A bridge building machine adaptive pose control system based on multi-source data fusion

Abstract

The present application relates to bridge construction equipment intelligent control technical field, especially in kind of based on multi-source data fusion's bridge machine adaptive pose control system, including abnormal identification module based on each fulcrum load rate of change determines load state;Space positioning management module responds to local load abnormality and determines load redistribution strategy;Trend traceability compensation module is based on historical load time sequence characteristic and determines compensation coefficient;Positioning fine adjustment execution module is based on four corner point space coordinates and executes pose adjustment;Effect evaluation correction module is based on residual deviation change and determines adjustment effect state;Parameter correction feedback module responds to effect degradation state and corrects compensation coefficient.The present application constructs load pose perception system through multi-source data fusion, realizes the screening of support system physical state with load rate of change as criterion, makes the adjustment process complete under the double guarantee of support balance and effect closed loop, effectively improves the pose reliability and parameter adaptive ability of bridge machine under the variable construction conditions.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for bridge construction equipment, and in particular to an adaptive attitude control system for bridge building machines based on multi-source data fusion. Background Technology

[0002] With the continuous development of bridge construction technology, bridge-building machines, as the core construction equipment for the installation, support, and pouring of bridge superstructures, are increasingly demonstrating improved intelligence. Modern bridge-building machines have integrated intelligent functions such as automatic walking, automatic formwork opening and closing, automatic adjustment of the basket height, and hydraulic adjustment of the rear anchor. They also utilize intelligent monitoring systems to collect key parameters in real time, such as tilt angle, tension, wind speed, strain, and walking status. Combined with integrated data transmission and analysis technology, this enables real-time monitoring, data analysis, and risk warning of the construction process. Regarding attitude control, existing technologies can automatically adjust the formwork elevation using BeiDou satellite positioning guidance. Some systems also construct intelligent control models by acquiring the bridge-building machine's operating status vectors to achieve standardized operations.

[0003] Chinese Patent Publication No. CN120927059A discloses a high-precision intelligent monitoring system for bridge-building machines based on BeiDou navigation. This system includes a BeiDou satellite positioning module. The output of the BeiDou satellite positioning module is electrically connected to a construction progress analysis module and a data acquisition and transmission module. The output of the data acquisition and transmission module is electrically connected to a data processing and analysis module. The output of the data processing and analysis module is electrically connected to an equipment health assessment module and a data storage and management module. This invention achieves high-precision monitoring of the bridge-building machine's position through the BeiDou satellite positioning module. Combined with multi-dimensional data collected by stress sensors, tilt sensors, etc., it can comprehensively and accurately grasp the operating status of the bridge-building machine. The construction progress analysis module can analyze the construction progress in real time, the equipment health assessment module can predict the remaining lifespan of the equipment, and the early warning and decision-making module makes timely decisions based on various data, realizing intelligent management of the bridge-building machine. However, the aforementioned high-precision intelligent monitoring system for bridge-building machines based on BeiDou navigation has the following problems:

[0004] Although the BeiDou-based high-precision intelligent monitoring system for bridge-building machines has achieved comprehensive monitoring and health assessment of the bridge-building machine's operating status, its technical solution is still limited to the application level of a single monitoring data source. It does not involve the field of attitude control driven by multi-source data fusion, lacks the ability to spatially locate and identify trends of abnormal loads on the support system, and also lacks an adaptive correction mechanism for control parameters based on attitude adjustment effect feedback. It cannot solve the technical problem of unreliable adjustment process caused by directly adjusting the attitude based on the position deviation. Summary of the Invention

[0005] To address this, the present invention provides an adaptive attitude adjustment control system for bridge-building machines based on multi-source data fusion, which overcomes the shortcomings of existing technologies in terms of lacking spatial positioning and trend recognition capabilities for abnormal loads on the support system, as well as the lack of an adaptive correction mechanism for control parameters based on attitude adjustment effect feedback, and the inability to solve the problem of unreliable adjustment process caused by directly adjusting attitude based on position deviation.

[0006] To achieve the above objectives, the present invention provides an adaptive attitude control system for bridge-building machines based on multi-source data fusion, comprising:

[0007] An anomaly identification module is used to determine the number of abnormal support points based on the degree of deviation of the current load of each support point from its respective historical average load, and to determine the load status of the support system based on the number of abnormal support points.

[0008] A spatial positioning and management module, which is connected to the anomaly identification module, is used to determine the spatial distribution pattern of the load anomaly of the support system based on the spatial location distribution of the abnormal support points in response to the support system being in a local load anomaly state, so as to determine the target stroke and execution strategy for load redistribution.

[0009] The trend tracing and compensation module is connected to the spatial positioning and management module. In response to the spatial positioning and management module completing load redistribution, it determines the time trend characteristics of load change based on the historical load data sequence of the abnormal fulcrum in the previous several cycles, so as to determine whether to perform adjustment and compensation operation, and determines the adjustment and compensation coefficient based on the average value of the historical load offset of the abnormal fulcrum in the preset cycle and the current load offset.

[0010] The pose fine-tuning execution module is connected to the anomaly identification module and the trend tracing and compensation module. It is used to respond to the support system that is in a stable and balanced load-bearing state after adjustment, and to determine the three-dimensional posture deviation based on the spatial coordinates of the four corner points of the bottom surface of the bridge-building machine template in order to perform pose adjustment operation.

[0011] The effect evaluation and correction module is connected to the pose fine-tuning execution module. In response to the completion of the pose adjustment operation, it obtains the change in residual deviation based on the residual deviation of the previous round and the residual deviation of the current round to determine the pose adjustment effect status.

[0012] The parameter correction feedback module, which is connected to the effect evaluation correction module and the trend tracing compensation module, is used to determine the correction requirement of the adjustment compensation coefficient in response to the pose adjustment effect being in a degraded state.

[0013] Furthermore, the anomaly detection module includes:

[0014] An anomaly identification unit is used to determine that the support system is in a local load anomaly state based on the load change rate of two or three support points exceeding the anomaly judgment threshold.

[0015] Furthermore, in response to the support system being in a state of local load abnormality, the spatial positioning management module performs abnormal location positioning and load redistribution processing.

[0016] Furthermore, the process of determining the spatial distribution pattern of load anomalies in the support system based on the spatial location distribution of the abnormal support points includes:

[0017] When the load change rate of the two support points exceeds the anomaly judgment threshold, the anomaly is judged as a single-sided concentrated anomaly, a single-row concentrated anomaly, or a diagonally dispersed anomaly, depending on the positional relationship of the anomaly support points.

[0018] When the load change rate of the three fulcrums exceeds the anomaly judgment threshold, the two fulcrums with the largest absolute value of the load change rate are selected as priority treatment objects. The corresponding anomaly mode is determined according to the positional relationship of the two fulcrums, and the load redistribution operation is performed. Then, the load redistribution operation is performed on the third fulcrum.

[0019] Furthermore, the spatial positioning governance module includes:

[0020] A single-row concentrated anomaly unit is used to perform synchronous adjustment of the vertical cylinders at the two support points of the abnormal row when a single-row concentrated anomaly is determined.

[0021] A single-sided concentrated anomaly unit is used to perform synchronous adjustment of the vertical cylinders at the two support points on the abnormal side when a single-sided concentrated anomaly is determined.

[0022] The diagonal dispersion anomaly unit is used to perform linkage adjustment on the vertical cylinders of the two abnormal support points when the diagonal dispersion anomaly is determined.

[0023] Furthermore, the trend tracing and compensation module includes:

[0024] The trend judgment subunit is used to determine the change attributes of the abnormal support point based on the consistency of the load change direction of the abnormal support point within a preset period.

[0025] When the load change direction of the abnormal fulcrum is completely consistent in each consecutive sampling period within a preset time, the load change attribute of the fulcrum is determined to be a trend change.

[0026] When the load change direction of the abnormal fulcrum is inconsistent in each consecutive sampling period within a preset time, the load change attribute of the fulcrum is determined to be an occasional fluctuation.

[0027] Furthermore, the trend tracing and compensation module also includes a compensation execution subunit.

[0028] When the trend judgment subunit determines that a trend change has occurred, it performs an adjustment and compensation operation based on the base coefficient and the adjustment and compensation coefficient.

[0029] When the trend judgment subunit determines that the fluctuation is occasional, the adjustment is performed based on the basic coefficient as the total governance coefficient.

[0030] Furthermore, the pose fine-tuning execution module includes:

[0031] The three-dimensional deviation determination unit is used to determine the maximum lateral deviation, maximum longitudinal deviation, and maximum vertical deviation based on the maximum coordinate deviation of the feature points at the four corners of the bottom surface of the bridge-building machine template in each axis, and to determine the three-dimensional deviation vector of the template based on the maximum axial deviation of the feature points at the four corners of the bottom surface of the template.

[0032] Furthermore, the effect evaluation and correction module includes:

[0033] The effect degradation state determination unit is used to determine that the pose adjustment effect is in an effect degradation state based on the residual deviation change being greater than zero.

[0034] Furthermore, the process of determining the correction requirements for the adjustment compensation coefficient includes:

[0035] When the effect evaluation and correction module determines that the effect is in a degraded state, it determines that the current value of the adjustment compensation coefficient exceeds the actual compensation requirement under the current working conditions, and adjusts the adjustment compensation coefficient down based on a preset ratio.

[0036] Compared with existing technologies, the advantages of this invention are as follows: This invention constructs a multi-source data fusion load pose joint sensing system by integrating pressure sensing data, historical periodic data, spatial coordinate data, and pose deviation data. It uses the load change rate at each support point as a criterion to achieve rapid screening and routing of the physical state of the support system. The spatial positioning and management module determines the spatial distribution pattern of load anomalies and the corresponding load redistribution strategy based on the spatial location distribution of abnormal support points, ensuring precise matching between the load management execution method and the location of the anomaly. The trend tracing and compensation module determines the temporal characteristics of load changes based on the historical load data sequence of abnormal support points and dynamically determines the adjustment compensation coefficient, ensuring precise correspondence between the compensation decision and the temporal characteristics of the anomaly. Finally, the pose fine-tuning execution module determines the three-dimensional attitude deviation based on the spatial coordinates of the four corner points of the template bottom surface and performs pose adjustment. The operation ensures that the attitude adjustment is based on the physical foundation of the balanced load-bearing support system. The effect evaluation and correction module determines the attitude adjustment effect state based on the residual deviation change, achieving a quantitative evaluation of the adjustment effect. The parameter correction feedback module responds to the effect degradation state by adaptively correcting the adjustment compensation coefficient, allowing the corrected parameters to directly affect the preceding stages of subsequent adjustment cycles, forming a complete closed loop of anomaly identification, spatial positioning, trend tracing, attitude fine-tuning, effect evaluation, and parameter correction. This invention, through the layer-by-layer fusion and cross-layer transmission of multi-source data, and the adaptive correction of the front-end control parameters by the parameter correction feedback module based on the actual adjustment effect, achieves complete connection and closed-loop feedback of multi-source data from the perception layer to the decision-making layer and then to the execution layer, effectively improving the attitude adjustment reliability and parameter self-adaptation capability of the bridge-building machine under varying construction conditions.

[0037] Furthermore, this invention compares the load change rate of each of the four support points with anomaly judgment thresholds in real time and counts the number of support points exceeding the thresholds, thereby dividing the support system into different operating states. Different states correspond to different handling strategies. In the steady-state operation state, the system directly enters the attitude adjustment phase. In the anomaly identification state, the system triggers load redistribution management. In the safety lockout state, the system unconditionally stops and locks out. This ensures that the system response mode is precisely matched with the actual risk level of the support system, avoiding response delays or malfunctions caused by ambiguous state boundaries in conventional systems, or excessive triggering of management processes when the support is normal. This effectively balances the operational efficiency of the bridge-building machine under continuous construction conditions and the structural safety under abnormal conditions.

[0038] Furthermore, this invention achieves precise location and targeted management of load anomalies in the support system through pattern-by-pattern recognition of the spatial positional relationships of abnormal support points. Different spatial distribution patterns correspond to different physical causes and management strategies. Single-sided concentrated anomalies and single-row concentrated anomalies are caused by two support points on the same side or in the same row deviating synchronously due to a common cause, requiring synchronous adjustment to eliminate torsional or pitch deformation trends. Diagonally dispersed anomalies are caused by two support points deviating due to their own independent causes, requiring coordinated adjustment to eliminate isolated anomalies while maintaining the overall attitude balance of the template. This ensures that the load management execution method is precisely matched with the location of the anomaly. In the case of multi-point dispersed anomalies, the two support points with the largest rate of change are selected for priority management, avoiding coupled oscillations caused by adjusting multiple support points simultaneously, effectively ensuring the structural safety and attitude stability of the load redistribution operation.

[0039] Furthermore, this invention adjusts the compensation coefficient to ensure that the compensation amount naturally corresponds to the strength of the trend factor; the stronger the trend, the greater the compensation, and the weaker the trend, the smaller the compensation, without the need to manually set a fixed compensation ratio. When there is a trend change, a compensation stroke is superimposed on the basic governance stroke, so that the adjustment action can eliminate the current offset while predicting and offsetting the additional offset that will appear in future cycles. When there are occasional fluctuations, no compensation stroke is superimposed to avoid over-responding to one-time disturbance factors and causing new attitude deviations.

[0040] Furthermore, this invention determines the state of the pose adjustment effect by the residual deviation change, so that the parameter correction decision corresponds precisely to the deviation change trend, realizing the adaptive optimization of system parameters. At the same time, the corrected parameters are directly used in the preceding steps of subsequent adjustments, so that the system continuously optimizes the control parameters based on the actual pose adjustment effect during long-term operation, effectively avoiding the decrease in adjustment accuracy caused by parameter mismatch when fixed parameters change under different working conditions, and providing a parameter self-optimization basis for the long-term reliable operation of the system under variable construction conditions.

[0041] Furthermore, this invention uses a parameter correction feedback module to trigger parameter correction in response to the effect degradation state. It uses whether the current round has passed through the trend tracing compensation module and performed compensation operations as the criterion to accurately locate the correction object, so that the parameter correction decision corresponds precisely to the adjustment path. This avoids parameter oscillation caused by blindly correcting all parameters without distinguishing the adjustment path. It also provides clear path basis for the timing and direction of correction of compensation coefficients and base coefficients, effectively realizing the adaptive optimization of control parameters under changing operating conditions, and providing a parameter self-correction basis for the continuous maintenance of system adjustment accuracy. Attached Figure Description

[0042] Figure 1 This is a unit connection diagram of the bridge-building machine adaptive attitude adjustment control system based on multi-source data fusion in an embodiment of the present invention;

[0043] Figure 2This is a schematic diagram of the process for determining the load state of a bridge-building machine in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the process for determining whether an adjustment and compensation operation needs to be performed in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the process for determining the pose adjustment effect state in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0047] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0048] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0049] Please see Figure 1 As shown, Figure 1 This is a unit connection diagram of the adaptive attitude adjustment control system for bridge-building machines based on multi-source data fusion in an embodiment of the present invention.

[0050] The bridge-building machine attitude adjustment control system provided in this embodiment of the invention includes:

[0051] An anomaly identification module is used to determine the number of abnormal support points based on the degree of deviation of the current load of each support point from its respective historical average load, and to determine the load status of the support system based on the number of abnormal support points.

[0052] The spatial positioning and management module, which is connected to the anomaly identification module, responds to the support system being in a local load anomaly state, and determines the spatial distribution pattern of the load anomaly of the support system based on the spatial location distribution of the anomaly support points, so as to determine the target stroke and execution strategy for load redistribution;

[0053] The trend tracing and compensation module is connected to the spatial positioning and management module. In response to the spatial positioning and management module completing load redistribution, it determines the time trend characteristics of load change based on the historical load data sequence of the abnormal fulcrum in the previous several cycles, so as to determine whether to perform adjustment and compensation operation, and determines the adjustment and compensation coefficient based on the average value of the historical load offset of the abnormal fulcrum in the preset cycle and the current load offset.

[0054] The pose fine-tuning execution module is connected to the anomaly identification module and the trend tracing and compensation module. It is used to respond to the support system that is in a stable and balanced load-bearing state after adjustment, and to determine the three-dimensional posture deviation based on the spatial coordinates of the four corner points of the bottom surface of the bridge-building machine template in order to perform pose adjustment operation.

[0055] The effect evaluation and correction module is connected to the pose fine-tuning execution module. In response to the completion of the pose adjustment operation, it obtains the change in residual deviation based on the residual deviation of the previous round and the residual deviation of the current round to determine the pose adjustment effect status.

[0056] The parameter correction feedback module, which is connected to the effect evaluation correction module and the trend tracing compensation module, is used to determine the correction requirement of the adjustment compensation coefficient in response to the pose adjustment effect being in a degraded state.

[0057] Specifically, this embodiment constructs a multi-source data fusion load pose joint sensing system by integrating pressure sensing data, historical periodic data, spatial coordinate data, and pose deviation data. It uses the load change rate at each support point as a criterion to achieve rapid screening and routing of the physical state of the support system. The spatial positioning governance module determines the spatial distribution pattern of load anomalies and the corresponding load redistribution strategy based on the spatial location distribution of abnormal support points, ensuring precise matching between the load governance execution method and the location of the anomaly. The trend tracing and compensation module determines the temporal characteristics of load changes based on the historical load data sequence of abnormal support points and dynamically determines the adjustment compensation coefficient, enabling compensation decisions to be made more accurate. The system precisely corresponds to the temporal characteristics of anomalies; the pose fine-tuning execution module determines the three-dimensional attitude deviation based on the spatial coordinates of the four corner points of the template bottom surface and performs pose adjustment operations, ensuring that the pose adjustment execution is based on the physical foundation of balanced load-bearing of the support system; the effect evaluation and correction module determines the pose adjustment effect state based on the residual deviation change, realizing a quantitative evaluation of the adjustment effect; the parameter correction feedback module responds to the effect degradation state and performs adaptive correction on the adjustment compensation coefficient, so that the corrected parameters directly act on the preceding links of subsequent adjustment cycles, forming a complete closed loop of anomaly identification, spatial positioning, trend tracing, pose fine-tuning, effect evaluation, and parameter correction. This invention, through the layer-by-layer fusion and cross-layer transmission of multi-source data, and the adaptive correction of the front-end control parameters by the parameter correction feedback module based on the actual adjustment effect, achieves complete connection and closed-loop feedback of multi-source data from the perception layer to the decision layer and then to the execution layer, effectively improving the pose adjustment reliability and parameter adaptive capability of the bridge-building machine under varying construction conditions.

[0058] In this embodiment, the support system of the bridge-building machine consists of four support points, which are respectively located at the four corners of the bottom surface of the bridge-building machine template (left front, right front, left rear, and right rear). The four support points jointly bear the bridge-building machine template and the construction load above it. Each support point is equipped with an independent pressure sensor to collect the load data borne by that support point in real time. The load distribution of the four support points directly reflects the degree of force balance of the bridge-building machine template support system. When the loads of the four support points are all within their respective historical normal ranges, it indicates that the support system is in a stable and balanced bearing state, and the template posture has a good physical basis. When the load of one or more support points deviates significantly from its historical normal level, it indicates that the load redistribution has occurred inside the support system, and the template posture deviation contains a non-negligible structural elastic deformation component.

[0059] In this embodiment, the attitude adjustment mechanism of the bridge-building machine includes four sets of vertical hydraulic cylinders and two lateral hydraulic cylinders on both sides. The four sets of vertical hydraulic cylinders are respectively set at the four support points, and each set of vertical hydraulic cylinders is independently controlled to achieve local adjustment of the height of the bottom surface of the template through the change of their respective strokes. The two lateral hydraulic cylinders are respectively set on the lateral sides of the bottom surface of the template and are used to drive the template to translate in the horizontal plane. The independent action of the four sets of vertical hydraulic cylinders at the four support points and the synchronous action of the two lateral hydraulic cylinders on both sides work together to achieve precise adjustment of the three-dimensional spatial attitude of the bridge-building machine template.

[0060] Please see Figure 2 As shown, Figure 2 This is a schematic diagram of the process for determining the load state of a bridge-building machine in an embodiment of the present invention.

[0061] In this embodiment, the anomaly identification module is used to determine the number of abnormal support points based on the degree of deviation of the current load of each support point from its respective historical average load, and to determine the load status of the support system based on the number of abnormal support points.

[0062] In this embodiment, the load change rate is determined based on the current load and the average load in the previous several periods. The load change rate is determined based on the absolute value of the difference between the current load and the average load in the previous several periods, and the ratio of the difference to the average load in the previous several periods.

[0063] Understandably, the load change rate reflects the degree to which the current load at each support point deviates from its historical normal state. The smaller the load change rate, the weaker the fluctuation of the load at that support point relative to its historical average level, indicating that the support system is in a stable equilibrium bearing state, the formwork posture is less affected by load fluctuations, and the posture adjustment process has a good physical basis. The larger the load change rate, the more the load at that support point deviates significantly from the historical normal level due to factors such as uneven concrete pouring, changes in slide rail friction, or changes in support conditions. Load redistribution occurs within the support system, and the formwork posture deviation contains a non-negligible component of structural elastic deformation. If posture adjustment is performed directly based on the posture deviation under this state, part of the hydraulic cylinder thrust will be used to offset elastic deformation rather than drive rigid body displacement, causing the adjustment result to deviate from the expected target.

[0064] In this embodiment, the anomaly identification module includes a steady-state passage unit, an anomaly identification unit, and a safety locking unit;

[0065] Among them, the steady-state passage unit is used to determine that the support system is in a stable equilibrium state based on the zero or one fulcrum load change rate exceeding the anomaly judgment threshold, and the system directly enters the pose fine-tuning execution module.

[0066] Anomaly identification unit is used to determine that the support system is in a local load anomaly state based on the load change rate of two or three support points exceeding the anomaly judgment threshold, and to perform anomaly location and load redistribution processing.

[0067] The safety locking unit is used to determine that the support system is in a dangerous state when the load change rate of all four support points exceeds the abnormal judgment threshold or the current load of any support point exceeds the structural safety limit. The system cuts off the power supply to all hydraulic valve groups and locks all actuator cylinders. The system is then reset after manual confirmation of safety.

[0068] In this embodiment, the anomaly judgment threshold is pre-set based on the distribution characteristics of the load change rate under normal operating conditions and critical abnormal conditions. This ensures that the anomaly judgment threshold can effectively distinguish between load fluctuations under normal operating conditions and continuous load shifts caused by changes in support status. If the anomaly judgment threshold is set too low, the warning range will be too wide, and small load fluctuations caused by normal temperature changes, uneven concrete pouring, or differences in rail friction may be judged as anomalies, leading to frequent triggering of abnormal location positioning and load redistribution processing by the system, thus affecting the continuity of operations. If the anomaly judgment threshold is set too high, the warning range will be too narrow, and load changes caused by genuine anomalies such as support settlement or eccentric loading may directly cross the warning range and enter the entire abnormal range, causing the system to lose the warning window period. Therefore, in this embodiment, the anomaly judgment threshold is set as a transition zone between the normal load fluctuation range and the critical range of support anomalies, enabling the system to reliably identify risks in the early stages when the support status undergoes substantial changes, while avoiding false alarms for normal construction disturbances.

[0069] Specifically, this embodiment compares the load change rate of the four support points with the anomaly judgment threshold in real time and counts the number of support points exceeding the threshold. Based on this, the support system is divided into different operating states. Different states correspond to different handling strategies. In the steady-state operation state, the system directly enters the attitude adjustment stage. In the anomaly identification state, the system triggers load redistribution management. In the safety lock state, the system unconditionally stops and locks. This ensures that the system response mode is accurately matched with the actual risk level of the support system. It avoids response delays or malfunctions caused by ambiguous state boundaries in conventional systems, or excessive triggering of management processes when the support is normal. It effectively balances the operational efficiency of the bridge-building machine under continuous construction conditions and the structural safety under abnormal conditions.

[0070] In this embodiment, the spatial positioning and management module is connected to the anomaly identification module. In response to the support system being in a local load anomaly state, it determines the spatial distribution pattern of the load anomaly of the support system based on the spatial location distribution of the abnormal support points, so as to determine the target travel and execution strategy for load redistribution.

[0071] In this embodiment, the spatial positioning governance module includes a single-row centralized anomaly unit, a single-sided centralized anomaly unit, and a diagonally dispersed anomaly unit;

[0072] Based on the fact that the load change rate of the fulcrum exceeds the anomaly judgment threshold, there are two fulcrums, and the spatial distribution pattern is determined based on the positional relationship of the abnormal fulcrums: when the two abnormal fulcrums are located on the same side, the current load anomaly pattern is judged to be a single-sided concentrated anomaly; when the two abnormal fulcrums are located in the same row, the current load anomaly pattern is judged to be a single-row concentrated anomaly; when the two abnormal fulcrums are located at opposite corners, the current load anomaly pattern is judged to be a diagonally dispersed anomaly.

[0073] When the load change rate of the three fulcrums exceeds the anomaly judgment threshold, the two fulcrums with the largest absolute value of the load change rate are selected as priority treatment targets. If the two fulcrums are located on the same side, treatment is performed according to the single-side centralized anomaly mode. If they are located in the same row, treatment is performed according to the single-row centralized anomaly mode. Then, a fine-tuning load redistribution operation is performed on the third fulcrum.

[0074] Understandably, a single-sided concentrated anomaly reflects the physical fact that the load is abnormally concentrated on one side of the bridge-building machine. The two abnormal support points deviate from the historical normal level simultaneously due to uneven loading during concrete pouring, changes in friction on one side of the slide rail, or changes in the support conditions on that side. A single-row concentrated anomaly reflects the physical fact that the load is abnormally concentrated on the front or rear row of the bridge-building machine. The two abnormal support points deviate from the historical normal level simultaneously due to the difference in the stiffness of the support foundations of the front and rear rows after the bridge-building machine moves forward along the slide rail. A diagonal dispersed anomaly reflects that there is no correlation between the two abnormal support points on the same side or in the same row. The two deviate from the historical normal level due to their own independent physical reasons, or due to a problem with a single leg causing the load to be redistributed in the diagonal direction.

[0075] When a single-sided concentrated abnormal unit is judged to be a single-sided concentrated abnormality, the vertical cylinders of the two support points on the abnormal side need to be synchronously adjusted so that the loads of the two support points on that side return to their respective historical normal ranges synchronously, so as to avoid the template from being torsional and deformed due to the two support points being higher and lower respectively.

[0076] When a single-row concentrated abnormal unit is judged to be a single-row concentrated abnormality, the vertical cylinders of the two support points of the abnormal row need to be synchronously adjusted so that the loads of the two support points of the row fall back to their respective historical normal ranges in a synchronous manner, so as to avoid the template from pitching deformation caused by the two support points being higher and lower than each other.

[0077] When a diagonally dispersed abnormal unit is identified, the vertical cylinders of the two abnormal support points need to be adjusted in a coordinated manner. The heavier support point retracts to unload and the lighter support point extends to load. The two actions are performed simultaneously to eliminate the two isolated abnormalities while maintaining the overall posture balance of the template.

[0078] Specifically, this embodiment achieves precise location and targeted management of load anomalies in the support system by identifying the spatial positional relationships of abnormal support points in a pattern-by-pattern manner. Different spatial distribution patterns correspond to different physical causes and management strategies. Single-sided concentrated anomalies and single-row concentrated anomalies are caused by two support points on the same side or in the same row deviating synchronously due to a common cause, requiring synchronous adjustment to eliminate torsional or pitch deformation trends. Diagonally dispersed anomalies are caused by two support points deviating due to their own independent causes, requiring coordinated adjustment to eliminate isolated anomalies while maintaining the overall attitude balance of the template. This ensures that the execution method of load management is precisely matched with the location of the anomaly. When there are multiple dispersed anomalies, the two support points with the largest rate of change are selected for priority management, avoiding coupled oscillations caused by adjusting multiple support points simultaneously, and effectively ensuring the structural safety and attitude stability of the load redistribution operation.

[0079] Please see Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the process of determining whether an adjustment and compensation operation needs to be performed in an embodiment of the present invention.

[0080] In this embodiment, the trend tracing and compensation module is connected to the spatial positioning and management module. In response to the spatial positioning and management module completing load redistribution, it determines the time trend characteristics of load change based on the historical load data sequence of the abnormal pivot point in the previous several cycles, so as to determine whether to perform adjustment and compensation operation, and determines the adjustment and compensation coefficient based on the average value of the historical load offset of the abnormal pivot point in the preset cycle and the current load offset.

[0081] In this embodiment, the trend tracing and compensation module includes a trend judgment subunit and a compensation execution subunit;

[0082] The trend judgment subunit is used to determine the change attribute of the abnormal support point based on the consistency of the load change direction of the abnormal support point within the recent preset time period: when the load change direction of the abnormal support point is completely consistent within the recent preset time period, the load change attribute of the support point is determined to be a trend change; when the load change direction of the abnormal support point is inconsistent within the preset time period, the load change attribute of the support point is determined to be an occasional fluctuation.

[0083] It is understood that the aforementioned trend change reflects the physical fact that the abnormal support load has been continuously drifting in one direction over multiple cycles; the load is not a random fluctuation, but rather increases or decreases cycle by cycle under the drive of continuous factors. If no intervention is applied, this trend will continue to affect the load distribution and formwork positioning accuracy in subsequent construction cycles. When it is determined to be a trend change, a compensation stroke needs to be added on the load redistribution stroke to offset the impact of the continuous drift factor in future cycles.

[0084] Occasional fluctuations reflect the physical fact that the abnormal support load deviates from its normal value due to temporary factors during construction. The direction of load change lacks consistency between adjacent periods. This deviation originates from the off-center loading during this pouring, changes in temporary load distribution, or instantaneous disturbances from environmental factors. When it is determined to be an occasional fluctuation, no compensation operation is performed, and subsequent attitude adjustment is carried out according to the actual state after load redistribution.

[0085] The compensation execution subunit is used to determine the adjustment compensation coefficient based on the average value of the historical load offset of the abnormal fulcrum within a preset period and the current load offset; when it is determined to be a trend change, the adjustment is performed by multiplying the base coefficient by the compensation coefficient as the total governance coefficient; when it is determined to be an occasional fluctuation, the adjustment is performed by using the base coefficient as the total governance coefficient.

[0086] In this embodiment, the basic coefficient is used to determine the basic adjustment range of the load redistribution operation in the spatial positioning governance unit, and serves as the basic gain of the load governance.

[0087] In this embodiment, the base coefficient is used to determine the basic adjustment range of the load redistribution operation in the spatial positioning treatment unit. Its default value is 1.0, which means that the treatment stroke is executed according to the normal treatment stroke. The default value is based on the theoretical stroke calculated based on physical parameters such as load deviation, cylinder area and structural stiffness, and 1.0 is defined as the neutral position. When the effect evaluation correction module determines that the effect is in a degraded state, the parameter correction feedback module increases the base coefficient to enhance the treatment force. When the adjustment effect is good, the base coefficient remains unchanged or is reduced to close to 1.0.

[0088] In this embodiment, the adjustment compensation coefficient is determined based on the average value of the preset periodic load offset and the current load offset. The adjustment compensation coefficient is determined based on the ratio of the sum of the average value of the preset periodic load offset and the current load offset to the current load offset.

[0089] The current load offset is determined based on the current offset load and the average load within a preset time period. The current load offset is determined based on the absolute value of the difference between the current load and the average load within a preset time period.

[0090] Specifically, this embodiment adjusts the compensation coefficient to ensure that the compensation amount naturally corresponds to the strength of the trend factor. The stronger the trend, the greater the compensation, and the weaker the trend, the smaller the compensation, without the need to manually set a fixed compensation ratio. When there is a trend change, the compensation stroke is superimposed on the basic governance stroke, so that the adjustment action can eliminate the current offset while predicting and offsetting the additional offset that will appear in future cycles. When there are occasional fluctuations, the compensation stroke is not superimposed to avoid over-responding to one-time disturbance factors and causing new attitude deviations.

[0091] In this embodiment, the pose fine-tuning execution module is connected to the anomaly identification module and the trend tracing and compensation module. It is used to respond to the support system that is in a stable and balanced load-bearing state after adjustment, and to determine the three-dimensional posture deviation based on the spatial coordinates of the four corner points of the bottom surface of the bridge-building machine template, so as to perform the pose adjustment operation.

[0092] In this embodiment, the pose fine-tuning execution module includes a three-dimensional deviation determination unit, which is used to collect three-dimensional spatial position data of the four corner feature points of the bottom surface of the bridge-building machine template in the local Cartesian coordinate system based on the total station. The difference between the current coordinates of each corner point and the design target coordinates is the deviation value of each axis. The maximum absolute value of the deviation values ​​of the four corner points on the same axis is taken to obtain the maximum lateral deviation, the maximum longitudinal deviation, and the maximum vertical deviation, respectively. The three maximum axial deviations constitute the three-dimensional deviation vector of the template.

[0093] Understandably, the maximum lateral deviation is the maximum deviation distance of the four corner points in the lateral direction, reflecting the most severe deviation of the template in the direction perpendicular to the bridge axis. When this value exceeds the standard, it indicates that the template as a whole has deviated from the design axis and there is lateral drift. The maximum longitudinal deviation is the maximum deviation distance of the four corner points in the longitudinal direction, reflecting the most severe deviation of the template in the direction along the bridge axis. When this value exceeds the standard, it indicates that the template as a whole has deviated from the design mileage position and there is longitudinal misalignment. The maximum vertical deviation is the maximum deviation distance of the four corner points in the vertical direction, reflecting the most severe deviation of the template in the elevation direction. When this value exceeds the standard, it indicates that the bottom surface of the template has deviated from the design elevation plane and there is pitch or roll tilt.

[0094] In this embodiment, the vertical leveling operation is specifically performed as follows: a measured spatial plane is constructed using the measured coordinates of any three non-collinear corner points among the four corner points, and a target spatial plane is constructed using the design target coordinates of the corresponding three corner points. The spatial rotation transformation relationship between the measured plane and the target plane is calculated, and the target stroke required by each of the four sets of vertical cylinders is deduced from the rotation transformation relationship. The four sets of vertical cylinders move synchronously according to their respective target strokes, so that the bottom plane of the template coincides with the target plane.

[0095] It is understandable that the bottom surface of the template is a rigid plane, and its spatial attitude is uniquely determined by the normal vector of the plane. When there is a deviation between the normal vector of the measured plane and the normal vector of the target plane, the measured plane can be rotated around the spatial axis by different stroke combinations of the four sets of vertical cylinders until the normal vector coincides with the normal vector of the target plane. The target stroke of the four sets of vertical cylinders is uniquely determined by the rotation matrix and translation vector between the measured plane and the target plane. The stroke of each cylinder is different to eliminate the tilt angle in the pitch and roll directions.

[0096] The specific execution method of the horizontal correction operation is as follows: taking the bridge design axis as the reference, when the center line of the template deviates from the design axis, drive the two lateral movement cylinders to push synchronously at the same speed, so that the center line of the template is translated to coincide with the design axis; during the synchronous pushing process, keep the stroke of the two lateral movement cylinders consistent to avoid introducing additional yaw angle during the translation process.

[0097] Specifically, this embodiment fuses the coordinate deviation information of the four corner points using a three-dimensional deviation vector, and uses the maximum deviation in each axis as the criterion to determine two modes: vertical deviation dominance and horizontal deviation dominance. When vertical deviation is dominant, the target stroke of each cylinder is determined by the three-point plane method, so that the four sets of cylinders move synchronously to eliminate the pitch and roll angles in one go. When horizontal deviation is dominant, the translational deviation is eliminated by the synchronous pushing of the lateral cylinders on both sides. When both vertical and horizontal deviations exceed the limit at the same time, the execution is strictly carried out in the order of vertical first and then horizontal, avoiding attitude oscillation and positioning loss of control caused by multi-directional coupling adjustment, and effectively ensuring the accuracy and efficiency of posture adjustment.

[0098] Please see Figure 4 As shown, Figure 4 This is a schematic diagram of the process for determining the pose adjustment effect state in an embodiment of the present invention.

[0099] In this embodiment, the effect evaluation and correction module is connected to the pose fine-tuning execution module. In response to the completion of the pose adjustment operation, it obtains the change in residual deviation based on the residual deviation of the previous round and the residual deviation of the current round to determine the pose adjustment effect status.

[0100] In this embodiment, after the pose fine-tuning execution module completes the pose adjustment, hydraulic cylinder locking, and template positioning, the total station is used to collect the spatial coordinates of the four corner points of the template again to obtain the current maximum axial deviation as the current residual deviation.

[0101] The change in residual deviation is determined based on the residual deviation after the previous round of attitude adjustment and the residual deviation after the current round. The change in residual deviation is determined based on the difference between the residual deviation after the current round and the residual deviation after the previous round of attitude adjustment.

[0102] Understandably, the current residual deviation reflects the degree to which the template deviates from the design target position after this adjustment, and is an indicator for measuring the final effect of this round of adjustment; the residual deviation of the previous round reflects the degree to which the template deviates from the design target position after the previous adjustment, and is a benchmark for measuring the historical adjustment effect; the change in residual deviation reflects the trend of residual deviation between the two rounds of adjustment. When the change in residual deviation is negative, it indicates that the residual deviation of this round is smaller than that of the previous round, the adjustment effect shows a convergent trend, and the system is gradually approaching the target pose; when the change in residual deviation is positive, it indicates that the residual deviation of this round is larger than that of the previous round, the adjustment effect shows a divergent trend, and the system is deviating from the target pose.

[0103] The effect evaluation and correction module includes an effect convergence state determination unit, an effect degradation state determination unit, and an effect stability state determination unit.

[0104] In this embodiment, the effect convergence state determination unit is used to determine that the pose adjustment effect is in the effect convergence state based on the residual deviation change being less than zero. This indicates that after the current round of adjustment, the template residual deviation has decreased compared to the previous round, the system is gradually approaching the target pose, the current control parameters are configured reasonably, and there is no need to perform parameter correction operations.

[0105] The effect degradation state determination unit is used to determine that the pose adjustment effect is in a degradation state based on the residual deviation change being greater than zero. This indicates that after the current adjustment is completed, the template residual deviation has increased compared to the previous round, the system is deviating from the target pose, the current control parameter configuration has a deviation, and parameter correction operation needs to be performed.

[0106] The effect stability determination unit is used to determine that the pose adjustment effect is in a stable state based on the fact that the change in residual deviation is equal to zero. This indicates that after the current adjustment is completed, the residual deviation of the template is basically the same as that of the previous round, the system is in a stable state, the current control parameters are configured reasonably, and there is no need to perform parameter correction operations.

[0107] Understandably, the change in residual deviation reflects the trend of the template residual deviation after the current attitude adjustment compared to the template residual deviation after the previous attitude adjustment. When the change is negative, the deviation tends to converge, indicating that the current control parameters match the current operating condition well and the adjustment strategy is effective. When the change is positive, the deviation tends to diverge, indicating that there is a deviation between the current control parameters and the current operating condition, and the corresponding parameter correction is executed. When the change is zero, the deviation does not change significantly, indicating that the system is in a stable state.

[0108] Specifically, this embodiment determines the state of the pose adjustment effect by the residual deviation change, so that the parameter correction decision corresponds precisely to the deviation change trend, and realizes the adaptive optimization of system parameters. At the same time, the corrected parameters are directly used in the preceding steps of subsequent adjustments, so that the system can continuously optimize the control parameters based on the actual pose adjustment effect during long-term operation. This effectively avoids the decrease in adjustment accuracy caused by parameter mismatch when fixed parameters change with working conditions, and provides a parameter self-optimization basis for the long-term reliable operation of the system under variable construction conditions.

[0109] In this embodiment, the parameter correction feedback module is connected to the effect evaluation correction module and the trend tracing compensation module to determine the correction requirement of the adjustment compensation coefficient in response to the pose adjustment effect being in a degraded state.

[0110] When the current adjustment cycle has passed through the trend tracing compensation module and the compensation operation has been performed, if the effect evaluation and correction module determines that the effect is in a degraded state, then the current value of the adjustment compensation coefficient is determined to be too large relative to the current operating conditions. The parameter correction feedback module then lowers the adjustment compensation coefficient by a preset ratio to determine the updated adjustment compensation coefficient.

[0111] It is understood that the effect degradation state indicates that the residual deviation of the template after the current adjustment cycle is completed is larger than the corresponding value of the previous adjustment cycle, indicating that there is a deviation between the value of the current adjustment compensation coefficient and the current operating conditions, and parameter correction operation needs to be performed; when the effect degradation state still occurs after the adjustment cycle has undergone compensation operation, it indicates that the superimposed compensation stroke has exceeded the actual drift compensation requirement under the current operating conditions, that is, the value of the adjustment compensation coefficient is relatively large, resulting in overcompensation, and the parameter correction feedback module reduces the adjustment compensation coefficient according to a preset ratio.

[0112] In this embodiment, the preset ratio is a pre-calibrated fixed reduction ratio, the value of which is determined based on the correction sensitivity of the adjustment compensation coefficient and the system stability requirements. If the reduction ratio is too large, the single correction amplitude will exceed the safety boundary, which may cause the adjustment compensation coefficient to jump from a large state to a small state, thus causing adjustment oscillation. If the reduction ratio is too small, the correction effect will be weak, and it will be impossible to converge the adjustment compensation coefficient to a value range that matches the current operating conditions within a finite adjustment period. This reduction ratio is determined to be a balance value between two extreme cases after engineering test verification, and a lower limit protection value is set. When the adjusted adjustment compensation coefficient is lower than the lower limit protection value, the lower limit protection value is used as the value of the adjustment compensation coefficient to avoid coefficient instability caused by over-correction.

[0113] Specifically, this embodiment uses a parameter correction feedback module to trigger parameter correction in response to the effect degradation state. It uses whether the current round has passed through the trend tracing compensation module and performed compensation operations as the criterion to accurately locate the correction object, so that the parameter correction decision corresponds precisely to the adjustment path. This avoids parameter oscillation caused by blindly correcting all parameters without distinguishing the adjustment path. It also provides clear path basis for the timing and direction of correction of compensation coefficients and base coefficients, effectively realizing adaptive optimization of control parameters under changing operating conditions, and providing a parameter self-correction basis for the continuous maintenance of system adjustment accuracy.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive attitude control system for a bridge-building machine based on multi-source data fusion, characterized in that, include: An anomaly identification module is used to determine the number of abnormal support points based on the degree of deviation of the current load of each support point from its respective historical average load, and to determine the load status of the support system based on the number of abnormal support points. A spatial positioning and management module, which is connected to the anomaly identification module, is used to determine the spatial distribution pattern of the load anomaly of the support system based on the spatial location distribution of the abnormal support points in response to the support system being in a local load anomaly state, so as to determine the target stroke and execution strategy for load redistribution. The trend tracing and compensation module is connected to the spatial positioning and management module. In response to the spatial positioning and management module completing load redistribution, it determines the time trend characteristics of load change based on the historical load data sequence of the abnormal fulcrum in the previous several cycles, so as to determine whether to perform adjustment and compensation operation, and determines the adjustment and compensation coefficient based on the average value of the historical load offset of the abnormal fulcrum in the preset cycle and the current load offset. The pose fine-tuning execution module is connected to the anomaly identification module and the trend tracing and compensation module. It is used to respond to the support system that is in a stable and balanced load-bearing state after adjustment, and to determine the three-dimensional posture deviation based on the spatial coordinates of the four corner points of the bottom surface of the bridge-building machine template in order to perform pose adjustment operation. The effect evaluation and correction module is connected to the pose fine-tuning execution module. In response to the completion of the pose adjustment operation, it obtains the change in residual deviation based on the residual deviation of the previous round and the residual deviation of the current round to determine the pose adjustment effect status. The parameter correction feedback module, which is connected to the effect evaluation correction module and the trend tracing compensation module, is used to determine the correction requirement of the adjustment compensation coefficient in response to the pose adjustment effect being in a degraded state.

2. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 1, characterized in that, The anomaly detection module includes: An anomaly identification unit is used to determine that the support system is in a local load anomaly state based on the load change rate of two or three support points exceeding the anomaly judgment threshold.

3. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 2, characterized in that, In response to the support system being in a state of local load abnormality, the spatial positioning management module performs abnormal location positioning and load redistribution processing.

4. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 3, characterized in that, The process of determining the spatial distribution pattern of load anomalies in the support system based on the spatial location distribution of abnormal support points includes: When the load change rate of the two support points exceeds the anomaly judgment threshold, the anomaly is judged as a single-sided concentrated anomaly, a single-row concentrated anomaly, or a diagonally dispersed anomaly, depending on the positional relationship of the anomaly support points. When the load change rate of the three fulcrums exceeds the anomaly judgment threshold, the two fulcrums with the largest absolute value of the load change rate are selected as priority treatment objects. The corresponding anomaly mode is determined according to the positional relationship of the two fulcrums, and the load redistribution operation is performed. Then, the load redistribution operation is performed on the third fulcrum.

5. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 4, characterized in that, The spatial positioning management module includes: A single-row concentrated anomaly unit is used to perform synchronous adjustment of the vertical cylinders at the two support points of the abnormal row when a single-row concentrated anomaly is determined. A single-sided concentrated anomaly unit is used to perform synchronous adjustment of the vertical cylinders at the two support points on the abnormal side when a single-sided concentrated anomaly is determined. The diagonal dispersion anomaly unit is used to perform linkage adjustment on the vertical cylinders of the two abnormal support points when the diagonal dispersion anomaly is determined.

6. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 5, characterized in that, The trend tracing and compensation module includes: The trend judgment subunit is used to determine the change attributes of the abnormal support point based on the consistency of the load change direction of the abnormal support point within a preset period. When the load change direction of the abnormal fulcrum is completely consistent in each consecutive sampling period within a preset time, the load change attribute of the fulcrum is determined to be a trend change. When the load change direction of the abnormal fulcrum is inconsistent in each consecutive sampling period within a preset time, the load change attribute of the fulcrum is determined to be an occasional fluctuation.

7. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 6, characterized in that, The trend tracing and compensation module also includes a compensation execution subunit. When the trend judgment subunit determines that a trend change has occurred, it performs an adjustment and compensation operation based on the base coefficient and the adjustment and compensation coefficient. When the trend judgment subunit determines that the fluctuation is occasional, the adjustment is performed based on the basic coefficient as the total governance coefficient.

8. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 7, characterized in that, The pose fine-tuning execution module includes: The three-dimensional deviation determination unit is used to determine the maximum lateral deviation, maximum longitudinal deviation, and maximum vertical deviation based on the maximum coordinate deviation of the feature points at the four corners of the bottom surface of the bridge-building machine template in each axis, and to determine the three-dimensional deviation vector of the template based on the maximum axial deviation of the feature points at the four corners of the bottom surface of the template.

9. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 8, characterized in that, The effect evaluation and correction module includes: The effect degradation state determination unit is used to determine that the pose adjustment effect is in an effect degradation state based on the residual deviation change being greater than zero.

10. The adaptive attitude control system for bridge-building machines based on multi-source data fusion according to claim 9, characterized in that, The process of determining the correction requirements for the adjustment compensation coefficient includes: When the effect evaluation and correction module determines that the effect is in a degraded state, it determines that the current value of the adjustment compensation coefficient exceeds the actual compensation requirement under the current working conditions, and adjusts the adjustment compensation coefficient down based on a preset ratio.

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