission technology, which can detect the surrounding channel interference intensity in real time, automatically switch the optimal communication frequency band, and avoid electromagnetic interference and metal shielding problems; it ensures that multi-source monitoring data is uploaded to the cloud server in a synchronous manner, and finally completes data storage, visualization display, data analysis and early warning push on the intelligent platform (46).

10. A monitoring method for an intelligent online monitoring system for seismic isolation bearings according to any one of claims 1-8, characterized in that, The data fusion and calibration in the monitoring method includes three interconnected modules: a sixth-order Kalman filter spatiotemporal synchronization fusion algorithm, a multi-parameter coupled slip early warning algorithm, and an IMU displacement online drift calibration algorithm. (I) Sixth-order Kalman filter spatiotemporal synchronization fusion algorithm The sixth-order Kalman filter spatiotemporal synchronization fusion algorithm constructs a six-dimensional state equation to synchronously reduce noise, synchronize and weightedly fuse triaxial angular acceleration, vertical pressure and bidirectional horizontal displacement, unify data timing, improve overall measurement accuracy, and solve the problems of timing misalignment, environmental noise interference and incompatibility of dimensions in multi-source data. The calculation formula is as follows: State prediction equation: Observational update equation: Optimal estimation equation for filtering: in, The state vector consists of six parameters that correspond sequentially to the angular acceleration along the X-axis of the support. α x Y-axis angular acceleration α y Z-axis angular acceleration α z Vertical bearing pressure F v X-axis horizontal displacement D x Y-axis horizontal displacement D y ; This is the theoretical prediction value for the current moment, calculated based on the previous sampling period; This is the optimal true state value for this sampling period, output after correction based on measured data. k The sampling period; A It is a 6×6 order state transition matrix, used to characterize the parameter iteration law of the seismic isolation bearing under low-speed vibration conditions; For the control matrix, This is an external active control input; since this online monitoring system has no active control components, it is set to zero by default. This represents the six-dimensional raw observation vector acquired in real time by the sensor. H This is the observation mapping matrix, used to establish the correspondence between theoretical state values ​​and sensor measured data; K k The dynamic Kalman gain matrix adaptively assigns weights to predicted and measured values. W, V These are process noise and observation noise, respectively, used to compensate for random errors caused by temperature disturbances, electromagnetic interference, and structural micro-vibrations. The sixth-order Kalman filter spatiotemporal synchronization fusion algorithm runs iteratively with a fixed sampling period. First, it predicts the current theoretical operating parameters of the seismic isolation bearing based on historical state data. Then, it compares the measured data collected in real time by each sensor and automatically corrects the error through a dynamic Kalman gain matrix. Finally, it outputs standardized six-dimensional data with noise reduction and time synchronization, providing accurate data support for damage early warning, displacement calibration, and condition rating. (II) Multi-parameter coupled slip early warning algorithm The multi-parameter coupled slip early warning algorithm combines the dual characteristics of vertical force change and three-dimensional torsional vibration to construct a dual-condition coupled judgment mechanism, avoiding the problems of false alarms and missed alarms in single-parameter early warning; The core calculation formula is as follows: Formula for calculating triaxial resultant angular acceleration: Critical slip comprehensive judgment criteria: in, α peak The peak value of the overall composite angular acceleration of the support is used to characterize the overall torsional vibration intensity and micro-shear deformation of the support. The instantaneous rate of change of vertical force is used to characterize the amplitude of sudden changes in vertical force on the support. T f The safety threshold for the rate of change of vertical force. The threshold values ​​are the synthetic angular acceleration warning thresholds; both threshold values ​​can be adaptively calibrated according to the bearing specifications, building seismic fortification intensity, and site seismic characteristics to adapt to various engineering scenarios. The multi-parameter coupled slip early warning algorithm reads the standardized parameters after Kalman filtering fusion in the above-mentioned sixth-order Kalman filter spatiotemporal synchronous fusion algorithm, and calculates the instantaneous rate of change of vertical force and the peak value of the composite angular acceleration respectively. The system sets dual judgment conditions. Only when both parameters exceed the preset safety threshold at the same time is the seismic isolation bearing determined to have entered a slip critical danger state and a graded early warning is triggered. Exceeding the limit of a single parameter will not trigger an early warning, effectively reducing the probability of false alarms caused by environmental interference. (III) Online IMU Displacement Drift Calibration Algorithm The IMU relies on the second integral of acceleration to calculate horizontal displacement. During long-term service, it is susceptible to temperature drift and component zero-bias error, which leads to distortion and deviation of displacement data. The IMU displacement online drift calibration algorithm uses the high-precision measured data of displacement sensor (43) as the true value to build a dynamic iterative correction model, realizes all-weather online calibration of displacement parameters, and does not require shutdown for disassembly and calibration. The core calculation formula is as follows: Accurate displacement formula after calibration: Iterative update formula for drift coefficient: in, The final high-precision calibration displacement value output by the algorithm; The original displacement value is obtained from the second integral calculation of the IMU acceleration. The true value of the measured displacement collected by the displacement sensor (43); The dynamic drift correction coefficient is updated iteratively based on changes in error. This is the iteration step size, used to control the convergence speed of the correction coefficients; The displacement error difference between two adjacent sampling periods serves as the core basis for coefficient iterative updates; When the IMU displacement online drift calibration algorithm is running, the system compares the error difference between the IMU integral displacement and the displacement measured by the displacement sensor (43) in real time, and dynamically updates the drift correction coefficient. The offset error caused by temperature drift and zero drift is offset by the linear correction formula. As the number of iterations increases, the correction coefficient gradually converges, taking into account both the short-term instantaneous measurement accuracy and the long-term service stability of the equipment.

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