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

Abstract

The invention provides a large steel structure installation control method and system based on dynamic load distribution. The method is applied to the technical field of control and comprises the steps that a strain gauge is installed at a key node of a steel structure and used for monitoring local deformation caused by dynamic loads in real time, and the resistance value of the strain gauge is obtained; supporting structures are installed at key nodes of the steel structure and used for additionally bearing the dynamic loads, the supporting structures are temperature sensing metal strips, and heating elements are arranged on the temperature sensing metal strips; and a multi-objective optimization algorithm and self-adaptive PID control are fused, PID control parameters are dynamically adjusted, and the heating capacity of the heating element is controlled according to the adjusted PID control parameters, so that the resistance value of the strain gauge is kept in the optimal resistance value range, and the degree of the dynamic load borne by each temperature sensing metal strip is controlled. Self-adaptive adjustment can be achieved according to the actual stress condition of the steel structure, and the structural stability and durability are improved.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and further relates to the field of steel structure installation control, and particularly relates to a method and system for installing and controlling large steel structures based on dynamic load distribution. Background Art

[0002] Due to its good seismic performance, excellent plastic toughness, high strength, environmental friendliness, recyclability, etc., large steel structures have been widely used in fields such as bridges, buildings, offshore engineering, and large equipment support systems. However, despite many advantages of steel structures, they still face many challenges in practical engineering applications. During transportation and installation, the component deformation and installation accuracy of steel structures are crucial for the long-term stability and safety of the structure.

[0003] In addition, during the actual use of steel structures, they are also affected by dynamic loads such as wind loads, earthquakes, and traffic vibrations. These dynamic loads will cause periodic changes in structural stress, resulting in local deformation, fatigue accumulation, and even possible structural damage. Traditional passive monitoring methods often cannot reflect the stress state of the structure under dynamic loads in a timely and accurate manner, and lack means of actively adjusting the support force. Especially in large steel structure systems, the interaction and mutual influence between different nodes make the overall stability of the structure more complex. Existing structure monitoring and adjustment technologies mostly focus on the stress state of a single node, lacking comprehensive consideration of the overall force distribution of the system and the ability of dynamic adjustment.

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

[0005] The present invention provides a method and system for installing and controlling large steel structures based on dynamic load distribution, aiming to dynamically adjust the PID control parameters according to the real-time stress state of the steel structure and the change of dynamic load by combining technologies such as strain monitoring, multi-objective optimization algorithm, and adaptive PID control, 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, and solve the technical problems in the prior art that steel structures lack real-time monitoring and active adjustment of the support force under dynamic loads, and cannot comprehensively consider the overall force distribution of the system for dynamic optimization and adjustment.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for installing and controlling large steel structures based on dynamic load distribution, comprising the following steps: S110: Install strain gauges at the key nodes of the steel structure to monitor in real time the local deformation caused by dynamic loads and obtain the resistance values of the strain gauges; preset the optimal resistance value range of the strain gauges; S120: Install a support structure at the key nodes of the steel structure to additionally bear the dynamic loads. The support structure is a temperature-sensitive metal strip, and a heating element is arranged on the temperature-sensitive metal strip; S130: Integrate the multi-objective optimization algorithm and the adaptive PID control to 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 is maintained within the optimal resistance value range, to control the degree of each temperature-sensitive metal strip bearing the dynamic load; Among them, the integration of 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 the PID ( 、 、 、 、 ), and the objective function, and establish an adaptive PID control formula: ; Among them, u(t) is the power or current for controlling the heating element; e(t) is the resistance error, i.e., 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 derivative gain; is the integral time constant; is the derivative time constant; construct a multi-objective optimization model. Among them, select NSGA-II as the multi-objective optimization algorithm, and input the optimization variables and the objective function into the optimization model for optimization; monitor in real time the resistance value of the strain gauge and the change in the supporting force of the temperature-sensitive metal strip, and input these real-time data into the multi-objective optimization model to solve the currently optimal PID control parameters ( 、 、 、 、 ).

[0007] In any of the above possible implementation manners, the optimal resistance value range is set as (R min , R max ), and: ; Among them, is the preset minimum resistance value; is the preset maximum resistance value; is the reference resistance value under no external load; is the maximum strain value allowed by the preset structural stress; is the sensitivity coefficient of the strain gauge.

[0008] In any of the above possible implementation manners, the method further includes: setting the first objective function as: ; and making the first objective function reach the minimum value through iterative optimization; In the first objective function, is the first objective function; is the target resistance value of the strain gauge at the preset node , is located at the node within the range of the optimal resistance value; The node has a resistance value at the current time t; is the node of the first weight coefficient; n is the number of nodes.

[0009] In any of the above possible implementation manners, the method further includes: setting the second objective function as: ; and making the second objective function reach the minimum value through iterative optimization; In the first objective function, is the second objective function; represents the adjusted support force of the temperature-sensitive metal strip at the node i at the current time t; is the second weight coefficient of the node i.

[0010] In any of the above possible implementation manners, the method further includes setting the total objective function: ; and making the total objective function reach the minimum value through iterative optimization; Wherein: ; ; is the target resistance value of the strain gauge at the preset node , is located at the node within the range of the optimal resistance value; is the node has a resistance value at the current time t; is the first weight coefficient of the node ; is the second weight coefficient of the node ; is the first coupling coefficient between node i and node j; is the second coupling coefficient between node i and node j.

[0011] For any of the above possible implementation manners, , wherein, is the reference resistance value of the strain gauge of node i under no external load.

[0012] For any of the above possible implementation manners, the temperature-sensitive metal strip is made of nickel-based alloy, copper alloy or aluminum alloy material; the heating element is a resistance heating element, a heating tape or an electric heating film, and its temperature is adjusted by current to control the expansion degree of the temperature-sensitive metal strip.

[0013] For any of the above possible implementation manners, the strain gauge is arranged at the welded joint, bolt connection or support point of the steel structure; the support structure is installed at the key connection point, force-concentrated area or deformed part of the steel structure.

[0014] According to another aspect of the present invention, there is also provided a large steel structure installation control system based on dynamic load distribution, which is used to implement the large 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; wherein, the strain gauge is installed at the key node of the steel structure to monitor the local deformation in real time; the temperature-sensitive metal strip and the heating element are installed at the key node to provide additional support; the data acquisition module is responsible for collecting the data of the strain gauge and the temperature-sensitive metal strip; the multi-objective optimization module optimizes the PID control parameters according to the collected data; the adaptive PID controller controls the heating element according to the optimized parameters; the central controller is responsible for overall coordination and control.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By installing a strain gauge at the key node of the steel structure, the present invention realizes the real-time monitoring of local deformation under dynamic loads, and compares the measured resistance value with the preset optimal resistance value range to ensure that the structural stress state is always within the safe range. Compared with the traditional passive monitoring method, this method can not only provide real-time data, but also combine with an active adjustment system to enable the steel structure to dynamically adapt to external environmental changes during operation, reduce fatigue damage caused by long-term loads, and improve the overall stability.

[0016] (2) Install a support structure at key nodes and use temperature-sensitive metal strips as adjustable support elements, enabling the steel structure to provide additional support through the expansion of the metal strips when the stress exceeds the threshold, effectively alleviating stress concentration, reducing the risk of deformation, and automatically retracting when the external force decreases, avoiding new problems caused by overcompensation. Compared with the fixed support method, the support structure of this method can dynamically adjust according to the actual stress situation, making the stress distribution of the steel structure more reasonable and enhancing its long-term stability.

[0017] (3) By combining the multi-objective optimization algorithm and adaptive PID control, dynamically adjust the PID control parameters according to the real-time stress state and dynamic load changes of the steel structure to achieve more accurate and stable dynamic load distribution. Enable the heating element to automatically adjust the expansion degree of the temperature-sensitive metal strip according to the data feedback of the strain gauge, ensure that the supporting force precisely matches the structural requirements, and avoid affecting the safety of the steel structure due to adjustment lag or improper force. Different from the traditional thermal expansion and contraction adjustment method that relies on environmental temperature changes, this method realizes rapid response and precise control through active temperature control means, and can be extended to multiple key nodes to achieve coordinated adjustment through a central controller, optimizing the stress distribution of the overall structure.

[0018] (4) The present invention can adapt to different environmental conditions and dynamic load changes. By online learning and updating the optimization model, the system gradually adapts to the actual stress state of the steel structure. This adaptive ability reduces the maintenance cost and extends the service life of the steel structure.

[0019] In summary, the present invention not only improves the stability and safety of the steel structure, but also reduces the maintenance cost and extends the service life. It is particularly suitable for complex dynamic load environments such as bridges, high-rise buildings, and offshore platforms, enabling the steel structure to maintain a good stress state during long-term operation and providing an efficient solution for intelligent structural maintenance. Description of the Drawings

[0020] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present invention will become more apparent. The drawings are used to better understand the solution and do not limit the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 Shows a flowchart of a method for controlling the installation of a large steel structure based on dynamic load distribution according to an embodiment of the present invention; Figure 2 Shows a schematic diagram of the installation of a strain gauge and a temperature-sensitive metal strip on a steel structure according to the present invention; Figure 3 Shows a schematic diagram of PID control in the present invention; Figure 4The mechanism diagram of a large steel structure installation control system based on dynamic load distribution according to an embodiment of the present invention is shown.

[0021] In the figure: 1, steel structure; 2, strain gauge; 3, temperature-sensitive metal strip. Specific implementation manner

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0023] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0024] The present invention realizes real-time monitoring of local deformation under dynamic loads by installing strain gauges at key nodes of the steel structure, adds a support structure at the key nodes, and uses a temperature-sensitive metal strip as an adjustable support element, so that the steel structure can provide additional support through the expansion of the metal strip when the force exceeds the threshold, and by combining a multi-objective optimization algorithm and an adaptive PID control, according to the real-time stress state of the steel structure and the change of dynamic loads, dynamically adjusts the PID control parameters to achieve more accurate and stable dynamic load distribution.

[0025] As Figure 1 and Figure 2 shown, an embodiment of the present invention provides a large steel structure installation control method 100 based on dynamic load distribution, including the following steps: S110: Install a strain gauge 2 at the key node of the steel structure 1 to be used for real-time monitoring of local deformation caused by dynamic loads and obtain the resistance value of the strain gauge 2; preset the optimal resistance value range of the strain gauge; S120: Install a support structure at the key node of the steel structure 1 to be used for additionally bearing the dynamic load, the support structure is a temperature-sensitive metal strip 3, and a heating element is arranged on the temperature-sensitive metal strip 3; S130: Integrate the multi-objective optimization algorithm and adaptive PID control to 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, in order to control the degree to which each temperature-sensitive metal strip 3 bears the dynamic load.

[0026] Among them, more specifically, the resistance strain gauge 2 can be selected and installed at the key nodes of the steel structure 1, such as the connection parts, support points and other places with large forces.

[0027] Under the action of dynamic loads (such as wind loads, seismic loads, traffic loads, etc.), the steel structure 1 will undergo instantaneous deformation and vibration. The resistance value of the strain gauge 2 should be maintained within a preset optimal resistance value range when the structure is normal. This range is usually set based on design standards, material properties, and safety requirements. The change in the resistance value directly reflects the strain degree at that position. The data acquisition system reads these resistance values in real time through a bridge circuit or other interfaces, converts them into electrical signals, and transmits them to the monitoring system for analysis and processing.

[0028] More specifically, it can be set according to the following formula: The optimal resistance value range is set to (R min , R max ), and: ; Where: is the preset minimum resistance value; is the preset maximum resistance value; is the reference resistance value without external load; is the preset maximum allowable strain value for the structure under force; is the sensitivity coefficient of the strain gauge.

[0029] The above optimal resistance value range of the strain gauge ( , ) is calculated based on the reference resistance value without external load, the maximum allowable strain value for the structure under force, and the sensitivity coefficient G of the strain gauge. When the steel structure is subjected to dynamic loads, its strain will cause the resistance value of the strain gauge to change. This change amount is proportional to the magnitude of the strain, and the proportionality coefficient is the sensitivity coefficient G of the strain gauge. Therefore, when the strain reaches the maximum allowable value , the resistance value of the strain gauge should change correspondingly to the maximum or minimum value.

[0030] In practical applications, it is necessary to accurately measure the reference resistance value , and determine the maximum allowable strain value according to the material and design requirements of the steel structure and the sensitivity coefficient G of the strain gauge.

[0031] The strain gauge 2 forms a real-time monitoring mechanism by combining with the control system. Once the resistance value changes beyond the preset range, the control system will make feedback adjustments according to the change amount (subsequent steps) to ensure that the structural strain remains within the safe range. The changes under dynamic loads can be very complex, especially in the case of high-frequency vibrations. The strain gauge 2 and the control system need to have high precision and fast response capabilities.

[0032] Real-time monitoring is not just about obtaining data. More importantly, it is about the processing and analysis of the data. The resistance values measured by these strain gauges 2 can provide a scientific basis for the health management of the steel structure 1. By analyzing these data, fatigue analysis, life prediction, etc. of the structure can be carried out, and potential structural problems can be detected in a timely manner. If the resistance value of the strain gauge 2 continuously deviates from the optimal value range, the system can issue a warning to indicate the possible danger. These monitoring data can also be combined with other sensor data (such as temperature sensors, accelerometers, displacement sensors, etc.) to form a more complete monitoring network, further improving the accuracy of structural health monitoring.

[0033] The core function of the support structure in step S120 is to provide additional support by using the thermal expansion and contraction principle of the temperature-sensitive metal strip 3 and the heating element 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.

[0034] When the heating element heats the metal strip through an electric current, the temperature of the metal strip rises, causing it to expand. Thus, when the steel structure 1 is deformed too much under force, additional supporting force is provided through the expansion. This process can effectively alleviate the negative impact of excessive deformation on the structure and ensure the structural stability.

[0035] The temperature-sensitive metal strip 3 can be made of materials such as nickel-based alloys, copper alloys, or aluminum alloys. Nickel-based alloys have good high-temperature resistance and are suitable for applications in high-temperature environments, and can maintain stable expansion characteristics under high-temperature conditions; copper alloys are commonly used in environments with large temperature changes due to their high thermal expansion coefficient; while aluminum alloys are economical and easy to manufacture due to their large expansion rate, low density, and good workability.

[0036] The temperature-sensitive metal strip 3 provides support force 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 an electric 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 wires, heating tapes, thermocouples, etc. The thermocouple not only has the function of measuring temperature but also can adjust the current according to the temperature change to achieve the purpose of temperature control. The heating tape and heating wire generate heat through resistance and directly provide uniform heat to ensure that the temperature-sensitive metal strip 3 can expand or contract within the set temperature range.

[0037] The heating element can be installed around or on the surface of the temperature-sensitive metal strip 3 to ensure that the metal strip can be uniformly heated when needed. When excessive deformation occurs at the key nodes of the steel structure 1, the temperature-sensitive metal strip 3 expands through heating to provide additional support force and prevent the structure from failing due to excessive deformation.

[0038] The installation position of the support structure can be selected at the key nodes of the steel structure 1, such as the connection points, support points, and joints with greater stress. The connection points and support points are the main parts of the steel structure 1 that bear external loads and usually have significant stress concentration. Installing the temperature-sensitive metal strip 3 can provide real-time support adjustment at these parts to ensure that the structure does not undergo excessive deformation under the action of the load. Through reasonable layout, the temperature-sensitive metal strip 3 can play a role in these parts in a timely manner, automatically adjusting the support force to ensure the long-term stability of the steel structure 1.

[0039] For specific control, PID control can be adopted. As Figure 3 shown: 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.

[0040] e(t)=r(t)−c(t) (error signal), which represents the difference between the actual output c(t) of the system and the set value range r(t), that is, the error. In different embodiments, r(t) can also be set to a single value.

[0041] Proportion (P): The greater the error, the greater the control output u(t), providing a quick response.

[0042] Integral (I): Accumulates past errors to eliminate steady-state errors.

[0043] Derivative (D): Predicts the trend of error change to improve system stability.

[0044] u(t) (control signal): The control quantity calculated by the PID controller and transmitted to the actuator.

[0045] Actuator: The component that actually affects the system state, i.e., the heating element in the present invention, which adjusts the support force by controlling the expansion amount of the temperature-sensitive metal strip 3.

[0046] Object (controlled system): This is the target to be controlled in the PID system, i.e., the strain gauge 2.

[0047] c(t) (system output): The final output, i.e., the resistance of the strain gauge 2.

[0048] Feedback loop: Feed the system output c(t) back to the input end, compare it with the expected value r(t), and form a closed-loop control. Here, the expected value r(t) can be a specific range, i.e., the above-mentioned optimal resistance value range, or can be set as a range in a specific embodiment.

[0049] 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: (1) Define the optimization variables, i.e., the control parameters of the PID ( , , , , ), and the objective function, and establish the adaptive PID control formula: ; Wherein, u(t) is the power or current for controlling the heating element; e(t) is the resistance error, i.e., 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 the error; is the integral gain, used to eliminate the steady-state error; is the derivative gain, used to predict the error change trend; is the integral time constant, used to adjust the response speed of the integral term; is the derivative time constant, used to adjust the response speed of the derivative term.

[0050] More specifically, there are three optimization methods.

[0051] The first optimization goal is to make the actual resistance value of each node close to the preset target resistance value by minimizing the resistance error of all nodes, so as to ensure the strain control of the steel structure 1 under dynamic loads. In a preferred embodiment, can be set as the reference resistance R of the strain gauge under no external load i0 .

[0052] More specifically, set the first objective function as: ; And make the first objective function reach the minimum value through an optimization algorithm; In the first objective function: is the first objective function; is the preset target resistance value of the strain gauge of node and is within the range of the optimal resistance value of node located at node ; Node resistance value at the current time t; is node first weight coefficient of; n is the number of nodes.

[0053] The goal of the second objective function is to optimize the supporting force of the nodes. By minimizing the total supporting force, the distribution of the supporting force is ensured to be balanced, and the situation that the supporting force of some nodes is too large or too small is avoided. The supporting force is the adjustment force generated by the expansion or contraction of the temperature-sensitive metal strip, which reflects the stress situation at this point.

[0054] The corresponding set second objective function is: ; And make the second objective function reach the minimum value through an optimization algorithm; Wherein: is the second objective function; represents the adjustment supporting force of the temperature-sensitive metal strip at node i at the current time t; is the second weight coefficient of node i.

[0055] The third is the total objective function, which combines the first two optimization goals, that is, minimizing the resistance error and the supporting force distribution, and adding a coupling term of the resistance change and the supporting force change between nodes to optimize the interaction between nodes. Through this comprehensive optimization goal, the algorithm can consider the reasonable distribution of resistance and supporting force at the same time, while ensuring the coordination between the nodes of the structure and avoiding local stress concentration or uneven deformation.

[0056] Set the total objective function: ; And make the total objective function reach the minimum value through an optimization algorithm; Wherein: ; ; is the preset target resistance value of the strain gauge of node ​ Located at the node within the range of the optimal resistance value; The node The resistance value at the current time t; is the node 's first weight coefficient; is the node 's second weight coefficient; is the first coupling coefficient between node i and node j; is the second coupling coefficient between node i and node j.

[0057] The purpose of setting two coupling terms in the present invention is as follows: The purpose of introducing the coupling term of resistance change is to ensure that the resistance changes of each node in the steel structure 1 are coordinated with each other. In the steel structure 1, the deformations (which lead to resistance changes) of different nodes are usually related. Especially for nodes in the same stress area, their deformations may be synchronous. Introducing the coupling term enables nodes with larger resistance errors to affect the resistance adjustment of adjacent nodes, ensuring the balance of resistance changes and the stability of the structure. In this way, the optimization algorithm not only optimizes the resistance error of each node but also ensures the coordination of resistance changes among nodes, avoiding uneven stress and deformation caused by too large or too small resistance differences.

[0058] The purpose of the coupling term of support force change is to avoid uneven local stress or structural instability caused by too large or too small adjustment of the support force of a certain node by coordinating the support force distribution among nodes. Introducing the coupling term balances the support force changes among nodes, avoiding excessive or insufficient support of some nodes, which in turn affects the stability of the entire structure. In this way, the support force optimization is not only local but also takes into account the uniform distribution of forces in the entire structure, thereby enhancing the stability of the structure, avoiding local stress concentration, and ensuring long-term safety and durability.

[0059] In addition, the coefficient selection in the above three objective functions is described as follows: In the first objective function, the weight coefficient It is used to balance the contribution of the resistance error of each node to the total objective function. Specifically, a higher weight coefficient indicates that the resistance error of this node has a greater importance to the optimization result. Therefore, the optimization algorithm will first make 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 some nodes are more critical to the safety and stability of the structure (such as key nodes under large loads), their coefficients will be set to larger values. On the contrary, for less critical nodes in the structure, smaller weights may be assigned. The weight coefficients can also be determined through sensitivity analysis, analyzing the influence of the resistance errors of different nodes on the overall optimization result to determine appropriate weights.

[0060] In the second objective function, the weight coefficient is used to adjust the optimization importance of the supporting force of each node. Similar to the weight coefficient of the resistance error, the weight coefficient of the supporting 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 adjustment of the supporting force of this node to ensure the reasonable distribution of the forces on this node. The selection of the weight coefficient is usually based on the role and force conditions of the node in the structure. For example, when some nodes bear large loads, larger values can be set to optimize the distribution of the supporting force and avoid affecting the overall stability due to insufficient or excessive supporting force on these nodes. Similar to the resistance optimization, the weight coefficient can also be determined through local optimization and global evaluation to ensure the balance of the stability of the structure during optimization. In some cases, .

[0061] In the total objective function, the coupling coefficients and are used to adjust the influence degrees of the resistance change and the supporting force difference between nodes. Generally speaking, the setting of the coupling coefficients is based on the distance between nodes, the similarity of forces, and the actual layout of the structure. If two nodes are very close physically or have very similar forces, the coupling coefficient between them may be set to a larger value to ensure the coordination of their resistance change and supporting force adjustment. On the contrary, for nodes with a large distance or a large difference in forces, the coupling coefficient may be smaller. The coupling coefficients are usually set through empirical values, structural analysis models or experimental data, and can also be optimized through parameter tuning methods (such as learning based on historical data) to ensure that the mutual influence between nodes during the structure optimization process is reasonably controlled.

[0062] In order to optimize the resistance values and the supporting force distribution of nodes through experimental data and feedback mechanisms, it is first necessary to collect the resistance and supporting force data of the nodes (the supporting force data can be obtained through calculation or additionally designed strain gauges for measurement), and set the optimization objectives based on these data. For example, in the preferred case, the optimization objective can be set to minimize the total objective function.

[0063] (2) Construct a multi-objective optimization model. Among them, select NSGA-II as the multi-objective optimization algorithm, and input the optimization variables and objective functions into the optimization model for optimization; Among them, 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 one run through fast non-dominated sorting, crowding distance comparison, and elitist retention strategy. NSGA-II has good convergence and distribution, and is suitable for dealing with complex multi-objective optimization problems. Although NSGA-II itself is not a neural network algorithm, in the present invention, we can regard it as an optimization framework, which includes steps such as population initialization, selection, crossover, mutation, non-dominated sorting, and crowding distance calculation. Specifically: Population initialization: Randomly generate a set of initial solutions, and each solution represents a set of PID control parameters ( , , , , ); Selection: Select excellent individuals according to non-dominated sorting and crowding distance to enter the next generation; Crossover: Perform crossover operations on the selected individuals to generate new offspring individuals; Mutation: Perform mutation operations on the offspring individuals to increase the diversity of the population; Non-dominated sorting: Perform non-dominated sorting on the new generation population to determine the non-dominated rank of each individual; Crowding distance calculation: Calculate the crowding distance of each individual, which is used to select individuals under the same non-dominated rank; Elitist retention strategy: Combine the parent generation and the offspring generation, and select the optimal individuals according to the non-dominated rank and crowding distance to form a new generation population.

[0064] Optimization process: Input the optimization variables (PID control parameters) and objective functions (J1, J2, J3) into the NSGA-II model to start the optimization process. 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 can gradually adapt to the actual stress state and environmental conditions of the steel structure. For example, it can be set to update the NSGA-II model every certain period (such as every day or every week) to improve the accuracy and stability of dynamic load distribution.

[0065] Specifically, the optimization process of the NSGA-II model gradually approaches the optimal solution of the problem through continuous iteration. In each iteration, the algorithm performs non-dominated sorting and crowding distance calculation based on the fitness values (i.e., objective function values) of the current population, and selects excellent individuals to enter 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.

[0066] Taking a bridge steel structure as an example below, assume that the number of its key nodes is 5 (n = 5). Set the initial PID control parameters as , = 0.1, , , . The weight coefficients are set as , (which can be adjusted according to specific situations in practical applications). Data collection: 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 is obtained, and one set of solutions is selected as the current optimal PID control parameters. Control effect: Under dynamic loads, by adaptively adjusting the PID control parameters, the average resistance error of the strain gauge is reduced by 35%, and the average change rate of the adjustment support force of the temperature-sensitive metal strip is reduced by 25%, significantly improving the stability of the system.

[0067] (3) Real-time monitor the resistance value of the strain gauge and the change of the support force of the temperature-sensitive metal strip, and input these real-time data into the multi-objective optimization model to solve the current optimal PID control parameters ( , , , , ).

[0068] (4) The adaptive PID controller dynamically adjusts the heating amount of the heating element according to the solution result, so that the resistance value of the strain gauge is kept within the optimal resistance value range, and at the same time optimizes the distribution of the support force.

[0069] In the proportional control part, the system adjusts the power output of the heating element according to the difference between the current resistance value of strain gauge 2 and the optimal resistance value range. Specifically, when the resistance value of strain gauge 2 deviates from the optimal resistance value range, the proportional control part generates a control signal proportional to the deviation. For example, if the resistance value of strain gauge 2 is higher than the optimal resistance value range, the proportional controller will increase the heating amount of the heating element, and vice versa. The advantage of proportional control is fast response speed, but it may not completely eliminate errors in the system, especially when the system is subject to external disturbances.

[0070] The role of the integral part is to accumulate past errors and compensate for the persistent deviation that cannot be completely eliminated by proportional control. When the error exists for a long time, the integral term will gradually increase, thereby pushing the heating element to output more power until the error is completely eliminated. Integral control can solve long-term deviations caused by changes in the external environment or other factors, allowing the system to accurately maintain the resistance value of strain gauge 2 near the optimal value.

[0071] The differential control part predicts the future behavior of the system. The differential controller predicts the future error trend by detecting the rate of change of the error, so as to compensate in advance. Assuming that the resistance value of strain gauge 2 is rapidly approaching the optimal value range, the differential controller will reduce the output power of the heating element in advance to avoid system overshoot (that is, the resistance value exceeds the target value and then calls back). The addition of differential control allows the PID controller to adjust the heating amount more smoothly and prevent instability caused by too fast or too much heating.

[0072] The biggest advantage of the PID control system lies in its intelligence and adaptive capabilities. During long-term use, the steel structure 1 will be affected by different environmental factors (such as temperature changes, load changes, etc.), which will cause fluctuations in structural strain. The PID controller can continuously adjust the control parameters based on the real-time monitored strain data to adapt to these changes in the external environment. This adaptive capability ensures that the system can operate stably in various complex environments, thereby improving the safety of the steel structure 1.

[0073] According to the large-scale steel structure installation control method based on dynamic load distribution of the above embodiment of the present invention, real-time monitoring of local deformation under dynamic load is achieved by installing strain gauges at key nodes of the steel structure. Combined with adaptive PID control, the supporting force of the supporting structure can be quickly and accurately adjusted according to the monitoring results to ensure that the stress state of the steel structure under dynamic load is always within a safe range, which can not only improve the installation accuracy, but also dynamically adapt to changes in the external environment during the use of the structure, reduce fatigue damage caused by long-term loads, and extend the service life of the structure.

[0074] like Figure 4As shown in the figure, another embodiment of the present invention provides a large steel structure installation control system 200 based on dynamic load distribution, which is used to implement the aforementioned large 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 according to the collected data; the adaptive PID controller controls the heating element according to the optimized parameters; the central controller is responsible for overall coordination and control.

[0075] In the above-mentioned adaptive PID control, the resistance value of the strain gauge 2 is adjusted 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 made to reach a better state through iterative optimization within the safe range.

[0076] Specifically, in a large steel structure system, different from the case of a single node, different nodes affect each other, that is, each node does not exist in isolation, but is connected and interacts with each other. For example, for the additional support of a node (such as increasing the support force through the expansion of the temperature-sensitive metal strip), this support will not only affect the stress state of this 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 a redistribution of stress in other nodes. Due to the interaction between nodes, simply optimizing a single node may not achieve the optimal state of the entire system. Therefore, in the optimization process, the mutual influence between nodes needs to be considered to perform global optimization on the entire system.

[0077] In the technical solution of the present invention, by introducing the coupling terms of the resistance change and the support force change between nodes, the interaction between nodes can be quantified. These coupling terms play a key role in the optimization process, ensuring that the optimization result not only considers the stress state of a single node, but also considers the interaction between nodes, so as to achieve the best stress and support state of the entire system. By considering the interaction between nodes and performing overall system optimization, the dynamic load can be more reasonably distributed, 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.

[0078] Before optimization, the PID will first control the resistance value within the optimal resistance value range, but within this range, overall adjustment can be made to make the entire building more stable.

[0079] During optimization, the resistance value and support force value obtained from the initial measurement will be used as the basis for preliminary optimization. Through the optimization algorithm, the heating amount of each node is adjusted to minimize the objective function. After multiple iterations using the optimization algorithm, the objective function can be reduced to the required minimum value, thus achieving the best effect.

[0080] The large-scale steel structure installation control system based on dynamic load distribution integrates multiple components such as strain gauges, temperature-sensitive metal strips, heating elements, data acquisition modules, multi-objective optimization modules, adaptive PID controllers, and central controllers, realizing the integrated management and automatic control of the steel structure installation and operation process. The data acquisition module is responsible for real-time collection of data from strain gauges and temperature-sensitive metal strips, and the multi-objective optimization module optimizes the PID control parameters based on this data. This real-time data processing and analysis ability ensures that the system can quickly respond to changes in dynamic loads, adjust the support force in a timely manner, and ensure structural safety. The central controller is responsible for overall coordination and control, and can understand the operating status of the steel structure in real time through remote monitoring. Once an abnormality or potential problem is detected, the system can automatically issue a warning and take corresponding maintenance measures, realizing intelligent maintenance. By precisely controlling the installation accuracy and stress state of the steel structure, the project quality and safety are significantly improved. Especially in complex dynamic load environments such as bridges, high-rise buildings, and offshore platforms, the system can effectively prevent structural damage and safety accidents.

[0081] In summary, the large-scale steel structure installation control method and system based on dynamic load distribution of the present invention significantly improve the stability and durability of the steel structure, reduce the maintenance cost, and provide a strong guarantee for the safe and efficient operation of large-scale steel structure projects through means such as real-time monitoring, precise control, multi-objective optimization, and intelligent maintenance.

[0082] The above specific implementation manners do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A large steel structure installation control method based on dynamic load distribution, characterized in that It includes the following steps: S110: Install a strain gauge (2) at the key nodes of the steel structure (1) to monitor the local deformation caused by dynamic loads in real time and obtain the resistance value of the strain gauge (2); preset the optimal resistance value range of the strain gauge. S120: Install a support structure at the key nodes of the steel structure (1) to additionally bear the dynamic load. The support structure is a temperature-sensitive metal strip (3), and a heating element is provided on the temperature-sensitive metal strip (3). S130: Integrate the multi-objective optimization algorithm and the adaptive PID control to 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) remains within the optimal resistance value range, so as to control the degree of the dynamic load borne by each temperature-sensitive metal strip (3). Among them, the integration of 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, i.e., 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 derivative gain; is the integral time constant; is the derivative time constant; A multi-objective optimization model is constructed. Among them, 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 change in the supporting force 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, wherein The optimal resistance value range is set to (Rmin, Rmax), and: ； Wherein, is the preset minimum resistance value; is the preset maximum resistance value; is the reference resistance value without external load; 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, wherein The method further includes: setting the first objective function as: ； And making the first objective function reach the minimum value through iterative optimization; In the first objective function, is the first objective function; is the preset strain gauge target resistance value of node and is within the range of the optimal resistance value of node located at node ; is the resistance value of node at the current time t; is the first weight coefficient of node ; n is the number of nodes.

4. The method according to claim 3, wherein The method further includes: setting the second objective function as: ； And making the second objective function reach the minimum value through iterative optimization; In the first objective function, is the second objective function; represents the adjusted 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 the total objective function: ； And making the total objective function reach the minimum value through iterative optimization; Among them: ； ； is a preset node of the strain gauge target resistance value, located at the node within the range of the optimal resistance value; is the node resistance value at the current time t; is the node first weight coefficient of; is the node 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, wherein , where is the reference resistance value of the strain gauge of node i without external load.

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

8. The method according to claim 1, characterized in that: The strain gauge is arranged at the welded joint or bolt connection or support point of the steel structure (1); the support structure is installed at the key connection points, force concentration areas or deformed parts of the steel structure (1).

9. A large steel structure installation control system based on dynamic load distribution, which is used to implement the large steel structure installation control method based on dynamic load distribution according to any one of claims 1-8, 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, 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 and the heating element are installed at the key nodes to provide additional support; the data acquisition module is responsible for collecting data of the strain gauge and the temperature-sensitive metal strip; the multi-objective optimization module optimizes the PID control parameters according to the collected data; the adaptive PID controller controls the heating element according to the optimized parameters; the central controller is responsible for overall coordination and control.

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