Cable-stayed bridge tower column channel installation method

Abstract

The invention relates to the technical field of building construction, in particular to a cable-stayed bridge tower column channel installation method which comprises the following steps: S1, establishing an intelligent scheduling system, monitoring meteorological changes, traffic conditions, equipment allocation and worker working efficiency in real time, and predicting weather trends in combination with machine learning; when the system is used, the hoisting path is optimized, the hoisting efficiency is improved, construction delay and potential safety hazards are reduced, automatic control and adjustment are conducted through an intelligent system, human intervention is reduced, the accuracy and automation level of operation are improved, the management efficiency of a construction site is improved, and the labor intensity of workers is lowered. Through real-time data processing of the wireless data transmission module and the central control platform, the real-time state of a site can be conveniently and rapidly obtained, a hoisting plan can be conveniently and timely adjusted, the working efficiency and safety can be improved, an alarm is automatically triggered and a project manager is reminded, so that the emergency situation can be rapidly dealt with, and the working efficiency and safety are improved. And the hoisting operation can be efficiently and safely carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and particularly relates to a method for installing a cable-stayed bridge tower column passage. Background Art

[0002] The cable-stayed bridge tower column passage refers to the tower column part in the cable-stayed bridge structure. It is one of the important components of the cable-stayed bridge. A cable-stayed bridge is a bridge structure in which the main beam is directly pulled on the bridge tower by many stay cables, and is composed of a compression-bearing tower, a tension-bearing cable, and a bending-bearing beam body. The main components of a cable-stayed bridge include the cable tower, the main beam, and the stay cables.

[0003] However, in the prior art, the following disadvantages will occur during use: The installation of the tower column and the passage usually involves multiple stages of prefabrication, transportation, hoisting, and connection installation. Each stage requires a long time to complete. Especially in complex geographical environments and adverse weather conditions, the installation of the tower column and the passage is often carried out in an open environment. Adverse weather conditions such as strong winds, heavy rains, and low temperatures will all have an adverse impact on the construction progress and quality. Extreme weather will cause the construction operation to be interrupted, and even the construction plan needs to be adjusted, which will affect the overall progress and cost of the project.

[0004] In summary, developing a method for installing a cable-stayed bridge tower column passage is still a key problem that urgently needs to be solved in the technical field of building construction. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art that the installation of the tower column and the passage usually involves multiple stages of prefabrication, transportation, hoisting, and connection installation, and each stage requires a long time to complete. Especially in complex geographical environments and adverse weather conditions, the construction period will be further extended. In addition, the installation of the tower column and the passage is often carried out in an open environment. Adverse weather conditions such as strong winds, heavy rains, and low temperatures will all have an adverse impact on the construction progress and quality. Extreme weather will cause the construction operation to be interrupted, and even the construction plan needs to be adjusted, which will affect the overall progress and cost of the project.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] The present invention provides a method for installing a cable-stayed bridge tower column passage, including the following steps: S1. Establish an intelligent scheduling system to monitor meteorological changes, traffic conditions, equipment allocation, and worker work efficiency in real time, and combine machine learning to predict weather trends;

[0008] S2. Select a time other than extreme weather according to the weather trend to transport modular prefabricated components to the construction site for hoisting work;

[0009] S3. During the hoisting process, the intelligent hoisting equipment is used to continuously sense the wind speed and temperature climate change factors, and the wind-resistant hoisting technology is adopted to automatically adjust the hoisting plan;

[0010] S4. The intelligent monitoring system is used to continuously monitor the hoisting plan of the tower column and the passage to obtain real-time monitoring data;

[0011] S5. The remote control system is used to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool.

[0012] Furthermore, in step S1, an intelligent scheduling system is established to continuously monitor meteorological changes, traffic conditions, equipment allocation, and worker work efficiency. The method of predicting weather trends by combining machine learning is as follows:

[0013] The intelligent scheduling system obtains the weather change data of temperature, wind speed, wind direction, and precipitation at the construction site in real time through the meteorological API interface to obtain the weather trend. Through the road traffic API, it obtains the traffic flow, traffic jam situation, and road passing capacity data from the prefabrication factory to the construction site to obtain traffic data. By collecting the historical operation data of workers, a regression model is used to analyze the work efficiency of workers and predict their operation performance. Based on the predicted operation performance, the scheduling system reasonably allocates tasks to obtain the predicted work efficiency. According to the historical data of equipment use, the factors of equipment use efficiency, failure rate, and maintenance cycle are analyzed, and the equipment scheduling is optimized through a machine learning model to obtain the equipment scheduling plan. Regression model: where Q(w) is the work efficiency of worker w, E(w) is the historical working hours of worker w, and R(w) is the historical task completion quality of worker w. is the coefficient of the regression model, T(w) is the personal ability assessment of worker w, ∈ is the error term of the regression model. Equipment scheduling optimization formula:

[0014] OptimizedSchedule(t) = α 1 ·Y e (t) + α 2 ·U e (t) - α 3 ·I e (t), where Y e (t) is the utilization rate of equipment e at time t, U e (t) is the failure rate of equipment e, I e (t) is the maintenance cycle of equipment e, α 1 , α 2 , α 3 are the weight coefficients of the optimization model, and OptimizedSchedule(t) is the scheduling priority of equipment e at time t.

[0015] Further, in step S2, the method of transporting modular precast components to the construction site for hoisting work during the time except for extreme weather according to the weather trend is as follows:

[0016] Use artificial intelligence to analyze according to the weather trend, predict the weather change data at the construction site within the next 1 to 3 days according to the construction time requirements, obtain the weather prediction result, make a judgment according to the weather prediction result, clarify the construction window period when the weather prediction result meets the construction requirements, automatically select a suitable time period according to the window period to generate a construction scheduling plan, plan the transportation route and time according to the scheduling plan and traffic data, and through GPS and real-time traffic monitoring, track the transportation progress of modular precast components in real time. In case of traffic jams and / or other accidents, the transportation time will be automatically adjusted and the arrival time will be generated. The weather change prediction formula: Where O(t - 1) is the temperature at time point t - 1, P(t - 1) is the wind speed at time point t - 1, A(t - 1) is the precipitation at time point t - 1, β 1 , β 2 , β 3 are the trained model parameters, is the prediction error term. The weather compliance judgment formula: Where WeatherForecast(t) represents whether the weather at time point t meets the construction requirements, P(t) is the wind speed at time point t, P max is the maximum safety threshold of the wind speed, O(t) is the temperature at time point t, O min is the minimum safety threshold of the temperature, A(t) is the precipitation at time point t, A max is the maximum safety threshold of the precipitation.

[0017] Further, in step S2, the method of transporting modular precast components to the construction site for hoisting work during the time except for extreme weather according to the weather trend is as follows:

[0018] Arrange the hoisting equipment according to the window period and the arrival time, and check the equipment status to ensure that the equipment can be put into work on time. Set the hoisting position at the construction site in advance to ensure that the hoisting passage is unobstructed. Arrange workers and technicians to perform the hoisting operation on site. During the hoisting process, the intelligent scheduling system continuously monitors the meteorological conditions on site to ensure that the factors of wind speed, wind direction and precipitation do not exceed the safety threshold. When the wind speed is too high, the wind direction deviates and / or the precipitation exceeds the standard, the hoisting operation will be suspended and / or postponed until the weather conditions return to normal. At the same time, check in real time whether the workers operate according to the safety requirements and monitor the safety status within the hoisting area. The traffic data influence formula: G actual (t) = χ 1 ·S(t) + χ 2·D(t) - χ 3 ·F(t), where G actual (t) is the actual transportation time, S(t) is the traffic flow at time point t, D(t) is the traffic congestion at time point t, F(t) is the road capacity at time point t, χ 1 , χ 2 , χ 3 ·F(t) is the weight coefficient, adjusting the transportation time formula: G new = G original + ΔG adjustment where ΔG adjustment = G actual (t), where G new is the adjusted transportation arrival time, G original is the original estimated arrival time, ΔG adjustment is the traffic adjustment time, G actual (t) is the traffic impact value at time point t.

[0019] Further, in step S3, during the hoisting process, by using intelligent hoisting equipment to real-time sense the wind speed and temperature climate change factors, and adopting anti-wind hoisting technology, the method for automatically adjusting the hoisting plan is as follows:

[0020] Install wind speed and temperature sensors on the hoisting equipment for real-time monitoring of the changes in wind speed and temperature at the construction site. The hoisting equipment is equipped with an intelligent automatic control system, which automatically adjusts the hoisting plan according to the changes in wind speed and temperature sensed by the wind speed and temperature sensors, in combination with the preset safety standards.

[0021] Further, in step S3, during the hoisting process, by using intelligent hoisting equipment to real-time sense the wind speed and temperature climate change factors, and adopting anti-wind hoisting technology, the method for automatically adjusting the hoisting plan is as follows:

[0022] According to the real-time monitored wind speed changes at the construction site, combined with the wind speed threshold of 10.8 m / s for judgment, when the wind speed exceeds the predetermined wind speed threshold, automatically suspend the hoisting operation until the wind speed returns to the safe range. The environmental temperature threshold for the construction site of the hoisting operation is -25°C to +40°C. According to the real-time monitored temperature changes at the construction site, carry out the hoisting operation in combination with the environmental temperature threshold. According to the wind speed changes, the hoisting equipment can automatically adjust the hoisting speed. When the wind speed is in the range of 7.8 m / s to 10.8 m / s, automatically slow down the hoisting speed to reduce the impact of the wind on the stability of the components during the hoisting process. At the same time, by adjusting the hoisting angle and optimizing the hoisting path, avoid excessive tilting force on the components caused by the wind, so as to ensure the safety of the hoisting process. Wind speed judgment formula: where P(t) is the real-time wind speed at time t, P maxis the maximum safety threshold of wind speed, WindSpeedCheck(t) is the wind speed check result, and the temperature judgment formula: where O(t) is the real-time temperature at time t, TemperatureCheck(t) is the temperature check result, and the hoisting speed adjustment formula: where H adjusted (t) is the adjusted hoisting speed at time t, H max is the maximum hoisting speed of the hoisting equipment, 7.8 is the lower limit of wind speed, 3.0 is the wind speed range between 7.8 m / s and 10.8 m / s, P(t) is the real-time wind speed at time t, and the hoisting angle adjustment formula: where δ adjusted (t) is the adjusted hoisting angle, δ max , δ min are the maximum and minimum adjustment ranges of the hoisting angle respectively, and P(t) is the real-time wind speed at time t.

[0023] Furthermore, in step S4, the intelligent monitoring system continuously monitors the hoisting plan of the tower column and the passage, and the method for obtaining the real-time monitoring data is as follows:

[0024] Install multiple sensors and wireless data transmission modules on the hoisting equipment, tower column and passage, including but not limited to: displacement sensors, load sensors, vibration sensors, temperature sensors, wind speed sensors and pressure sensors, and continuously monitor the operating state data of the hoisting equipment, the displacement data of the tower column and the passage, the load data and vibration data borne by the hoisting equipment.

[0025] Furthermore, in step S4, the intelligent monitoring system continuously monitors the hoisting plan of the tower column and the passage, and the method for obtaining the real-time monitoring data is as follows:

[0026] Fuse the operating state data, displacement data, load data and vibration data through multi-modal technology to obtain the monitoring data, and then transmit the monitoring data to the central control platform in real time. The central monitoring platform centrally processes and analyzes the monitoring data to provide the decision maker with the real-time status information of the hoisting plan. The data fusion formula:

[0027] where where J fused (t) is the fused monitoring data, k i is the weight value of the i-th sensor, L i (t) is the data collected by the i-th sensor at time t, and n is the total number of sensor types. The model formula for real-time transmission: J transmitted (t) = J fused (t - Δt transmission ), where Jtransmitted (t) is the monitoring data received at time t, J fused (t - Δt transmission ) is the fused data after delay, Δt transmission is the time delay in the transmission process.

[0028] Furthermore, in step S5, the method of using the remote control system to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool is as follows:

[0029] Interact with the construction site equipment through the remote control interface of the remote control system. The remote control system combines the central control platform to obtain the real-time status information of the construction site in real time, and remotely and automatically controls the working parameters of the lifting equipment according to the real-time status information, including but not limited to adjusting the lifting speed, changing the lifting path, and controlling the lifting angle. Analyze the bottlenecks in the lifting operation through the continuously accumulated real-time status information, and propose improvement measures to optimize the operation process. The control parameter adjustment formula of the lifting equipment: where Z lift (t) is the lifting speed adjusted according to the load, wind speed, and temperature at time t, V lift (t) is the lifting path adjusted according to the load and wind speed at time t, ε lift (t) is the lifting angle adjusted according to the wind speed and vibration state at time t, c load (t), c wind (t), c temperature (t), c vibration (t) are the load, wind speed, temperature, and vibration data accumulated from real-time monitoring and bottleneck analysis respectively: where B bottleneck (t) is the bottleneck index, N i {c i (t)} represents the function related to the i-th factor, φ i is the weight of the i-th factor, c i (t) is the data of the i-th factor obtained at time t.

[0030] Furthermore, in step S5, the method of using the remote control system to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool is as follows:

[0031] The visualization tool displays the real-time status information of the construction site through a graphical interface, including wind speed, load, equipment location, and tower column displacement, and presents it in the form of charts, curves, and / or 3D models. It uses a risk assessment model to determine whether the real-time status information on-site meets the predetermined safety standards. By continuously monitoring data changes, when abnormal data exceeding the wind speed and temperature thresholds is detected, the abnormal area is highlighted through the graphical interface, and project managers are prompted in the form of red warnings, flashing icons, and alarm sounds. The risk assessment formula: where M risk (t) is the overall risk assessment result, RiskCheck{c i (t), c safe,i} is the safety check function, γ i is the weight of the i-th data item. Alarm prompt: where Alarm(t) is the alarm status at time t, c wind (t) is the wind speed measured at time t, c safe,wind is the safety threshold of the wind speed, c temperature (t) is the temperature measured at time t, c safe,temperature is the safety threshold of the temperature.

[0032] Beneficial effects

[0033] Adopting the technical solution provided by the present invention, compared with the known public technologies, it has the following

[0034] beneficial effects:

[0035] When the present invention is in use, it optimizes the hoisting path, improves the hoisting efficiency, relies on an intelligent system for automatic control and adjustment, reduces human intervention, improves the accuracy and automation level of operations, and enhances the management efficiency of the construction site. Through the real-time data processing of the wireless data transmission module and the central control platform, it is convenient to quickly obtain the real-time status on-site, facilitate timely adjustment of the hoisting plan, and is conducive to improving the operation efficiency and safety. By automatically triggering an alarm and reminding project managers, it can respond to emergencies more quickly, which is conducive to the efficient and safe progress of the hoisting operation. Through intelligent weather prediction and scheduling, it is convenient to avoid hoisting operations in extreme weather, improve construction efficiency, automatically monitor traffic conditions and adjust the transportation time, ensure that precast components arrive at the construction site on time, avoid construction delays caused by traffic problems, and is conducive to avoiding the impact of bad weather conditions on the overall progress and cost of the cable-stayed bridge tower column channel installation. Description of the drawings

[0036] Figure 1 is a flowchart of a method for installing a cable-stayed bridge tower column channel according to the present invention. Detailed implementation manners

[0037] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but includes other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0039] The present invention will be further described in detail below in conjunction with the accompanying drawings:

[0040] Embodiment:

[0041] As Figure 1 shown, the present invention provides a method for installing a cable-stayed bridge tower column passage, including the following steps: S1. Establish an intelligent scheduling system to monitor meteorological changes, traffic conditions, equipment allocation, and worker work efficiency in real time, and combine machine learning to predict weather trends;

[0042] Further, in step S1, the method for establishing an intelligent scheduling system to monitor meteorological changes, traffic conditions, equipment allocation, and worker work efficiency in real time, and combine machine learning to predict weather trends is as follows:

[0043] The intelligent scheduling system obtains weather change data such as temperature, wind speed, wind direction, and precipitation at the construction site in real time through a meteorological API interface to obtain weather trends, obtains traffic data such as traffic flow, traffic jams, and road capacity from the prefabrication factory to the construction site through a road traffic API, analyzes the work efficiency of workers by collecting their historical operation data using a regression model and predicts their operation performance. Based on the predicted operation performance, the scheduling system reasonably allocates tasks to obtain predicted work efficiency. According to the historical data of equipment use, factors such as equipment use efficiency, failure rate, and maintenance cycle are analyzed, and the equipment scheduling is optimized through a machine learning model to obtain an equipment scheduling plan. Regression model: Among them, Q(w) is the work efficiency of worker w, E(w) is the historical working hours of worker w, R(w) is the historical task completion quality of worker w, are the coefficients of the regression model, T(w) is the personal ability assessment of worker w, ∈ is the error term of the regression model, and the equipment scheduling optimization formula:

[0044] OptimizedSchedule(t) = α 1 ·Y e (t) + α 2 ·U e (t) - α 3 ·I e (t), where Y e (t) is the utilization rate of equipment e at time t, U e (t) is the failure rate of equipment e, I e (t) is the maintenance cycle of equipment e, α 1 , α 2 , α 3 are the weight coefficients of the optimization model, and OptimizedSchedule(t) is the scheduling priority of equipment e at time t;

[0045] In this embodiment, through comprehensive weather prediction, traffic data, and worker efficiency prediction, the intelligent scheduling system can optimize the construction time and task allocation, thereby improving the overall execution efficiency of the project. Combining the work performance of workers and the usage status of equipment, it intelligently adjusts the task allocation and equipment scheduling, reduces resource waste and construction bottlenecks. Through the prediction of equipment failure rate and maintenance cycle, it reasonably arranges equipment scheduling to avoid construction delays caused by excessive equipment idle or equipment failures. At the same time, based on the prediction of worker work efficiency, it reasonably arranges tasks to improve the work efficiency of workers. By real-time monitoring of meteorological changes and traffic conditions, it is convenient to adjust the construction plan in advance to avoid the impact of bad weather or traffic jams on construction, ensuring the safety and smooth progress of the construction process. The introduction of machine learning and regression models makes the decision-making more scientific and data-driven, reduces the uncertainty of human judgment, and is conducive to improving the flexibility and adaptability of the construction process.

[0046] S2. According to the weather trend, select a time other than extreme weather to transport the modular precast components to the construction site for hoisting work;

[0047] Furthermore, in step S2, the method of transporting the modular precast components to the construction site for hoisting work according to the weather trend and selecting a time other than extreme weather is as follows:

[0048] Use artificial intelligence to analyze according to weather trends, predict the weather change data at the construction site within the next 1 to 3 days based on the construction time requirements, obtain the weather prediction result, make a judgment according to the weather prediction result, clarify the construction window period when the weather prediction result meets the construction requirements, automatically select a suitable time period according to the window period to generate a construction scheduling plan, plan the transportation route and time according to the scheduling plan and traffic data, and through GPS and real-time traffic monitoring, track the transportation progress of modular precast components in real time. In case of traffic jams and / or other accidents, the transportation time will be automatically adjusted and the arrival time will be generated. Weather change prediction formula:

[0049] where O(t - 1) is the temperature at time point t - 1, P(t - 1) is the wind speed at time point t - 1, A(t - 1) is the precipitation at time point t - 1, β 1 , β 2 , β 3 are the trained model parameters, is the prediction error term. Weather compliance judgment formula: where WeatherForecast(t) represents whether the weather meets the construction requirements at time point t, P(t) is the wind speed at time point t, P max is the maximum safety threshold of the wind speed, O(t) is the temperature at time point t, O min is the minimum safety threshold of the temperature, A(t) is the precipitation at time point t, A max is the maximum safety threshold of the precipitation.

[0050] Furthermore, in step S2, the method of transporting modular precast components to the construction site for hoisting work within the time except for extreme weather according to the weather trend is as follows:

[0051] Arrange hoisting equipment according to the window period and arrival time, and check the equipment status to ensure that the equipment can be put into work on time. Set the hoisting position at the construction site in advance to ensure that the hoisting passage is unobstructed. Arrange workers and technicians to perform hoisting operations on site. During the hoisting process, the intelligent scheduling system continuously monitors the meteorological conditions on site to ensure that the factors of wind speed, wind direction and precipitation do not exceed the safety threshold. When the wind speed is too high, the wind direction deviates and / or the precipitation exceeds the standard, the hoisting operation will be suspended and / or postponed until the weather conditions return to normal. At the same time, check in real time whether the workers operate according to safety requirements and monitor the safety status within the hoisting area. Traffic data influence formula: G actual (t) = χ 1 ·S(t) + χ 2 ·D(t) - χ 3 ·F(t), where G actual(t) is the actual transportation time, S(t) is the traffic flow at time point t, D(t) is the traffic congestion at time point t, F(t) is the road capacity at time point t, and χ 1 , χ 2 , χ 3 ·F(t) is the weight coefficient to adjust the transportation time formula: G new = G original + ΔG adjustment where ΔG adjustment = G actual (t), where G new is the adjusted transportation arrival time, G original is the original estimated arrival time, ΔG adjustment is the traffic adjustment time, and G actual (t) is the traffic impact value at time point t;

[0052] In this embodiment, through intelligent weather prediction and scheduling, it is convenient to avoid extreme weather for hoisting operations, improve construction efficiency, automatically monitor traffic conditions and adjust transportation time to ensure that precast components arrive at the construction site on time, avoid construction delays caused by traffic problems, automatically suspend hoisting operations by real-time monitoring of meteorological conditions and combining with safety thresholds, avoid the impact of extreme weather on the safety of hoisting operations, and combine weather, traffic and equipment status data. The system can intelligently adjust hoisting operation time and personnel arrangement, which is conducive to improving the utilization efficiency of resources. Through continuous monitoring and dynamic adjustment, it is convenient to detect and respond to potential construction risks in a timely manner.

[0053] S3. During the hoisting process, the intelligent hoisting equipment is used to real-time sense the wind speed and temperature climate change factors, and the wind-resistant hoisting technology is adopted to automatically adjust the hoisting plan;

[0054] Further, in step S3, during the hoisting process, the method of using the intelligent hoisting equipment to real-time sense the wind speed and temperature climate change factors and adopting the wind-resistant hoisting technology to automatically adjust the hoisting plan is as follows:

[0055] Install wind speed and temperature sensors on the hoisting equipment to be used for real-time monitoring of the changes in wind speed and temperature at the construction site. The hoisting equipment is equipped with an intelligent automatic control system, which automatically adjusts the hoisting plan according to the changes in wind speed and temperature sensed by the wind speed and temperature sensors and combining with the preset safety standards.

[0056] Further, in step S3, during the hoisting process, the method of using the intelligent hoisting equipment to real-time sense the wind speed and temperature climate change factors and adopting the wind-resistant hoisting technology to automatically adjust the hoisting plan is as follows:

[0057] Based on the real-time monitored wind speed changes at the construction site and combined with a wind speed threshold of 10.8 m / s for judgment, when the wind speed exceeds the predetermined wind speed threshold, the hoisting operation is automatically suspended until the wind speed returns to the safe range. The environmental temperature threshold for the construction site of the hoisting operation is -25°C to +40°C. According to the real-time monitored temperature changes at the construction site and combined with the environmental temperature threshold, the hoisting operation is carried out. According to the wind speed changes, the hoisting equipment can automatically adjust the hoisting speed. When the wind speed is in the range of 7.8 m / s to 10.8 m / s, the hoisting speed is automatically slowed down to reduce the influence of the wind on the stability of the components during hoisting. At the same time, by adjusting the hoisting angle and optimizing the hoisting path, the excessive tilting force generated by the wind on the components is avoided, thus ensuring the safety of the hoisting process. Wind speed judgment formula: where P(t) is the real-time wind speed at time t, P max is the maximum safety threshold of the wind speed, and WindSpeedCheck(t) is the wind speed check result. Temperature judgment formula:

[0058] where O(t) is the real-time temperature at time t, and TemperatureCheck(t) is the temperature check result. Hoisting speed adjustment formula: where H adjusted (t) is the adjusted hoisting speed at time t, H max is the maximum hoisting speed of the hoisting equipment, 7.8 is the lower limit of the wind speed, 3.0 is the wind speed range from 7.8 m / s to 10.8 m / s, and P(t) is the real-time wind speed at time t. Hoisting angle adjustment formula: where δ adjusted (t) is the adjusted hoisting angle, δ max , δ min are the maximum and minimum adjustment ranges of the hoisting angle respectively, and P(t) is the real-time wind speed at time t;

[0059] In this embodiment, by real-time sensing the wind speed and temperature changes and automatically adjusting the hoisting plan according to the set safety thresholds, the influence of wind and extreme weather on the hoisting operation is effectively avoided, ensuring the operation safety. By intelligently adjusting the hoisting speed and hoisting angle, the hoisting speed is slowed down in case of wind speed changes to avoid the impact on the stability of the components. At the same time, the hoisting path is optimized to improve the hoisting efficiency, reduce construction delays and safety hazards. Relying on the intelligent system for automatic control and adjustment reduces human intervention, improves the accuracy and automation level of the operation, and enhances the management efficiency of the construction site.

[0060] S4. Continuously monitor the hoisting plan of the tower column and the passage through the intelligent monitoring system to obtain real-time monitoring data;

[0061] Further, in step S4, the method for continuously monitoring the hoisting plan of the tower column and the passage by the intelligent monitoring system to obtain real-time monitoring data is as follows:

[0062] Install multiple sensors and wireless data transmission modules on the hoisting equipment, tower column and passage, including but not limited to: displacement sensors, load sensors, vibration sensors, temperature sensors, wind speed sensors and pressure sensors, and continuously monitor the operating state data of the hoisting equipment, the displacement data of the tower column and the passage, the load data and vibration data borne by the hoisting equipment.

[0063] Further, in step S4, the method for continuously monitoring the hoisting plan of the tower column and the passage by the intelligent monitoring system to obtain real-time monitoring data is as follows:

[0064] The operating state data, displacement data, load data and vibration data are fused through multi-modal technology to obtain monitoring data, and then the monitoring data is transmitted to the central control platform in real time. The central monitoring platform centrally processes and analyzes the monitoring data to provide real-time status information of the hoisting plan for decision-makers. The data fusion formula:

[0065] where Among them, J fused (t) is the fused monitoring data, k i is the weight value of the i-th sensor, L i (t) is the data collected by the i-th sensor at time t, n is the total number of sensor types, and the real-time transmission model formula: J transmitted (t) = J fused (t - Δt transmission ), where J transmitted (t) is the monitoring data received at time t, J fused (t - Δt transmission ) is the fused data after delay, and Δt transmission is the time delay during the transmission process;

[0066] In this embodiment, by installing multiple sensors, the key parameters of the hoisting equipment, tower column and passage are monitored in real time, which is convenient for timely discovering potential problems in the hoisting work. The multi-modal technology is used to fuse the data of different sensors, which improves the accuracy and comprehensiveness of the monitoring data, helps to accurately evaluate the safety and stability during the hoisting process. Through the real-time data processing of the wireless data transmission module and the central control platform, it is convenient to quickly obtain the real-time status on site, which is convenient for timely adjusting the hoisting plan and is beneficial to improving the operation efficiency and safety.

[0067] S5. Use the remote control system to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool;

[0068] Further, in step S5, the method of using the remote control system to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool is as follows:

[0069] Interact with the construction site equipment through the remote control interface of the remote control system. The remote control system combines the central control platform to obtain the real-time status information of the construction site in real time, and remotely and automatically controls the working parameters of the lifting equipment according to the real-time status information, including but not limited to adjusting the lifting speed, changing the lifting path, and controlling the lifting angle. Analyze the bottlenecks in the lifting operation based on the continuously accumulated real-time status information, and propose improvement measures to optimize the operation process. The control parameter adjustment formula for the lifting equipment: Where Z lift (t) is the lifting speed adjusted according to the load, wind speed, and temperature at time t, V lift (t) is the lifting path adjusted according to the load and wind speed at time t, ε lift (t) is the lifting angle adjusted according to the wind speed and vibration state at time t, c load (t), c wind (t), c temperature (t), c vibration (t) are the load, wind speed, temperature, and vibration data accumulated from real-time monitoring and bottleneck analysis respectively: Where B bottleneck (t) is the bottleneck index, N i {c i (t)} represents the function related to the i-th factor, φ i is the weight of the i-th factor, c i (t) is the data of the i-th factor obtained at time t.

[0070] Further, in step S5, the method of using the remote control system to monitor and adjust the site according to the real-time monitoring data in combination with the visualization tool is as follows:

[0071] The visualization tool displays the real-time status information of the construction site through the graphical interface, including wind speed, load, equipment location, and tower column displacement, and is displayed in the form of charts, curve graphs, and / or 3D models. Use the risk assessment model to judge whether the real-time status information of the site meets the predetermined safety standards. By continuously monitoring the data changes, when abnormal data exceeding the wind speed and temperature thresholds are detected, the abnormal area is highlighted through the graphical interface, and project managers are prompted in the form of red warnings, flashing icons, and alarm sounds. The risk assessment formula: Where M risk (t) is the overall risk assessment result, RiskCheck{c i (t), csafe,i} is a safety inspection function, γ i is the weight of the i-th data item, Alarm prompt: where Alarm(t) is the alarm status at time t, c wind (t) is the wind speed measured at time t, c safe,wind is the safety threshold of the wind speed, c temperature (t) is the temperature measured at time t, c safe,temperature is the safety threshold of the temperature;

[0072] In this embodiment, by real-time monitoring and automatically adjusting the working parameters of the hoisting equipment, it is convenient to reduce the risks caused by environmental changes. By continuously accumulating and analyzing real-time data, bottleneck problems in construction are discovered, and optimization measures are proposed in a timely manner, which is beneficial to improving construction efficiency and operation accuracy. The operation parameters are automatically adjusted according to the real-time change factors of wind speed and temperature. When abnormal data occurs, an alarm is automatically triggered to remind project managers, so as to respond to emergencies more quickly. Through visualization tools, project managers can grasp the on-site situation in real time, so as to make more accurate decisions, which is beneficial to the efficient and safe progress of hoisting operations.

[0073] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for installing a tower channel of a cable-stayed bridge, characterized in that: The following steps are involved: S1. Establish an intelligent dispatching system to monitor weather changes, traffic conditions, equipment deployment and worker efficiency in real time, and combine machine learning to predict weather trends; S2. Transport modular prefabricated components to the construction site for hoisting work at a time other than extreme weather conditions, based on weather trends; S3. During the hoisting process, intelligent hoisting equipment is used to sense wind speed and temperature climate change factors in real time, and wind-resistant hoisting technology is used to automatically adjust the hoisting plan; S4. Continuously monitor the tower column and channel hoisting plan through the intelligent monitoring system to obtain real-time monitoring data; S5. Use the remote control system to monitor and adjust the site based on real-time monitoring data combined with visualization tools.

2. A cable-stayed bridge tower channel installation method according to claim 1, characterized in that: In step S1, an intelligent dispatching system is established to monitor weather changes, traffic conditions, equipment deployment, and worker efficiency in real time. Combined with machine learning, the method for predicting weather trends is as follows: The intelligent dispatching system uses the meteorological API interface to obtain weather change data on the temperature, wind speed, wind direction and precipitation of the construction site in real time to obtain weather trends. Through the road traffic API, it obtains data on traffic flow, traffic congestion, and road capacity from the prefabrication factory to the construction site to obtain traffic data. By collecting workers' historical work data, the regression model is used to analyze the workers' work efficiency and predict their work performance. Based on the predicted work performance, the dispatching system reasonably allocates tasks to obtain predicted work efficiency. According to the historical data of equipment use, the factors of equipment use efficiency, failure rate, and maintenance cycle are analyzed. The equipment scheduling is optimized through the machine learning model to obtain the equipment scheduling plan. Regression model: Where Q(w) is the work efficiency of worker w, E(w) is the historical working time of worker w, and R(w) is the historical task completion quality of worker w. is the coefficient of the regression model, T(w) is the personal ability evaluation of worker w, ∈ is the error term of the regression model, and the equipment scheduling optimization formula is: OptimizedSchedule(t)=α1·Y e (t)+α2·U e (t)-α3·I e (t), where Y e (t) is the utilization rate of equipment e at time t, U e (t) is the failure rate of device e, I e (t) is the maintenance cycle of equipment e, α1, α2, α3 are the weight coefficients of the optimization model, and OptimizedSchedule(t) is the scheduling priority of equipment e at time t.

3. A cable-stayed bridge tower channel installation method according to claim 2, characterized in that: In step S2, the method for transporting the modular prefabricated components to the construction site for hoisting work is selected according to the weather trend within a time period other than extreme weather conditions: Artificial intelligence is used to analyze weather trends, and weather change data for the construction site within the next 1 to 3 days is predicted based on construction time requirements to obtain weather forecast results. Judgment is made based on weather forecast results. When the weather forecast results meet the construction requirements, the construction window period is clarified. According to the window period, the appropriate time period is automatically selected to generate a construction scheduling plan. The transportation route and time are planned according to the scheduling plan and traffic data. Through GPS and real-time traffic monitoring, the transportation progress of modular prefabricated components is tracked in real time. In case of traffic jams and / or other accidents, the transportation time will be automatically adjusted and the arrival time will be generated. The weather change prediction formula is: Where O(t-1) is the temperature at time t-1, P(t-1) is the wind speed at time t-1, A(t-1) is the precipitation at time t-1, β1, β2, β3 are the trained model parameters, is the forecast error term, and the weather meets the conditions to determine the formula: WeatherForecast(t) indicates whether the weather at time t meets the construction requirements, P(t) is the wind speed at time t, and P max is the maximum safety threshold of wind speed, O(t) is the temperature at time t, O min is the minimum safe threshold of temperature, A(t) is the precipitation at time t, A max It is the maximum safe threshold of precipitation.

4. A cable-stayed bridge tower channel installation method according to claim 3, characterized in that: In step S2, the method for transporting the modular prefabricated components to the construction site for hoisting work is selected according to the weather trend within a time period other than extreme weather conditions: Arrange the lifting equipment according to the window period and arrival time, and check the equipment status to ensure that the equipment can be put into work on time. Set the lifting position at the construction site in advance to ensure that the lifting channel is unobstructed, and arrange workers and technicians to perform lifting operations on site. During the lifting process, the intelligent scheduling system continuously monitors the meteorological conditions on site to ensure that the wind speed, wind direction and precipitation factors do not exceed the safety threshold. When the wind speed is too high, the wind direction deviates and / or the precipitation exceeds the standard, the lifting operation will be suspended and / or postponed until the weather conditions return to normal. At the same time, check in real time whether the workers are operating according to safety requirements and monitor the safety conditions in the lifting area. Traffic data influence formula: G actual (t)=χ1·S(t)+χ2·D(t)-χ3·F(t), where G actual G(t) is the actual transportation time, S(t) is the traffic flow at time point t, D(t) is the traffic congestion at time point t, F(t) is the road capacity at time point t, χ1, χ2, χ3·F(t) are weight coefficients, and the formula for adjusting the transportation time is: G new =G original +ΔG adjustment where ΔG adjustment =G actual (t), where G new is the adjusted transport arrival time, G original is the original estimated arrival time, ΔG adjustment is the traffic adjustment time, G actual (t) is the traffic impact value at time point t.

5. A cable-stayed bridge tower channel installation method according to claim 4, characterized in that: In step S3, during the hoisting process, the method of automatically adjusting the hoisting plan by using intelligent hoisting equipment to sense wind speed and temperature climate change factors in real time and adopting wind-resistant hoisting technology is as follows: Wind speed and temperature sensors are installed on the lifting equipment to monitor the changes in wind speed and temperature at the construction site in real time. The lifting equipment is equipped with an intelligent automatic control system, which senses the changes in wind speed and temperature based on the wind speed and temperature sensors and automatically adjusts the lifting plan based on preset safety standards.

6. A cable-stayed bridge tower column channel installation method according to claim 5, characterized in that: In step S3, during the hoisting process, the method of automatically adjusting the hoisting plan by using intelligent hoisting equipment to sense wind speed and temperature climate change factors in real time and adopting wind-resistant hoisting technology is as follows: According to the wind speed changes monitored in real time at the construction site, combined with the wind speed threshold of 10.8 m / s, when the wind speed exceeds the predetermined wind speed threshold, the hoisting operation is automatically suspended until the wind speed returns to a safe range. The ambient temperature threshold of the construction site for hoisting operations is -25°C to +40°C. According to the temperature changes monitored in real time at the construction site, combined with the ambient temperature threshold, hoisting operations are performed. The hoisting equipment can automatically adjust the hoisting speed according to the wind speed changes. When the wind speed is between 7.8 / s and 10.8 m / s, the hoisting speed is automatically slowed down to reduce the impact of wind on the stability of components during the hoisting process. At the same time, by adjusting the hoisting angle and optimizing the hoisting path, the wind is prevented from causing excessive tilting force on the components, thereby ensuring the safety of the hoisting process. Wind speed judgment formula: Where P(t) is the real-time wind speed at time t, P max is the maximum safety threshold of wind speed, WindSpeedCheck(t) is the wind speed check result, and the temperature judgment formula is: Where O(t) is the real-time temperature at time t, TemperatureCheck(t) is the temperature check result, and the lifting speed adjustment formula is: Among them, H adjusted (t) is the adjusted lifting speed at time t, H max is the maximum hoisting speed of the hoisting equipment, 7.8 is the lower limit of wind speed, 3.0 is the wind speed range from 7.8m / s to 10.8m / s, P(t) is the real-time wind speed at time t, and the hoisting angle adjustment formula is: where δ adjusted (t) is the adjusted lifting angle, δ max ,δ min are the maximum and minimum adjustment ranges of the lifting angle respectively, and P(t) is the real-time wind speed at time t.

7. A cable-stayed bridge tower channel installation method according to claim 6, characterized in that: In step S4, the lifting plan of the tower column and the channel is continuously monitored by the intelligent monitoring system, and the method for obtaining real-time monitoring data is as follows: Multiple sensors and wireless data transmission modules are installed on the hoisting equipment, towers and channels, including but not limited to: displacement sensors, load sensors, vibration sensors, temperature sensors, wind speed sensors and pressure sensors, to monitor the operating status data of the hoisting equipment, the displacement data of the towers and channels, and the load data and vibration data borne by the hoisting equipment in real time.

8. A cable-stayed bridge tower channel installation method according to claim 7, characterized in that: In step S4, the lifting plan of the tower column and the channel is continuously monitored by the intelligent monitoring system, and the method for obtaining real-time monitoring data is as follows: The operating status data, displacement data, load data and vibration data are integrated through multimodal technology to obtain monitoring data, which are then transmitted to the central control platform in real time. The monitoring data are centrally processed and analyzed through the central monitoring platform to provide decision makers with real-time status information of the lifting plan. The data fusion formula is: Among them J fused (t) is the fused monitoring data, k i is the weight value of the i-th sensor, L i (t) is the data collected by the i-th sensor at time t, n is the total number of sensor types, and the model formula for real-time transmission is: J transmitted (t) = J fused (t-Δt transmission ), where J transmitted (t) is the monitoring data received at time t, J fused (t-Δt transmission ) is the delayed fusion data, Δt transmission is the time delay of the transmission process.

9. A cable-stayed bridge tower channel installation method according to claim 8, characterized in that: In step S5, the method of using the remote control system to monitor and adjust the site based on real-time monitoring data in combination with visualization tools is as follows: The remote control system interacts with the construction site equipment through the remote control interface of the remote control system. The remote control system combines with the central control platform to obtain the real-time status information of the construction site in real time. According to the real-time status information, the working parameters of the lifting equipment are remotely and automatically controlled, including but not limited to adjusting the lifting speed, changing the lifting path and controlling the lifting angle. Through the continuous accumulation of real-time status information, the bottlenecks in the lifting operation are analyzed, and improvement measures are proposed to optimize the operation process. The control parameter adjustment formula of the lifting equipment is: Where Z lift (t) is the lifting speed adjusted according to load, wind speed and temperature at time t, V lift (t) is the lifting path adjusted according to load and wind speed at time t, ε lift (t) is the lifting angle adjusted according to wind speed and vibration state at time t, c load (t),c wind (t),c temperature (t),c vibration (t) are real-time monitoring of load, wind speed, temperature and vibration data accumulation and bottleneck analysis: Among them B bottleneck (t) is the bottleneck indicator, N i {c i (t)} represents the function related to the i-th factor, φ i is the weight of the ith factor, c i (t) is the data of the i-th factor obtained at time t.

10. A cable-stayed bridge tower channel installation method according to claim 8, characterized in that: In step S5, the method of using the remote control system to monitor and adjust the site based on real-time monitoring data in combination with visualization tools is as follows: The visualization tool displays the real-time status information of the construction site through a graphical interface, including wind speed, load, equipment location, and tower column displacement, and displays it in the form of charts, graphs, and / or 3D models. The risk assessment model is used to determine whether the real-time status information of the site meets the predetermined safety standards. By continuously monitoring data changes, when abnormal data exceeding the wind speed and temperature thresholds are detected, the abnormal area is highlighted through the graphical interface, and the project management personnel are prompted by red warnings, flashing icons, and alarm sounds. The risk assessment formula is: Among them, M risk (t) is the overall risk assessment result, RiskCheck{c i (t), c safe,i } is a safety check function, γ i is the weight of the ith data item, the alert prompt: Where Alarm(t) is the alarm status at time t, c wind (t) is the wind speed measured at time t, c safe,wind is the safety threshold of wind speed, c temperature (t) is the temperature measured at time t, c safe,temperature is the safety threshold of temperature.

Images