Remote real-time operation system for tower crane

Abstract

The invention provides a tower crane remote real-time operation system which comprises the following modules: a remote control system is used for receiving and converting an operation signal of a handheld assembly and transmitting the operation signal to a tower crane cab monitoring system, and the tower crane cab monitoring system is used for collecting operation parameters and environment data of a tower crane in real time and transmitting the operation signal to a remote control system; the operation signal and the collected data are sent to the tower crane lifting, amplitude-changing and corner integrated frequency converter control system, and the tower crane lifting, amplitude-changing and corner integrated frequency converter control system is used for controlling the lifting, amplitude-changing and corner actions of the tower crane in real time according to the operation signal or the collected data. The intelligent interaction control module cooperates with the multi-source data fusion analysis module and the building state sensing and feedback module, tower crane data, construction area building states and associated influence parameters are comprehensively integrated, the building state sensing and feedback module monitors building deformation and stress, it is guaranteed that construction and design are consistent in combination with a BIM model, the deviation is smaller than or equal to 3 mm, and construction safety and progress are guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of remote control, in particular to a tower crane remote real-time operating system. Background Art

[0002] Tower cranes, as essential equipment in construction, undertake crucial tasks such as lifting materials. In modern, large-scale construction projects, construction sites are complex and ever-changing. Traditional tower crane operation places operators in the crane cab, with limited visibility and limited visibility, creating significant safety risks. Furthermore, the expansion of construction sites and increasing construction requirements pose greater challenges to the accuracy, timeliness, and efficiency of tower crane operations. Therefore, remote tower crane operation has become an urgent need in the industry to improve operational safety and construction efficiency.

[0003] Currently, most tower cranes are equipped with intelligent operating systems. Some systems implement basic operating parameter monitoring, collecting data such as the crane's lifting height and luffing angle. Some also have simple abnormality alarm functions, such as issuing an alarm when the lifting weight exceeds a certain limit. For communication, wired networks or early wireless networks are used for data transmission.

[0004] For example, publication number CN112173973A, the remote real-time operating system of the tower crane can only collect the data of the tower crane itself, and cannot collect the building status and other influencing parameters in the construction area, making it difficult to fully integrate multiple information to optimize the operation strategy.

[0005] Therefore, in view of this, a tower crane remote real-time operating system is proposed. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a tower crane remote real-time operating system for solving the problems raised in the background technology.

[0007] A tower crane remote real-time operating system, including the following modules:

[0008] Remote control system: used to receive and convert the operation signals of the handheld components and transmit the operation signals to the tower crane cab monitoring system;

[0009] Tower crane cab monitoring system: used to collect the operating parameters and environmental data of the tower crane in real time, and send the operation signals and collected data to the tower crane lifting, luffing and angle-changing integrated inverter control system;

[0010] Tower crane lifting, luffing and turning integrated inverter control system: used to control the lifting, luffing and turning of the tower crane in real time according to the operating signal or collected data;

[0011] Intelligent interactive control module: integrated into the remote control system, providing a graphical operation interface, supporting one-touch commands, voice commands and gesture control, and dynamically feedback on operation status;

[0012] Multi-source data fusion analysis module: deployed in the tower crane cab monitoring system, used to clean, correlate and extract features of tower crane parameters and environmental data through edge computing, generate multi-dimensional parameter charts and trigger abnormal warnings;

[0013] Building status perception and feedback module: integrated into the tower crane's lifting, luffing and angle-changing integrated inverter control system, it uses lidar and strain sensors to monitor the deformation and stress of the construction building in real time, generates a building health report based on the BIM model, and dynamically adjusts the tower crane's operating parameters.

[0014] Preferably, the intelligent interactive control module includes a voice command recognition unit, a gesture control unit and a dynamic feedback unit. The voice command recognition unit supports noise reduction processing and multi-dialect adaptation. The gesture control unit captures gesture movements through a camera or infrared sensor and sets an anti-mistouch mechanism. The dynamic feedback unit displays the command execution status in real time through color gradient or progress bar animation.

[0015] Preferably, the multi-source data fusion analysis module includes a sensor data synchronization unit, an edge computing node and an abnormality threshold database. The sensor data synchronization unit is used to coordinate the data acquisition timing of the lifting height sensor, the amplitude angle sensor, the inclination sensor, the wind speed sensor and the temperature and humidity sensor. The edge computing node is deployed in the tower crane cab, and the edge computing node is used to generate thermal maps, 3D models and operation trend line charts in real time. The abnormality threshold database is used to pre-store overload, sway limit and environmental risk thresholds, and associate the optimization suggestion push logic.

[0016] Preferably, the remote control system and the tower crane cab monitoring system are connected via a 5G communication protocol and support a local redundant network as a backup link.

[0017] Preferably, the abnormal warning function of the multi-source data fusion analysis module includes overload warning and yaw over-limit warning. The motor load exceeds the preset safety value to trigger the overload warning, and automatically trigger the shutdown protection and push adjustment suggestions. The yaw over-limit warning calculates the tower body yaw amplitude in real time through the inclination sensor data, and starts the correction program when it exceeds the threshold.

[0018] Preferably, the system automatically adjusts the lifting path and optimizes the lifting sequence through a dynamic avoidance algorithm during actual construction.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. During use, the intelligent interactive control module collaborates with the multi-source data fusion analysis module and the building status perception and feedback module to comprehensively integrate tower crane data, construction area building status and related influencing parameters. The building status perception and feedback module monitors building deformation and stress, and combines the BIM model to ensure that construction is consistent with design, with a deviation of ≤3mm, to ensure construction safety and progress.

[0021] 2. When the present invention is in use, the multi-source data fusion analysis module ensures rapid data response, realizes 100% sensor data collection, quickly generates charts and issues early warnings, and reduces the risk of safety accidents caused by human errors.

[0022] 3. When the present invention is in use, the intelligent interactive control module provides multiple operation modes, simplifies the operation process, reduces the operation steps by more than 40%, lowers the operation threshold for non-professionals, and improves operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the tower crane intelligent control system architecture of the present invention;

[0024] Figure 2 This is a schematic diagram showing the code of the voice and gesture recognition technology of the intelligent interactive control module of the present invention;

[0025] Figure 3 This is a schematic diagram of the code presentation of the dynamic feedback mechanism of the intelligent interactive control module of the present invention;

[0026] Figure 4 This is a schematic diagram of the sensor data acquisition synchronization logic code of the multi-source data fusion analysis module of the present invention;

[0027] Figure 5 This is a schematic diagram of the data processing and early warning process code of the multi-source data fusion analysis module of the present invention;

[0028] Figure 6 This is a schematic diagram of the code for the collaborative operation of building monitoring and tower crane in the building status perception and feedback module of the present invention;

[0029] Figure 7 It is a simplified code diagram of the operation process of the intelligent interactive control module of the present invention. DETAILED DESCRIPTION

[0030] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0031] A tower crane remote real-time operating system, including the following modules:

[0032] Remote control system: used to receive and convert the operation signals of the handheld components and transmit the operation signals to the tower crane cab monitoring system;

[0033] Tower crane cab monitoring system: used to collect the operating parameters and environmental data of the tower crane in real time, and send the operation signals and collected data to the tower crane lifting, luffing and angle-changing integrated inverter control system;

[0034] Tower crane lifting, luffing and turning integrated inverter control system: used to control the lifting, luffing and turning of the tower crane in real time according to the operating signal or collected data;

[0035] Intelligent interactive control module: integrated into the remote control system, providing a graphical operation interface, supporting one-touch commands, voice commands and gesture control, and dynamically feedback on operation status;

[0036] Multi-source data fusion analysis module: deployed in the tower crane cab monitoring system, used to clean, correlate and extract features of tower crane parameters and environmental data through edge computing, generate multi-dimensional parameter charts and trigger abnormal warnings;

[0037] Building status perception and feedback module: integrated into the tower crane's lifting, luffing and angle-changing integrated inverter control system, it uses lidar and strain sensors to monitor the deformation and stress of the construction building in real time, generates a building health report based on the BIM model, and dynamically adjusts the tower crane's operating parameters.

[0038] Example 1: The intelligent interactive control module integrates a graphical operation interface, a voice command recognition unit, and a gesture control unit to achieve diversified input and dynamic feedback of user operation commands. The following technical means are used to simplify user operation steps and improve control intuitiveness:

[0039] The graphical interface is initialized. At this time, the predefined interface layout file is loaded. The file format is XML / JSON, and the UI component event listener is bound. A one-click command button is provided for automatic homing and preset trajectory execution.

[0040] Voice command processing involves using microphone hardware to collect voice signals, using a pre-trained speech recognition model (the DeepSpeech framework based on a deep neural network can be used), converting voice into text commands, and generating standardized operation commands through matching with a preset command library.

[0041] Gesture control processing uses a camera or infrared sensor to capture hand movements, uses the MediaPipeHandLandmark model to detect key point coordinates, and defines the mapping rules between gesture actions and commands. In this case, the gesture action is five fingers open, corresponding to the stop command;

[0042] Dynamic feedback is achieved by pushing the command execution status to the front-end interface in real time based on the WebSocket protocol, and intuitively displaying the operation progress through color gradients and progress bar animations;

[0043] The graphical interface initialization starts with the remote control terminal, parsing the interface configuration file, loading the buttons, sliders, and status indicator area components, and then registering event callback functions for each UI component. For example, clicking the "rise 10 meters" button triggers the on_l ift_command() method. This allows the predefined interface layout to be bound to events, reducing the number of manual configuration steps for users and ensuring a unified operation entry point.

[0044] The voice command processing first initializes the microphone hardware and sets the sampling rate to 16kHz and the bit depth to 16bit. It then calls the speech_recognition library to capture the audio stream in real time. It then loads the speech recognition model weights, performs endpoint detection, noise reduction, and feature extraction on the input audio, outputs a text command, and then matches the text command with the preset command library (stored in JSON format). If a match is successful, an operation command JSON message is generated. This lowers the operational threshold through voice recognition technology, allowing non-professionals to quickly get started and reduce manual input errors.

[0045] Gesture control processing first captures RGB images from the camera at 30fps, calls cv2.VideoCapture to obtain the video stream, and uses the MediaPipeHandLandmark model to detect the coordinates of 21 key points of the hand. The gesture feature vector is calculated and then matched to a predefined gesture action library based on the feature vector. For example, raising a fist corresponds to raising a hook. This non-contact interaction improves operational safety and avoids the risk of accidental touches caused by physical contact.

[0046] Dynamic feedback first establishes a WebSocket server to listen to the status update messages of the tower crane cab monitoring system and parse the status data, including the current height and motor load, to update the length and color attributes of the progress bar on the front-end interface. When an abnormal state is detected, a red flashing warning animation is triggered and a pop-up window prompt is pushed. This enhances the user's perception of the operating status through real-time visual feedback and reduces blind spots in human monitoring.

[0047] In addition, users can replace traditional multi-step operations with a single gesture or voice command, reducing the number of operation steps by more than 40%. The voice recognition unit supports noise reduction processing and multi-dialect adaptation, with a recognition accuracy rate of ≥95%. The gesture control unit sets an anti-mistouch mechanism, with a false trigger rate of ≤2% and a dynamic feedback delay of ≤200ms, ensuring that users can obtain the command execution status in a timely manner.

[0048] Example 2: The multi-source data fusion analysis module coordinates the data acquisition timing of multiple sensors, uses edge computing nodes to clean, correlate and extract features of tower crane operating parameters and environmental data, generates multi-dimensional visual charts and triggers abnormal warnings. Sensor data synchronization is to unify the acquisition timing of each sensor through a hardware timer to eliminate timestamp deviation. Edge computing processing is to deploy lightweight data processing algorithms on Jetson Nano edge devices to generate heat maps, 3D models and trend line charts in real time. In addition, the abnormal threshold warning is to preset overload, sway limit and environmental risk thresholds, and combine the optimization suggestion push logic to improve safety.

[0049] Sensor data synchronization first configures the acquisition frequencies of the lifting height sensor (10Hz), amplitude angle sensor (10Hz), and tilt sensor (20Hz). The STM32 microcontroller's hardware timer (TIM module) is then used to trigger ADC sampling, ensuring strict alignment of data acquisition cycles. This eliminates temporal asynchrony in multi-source data and ensures timing consistency for subsequent data fusion.

[0050] To deploy edge computing nodes, first install a Jetson Nano device in the tower crane cab, deploy the Ubuntu 20.04 system and Python 3.8 environment, then install the NumPy and Pandas libraries for data cleaning, and the Matplotlib and Seaborn libraries for chart generation. Finally, start the MQTT client to subscribe to sensor data topics, receive and cache data streams in real time, so that edge computing can reduce cloud dependence and ensure data processing latency ≤ 150ms.

[0051] The key step of data cleaning and correlation is to first call the remove_outliers() function to remove sensor outliers and filter out noisy data using the 3σ principle (standard deviation threshold = 3). Then, linear interpolation is used to align sensor data of different frequencies to generate a data frame with a unified timestamp. This improves data quality and prevents outliers from interfering with subsequent analysis and early warning logic.

[0052] To generate a visual chart, Matplotlib is first used to draw a line chart of the lifting height trend, with the horizontal axis representing the timestamp and the vertical axis representing the height value. Seaborn is then used to generate a heat map of environmental data, mapping wind speed, temperature, and humidity to a two-dimensional grid. The color depth represents the numerical value. Finally, the Three.js engine is used to render the 3D model of the tower crane, updating the boom angle and hook position in real time. This allows for a multi-dimensional visual display, helping users quickly locate abnormalities in the tower crane's operating status.

[0053] The abnormal warning logic first monitors the motor load current in real time. If it exceeds the preset safety threshold (such as 1500A), the shutdown protection command is triggered. Then, the over-sway warning calculates the tower body's sway angle based on the inclination sensor data. If it exceeds 5°, the hydraulic correction system is activated. Through active warning and automated correction, the risk of safety accidents caused by human error is reduced.

[0054] Furthermore, the sensor data acquisition coverage rate reaches 100%, the missing data interpolation filling error is ≤1%, and the end-to-end delay from data acquisition to chart generation is ≤200ms, meeting the real-time monitoring requirements. Finally, the overload warning false alarm rate is ≤0.5%, and the yaw correction response time is ≤500ms.

[0055] Example 3: The building status perception feedback module deploys lidar and strain sensors to monitor the surface deformation and structural stress of the construction building in real time. Combined with the BIM model comparison and analysis results, it dynamically adjusts the tower crane operating parameters. The lidar scanning uses a Velodyne VLP-16 radar to collect point cloud data on the building surface with an accuracy of ±2mm. The strain monitoring then measures the stress changes of key load-bearing structures through an FBG sensor array with a sensitivity of 0.1με. Finally, the BIM model comparison aligns the real-time scanning data with the design model to generate a deviation analysis report and construction progress forecast.

[0056] LiDAR data collection: First, a LiDAR is installed on the crane hook, with a scanning frequency of 20 Hz and a scanning angle of 220° × 30°. Point cloud data is then transmitted to the edge server via Ethernet. The data format is a floating-point XYZ coordinate sequence. This allows for high-precision building surface deformation data, providing spatial information support for dynamic avoidance.

[0057] Strain sensor data processing: First, deploy the FBG sensor array at the building beam-column node with a sampling rate of 1kHz. The wavelength demodulator outputs the strain value, and then according to the formula Calculate strain, where K = 0.001pm / με, to monitor the health of the building structure in real time and warn of potential collapse risks;

[0058] When comparing BIM models, first load the IFC format BIM model, extract the coordinates of key structural points, such as column tops and beam ends, then call the ICP algorithm to align the lidar point cloud with the model, calculate the deviation matrix, and finally generate a deviation heat map, marking high-risk areas with deviations exceeding 5mm. In this way, through digital comparison, it is possible to ensure that the construction progress is consistent with the design drawings and the deviation is ≤3mm.

[0059] In summary, from the above three embodiments, it can be concluded that the intelligent interactive control module, the multi-source data fusion analysis module and the building status perception feedback module include the following parameters in the entire system control process: the deviation degree of the tower crane placing objects, the response time of the picture information return, the time difference between the user operation and the tower crane start-up, the dynamic avoidance path planning time, the voice command recognition delay, the sensor data synchronization error, the overload warning response time and the influence of the ambient temperature and humidity on the deviation degree.

[0060] Table 1: Parameters of intelligent interactive control module

[0061] Parameter name Parameter Details Reduction ratio of operation steps >40％ Speech recognition accuracy ≥95％ Gesture control false trigger rate ≤2％ Dynamic feedback delay ≤200ms

[0062] The table above demonstrates that the intelligent interactive control module has significantly improved user convenience and provided real-time feedback. The reduction in operating steps by over 40% significantly simplifies the previously complex crane operation process through diversified input methods. For example, a single-touch command button consolidates tasks that previously required multiple manual steps, such as homing and executing a preset trajectory. This eliminates the tedious task of inputting individual commands, significantly improving operational efficiency. A speech recognition accuracy of ≥95% demonstrates that technologies such as the DeepSpeech framework, based on deep neural networks, combined with noise reduction and multi-dialect adaptation, can accurately convert user speech into operational commands. This lowers the barrier to entry for non-professionals, allowing more people to quickly master crane operation and reducing the risk of errors associated with manual input. A gesture control false trigger rate of ≤2% is achieved thanks to technologies such as the MediaPipeHandLandmark model, which accurately recognizes hand gestures captured by cameras or infrared sensors. Effective anti-accidental touch prevention mechanisms, such as defining a stop command for a five-finger spread, make contactless interaction safer and more reliable, preventing dangerous situations caused by accidental touches due to physical contact. The dynamic feedback delay is ≤200ms. With the help of the WebSocket protocol, the command execution status can be quickly pushed to the front-end interface in an intuitive way such as color gradient and progress bar animation. Users can understand the operation progress in a timely and clear manner. When an abnormal state occurs, the red flashing warning animation and pop-up prompts can allow users to quickly notice it, effectively reducing blind spots in human monitoring and ensuring smooth and safe operations in all aspects.

[0063] Table 2: Multi-source data fusion analysis module related parameters

[0064] Parameter name Parameter Details Sensor data collection coverage >40％ Missing data interpolation filling error ≥95％ Data processing delay ≤2％ End-to-end latency from data collection to chart generation ≤200ms Overload warning false alarm rate ≤0.5％ Deviation correction response time ≤500ms

[0065] The table above shows that the multi-source data fusion and analysis module plays a key role in ensuring the accuracy, timeliness, and safety of tower crane operation data. Sensor data acquisition coverage reaches 100%. The hardware timer (TIM module) of the STM32 microcontroller uniformly configures the acquisition timing of various sensors, including the lifting height sensor (10Hz), amplitude angle sensor (10Hz), and tilt sensor (20Hz). This ensures strict alignment of data acquisition cycles, eliminates temporal asynchrony in multi-source data, and provides solid timing consistency for subsequent data fusion. This ensures that all tower crane operation data is comprehensively and accurately collected, and the interpolation error for missing data is ≤1%.

[0066] In the data cleaning phase, the remove_outliers() function is called to use the 3σ principle (standard deviation threshold = 3) to remove sensor outliers. Then, linear interpolation is used to align sensor data of different frequencies and generate data frames with unified timestamps, which greatly improves data quality and prevents outliers from interfering with subsequent analysis and early warning logic. Data processing delay is ≤150ms. By installing a Jetson Nano device in the tower crane cab, deploying the Ubuntu 20.04 system and Python 3.8 environment, and installing the NumPy and Pandas libraries for data cleaning, and the Matplotlib and Seaborn libraries for chart generation, the MQTT client is started to subscribe to the sensor data topic to receive and cache data streams in real time. The use of edge computing technology greatly reduces dependence on the cloud and achieves fast data processing.

[0067] Among them, the end-to-end delay from data collection to chart generation is ≤200ms, which can meet the needs of real-time monitoring. Users can quickly obtain multi-dimensional visual charts generated by tower crane operating parameters and environmental data, such as lifting height trend line charts, environmental data heat maps, tower crane 3D models, etc., to assist in quickly locating abnormal tower crane operating status.

[0068] When the overload warning false alarm rate is ≤0.5%, the motor load current is monitored in real time. Once it exceeds the preset safety threshold (such as 1500A), the shutdown protection command is immediately triggered. The yaw correction response time is ≤500ms. The tower yaw angle is calculated based on the inclination sensor data. When it exceeds 5°, the hydraulic correction system is quickly started. Through active warning and automatic correction mechanism, the risk of safety accidents caused by human operational errors is effectively reduced.

[0069] Table 3: Building status perception feedback module related parameters

[0070] Parameter name Parameter Details LiDAR accuracy ±2mm Strain sensor sensitivity 0.1 BIM model comparison ensures consistency deviation ≤3mm LiDAR scanning frequency 20Hz LiDAR scanning angle 220°×30° Strain sensor sampling rate 1kHz

[0071] According to the above table, it can be concluded that the building status perception feedback module establishes a close connection between the tower crane and the construction building, ensuring construction safety and progress. The laser radar accuracy is ±2mm, and the Velodyne VLP-16 radar is installed on the tower crane hook. The scanning frequency is set to 20Hz, and the scanning angle is 220°×30°. The point cloud data of the floating-point XYZ coordinate sequence is transmitted to the edge server via Ethernet. It can obtain high-precision building surface deformation data, and provide accurate spatial information support for the dynamic avoidance of the tower crane during the lifting process, ensuring that the tower crane operation will not cause collisions and other damage to the building structure. The strain sensor sensitivity is 0.1, and the FBG sensor array is deployed at the building beam-column node. The sampling rate is 1kHz. The wavelength demodulator outputs the strain value and calculates the strain through the formula. It can monitor the stress changes of the key load-bearing structures of the building in real time, issue early warnings for potential collapse risks in time, and ensure the structural safety during the construction process.

[0072] Among them, the consistency deviation ensured by BIM model comparison is ≤3mm. First, the IFC format BIM model is loaded, the coordinates of key structural points such as column tops and beam ends are extracted, the ICP algorithm is called to align the lidar point cloud with the model, the deviation matrix is calculated and a deviation heat map is generated, and high-risk areas with deviations exceeding 5mm are marked. Through digital comparison, the consistency between the construction progress and the design drawings is strictly ensured, making the construction process more standardized and precise, avoiding engineering quality problems and safety hazards caused by excessive construction deviations. At the same time, the tower crane operating parameters can be dynamically adjusted according to the comparison results to ensure that the tower crane operation is compatible with the construction progress and safety requirements.

[0073] Judging from the effects of the innovative modules in the above technical solutions, the intelligent interactive control module greatly simplifies the operating process, reducing the number of operating steps by more than 40%, and improving operational efficiency. Its voice recognition accuracy is high, the gesture control false trigger rate is low, and the dynamic feedback delay is short, which significantly improves the user operating experience; the multi-source data fusion analysis module comprehensively and accurately collects data, and the data processing and early warning are timely to ensure the safe operation of the tower crane; the building status perception feedback module accurately monitors the deformation and stress of the building to ensure construction safety and progress.

[0074] In terms of feasibility, the technologies used in the intelligent interactive control module, such as the DeepSpeech framework and the MediaPipeHandLandmark model, are mature, and processes such as graphical interface initialization also have clear execution steps; the multi-source data fusion analysis module has specific technical solutions and equipment selection for each link from sensor data synchronization to chart generation; the building status perception feedback module's lidar, strain sensor and BIM model comparison technology also have practical application conditions.

[0075] Regarding technical challenges, the intelligent interactive control module needs to continuously optimize its speech recognition model to adapt to more complex environments and dialects, and improve gesture recognition accuracy. The multi-source data fusion and analysis module needs to further address the problem of extracting features and deeply correlating different types of data during data fusion. The building state perception and feedback module faces challenges in improving the stability of the LiDAR in complex construction environments and dynamically updating BIM models with greater accuracy. In terms of practicality, the intelligent interactive control module allows non-professionals to easily operate the tower crane, lowering the entry threshold. The multi-source data fusion and analysis module provides strong support for safe crane operation and reduces the risk of accidents. The building state perception and feedback module assists construction units in adjusting crane operating parameters in real time based on building status, improving construction quality and safety. Regarding the development cycle, the intelligent interactive control module involves the integration of mature technologies. With a well-developed development team and resources, the development cycle is expected to be relatively short, with basic functional development and testing completed in a few months. However, due to the complex technical steps and the need for detailed debugging, the development cycle of the multi-source data fusion and analysis module is expected to be longer, estimated at six months to a year. The development cycle of the building state perception and feedback module is also longer, likely around a year, due to the complex sensor deployment and model algorithms involved.

[0076] In addition, from an innovative perspective, the intelligent interactive control module innovates the operating method, realizes diversified input and real-time feedback, the multi-source data fusion analysis module innovates the data processing and early warning mechanism, and uses edge computing to improve efficiency; the building status perception feedback module innovates the way buildings and tower cranes are associated, and dynamically adjusts the operation of tower cranes based on real-time monitoring, opening up new paths for improving construction safety and efficiency.

[0077] All aspects of the present invention are within the scope of protection of this patent.

[0078] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A tower crane remote real-time operating system, characterized in that: Includes the following modules: Remote control system: used to receive and convert the operation signals of the handheld components and transmit the operation signals to the tower crane cab monitoring system; Tower crane cab monitoring system: used to collect the operating parameters and environmental data of the tower crane in real time, and send the operation signals and collected data to the tower crane lifting, luffing and angle-changing integrated inverter control system; Tower crane lifting, luffing and turning integrated inverter control system: used to control the lifting, luffing and turning of the tower crane in real time according to the operating signal or collected data; Intelligent interactive control module: integrated into the remote control system, providing a graphical operation interface, supporting one-touch commands, voice commands and gesture control, and dynamically feedback on operation status; Multi-source data fusion analysis module: deployed in the tower crane cab monitoring system, used to clean, correlate and extract features of tower crane parameters and environmental data through edge computing, generate multi-dimensional parameter charts and trigger abnormal warnings; Building status perception and feedback module: integrated into the tower crane's lifting, luffing and angle-changing integrated inverter control system, it uses lidar and strain sensors to monitor the deformation and stress of the construction building in real time, generates a building health report based on the BIM model, and dynamically adjusts the tower crane's operating parameters.

2. A tower crane remote real-time operating system according to claim 1, characterized in that: The intelligent interactive control module includes a voice command recognition unit, a gesture control unit and a dynamic feedback unit. The voice command recognition unit supports noise reduction processing and multi-dialect adaptation. The gesture control unit captures gesture movements through a camera or infrared sensor and sets an anti-mistouch mechanism. The dynamic feedback unit displays the command execution status in real time through color gradient or progress bar animation.

3. A tower crane remote real-time operating system according to claim 1, characterized in that: The multi-source data fusion analysis module includes a sensor data synchronization unit, an edge computing node and an abnormality threshold database. The sensor data synchronization unit is used to coordinate the data acquisition timing of the lifting height sensor, amplitude angle sensor, inclination sensor, wind speed sensor and temperature and humidity sensor. The edge computing node is deployed in the tower crane cab, and the edge computing node is used to generate thermal maps, 3D models and operation trend line charts in real time. The abnormality threshold database is used to pre-store overload, yaw limit and environmental risk thresholds, and associate optimization suggestion push logic.

4. A tower crane remote real-time operating system according to claim 1, characterized in that: The remote control system is connected to the tower crane cab monitoring system via the 5G communication protocol and supports a local redundant network as a backup link.

5. A tower crane remote real-time operating system according to claim 1, characterized in that: The abnormal warning functions of the multi-source data fusion analysis module include overload warning and yaw limit warning. When the motor load exceeds the preset safety value, the overload warning is triggered, and the shutdown protection is automatically triggered and adjustment suggestions are pushed. The yaw limit warning calculates the tower body yaw amplitude in real time through the inclination sensor data, and starts the correction program when it exceeds the threshold.

6. A tower crane remote real-time operating system according to claim 1, characterized in that: In actual construction, the system automatically adjusts the lifting path and optimizes the lifting sequence through a dynamic avoidance algorithm.

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