Method for measuring bidirectional displacement of object

Abstract

The invention discloses a method for measuring bidirectional displacement of an object. The method comprises the following steps: 1) initializing a system; 2) setting a calibration object; 3) data acquisition; 4) data processing; and 5) result display. According to the invention, high-precision bidirectional displacement measurement is realized through a multi-sensor fusion technology, and the system has the characteristics of strong real-time performance, high precision, high reliability and high data collaboration.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a method for measuring the bidirectional displacement of an object. Background Technology

[0002] With the development of industrial automation and precision measurement technology, the demand for accurate measurement of object displacement is increasing. Traditional displacement measurement methods have obvious limitations, especially in applications that require simultaneous acquisition of displacement and velocity information in multiple directions.

[0003] Existing displacement measurement technologies are mainly divided into three categories: 1. Mechanical contact displacement sensors (such as potentiometers, linear variable differential transformers (LVDTs), etc.): These can only measure unidirectional displacement and suffer from mechanical wear and contact interference. 2. Non-contact displacement sensors (such as laser rangefinders, ultrasonic rangefinders, etc.): Although they avoid mechanical contact, they are still mostly unidirectional measurements and are easily affected by environmental conditions. 3. Visual recognition displacement measurement methods: Theoretically, they can achieve multi-directional displacement measurement, but the algorithms are complex, the real-time performance is poor, and they are sensitive to lighting conditions. Furthermore, existing multi-directional displacement measurement systems typically employ multiple sensors operating independently, lacking an effective data fusion mechanism. This not only increases system complexity but may also introduce measurement errors, resulting in limited measurement accuracy and poor data coordination between sensors.

[0004] In the field of rail transit, bidirectional displacement measurement technology has significant application value. Traditionally, the performance evaluation of vehicle door systems mainly relies on simple mechanical measuring tools and is usually performed only in one direction. With the rapid development of rail transit technology and the increasing demands of passengers for a better riding experience, this traditional unidirectional measurement method can no longer meet modern needs.

[0005] Developing methods capable of simultaneously and accurately measuring the displacement and velocity of an object in two directions is crucial for engineers to gain a more comprehensive understanding of the motion characteristics of a vehicle door system and to analyze data on both lateral and longitudinal motion. Therefore, it is necessary to develop methods specifically designed to measure the displacement of an object in both directions. Summary of the Invention

[0006] Purpose of the invention: To address the shortcomings and defects of existing technologies, this invention provides a method for measuring the bidirectional displacement of an object. It achieves high-precision bidirectional displacement measurement through multi-sensor fusion technology, and features strong real-time performance, high accuracy, high reliability, and high data collaboration.

[0007] Technical solution: The present invention provides a method for measuring the bidirectional displacement of an object, characterized by comprising the following steps:

[0008] 1) System initialization: Connect the AC220V power supply of the measuring device or ensure that the lithium battery is fully charged; turn on the air switch, press the self-locking button, and the power indicator light will light up; the receiver controller and transmitter controller will power on and perform self-tests to initialize each subsystem; each sensor will perform self-calibration to ensure data accuracy; the touch screen will display the system ready status.

[0009] 2) Calibration setting: Install the calibration object on the object being measured, ensuring it is securely fixed; adjust the position of the vision sensor to ensure it can clearly capture the image of the calibration object; calibrate the vision sensor via the touchscreen; confirm that the vision sensor can correctly identify the calibration object;

[0010] 3) Data acquisition: The vision sensor acquires images of the calibration object in real time at a frequency of 30fps; the accelerometer acquires the object's acceleration data at a sampling rate of 100Hz; the attitude sensor acquires the object's tilt angle data in real time; the data acquired by the accelerometer and attitude sensor are transmitted to the transmitter controller for preprocessing; the preprocessed data is sent to the WiFi receiver via the WiFi transmitter, and then sent to the receiver controller by the WiFi receiver.

[0011] 4) Data Processing: The receiver controller performs visual sensor data processing to calculate displacement in the X / Y directions; the receiver controller performs accelerometer data processing for displacement data correction; the receiver controller performs multi-sensor data fusion processing; the receiver controller performs velocity and kinetic energy calculations; and the receiver controller performs data storage and output operations.

[0012] 5) Results display: The touchscreen displays the object's X-direction displacement curve in real time; the touchscreen displays the object's Y-direction displacement curve in real time; the touchscreen displays the object's velocity curve in real time; the touchscreen displays the object's kinetic energy curve in real time; and the touchscreen displays the object's attitude angle data.

[0013] 6) Situation Handling 1: When the system detects that the vision sensor has lost the image of the calibration object, the system will provide a warning; when the acceleration data exceeds the preset threshold, the system will also issue a warning and record the abnormal data for subsequent analysis.

[0014] 7) Scenario 2: The system supports automatic detection of operating status. When the quality of sensor data deteriorates, the sensor weights are automatically adjusted to ensure the reliability of measurement results. For sudden changes in acceleration, the system uses a threshold filtering mechanism to avoid interference from abnormal data.

[0015] 8) Measurement complete: Press the system stop button to stop data acquisition, save the collected data, and switch the system to standby mode.

[0016] In steps 3 and 4), data acquisition and processing include visual measurement and displacement calculation, acceleration signal processing for data correction, velocity calculation, kinetic energy calculation, and data fusion.

[0017] The aforementioned visual measurement and displacement calculation involve: a visual sensor acquiring an image of the calibration object, and obtaining the object's displacement in the horizontal direction, i.e., along the X-axis, through pixel coordinate transformation. The displacement calculation formula is as follows: Among them, S x S represents the displacement of an object in the X direction, in mm. y P represents the displacement of the object in the Y direction, in mm. x P represents the pixel offset in the X direction, in pixels. y , where Y is the pixel offset in pixels; D is the distance from the calibration object to the sensor in mm; f is the sensor focal length in pixels; the visual sensor data processing steps are: 1) Image preprocessing (grayscale conversion, filtering, binarization, etc.); 2) Calibration object feature point identification and extraction; 3) Pixel coordinates converted to actual distance; 4) Calculation of X / Y direction displacement;

[0018] The acceleration signal processing described above is used for data correction: the acceleration sensor collects the acceleration of the object, and the data is filtered through the following steps: 1) a moving average filter is used to remove high-frequency noise, and the average value of 5 consecutive data points is taken; 2) a threshold filter is used to eliminate sudden noise, and when the change between two adjacent data points exceeds 0.5g, it is judged as abnormal and corrected; 3) a low-pass filter is used to filter out signal components with a frequency greater than 10Hz, and retain the main motion acceleration information of the object.

[0019] The attitude sensor calibration steps are as follows: 1) Acquire the tilt angle data of the X and Y axes; 2) Correct the displacement deviation of the visual measurement using the cosine correction formula; 3) Correct the perspective transformation of the calibration object.

[0020] The velocity calculation is as follows: the velocity of the object is obtained by calculating the displacement differential.

[0021]

[0022] Among them, v x The velocity in the X direction is expressed in mm / s; v y Δt represents the velocity in the Y direction, in mm / s; Δt represents the sampling time interval, in seconds.

[0023] The aforementioned kinetic energy calculation: The formula for calculating the kinetic energy of an object is: Among them, E k ν is the kinetic energy of the object, measured in J; m is the mass of the object, measured in kg; v is the resultant velocity of the object, measured in m / s.

[0024] The data fusion process involves fusing data from various sensors using a weighted average algorithm. Weight coefficients Wv and Wa are assigned to the visual sensor and accelerometer data respectively, satisfying Wv + Wa = 1. Initially, Wv = 0.8 and Wa = 0.2, with visual sensor information dominating. The weights are dynamically adjusted as data quality changes during measurement; Wv is reduced when image clarity decreases. The final displacement S = Wv × Sv + Wa × Sa, where Sv is the visual measurement displacement and Sa is the displacement after acceleration data-assisted correction. Abnormal data detection is performed; when the rate of change exceeds a set threshold, the data is marked as abnormal and its weight is reduced. The processing results are stored in the receiver controller's memory. Data is transmitted to the WiFi receiver via a WiFi transmitter and then output to the touchscreen display.

[0025] In step 4), the visual sensor acquires an image of the calibration object and obtains the object's displacement in the horizontal direction, i.e., the X-axis and Y-axis, through pixel coordinate transformation; the accelerometer and attitude sensor acquire data, which is then processed by the transmitter controller and sent to the receiver controller via a WiFi transmitter; the data fusion part uses a weighted average algorithm to assign weight coefficients to the visual sensor and accelerometer data respectively, dynamically adjusts the weights, and finally achieves high-precision bidirectional displacement measurement.

[0026] In step 5), the touchscreen provides a data export function, supporting CSV, Excel, image, and report data formats; it also supports historical data query and playback.

[0027] The measuring device includes a power supply system, a control system, a sensor system, and a communication and display system. The power supply system includes an AC power supply, a switching power supply, a lithium battery, and a lithium battery charger. The control system includes a controller, a self-locking button, and a power indicator light. The sensor system includes a vision sensor, an acceleration sensor, and an attitude sensor. The communication and display system includes a WiFi transmitter, a WiFi receiver, a touch screen, LED lights, and a buzzer.

[0028] The AC power supply is a 220V AC power supply, the switching power supply is a 24V DC switching power supply, and the lithium battery is a 24V, 3000mAh lithium battery; the AC220V power supply powers the display screen and the DC24V switching power supply; the DC24V switching power supply powers other components.

[0029] The self-locking button is used to turn the device power on and off, and the power indicator light shows whether the device is in the on or off state.

[0030] The vision sensor has a resolution of 1280×720 pixels, a frame rate of 30fps, and a focal length of 50mm; the accelerometer has a range of ±8g, a resolution of 0.01g, and a sampling frequency of 100Hz; the attitude sensor has an angle range of ±180° and an accuracy of 0.1°.

[0031] The WiFi receiver receives processed signals from the visual sensor, accelerometer, and attitude sensor transmitted by the WiFi transmitter, processes them, and then sends the signals to the touchscreen. The touchscreen is DC powered and displays displacement, velocity, and kinetic energy curves. The WiFi transmitter uses the IEEE 802.11b / g / n standard, operates at a frequency of 2.4 GHz, and has a transmission distance of 50 m. The WiFi receiver uses the IEEE 802.11b / g / n standard, operates at a frequency of 2.4 GHz, and has a receiving sensitivity of -80 dBm.

[0032] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: The present invention combines high-performance sensors, data fusion technology and intelligent analysis system to achieve real-time, accurate and efficient measurement, providing reliable data support for the optimized design and operation and maintenance of the door system, and has important practical significance and application value.

[0033] This invention enables simultaneous measurement of the displacement of an object in both horizontal (X-axis and Y-axis) directions; improves the accuracy and reliability of displacement measurement through multi-sensor fusion technology; reduces measurement errors by employing data processing algorithms; achieves real-time monitoring and display of parameters such as displacement, velocity, and kinetic energy; has comprehensive data processing and display functions; supports WiFi wireless data transmission and lithium battery power supply, offering high flexibility and portability. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the device for measuring the bidirectional displacement of an object according to the present invention;

[0035] Figure 2 This is the electrical schematic diagram of the present invention;

[0036] Figure 3 This is a schematic diagram illustrating the measurement principle of the present invention;

[0037] Figure 4 This is a schematic diagram illustrating the visual measurement principle of the present invention;

[0038] Figure 5 This is a schematic diagram of the acceleration integral principle of the present invention;

[0039] Figure 6 This is a flowchart of the data fusion process of the present invention;

[0040] Figure 7 This is a system software architecture diagram of the present invention;

[0041] Figure 8 This is a graph for evaluating the measurement accuracy of the present invention. Detailed Implementation

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0043] Example:

[0044] The method for measuring the bidirectional displacement of an object in this embodiment:

[0045] First, a 20mm x 20mm checkerboard marker was installed on the outside of the train doors. Simultaneously, a measurement box containing an accelerometer, attitude sensor, transmitter controller, and WiFi transmitter was installed on the inside of the doors, powered by a built-in 24V lithium battery. Then, a measurement system containing a vision sensor, receiver controller, and display / communication system was installed at a fixed location on the platform.

[0046] After the train arrives at the station, the vision sensor captures images of the calibration objects on the door in real time at a frequency of 30fps, and obtains the displacement data of the door in the X and Y directions through pixel coordinate transformation. At the same time, the accelerometer on the door collects the door acceleration data at a sampling rate of 100Hz, and the attitude sensor collects the door tilt angle data in real time. After the data is preprocessed by the transmitter controller, it is transmitted to the receiving end on the platform through the WiFi transmitter.

[0047] The receiver controller fuses data from the vision and acceleration sensors to accurately measure the X / Y displacement during the opening and closing of the car door and calculate its velocity and kinetic energy, displaying the relevant curves in real time on the touchscreen. The system achieves a measurement accuracy of ±0.5mm in the X direction, ±1.0mm in the Y direction, and ±5mm / s in velocity measurement.

[0048] This measurement method can accurately record and analyze the opening and closing performance parameters of train doors, including key indicators such as opening and closing time, movement trajectory, speed curve, and resistance. It provides reliable technical support for the debugging, maintenance, and fault diagnosis of train door systems, and also ensures passenger safety.

[0049] Appendix of the present invention Figure 1This is a schematic diagram of a device for measuring the bidirectional displacement of an object, showing the components and their connections, including the power supply system, control system, sensor system, and communication system. The power supply system includes an AC power supply, a switching power supply, a lithium battery, and a lithium battery charger. The control system, centered on a controller, processes all sensor data. The sensor system includes a vision sensor, an accelerometer, and an attitude sensor to collect displacement and attitude information of the object. The communication and display system includes a touchscreen, a WiFi transmitter, and a WiFi receiver for data display and remote transmission. The WiFi communication module adopts the IEEE 802.11b / g / n standard, operates at a frequency of 2.4GHz, has a transmission distance of 50 meters, and a receiving sensitivity of -80dBm, meeting the system's remote data transmission requirements. The 7-inch touchscreen has a resolution of 1024×768, supports multi-touch, and provides users with an intuitive operating interface and data display.

[0050] Appendix Figure 2 This is an electrical schematic diagram, clearly showing the overall structure and electrical connections of the bidirectional displacement measurement system. The left side of the diagram represents the receiver (fixed measurement system), including the power supply system, vision measurement system, receiver control system, and display and communication system; the right side represents the transmitter (fixed to the measured object), including the mobile power supply system, sensor system, transmitter control system, and communication system. The two parts exchange information via WiFi wireless data transmission. The vision sensor, fixed to the receiver, acquires X / Y displacement data by observing a calibration object on the measured object; the accelerometer and attitude sensor, fixed to the measured object, transmit the collected data to the transmitter controller for preprocessing, and then send it back to the receiver via the WiFi transmitter to correct the displacement measurement results. The lithium battery operates at 24V, providing a 3000mAh capacity. The charger inputs AC220V and outputs DC25.2V / 2A charging current, ensuring power supply to the transmitter device housing during mobile measurement.

[0051] Appendix Figure 3 The diagram illustrates the measurement principle, explaining the method and data fusion process for horizontal (X-axis and Y-axis) displacement. This invention achieves bidirectional displacement measurement through multi-sensor fusion technology. X / Y-axis displacement is calculated using image processing techniques after acquiring images of a calibration object via a vision sensor; simultaneously, data from accelerometers and attitude sensors are used for correction. The system employs a weighted average algorithm to fuse the multi-sensor data, reducing measurement errors and improving accuracy.

[0052] Appendix Figure 4This diagram illustrates the principle of visual measurement, detailing the optical imaging principle, calibration object recognition process, pixel-to-distance conversion, and image processing flow of the visual measurement system. The visual measurement principle is based on camera imaging and image processing technology. The calibration object uses a black and white checkerboard pattern. The position of the calibration object is obtained through feature point extraction, and then the displacement of the object in the X / Y directions is calculated using the correspondence between pixel coordinates and actual distance. The visual sensor has a resolution of 1280×720 pixels, a frame rate of 30fps, and a focal length of 50mm. The grid size of the calibration object is 20mm×20mm, and the overall size is 100mm×100mm.

[0053] Appendix Figure 5 This diagram illustrates the principle of acceleration integration, showcasing a triaxial accelerometer, signal processing flow, error compensation mechanism, and detailed data correction process. Accelerometer data processing is used to correct measurement results. Accelerometer data undergoes a moving average filter to remove high-frequency noise, averaging five consecutive data points. Threshold filtering eliminates sudden noise; when the change between two adjacent data points exceeds 0.5g, it is considered an anomaly and corrected. A low-pass filter removes signal components with frequencies greater than 10Hz, retaining the object's primary acceleration information. The accelerometer has a range of ±8g, a resolution of 0.01g, and a sampling frequency of 100Hz.

[0054] Appendix Figure 6 This data fusion flowchart illustrates the complete processing flow, including the input data layer, data preprocessing layer, fusion processing layer, output layer, and system monitoring. Weighting coefficients Wv and Wa are assigned to the visual sensor and accelerometer data respectively, satisfying Wv + Wa = 1. Initially, Wv = 0.8 and Wa = 0.2, with visual sensor information dominating. The weights are dynamically adjusted as data quality changes during measurement; Wv decreases when image clarity deteriorates. The final displacement S = Wv × Sv + Wa × Sa, where Sv is the visual measurement displacement and Sa is the displacement after acceleration data-assisted correction. Simultaneously, the system monitoring module detects and processes abnormal data to ensure reliable system operation.

[0055] Appendix Figure 7 The system software architecture diagram illustrates the software structure, including the underlying driver layer, operating system layer, middleware layer, application layer, and cross-layer modules. The underlying driver layer provides hardware abstraction interfaces, the operating system layer is responsible for task scheduling and resource management, the middleware layer provides data processing and communication support, and the application layer implements specific functional modules. In addition, the system includes cross-layer modules such as system configuration management, power management, error handling, and a logging system.

[0056] Appendix Figure 8The measurement accuracy evaluation chart shows the evaluation results for X-direction displacement measurement accuracy, Y-direction displacement measurement accuracy, displacement measurement comparison, and motion trajectory reconstruction. The X-direction displacement measurement accuracy is ±0.5mm, the Y-direction displacement measurement accuracy is ±1.0mm, the velocity measurement accuracy is ±5mm / s, the angle measurement accuracy is ±0.1°, the system response time is 50ms, and the data refresh rate is 20Hz. Compared with traditional single-sensor measurement methods, this system has higher accuracy and better stability, especially showing a significant advantage in complex trajectory reconstruction.

[0057] The AC power supply of this invention is a standard AC220V AC power supply, used to power the display screen and the switching power supply. The switching power supply is an input AC220V AC power supply and an output DC24V DC power supply, used to provide voltage to other devices. The receiver controller is used to receive signals from the visual sensor, accelerometer, and attitude sensor processed by the transmitter controller from the WiFi receiver, and then send the processed signals to the touch screen. The transmitter controller is used to collect data from the accelerometer and attitude sensor, perform preprocessing, and then send the data to the receiver via the WiFi transmitter. The touch screen is DC powered and is used to display information such as displacement curves, velocity curves, and kinetic energy curves. The self-locking button is used to start and stop the device's power supply. Once pressed, the self-locking button will remain in the pressed state, and pressing it again will release the button. The power indicator light is used to indicate whether the device is in a powered-off or powered-on state. If the device is in a powered-on state, the power indicator light is on; if the device is in a powered-off state, the power indicator light is off.

[0058] The lithium battery is 24V with a 3000mAh capacity, providing a power source for the transmitter device housing. The device can operate without an external power source when the lithium battery is charged. The lithium battery charger inputs AC220V and outputs DC25.2V / 2A, used to charge the lithium battery. The vision sensor has a resolution of 1280×720 pixels, a frame rate of 30fps, and a focal length of 50mm. Installed in the fixed measurement system, it collects pixel data from the calibration object, converts it into distance data based on camera imaging principles, and transmits the collected data to the WiFi receiver via a WiFi transmitter before reaching the controller. The accelerometer has a range of ±8g, a resolution of 0.01g, and a sampling frequency of 100Hz. Installed in the transmitter device housing and fixed to the object being measured, it collects the acceleration of the object. The collected data is first transmitted to the transmitter controller for preprocessing, then transmitted to the WiFi receiver via a WiFi transmitter before reaching the receiver controller for data correction.

[0059] The attitude sensor has an angle range of ±180° and an accuracy of 0.1°. It is installed in the transmitter housing and fixed to the object under test. It is used to collect the tilt angle of the object. The collected data is first transmitted to the transmitter controller for preprocessing, then sent via a WiFi transmitter to a WiFi receiver, and finally transmitted to the receiver controller for data correction. The WiFi transmitter adopts the IEEE 802.11b / g / n standard, operates at a frequency of 2.4GHz, and has a transmission distance of 50 meters. It is installed in the transmitter housing and is used to remotely transmit various sensor data processed by the transmitter controller to the WiFi receiver. The WiFi receiver also adopts the IEEE 802.11b / g / n standard, operates at a frequency of 2.4GHz, and has a receiving sensitivity of -80dBm. It receives the data transmitted by the WiFi transmitter and transmits the data to the receiver controller. The calibration object provides a calibration image for the vision sensor. The calibration object uses a black and white checkerboard pattern, with each square measuring 20mm × 20mm, and the overall dimensions are 100mm × 100mm. It is fixed to the object under test. The touchscreen supports multi-touch and is used to collect position, velocity, and kinetic energy data from all sensors processed by the receiver controller, and to display the data as a graph on the touchscreen.

[0060] The present invention has the following accuracy measurements: X-direction displacement measurement accuracy: ±0.5mm; Y-direction displacement measurement accuracy: ±1.0mm; velocity measurement accuracy: ±5mm / s; angle measurement accuracy: ±0.1°; system response time: 50ms; data refresh rate: 20Hz.

[0061] Compared to traditional unidirectional displacement measurement methods, the bidirectional displacement measurement method of this invention can more comprehensively analyze the motion characteristics of the door system, especially effectively monitoring abnormalities such as Y-direction offset and swaying that may occur during the opening and closing of the door, thus improving the comprehensiveness and accuracy of the measurement.

[0062] This invention offers the following advantages: 1. Bidirectional Simultaneous Measurement: It can simultaneously measure the displacement changes of an object in both the horizontal X and Y axes. 2. Sensor Fusion Technology: Combining the advantages of visual measurement and acceleration measurement, they complement each other to improve overall measurement accuracy. 3. High Precision and Real-Time Performance: The system responds quickly and processes data efficiently, meeting real-time monitoring requirements. 4. Portability and Flexibility: It supports lithium battery power for mobile measurement and WiFi wireless data transmission for convenient remote monitoring. 5. User-Friendly Interface: The touchscreen displays intuitive data curves such as displacement, velocity, and kinetic energy, facilitating analysis and judgment. 6. Wide Range of Applications: Applicable to multiple fields such as industrial automation, rail transportation, construction engineering, and aerospace.

[0063] This invention addresses the problems of low accuracy, significant limitations of unidirectional measurement, and poor data coordination among multiple sensors in existing object displacement measurement methods. By using multi-sensor fusion technology, it achieves high-precision bidirectional displacement measurement, reducing measurement errors, improving measurement accuracy, enhancing system stability, and increasing applicability in complex application scenarios.

Claims

1. A method for measuring the bidirectional displacement of an object, characterized in that: Includes the following steps: 1) System initialization: Connect the AC220V power supply of the measuring device or ensure that the lithium battery is fully charged; turn on the air switch, press the self-locking button, and the power indicator light will light up; the receiver controller and transmitter controller will power on and perform self-tests to initialize each subsystem; each sensor will perform self-calibration to ensure data accuracy; The touchscreen displays the system's ready status; 2) Calibration setting: Install the calibration object on the object being measured, ensuring it is securely fixed; Adjust the position of the vision sensor to ensure it can clearly capture the image of the calibration object; calibrate the vision sensor via the touchscreen; confirm that the vision sensor can correctly identify the calibration object; 3) Data acquisition: The vision sensor acquires images of the calibration object in real time at a frequency of 30fps; the accelerometer acquires acceleration data of the object at a sampling rate of 100Hz. The attitude sensor collects the object's tilt angle data in real time; the data collected by the accelerometer and attitude sensor are transmitted to the transmitter controller for preprocessing. The preprocessed data is sent from the WiFi transmitter to the WiFi receiver, and then from the WiFi receiver to the receiver controller. 4) Data processing: The receiver controller performs visual sensor data processing to calculate displacement in the X / Y directions; the receiver controller performs accelerometer data processing for displacement data correction; the receiver controller performs multi-sensor data fusion processing. The receiver controller performs speed and kinetic energy calculations; the receiver controller performs data storage and output operations; 5) Results display: The touchscreen displays the object's X-direction displacement curve in real time; the touchscreen displays the object's Y-direction displacement curve in real time; the touchscreen displays the object's velocity curve in real time. The touchscreen displays the object's kinetic energy curve in real time; the touchscreen also displays the object's attitude and angle data. 6) Situation Handling 1: When the system detects that the vision sensor has lost the image of the calibration object, the system will provide a warning; when the acceleration data exceeds the preset threshold, the system will also issue a warning and record the abnormal data for subsequent analysis. 7) Scenario 2: The system supports automatic detection of operating status. When the quality of sensor data deteriorates, the sensor weights are automatically adjusted to ensure the reliability of measurement results. For sudden changes in acceleration, the system uses a threshold filtering mechanism to avoid interference from abnormal data. 8) Measurement complete: Press the system stop button to stop data acquisition, save the collected data, and switch the system to standby mode.

2. The method for measuring the bidirectional displacement of an object according to claim 1, characterized in that: In steps 3 and 4), data acquisition and processing include visual measurement and displacement calculation, acceleration signal processing for data correction, velocity calculation, kinetic energy calculation, and data fusion.

3. The method for measuring the bidirectional displacement of an object according to claim 2, characterized in that: The aforementioned visual measurement and displacement calculation: The visual sensor acquires an image of the calibration object, and obtains the object's displacement in the horizontal direction, i.e., the X-axis, through pixel coordinate transformation. The displacement calculation formula is as follows: Among them, S x S represents the displacement of an object in the X direction, in mm. y P represents the displacement of the object in the Y direction, in mm. x P represents the pixel offset in the X direction, in pixels. y , where is the pixel offset in the Y direction, in pixels; D is the distance from the calibration object to the sensor, in mm; f is the sensor focal length, in pixels; the visual sensor data processing steps are: 1) image preprocessing; 2) calibration object feature point identification and extraction; 3) pixel coordinates converted to actual distance; 4) calculation of X / Y direction displacement; The acceleration signal processing described above is used for data correction: the acceleration sensor collects the acceleration of the object, and the data is filtered through the following steps: 1) a moving average filter is used to remove high-frequency noise, and the average value of 5 consecutive data points is taken; 2) a threshold filter is used to eliminate sudden noise, and when the change between two adjacent data points exceeds 0.5g, it is judged as abnormal and corrected; 3) a low-pass filter is used to filter out signal components with a frequency greater than 10Hz, and retain the main motion acceleration information of the object. The attitude sensor calibration steps are as follows: 1) Acquire the tilt angle data of the X and Y axes; 2) Correct the displacement deviation of the visual measurement using the cosine correction formula; 3) Correct the perspective transformation of the calibration object. The velocity calculation is as follows: the velocity of the object is obtained by calculating the displacement differential. Among them, v x The velocity in the X direction is expressed in mm / s; v y Δt represents the velocity in the Y direction, in mm / s; Δt represents the sampling time interval, in seconds. The aforementioned kinetic energy calculation: The formula for calculating the kinetic energy of an object is: Among them, E k ν is the kinetic energy of the object, measured in J; m is the mass of the object, measured in kg; v is the resultant velocity of the object, measured in m / s. The data fusion process involves fusing data from various sensors using a weighted average algorithm. Weight coefficients Wv and Wa are assigned to the visual sensor and accelerometer data respectively, satisfying Wv + Wa = 1. Initially, Wv = 0.8 and Wa = 0.2, with visual sensor information dominating. The weights are dynamically adjusted as data quality changes during measurement; Wv is reduced when image clarity decreases. The final displacement S = Wv × Sv + Wa × Sa, where Sv is the visual measurement displacement and Sa is the displacement after acceleration data-assisted correction. Abnormal data detection is performed; when the rate of change exceeds a set threshold, the data is marked as abnormal and its weight is reduced. The processing results are stored in the receiver controller's memory. Data is transmitted to the WiFi receiver via a WiFi transmitter and then output to the touchscreen display.

4. The method for measuring the bidirectional displacement of an object according to claim 1, characterized in that: In step 4), the vision sensor acquires an image of the calibration object and obtains the object's displacement in the horizontal direction, i.e., the X-axis and Y-axis, through pixel coordinate transformation; after the accelerometer and attitude sensor acquire data, the data is processed by the transmitter controller and sent to the receiver controller via the WiFi transmitter. The data fusion part uses a weighted average algorithm to assign weight coefficients to the visual sensor and accelerometer data respectively, dynamically adjust the weights, and finally achieve high-precision bidirectional displacement measurement.

5. The method for measuring the bidirectional displacement of an object according to claim 1, characterized in that: In step 5), the touchscreen provides a data export function, supporting CSV, Excel, image, and report data formats; it also supports historical data query and playback.

6. The method for measuring the bidirectional displacement of an object according to claim 1, characterized in that: The measuring device includes a power supply system, a control system, a sensor system, and a communication and display system; the power supply system includes an AC power supply, a switching power supply, a lithium battery, and a lithium battery charger; the control system includes a controller, a self-locking button, and a power indicator light; the sensor system includes a vision sensor, an acceleration sensor, and an attitude sensor; the communication and display system includes a WiFi transmitter, a WiFi receiver, a touch screen, LED lights, and a buzzer.

7. The method for measuring the bidirectional displacement of an object according to claim 6, characterized in that: The AC power supply is a 220V AC power supply, the switching power supply is a 24V DC switching power supply, and the lithium battery is a 24V, 3000mAh lithium battery; the AC220V power supply powers the display screen and the DC24V switching power supply; the DC24V switching power supply powers other components.

8. The method for measuring the bidirectional displacement of an object according to claim 6, characterized in that: The self-locking button is used to turn the device power on and off, and the power indicator light shows whether the device is in the on or off state.

9. The method for measuring the bidirectional displacement of an object according to claim 6, characterized in that: The visual sensor has a resolution of 1280×720 pixels, a frame rate of 30fps, and a focal length of 50mm; the accelerometer has a range of ±8g, a resolution of 0.01g, and a sampling frequency of 100Hz; the attitude sensor has an angle range of ±180° and an accuracy of 0.1°.

10. The method for measuring the bidirectional displacement of an object according to claim 6, characterized in that: The WiFi receiver receives processed signals from the visual sensor, accelerometer, and attitude sensor transmitted by the WiFi transmitter, processes them, and then sends the signals to the touchscreen. The touchscreen is DC powered and displays displacement, velocity, and kinetic energy curves. The WiFi transmitter adopts the IEEE 802.11b / g / n standard, operates at a frequency of 2.4GHz, and has a transmission distance of 50m. The WiFi receiver adopts the IEEE 802.11b / g / n standard, operates at a frequency of 2.4GHz, and has a receiving sensitivity of -80dBm.

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