Integrated monitoring system and method for parameters in construction process of suspended derrick tower assembly

Abstract

The invention discloses a suspended derrick tower construction process parameter integrated monitoring system and method. The system comprises a sensing layer, a platform layer and an application layer which are connected in sequence. The sensing layer comprises a tension sensor group, a tilt angle sensor, a weight sensor and a wind speed and direction sensor and is used for collecting multi-source monitoring data in real time; the platform layer is used for performing space-time alignment and fusion processing on the multi-source monitoring data to form a multi-dimensional data stream; performing parallel analysis on the multi-dimensional data flow by using a built-in algorithm model to obtain state information and early warning information, and adjusting an early warning threshold value of the multi-dimensional data flow; the application layer comprises an on-site early warning terminal and a remote monitoring platform, and is used for receiving and displaying the state information and the early warning information, and extracting risk information based on the information. According to the invention, the overall stability and stress state of the suspension derrick can be comprehensively and truly evaluated from the system level, and the blind area of single-point monitoring is thoroughly eliminated.

Description

Technical Field

[0001] This invention relates to the field of suspended gantry tower construction technology, specifically to an integrated monitoring system and method for process parameters of suspended gantry tower construction. Background Technology

[0002] Currently, safety monitoring during the construction of suspended gantry towers mainly relies on the visual observation and personal experience of construction workers. Some simple monitoring methods have begun to be introduced in existing technologies, such as installing tilt sensors on the gantry or tension sensors on individual support ropes for independent monitoring of single parameters and simple threshold alarms. However, these existing technical solutions have significant drawbacks:

[0003] First, the monitoring dimensions are limited and lack a systematic approach. Existing technologies typically monitor only isolated parameters (such as the pole tilt angle or the tension of a single support rope), failing to simultaneously collect and correlate multiple key parameters, such as the support rope stress, lifting weight, pole posture, and environmental wind load. These parameters are independent of each other, failing to comprehensively and accurately reflect the overall mechanical state and stability of the suspended pole system, resulting in monitoring blind spots.

[0004] Secondly, the early warning mechanism is lagging and lacks initiative. Traditional alarm methods are based on fixed empirical thresholds, and an alarm is only triggered when a certain parameter exceeds a preset limit. At this time, the structural system is often close to or has reached a critical instability state, leaving very little reaction time for operators. The early warning lag is obvious, making it difficult to achieve truly effective prevention.

[0005] Finally, there is a lack of intelligent diagnostic capabilities. Existing technology cannot identify potential risks predicted by a combination of minor anomalies in multiple parameters (for example, when the lifting weight remains constant, a continuous minor increase in the gantry tilt angle accompanied by an abnormal change in the tension of the support rope on one side may indicate hidden risks such as loosening of connectors or foundation settlement). Due to the lack of adaptive analysis of construction conditions (such as lifting weight and gantry extension height), the fixed threshold mode is prone to false alarms or missed alarms in complex and variable construction scenarios. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide an integrated monitoring system and method for parameters in the construction process of suspended gantry tower assembly, which eliminates the blind spots of single-point monitoring and improves the level of construction safety, in order to solve the problems of independent monitoring of only one or a few parameters and delayed early warning in the existing technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solution:

[0008] The integrated monitoring system for construction process parameters of suspended gantry tower assembly includes a sensing layer, a platform layer, and an application layer connected in sequence.

[0009] The sensing layer includes a tension sensor array deployed on multiple support ropes, an inclination sensor deployed on the boom, a weight sensor integrated into the lifting system, and a wind speed and direction sensor deployed at the work site, used to collect multi-source monitoring data in real time.

[0010] The platform layer includes a data fusion center and an intelligent analysis engine module connected in sequence. The data fusion center is used to perform spatiotemporal alignment and fusion processing on multi-source monitoring data to form a multi-dimensional data stream. The intelligent analysis engine module is used to perform parallel analysis on the multi-dimensional data stream using built-in algorithm models to obtain status information and early warning information, and to adjust the early warning threshold of the multi-dimensional data stream.

[0011] The built-in algorithm models include a collaborative force balance model, a mechanical coupling analysis model, and an adaptive threshold model.

[0012] The application layer includes on-site early warning terminals and remote monitoring platforms, which are used to receive and display the status information and early warning information output by the intelligent analysis engine module, and extract risk information based on this information.

[0013] Furthermore, the multi-source monitoring data includes the tension of the support rope, the tilt angle of the jib, the lifting weight, the wind load, and the height of the jib;

[0014] The tension sensor group monitors the tension value of each supporting rope, the tilt sensor obtains the tilt angle and height of the gantry, the weight sensor obtains the lifting weight, and the wind speed and direction sensor obtains the wind load.

[0015] Furthermore, the tension values ​​of each supporting rope are calibrated and filtered before being input into the collaborative force balance model to calculate the force state of each supporting rope and obtain the force balance state of each supporting rope; the force balance state includes the average value, range, and standard deviation of the tension.

[0016] After filtering, denoising, and normalizing the gantry tilt angle, lifting weight, wind load, and gantry height, they are input into the mechanical coupling analysis model for coupling relationship analysis. The theoretical stress and strain values ​​of each stress node on the support rope are obtained. The theoretical stress and strain values ​​are compared with the stress and strain values ​​under normal conditions to obtain the deviation between the two. The changing trend of the stress node is obtained, and the changing trend corresponding to the deviation exceeding the predetermined threshold is marked as an abnormal mechanical state.

[0017] Among them, abnormal mechanical states include the pole tilt angle exceeding the normal working range, wind load influence, abnormal lifting weight, and multiple data anomalies.

[0018] Based on the current lifting weight and boom height, an adaptive threshold model is used to establish a functional relationship between multi-source monitoring data and corresponding thresholds. The threshold function relationship for the tension value is T(Q, H) = T0 + k1 × Q + k2 × H, and the threshold function relationship for the boom tilt angle is θ(Q, H) = θ0 × exp(-k3 × Q / H). When the lifting weight exceeds the preset segmented threshold, the boom tilt angle threshold tightening mechanism is triggered to adjust the boom tilt angle threshold. When the boom height exceeds the preset dangerous height threshold, the tension value threshold is increased proportionally to the height. This completes the dynamic adjustment of the multi-source monitoring data early warning threshold.

[0019] Where T represents the tension value, Q represents the lifting weight, H represents the boom height, T0 represents the initial value of the tension value, k1, k2, and k3 are all coefficients, θ represents the boom tilt angle, and θ0 represents the initial value of the boom tilt angle.

[0020] Furthermore, the degree of force equilibrium is assessed based on the state of force equilibrium;

[0021] If the ratio of the standard deviation to the mean is greater than 0.1, or if the ratio of the range to the mean is greater than 0.2 and less than or equal to 0.3, it indicates that the forces are unbalanced.

[0022] When the standard deviation is greater than 3kN, it indicates that the stress imbalance of the supporting rope is relatively serious, and a warning is issued.

[0023] When the ratio of the range to the average is greater than 0.3, it indicates that there is a significant difference in the stress on the supporting rope, and an early warning is issued.

[0024] Furthermore, in abnormal mechanical states, the normal operating range of the gantry tilt angle includes a continuous change time of less than 10 seconds and a tilt angle deviation of less than ±0.5°; wind load influence includes wind speed changes exceeding ±20%; abnormal lifting weight includes lifting weight changes exceeding ±5%; and multiple data co-occurrence anomalies include gantry tilt angle changes exceeding 2.5° and continuous changes exceeding 5 seconds, tension values ​​of the support ropes related to the gantry tilt angle changes exceeding 20% ​​and continuous changes exceeding 10 seconds, and wind speed changes exceeding 15% and continuous changes exceeding 10 seconds.

[0025] An early warning will be issued when an abnormal mechanical state occurs.

[0026] Furthermore, the built-in algorithm model of the intelligent analysis engine module also includes a trend prediction model, which predicts the future trend curve based on the time series of multidimensional data streams using time series algorithms.

[0027] Furthermore, at the application layer, risk information includes risk type, risk location, and handling recommendations;

[0028] Risk types include structural stability risk, environmental risk, equipment failure risk, load risk, and construction worker safety risk.

[0029] Furthermore, this invention also proposes an integrated monitoring method for parameters during the construction process of suspended gantry towers, including:

[0030] S1. Collect multi-source monitoring data during the construction process using a tension sensor group, tilt sensor, weight sensor, and wind speed and direction sensor.

[0031] S2. Perform spatiotemporal alignment and fusion processing on multi-source monitoring data to form a multi-dimensional data stream;

[0032] S3. Parallel analysis of multidimensional data streams is performed using a collaborative force balance model, a mechanical coupling analysis model, and an adaptive threshold model to obtain state information and early warning information, and the early warning thresholds of the multidimensional data streams are adjusted.

[0033] S4. Extract risk information based on status information and early warning information.

[0034] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0035] 1. By deploying a cluster of multiple types of sensors and utilizing a data fusion center to synchronously collect and correlate information from multiple sources such as the tension of the supporting rope, the tilt angle of the gantry, the lifting weight, and the environmental wind load, this invention can comprehensively and realistically assess the overall stability and stress state of the suspended gantry at the system level, eliminating the blind spots of single-point monitoring.

[0036] 2. Through innovative mechanical coupling analysis model and trend prediction model, this invention can keenly identify potential risk patterns (such as loose connectors, slight foundation settlement, and other hidden dangers) caused by minor abnormal changes in multiple parameters. This allows for early warning before the system condition deteriorates significantly, thus preventing accidents from happening in their infancy and achieving true proactive safety management. Attached Figure Description

[0037] Figure 1 This is an overall structural diagram of the system of the present invention. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0039] To achieve the above objectives, this invention proposes an integrated monitoring system for parameters during the construction process of suspended gantry towers, such as... Figure 1 As shown, it includes a perception layer, a platform layer, and an application layer connected in sequence.

[0040] The sensing layer includes tension sensor groups installed in series on the four main support ropes of the suspended mast system, two dual-axis tilt sensors installed at the top and middle of the mast, a weight sensor installed on the fixed pulley axle of the lifting trolley block, and an ultrasonic anemometer and wind direction sensor installed at the top of the mast, used for real-time acquisition of multi-source monitoring data. Specifically:

[0041] Multi-source monitoring data includes the tension value of the support ropes, the tilt angle of the gantry, the lifting weight, the wind load, and the gantry height; the tension sensor group monitors the tension value of each support rope, the tilt angle sensor obtains the tilt angle and height of the gantry, the weight sensor obtains the lifting weight, and the wind speed and direction sensor obtains the wind load.

[0042] The sensing layer transmits multi-source monitoring data to the on-site deployed gateway device via LoRa wireless communication. After aggregating all the data, the gateway device encrypts the data via a 4G / 5G DTU (Data Transmission Unit) and transmits it to the remote cloud platform. This hybrid networking approach addresses the needs of low power consumption, long-distance transmission, and remote platform access for sensors.

[0043] The platform layer comprises a data fusion center and an intelligent analysis engine module connected sequentially. The data fusion center performs spatiotemporal alignment (using the NTP protocol to ensure clock synchronization across sensors) and fusion processing on multi-source monitoring data, forming a unified multi-dimensional data stream (containing time, device ID, and parameter values). The intelligent analysis engine module utilizes built-in collaborative force balance models, mechanical coupling analysis models, adaptive threshold models, and trend prediction models to perform parallel analysis of the multi-dimensional data stream, obtaining status information and early warning information, and adjusting the early warning thresholds of the multi-dimensional data stream. Specifically:

[0044] After calibrating and filtering the tension values ​​of each supporting rope, they are input into the collaborative force balance model to calculate the force state of each supporting rope and obtain the force balance state of each supporting rope; the force balance state includes the average value, range, and standard deviation of the tension.

[0045] After filtering (e.g., low-pass filtering), denoising (e.g., Kalman filtering), and normalizing the gantry tilt angle, lifting weight, wind load, and gantry height, they are input into the mechanical coupling analysis model for coupling relationship analysis. The theoretical stress and strain values ​​of each stress node on the support rope are obtained. The theoretical stress and strain values ​​are compared with the stress and strain values ​​under normal conditions to obtain the deviation between the two. The changing trend of the stress node is obtained, and the changing trend corresponding to the deviation exceeding the predetermined threshold is marked as an abnormal mechanical state.

[0046] Abnormal mechanical states include pole tilt angle exceeding the normal working range, wind load influence, abnormal lifting weight, and multiple data anomalies.

[0047] The trend prediction model is based on time series algorithms to predict the time series of multidimensional data streams and obtain future trend curves.

[0048] Based on the current lifting weight and boom height, an adaptive threshold model is used to establish a functional relationship between multi-source monitoring data and corresponding thresholds. The threshold function relationship for the tension value is T(Q, H) = T0 + k1 × Q + k2 × H, and the threshold function relationship for the boom tilt angle is θ(Q, H) = θ0 × exp(-k3 × Q / H). When the lifting weight exceeds the preset segmented threshold, the boom tilt angle threshold tightening mechanism is triggered, and the boom tilt angle threshold is adjusted (e.g., the tilt angle is adjusted from 2° to 1.5°). When the boom height exceeds the preset dangerous height threshold (this threshold is 80%), the tension value threshold is increased proportionally to the height (e.g., for every 1 meter increase in height, the tension threshold increases by 0.5%). This completes the dynamic adjustment of the multi-source monitoring data early warning threshold.

[0049] Where T represents the tension value, Q represents the lifting weight, H represents the gantry height, T0 represents the initial value of the tension value, k1, k2, and k3 are all coefficients calibrated based on historical construction data or finite element analysis, θ represents the gantry tilt angle, and θ0 represents the initial value of the gantry tilt angle.

[0050] Assessing the degree of force equilibrium based on the state of force equilibrium:

[0051] If the ratio of the standard deviation to the mean is greater than 0.1, or if the ratio of the range to the mean is greater than 0.2 and less than or equal to 0.3, it indicates that the forces are unbalanced.

[0052] When the standard deviation is greater than 3kN, it indicates that the dispersion of the tension value of the supporting rope is too large and the uneven force is serious, which may threaten the structural safety of the suspended pole and issue a warning.

[0053] When the ratio of the range to the average is greater than 0.3, it indicates that the difference between the maximum and minimum values ​​of the tension in the supporting rope is too large relative to the average value, and the stress on each supporting rope is significantly different, which may affect the stability of the entire system. Timely handling and early warning are required.

[0054] In abnormal mechanical conditions, the normal operating range of the gantry tilt angle includes a continuous change time of less than 10 seconds and a tilt angle deviation of less than ±0.5°; wind load effects include wind speed changes exceeding ±20%; abnormal lifting weight includes lifting weight changes exceeding ±5%; multiple data co-occurrence anomalies include gantry tilt angle changes exceeding 2.5° and continuous changes exceeding 5 seconds, tension values ​​of the support ropes related to the gantry tilt angle changes exceeding 20% ​​and continuous changes exceeding 10 seconds, and wind speed changes exceeding 15% and continuous changes exceeding 10 seconds.

[0055] An early warning will be issued when an abnormal mechanical state occurs.

[0056] The application layer includes on-site early warning terminals (using industrial-grade explosion-proof tablets running a dedicated monitoring app) and a remote monitoring platform, used to receive and display status and early warning information output by the intelligent analysis engine module, and extract risk information based on this information. Specifically:

[0057] The interface displays all parameters in real time in both numerical and graphical formats, and uses traffic light colors (green, yellow, red) to indicate the system's safety level. When a warning is issued, the screen highlights the warning information, such as: "Note: The northeast support rope is under 20% excessive stress; adjustment is recommended," and triggers the built-in buzzer for an audible alert.

[0058] Remote monitoring platform: Safety management personnel at the company headquarters can log in to the monitoring platform via a web browser to view the status of multiple construction sites in real time, receive alarm information pushed by WeChat or SMS, and access historical data to generate safety reports.

[0059] Risk information includes the type of risk, its location, and recommendations for handling it;

[0060] Risk types include structural stability risk, environmental risk, equipment failure risk, load risk, and construction worker safety risk;

[0061] To address the risk of uneven stress on the support ropes, the recommended course of action is as follows:

[0062] 1. Adjust the support ropes: Make appropriate adjustments to the support ropes that are under too much or too little stress, such as by tightening or loosening the support ropes to balance the stress on each support rope;

[0063] 2. Check the connection points: Check whether the connection points between the supporting rope and the pole and other structures are firm, and whether there is any looseness or deformation. If there are any problems, reinforce or replace the connection parts in time.

[0064] 3. Re-plan the lifting scheme: Assess factors such as the weight and center of gravity of the object to be lifted. If the distribution of the object is unreasonable, resulting in uneven stress on the supporting rope, the lifting scheme can be re-planned, and the position or method of the object to be lifted can be adjusted.

[0065] Regarding the risk of abnormal mechanical states, the recommended handling methods are as follows:

[0066] 1. Stop lifting operations: When an abnormal mechanical condition is detected, immediately stop the current lifting operation to prevent the situation from deteriorating further;

[0067] 2. Check equipment and environment: Conduct a comprehensive inspection of equipment such as the boom and lifting system to check for any equipment malfunctions or damage; at the same time, check the environmental conditions of the work site, such as whether there are any sudden strong winds, ground subsidence, or other situations that may affect the construction.

[0068] 3. Conduct mechanical analysis and evaluation: Organize professional and technical personnel to conduct in-depth analysis and evaluation of abnormal mechanical states, determine the degree of risk and possible solutions, and reinforce or adjust the pole structure if necessary.

[0069] Regarding the risk of multi-source monitoring data exceeding the threshold, the recommended handling measures are as follows:

[0070] 1. Reduce lifting weight: If the risk of multi-source monitoring data exceeding the threshold is due to excessive lifting weight, the lifting weight can be appropriately reduced to keep it within a safe range;

[0071] 2. Adjust the pole height: If the risk of multi-source monitoring data exceeding the threshold is related to the pole height, the pole height can be adjusted appropriately to reduce the value of multi-source monitoring data;

[0072] 3. Strengthen monitoring and observation: After taking corresponding measures, strengthen the monitoring and observation of multi-source monitoring data to ensure that the data is restored to a safe range, and continue to observe for a period of time to prevent the risk from recurring.

[0073] This invention also proposes an integrated monitoring method for parameters during the construction process of suspended gantry towers, including:

[0074] S1. Before the tower erection construction, install all sensors and complete system debugging. Collect multi-source monitoring data during the construction process at a frequency of 1Hz through tension sensor group, tilt sensor, weight sensor and wind speed and direction sensor.

[0075] S2. Perform spatiotemporal alignment and fusion processing on multi-source monitoring data to form a multi-dimensional data stream;

[0076] S3. Parallel analysis of multidimensional data streams is performed using a collaborative force balance model, a mechanical coupling analysis model, and an adaptive threshold model to obtain state information and early warning information, and the early warning thresholds of the multidimensional data streams are adjusted.

[0077] S4. Extract risk information based on status information and early warning information.

[0078] Example:

[0079] The tension sensor group is a spoke-type force sensor based on the strain principle, with a range of 0-100kN and an accuracy of ±0.5%FS. The housing has an IP67 protection rating, making it suitable for field operation environments.

[0080] The dual-axis tilt sensor has a range of ±30° and an accuracy of ±0.1°.

[0081] The weight sensor is a pressure-side sensor with a range of 0-10 tons.

[0082] The range of the ultrasonic anemometer is 0-60m / s.

[0083] When the lifting weight is between 1 and 5 tons, the threshold for the boom tilt angle is 5° to 8°; when the lifting weight is between 5 and 10 tons, the threshold for the boom tilt angle is lowered by 15%, that is, the warning threshold becomes 4.25° to 6.8°; when the lifting weight is greater than 10 tons, the threshold for the boom tilt angle is lowered by 30%, that is, the threshold becomes 3.5° to 5.6°.

[0084] When the pole height is 10-20 meters, the threshold value of the supporting rope tension is 10-20 kN; when the pole height is 20-30 meters, the threshold value of the supporting rope tension is increased by 20%, that is, the threshold value becomes 12-24 kN; when the pole height is greater than 30 meters, the threshold value of the supporting rope tension is increased by 35%, that is, the threshold value becomes 13.5-27 kN.

[0085] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An integrated monitoring system for construction process parameters of suspended gantry tower assembly, characterized in that, It includes the perception layer, platform layer, and application layer, which are connected in sequence; The sensing layer includes a tension sensor group deployed on multiple support ropes, an inclination sensor deployed on the boom, a weight sensor integrated into the lifting system, and a wind speed and direction sensor deployed at the work site, for real-time collection of multi-source monitoring data; The platform layer includes a data fusion center and an intelligent analysis engine module connected in sequence; the data fusion center is used to perform spatiotemporal alignment and fusion processing on multi-source monitoring data to form a multi-dimensional data stream. The intelligent analysis engine module is used to perform parallel analysis of multidimensional data streams using built-in algorithm models, obtain status information and early warning information, and adjust the early warning thresholds of multidimensional data streams. The built-in algorithm models include a collaborative force balance model, a mechanical coupling analysis model, and an adaptive threshold model. The application layer includes on-site early warning terminals and remote monitoring platforms, which are used to receive and display the status information and early warning information output by the intelligent analysis engine module, and extract risk information based on this information.

2. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 1, characterized in that, Multi-source monitoring data includes the tension of the support rope, the tilt angle of the gantry, the lifting weight, the wind load, and the height of the gantry; The tension sensor group monitors the tension value of each supporting rope, the tilt sensor obtains the tilt angle and height of the gantry, the weight sensor obtains the lifting weight, and the wind speed and direction sensor obtains the wind load.

3. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 2, characterized in that, After calibrating and filtering the tension values ​​of each supporting rope, they are input into the collaborative force balance model to calculate the force state of each supporting rope and obtain the force balance state of each supporting rope; the force balance state includes the average value, range, and standard deviation of the tension. After filtering, denoising, and normalizing the gantry tilt angle, lifting weight, wind load, and gantry height, they are input into the mechanical coupling analysis model for coupling relationship analysis. The theoretical stress and strain values ​​of each stress node on the support rope are obtained. The theoretical stress and strain values ​​are compared with the stress and strain values ​​under normal conditions to obtain the deviation between the two. The changing trend of the stress node is obtained, and the changing trend corresponding to the deviation exceeding the predetermined threshold is marked as an abnormal mechanical state. Among them, abnormal mechanical states include the pole tilt angle exceeding the normal working range, wind load influence, abnormal lifting weight, and multiple data anomalies. Based on the current lifting weight and boom height, an adaptive threshold model is used to establish a functional relationship between multi-source monitoring data and corresponding thresholds. The threshold function relationship for the tension value is T(Q, H) = T0 + k1 × Q + k2 × H, and the threshold function relationship for the boom tilt angle is θ(Q, H) = θ0 × exp(-k3 × Q / H). When the lifting weight exceeds the preset segmented threshold, the boom tilt angle threshold tightening mechanism is triggered to adjust the boom tilt angle threshold. When the boom height exceeds the preset dangerous height threshold, the tension value threshold is increased proportionally to the height. This completes the dynamic adjustment of the multi-source monitoring data early warning threshold. Where T represents the tension value, Q represents the lifting weight, H represents the boom height, T0 represents the initial value of the tension value, k1, k2, and k3 are all coefficients, θ represents the boom tilt angle, and θ0 represents the initial value of the boom tilt angle.

4. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 3, characterized in that, Assess the degree of force equilibrium based on the force equilibrium state; If the ratio of the standard deviation to the mean is greater than 0.1, or if the ratio of the range to the mean is greater than 0.2 and less than or equal to 0.3, it indicates that the forces are unbalanced. When the standard deviation is greater than 3kN, it indicates that the stress imbalance of the supporting rope is relatively serious, and a warning is issued. When the ratio of the range to the average is greater than 0.3, it indicates that there is a significant difference in the stress on the supporting rope, and an early warning is issued.

5. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 3, characterized in that, In abnormal mechanical conditions, the normal operating range of the gantry tilt angle includes a continuous change time of less than 10 seconds and a tilt angle deviation of less than ±0.5°; wind load effects include wind speed changes exceeding ±20%; abnormal lifting weight includes lifting weight changes exceeding ±5%; multiple data co-occurrence anomalies include gantry tilt angle changes exceeding 2.5° and continuous changes exceeding 5 seconds, tension values ​​of the support ropes related to the gantry tilt angle changes exceeding 20% ​​and continuous changes exceeding 10 seconds, and wind speed changes exceeding 15% and continuous changes exceeding 10 seconds. An early warning will be issued when an abnormal mechanical state occurs.

6. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 1, characterized in that, The built-in algorithm model of the intelligent analysis engine module also includes a trend prediction model, which predicts the future trend curve based on the time series of multidimensional data streams using time series algorithms.

7. The integrated monitoring system for construction process parameters of suspended gantry tower assembly according to claim 1, characterized in that, In the application layer, risk information includes risk type, risk location, and handling recommendations; Risk types include structural stability risk, environmental risk, equipment failure risk, load risk, and construction worker safety risk.

8. A method for integrating and monitoring parameters of the suspended gantry tower construction process as described in any one of claims 1-7, characterized in that, include: S1. Collect multi-source monitoring data during the construction process using a tension sensor group, tilt sensor, weight sensor, and wind speed and direction sensor. S2. Perform spatiotemporal alignment and fusion processing on multi-source monitoring data to form a multi-dimensional data stream; S3. Parallel analysis of multidimensional data streams is performed using a collaborative force balance model, a mechanical coupling analysis model, and an adaptive threshold model to obtain state information and early warning information, and the early warning thresholds of the multidimensional data streams are adjusted. S4. Extract risk information based on status information and early warning information.

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