A Real-Time Mechanical Calibration Data Monitoring System Based on Multiple Sensors

Abstract

This invention provides a real-time mechanical calibration data monitoring system based on multiple sensors, belonging to the field of data monitoring technology. It includes a system hardware architecture and a system software architecture. The system hardware architecture includes a sensor module for collecting data, a data acquisition module for data acquisition, a data calibration unit for data calibration, a data processing and analysis module for data processing, and a data storage and communication module for data storage and transmission. The data calibration unit includes static calibration data acquisition, dynamic calibration data acquisition, and environmental compensation data acquisition. The system software architecture includes a system configuration and calibration interface, a real-time monitoring dashboard, and a data analysis and report generation module. By setting up multi-sensor fusion and complementarity, coupled with a rigorous calibration process, the system effectively reduces the error of a single sensor, improves the reliability of measurement data, and meets the requirements of high-precision mechanical monitoring.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, specifically to a real-time mechanical calibration data monitoring system based on multiple sensors. Background Technology

[0002] In numerous fields of scientific research, industrial production, and engineering construction, the precise measurement of mechanical quantities plays a crucial role. For example, in the aerospace field, the development of aircraft engines requires precise monitoring of the complex stresses and vibrations experienced by the blades to ensure the reliability and safety of the engine under extreme conditions such as high-speed operation and high temperature and pressure. In the automotive manufacturing industry, vehicle crash testing, mechanical performance evaluation of chassis suspension systems, and torque monitoring of power transmission components are all directly related to the overall performance, driving comfort, and safety of automobiles. In the design, construction, operation, and maintenance phases of civil engineering structures such as large bridges and high-rise buildings, the real-time and accurate measurement of mechanical parameters such as gravity, wind force, and seismic force acting on the structure is an important basis for ensuring structural stability and durability. In the field of precision machining, the precise control of mechanical quantities such as cutting force and grinding force is of decisive significance for ensuring machining accuracy, optimizing machining processes, and extending tool life.

[0003] In order to meet the needs of mechanical measurement, some existing monitoring systems have attempted to use a combination of multiple sensors, but these systems still have a series of problems;

[0004] In terms of data acquisition, many existing systems cannot achieve high-precision synchronous acquisition of multiple sensors. Due to the differences in response time, sampling frequency and other characteristics of different sensors, if data cannot be acquired in a precise and synchronous manner, the spatiotemporal consistency between the data from multiple sensors will be difficult to guarantee, which will affect the accuracy of subsequent comprehensive analysis of the mechanical system. For example, when monitoring the vibration and force of a high-speed rotating mechanical component at the same time, if the force sensor and the acceleration sensor acquire data at different times, it will be impossible to accurately determine the causal relationship between force and vibration and the real-time response characteristics.

[0005] In the data calibration stage, existing calibration methods are not perfect or comprehensive enough. Some systems only perform simple static calibration, ignoring the calibration of the dynamic response characteristics of sensors in dynamic force measurement scenarios. In actual engineering, many mechanical phenomena are dynamically changing, such as impact and vibration. The dynamic performance of sensors has a crucial impact on the accuracy of measurement results. For example, in earthquake monitoring, if the dynamic calibration of the accelerometer is inaccurate, the true characteristics of seismic waves cannot be accurately recorded, thus affecting the reliability of earthquake early warning and disaster assessment. Moreover, most existing systems lack effective compensation mechanisms for the impact of environmental factors. Environmental factors such as temperature, humidity, and air pressure can change the physical characteristics of sensors and the mechanical properties of the measured object, thereby introducing measurement errors. For example, temperature changes can cause changes in the resistance value of strain gauge force sensors, which in turn affects the measurement accuracy. However, many existing systems have failed to fully consider and compensate for the impact of such environmental factors.

[0006] In terms of data processing, storage, and communication, some systems have limited data processing capabilities, which cannot meet the needs of rapid processing of large amounts of data and analysis of complex mechanical models. At the same time, the security and reliability of data storage are insufficient, and the compatibility and transmission rate of communication interfaces are also difficult to meet the requirements of remote monitoring and multi-terminal collaborative work. For example, on large industrial production lines, if the monitoring system cannot process a large amount of mechanical data in a timely manner and reliably transmit it to the remote monitoring center, it will be impossible to achieve real-time and effective control of the production process. Once a mechanical anomaly occurs, it may lead to serious production accidents and economic losses.

[0007] In summary, existing mechanical measurement and monitoring systems have many shortcomings in terms of multi-sensor fusion, data acquisition synchronization, calibration comprehensiveness, environmental adaptability, and data processing, storage, and communication. They are unable to meet the urgent needs of modern scientific research, industrial production, and engineering construction for high-precision, real-time, and reliable mechanical measurement and monitoring. Therefore, developing a real-time mechanical calibration data monitoring system based on multiple sensors has extremely important practical significance and application value. Summary of the Invention

[0008] (a) Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides a real-time mechanical calibration data monitoring system based on multiple sensors. This system solves many deficiencies in existing mechanical measurement and monitoring systems in terms of multi-sensor fusion, data acquisition synchronization, calibration comprehensiveness, environmental adaptability, and data processing, storage, and communication. These deficiencies make it difficult to meet the urgent needs of modern scientific research, industrial production, and engineering construction for high-precision, real-time, and reliable mechanical measurement and monitoring. Therefore, developing a real-time mechanical calibration data monitoring system based on multiple sensors has extremely important practical significance and application value.

[0010] (II) Technical Solution

[0011] To achieve the above objectives, the present invention provides the following technical solution: a real-time mechanical calibration data monitoring system based on multiple sensors, comprising a system hardware architecture and a system software architecture. The system hardware architecture includes a sensor module for collecting data, a data acquisition module for acquiring data, a data calibration unit for calibrating data, a data processing and analysis module for data processing, and a data storage and communication module for data storage and transmission. The data calibration unit includes static calibration data acquisition, dynamic calibration data acquisition, and environmental compensation data acquisition. The system software architecture includes a system configuration and calibration interface, a real-time monitoring dashboard, and a data analysis and report generation module.

[0012] Furthermore, the static calibration data acquisition process includes the following steps: first, zero-point calibration is performed, then single-point calibration is performed, and finally multi-point calibration is performed. The main purpose is to determine the basic characteristic parameters of the sensor under static conditions, such as zero-point offset, sensitivity, linearity, hysteresis, and repeatability. These parameters can reflect the measurement accuracy of the sensor under stable conditions, providing a basis for subsequent accurate measurements.

[0013] Furthermore, the dynamic calibration data acquisition process includes the following steps:

[0014] S1: Identify the calibration equipment and excite the signal source;

[0015] S2: Install and connect the sensors and calibration equipment;

[0016] S3: Set data acquisition parameters;

[0017] S4: Dynamic stimulus and data acquisition;

[0018] S5: Analyze and process the data;

[0019] S6: Determine the dynamic calibration coefficients and error correction;

[0020] S7: Verification and recording of calibration results.

[0021] Furthermore, the dynamic calibration data acquisition mainly focuses on the sensor's response characteristics to input physical quantities that change rapidly over time, including the sensor's response speed, frequency response, and dynamic sensitivity. Dynamic calibration is mainly used to ensure that the sensor can accurately capture and reflect these dynamic information during the measurement of rapidly changing dynamic forces, vibrations, and impacts.

[0022] Furthermore, the environmental compensation data acquisition includes temperature and humidity calibration and other environmental factor calibration. The temperature and humidity calibration involves acquiring the sensor's output signal under different temperature and humidity conditions, establishing a relationship model between temperature and humidity and the sensor output, and then compensating the sensor's measurement results in real time based on the actual temperature and humidity changes in the measurement environment to eliminate the impact of temperature and humidity changes on the sensor's accuracy.

[0023] Furthermore, the dynamic calibration in S1 requires a dedicated dynamic force source or displacement source device and a suitable excitation signal.

[0024] Furthermore, the dynamic calibration in S2 requires that the sensor be firmly installed in a position that can receive dynamic excitation, and that the installation method and force direction of the sensor conform to the actual working conditions. At the same time, the sensor output signal and the standard signal generated by the calibration equipment must be synchronously connected to the data acquisition system.

[0025] Furthermore, the data acquisition parameter settings for dynamic calibration in S3 mainly consider the frequency range of the dynamic excitation signal and the response characteristics of the sensor, requiring a higher sampling frequency and a suitable acquisition duration to fully capture the details and changes of the dynamic signal.

[0026] Furthermore, in S4, during dynamic calibration, the calibration equipment is activated to generate a dynamic excitation signal, causing the sensor to be subjected to dynamic force or displacement. At the same time, the data acquisition system synchronously acquires the standard signal and the sensor's output signal.

[0027] Furthermore, the data analysis for dynamic calibration in S5 involves time-domain and frequency-domain analysis. In the time domain, the dynamic response time and amplitude differences are calculated and compared. In the frequency domain, Fourier transform is performed to calculate the amplitude-frequency characteristics and phase-frequency characteristics.

[0028] (III) Beneficial Effects

[0029] This invention provides a real-time mechanical calibration data monitoring system based on multiple sensors. It has the following beneficial effects:

[0030] 1. In this solution, by setting up multi-sensor fusion and complementarity, and with a strict calibration process, the error of a single sensor is effectively reduced, the reliability of measurement data is improved, and the requirements of high-precision mechanical monitoring are met.

[0031] 2. In this solution, by setting up high-speed synchronous acquisition and rapid data processing, we can ensure the real-time acquisition and calibration analysis of mechanical data, and realize real-time tracking and feedback of dynamically changing force fields, which helps to detect abnormal working conditions in a timely manner.

[0032] 3. In this solution, an environmental compensation mechanism is set up to ensure that the system can work stably under complex temperature, humidity, electromagnetic interference and other conditions, maintain measurement accuracy and broaden the applicable scenarios.

[0033] 4. In this solution, by improving communication and software functions to support remote data interaction, geographical limitations are broken, and centralized management and operation of distributed mechanical monitoring projects are realized. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of a real-time mechanical calibration data monitoring system based on multiple sensors proposed in this invention.

[0035] Figure 2 This is a flowchart of the data calibration unit of a real-time mechanical calibration data monitoring system based on multiple sensors proposed in this invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example:

[0038] like Figure 1-2 As shown, this embodiment of the invention provides a real-time mechanical calibration data monitoring system based on multiple sensors, including a system hardware architecture and a system software architecture. The system hardware architecture includes a sensor module for collecting data, a data acquisition module for acquiring data, a data calibration unit for calibrating data, a data processing and analysis module for data processing, and a data storage and communication module for data storage and transmission. The data calibration unit includes static calibration data acquisition, dynamic calibration data acquisition, and environmental compensation data acquisition. The system software architecture includes a system configuration and calibration interface, a real-time monitoring dashboard, and a data analysis and report generation module.

[0039] The sensor module includes a force sensor, a displacement sensor, an acceleration sensor, and a temperature and humidity sensor;

[0040] Force sensors: High-precision piezoelectric and strain gauge force sensors are selected and configured reasonably according to the measurement range and accuracy requirements. They are distributed at key stress points and are responsible for directly measuring information such as the magnitude and direction of the force. For example, they can be installed at the joints of a robotic arm to detect the load force during operation; or deployed on the bridge support structure to measure the pressure on the piers when vehicles pass.

[0041] Displacement sensors, such as laser displacement sensors and optical grating rulers, measure the displacement changes of an object after it is subjected to force, and assist in the analysis of mechanical behavior. For example, they are used to monitor the elongation of test specimens in material tensile tests and to measure and calculate mechanical performance indicators such as elastic modulus by measuring the force value.

[0042] Accelerometers capture the acceleration of moving objects, indirectly reflecting their stress state. Especially under vibration conditions, they help us understand the dynamic stress and response of structures and can be applied to monitoring the vibration stress of aero-engine blades.

[0043] Temperature and humidity sensor: Since temperature and humidity can affect the accuracy of the sensor and the mechanical properties of the measured object (thermal expansion and contraction change the size, and humidity causes changes in material properties), real-time collection of environmental temperature and humidity data is used for subsequent calibration and compensation.

[0044] The data acquisition module has multi-channel synchronous acquisition capability, adapting to different sensor output signal types (analog voltage, current, digital pulse, etc.). It uses a high-precision ADC (analog-to-digital converter) to quantize and convert analog signals into digital data, and performs protocol parsing and format standardization on digital signals. For example, the 0-10V voltage output of the force sensor is sampled and converted with 16-bit ADC precision to ensure data resolution and accuracy. At the same time, it strictly controls the synchronization error of the acquisition time of each channel to the microsecond level to ensure the spatiotemporal consistency of multi-sensor data, laying the foundation for accurate mechanical analysis.

[0045] The data processing and analysis module utilizes a microprocessor (such as a high-performance ARM processor or an industrial PC) to run data processing algorithms. On one hand, it filters and reduces noise in the calibrated data (using mean filtering, Kalman filtering, etc. to remove measurement noise) and extracts effective mechanical characteristic parameters (such as peak force, mean force, vibration frequency components, etc.). On the other hand, it conducts data analysis based on mechanical theoretical models (constitutive equations of mechanics of materials, finite element models of structural mechanics, etc.) to evaluate the mechanical properties and health status of the tested object, such as determining whether the mechanical structure is overloaded or whether the internal stress of the material exceeds the standard, thus posing a risk of failure.

[0046] The data storage and communication module has built-in storage units and communication interfaces. Storage units are equipped with large-capacity local storage media (hard drives, SD cards, etc.) to save raw collected data, calibration records, and analysis results according to time series, event triggering, and other strategies, facilitating historical review and fault diagnosis. It also supports periodic data cleaning and archiving to optimize storage space utilization. Communication interfaces include Ethernet, Wi-Fi, and RS485, and comply with protocols such as Modbus, TCP / IP, and MQTT to transmit real-time calibration monitoring data to remote monitoring centers and cloud platforms, enabling data sharing and multi-terminal collaborative management. This allows engineers to remotely view and analyze on-site mechanical conditions and issue control commands.

[0047] System configuration and calibration interface: Provides a visual operation interface for users to enter parameters such as sensor model, range, and installation location, start static and dynamic calibration processes, view calibration progress and result reports, and adjust calibration coefficients to ensure that the system is accurately adapted to different measurement tasks;

[0048] Real-time monitoring dashboard: Dynamically displays real-time data and mechanical characteristics of each sensor after calibration using intuitive charts (line graphs, bar graphs, dashboards, etc.), with threshold alarms (such as flashing red when the force exceeds the set safety value), allowing users to grasp the real-time mechanical status of the monitored object at a glance;

[0049] Data analysis and report generation functions: The built-in data analysis toolset can perform in-depth analysis of historical or selected time period data as needed (trend analysis, correlation analysis, etc.) and automatically generate graphic and text-rich mechanical monitoring reports, including data statistics, status assessment conclusions, improvement suggestions, etc., to serve engineering decision-making and operation and maintenance optimization.

[0050] The static calibration data acquisition process includes the following steps: first, zero-point calibration, then single-point calibration, and finally multi-point calibration. The main purpose is to determine the basic characteristic parameters of the sensor under static conditions (when the input physical quantity does not change with time), such as zero-point offset, sensitivity, linearity, hysteresis, and repeatability. These parameters can reflect the measurement accuracy of the sensor under steady-state conditions and provide a basis for subsequent accurate measurements.

[0051] Zero-point calibration involves measuring the sensor's output signal without any input physical quantity (such as force or displacement) and recording this signal value as the zero-point offset. This step is to eliminate non-zero outputs caused by the sensor's own characteristics (such as internal circuit offsets or mechanical prestress) when it is not in operation, ensuring that the output is zero when there is no input. For example, for a force sensor, the tiny voltage value it outputs when no external force is applied is the zero-point offset, which needs to be subtracted in subsequent measurements.

[0052] Single-point calibration involves applying a known and stable standard physical quantity to the sensor, measuring the sensor's output signal, and calculating its sensitivity. Sensitivity is defined as the ratio of the change in the sensor's output signal to the corresponding change in the input physical quantity. For example, if a standard force of 10N is applied to a force sensor with a range of 100N, and the output voltage changes by 1V, the sensitivity is 0.1V / N. This step can preliminarily determine the sensor's measurement gain characteristics.

[0053] Multi-point calibration involves selecting multiple different standard physical quantity values ​​(typically including 0%, 20%, 40%, 60%, 80%, 100% of the range) across the entire measurement range of the sensor. These standard physical quantities are applied sequentially, and the corresponding sensor output signals are acquired. Through data fitting methods (such as least squares), the best-fit straight line or curve equation between the sensor output and the input physical quantity is obtained. This is used to determine the static characteristic indicators of the sensor, such as linearity, hysteresis, and repeatability. For example, by applying different forces multiple times and recording the output voltage of the force sensor, an input-output curve is plotted, and the degree of deviation of the curve from the ideal straight line is observed to evaluate linearity.

[0054] Static calibration data acquisition has relatively relaxed environmental requirements. The main requirement is to maintain relatively stable ambient temperature and humidity to reduce the impact of environmental factors on the calibration results. Generally, the temperature should be controlled at 20℃-25℃ and the humidity at 40%-60%. This is because the input physical quantities are stable during static calibration, and the impact of environmental factors on the calibration results is relatively small and easy to control.

[0055] The process of dynamic calibration data acquisition includes the following steps:

[0056] S1: Determine the calibration equipment and excitation signal source. Dynamic calibration requires specialized dynamic force or displacement source equipment and a suitable excitation signal.

[0057] Calibration equipment: Dynamic calibration requires the use of standard dynamic force or displacement source equipment, such as electromagnetic exciters, Hopkinson bars (for dynamic force calibration under high strain rates), etc. These devices can generate dynamic force or displacement signals with known characteristics as a reference for calibration. At the same time, a high-precision data acquisition system is also required to synchronously acquire standard signals and output signals of the sensor being calibrated.

[0058] Excitation signal source: Select an appropriate excitation signal according to the actual application scenario and sensor type. For example, for acceleration sensors that measure mechanical vibration, sine wave excitation signals are often used to simulate actual vibration conditions. For impact force measurement scenarios, such as in automobile crash tests, half-sine wave or rectangular pulse impact pulse signals are used as excitation. The frequency, amplitude, duration and other parameters of these excitation signals should be set according to the sensor's working range and calibration requirements.

[0059] S2: Install and connect the sensor and calibration equipment. Dynamic calibration requires that the sensor be firmly installed in a position that can receive dynamic excitation, and that the installation method and force direction of the sensor be consistent with the actual working conditions. At the same time, the sensor output signal and the standard signal generated by the calibration equipment should be synchronously connected to the data acquisition system.

[0060] The sensor to be calibrated (such as a force sensor or accelerometer) should be securely mounted in a location that can receive dynamic excitation, ensuring that the sensor's mounting method and force direction conform to the actual working conditions. For example, when calibrating an accelerometer using an electromagnetic vibrator, the accelerometer should be mounted on the vibrator's vibration table, and the sensor's sensitive axis should be aligned with the vibration direction.

[0061] Connect the sensor's output signal line to the corresponding channel of the data acquisition system, and at the same time connect the standard signal generated by the calibration equipment (such as the drive signal of the electromagnetic exciter or the output signal of the reference accelerometer) to another channel of the data acquisition system for synchronous acquisition.

[0062] S3: Set data acquisition parameters. The data acquisition parameters for dynamic calibration (such as sampling frequency, acquisition duration, triggering mode) should be set mainly considering the frequency range of the dynamic excitation signal and the response characteristics of the sensor. Higher sampling frequency and appropriate acquisition duration are required to fully capture the details and changes of the dynamic signal.

[0063] Sampling frequency: Based on the frequency range of the dynamic excitation signal and the response characteristics of the sensor, set a sufficiently high data sampling frequency. Typically, the sampling frequency should be at least 5-10 times the highest frequency component of the excitation signal to ensure that the details and changes of the signal can be accurately captured. For example, if the highest frequency of the excitation signal is 1000Hz, then the sampling frequency should be set to 5000Hz-10000Hz.

[0064] Acquisition duration: The acquisition duration should cover the entire dynamic excitation process, including the rising edge, steady state, and falling edge of the signal. For example, for a pulse excitation signal with a duration of 1 second, the acquisition duration can be set to 1.2-1.5 seconds to ensure complete acquisition of signal data.

[0065] Triggering method: Select an appropriate triggering method, such as external triggering or signal level triggering. External triggering usually uses the trigger signal of the calibration equipment to start data acquisition, ensuring that the acquired signal is synchronized with the excitation signal. Signal level triggering starts acquisition when the sensor output signal reaches a certain level value. This method is suitable for situations such as self-excited vibration.

[0066] S4: Dynamic excitation and data acquisition. In dynamic calibration, the calibration equipment is activated to generate a dynamic excitation signal, causing the sensor to be subjected to dynamic force or displacement. At the same time, the data acquisition system synchronously acquires the standard signal and the sensor's output signal.

[0067] The calibration equipment is started to generate a set dynamic excitation signal, which causes the sensor to be subjected to dynamic force or displacement. During the excitation process, the data acquisition system synchronously acquires the standard signal and the sensor output signal according to the preset parameters. For example, when using an electromagnetic vibrator to generate a sine wave to excite the accelerometer, the vibrator vibrates at a set frequency and amplitude. The data acquisition system simultaneously acquires the drive signal of the vibrator (as a standard signal) and the output signal of the accelerometer.

[0068] S5: Analyze and process the data. The data analysis for dynamic calibration involves time domain and frequency domain analysis. In the time domain, it is necessary to calculate the dynamic response time and compare amplitude differences, etc. In the frequency domain, it is necessary to perform Fourier transform and calculate the amplitude-frequency characteristics and phase-frequency characteristics.

[0069] Time-domain analysis:

[0070] Calculate dynamic response time: By observing parameters such as rise time, peak time and recovery time of the sensor output signal, the response speed of the sensor can be evaluated. For example, under a step force excitation, the time it takes for the sensor output to rise from 10% to 90% of the peak value can be recorded to measure the sensor's ability to respond quickly to dynamic force changes.

[0071] Comparing amplitude differences: The amplitude of the sensor output signal is compared with the amplitude of the standard signal to calculate the amplitude error. For example, under sine wave excitation, by collecting data multiple times, the ratio of the average amplitude of the sensor output to the average amplitude of the standard signal is calculated to obtain the amplitude error percentage, which is used to evaluate the gain characteristics of the sensor under dynamic conditions.

[0072] Frequency domain analysis:

[0073] Fourier Transform: Performing a Fast Fourier Transform (FFT) on the acquired time-domain signal (including standard signals and sensor output signals) transforms the signal from the time domain to the frequency domain, thus obtaining the spectral distribution of the signal and displaying the amplitude and phase information of different frequency components;

[0074] Calculate amplitude-frequency and phase-frequency characteristics: Based on the frequency domain analysis results, calculate the sensor's amplitude-frequency characteristics (the relationship between the ratio of the sensor's output signal amplitude to the standard signal amplitude at different frequencies) and phase-frequency characteristics (the relationship between the phase difference between the sensor's output signal phase and the standard signal phase at different frequencies). For example, by plotting the amplitude-frequency characteristic curve and the phase-frequency characteristic curve, the sensor's gain and phase delay at different frequencies can be seen intuitively, thereby evaluating the sensor's frequency response characteristics.

[0075] S6: Determine the dynamic calibration coefficients and error corrections. Based on the sensor's dynamic response characteristics obtained from data analysis, such as amplitude-frequency characteristics, phase-frequency characteristics, and amplitude error, determine the dynamic calibration coefficients. These coefficients are used to correct the actual measurement data of the sensor to improve the accuracy of dynamic measurements. For example, if a deviation in the amplitude-frequency characteristics of the sensor at a certain frequency is found, a correction coefficient is calculated. When actually measuring the dynamic signal in that frequency range, the sensor output is multiplied by the correction coefficient to obtain a more accurate measurement result.

[0076] S7: Verification and recording of calibration results;

[0077] Verification: Perform dynamic measurements again using the calibrated sensor, and compare the measurement results with known standard values ​​or reference signals to verify the calibration effect. If the error is within the allowable range, the dynamic calibration is successful; otherwise, the calibration process needs to be checked again or the calibration coefficients need to be adjusted.

[0078] Recording: All data during the dynamic calibration process, including calibration equipment parameters, excitation signal characteristics, acquired data, analysis results, calibration coefficients, etc., are recorded in detail as part of the sensor calibration archive. These records are of great significance for subsequent sensor performance evaluation, troubleshooting, and quality traceability.

[0079] Dynamic calibration data acquisition mainly focuses on the sensor's response characteristics to input physical quantities that change rapidly over time, including the sensor's response speed, frequency response (amplitude-frequency characteristics and phase-frequency characteristics), and dynamic sensitivity. Dynamic calibration is mainly used to ensure that the sensor can accurately capture and reflect these dynamic information in the process of measuring rapidly changing mechanical processes such as dynamic forces, vibrations, and shocks.

[0080] In addition to considering factors such as ambient temperature and humidity, dynamic calibration data acquisition also needs to take into account interference factors such as surrounding electromagnetic fields and vibrations. This is because dynamic calibration involves the measurement and analysis of minute and rapidly changing signals, and any external interference may affect the accuracy of the calibration results. For example, when performing high-precision dynamic calibration of accelerometers, it is necessary to avoid electromagnetic field interference generated by the start-up of large motors nearby and the effects of vibrations from other equipment.

[0081] Environmental compensation data acquisition includes temperature and humidity calibration and other environmental factor calibration. Temperature and humidity calibration involves collecting the sensor's output signal under different temperature and humidity conditions, establishing a relationship model between temperature and humidity and sensor output, and then compensating the sensor's measurement results in real time based on the actual temperature and humidity changes in the measurement environment to eliminate the impact of temperature and humidity changes on sensor accuracy.

[0082] Based on temperature and humidity sensor data, and according to a pre-calibrated temperature and humidity-error model (obtained through extensive testing and statistics under various temperature and humidity conditions), the system compensates and corrects force, displacement, and other measurement data in real time, eliminating interference from environmental factors. For example, the stress measurement value is adjusted by a specific coefficient for every 1°C change in temperature, thereby improving the system's applicability under all operating conditions.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time mechanical calibration data monitoring system based on multiple sensors, characterized in that: This includes system hardware architecture and system software architecture; The system hardware architecture includes a sensor module for collecting data, a data acquisition module for acquiring data, a data calibration unit for calibrating data, a data processing and analysis module for data processing, and a data storage and communication module for data storage and transmission. The data calibration unit includes static calibration data acquisition, dynamic calibration data acquisition, and environmental compensation data acquisition; The system software architecture includes a system configuration and calibration interface, a real-time monitoring dashboard, and a data analysis and report generation module.

2. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 1, characterized in that: The static calibration data acquisition process includes the following steps: first, zero-point calibration is performed, then single-point calibration is performed, and finally multi-point calibration is performed. The main purpose is to determine the basic characteristic parameters of the sensor under static conditions, such as zero-point offset, sensitivity, linearity, hysteresis, and repeatability. These parameters can reflect the measurement accuracy of the sensor under stable conditions and provide a basis for subsequent accurate measurements.

3. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 1, characterized in that: The process of acquiring dynamic calibration data includes the following steps: S1: Identify the calibration equipment and excite the signal source; S2: Install and connect the sensors and calibration equipment; S3: Set data acquisition parameters; S4: Dynamic stimulus and data acquisition; S5: Analyze and process the data; S6: Determine the dynamic calibration coefficients and error correction; S7: Verification and recording of calibration results.

4. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 1, characterized in that: The dynamic calibration data acquisition mainly focuses on the sensor's response characteristics to input physical quantities that change rapidly over time, including the sensor's response speed, frequency response, and dynamic sensitivity. Dynamic calibration is mainly used to ensure that the sensor can accurately capture and reflect these dynamic information during the measurement of rapidly changing dynamic forces, vibrations, and impacts.

5. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 1, characterized in that: The environmental compensation data acquisition includes temperature and humidity calibration and other environmental factor calibration. The temperature and humidity calibration involves collecting the sensor's output signal under different temperature and humidity conditions, establishing a relationship model between temperature and humidity and the sensor output, and then compensating the sensor's measurement results in real time based on the actual temperature and humidity changes in the measurement environment to eliminate the impact of temperature and humidity changes on the sensor's accuracy.

6. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 3, characterized in that: The dynamic calibration in S1 requires a dedicated dynamic force or displacement source device and a suitable excitation signal.

7. The real-time mechanical calibration data monitoring system based on multiple sensors according to claim 3, characterized in that: The dynamic calibration in S2 requires that the sensor be firmly installed in a position that can receive dynamic excitation, and that the installation method and force direction of the sensor be consistent with the actual working conditions. At the same time, the sensor output signal and the standard signal generated by the calibration equipment should be synchronously connected to the data acquisition system.

8. A real-time mechanical calibration data monitoring system based on multiple sensors according to claim 3, characterized in that: The data acquisition parameter settings for dynamic calibration in S3 mainly consider the frequency range of the dynamic excitation signal and the response characteristics of the sensor, requiring a higher sampling frequency and a suitable acquisition duration to fully capture the details and changes of the dynamic signal.

9. A real-time mechanical calibration data monitoring system based on multiple sensors according to claim 3, characterized in that: In S4, during dynamic calibration, the calibration equipment is activated to generate a dynamic excitation signal, causing the sensor to be subjected to dynamic force or displacement. At the same time, the data acquisition system synchronously acquires the standard signal and the sensor's output signal.

10. A real-time mechanical calibration data monitoring system based on multiple sensors according to claim 3, characterized in that: The data analysis for dynamic calibration in S5 involves time-domain and frequency-domain analysis. In the time domain, the dynamic response time and amplitude differences need to be calculated and compared. In the frequency domain, Fourier transform is performed to calculate the amplitude-frequency characteristics and phase-frequency characteristics.

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