Urban rail vehicle maintenance critical dimension detection method and system

Abstract

The invention discloses an urban rail vehicle maintenance critical dimension detection method and system, relates to the technical field of urban rails, reduces the time and errors of manual operation through automatic measurement and data processing, improves the measurement efficiency and precision, realizes the unified management and backup of measurement data through a data storage module and cloud storage, and improves the measurement accuracy. The method provides convenience for workers to carry out self-inspection and tracing, provides a basis for big data analysis, is helpful for finding wear rules and fault trends of vehicle parts, and provides a more scientific decision basis for vehicle maintenance, and comprises the following steps: S1, equipment calibration and calibration; s11, before detection, the measuring equipment is verified, and it is ensured that length information and rotation angle information of the articulated arm are accurate; by means of the high-precision calibration tool and method, all joints of the articulated arm are calibrated, and the length measurement precision of the articulated arm at different angles is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban rail technology, and in particular to a key dimension detection method and system for urban rail vehicle maintenance. BACKGROUND

[0002] Since the 20th century, the urban rail transit industry has experienced explosive growth, not only alleviating urban traffic pressure and improving residents' travel experience, but also driving the development of related industries and urban economy. In recent years, the rail industry has entered the post-market maintenance stage, and more and more urban rail vehicles have entered the overhaul time node.

[0003] In traditional metro vehicle maintenance work, the size measurement of many components relies on manual contact measurement. This method not only has many error factors, but also the measurement error becomes even larger when facing smaller size measurement or manual contact measurement.

[0004] On-site size measurement is manual measurement, which has high error. Due to different tools and target objects to be detected, the detection error is large, which affects the safety of the vehicle. At the same time, the efficiency is low, and manual measurement is time-consuming and laborious. Especially when a large amount of data needs to be measured, it will occupy a large amount of human resources and reduce work efficiency. At the same time, manual recording of measurement data is prone to writing errors, omissions or repeated records, with high error rate, affecting the accuracy and reliability of the data. SUMMARY

[0005] The purpose of the present application is to provide a key dimension detection method and system for urban rail vehicle maintenance to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solution: a key dimension detection method for urban rail vehicle maintenance, comprising the following steps:

[0007] S1, equipment calibration and calibration, detecting the length and rotation angle of the joint arm, calibrating the detection accuracy of the laser measurement, and obtaining the relationship between the detected object and the pixel displacement;

[0008] S2, detection information input, inputting the project name, the type to be measured, the sequence of detection, the number of key points, the calculation method of key points, and the qualified value and other related information;

[0009] S3, automatic measurement execution, adjusting the joint arm and the laser incidence angle according to the input information, measuring the item point one by one, and using a quartic polynomial to fit the measurement data;

[0010] S4, data processing and management, automatically filling and recording the measured information according to the input item point, and storing and uploading to the cloud.

[0011] Preferably, the S1 comprises the steps of:

[0012] S11, verifying the measuring device before detection to ensure that the joint arm length information and the rotation angle information are accurate;

[0013] S12, calibrating the laser measuring module using a standard calibration block based on the principle of laser triangulation, and automatically recording the calibration required for this measurement.

[0014] Preferably, the S3 comprises the steps of:

[0015] S31, moving the coordinate base module according to the target to be detected to determine the world coordinates required for calculation;

[0016] S32, adjusting the joint arm to ensure the incident angle of the laser vision module, so that the laser can smoothly shoot on the detection point;

[0017] S33, using a quartic polynomial to fit the measurement data to reduce measurement error.

[0018] Preferably, the S4 comprises the steps of:

[0019] S41, determining whether the detection data is qualified according to the preset qualified data;

[0020] S42, selecting a preset measurement item to retest the item;

[0021] S43, filling the data to the preset item and storing the data to upload to the cloud for saving

[0022] Preferably, the S4 further comprises the steps of:

[0023] Environment monitoring correction, real-time collection of temperature, humidity and other parameters of the measurement environment, real-time correction of the original measurement data based on the preset environment parameter-measurement error compensation model, and elimination of the influence of environmental factors on the measurement accuracy;

[0024] Device state diagnosis and early warning, real-time monitoring of joint arm motion parameters, laser transmitter power, CCD camera imaging quality and other device running state data, when the device running parameters are detected to be out of the normal range or abnormal fluctuations occur, the fault early warning mechanism is automatically triggered and the fault positioning report is generated

[0025] The urban rail vehicle maintenance key size detection device provided by the application comprises:

[0026] The coordinate base module provides basic world coordinate information for measurement;

[0027] The joint arm module provides multi-angle selection for detecting targets and provides relevant coordinate movement information for the base;

[0028] The laser vision module, equipped with a laser emitter and a CCD camera, is used to collect point or contour information of the target to be measured.

[0029] The calculation module is used to calculate the optimized collected data to obtain the distance size and other related information of the detected point or surface.

[0030] The display module is used to display the basic information of the device and the related information of the measurement.

[0031] The data storage module is used to store measurement data and automatically complete the measurement form.

[0032] Preferably, the calculation module is built-in with a quartic polynomial fitting algorithm and an environmental error compensation model for fitting processing and environmental factor correction of the measurement data.

[0033] Preferably, the data storage module can realize local encrypted storage, cloud synchronization backup and historical data retrieval of the measurement data.

[0034] Preferably, the environmental monitoring correction module is used to monitor the temperature, humidity and other parameters of the measurement environment in real time and transmit them to the calculation module.

[0035] Preferably, the fault diagnosis and early warning module is used to monitor the running state of the measurement device in real time, diagnose and warn the device fault, and predict and warn the potential fault of the vehicle parts.

[0036] Compared with the prior art, the beneficial effects of the present application are:

[0037] 1. Through automatic measurement and data processing, the time and error of manual operation are reduced, and the measurement efficiency and accuracy are improved.

[0038] 2. Through the data storage module and cloud storage, the unified management and backup of measurement data are realized, which is convenient for staff to check and trace back, and provides a basis for big data analysis, which helps to find the wear rule and fault trend of vehicle parts, and provides a more scientific decision basis for vehicle maintenance and maintenance.

[0039] 3. Through real-time monitoring of environmental parameters and data compensation, the influence of environmental factors on measurement results is reduced, and through fault diagnosis and early warning function, device faults and potential faults of vehicle parts can be found in time to avoid safety accidents and economic losses caused by device faults and part faults. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The scheme flowchart of the present application;

[0041] Figure 2 The laser triangulation principle diagram of the present application. DETAILED DESCRIPTION

[0042] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0043] The technical solutions of the present application will be further described in detail below in combination with specific embodiments. A key dimension detection method for urban rail vehicle maintenance, comprising: S1, equipment calibration and calibration;

[0044] S11, before detection, verify the measuring equipment to ensure that the joint arm length information and the rotation angle information are accurate; calibrate each joint of the joint arm through high-precision calibration tools and methods to ensure the length measurement accuracy at different angles.

[0045] S12, according to the principle of laser triangulation, use a standard calibration block to calibrate the laser measurement module, and automatically input the calibration parameters required for this measurement. Specifically, place the standard calibration block at a specific position, the laser transmitter emits laser to the calibration block, the CCD camera collects the reflected light image, and the relationship between the detection object and the pixel displacement is determined by calculating the position change of the laser point in the image, thereby completing the calibration of the laser measurement module.

[0046] S2, detection information input;

[0047] S21, according to the actual measurement situation, preset the measurement items, such as the size measurement of each part of the vehicle bogie, the gap measurement of the vehicle body connection part, etc.

[0048] S22, preset the number of key points and the calculation method of the key points required for the measurement items. For example, for a certain part of the bogie, it is determined that the number of key points to be measured is 5, and the coordinate calculation method of each key point is preset. S23, preset the qualified value of the detection, according to the relevant standards and requirements of urban rail vehicle maintenance, set the corresponding qualified range for each measurement item.

[0049] S3, automatic measurement execution

[0050] S31, according to the target to be detected, moving the coordinate base module, determining the world coordinates required for calculation. By moving the base, the measurement system is placed in the appropriate position, ensuring that the size of the target to be detected can be accurately measured.

[0051] S32, adjust the joint arm, ensure the incident angle of the laser vision module, so that the laser can smoothly shoot on the detection point. Through the multi-degree of freedom movement of the joint arm, the position and angle of the laser vision module are adjusted to ensure that the laser beam is perpendicular or approximately perpendicular to the detection point, so as to improve the measurement accuracy.

[0052] S33, using a quartic polynomial to fit the measurement data, reducing the measurement error. Due to various interference factors in actual measurement, there is a certain fluctuation in the measurement data, and through quartic polynomial fitting, the measurement data can be smoothed to improve the accuracy of the measurement results.

[0053] S4, data processing and management

[0054] S41, according to the preset qualified data, the detection data is judged whether it is qualified. The measured data is compared with the preset qualified range to judge whether the measurement point is qualified, and the corresponding judgment result is given.

[0055] S42, select the preset measurement project, and retest this project. If there is doubt about the result of a certain measurement project or further verification is needed, the project can be selected for retesting to ensure the reliability of the measurement result.

[0056] S43, fill in the data to the preset project, and store the data, upload to the cloud for saving. The data storage module automatically fills in the measurement data to the preset measurement form, and stores the data in the local database, and uploads to the cloud server at the same time, realizing the unified management and backup of the data, and facilitating the subsequent query and analysis.

[0057] The S4 further comprises the steps of: environmental monitoring, real-time monitoring of the temperature, humidity and other parameters of the measurement environment, and compensation of the measurement data according to the monitoring results, since the changes of environmental factors such as temperature and humidity may affect the accuracy of the measurement equipment and the size of the target to be detected, by real-time monitoring of the environmental parameters and establishing a corresponding compensation model, the measurement data is corrected to improve the accuracy of the measurement results, and the data of multiple measurements is statistically analyzed to optimize the measurement parameters and algorithms. Through statistical analysis of a large amount of measurement data, problems and optimization space in the measurement process are found out, and measurement parameters and algorithms are continuously adjusted to improve the performance and accuracy of the measurement system; fault diagnosis and early warning, real-time monitoring of the running state of the measurement equipment, diagnosis and early warning of the faults of the equipment, by installing various sensors and monitoring modules in the measurement equipment, real-time collection of the running data of the equipment, such as the movement speed of the articulated arm, the working current of the laser emitter, etc., through analysis of these data, the hidden dangers of the equipment are found out in time, and warning signals are sent out for timely maintenance and maintenance.

[0058] A key dimension detection device for urban rail vehicle maintenance, comprising:

[0059] Coordinate base module: provides basic world coordinate information for measurement, and is movable to adapt to different measurement positions.

[0060] Articulated arm module: has multiple degrees of freedom, provides multiple angle options for the detection target, and provides relevant coordinate movement information for the base.

[0061] Laser vision module: includes a laser emitter and a CCD camera, used to collect point or contour information of the target to be measured.

[0062] Display module: used to display basic information of the equipment and related information of the measurement, such as measurement items, measurement results, qualification determination, etc.

[0063] Data storage module: used to store measurement data, automatically complete the measurement form, and realize cloud uploading and management of data.

[0064] Environmental monitoring module: used to real-time monitor the temperature, humidity and other parameters of the measurement environment, and provide basis for data compensation. Fault diagnosis module: used to real-time monitor the running state of the measurement equipment, diagnose and warn the equipment faults, and predict and warn the potential faults of the vehicle parts.

[0065] The calculation module is built-in with a quartic polynomial fitting algorithm and an environmental error compensation model, used for fitting processing and environmental factor correction of the measurement data.

[0066] The data storage module can realize local encryption storage, cloud synchronization backup and historical data retrieval of measurement data.

[0067] The environmental monitoring module monitors the temperature, humidity and other parameters of the measurement environment in real time, and compensates the measurement data according to the monitoring results. Since the changes of environmental factors such as temperature and humidity may affect the accuracy of the measurement device and the size of the target to be detected, by monitoring the environmental parameters in real time and establishing a corresponding compensation model, the measurement data is corrected to improve the accuracy of the measurement results.

[0068] The environmental monitoring and data compensation, temperature sensor, humidity sensor and environmental parameter acquisition circuit, are used for real-time monitoring of the temperature, humidity and other parameters of the measurement environment and transmitting to the calculation module, based on the preset environmental parameter-measurement error compensation model, the original measurement data is corrected in real time, and the influence of environmental factors on the measurement accuracy is eliminated.

[0069] The fault diagnosis and early warning module monitors the running state of the measurement device in real time, diagnoses and warns the faults of the device, by installing various sensors and monitoring modules in the measurement device, the running data of the device is collected in real time, such as the movement speed of the articulated arm, the working current of the laser emitter, etc., through the analysis of these data, the hidden danger of the device is found in time, and the early warning signal is sent out, so as to carry out maintenance and maintenance in time.

[0070] The working principle of the city rail vehicle maintenance key size detection method is the same as that of the city rail vehicle maintenance key size detection system, and will not be described one by one.

[0071] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be combined appropriately to form other embodiments that those skilled in the art can understand.

Claims

1. A method for detecting key dimensions in urban rail vehicle maintenance, characterized in that, Includes the following steps: S1. Equipment calibration and standardization: Detect the length and rotation angle of the articulated arm, calibrate the detection accuracy of laser measurement, and obtain the relationship between the detected object and pixel displacement. S2. Input test information: Input the test item name, the category to be measured, the test order, the number of key points, the calculation method of key points, and the relevant information of the qualified value. S3. Automatic measurement execution: Based on the entered information, adjust the articulated arm and the laser incident angle, measure each measurement point one by one, and fit the measurement data using a fourth-order polynomial. S4. Data processing and management: Automatically fill in and record the measured information according to the entered items, and store and upload it to the cloud.

2. The method for detecting key dimensions for urban rail vehicle maintenance according to claim 1, characterized in that, S1 includes the following steps: S11. Before conducting the test, the measuring equipment should be verified to ensure that the joint arm length information and rotation angle information are accurate. S12. Using the principle of laser triangulation, the laser measurement module is calibrated using a standard calibration block, and the calibration required for this measurement is automatically entered.

3. The method for detecting key dimensions for urban rail vehicle maintenance according to claim 1, characterized in that, S3 includes the following steps: S31. Based on the target to be detected, move the coordinate base module to determine the world coordinates required for calculation; S32. Adjust the articulated arm to ensure the incident angle of the laser vision module so that the laser can be steadily projected onto the point to be detected. S33. Use a fourth-order polynomial to fit the measurement data to reduce measurement error.

4. The method for detecting key dimensions for urban rail vehicle maintenance according to claim 1, characterized in that, S4 includes the following steps: S41. Determine whether the test data is qualified based on the preset qualified data; S42. Select the preset measurement item and re-measure this item; S43. Fill in the data into the preset items, store the data, and upload it to the cloud for saving.

5. The method for detecting key dimensions of urban rail vehicle maintenance according to claim 4, further comprising the following steps after step S4: Environmental monitoring correction: Real-time acquisition of temperature and humidity parameters of the measurement environment; Based on a preset environmental parameter-measurement error compensation model, real-time correction of the original measurement data to eliminate the influence of environmental factors on measurement accuracy. The equipment status diagnosis and early warning system monitors the joint arm motion parameters, laser emitter power, CCD camera imaging quality, and equipment operating status data in real time. When the equipment operating parameters are detected to be outside the normal range or to fluctuate abnormally, the system automatically triggers a fault early warning mechanism and generates a fault location report.

6. A system for detecting key dimensions in the maintenance of urban rail vehicles, characterized in that, include: The coordinate base module provides basic world coordinate information for measurement. The articulated arm module provides multiple angle options for the detection target and provides relevant coordinate movement information for the base; The laser vision module, equipped with a laser emitter and a CCD camera, is used to acquire the position or contour information of the target to be measured. The calculation module is used to calculate and optimize the collected data to obtain distance and dimension information of the points or surfaces to be detected. The display module is used to display basic information about the device and measurement-related information. The data storage module is used to store measurement data and measurement forms that are automatically filled out.

7. The system for detecting key dimensions of urban rail vehicle maintenance according to claim 6, characterized in that, The calculation module incorporates a fourth-order polynomial fitting algorithm and an environmental error compensation model, which are used to fit the measurement data and correct for environmental factors.

8. The system for detecting key dimensions of urban rail vehicle maintenance according to claim 6, characterized in that, The data storage module enables local encrypted storage of measurement data, cloud-synchronized backup, and retrieval and access to historical data.

9. The system for detecting key dimensions of urban rail vehicle maintenance according to claim 6, comprising: The environmental monitoring and correction module is used to monitor and measure the temperature and humidity parameters of the environment in real time and transmit them to the calculation module; the fault diagnosis and early warning module is used to monitor the operating status of the measuring equipment in real time, diagnose and warn of equipment faults, and predict and warn of potential faults in vehicle components.

Images