A large steel structure installation control method and system based on dynamic load distribution

Abstract

The present invention provides a large-scale steel structure installation control method and system based on dynamic load distribution. Applied to the field of control technology, the method includes: installing strain gauges at key nodes of the steel structure to monitor the local deformation caused by dynamic loads in real time and obtain the resistance value of the strain gauge; installing a support structure at the key nodes of the steel structure to additionally bear the dynamic load, the support structure is a temperature-sensitive metal strip, and the temperature-sensitive metal strip is provided with a heating element; integrating a multi-objective optimization algorithm and adaptive PID control, dynamically adjusting the PID control parameters, and controlling the heating amount of the heating element according to the adjusted PID control parameters, so that the resistance value of the strain gauge is maintained in the optimal resistance value range, so as to control the degree to which each temperature-sensitive metal strip bears the dynamic load. The present invention can adaptively adjust according to the actual stress conditions of the steel structure to improve the stability and durability of the structure.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and further to the field of steel structure installation control, and in particular to a large-scale steel structure installation control method and system based on dynamic load distribution. Background Art

[0002] Large steel structures are widely used in bridges, buildings, marine engineering, and large-scale equipment support systems due to their excellent seismic resistance, excellent plasticity and toughness, high strength, environmental friendliness, and recyclability. However, despite these advantages, steel structures still face numerous challenges in practical engineering applications. During transportation and installation, component deformation and installation accuracy are crucial to the long-term stability and safety of the structure.

[0003] In addition, during actual use, steel structures are also affected by dynamic loads such as wind loads, earthquakes, and traffic vibrations. These dynamic loads can cause periodic changes in structural stress, leading to local deformation, fatigue accumulation, and even structural damage. Traditional passive monitoring methods are often unable to timely and accurately reflect the stress state of the structure under dynamic loads, and lack the means to actively adjust the supporting force. Especially in large-scale steel structure systems, the interaction and mutual influence between different nodes make the overall stability of the structure more complicated. Existing structural monitoring and adjustment technologies mostly focus on the stress state of a single node, lacking comprehensive consideration of the overall stress distribution of the system and dynamic adjustment capabilities.

[0004] Therefore, developing an installation control method that can monitor the local deformation of steel structures under dynamic loads in real time and actively adjust the support force according to the monitoring results to achieve optimization of the overall force distribution of the structure is of great significance for improving the stability and durability of large steel structures. Summary of the Invention

[0005] The present invention provides a large-scale steel structure installation control method and system based on dynamic load distribution. The method aims to dynamically adjust the PID control parameters according to the real-time stress state and dynamic load changes of the steel structure by combining technical means such as strain monitoring, multi-objective optimization algorithm and adaptive PID control, so as to achieve more accurate and stable dynamic load distribution, realize precise control of the installation accuracy of the steel structure and effectively improve the long-term stability. It solves the technical problems in the prior art of the lack of real-time monitoring and active adjustment of the supporting force of the steel structure under dynamic load, and the inability to fully consider the overall stress distribution of the system for dynamic optimization and adjustment.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] A large steel structure installation control method based on dynamic load distribution includes the following steps:

[0008] S110: Installing strain gauges at key nodes of the steel structure to monitor local deformation caused by dynamic loads in real time and obtain resistance values ​​of the strain gauges; presetting an optimal resistance value range of the strain gauges;

[0009] S120: Installing a support structure at a key node of the steel structure to additionally bear the dynamic load, wherein the support structure is a temperature-sensitive metal strip, and a heating element is provided on the temperature-sensitive metal strip;

[0010] S130: integrating a multi-objective optimization algorithm and adaptive PID control, dynamically adjusting PID control parameters, and controlling the heating amount of the heating element according to the adjusted PID control parameters, so that the resistance value of the strain gauge is maintained within the optimal resistance value range, thereby controlling the degree to which each temperature-sensitive metal strip bears the dynamic load;

[0011] The method of integrating the multi-objective optimization algorithm and the adaptive PID control to dynamically adjust the PID control parameters includes:

[0012] Define the optimization variables, i.e. the control parameters of PID ( 、 、 、 、 ) and the objective function, and establish the adaptive PID control formula:

[0013] ;

[0014] Where u(t) is the power or current controlling the heating element; e(t) is the resistance error, which is the difference between the actual resistance value at the current moment t and the optimal resistance value range; is the proportional gain, is the integral gain, is the differential gain; is the integration time constant; is the differential time constant; a multi-objective optimization model is constructed, wherein NSGA-II is selected as the multi-objective optimization algorithm, and the optimization variables and objective functions are input into the optimization model for optimization; the resistance value of the strain gauge and the supporting force change of the temperature-sensitive metal strip are monitored in real time, and these real-time data are input into the multi-objective optimization model to solve the current optimal PID control parameters ( 、 、 、 、 ).

[0015] As in any of the above possible implementations, the optimal resistance value range is set to (R min , Rmax ),and:

[0016] ;

[0017] in, is the preset minimum resistance value; is the preset maximum resistance value; It is the reference resistance value without external load; It is the maximum strain value allowed by the preset structural stress; is the sensitivity coefficient of the strain gauge.

[0018] As in any of the above possible implementations, the method further includes: setting the first objective function to:

[0019] ;

[0020] And through iterative optimization, the first objective function is minimized;

[0021] In the first objective function, is the first objective function; It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; node The resistance value at the current time t; is a node The first weight coefficient of ; n is the number of nodes.

[0022] As in any of the above possible implementations, the method further includes: setting the second objective function to:

[0023] ;

[0024] And through iterative optimization, the second objective function is minimized;

[0025] In the first objective function, is the second objective function; represents the adjustment support force of the temperature-sensitive metal strip at node i at the current time t; is the second weight coefficient of node i.

[0026] As in any of the above possible implementations, the method further includes setting an overall objective function:

[0027] ;

[0028] And through iterative optimization, the total objective function is minimized;

[0029] in:

[0030] ;

[0031] ;

[0032] It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; is a node The resistance value at the current time t; is a node The first weight coefficient of is a node The second weight coefficient of is the first coupling coefficient between node i and node j; is the second coupling coefficient between node i and node j.

[0033] As any of the above possible implementations, ,in, is the strain gauge reference resistance value at node i without external load.

[0034] As in any possible embodiment above, the temperature-sensitive metal strip is made of nickel-based alloy, copper alloy or aluminum alloy; the heating element is a resistance heating element, a heating belt or an electric heating film, and its temperature is adjusted by electric current to control the expansion degree of the temperature-sensitive metal strip.

[0035] In any of the possible implementations above, the strain gauge is arranged at the welded joint, bolted connection or support point of the steel structure; the support structure is installed at the key connection point, stress concentration area or deformation site of the steel structure.

[0036] According to another aspect of the present invention, a large-scale steel structure installation control system based on dynamic load distribution is provided, which is used to implement the large-scale steel structure installation control method based on dynamic load distribution as described in the first aspect. The system includes: a strain gauge, a temperature-sensitive metal strip, a heating element, a data acquisition module, a multi-objective optimization module, an adaptive PID controller, and a central controller;

[0037] Among them, strain gauges are installed at key nodes of the steel structure to monitor local deformation in real time; temperature-sensitive metal strips and heating elements are installed at key nodes to provide additional support; the data acquisition module is responsible for collecting data from strain gauges and temperature-sensitive metal strips; the multi-objective optimization module optimizes PID control parameters based on the collected data; the adaptive PID controller controls the heating element according to the optimized parameters; and the central controller is responsible for overall coordination and control.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The present invention achieves real-time monitoring of local deformation under dynamic loads by installing strain gauges at key nodes of the steel structure. The measured resistance value is then compared with a preset optimal resistance range to ensure that the structural stress state is always within a safe range. Compared with traditional passive monitoring methods, this method not only provides real-time data but also integrates an active adjustment system to enable the steel structure to dynamically adapt to changes in the external environment during operation, reducing fatigue damage caused by long-term loads and improving overall stability.

[0040] (2) Add support structures at key nodes and use temperature-sensitive metal strips as adjustable support elements. This allows the steel structure to provide additional support through the expansion of the metal strips when the force exceeds the threshold, effectively alleviating stress concentration and reducing the risk of deformation. At the same time, the strips automatically retract when the external force decreases, avoiding new problems caused by overcompensation. Compared with fixed support methods, the support structure of this method can be dynamically adjusted according to the actual force conditions, making the force distribution of the steel structure more reasonable and enhancing its long-term stability.

[0041] (3) By combining a multi-objective optimization algorithm with adaptive PID control, the PID control parameters are dynamically adjusted according to the real-time stress state and dynamic load changes of the steel structure, achieving a more accurate and stable dynamic load distribution. The heating element can automatically adjust the expansion degree of the temperature-sensitive metal strip according to the data feedback of the strain gauge, ensuring that the supporting force accurately matches the structural requirements and avoiding the safety of the steel structure affected by adjustment lag or improper force. Unlike the traditional thermal expansion and contraction adjustment method that relies on changes in ambient temperature, this method achieves rapid response and precise control through active temperature control means, and can be expanded to multiple key nodes, achieving coordinated adjustment through a central controller to optimize the force distribution of the entire structure.

[0042] (4) The present invention can adapt to different environmental conditions and changes in dynamic loads. By learning and updating the optimization model online, the system gradually adapts to the actual stress state of the steel structure. This adaptive capability reduces maintenance costs and extends the service life of the steel structure.

[0043] In summary, the present invention not only improves the stability and safety of steel structures, but also reduces maintenance costs and extends service life. It is particularly suitable for complex dynamic load environments such as bridges, high-rise buildings, and offshore platforms, enabling steel structures to maintain a good stress state during long-term operation, providing an efficient solution for intelligent structural maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0045] Figure 1 A flow chart showing a large steel structure installation control method based on dynamic load distribution according to an embodiment of the present invention is shown;

[0046] Figure 2 A schematic diagram showing the installation of the strain gauge and the temperature-sensitive metal strip of the present invention on a steel structure is shown;

[0047] Figure 3 Shown is a schematic diagram of PID control in the present invention;

[0048] Figure 4 The diagram shows a mechanism of a large steel structure installation control system based on dynamic load distribution according to an embodiment of the present invention.

[0049] In the figure: 1. Steel structure; 2. Strain gauge; 3. Temperature-sensitive metal strip. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0052] The present invention achieves real-time monitoring of local deformation under dynamic loads by installing strain gauges at key nodes of the steel structure, adds support structures at key nodes, and uses temperature-sensitive metal strips as adjustable support elements, so that the steel structure can provide additional support through the expansion of the metal strips when the force exceeds a threshold. By combining a multi-objective optimization algorithm and adaptive PID control, the PID control parameters are dynamically adjusted according to the real-time stress state and dynamic load changes of the steel structure, thereby achieving more accurate and stable dynamic load distribution.

[0053] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a large steel structure installation control method 100 based on dynamic load distribution, comprising the following steps:

[0054] S110: Installing strain gauges 2 at key nodes of the steel structure 1 to monitor local deformation caused by dynamic loads in real time and obtain resistance values ​​of the strain gauges 2; presetting an optimal resistance value range of the strain gauges;

[0055] S120: Installing a support structure at a key node of the steel structure 1 to additionally bear the dynamic load, the support structure being a temperature-sensitive metal strip 3 on which a heating element is provided;

[0056] S130: Integrate the multi-objective optimization algorithm and adaptive PID control, dynamically adjust the PID control parameters, and control the heating amount of the heating element according to the adjusted PID control parameters, so that the resistance value of the strain gauge 2 is maintained within the optimal resistance value range, so as to control the extent to which each of the temperature-sensitive metal strips 3 bears the dynamic load.

[0057] Specifically, the resistance strain gauge 2 may be selected and installed at key nodes of the steel structure 1, such as connection parts, support points and other places with greater stress.

[0058] Steel structure 1 experiences instantaneous deformation and vibration under dynamic loads (such as wind, seismic, and traffic). When the structure is functioning normally, the resistance of strain gauge 2 should remain within a preset optimal resistance range, typically determined based on design standards, material properties, and safety requirements. Changes in resistance directly reflect the degree of strain at that location. A data acquisition system reads these resistance values ​​in real time through a bridge circuit or other interface, converts them into electrical signals, and transmits them to a monitoring system for analysis and processing.

[0059] More specifically, it can be set according to the following formula:

[0060] The optimal resistance value range is set as (R min , R max ),and:

[0061] ;

[0062] in: is the preset minimum resistance value; is the preset maximum resistance value; It is the reference resistance value without external load; It is the maximum strain value allowed by the preset structural stress; is the sensitivity coefficient of the strain gauge.

[0063] The optimal resistance value range of the strain gauge mentioned above ( , ) is based on the reference resistance value without external load , the maximum strain value allowed by the structural stress And the sensitivity coefficient G of the strain gauge is used to calculate. When the steel structure is subjected to dynamic load, its strain will cause the resistance value of the strain gauge to change. This change is proportional to the size of the strain, and the proportional coefficient is the sensitivity coefficient G of the strain gauge. Therefore, when the strain reaches the maximum allowable value When the resistance value of the strain gauge changes to the maximum or minimum value accordingly.

[0064] In practical applications, it is necessary to accurately measure the reference resistance value without external load , and determine the maximum allowable strain value based on the material and design requirements of the steel structure and the sensitivity coefficient G of the strain gauge.

[0065] Strain gauge 2, combined with a control system, forms a real-time monitoring mechanism. If the resistance value changes beyond a preset range, the control system will provide feedback adjustments based on the change (in subsequent steps) to ensure that the structural strain remains within a safe range. Changes under dynamic loads can be complex, especially in the presence of high-frequency vibrations. Therefore, the strain gauge 2 and control system must possess high precision and rapid response capabilities.

[0066] Real-time monitoring isn't just about acquiring data; more importantly, it involves processing and analyzing that data. The resistance values ​​measured by these strain gauges 2 provide a scientific basis for health management of the steel structure 1. This data analysis enables fatigue analysis and lifespan prediction, identifying potential structural issues in a timely manner. If the resistance values ​​of the strain gauges 2 consistently deviate from the optimal range, the system will issue an early warning, alerting potential hazards. This monitoring data can also be combined with data from other sensors (such as temperature sensors, accelerometers, and displacement sensors) to form a more comprehensive monitoring network, further enhancing the accuracy of structural health monitoring.

[0067] The core function of the support structure in step S120 is to provide additional support by using the temperature-sensitive metal strip 3 and the heating element in combination with the principle of thermal expansion and contraction to adjust the deformation of the steel structure 1. The temperature-sensitive metal strip 3 itself has the characteristic of expanding or contracting with temperature changes.

[0068] When the heating element passes an electric current through the metal strip, the temperature of the metal strip increases, causing it to expand. This expansion provides additional support when the steel structure 1 is subjected to excessive deformation. This process can effectively alleviate the negative impact of excessive deformation on the structure and ensure structural stability.

[0069] The temperature-sensitive metal strip 3 can be made of nickel-based alloys, copper alloys, or aluminum alloys. Nickel-based alloys have excellent high-temperature resistance and are suitable for applications in high-temperature environments, maintaining stable expansion characteristics under high-temperature conditions. Copper alloys, due to their high thermal expansion coefficient, are often used in environments with large temperature fluctuations. Aluminum alloys, due to their high expansion rate, low density, and good processability, are economical and easy to manufacture.

[0070] The temperature-sensitive metal strip 3 provides support through expansion, so a heating element is required to control its temperature. In practical applications, for example, a resistance heating element. The resistance heating element generates heat when current flows through a conductor with a certain resistance, thereby heating and expanding the temperature-sensitive metal strip 3. Common resistance heating elements include nickel-chromium alloy wire, heating tape, thermocouple, etc. Thermocouples not only have a temperature measurement function, but can also adjust the current according to temperature changes to achieve the purpose of temperature control. The heating tape and heating wire directly provide uniform heat through resistance heat, ensuring that the temperature-sensitive metal strip 3 can expand or contract within the set temperature range.

[0071] Heating elements can be installed around or on the surface of the temperature-sensitive metal strip 3 to ensure uniform heating of the strip when needed. When a critical joint of the steel structure 1 experiences excessive deformation, the temperature-sensitive metal strip 3 expands upon heating, providing additional support and preventing structural failure due to excessive deformation.

[0072] The support structure can be installed at key nodes of the steel structure 1, such as joints, support points, and highly stressed joints. Joints and support points are the primary locations within the steel structure 1 subject to external loads, and are typically subject to significant stress concentration. Installing temperature-sensitive metal strips 3 can provide real-time support adjustments at these locations, ensuring that the structure does not deform excessively under load. Through proper layout, the temperature-sensitive metal strips 3 can function promptly at these locations, automatically adjusting the support force and ensuring the long-term stability of the steel structure 1.

[0073] For specific control, PID control can be used. Figure 3 As shown:

[0074] r(t) (reference input) represents the set value or expected value of the system, such as the optimal resistance value range of the strain gauge 2 in the present invention.

[0075] e(t) = r(t) − c(t) (the error signal) represents the difference between the actual system output c(t) and the set value range r(t), i.e., the error. In various embodiments, r(t) can also be set to a single value.

[0076] Proportional (P): The larger the error, the larger the control output u(t), providing a faster response.

[0077] Integration (I): Accumulates past errors to eliminate steady-state errors.

[0078] Differentiation (D): Predict error change trends and improve system stability.

[0079] u(t) (control signal): The control quantity calculated by the PID controller and transmitted to the actuator.

[0080] Actuator: The component that actually affects the system status, that is, the heating element in the present invention, which adjusts the supporting force by controlling the expansion amount of the temperature-sensitive metal strip 3.

[0081] Object (controlled system): This is the target to be controlled in the PID system, i.e., strain gauge 2.

[0082] c(t) (system output): Final output, i.e. resistance of strain gauge 2.

[0083] Feedback loop: The system output c(t) is fed back to the input and compared with the desired value r(t), forming a closed-loop control. The desired value r(t) here can be a specific range, i.e., the optimal resistance range mentioned above, or it can be set to a range in specific embodiments.

[0084] Preferably, in the present invention, the PID control is an adaptive PID control. By integrating the multi-objective optimization algorithm and the adaptive PID control, the PID control parameters are dynamically adjusted, including:

[0085] (1) Define the optimization variables, i.e., the control parameters of PID ( 、 、 、 、 ) and the objective function, and establish the adaptive PID control formula:

[0086] ;

[0087] Where u(t) is the power or current controlling the heating element; e(t) is the resistance error, which is the difference between the actual resistance value at the current moment t and the optimal resistance value range; is the proportional gain, used to quickly respond to errors; is the integral gain, used to eliminate steady-state error; is the differential gain, which is used to predict the error change trend; is the integral time constant, which is used to adjust the response speed of the integral term; is the differential time constant, which is used to adjust the response speed of the differential term.

[0088] More specifically, there are three optimization methods.

[0089] The goal of the first optimization is to minimize the resistance error of all nodes so that the actual resistance value of each node is Close to the preset target resistance value , thereby ensuring the strain control of the steel structure 1 under dynamic load. In a preferred embodiment, Set to the strain gauge reference resistance R under no external load i0 .

[0090] More specifically, the first objective function is set as:

[0091] ;

[0092] And make the first objective function reach the minimum value through the optimization algorithm;

[0093] In the first objective function:

[0094] is the first objective function; It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; node The resistance value at the current time t; is a node The first weight coefficient of ; n is the number of nodes.

[0095] The second objective function aims to optimize the support force at each node, ensuring a balanced distribution of support force by minimizing the total support force and preventing excessive or insufficient support force at certain nodes. The support force is the regulating force generated by the expansion or contraction of the temperature-sensitive metal strip, reflecting the stress at that point.

[0096] The corresponding second objective function is:

[0097] ;

[0098] And the second objective function is minimized through the optimization algorithm;

[0099] in: is the second objective function; represents the adjustment support force of the temperature-sensitive metal strip at node i at the current time t; is the second weight coefficient of node i.

[0100] The third optimization objective is the overall objective function, which combines the first two optimization objectives—minimizing resistance error and support force distribution—and incorporates a coupling term for inter-node resistance and support force variations to optimize the interactions between nodes. This combined optimization objective allows the algorithm to simultaneously consider the proper distribution of resistance and support force while ensuring coordination among structural nodes, avoiding localized stress concentrations or uneven deformation.

[0101] Set the overall objective function:

[0102] ;

[0103] And the total objective function is minimized through the optimization algorithm;

[0104] in:

[0105] ;

[0106] ;

[0107] It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; node The resistance value at the current time t; is a node The first weight coefficient of is a node The second weight coefficient of is the first coupling coefficient between node i and node j; is the second coupling coefficient between node i and node j.

[0108] The purpose of setting two coupling items in the present invention is:

[0109] The purpose of introducing a coupling term for resistance change is to ensure that the resistance changes at each node in Steel Structure 1 are coordinated. In Steel Structure 1, the deformations (and thus the resistance changes) at different nodes are often correlated, especially at nodes in the same stress region, where their deformations may be synchronized. The introduction of the coupling term ensures that nodes with large resistance errors affect the resistance adjustments of adjacent nodes, ensuring balanced resistance changes and structural stability. This optimization algorithm not only optimizes the resistance error at each node but also ensures coordinated resistance changes across nodes, avoiding uneven stress and deformation caused by excessive or insufficient resistance differences.

[0110] The purpose of the coupling term for support force variation is to coordinate the distribution of support forces across nodes, avoiding localized force imbalances or structural instability caused by over- or under-adjustment of support forces at a single node. The introduction of the coupling term balances the variation in support forces across nodes, preventing over- or under-support at certain nodes, which could affect the stability of the entire structure. In this way, support force optimization is not only localized but also considers the uniform distribution of forces across the entire structure, thereby improving structural stability, avoiding localized stress concentrations, and ensuring long-term safety and durability.

[0111] In addition, the selection of coefficients in the above three objective functions is explained as follows:

[0112] In the first objective function, the weight coefficient It is used to balance the contribution of each node's resistance error to the overall objective function. Specifically, a higher weight coefficient indicates that the node's resistance error is more important to the optimization results, so the optimization algorithm will prioritize making the resistance values ​​of these nodes close to the target resistance values. These coefficients can usually be set through empirical analysis or structural analysis. For example, if certain nodes are more critical to the safety and stability of the structure (such as key nodes that bear large loads), they will be set to larger values. Conversely, less critical nodes in the structure may be assigned smaller weights. Weight coefficients can also be determined through sensitivity analysis, analyzing the impact of different node resistance errors on the overall optimization results to determine appropriate weights.

[0113] In the second objective function, the weight coefficient Used to adjust the optimization importance of the support force of each node. Similar to the weight coefficient of resistance error, the weight coefficient of support force determines the role of the node in the optimization process. By setting a larger weight coefficient, the optimization process will pay more attention to the support force adjustment of the node to ensure that the force of the node is reasonably distributed. The selection of weight coefficient is usually based on the role and force of the node in the structure. For example, when some nodes are under heavy loads, a larger value can be set to optimize the distribution of support force to avoid these nodes affecting the overall stability due to insufficient or excessive support force. Like resistance optimization, the weight coefficient can also be determined through local optimization and global evaluation to ensure that the stability of the structure is balanced during optimization. In some cases, you can choose .

[0114] In the overall objective function, the coupling coefficient and It is used to adjust the degree of influence of resistance changes and support force differences between nodes. Generally speaking, the setting of the coupling coefficient is based on the distance between the nodes, the similarity of the forces, and the actual layout of the structure. If two nodes are physically very close or have very similar forces, the coupling coefficient between them may be set to a larger value to ensure that their resistance changes and support force adjustments are coordinated with each other. Conversely, for nodes that are farther apart or have large differences in forces, the coupling coefficient may be smaller. The coupling coefficient is usually set based on empirical values, structural analysis models, or experimental data. It can also be optimized through parameter tuning methods (such as learning based on historical data) to ensure that the mutual influence between nodes is reasonably controlled during the structural optimization process.

[0115] In order to optimize the resistance value and support force distribution of the node through experimental data and feedback mechanism, it is first necessary to collect the resistance and support force data of the node (the support force data can be obtained by calculation, or additional strain gauges can be designed for measurement), and set optimization goals based on these data. For example, in the best case, the optimization goal can be set to minimize the total objective function.

[0116] (2) Construct a multi-objective optimization model, where NSGA-II is selected as the multi-objective optimization algorithm, and the optimization variables and objective functions are input into the optimization model for optimization;

[0117] NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a multi-objective optimization algorithm based on the Pareto optimal solution set. It finds multiple Pareto optimal solutions in a single run through fast non-dominated sorting, crowding distance comparison, and an elite retention strategy. NSGA-II has good convergence and distribution properties, making it suitable for handling complex multi-objective optimization problems. Although NSGA-II itself is not a neural network algorithm, in this invention, it can be viewed as an optimization framework that includes steps such as population initialization, selection, crossover, mutation, non-dominated sorting, and crowding distance calculation. Specifically:

[0118] Population initialization: Randomly generate a set of initial solutions, each solution represents a set of PID control parameters ( 、 、 、 、 );

[0119] Selection: Select excellent individuals to enter the next generation based on non-dominated sorting and crowding distance;

[0120] Crossover: Perform a crossover operation on the selected individuals to generate new offspring individuals;

[0121] Mutation: Perform mutation operations on offspring individuals to increase the diversity of the population;

[0122] Non-dominated sorting: Perform non-dominated sorting on the new generation population to determine the non-dominated rank of each individual;

[0123] Crowding distance calculation: Calculate the crowding distance of each individual to select individuals with the same non-dominated level;

[0124] Elite retention strategy: merge the parent and offspring generations, and select the best individuals to form a new generation population based on the non-dominated level and crowding distance.

[0125] Optimization Process: The optimization variables (PID control parameters) and the objective function (J1, J2, and J3) are input into the NSGA-II model, and the optimization process begins. As the number of iterations increases, the population gradually converges to the Pareto optimal solution set. As the steel structure operates, new data is continuously collected and used to update the NSGA-II model. Through online learning, the optimization model gradually adapts to the actual stress state and environmental conditions of the steel structure. For example, the NSGA-II model can be updated at regular intervals (such as daily or weekly) to improve the accuracy and stability of dynamic load distribution.

[0126] Specifically, the NSGA-II model's optimization process gradually approaches the optimal solution through continuous iteration. In each iteration, the algorithm performs non-dominated sorting and crowding distance calculations based on the current population's fitness (i.e., the objective function value), selecting the best individuals for the next generation. Through crossover and mutation operations, the algorithm generates new offspring individuals, increasing the diversity of the population. As the number of iterations increases, the population gradually converges to the Pareto optimal solution set, thus achieving the optimization goal.

[0127] The following example takes a bridge steel structure as an example, assuming that the number of its key nodes is 5 (n=5). The initial PID control parameters are set as , =0.1, , , The weight coefficient is set to 、 (It can be adjusted according to specific circumstances in actual applications). Data acquisition: Use high-precision strain gauges and sensors to collect data every 1 second. Optimization process: Input the collected data into the NSGA-II model, set the population size to 100, the maximum number of iterations to 1000, the crossover probability to 0.9, and the mutation probability to 0.1. After optimization, a set of Pareto optimal solution sets are obtained, and one of the solutions is selected as the current optimal PID control parameters. Control effect: Under the action of dynamic load, by adaptively adjusting the PID control parameters, the resistance error of the strain gauge is reduced by an average of 35%, and the rate of change of the adjustment support force of the temperature-sensitive metal strip is reduced by an average of 25%, and the stability of the system is significantly improved.

[0128] (3) Real-time monitoring of the resistance value of the strain gauge and the change in the supporting force of the temperature-sensitive metal strip, inputting these real-time data into the multi-objective optimization model, and solving the current optimal PID control parameters ( 、 、 、 、 ).

[0129] (4) The adaptive PID controller dynamically adjusts the heating amount of the heating element according to the solution results, so that the resistance value of the strain gauge remains within the optimal resistance value range and optimizes the distribution of the supporting force.

[0130] In the proportional control section, the system adjusts the power output of the heating element based on the difference between the current resistance value of strain gauge 2 and the optimal resistance range. Specifically, when the resistance value of strain gauge 2 deviates from the optimal resistance range, the proportional control section generates a control signal proportional to the magnitude of the deviation. For example, if the resistance value of strain gauge 2 is higher than the optimal resistance range, the proportional controller will increase the heating output of the heating element; otherwise, it will reduce the heating output. Proportional control has the advantage of fast response, but it may not completely eliminate errors in the system, especially when the system is subject to external disturbances.

[0131] The integral component accumulates past errors, compensating for persistent deviations that were not fully eliminated by proportional control. When the error persists for a long time, the integral term gradually increases, driving the heating element to output more power until the error is completely eliminated. Integral control accounts for long-term deviations caused by environmental changes or other factors, allowing the system to accurately maintain the resistance of strain gauge 2 near its optimal value.

[0132] The differential control component predicts the system's future behavior. By monitoring the rate of change of the error, the differential controller predicts future error trends and enables proactive compensation. Assuming that the resistance of strain gauge 2 is rapidly approaching its optimal range, the differential controller will proactively reduce the heating element's output power to avoid system overshoot (i.e., adjusting the resistance after exceeding the target value). The addition of differential control allows the PID controller to more smoothly adjust the heating amount, preventing instability caused by overheating or excessive heating.

[0133] The greatest advantage of the PID control system lies in its intelligence and adaptability. Over the long term, steel structure 1 will be affected by various environmental factors (such as temperature and load variations), which can cause fluctuations in structural strain. The PID controller can continuously adjust control parameters based on real-time strain data to adapt to these changes. This adaptability ensures stable system operation in a variety of complex environments, thereby improving the safety of steel structure 1.

[0134] According to the aforementioned embodiment of the present invention, a large-scale steel structure installation control method based on dynamic load distribution achieves real-time monitoring of local deformation under dynamic loads by installing strain gauges at key nodes of the steel structure. Combined with adaptive PID control, the support force of the supporting structure can be quickly and accurately adjusted based on the monitoring results, ensuring that the stress state of the steel structure under dynamic loads remains within a safe range. This not only improves installation accuracy but also allows the structure to dynamically adapt to changes in the external environment during use, reducing fatigue damage caused by long-term loads and extending the structure's service life.

[0135] like Figure 4As shown, another embodiment of the present invention provides a large-scale steel structure installation control system 200 based on dynamic load distribution, which is used to implement the aforementioned large-scale steel structure installation control method 100 based on dynamic load distribution. The control system 200 is composed of a strain gauge, a temperature-sensitive metal strip, a heating element, a data acquisition module, a multi-objective optimization module, an adaptive PID controller, and a central controller. The strain gauge is installed at the key nodes of the steel structure to monitor local deformation in real time; the temperature-sensitive metal strip 3 and the heating element are installed at the key nodes to provide additional support; the data acquisition module is responsible for collecting data from the strain gauge and the temperature-sensitive metal strip; the multi-objective optimization module optimizes the PID control parameters based on the collected data; the adaptive PID controller controls the heating element based on the optimized parameters; and the central controller is responsible for overall coordination and control.

[0136] In the above adaptive PID control, the resistance value of the strain gauge 2 is kept within a safe range by adjusting the temperature-sensitive metal strip 3 and the heating element. In a further practical example, the entire system can be optimized through iterative optimization within the safe range to achieve a better state.

[0137] Specifically, in a large steel structure system, unlike the case of a single node, different nodes influence each other, that is, each node does not exist in isolation, but is interconnected and interacts with each other. For example, additional support for a node (such as increasing the support force by the expansion of a temperature-sensitive metal strip) will not only affect the stress state of the node, but may also have an indirect impact on other nodes through structural connections. For example, an increase in the support force of a node may reduce the deformation of its adjacent nodes, but may also cause stress redistribution at other nodes. Due to the interaction between nodes, simply optimizing a single node may not achieve the optimal state of the system as a whole. Therefore, during the optimization process, it is necessary to consider the mutual influence between nodes and perform global optimization of the entire system.

[0138] In the technical solution of the present invention, by introducing coupling terms for changes in resistance and support force between nodes, the interactions between nodes can be quantified. These coupling terms play a key role in the optimization process, ensuring that the optimization results not only consider the stress state of a single node, but also the mutual influence between nodes, thereby achieving the optimal stress and support state of the entire system. By considering the interactions between nodes and optimizing the entire system, dynamic loads can be distributed more reasonably, avoiding problems such as local stress concentration and uneven deformation. This helps to improve the overall stability and durability of the steel structure and extend its service life.

[0139] Before optimization, PID will first control the resistance value within the optimal resistance value range, but overall adjustments can be made within this range to make the building more stable as a whole.

[0140] During optimization, the resistance and support force values ​​obtained from initial measurements serve as the basis for preliminary optimization. The optimization algorithm adjusts the heating amount at each node to minimize the objective function. After multiple iterations of the optimization algorithm, the objective function is reduced to the desired minimum value, achieving optimal results.

[0141] This large-scale steel structure installation control system, based on dynamic load distribution, integrates multiple components, including strain gauges, temperature-sensing metal strips, heating elements, a data acquisition module, a multi-objective optimization module, an adaptive PID controller, and a central controller. This system enables integrated management and automated control of the steel structure's installation and operation processes. The data acquisition module collects real-time data from the strain gauges and temperature-sensing metal strips, and the multi-objective optimization module optimizes the PID control parameters based on this data. This real-time data processing and analysis capability ensures the system can rapidly respond to changes in dynamic loads, adjusting support forces promptly to ensure structural safety. The central controller provides overall coordination and control, providing real-time visibility into the steel structure's operational status through remote monitoring. Upon detecting anomalies or potential problems, the system automatically issues warnings and initiates appropriate maintenance measures, achieving intelligent maintenance. By precisely controlling the installation accuracy and stress state of the steel structure, the system significantly improves project quality and safety. This system can effectively prevent structural damage and safety incidents, particularly in complex dynamic load environments such as bridges, high-rise buildings, and offshore platforms.

[0142] In summary, the large-scale steel structure installation control method based on dynamic load distribution and its system of the present invention significantly improve the stability and durability of steel structures and reduce maintenance costs through real-time monitoring, precise control, multi-objective optimization and intelligent maintenance, thus providing a strong guarantee for the safe and efficient operation of large-scale steel structure projects.

[0143] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A large steel structure installation control method based on dynamic load distribution, characterized in that: The steps include: S110: Installing a strain gauge (2) at a key node of the steel structure (1) to monitor local deformation caused by dynamic loads in real time and obtain a resistance value of the strain gauge (2); presetting an optimal resistance value range of the strain gauge; S120: Installing a support structure at a key node of the steel structure (1) for additionally bearing the dynamic load, the support structure being a temperature-sensitive metal strip (3), and a heating element being provided on the temperature-sensitive metal strip (3); S130: integrating a multi-objective optimization algorithm and adaptive PID control, dynamically adjusting PID control parameters, and controlling the heating amount of the heating element according to the adjusted PID control parameters, so that the resistance value of the strain gauge (2) is maintained within the optimal resistance value range, thereby controlling the degree to which each of the temperature-sensitive metal strips (3) bears the dynamic load; The method of integrating the multi-objective optimization algorithm and the adaptive PID control to dynamically adjust the PID control parameters includes: Define the optimization variables, i.e. the control parameters of PID ( 、 、 、 、 ) and the objective function, and establish the adaptive PID control formula: ； Where u(t) is the power or current controlling the heating element; e(t) is the resistance error, which is the difference between the actual resistance value at the current moment t and the optimal resistance value range; is the proportional gain, is the integral gain, is the differential gain; is the integration time constant; is the differential time constant; a multi-objective optimization model is constructed, wherein NSGA-II is selected as the multi-objective optimization algorithm, and the optimization variables and objective functions are input into the optimization model for optimization; the resistance value of the strain gauge and the supporting force change of the temperature-sensitive metal strip are monitored in real time, and these real-time data are input into the multi-objective optimization model to solve the current optimal PID control parameters ( 、 、 、 、 ).

2. The method according to claim 1, characterized in that The optimal resistance value range is set to (Rmin, Rmax), and: ； in, is the preset minimum resistance value; is the preset maximum resistance value; It is the reference resistance value without external load; It is the maximum strain value allowed by the preset structural stress; is the sensitivity coefficient of the strain gauge.

3. The method according to claim 1, characterized in that The method further includes setting the first objective function to: ； And through iterative optimization, the first objective function is minimized; In the first objective function, is the first objective function; It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; node The resistance value at the current time t; is a node The first weight coefficient; n is the number of nodes.

4. The method according to claim 3, characterized in that The method further includes setting a second objective function as: ； And through iterative optimization, the second objective function is minimized; In the first objective function, is the second objective function; represents the adjustment support force of the temperature-sensitive metal strip at node i at the current time t; is the second weight coefficient of node i.

5. The method according to claim 4, characterized in that: The method further includes setting an overall objective function: ； And through iterative optimization, the total objective function is minimized; in: ； ； It is a preset node The target resistance value of the strain gauge is Located at the node within the optimal resistance value range; is a node The resistance value at the current time t; is a node The first weight coefficient of is a node The second weight coefficient of is the first coupling coefficient between node i and node j; is the second coupling coefficient between node i and node j.

6. The method according to claim 5, characterized in that ,in, is the strain gauge reference resistance value at node i without external load.

7. The method according to claim 1, characterized in that: The temperature-sensitive metal strip (3) is made of nickel-based alloy, copper alloy or aluminum alloy; the heating element is a resistance heating element, a heating belt or an electric heating film, and its temperature is adjusted by electric current to control the expansion degree of the temperature-sensitive metal strip (3).

8. The method according to claim 1, wherein: The strain gauge is arranged at a welded joint, a bolted joint or a supporting point of the steel structure (1); and the supporting structure is installed at a key connection point, a stress-concentrated area or a deformed part of the steel structure (1).

9. A large-scale steel structure installation control system based on dynamic load distribution, used to implement the large-scale steel structure installation control method based on dynamic load distribution according to any one of claims 1 to 8, characterized in that: The system includes: a strain gauge, a temperature-sensitive metal strip, a heating element, a data acquisition module, a multi-objective optimization module, an adaptive PID controller and a central controller; Among them, strain gauges are installed at key nodes of the steel structure to monitor local deformation in real time; temperature-sensitive metal strips and heating elements are installed at key nodes to provide additional support; the data acquisition module is responsible for collecting data from strain gauges and temperature-sensitive metal strips; the multi-objective optimization module optimizes PID control parameters based on the collected data; the adaptive PID controller controls the heating element according to the optimized parameters; and the central controller is responsible for overall coordination and control.

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