Assembled counterforce frame and intelligent pre-pressing control system thereof

Abstract

The application is suitable for the technical field of cantilever beam construction monitoring equipment, and provides an assembly type counterforce frame and an intelligent pre-pressing control system, which comprises a cantilever beam main body, a hanging basket main truss mechanism and a counterforce frame mechanism, the hanging basket main truss mechanism is arranged at the top of the cantilever beam main body, and the counterforce frame mechanism is arranged at one side of the cantilever beam main body. In the scheme, the hanging basket main truss mechanism and the counterforce frame mechanism are arranged in an assembly type structure, so that the monitoring system can be quickly installed and disassembled, the dynamic forward movement demand of the hanging basket in cantilever pouring construction is met, the problem that the traditional monitoring equipment needs to be frequently debugged is solved, and through the arrangement of multiple first visual measuring points and second visual measuring points, in combination with rear displacement measuring points, front displacement measuring points and displacement meters, the stress change, deformation amount and displacement trajectory of the hanging basket main truss mechanism and the counterforce frame mechanism in the forward movement process are captured in real time, the omnibearing data acquisition of the structural bearing capacity and safety is realized, and the monitoring system can continuously cover the whole construction process without manual adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cantilever construction monitoring equipment, in particular to a detachable counterforce frame and an intelligent pre-pressing control system thereof. BACKGROUND

[0002] In the cantilever pouring construction of a bridge, the main truss of the hanging basket as a load-bearing structure directly affects the construction safety and structural quality in terms of actual bearing capacity and safety reliability. Real-time monitoring of the stress state (such as stress and load) and deformation condition (such as displacement and deflection) of the hanging basket is a key link for providing adjustment basis for the construction process and preventing safety accidents. At present, the industry mainly uses two types of technical solutions for hanging basket monitoring, namely long-distance measurement and close-range measurement. However, both have significant technical bottlenecks.

[0003] 1. Long-distance measurement technology

[0004] Long-distance measurement technology uses a single device (such as a total station or a laser scanner) to monitor the overall hanging basket from a long distance, with a wide visual coverage range. This technology allows for convenient measurement with a single device, simple device deployment, and does not require intensive sensor placement. A single device can complete large-scale data acquisition, making it suitable for macroscopic monitoring of the overall shape of the hanging basket. However, long-distance measurement is difficult to manage, as it often requires complex data transmission and processing systems. In particular, in the case of multiple hanging baskets being constructed simultaneously or in complex working conditions, the data acquisition frequency, precision, and multi-source data integration efficiency of a single device are difficult to coordinate, resulting in insufficient real-time and systematicity of the monitoring data, as well as insufficient detail precision. Long-distance measurement has low sensitivity to subtle deformation or stress concentration phenomena at key nodes of the hanging basket (such as main truss node welds and pin connection positions), making it unable to meet high-precision monitoring requirements.

[0005] 2. Close-range measurement technology

[0006] Close-range measurement technology uses multiple close-range monitoring devices (such as high-precision displacement meters and strain gauges) to densely measure local areas of the hanging basket. This technology has a narrow visual coverage range and requires multiple devices to work together. The cost of close-range measurement devices is relatively low, and close-range placement allows for high-frequency monitoring of local structures. Additionally, data management can be facilitated through centralized wiring or local storage. However, close-range measurement devices (such as optical measurement instruments) need to be frequently adjusted to adapt to dynamic deformation during hanging basket construction. The focusing process is easily disturbed by factors such as construction vibration and environmental light, resulting in unstable measurement precision. Whether the precision meets engineering requirements needs to be verified through a large number of field tests. Moreover, multiple devices need to be synchronized for calibration and data synchronization. If the hanging basket structure is complex or the construction environment is variable, the problem of visual angle overlap, data redundancy, or blind spots between devices is prominent, increasing the difficulty of installation and debugging and the maintenance cost of the monitoring system.

[0007] In the related art, long-distance measurement is convenient for single-device operation but lacks precision, and short-distance measurement precision relies on test verification and is complex to manage, lacking technical solutions that take into account efficient management and high-precision monitoring. Moreover, long-distance devices are high in cost but have limited applicability, and short-distance devices are low in cost but need to solve the problems of focusing precision and multi-device collaboration. Existing technologies cannot achieve an optimal balance between cost control and monitoring reliability, and both methods are difficult to adapt to the dynamic changes in the structure form in the hanging basket construction (such as the gradual movement of the hanging basket and the change in load distribution during cantilever pouring), and the device parameters need to be adjusted frequently by hand.

[0008] In the preloading test of large structures, the core limitation of existing technologies is the passive and static risk response mode. The traditional method mainly relies on a fixed safety threshold set based on an idealized structure model, and an alarm is triggered only when the real-time monitoring data reaches this threshold. This status quo leads to the inability to effectively respond to the highly nonlinear behavior of the structure when approaching the limit load, and the inability to handle the deviation of the real mechanical response due to material unevenness, construction flaws, and other uncertain factors. As a result, the alarm may be issued too late to provide effective early warning time before a sudden failure occurs.

[0009] The above status and deficiencies mainly result from the limitations of the risk assessment mechanism. Once the theoretical model relied on by the traditional method is established, it remains unchanged, and its early warning mechanism only monitors whether the physical quantity exceeds the limit, which is a kind of lagging state monitoring rather than a forward-looking trend warning. This leads to the system's inability to identify early dynamic characteristics of structural instability, i.e., the accelerating expansion trend of the difference between physical response and model prediction. The final result is that the system cannot generate a forward-looking warning signal before the structure enters an irreversible destruction process, and lacks a closed-loop control means to actively suppress the development of risks, greatly affecting the safety and reliability of large structure testing. SUMMARY

[0010] The purpose of the present application is to provide an assembled counterforce frame and an intelligent preloading control system thereof to solve the problems raised in the background art.

[0011] The technical solution of the present application is an assembled counterforce frame, comprising: a cantilever beam main body, a hanging basket main truss mechanism, and a counterforce frame mechanism. The hanging basket main truss mechanism is arranged at the top of the cantilever beam main body, and the counterforce frame mechanism is arranged on one side of the cantilever beam main body, and the hanging basket main truss mechanism and the counterforce frame mechanism are connected to each other and combined to form an integral structure.

[0012] The hanging basket main truss mechanism comprises a channel steel base, a connecting seat, a supporting rod, a supporting channel steel and a butt joint channel steel, the channel steel base is fixed at the top of the cantilever beam body, the connecting seat is arranged on the channel steel base, the supporting rod is vertically fixed on the connecting seat, the supporting channel steel is transversely fixed at the top of the supporting rod and parallel to the connecting seat, and the butt joint channel steel is transversely fixed at one end of the supporting channel steel and perpendicular to the supporting channel steel.

[0013] The counterforce frame mechanism comprises a counterforce frame cross bar, a first bottom supporting plate, a second bottom supporting plate and a counterforce frame connecting rod, the counterforce frame cross bar is fixed on one side of the cantilever beam body, the first bottom supporting plate is fixed at the bottom of the counterforce frame cross bar, the second bottom supporting plate is fixed at the bottom of the first bottom supporting plate and parallel to the butt joint channel steel, and the counterforce frame connecting rod is vertically connected between the second bottom supporting plate and the butt joint channel steel.

[0014] Preferably, the hanging basket main truss mechanism further comprises strain rods, rear displacement measuring points and front displacement measuring points, the strain rods are obliquely fixed between the connecting seat and the supporting channel steel, the strain rods are symmetrically arranged in front of and behind and left and right of the connecting seat, at least four strain rods are arranged, and the strain rods are combined to form a parallelogram structure, the rear displacement measuring points are arranged at the tail end of the connecting seat, the front displacement measuring points are arranged at the front end of the connecting seat, and displacement meters are arranged on the rear displacement measuring points and the front displacement measuring points.

[0015] Preferably, the butt joint channel steel is provided with first visual measuring points, and at least three first visual measuring points are equidistantly arranged.

[0016] Preferably, the counterforce frame mechanism comprises first counterforce frame pull rods, butt joint rods, first limiting seats, transverse reinforcing rods, stable pull rods, second limiting seats and second counterforce frame pull rods, the first counterforce frame pull rods are obliquely fixed between the cantilever beam body and the counterforce frame cross bar, one end of the butt joint rod is fixed on the cantilever beam body, the first limiting seat is fixed at the other end of the butt joint rod, one end of the stable pull rod is fixed on the butt joint channel steel, the second limiting seat is fixed at the other end of the stable pull rod, the first limiting seat and the second limiting seat are correspondingly arranged, the transverse reinforcing rod is slidably connected between the first limiting seat and the second limiting seat, counterforce frame strain measuring points are arranged at the connection positions of the second counterforce frame pull rods and the counterforce frame cross bar and the cantilever beam body, second visual measuring points are arranged on the second bottom supporting plate, a reinforcing cross bar is horizontally fixed at the top of the connecting seat, and a reinforcing inclined pull rod is obliquely connected between the reinforcing cross bar and the strain rod.

[0017] An intelligent pre-pressing control system comprises:

[0018] A data acquisition and fusion module is configured to acquire sensor data of the counterforce frame and determine an accurate state vector of a key measuring point of the structure.

[0019] The digital twin model calibration module is configured to determine a model deviation vector between the structure key point accurate state vector and a model predicted state vector calculated by using a preset digital twin model, and update model parameters of the digital twin model based on the model deviation vector.

[0020] The model deviation evaluation and early warning module is configured to analyze a time sequence of the model deviation vector, and generate a predictive failure early warning signal when a divergence trend of the model deviation vector meets a preset failure early warning rule.

[0021] The dynamic loading control decision module is configured to determine a dynamic loading inhibition factor based on the model deviation vector and a time derivative in response to the predictive failure early warning signal, and generate a loading instruction rate in combination with a preset reference loading rate.

[0022] Preferably, the data acquisition and fusion module determines the structure key point accurate state vector, including:

[0023] The real-time data from the multiple-source heterogeneous sensors arranged on the counterforce frame system are collected, and a Kalman filtering algorithm is used to perform fusion processing on the real-time data to generate the structure key point accurate state vector.

[0024] The digital twin model calibration module determines the model deviation vector, including:

[0025] The digital twin model is driven to calculate the model predicted state vector, and a vector difference between the structure key point accurate state vector and the model predicted state vector is calculated to generate the model deviation vector.

[0026] Preferably, the digital twin model calibration module updates the model parameters, including:

[0027] The norm of the structure key point accurate state vector and the norm of the model predicted state vector are calculated respectively, a multiplication correction proportion factor is determined based on a ratio of the norm of the structure key point accurate state vector to the norm of the model predicted state vector and a preset adjustment index, and the multiplication correction proportion factor is applied to the current model parameters to determine the updated model parameters.

[0028] The preset failure early warning rule is defined as:

[0029] A first-order time derivative of the norm of the model deviation vector exceeds a preset speed threshold;

[0030] A second-order time derivative of the norm of the model deviation vector is a positive value.

[0031] Preferably, the dynamic loading control decision module determines the dynamic loading inhibition factor, including:

[0032] The time derivative of the norm of the model deviation vector is divided by the norm of the model deviation vector to obtain an instantaneous relative growth rate of the deviation; and the instantaneous relative growth rate of the deviation is multiplied by a preset damping time constant to obtain an initial damping factor;

[0033] If the initial damping factor is negative, the dynamic loading damping factor is set to 0; otherwise, the initial damping factor is set as the dynamic loading damping factor.

[0034] Preferably, the dynamic loading control decision module generates a loading instruction rate, comprising:

[0035] The dynamic loading damping factor is subtracted from the numerical value to obtain a loading rate adjustment coefficient, and the loading rate adjustment coefficient is multiplied by a preset reference loading rate to generate the loading instruction rate;

[0036] The interval of the loading rate adjustment coefficient is defined as:

[0037] If the loading rate adjustment coefficient is positive and less than or equal to 1, the loading instruction rate instructs the loading system to continue loading at a reduced or unchanged rate;

[0038] If the loading rate adjustment coefficient is negative, the loading instruction rate instructs the loading system to perform reverse unloading.

[0039] The present application provides an assembly type counterforce frame and an intelligent preloading control system thereof, which has the following improvements and advantages compared with the prior art.

[0040] In the present scheme, the hanging basket main truss mechanism and the counterforce frame mechanism are set as an assembly type structure, realizing rapid installation and disassembly of the monitoring system, adapting to the dynamic forward movement demand of the hanging basket in cantilever pouring construction, solving the problem of frequent debugging of traditional monitoring equipment, and through the arrangement of multiple first visual measurement points and second visual measurement points, combining the rear displacement measurement point, the front displacement measurement point and the displacement meter, the stress change, the deformation amount and the displacement trajectory of the hanging basket main truss mechanism and the counterforce frame mechanism in the forward movement process are captured in real time, realizing omnibearing data acquisition of the structural bearing capacity and safety, and the design that the counterforce frame mechanism moves forward synchronously with the hanging basket main truss mechanism enables the monitoring system to continuously cover the whole construction process without manual adjustment, avoiding the monitoring blind area caused by fixed viewing angle in traditional close-range measurement, and eliminating the defect of insufficient local detail accuracy in long-distance measurement, and the integrated deployment of multiple source monitoring points reduces the number of equipment and the complexity of wiring, and combined with the automatic monitoring mode of synchronous forward movement, the management difficulty of multiple equipment cooperation is reduced, and the cost investment of frequent focusing and calibration in the traditional scheme is avoided, realizing the balance of monitoring efficiency and economy, so that it can adapt to complex working conditions such as load distribution change and structural form dynamic adjustment in cantilever pouring process, and provide accurate basis for construction adjustment through real-time data feedback, significantly improving the safety and reliability of the hanging basket construction.

[0041] 1. The core of the present application is not to monitor whether the physical quantity is out of limit, but to analyze the dynamic evolution trend of the model deviation vector through the model deviation evaluation and early warning module. It identifies the early dynamic characteristics of system instability, rather than the lagging state results, so that it can generate a predictive failure warning signal with foresight before the structure enters the irreversible destruction process;

[0042] 2. The digital twin model calibration module in the present application introduces an adaptive calibration law; the norm ratio of the measured response of the entire structure to the model predicted response is converted into a multiplicative correction factor for the model parameters; this ensures that the digital twin model continuously approximates the true state of the physical structure throughout the loading process, and its fidelity dynamically improves, thereby providing a more accurate benchmark over time for early warning and control;

[0043] 3. The dynamic loading control decision module of the present application constitutes a complete negative feedback loop; a potential, divergent failure trend is actively suppressed in the embryonic state by smoothly and automatically reducing the loading rate or even reversing the unloading. Through the collaborative work of the data acquisition and fusion module, the digital twin model calibration module, the model deviation evaluation and early warning module, and the dynamic loading control decision module, a passive alarm system that relies on static thresholds is upgraded to an intelligent system that can dynamically perceive, adaptively recognize, prospectively predict, and actively close-loop control; the predictive and avoidance capabilities for structural nonlinear behavior and sudden destruction are greatly improved, thereby enhancing the safety, reliability, and intelligent level of the large structure preloading test process. BRIEF DESCRIPTION OF DRAWINGS

[0044] The present application will be further explained in conjunction with the accompanying drawings and examples:

[0045] Figure 1 is a schematic diagram of the overall structure of the present application;

[0046] Figure 2 is a schematic diagram of the top connection structure of the cantilever beam body of the present application;

[0047] Figure 3 is a schematic diagram of the transverse reinforcing rod and its connection structure of the present application;

[0048] Figure 4 is a schematic diagram of the counterforce frame mechanism structure of the present application;

[0049] Figure 5 is a flowchart of the system of the present application.

[0050] In the figure: 1, cantilever beam main body; 2, hanging basket main truss mechanism; 21, channel steel base; 22, connecting seat; 23, support rod; 24, bearing channel steel; 25, butt joint channel steel; 26, strain rod; 27, rear displacement measuring point; 28, front displacement measuring point; 29, first visual measuring point; 3, counterforce frame mechanism; 31, counterforce frame crossbar; 32, first bottom support plate; 33, second bottom support plate; 34, counterforce frame connecting rod; 35, first counterforce frame pull rod; 36, butt joint rod; 37, first limiting seat; 38, transverse reinforcing rod; 39, stabilizing pull rod; 310, second limiting seat; 311, second counterforce frame pull rod; 312, counterforce frame strain measuring point; 313, second visual measuring point; 4, reinforcing crossbar; 5, reinforcing inclined pull rod. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with specific examples.

[0052] The embodiment of the present application provides an assembled counterforce frame, as shown in the figure, comprising: a cantilever beam main body 1, a hanging basket main truss mechanism 2 and a counterforce frame mechanism 3, the hanging basket main truss mechanism 2 is arranged at the top of the cantilever beam main body 1, the counterforce frame mechanism 3 is arranged at one side of the cantilever beam main body 1, and the hanging basket main truss mechanism 2 and the counterforce frame mechanism 3 are connected with each other and combined to form an integral structure. Figures 1-4

[0053] The hanging basket main truss mechanism 2 comprises a channel steel base 21, a connecting seat 22, a support rod 23, a bearing channel steel 24 and a butt joint channel steel 25, the channel steel base 21 is fixed at the top of the cantilever beam main body 1, the connecting seat 22 is arranged on the channel steel base 21, the support rod 23 is vertically fixed on the connecting seat 22, the bearing channel steel 24 is transversely fixed at the top of the support rod 23 and parallel to the connecting seat 22, and the butt joint channel steel 25 is transversely fixed at one end of the bearing channel steel 24 and perpendicular to the bearing channel steel 24.

[0054] The counterforce frame mechanism 3 comprises a counterforce frame crossbar 31, a first bottom support plate 32, a second bottom support plate 33 and a counterforce frame connecting rod 34, the counterforce frame crossbar 31 is fixed at one side of the cantilever beam main body 1, the first bottom support plate 32 is fixed at the bottom of the counterforce frame crossbar 31, the second bottom support plate 33 is fixed at the bottom of the first bottom support plate 32 and parallel to the butt joint channel steel 25, and the counterforce frame connecting rod 34 is vertically connected between the second bottom support plate 33 and the butt joint channel steel 25.

[0055] ​The hanging basket main truss mechanism 2 further comprises strain rods 26, a rear displacement measuring point 27 and a front displacement measuring point 28, the strain rods 26 are fixed obliquely between the connecting seat 22 and the supporting channel steel 24, there are at least four strain rods 26 symmetrically arranged in front of and behind and left and right of the strain rods 26, and the strain rods 26 are combined to form a parallelogram structure, the rear displacement measuring point 27 is arranged at the tail end of the connecting seat 22, the front displacement measuring point 28 is arranged at the front end of the connecting seat 22, and displacement meters are arranged on the rear displacement measuring point 27 and the front displacement measuring point 28; the butt joint channel steel 25 is provided with first visual measuring points 29, and there are at least three first visual measuring points 29 arranged equidistantly;

[0056] The counterforce frame mechanism 3 comprises a first counterforce frame pull rod 35, a butt joint rod 36, a first limiting seat 37, a transverse reinforcing rod 38, a stabilizing pull rod 39, a second limiting seat 310 and a second counterforce frame pull rod 311, the first counterforce frame pull rod 35 is fixed obliquely between the cantilever beam body 1 and the counterforce frame cross rod 31, one end of the butt joint rod 36 is fixed on the cantilever beam body 1, the first limiting seat 37 is fixed on the other end of the butt joint rod 36, one end of the stabilizing pull rod 39 is fixed on the butt joint channel steel 25, the second limiting seat 310 is fixed on the other end of the stabilizing pull rod 39, the first limiting seat 37 and the second limiting seat 310 are arranged in one-to-one correspondence, and the transverse reinforcing rod 38 is slidably connected between the first limiting seat 37 and the second limiting seat 310;

[0057] The second counterforce frame pull rod 311 and the connecting position of the counterforce frame cross rod 31 and the cantilever beam body 1 are both provided with counterforce frame strain measuring points 312, and the second bottom supporting plate 33 is provided with second visual measuring points 313.

[0058] It should be noted that, due to the problems of management inconvenience, insufficient detail accuracy in long-distance measurement in the existing hanging basket main truss bearing capacity and safety reliability monitoring, and the problems of unstable focusing accuracy, low multi-device cooperation efficiency and the need for test verification accuracy in close-range measurement, in order to solve this problem, the hanging basket main truss mechanism 2 and the counterforce frame mechanism 3 are arranged in the scheme, by arranging the hanging basket main truss mechanism 2 and the counterforce frame mechanism 3 as a detachable structure, the quick installation and disassembly of the monitoring system are realized, which adapts to the dynamic forward movement demand of the hanging basket in the cantilever pouring construction, solves the problem that the traditional monitoring equipment needs to be frequently debugged, and through the arrangement of multiple first visual measuring points 29 and second visual measuring points 313, combined with the rear displacement measuring point 27, the front displacement measuring point 28 and the displacement meter, the stress change, deformation and displacement trajectory of the hanging basket main truss mechanism 2 and the counterforce frame mechanism 3 in the forward movement process are captured in real time, realizing the all-around data acquisition of the structural bearing capacity and safety, and the design that the counterforce frame mechanism 3 moves forward synchronously with the hanging basket main truss mechanism 2 makes the monitoring system continuously cover the whole construction process without manual adjustment, avoiding the monitoring blind area caused by fixed viewing angle in traditional close-range measurement, and eliminating the defect of insufficient local detail accuracy in long-distance measurement, and the integrated deployment of multiple source monitoring points reduces the number of equipment and the complexity of wiring, combined with the automatic monitoring mode of synchronous forward movement, not only reduces the management difficulty of multi-device cooperation, but also avoids the cost investment of frequent focusing and calibration in the traditional scheme, realizes the balance of monitoring efficiency and economy, and adapts to the complex working conditions such as load distribution change and structure form dynamic adjustment in the cantilever pouring process, provides accurate basis for construction adjustment through real-time data feedback, and significantly improves the safety and reliability of the hanging basket construction.

[0059] The scheme mainly includes a cantilever beam body 1, a hanging basket main truss mechanism 2 and a counterforce frame mechanism 3, in use, first fix the channel steel base 21 on the top of the cantilever beam body 1, then sequentially assemble and connect each part, then assemble and connect the counterforce frame cross bar 31 and its part connecting structure, then butt joint the second bottom supporting plate 33 with the butt joint channel steel 25 through the counterforce frame connecting rod 34, then set the rear displacement measuring point 27 and the front displacement measuring point 28 at the front and rear ends of the connecting seat 22 respectively, monitor the displacement of the hanging basket main truss mechanism 2 when moving through the rear displacement measuring point 27, the front displacement measuring point 28 and the displacement meter, and at the same time capture the stress change, deformation and displacement trajectory of the hanging basket main truss mechanism 2 and the counterforce frame mechanism 3 in the forward movement process through the first visual measuring point 29 and the second visual measuring point 313, to realize the all-around data acquisition of the structural bearing capacity and safety.

[0060] As shown in Figures 1-4 The top of the connecting seat 22 is horizontally fixed with a reinforcing cross bar 4, and a reinforcing inclined pull rod 5 is obliquely connected between the reinforcing cross bar 4 and the strain rod 26.

[0061] The structural strength of the overall structure composed of the connecting seat 22, the support rod 23, the bearing channel steel 24 and the butt joint channel steel 25 is further improved by reinforcing the inclined pull rod 5.

[0062] Embodiment 2:

[0063] Please refer to Figure 5 The present application provides an intelligent preloading control system, comprising:

[0064] A data acquisition and fusion module is configured to acquire sensor data of the reaction frame and determine an accurate state vector of a key measurement point of the structure.

[0065] A digital twin model calibration module is configured to determine a model deviation vector between the accurate state vector of the key measurement point of the structure and a model predicted state vector calculated by using a preset digital twin model, and update model parameters of the digital twin model based on the model deviation vector.

[0066] A model deviation evaluation and early warning module is configured to analyze a time sequence of the model deviation vector, and generate a predictive failure early warning signal when a divergence trend of the model deviation vector meets a preset failure early warning rule.

[0067] A dynamic loading control decision module is configured to determine a dynamic loading inhibition factor based on the model deviation vector and a time derivative in response to the predictive failure early warning signal, and generate a loading instruction rate in combination with a preset reference loading rate.

[0068] In one embodiment, the intelligent preloading control system constructs a complete closed-loop control architecture through the serialized information processing of the four internal logic modules; the data acquisition and fusion module serves as a perception interface of the physical world and provides high-precision state data for the digital twin model calibration module; the deviation vector output by the model calibration module and the continuously calibrated digital twin model provide accurate basis for the deviation evaluation module and the model prediction of the next calculation period, respectively; the risk evaluation result output by the model deviation evaluation and early warning module directly drives the dynamic loading control decision module to generate a control instruction; the instruction is finally applied to the physical loading system, and the resulting structural response change is captured by the data acquisition and fusion module at the next moment, thereby starting a new round of closed-loop iteration; the overall technical effect of this architecture lies in its ability to dynamically adapt to the nonlinear mechanical behavior of the structure, predict and actively suppress the deviation divergence trend to avoid sudden damage, and significantly improve the safety and control reliability of large-scale structure testing.

[0069] The core limitation of the prior art is its passive and static risk response mode; the traditional method sets a safety threshold based on an idealized structural model, and an alarm is triggered when the monitoring data reaches this threshold; this approach cannot effectively deal with the highly nonlinear behavior of the structure when it approaches the limit load, nor can it handle the deviation of the real mechanical response caused by uncertainties such as material inhomogeneity and construction defects; as a result, the alarm may be too late to provide effective early warning before a sudden failure.

[0070] Embodiment 3:

[0071] The data acquisition and fusion module determines the accurate state vector of the key measurement points of the structure, including:

[0072] Real-time data from multiple source heterogeneous sensors deployed on the reaction frame system are collected, and Kalman filtering algorithm is used for fusion processing of the real-time data to generate the accurate state vector of the key measurement points of the structure;

[0073] To achieve accurate perception of the structure state, the data acquisition and fusion module collects data from multiple source heterogeneous sensors deployed on the reaction frame system, such as long-distance visual measurement devices and short-distance strain gauges and displacement meters; to obtain the optimal state estimation of the structure at any time , the system applies Kalman filtering algorithm to real-time fusion processing of the collected multi-source data; the application of this algorithm aims to systematically overcome the measurement noise and inherent bias of a single type of sensor, and through the iterative optimization of state prediction and measurement update, an accurate state vector of the key measurement points of the structure with high confidence is outputted ; this vector is the only data input for all subsequent analysis and control modules, and is a necessary prerequisite for high-fidelity digital twin calibration and accurate control decision-making.

[0074] Embodiment 4:

[0075] The digital twin model calibration module determines the model bias vector, including:

[0076] The digital twin model is driven to calculate the model predicted state vector, and the vector difference between the accurate state vector of the key measurement points of the structure and the model predicted state vector is calculated to generate the model bias vector;

[0077] The digital twin model calibration module updates the model parameters, including:

[0078] The norm of the accurate state vector of the structure key measuring point and the norm of the model predicted state vector are calculated respectively; based on the ratio of the norm of the accurate state vector of the structure key measuring point to the norm of the model predicted state vector and a preset adjustment index, a multiplication correction scale factor is determined, and the multiplication correction scale factor is applied to the current model parameter to determine the updated model parameter;

[0079] The core function of the digital twin model calibration module is to establish and maintain the consistency between the physical structure and the digital model; an internal configuration is a high-fidelity structure digital twin model based on the finite element method, which is pre-constructed The initial parameters of the model are derived from the design standards or laboratory test data of the material; after receiving the accurate state vector , the module drives the digital twin model under the current load condition to calculate a model predicted state vector ; then, by calculating the vector difference , the system can quantify the instantaneous deviation between the physical reality and the digital simulation, wherein, is the model deviation vector at the current time;

[0080] In order to make the digital twin model continuously approach the true mechanical behavior of the structure during the loading process, the module uses an adaptive calibration law based on the overall response to iteratively update the key physical parameters of the model, such as the equivalent elastic modulus of a specific region; the internal logic of this calibration law is to use the overall information of the vector composed of all measuring point responses to avoid the one-sidedness that may be introduced by relying on single measuring point data; the mathematical expression is as follows:

[0081] ;

[0082] represents the updated model parameter in the i th iteration step; represents the current model parameter in the i th step; is the measured response vector in the i th step; is the model predicted response vector in the i th step; represents the Euclidean norm of the vector; is a preset, dimensionless adjustment index; the adjustment index is used to control the convergence speed and stability of the correction process, and its value is greater than 0, and is set through prior simulation analysis of similar structures or based on empirical data;

[0083] For example, a specific prior simulation analysis method is as follows:

[0084] Construct a baseline finite element model and define a set of known model parameters that represent the true physical properties of the structure. ;

[0085] From a model parameter with initial bias For example, compared to Starting at 15% lower, a series of simulated loads are applied;

[0086] In different Take values, such as from 0.1 to 2.0, with a step size of 0.1, run the iterative correction program, and record the model parameters after each iteration. convergence to Number of iterations required ;

[0087] Choose the one that guarantees a relatively fast convergence speed, i.e. Smaller size, while avoiding violent oscillations during convergence, i.e. Value not around Large fluctuations The value serves as the optimal adjustment index. This method transforms empirical settings into a quantifiable and reproducible calibration process.

[0088] In each iteration, the formula transforms the overall magnitude difference between the measured response and the model-predicted response into a norm ratio for the model parameters. The multiplication correction factor ensures the physical correctness of the parameter adjustment direction. Through the continuous execution of this iterative process, the fidelity of the digital twin model is dynamically improved throughout the loading process, thus providing a more accurate model basis for subsequent risk assessment and control decisions.

[0089] This invention constructs a cognitive upgrade from static models to adaptive digital twins; unlike traditional methods that rely on theoretical models that remain unchanged once established, this invention introduces an adaptive calibration law into its digital twin model calibration module; this is achieved by applying a model parameter update formula. ,in For model parameters, This is the measured response vector. To predict the response vector for the model, To adjust the index, the system can continuously iterate and correct the key parameters of the digital twin model based on the overall response difference between the key measurement point structural precise state vector and the model predicted state vector; the practical significance of this formula is that it converts the norm ratio of the entire structure's measured response and model predicted response into a multiplication correction factor for model parameters such as elastic modulus; this ensures that the digital twin model continuously approximates the true state of the physical structure throughout the loading process, with its fidelity dynamically improving, thereby providing increasingly accurate benchmarks over time for early warning and control.

[0090] Embodiment 5:

[0091] The preset failure warning rule is defined as:

[0092] The first-order time derivative of the norm of the model deviation vector exceeds a preset speed threshold;

[0093] The second-order time derivative of the norm of the model deviation vector is positive;

[0094] The core function of the model deviation evaluation and early warning module is to achieve predictive early warning of structural failure; the judgment basis is not a traditional fixed threshold, but a preset failure warning rule based on dynamic trends; the triggering condition of this rule is defined as the logical AND of two mathematical conditions: first, the first-order time derivative of the norm of the model deviation vector exceeds a preset speed threshold ; second, the second-order time derivative of the norm is positive; The physical meaning of this rule is to capture the specific moment when the difference between model prediction and actual response is accelerating; the first-order derivative exceeding the threshold indicates that the deviation growth rate is abnormal, and the second-order derivative being positive confirms that this growth trend is intensifying rather than slowing down; this state is a reliable early sign of internal damage accumulation, entering a non-stable state, or imminent local destruction; among them, the speed threshold

[0095] is a key parameter determined by statistical analysis of industry standards and historical failure data of similar structures, ensuring the sensitivity and reliability of early warning;

[0096] In the absence of historical data or industry standards, this threshold can be conservatively set by the following simulation method based on the digital twin model:

[0097] Using the calibrated digital twin model, simulate the loading process until the structure reaches the theoretical ultimate bearing capacity or significant nonlinear large deformation occurs;

[0098] Record the first-order time derivative of the model deviation norm ​the change curve of the derivative of the norm of the model deviation vector;

[0099] By analyzing the curve, especially in the stage before the simulation failure, the peak value of a stable growth interval of the derivative value before entering the final destruction stage or the maximum value in a short period of time before the theoretical failure point is extracted, denoted as ;

[0100] The preset speed threshold is set as a safe discount value of the simulation peak value, for example , this method does not rely on external data, and provides a reasonable and safe initial threshold for the early warning system through self-simulation;

[0101] Once the rule is triggered, the system immediately generates a predictive failure warning signal with the highest priority to gain time for taking proactive intervention measures;

[0102] The present application realizes the paradigm shift from state monitoring to trend warning; the core of the present application is not to monitor whether the physical quantity exceeds the limit, but to analyze the dynamic evolution trend of the model deviation vector through the model deviation evaluation and warning module; the preset failure warning rule applied by the module is to simultaneously judge whether the first order time derivative of the norm of the deviation exceeds the preset speed threshold and whether the second order time derivative of the norm of the deviation is positive, which is the moment when the difference between the physical structure and the digital model "accelerates and expands"; the physical meaning of this criterion is that it identifies the early dynamic characteristics of system instability, rather than the lagging state result, so that it can generate a predictive failure warning signal with foresight before the structure enters the irreversible destruction process.

[0103] Embodiment 6:

[0104] The dynamic loading control decision module determines the dynamic loading suppression factor, including:

[0105] The ratio of the time derivative of the norm of the model deviation vector to the norm of the model deviation vector is calculated to obtain the instantaneous relative growth rate of the deviation; the initial suppression factor is obtained by multiplying the instantaneous relative growth rate of the deviation by the preset suppression time constant;

[0106] If the initial suppression factor is negative, the dynamic loading suppression factor is set to 0; otherwise, the initial suppression factor is set as the dynamic loading suppression factor;

[0107] The dynamic loading control decision module generates a loading instruction rate, including:

[0108] Subtract the dynamic loading suppression factor from the value in numeral 1 to obtain a loading rate adjustment coefficient, and multiply the loading rate adjustment coefficient with a preset reference loading rate to generate a loading instruction rate;

[0109] Define an interval for the loading rate adjustment coefficient:

[0110] If the loading rate adjustment coefficient is positive and less than or equal to 1, the loading instruction rate instructs the loading system to continue loading at a reduced or unchanged rate;

[0111] If the loading rate adjustment coefficient is negative, the loading instruction rate instructs the loading system to perform reverse unloading;

[0112] The dynamic loading control decision module is the final execution end of the control loop, and the core is to actively adjust the loading rate based on a dynamic loading suppression factor The calculation of this factor is the core link to realize proactive risk avoidance; the internal control law aims to establish a direct functional relationship between the dynamic characteristics of model deviation and the loading rate adjustment, and through a negative feedback mechanism, the expansion trend of the deviation is actively suppressed in the embryonic state;

[0113] The dynamic loading suppression factor is determined by the following formula:

[0114] ;

[0115] Wherein, is the dimensionless dynamic loading suppression factor; is a preset suppression time constant with a time dimension; is the response deviation vector at the current time; is the first-order time derivative of the norm of the deviation vector; the expression represents the instantaneous relative growth rate of the deviation, and its dimension is the reciprocal of time; to ensure dimensionless characteristics, the dimension of the suppression time constant is time, and its value is calibrated through simulation analysis of the target structure to seek the optimal balance between control response speed and system stability;

[0116] The calibration process can be designed as follows:

[0117] In the digital twin simulation environment, simulate a virtual disturbance that causes the deviation to appear an accelerating divergence trend;

[0118] Set a comprehensive performance evaluation function that includes the control response time and the system overshoot, for example , wherein is the time required from the early warning signal trigger to the successful suppression of the loading rate to a safe level, This refers to the maximum overshoot of the structural response, such as displacement or stress, during this suppression process. and These are weighting coefficients set based on security requirements;

[0119] By running a series of different Value, calculate each Performance function corresponding to the value The value;

[0120] Choose to The smallest one The value, as the final value that achieves the optimal balance between response speed and system stability, transforms the abstract process of seeking balance into a parameter optimization problem with a clear optimization objective.

[0121] If the calculated initial inhibition factor is negative, then... Set to 0 to avoid unnecessary loading suppression when the deviation converges safely;

[0122] In determining the inhibitory factor Then, the module generates the final load instruction rate through the following logic. :

[0123] ;

[0124] in, It is the rate of instructions output to the loading device; This is the preset baseline loading rate;

[0125] This control scheme enables intelligent and smooth adjustment of the loading process; when the system is running smoothly, Approaching 0, the loading process is at a baseline rate. Proceed; once a trend of accelerated expansion of the deviation is detected, The value will increase rapidly, thus through This adjustment factor actively reduces the instruction rate. In extreme cases, if the deviation diverges drastically, The value may be greater than 1, in which case the adjustment coefficient should be used. When the value becomes negative, the system will instruct the loading device to perform reverse unloading; this mechanism avoids potential structural failure risks through active negative feedback control, which constitutes the core safety guarantee of this invention.

[0126] The present application completes the control innovation from open-loop alarm to closed-loop active control; the prior art usually relies on manual intervention or simple open-loop instructions after triggering the alarm, the response is slow and the control is rough; the dynamic loading control decision module of the present application constitutes a complete negative feedback closed loop; it calculates an inhibition factor proportional to the relative growth rate of the deviation based on the dynamic characteristics of the model deviation vector , wherein is a dynamic loading inhibition factor, is an inhibition time constant , wherein is an instruction rate, is a reference rate

[0127] In summary, the present application improves a passive alarm system relying on static threshold to an intelligent system capable of dynamic perception, adaptive cognition, forward-looking prediction and active closed-loop control through the collaborative work of the data acquisition and fusion module, the digital twin model calibration module, the model deviation evaluation and early warning module and the dynamic loading control decision module; its beneficial effects are to greatly improve the prediction and avoidance ability of structural nonlinear behavior and sudden destruction, thereby enhancing the safety, reliability and intelligent level of the large structure preloading test process.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. An intelligent pre-press control system, characterized in that, The method comprises the following steps: a data acquisition and fusion module is used to acquire sensor data of the counterforce frame and determine an accurate state vector of a key measuring point of the structure; a digital twin model calibration module is used to determine a model deviation vector between the accurate state vector of the key measuring point of the structure and a model predicted state vector calculated by using a preset digital twin model, and update model parameters of the digital twin model based on the model deviation vector; a model deviation evaluation and early warning module is used to analyze a time sequence of the model deviation vector, and generate a predictive failure early warning signal when a divergence trend of the model deviation vector meets a preset failure early warning rule; a dynamic loading control decision module is used to determine a dynamic loading inhibition factor based on the model deviation vector and a time derivative in response to the predictive failure early warning signal, and generate a loading instruction rate in combination with a preset reference loading rate; the data acquisition and fusion module determines the accurate state vector of the key measuring point of the structure, which comprises the following steps: real-time data from multiple source heterogeneous sensors arranged on the counterforce frame system are collected, and the real-time data are processed by using a Kalman filtering algorithm to generate the accurate state vector of the key measuring point of the structure; the digital twin model calibration module determines the model deviation vector, which comprises the following steps: the digital twin model is driven to calculate the model predicted state vector, and a vector difference between the accurate state vector of the key measuring point of the structure and the model predicted state vector is calculated to generate the model deviation vector; the digital twin model calibration module updates the model parameters, which comprises the following steps: a norm of the accurate state vector of the key measuring point of the structure and a norm of the model predicted state vector are calculated respectively; a multiplication correction proportion factor is determined based on a ratio of the norm of the accurate state vector of the key measuring point of the structure to the norm of the model predicted state vector and a preset adjustment index, and the multiplication correction proportion factor is applied to current model parameters to determine updated model parameters; the preset failure early warning rule is defined as: a first order time derivative of a norm of the model deviation vector exceeds a preset speed threshold value; a second order time derivative of the norm of the model deviation vector is a positive value; the dynamic loading control decision module determines the dynamic loading inhibition factor, which comprises the following steps: a ratio of a time derivative of the norm of the model deviation vector to the norm of the model deviation vector is calculated to obtain an instantaneous relative growth rate of the deviation; the instantaneous relative growth rate of the deviation is multiplied by a preset inhibition time constant to obtain an initial inhibition factor; if the initial inhibition factor is a negative value, the dynamic loading inhibition factor is set to 0; otherwise, the initial inhibition factor is set to the dynamic loading inhibition factor; the dynamic loading control decision module generates the loading instruction rate, which comprises the following steps: the dynamic loading inhibition factor is subtracted from a numerical value 1 to obtain a loading rate adjustment coefficient, and the loading rate adjustment coefficient is multiplied by the preset reference loading rate to generate the loading instruction rate; an interval of the loading rate adjustment coefficient is defined as: if the loading rate adjustment coefficient is a positive value and less than or equal to 1, the loading instruction rate instructs the loading system to continue loading at a reduced or unchanged rate; if the loading rate adjustment coefficient is a negative value, the loading instruction rate instructs the loading system to perform reverse unloading.

2. The assembled reaction frame comprising the intelligent pre-pressing control system of claim 1, characterized in that, The method comprises the following steps: Cantilever beam body (1), hanging basket main truss mechanism (2) and counterforce frame mechanism (3), the hanging basket main truss mechanism (2) is arranged at the top of the cantilever beam body (1), the counterforce frame mechanism (3) is arranged at one side of the cantilever beam body (1), and the hanging basket main truss mechanism (2) and the counterforce frame mechanism (3) are connected with each other and combined to form an integral structure; The hanging basket main truss mechanism (2) comprises a channel steel base (21), a connecting seat (22), a supporting rod (23), a supporting channel steel (24) and a butt joint channel steel (25), the channel steel base (21) is fixed at the top of the cantilever beam body (1), the connecting seat (22) is arranged on the channel steel base (21), the supporting rod (23) is vertically fixed on the connecting seat (22), the supporting channel steel (24) is transversely fixed at the top of the supporting rod (23) and is parallel to the connecting seat (22), and the butt joint channel steel (25) is transversely fixed at one end of the supporting channel steel (24) and is perpendicular to the supporting channel steel (24). The counterforce frame mechanism (3) comprises a counterforce frame cross bar (31), a first bottom supporting plate (32), a second bottom supporting plate (33) and a counterforce frame connecting rod (34), the counterforce frame cross bar (31) is fixed at one side of the cantilever beam body (1), the first bottom supporting plate (32) is fixed at the bottom of the counterforce frame cross bar (31), the second bottom supporting plate (33) is fixed at the bottom of the first bottom supporting plate (32) and is parallel to the butt joint channel steel (25), and the counterforce frame connecting rod (34) is vertically connected between the second bottom supporting plate (33) and the butt joint channel steel (25).

3. The modular reaction wall of claim 2, wherein, The hanging basket main truss mechanism (2) further comprises a strain rod (26), a rear displacement measuring point (27) and a front displacement measuring point (28), the strain rod (26) is obliquely fixed between the connecting seat (22) and the supporting channel steel (24), the strain rod (26) is symmetrically provided with at least four rods in front and back and left and right, and is combined to form a parallelogram structure, the rear displacement measuring point (27) is arranged at the tail end of the connecting seat (22), the front displacement measuring point (28) is arranged at the front end of the connecting seat (22), and displacement meters are arranged on the rear displacement measuring point (27) and the front displacement measuring point (28).

4. The modular reaction wall of claim 2, wherein, The butt joint channel steel (25) is provided with a first visual measuring point (29), and the first visual measuring point (29) is equidistantly provided with at least three.

5. The modular reaction wall of claim 2, wherein, The counterforce frame mechanism (3) comprises a first counterforce frame pull rod (35), a butt joint rod (36), a first limiting seat (37), a transverse reinforcing rod (38), a stabilizing pull rod (39), a second limiting seat (310) and a second counterforce frame pull rod (311), the first counterforce frame pull rod (35) is fixed between the cantilever beam body (1) and the counterforce frame cross bar (31) in an inclined manner, one end of the butt joint rod (36) is fixed on the cantilever beam body (1), the first limiting seat (37) is fixed on the other end of the butt joint rod (36), one end of the stabilizing pull rod (39) is fixed on the butt joint channel steel (25), the second limiting seat (310) is fixed on the other end of the stabilizing pull rod (39), the first limiting seat (37) and the second limiting seat (310) are arranged in one-to-one correspondence, the transverse reinforcing rod (38) is slidably connected between the first limiting seat (37) and the second limiting seat (310), the second counterforce frame pull rod (311) and the connecting position of the counterforce frame cross bar (31) and the cantilever beam body (1) are provided with counterforce frame strain measuring points (312), the second bottom supporting plate (33) is provided with second visual measuring points (313), the top of the connecting seat (22) is horizontally fixed with a reinforcing cross bar (4), and a reinforcing inclined pull rod (5) is obliquely connected between the reinforcing cross bar (4) and the strain rod (26).

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