An environmental perception-based bridge machine operation parameter intelligent regulation system and method

Abstract

The present application relates to the technical field of intelligent data detection processing, and specifically discloses a bridge girder erection machine operation parameter intelligent regulation and control system and method based on environment perception, which collects multi-source monitoring data, extracts operation influence characteristics, analyzes hoist synchronization deviation indexes, builds a hoist consistency discrimination strategy through the hoist synchronization deviation indexes, determines whether the hoist synchronization action consistency meets the standard, constructs a stability risk discrimination model for the hoist meeting the standard, judges whether there is a potential hoisting stability risk, analyzes the influence factors of the hoist action consistency based on the stability risk discrimination model when there is a potential hoisting stability risk, determines the core influence factors, and performs intelligent regulation and control according to the core influence factors; the present application can effectively improve the stability, safety and intelligent level of the bridge girder erection machine operation process.

Description

Technical Field

[0001] This invention relates to the field of intelligent data detection and processing technology, and more specifically, to an intelligent control system and method for bridge erecting machine operation parameters based on environmental perception. Background Technology

[0002] The bridge erecting machine uses front and rear hoisting winches to lift and move the bridge beams. The front and rear winch systems need to maintain a high degree of consistency to ensure the stability of the beams during hoisting. However, in actual construction environments, bridge erecting machines operate outdoors for extended periods and are susceptible to environmental factors, which can lead to problems such as uneven track spacing, localized track settlement, insufficient track flatness, and mechanical wear. All of these factors can affect the operating condition of the bridge erecting machine.

[0003] When the track of the bridge erecting machine has uneven spacing or local deformation, the main beam of the machine will undergo slight posture changes during longitudinal movement, resulting in changes in the force distribution between the front and rear lifting points and causing differences in the actual load on the front and rear lifting winches. Simultaneously, with long-term use, key components of the winches will experience varying degrees of mechanical wear or clearance changes, leading to differences in transmission efficiency and response speed. These factors are gradually amplified through the mechanical transmission chain, causing the front and rear winches to produce different actual action responses under the same control commands. Existing bridge erecting machine control systems typically rely on preset control parameters or human experience for operation, exhibiting weak perception of changes in the construction environment and equipment operating status, making it difficult to promptly identify factors such as changes in track condition, equipment wear, and changes in lifting load distribution. When these factors are combined, traditional control methods struggle to achieve consistent adjustment of the actions of the front and rear lifting winches, thus affecting the stability and safety of the beam lifting process.

[0004] Therefore, it is necessary to provide an intelligent control system and method for bridge erecting machine operation parameters based on environmental perception to solve the above-mentioned technical problems. In order to solve the above problems, a technical solution is provided. Summary of the Invention

[0005] To overcome the aforementioned deficiencies in the prior art, this invention provides an intelligent control system and method for bridge erecting machine operation parameters based on environmental perception. This system addresses the problem that existing bridge erecting machines face difficulties in synchronous control and monitoring of the front and rear beam hoisting winches due to factors such as uneven track spacing and mechanical wear, which makes it difficult to affect the stability and safety of the beam hoisting process.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An intelligent control system for bridge erecting machine operation parameters based on environmental perception includes a core processor, a multi-source data acquisition module, a synchronization deviation analysis module, a winch consistency judgment module, a bridge erecting machine operation status evaluation module, and an influencing factor determination module. The multi-source data acquisition module is used to collect multi-source monitoring data in real time by deploying a multi-source sensor network on the bridge erecting machine. After preprocessing the multi-source monitoring data, an operational perception dataset is formed. The multi-source monitoring data includes operating environment data and equipment operating status parameters. The synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index. The winch consistency discrimination module is used to build a winch consistency discrimination strategy based on the winch synchronization deviation index to determine whether the winch synchronization action consistency meets the standard. The bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks. The influencing factor determination module is used to analyze the influencing factors of winch action consistency based on the stability risk discrimination model when there are potential hoisting stability risks, identify the core influencing factors, and carry out intelligent control based on the core influencing factors.

[0007] As a further aspect of the present invention, the operating environment data includes the operating environment wind speed; The equipment operating status parameters include the track spacing of the bridge erecting machine, track settlement change parameters, and beam hoisting winch operating parameters; the beam hoisting winch operating parameters include drum speed, wire rope tension, lifting displacement parameters, and equipment vibration data.

[0008] As a further aspect of the present invention, the synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index, as detailed below: By extracting features from multi-source monitoring data in the operational awareness dataset, operational impact features are obtained, and after normalization, an operational impact feature parameter set is formed; the operational impact features include a first key feature parameter and a second key feature parameter. A winch action consistency assessment model is constructed based on the set of operational impact feature parameters. The degree of winch action consistency is calculated by weighting and fusing the first key feature parameter.

[0009] As a further aspect of the present invention, the specific steps for feature extraction of multi-source monitoring data in the operational sensing dataset are as follows: based on the multi-source monitoring data, calculate the difference in drum speed, difference in lifting displacement parameters, and difference in wire rope tension between the two winches at the same moment as the first key feature parameters; simultaneously extract the vibration amplitude, track spacing change parameters, and track settlement change parameters as the second key feature parameters.

[0010] As a further aspect of the present invention, the winch consistency discrimination module is used to establish a winch consistency discrimination strategy based on the winch synchronization deviation index to determine whether the consistency of the winch's synchronous actions meets the standard, as detailed below: By comparing the extracted winch synchronization deviation index with the preset synchronization deviation threshold, if the winch synchronization deviation index is greater than or equal to the preset synchronization deviation threshold, the winch synchronization consistency does not meet the standard; if the winch synchronization deviation index is less than the preset synchronization deviation threshold, the winch synchronization consistency meets the standard.

[0011] As a further aspect of the present invention, the bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks, as detailed below: Based on the real-time calculation of the operating environment wind speed change parameters according to the operating environment data, the second key feature parameter is extracted to analyze the track stability coefficient; Extract the hoist synchronization deviation index, track stability coefficient and operating environment wind speed variation parameters, normalize them and input them into the stability risk discrimination model to output the operating stability assessment factor. The system uses operational stability assessment factors to determine in real time whether there are potential hoisting stability risks. Specifically, it compares the operational stability assessment factors with preset stability assessment thresholds. If the operational stability assessment factors are greater than or equal to the preset stability assessment thresholds, there are potential hoisting stability risks. If the operational stability assessment factors are less than the preset stability assessment thresholds, there are no potential hoisting stability risks.

[0012] As a further aspect of the present invention, the operating environment wind speed change parameter is calculated and obtained in real time based on the operating environment data. Specifically, the difference between the operating environment data at adjacent time points is calculated as the environmental wind speed change parameter by obtaining the operating environment data in real time. The second key feature parameter is extracted to analyze the track stability coefficient. Specifically, the vibration amplitude, track spacing variation parameter, and track settlement variation parameter are extracted from the second key feature parameter. After normalization, a track stability analysis model is constructed to obtain the track stability coefficient.

[0013] As a further aspect of the present invention, the influencing factor determination module is used to analyze the influencing factors of winch operation consistency based on a stability risk discrimination model when there is a potential hoisting stability risk, and to determine the core influencing factors, as follows: Based on the stability risk discrimination model, the track stability coefficient and the wind speed change parameters of the operating environment are obtained, and the influencing factors are judged according to the track stability coefficient and the wind speed change parameters of the operating environment. The influencing factor identification is as follows: the orbit stability coefficient is compared with the preset stability threshold. If the orbit stability coefficient is greater than or equal to the preset stability threshold, then the orbit stability coefficient is the core influencing factor; if the orbit stability coefficient is less than the preset stability threshold, then the orbit stability coefficient is not the core influencing factor. The operating environment wind speed change parameter is compared with the preset wind speed change threshold. If the operating environment wind speed change parameter is greater than or equal to the preset wind speed change threshold, then the operating environment wind speed change parameter is the core influencing factor; if the operating environment wind speed change parameter is less than the preset wind speed change threshold, then the operating environment wind speed change parameter is not the core influencing factor.

[0014] A method for intelligent control of bridge erecting machine operating parameters based on environmental perception includes the following steps: By deploying a multi-source sensor network on the bridge erecting machine to collect multi-source monitoring data in real time, and then preprocessing the multi-source monitoring data to form an operational perception dataset; the multi-source monitoring data includes operating environment data and equipment operating status parameters; Based on the operational perception dataset, operational impact features are extracted, and a winch action consistency evaluation model is constructed according to these operational impact features. The winch synchronization deviation index is then analyzed. A winch consistency discrimination strategy is established by using the winch synchronization deviation index to determine whether the winch synchronization action consistency meets the standard. For winches that meet the standards, the corresponding winch synchronization deviation index and operation impact characteristics are extracted, a stability risk discrimination model is constructed, and the current bridge erecting machine operation status is comprehensively evaluated to determine whether there are potential hoisting stability risks. When there are potential hoisting stability risks, the factors affecting the consistency of winch operation are analyzed based on the stability risk discrimination model to identify the core influencing factors, and intelligent control is carried out based on the core influencing factors.

[0015] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of an intelligent control system for bridge erecting machine operation parameters based on environmental perception, as described above.

[0016] The technical effects and advantages of this invention, a bridge erecting machine operation parameter intelligent control system and method based on environmental perception, are as follows: This invention utilizes multi-source data fusion and synchronization deviation analysis to accurately identify inconsistencies in winch movements, improving the synchronization control accuracy of the hoisting process. By introducing factors such as track stability and environmental wind speed to construct a comprehensive risk assessment model, the system not only focuses on the equipment's physical condition but also considers the external environment and foundation conditions, significantly improving the comprehensiveness and accuracy of risk identification. Through the determination of core influencing factors and targeted control strategies, cause-based control can be achieved, avoiding blind adjustments, improving control efficiency and reducing energy consumption. This invention can effectively prevent safety hazards such as beam tilting, swaying, and even instability, improving the safety, stability, and intelligence level of bridge erecting machine hoisting operations, while reducing manual intervention and enhancing the system's adaptability in complex construction environments. Attached Figure Description

[0017] Figure 1 A system block diagram of an intelligent control system for bridge erecting machine operation parameters based on environmental perception, provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating an intelligent control method for bridge erecting machine operation parameters based on environmental perception, provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described technical solutions are only a part of this invention, and not all of it. All other technical solutions obtained by those skilled in the art based on the technical solutions of this invention without inventive effort are within the scope of protection of this invention.

[0019] like Figure 1 The diagram shown is a system block diagram of an intelligent control system for bridge erecting machine operation parameters based on environmental perception, provided by an embodiment of the present invention. The intelligent control system for bridge erecting machine operation parameters based on environmental perception includes a core processor, a multi-source data acquisition module, a synchronization deviation analysis module, a winch consistency discrimination module, a bridge erecting machine operation status evaluation module, and an influencing factor determination module. The core processor is connected to the multi-source data acquisition module, the synchronization deviation analysis module, the winch consistency discrimination module, the bridge erecting machine operation status evaluation module, and the influencing factor determination module, respectively. The multi-source data acquisition module is connected to the synchronization deviation analysis module, the synchronization deviation analysis module is connected to the winch consistency discrimination module, the winch consistency discrimination module is connected to the bridge erecting machine operation status evaluation module, and the bridge erecting machine operation status evaluation module is connected to the influencing factor determination module.

[0020] The multi-source data acquisition module is used to collect multi-source monitoring data in real time by deploying a multi-source sensor network on the bridge erecting machine. After preprocessing the multi-source monitoring data, an operational perception dataset is formed. The multi-source monitoring data includes operating environment data and equipment operating status parameters. The preprocessing includes filtering, outlier removal, and data normalization to eliminate noise and acquisition errors. The synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index. The winch consistency discrimination module is used to build a winch consistency discrimination strategy based on the winch synchronization deviation index to determine whether the winch synchronization action consistency meets the standard. The bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks. The influencing factor determination module is used to analyze the influencing factors of winch action consistency based on the stability risk discrimination model when there are potential hoisting stability risks, identify the core influencing factors, and carry out intelligent control based on the core influencing factors.

[0021] The operating environment data includes the operating environment wind speed; The equipment operating status parameters include the track spacing of the bridge erecting machine, track settlement change parameters, and beam hoisting winch operating parameters; the beam hoisting winch operating parameters include drum speed, wire rope tension, lifting displacement parameters, and equipment vibration data.

[0022] Operating environment data is mainly used to reflect the disturbance impact of the external environment of the construction site on the hoisting process. A typical parameter is the operating environment wind speed. Wind speed not only directly acts on the beam surface to generate additional wind load, but also causes the beam to sway and fluctuate in attitude during hoisting. Its impact is more significant, especially in high-altitude operations or hoisting scenarios of large-span beams.

[0023] Equipment operating status parameters characterize the structural state and operational performance of the bridge erecting machine, including the track spacing, track settlement variation parameters, and hoisting winch operating parameters. Track spacing reflects the geometric relationship between the left and right tracks; uneven changes in this spacing can lead to deviations in the machine's operating posture. Track settlement variation parameters describe the local settlement or deformation of the tracks under construction loads and foundation conditions, directly affecting the bridge erecting machine's stability and the stress distribution on the main beam. Hoisting winch operating parameters are core data reflecting the operating status of the hoisting execution unit, specifically including drum speed, wire rope tension, lifting displacement parameters, and equipment vibration data. Drum speed characterizes the winch's operating speed and dynamic response characteristics; wire rope tension reflects the stress on the lifting points and load distribution; lifting displacement parameters describe the spatial position changes of the beam during hoisting; and equipment vibration data identifies whether the mechanical transmission system operates smoothly and whether there are abnormal impacts or wear.

[0024] By comprehensively collecting and analyzing the above-mentioned multi-dimensional operating parameters, the operating status of the bridge erecting machine in complex construction environments can be fully characterized, providing data support for subsequent action consistency assessment and intelligent control.

[0025] Preferably, the synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index, as follows: By extracting features from multi-source monitoring data in the operational awareness dataset, operational impact features are obtained, and after normalization, an operational impact feature parameter set is formed; the operational impact features include a first key feature parameter and a second key feature parameter. A winch action consistency assessment model is constructed based on a set of operational influence characteristic parameters. The model calculates the degree of winch action consistency by weighted fusion of the first key characteristic parameter. Specifically, the drum speed difference, lifting displacement parameter difference, and wire rope tension difference are used as input variables. A winch synchronization deviation index is formed through a weighted combination method. This index characterizes the degree of deviation in the consistency of the actions of the front and rear winches. A larger synchronization deviation index indicates a more significant difference in the actions of the front and rear winches, and a higher risk of the beam tilting or changing its posture during hoisting.

[0026] Preferably, the specific steps for feature extraction from the multi-source monitoring data in the operational sensing dataset are as follows: based on the multi-source monitoring data, calculate the difference in drum speed, lifting displacement parameter, and wire rope tension between the two winches at the same moment as the first key feature parameter; simultaneously extract the vibration amplitude, track spacing change parameter, and track settlement change parameter as the second key feature parameter.

[0027] In the bridge erection construction embodiment of the present invention, when the bridge erecting machine is performing precast box girder hoisting operations, the front and rear hoisting winches need to maintain height synchronization to ensure the stability of the girder posture. To this end, multi-source sensors are deployed at key parts of the bridge erecting machine to collect real-time operational sensing datasets, including the drum speeds of the front and rear winches, wire rope tension, lifting displacement parameters, equipment vibration signals, track spacing changes, and track settlement monitoring data. During operation, the synchronization deviation analysis module first extracts features from the aforementioned multi-source monitoring data, calculating the difference in drum speeds, lifting displacement parameters, and wire rope tension between the two winches within the same time frame, using these as the first key feature parameters reflecting the consistency of winch actions. Simultaneously, vibration amplitude, track spacing changes, and track settlement changes are extracted from the perspective of structure and operating environment, serving as the second key feature parameters reflecting the basic operating state of the equipment. Subsequently, the extracted feature parameters are normalized to eliminate the influence of dimensions, forming a unified set of operational influence feature parameters.

[0028] Based on the above, a winch action consistency assessment model is constructed. The first key characteristic parameter is used as the core input variable, and weighted fusion is performed using preset weights to calculate the winch synchronization deviation index. In specific implementation, for example, when the difference in drum speed between the front and rear winches gradually increases, and the tension difference in the wire rope shows significant fluctuations, the synchronization deviation index calculated by the model will rise significantly, indicating a decrease in the consistency of the front and rear winch actions. Further analysis combined with the second key characteristic parameter reveals that if this is accompanied by local changes in track spacing or abnormal track settlement, it indicates a correlation between the synchronization deviation and changes in track condition. Based on this, it can be determined that the beam exhibits a tendency for attitude deviation during hoisting, and this result can be promptly output to the subsequent stability risk assessment and operation parameter control module, achieving dynamic assessment and intelligent control of the bridge erecting machine's operating status. Through this method, winch synchronization anomalies can be effectively identified in real construction scenarios, providing early warning of beam tilting risks, thereby significantly improving the safety and stability of bridge erecting machine hoisting operations.

[0029] Preferably, the winch consistency discrimination module is used to establish a winch consistency discrimination strategy based on the winch synchronization deviation index to determine whether the consistency of the winch's synchronous actions meets the standard, as follows: By comparing the extracted winch synchronization deviation index with the preset synchronization deviation threshold, if the winch synchronization deviation index is greater than or equal to the preset synchronization deviation threshold, the winch synchronization consistency does not meet the standard; if the winch synchronization deviation index is less than the preset synchronization deviation threshold, the winch synchronization consistency meets the standard.

[0030] In one embodiment of the present invention, during the lifting and longitudinal movement of the bridge girder by the bridge erecting machine, the front and rear lifting winches must operate synchronously to ensure the stability of the girder's posture. During operation, the synchronization deviation analysis module calculates the winch synchronization deviation index in real time and transmits this index to the winch consistency judgment module. The module pre-sets a synchronization deviation threshold based on equipment performance parameters, construction specifications, and historical operating data. In specific implementation, when the bridge erecting machine performs girder lifting operations, if the current winch synchronization deviation index is detected to be less than the preset synchronization deviation threshold, it is determined that the consistency of the front and rear winch actions meets the standard, and the equipment is allowed to continue operating according to the current operating parameters. Conversely, when the synchronization deviation index gradually increases and reaches or exceeds the preset threshold due to factors such as uneven track, mechanical wear, or uneven load distribution, the winch consistency judgment module will determine that the current synchronization action consistency does not meet the standard and send an abnormal warning signal to the control system. At this time, the subsequent stability risk assessment and operation parameter control mechanism is further triggered to dynamically adjust parameters such as winch operating speed and synchronization control coefficient. If necessary, speed limiting or suspension operations are implemented to avoid the risk of tilting or instability of the girder during the lifting process. This implementation method enables rapid identification and response to the consistency of winch movements in real construction scenarios, effectively ensuring the safety and stability of the bridge erecting machine operation process.

[0031] Preferably, the bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks, as follows: Based on the real-time calculation of the operating environment wind speed change parameters according to the operating environment data, the second key feature parameter is extracted to analyze the track stability coefficient; The hoist synchronization deviation index, track stability coefficient, and ambient wind speed variation parameters are extracted, normalized, and then input into the stability risk discrimination model to output the operational stability assessment factor. The formula for the stability risk discrimination model is:

[0032] In the formula: As an operational stability assessment factor, This is the weighting factor for the orbital stability coefficient. The orbital stability coefficient, This is the weighting coefficient for the hoist synchronization deviation index. Let be the hoist synchronization deviation index at time t. For the testing cycle, The weighting coefficients for the environmental wind speed variation parameters. Let t be the environmental wind speed change parameter at time t.

[0033] The system uses operational stability assessment factors to determine in real time whether there are potential hoisting stability risks. Specifically, it compares the operational stability assessment factors with preset stability assessment thresholds. If the operational stability assessment factors are greater than or equal to the preset stability assessment thresholds, there are potential hoisting stability risks. If the operational stability assessment factors are less than the preset stability assessment thresholds, there are no potential hoisting stability risks.

[0034] In one embodiment of the invention, environmental monitoring devices deployed in the bridge erecting machine's operating area collect real-time environmental data, focusing on acquiring instantaneous wind speed information in the construction area. Wind speed data is continuously sampled at fixed time intervals, and the difference between wind speed values ​​at adjacent time points is calculated to obtain a wind speed variation parameter, which characterizes the degree of wind speed fluctuation and abrupt changes. For example, when the wind speed difference increases significantly in a short period of time, it indicates the presence of gusts or unstable airflow at the construction site, which will disturb the stability of the beam hoisting. This parameter is then used as a key input in subsequent stability analysis.

[0035] Simultaneously, a second key feature parameter is extracted from the operational sensing dataset to comprehensively evaluate the stability of the bridge erecting machine's track. Specifically, vibration amplitude data collected by the equipment's vibration sensors, along with track spacing and settlement variation parameters acquired by the track monitoring module, are acquired and normalized to eliminate dimensional differences. Based on this, a track stability analysis model is constructed to integrate and calculate various parameters within the monitoring period: on the one hand, vibration amplitude reflects the overall stability of the equipment's operation; on the other hand, the combined impact of track geometry changes is quantified by taking the square root of the sum of the squares of track spacing and settlement variations at each moment and accumulating the results. By introducing weighting coefficients to weight different factors, a track stability coefficient is finally obtained. For example, when there is localized settlement or uneven spacing variation in the track at the construction site, this accumulated term will significantly increase, leading to an increase in the track stability coefficient, indicating that the track's operating state tends to be unstable. This track stability coefficient, along with wind speed variation parameters, is input into subsequent models to determine the comprehensive stability of the bridge erecting machine's operating environment and structural state, providing a basis for risk warning and parameter control during the hoisting process.

[0036] Preferably, the operating environment wind speed change parameter is calculated and obtained in real time based on the operating environment data. Specifically, the difference between the operating environment data at adjacent time points is calculated as the environmental wind speed change parameter by obtaining the operating environment data in real time. The second key feature parameter is extracted to analyze the track stability coefficient. Specifically, the vibration amplitude, track spacing variation parameter, and track settlement variation parameter are extracted from the second key feature parameter. After normalization, a track stability analysis model is constructed to obtain the track stability coefficient. The formula for the track stability analysis model is as follows: ;

[0037] In the formula: The orbital stability coefficient, The weighting coefficient for vibration amplitude. To monitor the vibration amplitude during the monitoring period, The weighting coefficients for changes in the operating trajectory. For the testing cycle, Let t be the parameter representing the change in track spacing. Let be the parameters representing the change in track settlement at time t.

[0038] In one embodiment of the present invention, after the bridge erecting machine completes the consistency judgment of the winch's actions and confirms that its synchronous actions meet the standards, it enters the operation status evaluation stage. First, wind speed data is acquired in real time through environmental monitoring devices deployed in the construction area, and the wind speed change parameter of the operating environment is calculated by the wind speed difference between adjacent time points to reflect the degree of wind speed fluctuation in the construction environment. At the same time, a second key feature parameter is extracted from the operation perception dataset, and a comprehensive analysis is performed on the equipment vibration amplitude, changes in the track spacing, and changes in track settlement to construct a track stability analysis model and calculate the track stability coefficient, thereby characterizing the overall stability state of the bridge erecting machine's operating track.

[0039] Based on this, the winch synchronization deviation index, track stability coefficient, and wind speed variation parameters of the operating environment are further extracted. After normalization of these multi-dimensional parameters, they are input into the stability risk discrimination model. By weighted fusion calculation of the track stability coefficient, the winch synchronization deviation index at each moment within the detection period, and the wind speed variation parameters, the operational stability assessment factor is obtained.

[0040] In practical implementation, for example, when there is localized track settlement and significant wind speed fluctuations at the construction site, the track stability coefficient and wind speed variation term will increase significantly. Simultaneously, if the winch synchronization deviation fluctuates within a certain range, the combined effect of these three factors will cause the operational stability assessment factor to rise rapidly. The operational stability assessment factor is compared in real time with a preset stability assessment threshold: when the assessment factor is less than the threshold, the current bridge erecting machine is considered to be in a stable operating state, and the hoisting operation can proceed normally; when the assessment factor reaches or exceeds the threshold, a potential hoisting stability risk is identified, and an early warning signal is issued. This is then linked to the subsequent operation parameter control module to dynamically adjust the winch operating speed, synchronization control strategy, etc., and, if necessary, execute speed limits or suspend operations. Through this implementation method, dynamic assessment and risk prediction of the bridge erecting machine's operating state can be achieved in a real construction environment, effectively avoiding beam tilting or hoisting instability caused by track instability or environmental disturbances.

[0041] Preferably, the influencing factor determination module is used to analyze the influencing factors of winch action consistency based on a stability risk discrimination model when there are potential hoisting stability risks, and to determine the core influencing factors, as follows: Based on the stability risk discrimination model, the track stability coefficient and the wind speed change parameters of the operating environment are obtained, and the influencing factors are judged according to the track stability coefficient and the wind speed change parameters of the operating environment. The influencing factor identification is as follows: the orbit stability coefficient is compared with the preset stability threshold. If the orbit stability coefficient is greater than or equal to the preset stability threshold, then the orbit stability coefficient is the core influencing factor; if the orbit stability coefficient is less than the preset stability threshold, then the orbit stability coefficient is not the core influencing factor. The operating environment wind speed change parameter is compared with the preset wind speed change threshold. If the operating environment wind speed change parameter is greater than or equal to the preset wind speed change threshold, then the operating environment wind speed change parameter is the core influencing factor; if the operating environment wind speed change parameter is less than the preset wind speed change threshold, then the operating environment wind speed change parameter is not the core influencing factor.

[0042] In one embodiment of the present invention, when the bridge erecting machine operation status assessment module determines that there is a potential hoisting stability risk, it enters the influencing factor determination stage. At this time, the influencing factor determination module extracts key parameters based on the output results of the stability risk discrimination model, including the track stability coefficient and the wind speed change parameters of the operating environment, and performs discrimination analysis on the two types of factors respectively. In specific implementation, the currently calculated track stability coefficient is first compared with the preset track stability threshold. For example, if the track spacing changes and the cumulative settlement increases due to uneven foundation settlement or track laying errors during construction, causing the track stability coefficient to reach or exceed the preset threshold, the track condition is determined to be the main source of risk, that is, the track stability coefficient is the core influencing factor; conversely, if the track stability coefficient is lower than the threshold, it indicates that the overall track condition is controllable and is not considered the current main influencing factor.

[0043] Simultaneously, the system analyzes environmental wind speed changes, comparing real-time calculated wind speed variations with preset threshold values. For example, during actual hoisting, if a sudden gust or significant wind speed fluctuation causes the wind speed variation to exceed the threshold, the environmental wind speed change is identified as the core influencing factor; if the wind speed change is relatively stable, it is not considered a primary source of influence. This dual-discrimination mechanism allows for the rapid identification of dominant factors affecting the consistency of winch movements and hoisting stability in complex construction environments. When the determination results indicate that track or wind speed factors are the core influencing factors, this information is transmitted to the subsequent operational parameter control module, allowing for targeted adjustments to the winch's operating strategy. For instance, it optimizes synchronization control and operating speed to address track issues, or reduces lifting speed and enhances stability control to address wind speed disturbances, thereby achieving precise control. This approach effectively distinguishes different risk sources in real construction scenarios, improving the targeting and efficiency of risk management and further ensuring the safety and stability of bridge erecting machine hoisting operations.

[0044] Example 2:

[0045] In this embodiment of the invention, when the bridge erecting machine performs beam hoisting and erection operations, it first deploys a multi-source sensor network in key parts of the machine, such as the front and rear beam hoisting winches, the main beam structure, the running track, and the construction environment area, to collect operating environment data and equipment operating status parameters in real time. After preprocessing the collected multi-source monitoring data such as filtering, noise reduction, and time synchronization, a unified operating perception dataset is formed, providing a reliable data foundation for subsequent analysis.

[0046] During hoisting operations, operational impact features are extracted based on the operational perception dataset. Key parameters such as the difference in drum speed, lifting displacement, and wire rope tension between the front and rear winches are calculated. Combined with features like vibration amplitude, track spacing changes, and track settlement, a winch action consistency assessment model is constructed. A winch synchronization deviation index is calculated in real time to quantify the consistency of the front and rear winch actions. Subsequently, by comparing this synchronization deviation index with a preset threshold, a winch consistency judgment strategy is established: when the synchronization deviation index is within the allowable range, the winch synchronization action is deemed to meet the standard, and the equipment is allowed to continue normal operation; if it does not meet the standard, an alarm or control mechanism can be directly triggered.

[0047] For operating conditions where the consistency of actions meets the standard, the hoist synchronization deviation index and operational impact characteristics for the corresponding time period are further extracted to construct a stability risk discrimination model. This model integrates the track stability coefficient, synchronization deviation index, and environmental wind speed variation parameters to obtain an operational stability assessment factor, which is then compared with a preset threshold to comprehensively evaluate the current operating status of the bridge erecting machine. For example, in cases of local track settlement or increased wind speed fluctuations, even if the hoist actions are basically synchronized, the system may still identify potential hoisting stability risks.

[0048] When potential risks are identified, the analysis further examines the factors affecting the consistency of winch operation based on the output of the stability risk discrimination model. It determines whether the track stability coefficient and wind speed variation parameters exceed corresponding thresholds, thereby identifying the core influencing factors. For example, if the track stability coefficient exceeds the limit while wind speed variation is small, the track condition can be identified as the primary source of risk; conversely, environmental wind speed disturbances may dominate. Finally, targeted intelligent control strategies are implemented based on the identified core influencing factors. These strategies include adjusting the operating speed and synchronization control parameters of the front and rear winches, optimizing the lifting rhythm, or reducing or even suspending operations when wind speeds are high. This enables dynamic perception, risk prediction, and precise control of the bridge erecting machine's hoisting process in real construction scenarios, effectively ensuring the safety and stability of beam hoisting operations.

[0049] Example 3:

[0050] A method for intelligent control of bridge erecting machine operating parameters based on environmental perception includes the following steps: Step S1: Multi-source monitoring data is collected in real time by deploying a multi-source sensor network on the bridge erecting machine. After preprocessing the multi-source monitoring data, an operational perception dataset is formed. The multi-source monitoring data includes operating environment data and equipment operating status parameters. Step S2: Extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index; Step S3: Establish a winch consistency judgment strategy by using the winch synchronization deviation index to determine whether the consistency of the winch's synchronous action meets the standard. Step S4: For winches that meet the standards, extract the corresponding winch synchronization deviation index and operation impact characteristics, construct a stability risk discrimination model, comprehensively evaluate the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks. Step S5: When there is a potential risk to hoisting stability, analyze the influencing factors of winch action consistency based on the stability risk discrimination model, determine the core influencing factors, and carry out intelligent control based on the core influencing factors.

[0051] like Figure 2 The diagram shown is a flowchart illustrating an intelligent control method for bridge erecting machine operating parameters based on environmental perception, according to an embodiment of the present invention. This method can be used to execute... Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0052] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of an intelligent control system for bridge erecting machine operation parameters based on environmental perception as described above.

[0053] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0054] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0055] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0056] Through the above embodiments, this invention utilizes multi-source data fusion and synchronization deviation analysis to accurately identify inconsistencies in winch movements, thereby improving the synchronization control accuracy of the hoisting process. By introducing factors such as track stability and ambient wind speed to construct a comprehensive risk assessment model, the system not only focuses on the equipment's physical condition but also considers the external environment and foundation conditions, significantly improving the comprehensiveness and accuracy of risk identification. Through the determination of core influencing factors and targeted control strategies, cause-based control can be achieved, avoiding blind adjustments, improving control efficiency, and reducing energy consumption. This invention can effectively prevent safety hazards such as beam tilting, swaying, and even instability, improving the safety, stability, and intelligence level of bridge erecting machine hoisting operations, while reducing manual intervention and enhancing the system's adaptability in complex construction environments.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0058] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A bridge erecting machine operation parameter intelligent control system based on environmental perception, characterized in that, It includes a core processor, a multi-source data acquisition module, a synchronization deviation analysis module, a winch consistency judgment module, a bridge erecting machine operation status assessment module, and an influencing factor determination module; The multi-source data acquisition module is used to collect multi-source monitoring data in real time by deploying a multi-source sensor network on the bridge erecting machine. After preprocessing the multi-source monitoring data, an operational perception dataset is formed. The multi-source monitoring data includes operating environment data and equipment operating status parameters. The synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index. The winch consistency discrimination module is used to build a winch consistency discrimination strategy based on the winch synchronization deviation index to determine whether the winch synchronization action consistency meets the standard. The bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks. The influencing factor determination module is used to analyze the influencing factors of winch action consistency based on the stability risk discrimination model when there are potential hoisting stability risks, identify the core influencing factors, and carry out intelligent control based on the core influencing factors.

2. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 1, characterized in that, Operating environment data includes the wind speed in the operating environment; The equipment operating status parameters include the track spacing of the bridge erecting machine, track settlement change parameters, and beam hoisting winch operating parameters; the beam hoisting winch operating parameters include drum speed, wire rope tension, lifting displacement parameters, and equipment vibration data.

3. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 1, characterized in that, The synchronization deviation analysis module is used to extract operational impact features based on the operational perception dataset, construct a winch action consistency evaluation model based on the operational impact features, and analyze the winch synchronization deviation index, as detailed below: By extracting features from multi-source monitoring data in the operational awareness dataset, operational impact features are obtained, and after normalization, an operational impact feature parameter set is formed; the operational impact features include a first key feature parameter and a second key feature parameter. A winch action consistency assessment model is constructed based on the set of operational impact feature parameters. The degree of winch action consistency is calculated by weighting and fusing the first key feature parameter.

4. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 3, characterized in that, The specific steps for feature extraction from the multi-source monitoring data in the operational sensing dataset are as follows: Based on the multi-source monitoring data, calculate the difference in drum speed, lifting displacement parameters, and wire rope tension between the two winches at the same moment as the first key feature parameters; at the same time, extract the vibration amplitude, track spacing change parameters, and track settlement change parameters as the second key feature parameters.

5. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 1, characterized in that, The winch consistency judgment module is used to establish a winch consistency judgment strategy based on the winch synchronization deviation index, and to determine whether the winch's synchronous action consistency meets the standard, as detailed below: By comparing the extracted winch synchronization deviation index with the preset synchronization deviation threshold, if the winch synchronization deviation index is greater than or equal to the preset synchronization deviation threshold, the winch synchronization consistency does not meet the standard; if the winch synchronization deviation index is less than the preset synchronization deviation threshold, the winch synchronization consistency meets the standard.

6. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 1, characterized in that, The bridge erecting machine operation status assessment module is used to extract the corresponding winch synchronization deviation index and operation impact characteristics for winches that meet the standards, construct a stability risk discrimination model, comprehensively assess the current bridge erecting machine operation status, and determine whether there are potential hoisting stability risks, as detailed below: Based on the real-time calculation of the operating environment wind speed change parameters according to the operating environment data, the second key feature parameter is extracted to analyze the track stability coefficient; Extract the hoist synchronization deviation index, track stability coefficient and operating environment wind speed variation parameters, normalize them and input them into the stability risk discrimination model to output the operating stability assessment factor. Based on operational stability assessment factors, it is determined in real time whether there are potential hoisting stability risks; Specifically, the operational stability assessment factor is compared with the preset stability assessment threshold. If the operational stability assessment factor is greater than or equal to the preset stability assessment threshold, there is a potential risk to hoisting stability; if the operational stability assessment factor is less than the preset stability assessment threshold, there is no potential risk to hoisting stability.

7. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 6, characterized in that, The operating environment wind speed change parameter is calculated and obtained in real time based on the operating environment data. Specifically, the difference between the operating environment data at adjacent time points is calculated as the environmental wind speed change parameter by acquiring the operating environment data in real time. The second key feature parameter is extracted to analyze the track stability coefficient. Specifically, the vibration amplitude, track spacing variation parameter, and track settlement variation parameter are extracted from the second key feature parameter. After normalization, a track stability analysis model is constructed to obtain the track stability coefficient.

8. The intelligent control system for bridge erecting machine operation parameters based on environmental perception according to claim 1, characterized in that, The influencing factor determination module is used to analyze the influencing factors of winch operation consistency based on a stability risk discrimination model when there are potential hoisting stability risks, and to determine the core influencing factors, as follows: Based on the stability risk discrimination model, the track stability coefficient and the wind speed change parameters of the operating environment are obtained, and the influencing factors are judged according to the track stability coefficient and the wind speed change parameters of the operating environment. The influencing factor identification is as follows: the orbit stability coefficient is compared with the preset stability threshold. If the orbit stability coefficient is greater than or equal to the preset stability threshold, then the orbit stability coefficient is the core influencing factor; if the orbit stability coefficient is less than the preset stability threshold, then the orbit stability coefficient is not the core influencing factor. The operating environment wind speed change parameter is compared with the preset wind speed change threshold. If the operating environment wind speed change parameter is greater than or equal to the preset wind speed change threshold, then the operating environment wind speed change parameter is the core influencing factor; if the operating environment wind speed change parameter is less than the preset wind speed change threshold, then the operating environment wind speed change parameter is not the core influencing factor.

9. A method for intelligent control of bridge erecting machine operating parameters based on environmental perception, applied to the intelligent control system for bridge erecting machine operating parameters based on environmental perception as described in any one of claims 1-8, characterized in that, Includes the following steps: By deploying a multi-source sensor network on the bridge erecting machine to collect multi-source monitoring data in real time, and then preprocessing the multi-source monitoring data to form an operational perception dataset; the multi-source monitoring data includes operating environment data and equipment operating status parameters; Based on the operational perception dataset, operational impact features are extracted, and a winch action consistency evaluation model is constructed according to these operational impact features. The winch synchronization deviation index is then analyzed. A winch consistency discrimination strategy is established by using the winch synchronization deviation index to determine whether the winch synchronization action consistency meets the standard. For winches that meet the standards, the corresponding winch synchronization deviation index and operation impact characteristics are extracted, a stability risk discrimination model is constructed, and the current bridge erecting machine operation status is comprehensively evaluated to determine whether there are potential hoisting stability risks. When there are potential hoisting stability risks, the factors affecting the consistency of winch operation are analyzed based on the stability risk discrimination model to identify the core influencing factors, and intelligent control is carried out based on the core influencing factors.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the intelligent control system and method for bridge erecting machine operation parameters based on environmental perception as described in any one of claims 1-8.

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