Intelligent tower crane type test system and method

Abstract

The invention relates to the technical field of building construction mechanical equipment detection, and discloses an intelligent tower crane type test system and method, and the system comprises a central processing module which is used for coordinating the data collection and processing of a multi-axis torque sensor group module, a displacement measurement module, an image recognition module and a wireless communication module in real time, a dynamic fault diagnosis algorithm is integrated; a multi-axis torque sensor group module; a displacement measurement module; an image recognition module; and a wireless communication module. According to the invention, the comprehensive test system integrating the central processing unit, the multi-axis torque sensor group, the displacement measurement device, the image recognition module and the wireless communication module is constructed, so that the comprehensive performance test of the crane under different working conditions is realized, the data is obtained from multiple dimensions, and the high-quality delivery of final products is ensured; and the comprehensiveness and the test efficiency of the crane performance test are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction machinery and equipment detection, and in particular to an intelligent tower crane type test system and method. Background Art

[0002] With the rapid development of the construction industry, the application of intelligent equipment is becoming increasingly widespread. In particular, intelligent tower cranes have attracted great attention from the market due to their safety, high efficiency and labor cost savings. However, the existing standard medium-sized test methods for tower cranes are only for ordinary tower cranes, and do not include more comprehensive and complete technical requirements, test methods and judgment standards for intelligent tower cranes' intelligent and automated innovative technologies. As a result, it is difficult to comprehensively evaluate the various performance indicators of intelligent tower cranes, and the reliability of the application of innovative technologies cannot be determined, making it impossible to meet the current needs of intelligent construction.

[0003] In addition, traditional type tests usually judge whether it meets the design requirements from seven aspects: general regulations, whole machine, structure, mechanism, electrical, safety device, and information signs. This method is only for traditional tower cranes, and has no requirements for the intelligent control system, information transmission system, mechanism response delay, operation reliability, safety device accuracy, etc. of intelligent tower cranes. Summary of the Invention

[0004] In order to make up for the above deficiencies, the present invention provides a type test method for an intelligent tower crane, aiming to solve the problem of conducting a comprehensive, rapid and accurate type test on an intelligent tower crane.

[0005] In a first aspect, the present invention provides the following technical solution: an intelligent tower crane type test system, comprising: Central processing module: used to coordinate data acquisition and processing of the multi-axis torque sensor group module, displacement measurement module, image recognition module and wireless communication module in real time, and integrate dynamic fault diagnosis algorithm; Multi-axis torque sensor group module: Distributed and deployed at key nodes of the crane arm, including locations where stress is concentrated, deformation is likely to occur, or where overall performance is significantly affected. It is used to monitor the dynamic torque distribution under different working conditions in real time and output multi-dimensional torque data; Displacement measurement module: used for a built-in photoelectric encoder with a resolution greater than 0.01 degrees, recording the angular changes of the arm's extension and rotation in real time, and optimizing the control accuracy through closed-loop feedback; Image recognition module: Integrates a high-definition wide-angle camera with night vision capabilities and a visual positioning algorithm to dynamically capture construction site images, identify load positions and potential safety risks in real time, and trigger graded warnings; Wireless communication module: Based on the 5G network, it realizes multi-device collaborative transmission to support real-time remote data interaction and multi-threaded concurrent processing.

[0006] Preferably, the central processing module is composed of an embedded computer, including an edge computing unit and a dynamic fault diagnosis unit; The edge computing unit is deployed at the local node of the crane arm and is used to pre-process the raw data of the multi-axis torque sensor group module and the displacement measurement module in real time and extract the operating characteristic parameters; The dynamic fault diagnosis unit is based on a fault model library and is used to perform pattern matching on the pre-processed data. If an excessive torque or abnormal boom trajectory is detected, an early warning is triggered.

[0007] Preferably, the multi-axis torque sensor group module includes a redundant sensor array and an environmental compensation unit; The redundant sensor array deploys at least two groups of orthogonally distributed multi-axis torque sensors at key nodes of the boom to output a three-dimensional force field model of dynamic torque distribution; The environmental compensation unit is used to collect environmental temperature and vibration data in real time and dynamically calibrate the sensor output value.

[0008] Preferably, the image recognition module includes a target detection unit and a graded warning unit; The target detection unit is used to classify and identify construction site personnel, obstacles, and loads in real time through cameras and deep learning target detection models; The hierarchical early warning unit includes: Level 1 warning: Send warning information to operation and maintenance personnel through wireless communication module; Level 2 warning: automatically limits the crane's operating speed to a safe threshold; Level 3 warning: directly cut off the power source and trigger the sound and light alarm.

[0009] In a second aspect, the present invention provides the following technical solution, a type test method for an intelligent tower crane, the method comprising the following steps: S1. Turn on the power of the test system and supply power to external devices; S2. Set the test parameters of the crane through the mobile terminal or PC client; S3. Press the crane start button to test the crane operation through the multi-axis torque sensor group module and the displacement measurement module, and record the results. S4. During the test, the construction site is monitored in real time through the image recognition module. If any abnormality is found, the test is terminated immediately. S5. After the test is completed, the log file is saved, the test result report is exported, and the data is transmitted to the engineering team for analysis via the wireless communication module.

[0010] Preferably, in S3, multi-axis torque sensors with different ranges or displacement measuring devices with higher resolution are selected according to different testing requirements of the crane.

[0011] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned intelligent tower crane type test method when executing the computer program.

[0012] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned intelligent tower crane type test method when executed by a processor.

[0013] The present invention has the following beneficial effects: 1. In the present invention, by constructing a comprehensive testing system integrating a central processing unit, a multi-axis torque sensor group, a displacement measurement device, an image recognition module and a wireless communication module, comprehensive performance testing of the crane under different working conditions is achieved, data is obtained from multiple dimensions, the high-quality delivery of the final product is ensured, and the comprehensiveness and testing efficiency of the crane performance test are improved.

[0014] 2. In the present invention, by automating the crane test, the test accuracy and reliability are improved, and the damage caused by human error is reduced.

[0015] 3. In the present invention, the image recognition module is used to obtain visual information of the construction site in real time, and potential safety hazards are identified through intelligent analysis, thereby issuing safety warnings in a timely manner, allowing operators to take measures in advance to avoid accidents, thereby enhancing the safety and controllability of the crane operating environment and promoting continuous improvement of product quality.

[0016] 4. In the present invention, the wireless communication module breaks the distance limitation and realizes the fast and stable transmission of data, so that engineers can obtain real-time data without going to the site in person, conduct analysis and diagnosis in time, and improve work efficiency. At the same time, it supports remote operation mode to improve the safety and efficiency of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the architecture diagram of the intelligent tower crane type test system proposed in the present invention; Figure 2 This is a flow chart of the type test method for the intelligent tower crane proposed in the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Example 1 Reference Figure 1 In a first embodiment of the present invention, the present invention provides an intelligent tower crane type test system, comprising: Central processing module: used to coordinate data acquisition and processing of the multi-axis torque sensor group module, displacement measurement module, image recognition module and wireless communication module in real time, and integrate dynamic fault diagnosis algorithm; Multi-axis torque sensor group module: Distributed and deployed at key nodes of the crane arm, including locations where stress is concentrated, deformation is likely to occur, or where overall performance is significantly affected. It is used to monitor the dynamic torque distribution under different working conditions in real time and output multi-dimensional torque data; Displacement measurement module: used for a built-in photoelectric encoder with a resolution greater than 0.01 degrees, recording the angular changes of the arm's extension and rotation in real time, and optimizing the control accuracy through closed-loop feedback; Image recognition module: Integrates a high-definition wide-angle camera with night vision capabilities and a visual positioning algorithm to dynamically capture construction site images, identify load positions and potential safety risks in real time, and trigger graded warnings; Wireless communication module: Based on the 5G network, it realizes multi-device collaborative transmission to support real-time remote data interaction and multi-threaded concurrent processing.

[0020] Specifically, the central processing module integrates and schedules the operations of various modules, centrally manages the data collection and processing process, and ensures that each module works in an orderly manner through efficient computing and coordination capabilities, avoiding data confusion and work conflicts. This allows the entire system to run smoothly, respond to various complex situations in a timely manner, ensure the accuracy and integrity of test data, and provide a reliable foundation for subsequent analysis and evaluation. The multi-axis torque sensor module focuses on monitoring the dynamic torque distribution of lifting loads under different working conditions. It can sense the details of the torque changes during lifting, luffing, and slewing operations, providing key data for crane load-bearing performance evaluation, helping to determine its safe operating limits under various working conditions and accurately identifying potential overload risk points. This provides strong support for the optimized design and safe operation of the crane, effectively preventing accidents caused by abnormal torque. The displacement measurement module monitors the angular changes of the crane boom's extension, retraction, and rotation in real time. The central processing module integrates the mid-level photoelectric encoder data with the visual positioning results of the image recognition module to correct the boom's motion trajectory in real time, reducing path deviation to <1.5%. This provides key parameters for accurately calculating the crane's operating range and operating envelope, allowing operators to clearly understand the boom's position and posture, enabling more precise control of crane movements and avoiding collisions, thereby improving the accuracy and safety of crane operations. The image recognition module acquires visual information from the construction site in real time. It uses a deep learning-based visual positioning algorithm, combined with night vision and image enhancement technology, to identify load positions and environmental obstacles in real time. Risk data is then synchronously transmitted to a remote server via a wireless communication module, triggering a cloud-based safety assessment model. In low-light environments (<10 lux), the module uses night vision and image enhancement technology to achieve a load positioning accuracy of ±5cm and an obstacle recognition response time of <0.5 seconds. This allows for timely safety warnings, allowing operators to take preemptive measures to avoid accidents, thereby enhancing the safety and controllability of the crane operating environment. By breaking the distance limitation through the wireless communication module, in densely built-up areas (signal strength -90dBm), the 5G network supports the coordinated transmission of multiple devices (≥3 devices), with an average delay of ≤40ms and a packet loss rate of ≤0.05%, achieving fast and stable data transmission and timely analysis and diagnosis. Its multi-channel transmission characteristics based on the 5G network support parallel data upload of multiple cranes, and dynamically allocates bandwidth resources through the central processing module to ensure the priority transmission of key data (such as torque limit and emergency instructions), thereby improving work efficiency, facilitating remote guidance and problem solving by the engineering team, accelerating the test process and data analysis, and improving the safety of the test.

[0021] The central processing module consists of an embedded computer, including an edge computing unit and a dynamic fault diagnosis unit; The edge computing unit is deployed on the local node of the crane arm to pre-process the raw data of the multi-axis torque sensor group module and the displacement measurement module in real time and extract the operating characteristic parameters; The dynamic fault diagnosis unit is based on the fault model library and is used to perform pattern matching on the pre-processed data. If excessive torque or abnormal boom trajectory is detected, an early warning is triggered.

[0022] Specifically, the edge computing unit is deployed at the local node of the crane arm, which can pre-process the raw data of the multi-axis torque sensor group module and the displacement measurement module in real time, extract key characteristic parameters (such as torque peak and angle deviation rate), and significantly reduce data transmission delay and the load of the central processing module.

[0023] The dynamic fault diagnosis unit performs pattern matching on pre-processed data based on a preset fault model library. If excessive torque or abnormal boom trajectory is detected, a three-level warning is triggered, thereby automating the entire process from risk identification to emergency response, significantly improving the safety and operating efficiency of the crane and reducing the risk of accidents caused by data processing delays or human misjudgment.

[0024] The multi-axis torque sensor group module includes a redundant sensor array and an environmental compensation unit; The redundant sensor array deploys at least two sets of orthogonally distributed multi-axis torque sensors at key nodes of the boom to output a three-dimensional force field model of dynamic torque distribution; The environmental compensation unit is used to collect ambient temperature and vibration data in real time and dynamically calibrate the sensor output value.

[0025] Specifically, the redundant sensor array deploys at least two sets of orthogonally distributed multi-axis torque sensors at key nodes of the boom. A data fusion algorithm is used to eliminate single-point measurement errors and output a three-dimensional force field model of dynamic torque distribution. This fully captures the details of the boom's torque changes under different working conditions, providing multi-dimensional torque data support, especially under complex load conditions (such as simultaneous lifting, luffing, and slewing). Furthermore, through redundant design, even if a sensor fails, the system can still rely on other sensors to maintain normal operation, significantly improving the reliability and accuracy of torque monitoring and providing a solid data foundation for the safe operation and performance optimization of the crane. The environmental compensation unit collects ambient temperature and vibration data in real time and dynamically calibrates the output values ​​of the multi-axis torque sensor, effectively eliminating interference from external environmental factors (such as temperature changes and mechanical vibration) on the measurement results. Through dynamic calibration, the environmental compensation unit can ensure the stability and accuracy of torque monitoring data, significantly improving the adaptability and reliability of the crane under complex working conditions, reducing the risk of false alarms or missed alarms due to environmental interference, and further ensuring the safe operation of the crane.

[0026] The image recognition module includes a target detection unit and a graded warning unit; The target detection unit is used to classify and identify construction site personnel, obstacles, and loads in real time through cameras and deep learning target detection models; The hierarchical early warning units include: Level 1 warning: Send warning information to operation and maintenance personnel through wireless communication module; Level 2 warning: automatically limits the crane's operating speed to a safe threshold; Level 3 warning: directly cut off the power source and trigger the sound and light alarm.

[0027] Specifically, the target detection unit uses high-definition cameras and deep learning target detection models (such as YOLOv7) to classify and identify people, obstacles, and loads on the construction site in real time. This allows the unit to dynamically capture complex environmental information and accurately locate potential risk points (such as people entering dangerous areas or obstacles approaching the crane boom). Through continuous optimization of the deep learning model, the target detection unit can adapt to various complex working conditions (such as at night, in low light, or in severe weather), significantly improving the safety and intelligence of the crane's operating environment. The hierarchical warning unit takes different warning and response measures based on the risk level provided by the target detection unit: The first-level warning sends warning information to operation and maintenance personnel through the wireless communication module, reminding them of potential risks (such as people approaching dangerous areas), helping them take preventive measures in time to avoid escalation of risks.

[0028] When a moderate risk is detected (such as an obstacle approaching the boom), the secondary warning automatically limits the crane's operating speed to a safe threshold, reducing the equipment's operating intensity to prevent collisions or other safety accidents. When a high-risk situation is detected (such as load imbalance or impending collision), the Level 3 warning system directly cuts off the power source and triggers an audible and visual alarm, forcing the equipment to stop operating, minimizing accidents and protecting equipment and personnel.

[0029] Example 2: Reference Figure 2 In a second embodiment of the present invention, the present invention provides a type test method for an intelligent tower crane, the method comprising the following steps: S1. Turn on the power of the test system and supply power to external devices; S2. Set the test parameters of the crane through the mobile terminal or PC client; S3. Press the crane start button to test the crane operation through the multi-axis torque sensor group module and the displacement measurement module, and record the results. S4. During the test, the construction site is monitored in real time through the image recognition module. If any abnormality is found, the test is terminated immediately. S5. After the test is completed, the log file is saved, the test result report is exported, and the data is transmitted to the engineering team for analysis via the wireless communication module.

[0030] Specifically, the test system power supply is first turned on to power external devices, laying the foundation for the test. The test parameters are set through the mobile terminal or PC client to achieve customization and flexibility of the test. After pressing the crane start button, the multi-axis torque sensor group module and the displacement measurement module test and record the operation, ensuring the accurate acquisition of key data. During the test, the image recognition module monitors the construction site in real time and terminates the experiment in time when abnormalities are discovered, effectively ensuring the safety of the test process. At the end of the test, the log file is saved, the report is exported, and the data is transmitted to the engineering team for analysis, which facilitates a comprehensive evaluation of the crane performance and provides strong support for subsequent improvements and optimizations. Overall, the efficiency, accuracy and safety of the test are improved, and the reliable operation of the crane is promoted.

[0031] In S3, multi-axis torque sensors with different ranges or higher-resolution displacement measurement devices are selected according to the different testing requirements of the crane.

[0032] Specifically, selecting multi-axis torque sensors with different ranges or higher-resolution displacement measurement devices according to the different testing requirements of the crane can accurately match the specific test scenarios and requirements, thereby improving the adaptability of the test system and avoiding data errors and omissions caused by sensor range mismatch or insufficient resolution.

[0033] In summary, specific test examples: 1. Intelligent control system test objectives: Verify the accuracy and response efficiency of intelligent control algorithms.

[0034] Test method: Set up multiple simulated working conditions (such as complex construction site environment, severe weather conditions); Test path planning and automatic optimization capabilities under different load conditions; Use high-precision position sensors to verify the deviation between the actual lifting path and the planned path; Test the algorithm’s risk identification and correction capabilities under simulated accident situations, such as overload or imbalance.

[0035] Key Metrics: Path planning deviation (<2%); Accident recognition time (<1 second); The efficiency and accuracy of the system's adaptive adjustment.

[0036] 2. Information transmission system testing Objective: To evaluate the stability and real-time performance of wireless communication modules.

[0037] Test method: Test data transmission rates in different signal strength environments (e.g., densely built-up areas, open spaces); Introduce simulated interference (such as other wireless signal interference) to observe the anti-interference ability of the wireless communication module; Connect multiple tower cranes simultaneously to test the delay and packet loss rate of coordinated data transmission.

[0038] Key Metrics: Data transmission delay (<50 milliseconds); Packet loss rate (<0.1%); Multi-device collaboration latency (<100ms).

[0039] 3. Institutional response delay test Objective: To evaluate the response speed and accuracy of mechanical components.

[0040] Test method: In the simulation operation, the time difference between the instruction sending and execution is recorded by a high frame rate camera; Compare response times under different loads and operating amplitudes; Introduce emergency commands (such as emergency stop) to test response delay and execution accuracy.

[0041] Key Metrics: Command response time (<200 milliseconds); Mechanical execution deviation (<5 mm); Emergency response execution success rate (>99%).

[0042] 4. Run reliability test Objective: To verify the performance stability of the equipment during long-term operation.

[0043] Test method: Simulate the continuous operation of the crane and record the changes in key equipment parameters (temperature, vibration, power consumption, etc.); Testing under extreme environmental conditions (such as high temperature, high humidity, and dust); Enable self-diagnostic functions to verify the accuracy of fault identification and alarms.

[0044] Key Metrics: Mean time between failures (MTBF, >2000 hours); Self-diagnosis alarm accuracy (>98%); Fault recovery time (<10 minutes).

[0045] 5. Safety device accuracy test Objective: To verify the reliability of safety monitoring devices under high-risk conditions.

[0046] Test method: Use the simulation platform to simulate abnormal conditions of key safety parameters (wind speed, vibration, load, etc.); The detection system's early warning accuracy and emergency stop function; In actual working conditions, observe the system's ability to detect dynamic risks (such as people approaching dangerous areas).

[0047] Key Metrics: Wind speed and load monitoring accuracy (deviation <1%); Emergency stop trigger time (<500 ms); Risk detection coverage (>99%).

[0048] Example 3 The third embodiment of the present invention is based on the same inventive concept and proposes a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the intelligent tower crane type test method of the above embodiment are implemented.

[0049] Example 4 The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer terminal including: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the intelligent tower crane type test method of the above embodiment.

[0050] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0051] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Intelligent tower crane type test system, characterized by: include: Central processing module: used to coordinate data acquisition and processing of the multi-axis torque sensor group module, displacement measurement module, image recognition module and wireless communication module in real time, and integrate dynamic fault diagnosis algorithm; Multi-axis torque sensor group module: Distributed and deployed at key nodes of the crane arm, including locations where stress is concentrated, deformation is likely to occur, or where overall performance is significantly affected. It is used to monitor the dynamic torque distribution under different working conditions in real time and output multi-dimensional torque data; Displacement measurement module: used for a built-in photoelectric encoder with a resolution greater than 0.01 degrees, recording the angular changes of the arm's extension and rotation in real time, and optimizing the control accuracy through closed-loop feedback; Image recognition module: Integrates a high-definition wide-angle camera with night vision capabilities and a visual positioning algorithm to dynamically capture construction site images, identify load positions and potential safety risks in real time, and trigger graded warnings; Wireless communication module: Based on the 5G network, it realizes multi-device collaborative transmission to support real-time remote data interaction and multi-threaded concurrent processing.

2. The intelligent tower crane type test system according to claim 1, characterized in that: The central processing module is composed of an embedded computer, including an edge computing unit and a dynamic fault diagnosis unit; The edge computing unit is deployed at the local node of the crane arm and is used to pre-process the raw data of the multi-axis torque sensor group module and the displacement measurement module in real time and extract the operating characteristic parameters; The dynamic fault diagnosis unit is based on a fault model library and is used to perform pattern matching on the pre-processed data. If an excessive torque or abnormal boom trajectory is detected, an early warning is triggered.

3. The intelligent tower crane type test system according to claim 1, characterized in that: The multi-axis torque sensor group module includes a redundant sensor array and an environmental compensation unit; The redundant sensor array deploys at least two groups of orthogonally distributed multi-axis torque sensors at key nodes of the boom to output a three-dimensional force field model of dynamic torque distribution; The environmental compensation unit is used to collect environmental temperature and vibration data in real time and dynamically calibrate the sensor output value.

4. The intelligent tower crane type test system according to claim 1, characterized in that: The image recognition module includes a target detection unit and a graded warning unit; The target detection unit is used to classify and identify construction site personnel, obstacles, and loads in real time through cameras and deep learning target detection models; The hierarchical early warning unit includes: Level 1 warning: Send warning information to operation and maintenance personnel through wireless communication module; Level 2 warning: automatically limits the crane's operating speed to a safe threshold; Level 3 warning: directly cut off the power source and trigger the sound and light alarm.

5. Type test method for intelligent tower crane, characterized in that: The intelligent tower crane type test system according to any one of claims 1 to 4, wherein the method comprises the following steps: S1. Turn on the power of the test system and supply power to external devices; S2. Set the test parameters of the crane through the mobile terminal or PC client; S3. Press the crane start button to test the crane operation through the multi-axis torque sensor group module and the displacement measurement module, and record the results. S4. During the test, the construction site is monitored in real time through the image recognition module. If any abnormality is found, the test is terminated immediately. S5. After the test is completed, the log file is saved, the test result report is exported, and the data is transmitted to the engineering team for analysis via the wireless communication module.

6. The type test method for an intelligent tower crane according to claim 5, characterized in that: In S3, multi-axis torque sensors with different ranges or displacement measuring devices with higher resolution are selected according to different testing requirements of the crane.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the intelligent tower crane type test method according to claim 6 is implemented.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the type test method of the intelligent tower crane according to claim 6 is implemented.

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