Intelligent management method and system for tower crane operation based on digital twinning

Abstract

The application provides a tower crane operation intelligent management method and system based on digital twinning, relates to the technical field of tower crane operation management, and comprises the following steps: tracking construction progress by using an APF combination sequence, obtaining production requirements, outputting operation requirements, selecting an optimal tower crane and a best hoisting control strategy, forming operation instructions, and distributing the operation instructions to a tower crane operation terminal; the tower crane operation terminal is connected with an edge node, the safety state of a tower crane corresponding to the operation instructions is tracked, and a reminder instruction is sent along the way to inform workers along the way to perform safety management. Through the application, the technical problem of low operation efficiency caused by the limitation of traditional methods and the difficulty in realizing optimal resource allocation can be solved, the safety state of the tower crane is monitored in real time by tracking the construction progress through the APF combination sequence and distributing operation instructions by combining an optimal hoisting algorithm, intelligent management of tower crane operation scheduling is realized, and the technical effect of improving overall construction efficiency is achieved.

Description

Technical Field

[0001] This application relates to the field of tower crane operation management technology, and in particular to a method and system for intelligent management of tower crane operations based on digital twins. Background Technology

[0002] With the development of industrial technology, the demand for large-scale construction is increasing, especially in high-altitude operations. Large-scale hoisting services are now widely used in many key areas, and tower crane operation management is gradually developing towards automation and intelligence.

[0003] Currently, existing tower crane operation management still has some shortcomings in terms of efficiency, safety, and resource utilization.

[0004] In summary, existing technologies suffer from the problem of low operational efficiency due to the limitations of traditional methods, making it difficult to achieve optimal resource allocation. Summary of the Invention

[0005] The purpose of this application is to provide a digital twin-based intelligent management method and system for tower crane operations, in order to solve the technical problem in the prior art that the limitation of traditional methods makes it difficult to achieve optimal resource allocation, resulting in low operational efficiency.

[0006] In view of the above problems, this application provides a method and system for intelligent management of tower crane operations based on digital twins.

[0007] Firstly, this application provides a digital twin-based intelligent management method for tower crane operations. This method is implemented through a digital twin-based intelligent management system for tower crane operations. The method includes: tracking the construction progress of tower crane operations using an APF (Advanced Persistent Function) combination sequence to obtain the current construction progress; obtaining production requirements and, in conjunction with the current construction progress, outputting operation requirements, which include hoisting material requirements, current hoisting position, unloading position, hoisting time, and the tower crane operation status within the hoisting time window; based on the operation requirements, using an optimal hoisting algorithm to select the optimal tower crane and the best hoisting control strategy, forming operation instructions and allocating them to the corresponding tower crane operation terminals; after receiving the operation instructions, the tower crane operation terminals connect to the tower crane safety management edge node, track the tower crane safety status during the hoisting process corresponding to the operation instructions, and issue along-the-way reminder instructions; based on the along-the-way reminder instructions, notifying along-the-way personnel for safety management.

[0008] Secondly, this application also provides a digital twin-based intelligent management system for tower crane operations, used to execute the method described in the first aspect. The system includes: a construction progress acquisition module, used to track the construction progress of tower crane operations using an APF (Advanced Persistent Function) combination sequence to acquire the current construction progress; a production demand acquisition module, used to acquire production demands, and in conjunction with the current construction progress, output operation demands, including hoisting material requirements, current hoisting position, unloading position, hoisting time, and the tower crane operation status within the hoisting time window; an optimal hoisting algorithm module, used to select the optimal tower crane and the best hoisting control strategy based on the operation demands, using the optimal hoisting algorithm, and form operation instructions to be allocated to the corresponding tower crane operation terminals; a reminder instruction issuance module, used by the tower crane operation terminal to connect to the tower crane safety management edge node after receiving the operation instructions, track the tower crane safety status during the hoisting process corresponding to the operation instructions, and issue reminder instructions along the way; and a safety management notification module, used to notify personnel along the route to perform safety management based on the reminder instructions along the way.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: This system utilizes APF (Advanced Pilot Function) combination sequences to track the construction progress of tower crane operations and obtain the current construction progress. It then acquires production requirements and, based on the current construction progress, outputs operational requirements. These requirements include the required lifting materials, the current lifting location, the unloading location, the lifting time, and the tower crane's operational status within the lifting time window. Based on these operational requirements, an optimal lifting algorithm is used to select the optimal tower crane and the best lifting control strategy, generating operational instructions that are then distributed to the corresponding tower crane operation terminals. Upon receiving the operational instructions, the tower crane operation terminals connect to the tower crane safety management edge node, track the tower crane's safety status during the lifting process corresponding to the operational instructions, and issue along-the-path warning instructions. Based on these warning instructions, personnel along the route are notified to conduct safety management. By tracking construction progress through APF combination sequences, distributing operational instructions using the optimal lifting algorithm, and monitoring the tower crane's safety status in real time, intelligent management of tower crane operation scheduling is achieved, thereby improving overall construction efficiency.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the intelligent management method for tower crane operations based on digital twins proposed in this application. Figure 2 This is a schematic diagram of the intelligent management system for tower crane operations based on digital twins, as described in this application.

[0013] Figure labeling: Module 11 for obtaining construction progress, Module 12 for obtaining production requirements, Module 13 for the optimal hoisting algorithm, Module 14 for issuing reminder instructions, and Module 15 for safety management notification. Detailed Implementation

[0014] This application provides a digital twin-based intelligent management method and system for tower crane operations, solving the technical problem of low operational efficiency caused by the limitations of traditional methods, which make it difficult to achieve optimal resource allocation. By tracking construction progress through APF combination sequences, allocating work instructions in conjunction with the optimal lifting algorithm, and monitoring the safety status of tower cranes in real time, intelligent management of tower crane operation scheduling is achieved, thereby improving the overall construction efficiency.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1 Please see the appendix Figure 1 This application provides a method for intelligent management of tower crane operations based on digital twins, wherein the method is applied to an intelligent management system for tower crane operations based on digital twins, and the method specifically includes the following steps: Step 1: Use the APF combined sequence to track the construction progress of the tower crane operation and obtain the current construction progress.

[0017] Specifically, real-time data is collected through sensors and monitoring systems installed on the tower crane, including the crane's position, movement status, and load. This collected data is used to build a potential field model of the construction site, reflecting the crane's operating environment and construction progress. The potential field model is analyzed to predict and calculate the current construction progress, including completed and remaining work. APF (Automatic Power Processing) is an algorithm based on artificial intelligence and machine learning used to simulate and control robots or equipment, such as tower cranes, in dynamic environments. In tower crane operations, APF can be used to simulate and analyze the crane's operational progress and environment, including its movement path, work area, and potential obstacles, to optimize workflows and improve efficiency. Combination sequences refer to combining multiple APF algorithms or models to handle more complex operating environments. In tower crane operations, this may involve considering multiple work points, different tower cranes, and various operational constraints simultaneously. Through combination sequences, the system can track the real-time position and status of the tower crane, thereby obtaining the current construction progress and achieving real-time tracking and optimized management of the construction schedule.

[0018] Step 2: Obtain production requirements and, in conjunction with the current construction progress, output work requirements. These work requirements include hoisting material requirements, current hoisting position, unloading position, hoisting time, and tower crane operation status within the hoisting time window.

[0019] Specifically, the system collects production demand information from the construction site management system and managers of each workflow section, including the type, quantity, and purpose of the materials to be hoisted. This acquired production demand is then combined with the current construction progress tracked through the APF (Advanced Planning and Design) sequence to ensure that operational requirements match the actual conditions at the construction site, avoiding operational conflicts or delays caused by information asymmetry. Based on this combined information, the system outputs specific operational requirements, including the type and quantity of materials to be hoisted, the start and end positions of the hoisting, the specific time of the hoisting, and the expected operational status of the tower crane during the hoisting period. By integrating real-time data and demand information, precise operational instructions are provided to the construction site, reducing uncertainties during the operational process.

[0020] Step 3: Based on the aforementioned operational requirements, the optimal tower crane and best hoisting control strategy are selected using the optimal hoisting algorithm, and operational instructions are generated and distributed to the corresponding tower crane operation terminals.

[0021] Specifically, based on the output operational requirements, the system analyzes these requirements using an optimal lifting algorithm, taking into account factors such as the tower crane's location, workload, and performance. The system selects the most suitable tower crane for the task based on the operational requirements (weight of the material to be lifted, lifting location, etc.) and the tower crane's performance parameters (such as lifting capacity and operating radius). The optimal lifting control strategy considers factors such as safety distance, work path optimization, and obstacle avoidance, determining the detailed steps for completing the lifting task, including lifting, moving, and unloading. Based on the results of the optimal lifting algorithm, specific operational instructions are generated, including the tower crane's operational sequence and lifting control parameters. These instructions are then assigned to the corresponding tower crane operation terminals, which can be the tower crane operator's work equipment or a control panel fixed to the tower crane. The optimal lifting algorithm is a decision support tool based on digital models and algorithms, used to analyze different lifting schemes and select the optimal solution. The optimal lifting schemes and strategies provided by the system offer clear decision support for site managers and tower crane operators, reducing the uncertainty of human judgment.

[0022] Step 4: After receiving the operation instruction, the tower crane operation terminal connects to the tower crane safety management edge node, tracks the tower crane safety status during the hoisting process corresponding to the operation instruction, and issues reminder instructions along the way.

[0023] Specifically, the tower crane operation terminal receives the system's operation instructions, clarifying the tower crane's operational content and requirements. It establishes a connection with the tower crane safety management edge node via wireless or wired means to monitor key safety indicators of the tower crane in real time, such as position, load, and speed, as well as surrounding environmental information, ensuring the tower crane remains in a safe state throughout operation. If the tower crane's operational status presents a safety hazard, the edge node will issue a warning instruction along the route to the tower crane operator. The safety management edge node is an intelligent device installed on the tower crane, used to collect and analyze the tower crane's operational data in real time, including its position, speed, load, and tilt angle. The edge node is a computing device located near the tower crane, responsible for processing and analyzing real-time data to ensure the safety of tower crane operations. The edge node analyzes the monitored data to determine whether the tower crane is within a safe operating range. If there are potential safety hazards, such as approaching overload, exceeding operating limits, or a collision risk, the edge node will trigger an alarm and issue a warning instruction along the route. These instructions can be audible, visual, or tactile signals, reminding the operator to pay attention to the current operational status and take appropriate safety measures to ensure the tower crane operates within a safe range. By tracking and issuing early warnings in real time, the occurrence of safety accidents at construction sites can be reduced.

[0024] Step 5: Based on the aforementioned reminder instructions along the route, notify the personnel working along the route to carry out safety management.

[0025] Specifically, when a potential safety risk is detected, the tower crane safety management edge node or central control system promptly notifies relevant personnel at the construction site through effective communication means (such as radio, audible alarms, and mobile device push notifications). This ensures that workers along the route take appropriate safety measures, including evacuating the danger zone, stopping work, adjusting work methods, or adding safety precautions. Timely and effective communication and response help reduce the occurrence of accidents at the construction site.

[0026] Furthermore, step one of this application includes: The location and motion data of the tower crane group at the construction site are collected in real time through the BeiDou satellite navigation system; the location and motion data of the tower crane group are processed using big data analysis technology to form an APF combination sequence; and the current construction progress is determined by pattern recognition of the APF combination sequence through machine learning algorithms.

[0027] Specifically, at the construction site, the BeiDou Navigation Satellite System collects real-time information on the position, speed, and acceleration of each tower crane, transmitting this data to a central processing system. Big data analytics is used to process this data, extracting valuable information through data mining and analysis. Based on this big data analytics structure, the system constructs an APF (Adaptive Potential Field) combination sequence, forming a sequence that reflects the tower crane's operating environment and dynamics. Furthermore, machine learning algorithms are used to perform pattern recognition on the APF combination sequence, automatically learning and recognizing patterns and regularities in tower crane operations, understanding the dynamic characteristics of tower crane operations, and determining the current construction progress accordingly.

[0028] The BeiDou Navigation Satellite System is a global satellite navigation system independently developed by China. It provides high-precision position and velocity information and is suitable for various positioning and navigation applications, including tower crane operations at construction sites. Big data analytics is a method for processing and analyzing large amounts of data to extract valuable information. In tower crane operations, this includes cleaning, integrating, analyzing, and mining the collected position and motion data to understand the dynamics of the construction site. Machine learning algorithms are an artificial intelligence technique that allows computers to learn from data and make decisions or predictions. In tower crane operations, machine learning algorithms can use linear regression or time series analysis to predict construction progress.

[0029] In a specific example, a construction site has eight tower cranes operating simultaneously. Each crane is equipped with a BeiDou satellite navigation system, collecting real-time position and motion data, including speed and direction. This data is transmitted wirelessly to a central processing system, where it is cleaned, integrated, analyzed, and visualized using big data analytics. The system constructs an APF (Advanced Position Factor) combination sequence to simulate the virtual potential field of the construction site, reflecting the operating environment and dynamics of the tower cranes. A linear regression algorithm is selected, and a machine learning model is trained using historical data. The model performs pattern recognition on the APF combination sequence to discover potential patterns and regularities. Based on the learned model, the system predicts that lifting operations in one area will be completed within the next few hours, thus allowing for advance planning of the next phase of work, such as adjusting work schedules or reallocating resources. Real-time monitoring and predictive capabilities help on-site managers better coordinate operations and improve construction efficiency.

[0030] Furthermore, step three of this application includes: Based on the required operation time in the operation requirements, connect the tower crane operation groups at the tower crane construction site; sort the tower crane operation groups at the tower crane construction site by their busyness to obtain a tower crane construction busyness sequence; sort the tower crane operation groups at the tower crane construction site by their transport distance from smallest to largest to obtain a work equipment transport distance sequence; and select the optimal tower crane by combining the tower crane construction busyness sequence, the work equipment transport distance sequence, and the work equipment performance.

[0031] Specifically, based on the required operation time in the work requirements, the system uses the BeiDou Navigation Satellite System to connect all tower cranes at the construction site in real time, transmitting the position and movement data of each crane to the central control system. The system analyzes the current workload and waiting tasks for each tower crane, obtaining a sequence of crane activity levels. Based on the distance from each tower crane's current position to the target work point, the cranes are sorted from shortest to longest transport distance, resulting in a sequence of transport distances for the work equipment. Taking into account the crane activity level, transport distance, and performance parameters (such as lifting capacity, load capacity, and moving speed), the system selects the most suitable tower crane for the current work requirements. For example, the system at the construction site, based on the crane activity level and transport distance, analyzes and finds that tower crane 1 has the best performance and is currently the busiest; tower crane 2 has the best performance and three waiting tasks; tower crane 3 has moderate performance, one waiting task, and is closest to the current work point; and tower crane 4 has the best performance, no waiting tasks, and is the second closest to the current work point. Considering these factors, the system selects tower crane 4 to complete the current lifting task. By comprehensively considering multiple factors, the system can achieve optimal resource allocation, reducing unnecessary waiting and movement time.

[0032] Furthermore, this application also includes the following steps: Collect and analyze historical operation data of tower cranes, including the number of lifting operations, operation efficiency, and safety accident records; use statistical methods to establish a tower crane operation safety prediction model; based on the tower crane operation safety prediction model, evaluate the expected performance of the tower crane group at the construction site under the operation requirements, and select the optimal tower crane.

[0033] Specifically, operational data of tower cranes over a past period is collected from their log files, safety reports, and work records, including the number of lifting operations, operational efficiency, and safety incident records. Statistical methods, such as regression analysis and time series analysis, are used to analyze the collected data and identify key factors affecting tower crane operational safety. Predictive models are established to forecast future operational safety risks. Based on current operational requirements, such as the type and weight of the material being lifted, and the lifting height, the operational performance of each tower crane is predicted. Combining historical data and the predictive model, the expected performance of each tower crane under the current operational requirements is evaluated, and the most suitable tower crane is selected. For example, if an operational requirement is to lift a 50-ton load, the system needs to evaluate the lifting capacity, safety, and operational efficiency of each tower crane. First, tower cranes with a lifting capacity of 50 tons or less are excluded. Then, considering both the crane's performance and safety, the tower crane with the best performance and highest safety is selected to complete the operational requirement. By combining the historical performance of tower cranes with the specific needs of current operations, predictive models can be used to assess the safety of tower cranes and avoid using tower cranes that are unsuitable for current operational requirements, thus helping to reduce the risk of safety accidents.

[0034] Furthermore, step four of this application includes: Connect to on-site monitoring equipment to acquire environmental monitoring data, including wind speed and wind direction data; based on the tower crane safety management edge node, receive and parse the operation instructions of multiple tower crane operation terminals corresponding to the tower crane operation group at the tower crane construction site, and perform wind deflection angle compensation in combination with the environmental monitoring data.

[0035] Specifically, various environmental monitoring devices, such as anemometers, humidity and temperature meters, barometers, ultraviolet detectors, and surveillance cameras, are installed to monitor environmental data at the construction site in real time, as well as the operational status of each tower crane. These monitoring devices are connected via wired or wireless means to collect data from the construction site in real time, such as wind speed and direction. The tower crane safety management edge node receives operational instructions from multiple connected tower crane operation terminals, such as lifting position, height, and speed. The environmental monitoring data is analyzed to calculate its impact on tower crane operations, particularly wind speed and direction data, including wind skew angle. The system automatically adjusts tower crane operation instructions, performing wind skew angle compensation to ensure operational safety. Wind skew angle compensation refers to calculating the wind skew angle of the lifted object based on wind direction and speed during tower crane operations, automatically adjusting the tower crane's lifting path and angle to maintain the stability of the lifted object and avoid deviations or safety risks caused by wind. Assume the wind speed at the construction site is 15 m / s, and the wind direction is southeast. Tower cranes need to lift heavy objects from one point to another. Due to the influence of wind speed and direction, the lifting path may deviate from the target. By implementing wind skew angle compensation, the system automatically adjusts the tower crane's operating commands, causing the lifting path to veer towards the northwest to counteract the influence of southeast winds. Through real-time environmental monitoring and wind skew angle compensation, the accuracy of the lifting path is ensured, reducing the risk of safety accidents caused by wind.

[0036] Furthermore, this application also includes the following steps: Based on the tower crane safety management edge node, sensor monitoring data is extracted, including lifting weight, lifting speed, and rotation speed. Using edge computing technology, the sensor monitoring data is synchronized to the tower crane safety management edge node. At the tower crane safety management edge node, the wind deflection angle is calculated by combining the environmental monitoring data and the sensor monitoring data, and the tower crane's operating parameters are adjusted in real time.

[0037] Specifically, each tower crane is equipped with various sensors, such as a load cell to monitor the load in real time, a speed sensor to monitor the descent speed of the load, a rotation speed sensor to monitor the crane's rotation speed, and an anemometer and wind vane to monitor wind speed and direction in real time, collecting key parameters. Data processing and analysis, such as filtering, compression, and classification, are performed using edge computing devices. The processed data is then synchronized to the tower crane safety management edge node, a small, efficient computing device installed near or on the tower crane. The system integrates data from environmental monitoring (such as wind speed and direction) and sensor monitoring (such as load, speed, and rotation speed) to calculate the wind's impact on tower crane operations, i.e., the wind skew angle. Based on the calculated wind skew angle, the tower crane's operating parameters are adjusted in real time, including changing the lifting path, adjusting the load, and adjusting the rotation speed, to counteract the wind's influence. Edge computing technology is a distributed computing model that shifts data processing and analysis tasks from centralized cloud servers to the edge of the network, i.e., the source of data generation. The main purpose of this technology is to reduce data transmission latency, improve real-time processing capabilities, and alleviate the burden on centralized data centers. For example, a tower crane is equipped with various sensors and an edge computing device. When the tower crane is performing lifting operations, the sensors collect data in real time, such as lifting weight, lifting speed, and rotation speed. The edge computing device performs preliminary processing on this data, calculating the real-time changes in lifting weight and lifting speed, as well as the wind skew angle. Based on the calculated wind skew angle, the edge computing device can adjust the tower crane's operating parameters in real time, changing the lifting path or adjusting the lifting weight, and can also adjust the tower crane's rotation speed to ensure the lifted object is placed in the optimal position, compensate for the wind skew angle, and reduce swaying and errors caused by wind. Through real-time monitoring and analysis, the system can automatically adjust the tower crane's operating parameters to cope with environmental changes.

[0038] Furthermore, step five of this application includes: Based on the aforementioned reminder instructions along the route, reminder instructions are sent to workers along the route; the sending and receiving of the reminder instructions, as well as the response of the workers along the route, are recorded.

[0039] Specifically, when a potential safety risk is detected, the system automatically generates a warning instruction along the route and sends it to personnel working along the route. The system records the sending time, method, and recipients of the warning instruction, tracking its delivery to ensure all personnel receive the safety reminder. Simultaneously, it records the responses of personnel along the route to the warning instruction, such as whether they took safety measures or evacuated the danger zone. Based on this, the effectiveness of the warning system is evaluated, and appropriate improvements are made. For example, if a tower crane is performing hoisting operations and the system detects that the crane is approaching its maximum working capacity and there is a risk of collision, the system immediately generates a warning instruction along the route and pushes it to personnel working along the route via mobile devices. Upon seeing the warning, personnel along the route immediately take measures to stop work. The system records the sending time, method, recipients, response status, and countermeasures of the warning instruction. By automatically generating and sending warnings and recording actual conditions, the system helps prevent hazards from occurring and provides data support for subsequent safety management.

[0040] In summary, the intelligent management method for tower crane operations based on digital twins provided in this application has the following technical effects: This system utilizes APF (Advanced Pilot Function) combination sequences to track the construction progress of tower crane operations and obtain the current construction progress. It then acquires production requirements and, based on the current construction progress, outputs operational requirements. These requirements include the required lifting materials, the current lifting location, the unloading location, the lifting time, and the tower crane's operational status within the lifting time window. Based on these operational requirements, an optimal lifting algorithm is used to select the optimal tower crane and the best lifting control strategy, generating operational instructions that are then distributed to the corresponding tower crane operation terminals. Upon receiving the operational instructions, the tower crane operation terminals connect to the tower crane safety management edge node, track the tower crane's safety status during the lifting process corresponding to the operational instructions, and issue along-the-path warning instructions. Based on these warning instructions, personnel along the route are notified to conduct safety management. By tracking construction progress through APF combination sequences, distributing operational instructions using the optimal lifting algorithm, and monitoring the tower crane's safety status in real time, intelligent management of tower crane operation scheduling is achieved, thereby improving overall construction efficiency.

[0041] Example 2 Based on the same inventive concept as the intelligent tower crane operation management method based on digital twins in the foregoing embodiments, this application also provides an intelligent tower crane operation management system based on digital twins. Please refer to the appendix. Figure 2 The system includes: The construction progress acquisition module 11 is used to track the construction progress of tower crane operations using the APF combination sequence and acquire the current construction progress.

[0042] The production demand acquisition module 12 is used to acquire production demand and, in conjunction with the current construction progress, output operation requirements. The operation requirements include hoisting material requirements, current hoisting position, unloading position, hoisting time, and tower crane operation status within the hoisting time window.

[0043] The optimal lifting algorithm module 13 is used to select the optimal tower crane and the best lifting control strategy based on the operation requirements, and to generate operation instructions to be distributed to the corresponding tower crane operation terminals.

[0044] The reminder instruction module 14 is used by the tower crane operation terminal to connect to the tower crane safety management edge node after receiving the operation instruction, track the tower crane safety status during the hoisting process corresponding to the operation instruction, and issue reminder instructions along the way.

[0045] Safety management notification module 15, which is used to notify workers along the route to carry out safety management based on the reminder instructions along the route.

[0046] Furthermore, the construction progress acquisition module 11 in the system is also used for: The location and motion data of the tower crane group at the construction site are collected in real time through the BeiDou satellite navigation system; the location and motion data of the tower crane group are processed using big data analysis technology to form an APF combination sequence; and the current construction progress is determined by pattern recognition of the APF combination sequence through machine learning algorithms.

[0047] Furthermore, the optimal hoisting algorithm module 13 in the system is also used for: Based on the required operation time in the operation requirements, connect the tower crane operation groups at the tower crane construction site; sort the tower crane operation groups at the tower crane construction site by their busyness to obtain a tower crane construction busyness sequence; sort the tower crane operation groups at the tower crane construction site by their transport distance from smallest to largest to obtain a work equipment transport distance sequence; and select the optimal tower crane by combining the tower crane construction busyness sequence, the work equipment transport distance sequence, and the work equipment performance.

[0048] Furthermore, the optimal hoisting algorithm module 13 in the system is also used for: Collect and analyze historical operation data of tower cranes, including the number of lifting operations, operation efficiency, and safety accident records; use statistical methods to establish a tower crane operation safety prediction model; based on the tower crane operation safety prediction model, evaluate the expected performance of the tower crane group at the construction site under the operation requirements, and select the optimal tower crane.

[0049] Furthermore, the reminder instruction issuing module 14 in the system is also used for: Connect to on-site monitoring equipment to acquire environmental monitoring data, including wind speed and wind direction data; based on the tower crane safety management edge node, receive and parse the operation instructions of multiple tower crane operation terminals corresponding to the tower crane operation group at the tower crane construction site, and perform wind deflection angle compensation in combination with the environmental monitoring data.

[0050] Furthermore, the reminder instruction issuing module 14 in the system is also used for: Based on the tower crane safety management edge node, sensor monitoring data is extracted, including lifting weight, lifting speed, and rotation speed. Using edge computing technology, the sensor monitoring data is synchronized to the tower crane safety management edge node. At the tower crane safety management edge node, the wind deflection angle is calculated by combining the environmental monitoring data and the sensor monitoring data, and the tower crane's operating parameters are adjusted in real time.

[0051] Furthermore, the security management notification module 15 in the system is also used for: Based on the aforementioned reminder instructions along the route, reminder instructions are sent to workers along the route; the sending and receiving of the reminder instructions, as well as the response of the workers along the route, are recorded.

[0052] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The intelligent tower crane operation management method and specific examples based on digital twins in Embodiment 1 are also applicable to the intelligent tower crane operation management system based on digital twins in this embodiment. Through the foregoing detailed description of the intelligent tower crane operation management method based on digital twins, those skilled in the art can clearly understand the intelligent tower crane operation management system based on digital twins in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As the system disclosed in the embodiments corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0053] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0054] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent management of tower crane operations based on digital twins, characterized in that: The method includes: The construction progress of tower crane operations is tracked using APF combined sequences to obtain the current construction progress; Obtain production requirements, combine them with the current construction progress, and output work requirements. The work requirements include hoisting material requirements, current hoisting position, unloading position, hoisting time, and tower crane operation status within the hoisting time window. Based on the aforementioned operational requirements, the optimal tower crane and the best hoisting control strategy are selected using the optimal hoisting algorithm, and operational instructions are generated and distributed to the corresponding tower crane operation terminals. After receiving the operation instruction, the tower crane operation terminal connects to the tower crane safety management edge node, tracks the tower crane safety status during the hoisting process corresponding to the operation instruction, and issues reminder instructions along the way. Based on the aforementioned reminder instructions along the route, personnel working along the route are notified to conduct safety management. Among these, the optimal lifting algorithm is used to select the optimal tower crane and the best lifting control strategy, including: Based on the required time of the operation in the aforementioned operation requirements, connect the tower crane operation group at the tower crane construction site; Based on the busyness ranking of the tower crane operation groups at the tower crane construction site, a tower crane construction busyness sequence is obtained. Based on the tower crane operation group at the tower crane construction site, the transport distances of the tower cranes are sorted from smallest to largest to obtain the transport distance sequence of the operating equipment. The optimal tower crane is selected based on the tower crane construction busyness sequence, the equipment transport distance sequence, and the equipment performance. The method involves using APF (Advanced Persistent Functions) combined sequences to track the construction progress of tower crane operations and obtain the current construction progress, including: The location and movement data of the tower cranes at the tower crane construction site are collected in real time through the BeiDou satellite navigation system. The location and motion data of the tower crane operation group are processed using big data analytics to form an APF (Advanced Position Flow) combination sequence. The current construction progress is predicted by performing pattern recognition on the APF combination sequence using machine learning algorithms. The system connects to the edge nodes of tower crane safety management, tracks the safety status of the tower crane during the hoisting process corresponding to the operation instructions, and issues reminder instructions along the way, including: Connect to on-site monitoring equipment to obtain environmental monitoring data, including wind speed data and wind direction data; Based on the tower crane safety management edge node, the system receives and parses the operation instructions from multiple tower crane operation terminals corresponding to the tower crane operation group at the tower crane construction site, and performs wind deflection angle compensation in conjunction with the environmental monitoring data, including: Based on the tower crane safety management edge node, sensor monitoring data is extracted, including lifting weight, lifting speed, and rotation speed. Using edge computing technology, the sensor monitoring data is synchronized to the tower crane safety management edge node; At the edge node of tower crane safety management, the wind deflection angle is calculated by combining the environmental monitoring data and the sensor monitoring data, and the operating parameters of the tower crane are adjusted in real time.

2. The method as described in claim 1, characterized in that, The method for selecting the optimal tower crane based on the performance of the operating equipment includes: Collect and analyze historical operation data of tower cranes, including the number of lifting operations, operation efficiency, and safety accident records; A safety prediction model for tower crane operations was established using statistical methods. Based on the tower crane operation safety prediction model, the expected performance of the tower crane group at the tower crane construction site under the operation requirements is evaluated, and the optimal tower crane is selected.

3. The method as described in claim 1, characterized in that, Based on the aforementioned route-side reminder instructions, personnel working along the route are notified to conduct safety management. The method includes: Based on the aforementioned reminder instructions along the route, reminder instructions are sent to the workers along the route; Record the sending and receiving of the reminder instructions, as well as the response of the personnel working along the route.

4. A tower crane operation intelligent management system based on digital twins, characterized in that: The system comprises: steps for implementing the method according to any one of claims 1 to 3, wherein the system includes: The construction progress acquisition module is used to track the construction progress of tower crane operations using the APF combination sequence and acquire the current construction progress. The production demand acquisition module is used to acquire production demand, and output operation demand in combination with the current construction progress. The operation demand includes hoisting material demand, current hoisting position, unloading position, hoisting time, and tower crane operation status within the hoisting time window. The optimal lifting algorithm module is used to select the optimal tower crane and the best lifting control strategy based on the operation requirements, and to generate operation instructions to be distributed to the corresponding tower crane operation terminals. The reminder instruction module is used by the tower crane operation terminal to connect to the tower crane safety management edge node after receiving the operation instruction, track the tower crane safety status during the hoisting process corresponding to the operation instruction, and issue reminder instructions along the way. The safety management notification module is used to notify personnel working along the route to conduct safety management based on the route reminder instructions.

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