In major infrastructure projects such as high-speed rail and highway, bridge-building machines are used as the core construction equipment, and the state of their outriggers in place directly determines the stability of the equipment and the safety of the operation. The traditional method of relying on manual visual inspection is susceptible to factors such as complex operating environment, fatigue and negligence of personnel, and there is a risk of missed inspection and misjudgment, which has become a weak link in construction safety management. Micro-technology deeply cultivates the field of intelligent engineering equipment, independent research and developmentAI detection system for the pins of the legs of the bridge-building machine in place, Using visual AI technology to achieve accurate identification and intelligent early warning of sales status, and completely solve the problem of industry detection.
Technology core: AI vision-driven precision detection system
With “visual perception + intelligent algorithm” as the core architecture, the system builds a closed loop of automated detection of the whole process to replace the uncertainty of manual judgment from the root cause.
Multi-dimensional perception hardware deployment
A high-definition vision camera is accurately arranged at the key position of the outrigger pin of the bridge-building machine to form a field of view without dead ends, which can fully cover the outrigger shaft pins of different models and specifications. The camera adopts industrial-grade protection design, has dust-proof, waterproof, and vibration-resistant properties, and has a protection level of IP65 or higher. It can maintain stable image acquisition capabilities even in harsh construction environments such as high altitude and open air, dust splashing, strong light and heavy rain.
Core advantages of self-developed AI algorithms
Equipped with a deep learning algorithm model independently developed by Weite, it realizes the accurate recognition of sales characteristics through tens of millions of sample training.：
Full type adaptation recognition: It can automatically detect shaft pins of different shapes, sizes, and lengths, without the need for algorithm reconstruction for specific equipment, and it is extremely adaptable.；
Scene adaptive learning: The algorithm is localized and deployed and has independent learning capabilities. It can adapt to scene fluctuations such as lighting changes and angle offsets in construction in real time, and continuously optimize the recognition accuracy.；
Millisecond response processing: The detection time of each frame is less than 100ms, and the delay of the whole process from image acquisition to status judgment is controlled within 0.1 seconds to ensure that the detection is synchronized with the job process.
Core functions: full-process security protection and intelligent control
Through the complete link of “real–time monitoring–intelligent analysis–early warning control-data retention”, the system realizes the whole life cycle management of the sales status of the outrigger.
Accurate status recognition and abnormal warning
With the help of AI vision algorithm, continuous scanning and analysis of the position of the piercing pin can accurately determine whether the piercing pin is completely in place, whether there is loosening, offset and other abnormal conditions. When it detects that the pin is not in place or is loose, the system immediately triggers a multi-level early warning mechanism: the local sound and light alarm is activated immediately, and the main control screen of the cab synchronously displays abnormal location and status prompts. At the same time, it can be linked with the main control system of the bridge-building machine through industrial communication protocols such as Modbus to forcibly cut off the danger.The operation action realizes the active protection of "shutdown when abnormality is found".
Full-scene anti-interference and reliability assurance
In view of the complex environment of engineering construction, the system is optimized through multiple algorithms to achieve strong anti-interference capabilities: image noise reduction technology is used to eliminate the influence of dust, rain and mist on visual acquisition, and dynamic exposure adjustment is used to adapt to changes in day and night lighting to ensure that the recognition accuracy rate is always maintained at more than 99%. At the same time, it is equipped with a dual power supply redundant architecture, which supports automatic switching between 220VAC industrial power and 24VDC emergency power to avoid detection interruptions caused by voltage fluctuations.
Data management and traceability
The system has perfect data recording and analysis functions, and automatically stores the data of the whole process of wear-out testing, including detection time, equipment number, wear-out status, early warning information, etc. The data retention period is not less than 30 days, which meets the safety supervision requirements of special equipment. It can be connected to the centralized project management platform through a standard communication interface, supports remote viewing on the PC and mobile terminals, realizes visual display of inspection data, historical query and report generation, and provides data support for equipment operation and maintenance and safety audit.
Industry value: upgrade from passive protection to active prevention
Completely eliminate human-made safety hazards
Through the “human judgment” of the machine on behalf of the machine, the problems of missed inspections and misjudgments in manual visual inspection are avoided, and major safety risks such as overturning and instability of the bridge-building machine caused by failure to put the pins in place are eliminated. According to engineering practice data, it can reduce outrigger-related accidents caused by human negligence by more than 90%..
Significantly improve construction efficiency
The inspection process does not require manual intervention, and it is carried out synchronously with the operation process of the bridge-building machine, eliminating the round-trip confirmation time of traditional manual inspection, and the average daily operating efficiency of a single equipment is increased by more than 15%. In the application of major projects such as the Chongqing-Kunming High-speed Railway, the operation of the outriggers of the bridge-building machine has been realized “on-the-spot inspection and on-the-go”, effectively guaranteeing the construction progress.
Adapt to the needs of full-scene engineering
Whether it is the high-precision requirements of high-speed rail box girder erection or the complex working conditions of highway bridge construction, the system can be flexibly adapted to different tonnages and types of bridge-building machines, supporting the installation of new machines and the transformation of in-service equipment. The transformation process adopts non-contact installation, without the need to change the structure of the original equipment, and the installation and commissioning cycleIt only takes 1-2 working days.
As the core component of the informationized inspection system of the micro-special bridge-building machine, the AI inspection system with the legs in place has been successfully applied to a number of major engineering projects of China Railway, China Communications and other enterprises, and technological innovation is used to promote the transformation of construction safety management from “passive response” to “active prevention”. In the future, micro-technology will continue to deepen the integration and innovation of AI vision and engineering equipment, provide full-dimensional safety monitoring solutions for more infrastructure scenarios, and help the industry achieve the dual goals of efficient construction and intrinsically safe.
