A substation frame assembly construction monitoring method and system

Abstract

The invention discloses a substation frame assembly construction monitoring method and system, which specifically relates to the technical field of substation frame assembly monitoring, and is used to solve the problem of inaccurate monitoring of a structural structure during an assembly process. The invention determines key stress nodes and displacement areas of a steel structure frame through finite element analysis, arranges sensors, and corrects sensor measurement errors in combination with quaternion attitude calibration and Kalman filtering, formulates a data acquisition protocol, sets a sampling frequency and a data format, uses a phase-locked loop and a timestamp technology to perform data timing synchronization and spatial alignment, constructs a stress-displacement state space based on synchronized data, extracts key variables, performs nonlinear mapping and dimensionality reduction, establishes a state equation, analyzes trajectories and detects singular points, uses the analysis results for real-time monitoring of a step-by-step locking process, adjusts a torque application strategy through feedback control, dynamically optimizes the locking process, and ensures the stability and safety of the structural structure.

Description

Technical Field

[0001] The present invention relates to the technical field of substation frame assembly monitoring, and more specifically, to a substation frame assembly construction monitoring method and system. Background Art

[0002] In the construction of modern power facilities, the assembly and installation of steel structure racks in substations is one of the key links to ensure the safe and stable operation of power systems. The stress conditions of steel structure racks are complex, especially during the gradual tightening process. The stress distribution and displacement changes of the structure directly affect the stability and safety of the entire architecture. If these key parameters are not accurately monitored and controlled, it may lead to structural instability and even cause serious safety accidents.

[0003] Deficiencies of existing technologies: Traditional monitoring methods often rely on the monitoring of a single parameter, such as torque or stress, which cannot fully reflect the dynamic behavior of the structure during the locking process. In addition, data collection and processing have problems such as inconsistent timing and spatial alignment errors, which affect the accuracy of monitoring and easily cause insufficient locking during the assembly process, increasing safety risks. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for monitoring the assembly construction of a substation framework, so as to solve the problem of inaccurate monitoring of the framework structure during the assembly process in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A substation frame assembly construction monitoring method comprises the following steps:

[0007] Based on finite element analysis, the key stress nodes and displacement areas of the steel structure frame are determined, sensors are arranged, and quaternion is used for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors;

[0008] According to the sensor layout and calibration results, formulate data acquisition protocols, set sampling frequency and data format, and use phase-locked loop and time stamp technology to synchronize the timing and align the multi-sensor data in space;

[0009] Based on the collected and synchronized sensor data, the stress-displacement state space is constructed, key variables are extracted for nonlinear mapping and dimensionality reduction, and state equations are established to analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process;

[0010] During the gradual locking process, the torque and displacement data of each node are collected in real time, and combined with the displacement-stress correlation analysis results, the structural stress distribution is dynamically evaluated, and the torque application strategy is adjusted through feedback control to optimize the locking process.

[0011] In a preferred embodiment, based on finite element analysis, the key stress nodes and displacement areas of the steel structure frame are determined, sensors are arranged, and quaternion is used for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors. The specific process is as follows:

[0012] Before assembling the steel structure frame of the substation, determine the spatial distribution of key stress nodes and potential displacement points based on structural mechanics analysis;

[0013] The stress conditions of the steel structure frame are simulated through finite element analysis to determine the stress concentration area and the displacement area;

[0014] According to the simulation results, the displacement sensors are arranged and the initial position and initial attitude of the sensors are calibrated;

[0015] Use quaternion representation to calibrate the initial posture of the sensor, calculate the initial angle of the sensor to the reference plane, and correct the displacement data according to the initial angle;

[0016] After the sensor is installed and the initial posture is determined, initial calibration is performed and the Kalman filter algorithm is used to correct the sensor data in real time.

[0017] In a preferred embodiment, according to the sensor arrangement and calibration results, a data acquisition protocol is formulated, the sampling frequency and data format are set, and a phase-locked loop and timestamp technology are used to perform time synchronization and spatial alignment on multi-sensor data. The specific process is as follows:

[0018] Determine the initial angle and attitude adjustment parameters of the sensor setting according to the initial position and attitude data of the sensor;

[0019] Perform real-time data acquisition on the calibrated displacement sensor and set the data acquisition protocol, which includes setting the data sampling frequency, data format, transmission method and error correction mechanism;

[0020] The phase-locked loop and timestamp synchronization technology are used to perform timing synchronization and data alignment on the collected data.

[0021] In a preferred embodiment, based on the collected and synchronized sensor data, the stress-displacement state space is constructed, key variables are extracted for nonlinear mapping and dimensionality reduction, and the state equation is established. The specific process is as follows:

[0022] Perform displacement and stress correlation analysis on the data collected by the calibrated and set-up sensors;

[0023] Construct the stress-displacement state space and select key variables for constructing the stress-displacement state space, including different dimensional characteristics of displacement and stress;

[0024] In the construction of state space, the displacement and stress states of the structure in different directions are determined based on the linear displacement and stress components;

[0025] Determine the local stress concentration and energy accumulation phenomenon based on the strain energy density and local bending stress;

[0026] Perform nonlinear feature extraction and transformation on the selected state variables to map the variables to a new state space;

[0027] Nonlinear feature extraction uses the kernel function method to map high-dimensional features into a high-dimensional reproducing kernel Hilbert space;

[0028] After the high-dimensional state space is constructed, principal component analysis combined with singular value decomposition is used for compression and optimization, and the state equation describing the dynamic behavior of the system is established for the state space after dimensionality reduction and optimization.

[0029] In a preferred embodiment, the trajectory is analyzed and singular points are detected, the dynamic behavior of the structure is evaluated, and the analysis results are used to monitor the gradual locking process. The specific process is as follows:

[0030] Generate trajectories in the state space based on the constructed state space and determine the evolutionary behavior in the time dimension;

[0031] The trajectory in the state space includes the time variation of stress and displacement. The state equation is solved using the numerical integration method to obtain the trajectory curve that varies with time.

[0032] Analyze the state space trajectory, calculate the velocity and acceleration of the trajectory, and identify the points where the trajectory changes suddenly or drastically;

[0033] The singular points in the state space trajectory are detected and the sensitivity analysis of the singular points is performed. Based on the distribution of the singular points and the sensitivity analysis results, an early warning strategy is formulated.

[0034] In a preferred embodiment, during the step-by-step locking process, the torque and displacement data of each node are collected in real time, and combined with the displacement-stress correlation analysis results, the structural stress distribution is dynamically evaluated, and the torque application strategy is adjusted through feedback control to optimize the locking process. The specific process is as follows:

[0035] During the step-by-step locking process, the moment and displacement data of each locking node are collected in real time as the basic input for displacement-stress correlation analysis;

[0036] Perform dynamic correlation analysis of the moment-displacement curve, combine the real-time collected moment and displacement data with the displacement-stress correlation analysis results in the state space, and evaluate the impact of the locking process on the overall structural stress distribution;

[0037] Calculate the slope of the torque displacement curve to determine the stable state of the node behavior during the locking process;

[0038] Based on the real-time torque and displacement data during the locking process, combined with displacement-stress correlation analysis, the stress distribution of the structure is dynamically evaluated, potential danger areas are identified, and early warning signals are generated;

[0039] According to the results of dynamic stress evaluation, real-time feedback control is implemented to optimize the locking process.

[0040] In a preferred embodiment, according to the stress dynamic evaluation result, real-time feedback control is implemented to optimize the locking process. The specific steps are as follows:

[0041] Formulate feedback control strategies based on the magnitude and changing trend of stress deviation;

[0042] If the stress value of a node deviates from the reference stress value, the torque applied to the node is adjusted immediately, and the speed or sequence of torque application is adjusted through a differential feedback mechanism;

[0043] Based on the feedback control strategy, the torque applied to each locking node is automatically adjusted, including increasing or decreasing the applied torque, changing the order of force application, and adjusting the locking speed;

[0044] Each time the feedback control strategy is adjusted, new stress and displacement data are monitored and the next round of stress assessment and feedback control is carried out.

[0045] A substation frame assembly construction monitoring system, used to implement the above-mentioned substation frame assembly construction monitoring method, comprising:

[0046] The sensor calibration module determines the key stress nodes and displacement areas of the steel structure frame based on finite element analysis, arranges sensors, and uses quaternions for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors;

[0047] The data processing module is used to formulate data acquisition protocols, set sampling frequency and data format according to sensor layout and calibration results, and use phase-locked loop and time stamp technology to synchronize the timing and align the spatial data of multiple sensors;

[0048] The state analysis module is used to construct the stress-displacement state space based on the collected and synchronized sensor data, extract key variables for nonlinear mapping and dimensionality reduction, establish state equations, analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process;

[0049] The locking control module is used to collect the torque and displacement data of each node in real time during the gradual locking process, and dynamically evaluate the structural stress distribution in combination with the displacement-stress correlation analysis results, adjust the torque application strategy through feedback control, and optimize the locking process.

[0050] Technical effects and advantages of the present invention:

[0051] The present invention determines the key stress nodes and displacement areas of the steel structure frame through a finite element analysis method, arranges sensors, and combines quaternion attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors, providing reliable basic data for data acquisition. According to the sensor arrangement and calibration results, an accurate data acquisition protocol is formulated, the appropriate sampling frequency and data format are set, and the phase-locked loop and timestamp technology are used to ensure the timing synchronization and spatial alignment of multi-sensor data, thereby ensuring the consistency and accuracy of the data.

[0052] Based on the collected and synchronized high-quality sensor data, the present invention constructs the stress-displacement state space, extracts key variables, and optimizes the data dimension through nonlinear mapping and dimensionality reduction technology, and then establishes the state equation for analyzing the trajectory and detecting singular points to evaluate the dynamic behavior of the structure. The analysis results are applied to the real-time monitoring of the step-by-step locking process. By collecting the torque and displacement data of the nodes in real time and combining the results of the stress-displacement correlation analysis, the stress distribution of the structure is dynamically evaluated, and the torque application strategy is optimized through feedback control, thereby ensuring the stability of the locking process and the overall safety of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a flow chart of a method for monitoring substation frame assembly construction.

[0054] Figure 2 The present invention is a schematic structural diagram of a substation framework assembly construction monitoring system. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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 creative work are within the scope of protection of the present invention.

[0056] Example 1: Figure 1 As shown, a substation frame assembly construction monitoring method includes the following steps:

[0057] Based on finite element analysis, the key stress nodes and displacement areas of the steel structure frame are determined, sensors are arranged, and quaternion is used for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors;

[0058] According to the sensor layout and calibration results, formulate data acquisition protocols, set sampling frequency and data format, and use phase-locked loop and time stamp technology to synchronize the timing and align the multi-sensor data in space;

[0059] Based on the collected and synchronized sensor data, the stress-displacement state space is constructed, key variables are extracted for nonlinear mapping and dimensionality reduction, and state equations are established to analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process;

[0060] During the gradual locking process, the torque and displacement data of each node are collected in real time, and combined with the displacement-stress correlation analysis results, the structural stress distribution is dynamically evaluated, and the torque application strategy is adjusted through feedback control to optimize the locking process.

[0061] Before assembling the substation structure frame, the spatial distribution of key stress nodes and potential displacement points is determined based on structural mechanics analysis. The stress conditions of the steel structure frame are simulated by the Finite Element Analysis (FEA) method to determine the stress concentration areas and areas where displacement may occur. Displacement sensors are arranged according to the simulation results so that the stress and displacement changes of each key point can be monitored in real time.

[0062] Through finite element analysis, the displacement vector of each key node is calculated, and sensors are arranged in areas with large displacement to ensure that the sensors can accurately monitor the displacement changes in stress concentration areas. The stiffness matrix and force vector are obtained based on the material properties of the structure and the distribution of external forces. The displacement vector is obtained through inverse matrix operation to determine the sensor layout position;

[0063] The initial position and attitude of the sensor are determined. That is, after the sensor is located, the initial position and attitude calibration is performed to ensure that the displacement data measured accurately reflects the actual displacement. The initial attitude of the sensor is calibrated using the quaternion representation method, the initial angle of the sensor relative to the reference plane is calculated, and the displacement data is corrected according to this angle. The quaternion representation formula is as follows: , where q is the quaternion attitude representation of the sensor, is the rotation angle of the sensor relative to the reference plane, v is the unit vector of the sensor rotation axis;

[0064] By using quaternions to represent the attitude of the sensor, the universal lock problem in the Euler angle representation can be avoided. According to the initial attitude information of the sensor, the displacement data of the sensor during the actual measurement process can be corrected to ensure data accuracy. The advantage of quaternions is that they can concisely represent rotations in three-dimensional space and avoid the singularity of the conventional Euler angle representation.

[0065] After the sensor is installed and the initial posture is determined, the initial calibration is performed. The Kalman filter algorithm is used to correct the sensor data in real time to eliminate the random noise and system error of the sensor. The purpose of the initial calibration is to make the sensor output zero data when it is not affected by external forces. The formula is: ,in, is the estimated displacement after correction at time k, For the moment The predicted displacement value of is the Kalman gain, which represents the weight distribution between the measured data and the predicted data; is the measurement data at time k; H is the observation matrix, which represents the relationship between the measurement data and the displacement state. Kalman filtering continuously corrects the sensor's measurement data through iterative updates to reduce the impact of noise on displacement measurement. In the initial calibration stage, through multiple iterations, the sensor's measurement data in the absence of external force is adjusted to a value close to zero, thereby ensuring that the reference point for subsequent measurements is correct.

[0066] Starting from the sensor layout, the results of finite element analysis directly affect the sensor layout plan and its position. After the layout is completed, the initial position and attitude data of the sensor are used to determine its initial angle and attitude adjustment parameters. These data are then passed to the initial calibration process as the basic data for calibration. The calibrated sensor data will be directly used in the subsequent real-time monitoring and analysis stages.

[0067] Perform real-time data acquisition on the displacement sensor and set the data acquisition protocol. The data acquisition protocol includes setting the data sampling frequency, data format, transmission method and error correction mechanism to meet the needs of real-time monitoring and analysis. The specific steps are as follows:

[0068] The data acquisition protocol is designed to maximize the accuracy and transmission efficiency of sensor data, select an appropriate sampling frequency fs to capture structural dynamic changes, avoid undersampling or oversampling, and use a binary data format. To reduce transmission bandwidth requirements while ensuring high data fidelity, Indicates the nth binary position or bit. Bits together constitute a complete data unit measured by the sensor. According to the setting of the data format, bits can represent the numerical information output by the sensor, such as displacement, stress, etc., which are binary-coded to facilitate digital signal processing and transmission. For example, It can represent the displacement and stress collected by the sensor respectively;

[0069] Adopt time slot-based wireless transmission protocols, such as time division multiple access (TDMA), to reduce transmission delay and improve anti-interference capability. Error correction mechanisms introduce forward error correction (FEC) coding, such as convolutional coding, to detect and correct data errors that may occur during transmission. The formulation of data acquisition protocols ensures high-quality transmission of real-time data and provides a reliable data foundation for subsequent edge computing processing.

[0070] Perform time synchronization and data alignment on the collected data to achieve time synchronization and spatial alignment of multi-sensor data, ensure the consistency of each sensor data in time and space, and lay the foundation for accurate real-time analysis. Use phase-locked loop (PLL) and timestamp synchronization technology to achieve high-precision data alignment;

[0071] In a multi-sensor system, the timing synchronization of each sensor data is crucial. Phase-locked loop (PLL) technology is used to ensure that the sampling clock of each sensor is synchronized with the central clock to reduce the error caused by clock drift. A high-precision timestamp mechanism is introduced to mark the sampling time for each sensor data packet. Through interpolation algorithms such as spline interpolation, the data of different sensors are time-aligned to form a unified time series data set. , where the element Represents the synchronous data collected by the nth sensor at time t.

[0072] In terms of spatial alignment, based on the geometric position of the sensor layout, the rotation matrix R and translation vector T are used to transform the coordinates of the displacement data. That is, all data are represented in a unified reference coordinate system. The realization of timing synchronization and data alignment improves the consistency and comparability of multi-source data and enhances the accuracy of subsequent analysis.

[0073] The displacement and stress correlation analysis is performed on the data collected by the calibrated and set sensors. Step 1: First, construct the stress-displacement state space, select the key variables for constructing the stress-displacement state space, including the different dimensional characteristics of displacement and stress, and extract the core variables reflecting the dynamic behavior of the structure by analyzing the distribution of displacement and stress at different spatial positions and time points. These variables include not only linear displacement and stress components, but also nonlinear characteristics such as strain energy density, local bending stress, shear force, etc. The construction formula is: ,in, are the displacement components of the structure in the x, y, and z directions, respectively. are the stress components of the structure in the x, y, and z directions, respectively. is the strain energy density, which represents the energy concentration in the local area of ​​the structure; is the local bending stress, which indicates the bending strength of the structure at a specific node; the superscript T indicates transposition;

[0074] In the construction of the state space, linear displacement and stress components are the most basic variables, reflecting the displacement and stress states of the structure in different directions. However, in order to have a deeper understanding of the nonlinear behavior of the structure, it is also necessary to introduce strain energy density and local bending stress. These variables can capture the complex local stress concentration and energy accumulation phenomena, providing a more comprehensive description of the structural stability.

[0075] The selected state variables are subjected to nonlinear feature extraction and transformation, and the variables are mapped to a new state space to reveal deeper dynamic relationships. Nonlinear feature extraction uses the kernel function method to map high-dimensional features to a high-dimensional reproducing kernel Hilbert space (RKHS), thereby enhancing the expressive power of the state space. The formula is: ,in, is the state vector after mapping; It is a kernel function mapping, which is used to nonlinearly map the original variable V(t) to a high-dimensional state space; is the nth characteristic component after kernel function mapping;

[0076] The core of the nonlinear feature extraction step is to use the kernel function method to map the original physical variables to a high-dimensional reproducing kernel Hilbert space. Commonly used kernel functions include Gaussian kernel, radial basis function, etc. Through these kernel functions, nonlinear features can be effectively extracted, making the representation of the state space more comprehensive and accurate.

[0077] After the high-dimensional state space is constructed, it is compressed and optimized using a dimensionality reduction algorithm to retain the most important feature dimensions and reduce computational complexity. Common dimensionality reduction methods include principal component analysis (PCA) combined with singular value decomposition (SVD). By retaining the principal components with the strongest explanatory power, an optimized state space Y(t) is constructed. Principal component analysis combined with singular value decomposition can effectively identify and retain the most important feature dimensions, construct a simplified state space, and significantly reduce computational complexity while retaining the main information.

[0078] The state equation describing the system dynamic behavior is established by using the state space after dimensionality reduction optimization. This state equation will be used for subsequent stress-displacement correlation analysis and early warning mechanism, that is, through the nonlinear state space modeling method, the state vector is associated with the time derivative to construct a set of equations describing the system dynamic characteristics: , ,in, is the time derivative of the state vector, which represents the dynamic change of the system; A(t) is the dynamic matrix in the state space, which describes the mutual influence between states; B(t) is the external input vector, which reflects the influence of external force or other interference on the system; S(t) is the stress vector; C(t) is the output matrix, which describes the contribution of the state vector to the stress; D(t) is an additional term, which represents the nonlinear correction;

[0079] Step 2: State space trajectory analysis and singularity detection, including:

[0080] Based on the state space Y(t) constructed in the previous step, the trajectory in the state space is generated, and its evolution behavior in the time dimension is analyzed. The state space trajectory reflects the dynamic behavior of the structure during the construction process, including the time variation of stress and displacement. The state equation is solved using the numerical integration method to obtain the trajectory curve that varies with time, expressed as: , Y(t+Δt) is the state vector at time t+Δt, A(t) and B(t) are the dynamic matrix and external input vector of the state equation respectively, and Δt is the time step;

[0081] Solving the state equations through numerical integration methods (such as the Runge-Kutta method) can generate the trajectory of the system in the state space. The shape of the trajectory reflects the dynamic behavior of the system at different time points, such as whether there is periodic motion, oscillation or divergence trend. The evolution of the trajectory reveals the response of the structure under external loads and changes in internal stress distribution;

[0082] Conduct trajectory differential geometry characteristic analysis, differential geometry tools, analyze the state space trajectory, calculate the velocity (tangent vector) and acceleration (curvature vector) of the trajectory, and identify possible trajectory mutation points or drastic change points, which may indicate structural instability or abnormality. , , where ξ(t) is the velocity vector of the state-space trajectory, indicating the tangent direction of the trajectory; κ(t) is the acceleration vector of the state-space trajectory, indicating the curvature or bending degree of the trajectory;

[0083] By calculating the velocity vector and acceleration vector of the trajectory, the motion characteristics of the trajectory in the state space can be determined. The velocity vector describes the tangent direction of the trajectory, while the acceleration vector reflects the curvature of the trajectory, that is, the bending change of the trajectory. A sharp change in curvature usually corresponds to a significant change in the dynamic characteristics of the system, which may indicate that the system is about to experience instability or severe vibration.

[0084] Singular point detection and sensitivity analysis are performed. In the state space trajectory, the singular points in the trajectory are detected by analyzing the determinant value of the Jacobian matrix. Singular points usually indicate that the system has undergone significant dynamic changes or potential instability at this point. Sensitivity analysis of singular points is performed to evaluate their impact on overall stability. The formula is: , , where Δ(t) is the singular point detection index of the state space trajectory, which represents the determinant value of the Jacobian matrix based on the velocity vector; S(Δ) is the sensitivity index of the singular point, which represents the sensitivity of the singular point to the system parameter p;

[0085] The state space trajectory and detected singular points are visualized to intuitively display the dynamic evolution process and potential risk areas of the structure. Based on the distribution of singular points and sensitivity analysis results, corresponding early warning strategies are formulated, including early warning, real-time monitoring and emergency response measures to prevent structural instability or damage. , , where P(t) is the warning probability function based on trajectory singularity points and curvature changes; E(t) is the cumulative warning index, which represents the overall warning level at time t.

[0086] A set of differential equations is used to analyze the dynamic correlation between stress and displacement, revealing the mutual influence mechanism between the two in the time dimension. The differential coherence theory is used to analyze the dynamic correlation of stress and displacement and identify the key coupling modes and transmission paths.

[0087] Real-time monitoring and dynamic control in the assembly construction of the structural frame requires the monitoring of the gradual locking process and the aforementioned displacement stress correlation analysis. The gradual locking process is monitored and feedback is performed during the assembly construction of the substation structure. The torque change and corresponding displacement of each locking node are monitored in real time to ensure seamless connection between the monitoring process and the displacement stress analysis. The torque applied by the locking tool is measured by a torque sensor, and the displacement response of the node is monitored by a displacement sensor. The locking state of the node is analyzed through the torque displacement curve. The specific steps are as follows:

[0088] During the gradual tightening process, the torque and displacement data of each tightening node are collected in real time. The data will serve as the basic input for the subsequent displacement-stress correlation analysis. , ,in, is the torque of the i-th locking node at time t; is the initial torque, is the torque increment function; is the displacement of the i-th locking node at time t, is the displacement change rate;

[0089] Perform dynamic correlation analysis of the moment-displacement curve, combine the real-time collected moment and displacement data with the displacement-stress correlation analysis results in the state space, and evaluate the impact of the locking process on the overall structural stress distribution; ,in, is the slope of the moment-displacement curve of the ith node at time t;

[0090] By calculating the slope of the moment displacement curve, it is possible to determine whether the node behavior during the locking process is stable. Combining these slopes with the results of the displacement-stress correlation analysis, the dynamic impact of the locking process on the structural stress distribution can be identified. The locking process will not cause instability in the overall structure.

[0091] Based on the real-time torque and displacement data during the locking process, combined with displacement-stress correlation analysis, the stress distribution of the structure is dynamically evaluated, potential danger areas are identified, and early warning signals are generated. , where is the stress of the i-th node at time t; is the cross-sectional area of ​​the ith node; , where It is the sum of the stress of the entire structure. By converting the real-time monitored moment data into stress and calculating the overall stress sum, the overall stress condition of the structure can be evaluated.

[0092] According to the results of dynamic stress assessment, real-time feedback control is implemented to optimize the locking process. The specific steps are as follows:

[0093] During the gradual tightening process, the stress and displacement changes of each locking node are monitored in real time. Through sensor data, the stress value and displacement response of each node are continuously received and recorded to ensure that the force conditions during the tightening process can be dynamically tracked.

[0094] Analyze the data collected in real time and calculate the deviation between the actual stress value of each locking node and the preset reference stress value. The stress deviation is used to evaluate whether the current locking process has achieved the expected effect, that is, whether the structure has been locked to the stable state required by the design;

[0095] According to the size and change trend of stress deviation, a feedback control strategy is formulated. If the stress value of a node deviates from the reference stress value, the torque applied to the node is adjusted immediately to reduce the deviation. The strength of proportional feedback depends on the size of the deviation. The larger the deviation, the greater the adjustment strength. For nodes with faster stress changes, the speed or sequence of torque application is adjusted through the differential feedback mechanism to prevent excessive stress concentration or fluctuation. Differential feedback is mainly used to quickly respond to dynamic changes and smooth stress fluctuations.

[0096] Based on the feedback control strategy, the torque applied to each locking node is automatically adjusted. The specific operations include increasing or decreasing the applied torque, changing the order of force application, adjusting the locking speed, etc., to ensure that the stress of each node gradually approaches the reference value and the stress distribution of the overall structure remains uniform and stable;

[0097] During the locking process, the system continuously cycles through monitoring, analysis, feedback, and adjustment operations. After each adjustment, the system immediately monitors new stress and displacement data, and conducts the next round of stress evaluation and feedback control. Through this continuous optimization cycle, stress concentration points are gradually eliminated, local over-locking or under-locking is prevented, and the entire locking process is ensured to be stable and controllable.

[0098] On the basis of feedback control, the overall locking strategy is further optimized, including:

[0099] Locking sequence optimization: Based on historical data and real-time feedback, the locking sequence is adjusted to disperse stress and reduce stress concentration;

[0100] Optimization of torque application mode: adopt the method of applying force in stages or grades, gradually increase or decrease the force according to the real-time changes in stress distribution, and prevent local damage or deformation caused by excessive force at one time; optimization of locking speed: dynamically adjust the locking speed according to the results of dynamic stress assessment. At nodes with drastic stress changes, slow down the locking speed to give the structure enough time to buffer stress changes; at nodes with stable stress, speed up the locking speed to improve construction efficiency.

[0101] If during the locking process, the system detects that the stress of certain nodes exceeds the safety threshold or the stress change trend is abnormal, the early warning mechanism will be triggered immediately to remind construction personnel of possible risks. The system can also automatically execute emergency response measures, such as temporarily stopping the locking operation, reallocating the force application sequence, or enabling alternate locking paths to prevent structural instability or damage;

[0102] After the locking process is completed, the system generates a complete record of the locking process, including stress changes at each node, torque adjustment history, locking sequence and speed, and other information. By evaluating these data, the construction team can summarize the experience of the locking process and provide optimization reference for subsequent construction. When the stress of all nodes reaches or approaches the reference stress value and the stress distribution is uniform, the system confirms that the locking process has been completed and prompts the construction personnel to conduct a final structural inspection and confirmation to ensure that the entire architecture is stable and reliable.

[0103] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and requirements, and are usually adjusted and optimized based on factors such as historical data and real-time data.

[0104] The present invention determines the key stress nodes and displacement areas of the steel structure frame through a finite element analysis method, arranges sensors, and combines quaternion attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors, providing reliable basic data for data acquisition. According to the sensor arrangement and calibration results, an accurate data acquisition protocol is formulated, the appropriate sampling frequency and data format are set, and the phase-locked loop and timestamp technology are used to ensure the timing synchronization and spatial alignment of multi-sensor data, thereby ensuring the consistency and accuracy of the data.

[0105] Based on the collected and synchronized high-quality sensor data, the present invention constructs the stress-displacement state space, extracts key variables, and optimizes the data dimension through nonlinear mapping and dimensionality reduction technology, and then establishes the state equation for analyzing the trajectory and detecting singular points to evaluate the dynamic behavior of the structure. The analysis results are applied to the real-time monitoring of the step-by-step locking process. By collecting the torque and displacement data of the nodes in real time and combining the results of the stress-displacement correlation analysis, the stress distribution of the structure is dynamically evaluated, and the torque application strategy is optimized through feedback control, thereby ensuring the stability of the locking process and the overall safety of the structure.

[0106] Embodiment 2: A substation frame assembly construction monitoring system, such as Figure 2 As shown, it specifically includes the following modules:

[0107] The sensor calibration module determines the key stress nodes and displacement areas of the steel structure frame based on finite element analysis, arranges sensors, and uses quaternions for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors;

[0108] The data processing module is used to formulate data acquisition protocols, set sampling frequency and data format according to sensor layout and calibration results, and use phase-locked loop and time stamp technology to synchronize the timing and align the spatial data of multiple sensors;

[0109] The state analysis module is used to construct the stress-displacement state space based on the collected and synchronized sensor data, extract key variables for nonlinear mapping and dimensionality reduction, establish state equations, analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process;

[0110] The locking control module is used to collect the torque and displacement data of each node in real time during the gradual locking process, and dynamically evaluate the structural stress distribution in combination with the displacement-stress correlation analysis results, adjust the torque application strategy through feedback control, and optimize the locking process.

[0111] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0112] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0113] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for monitoring substation frame assembly construction, characterized in that: The steps include: Based on finite element analysis, the key stress nodes and displacement areas of the steel structure frame are determined, sensors are arranged, and quaternion is used for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors; According to the sensor layout and calibration results, formulate data acquisition protocols, set sampling frequency and data format, and use phase-locked loop and time stamp technology to synchronize the timing and align the multi-sensor data in space; Based on the collected and synchronized sensor data, the stress-displacement state space is constructed, key variables are extracted for nonlinear mapping and dimensionality reduction, and state equations are established to analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process; During the gradual locking process, the torque and displacement data of each node are collected in real time, and combined with the displacement-stress correlation analysis results, the structural stress distribution is dynamically evaluated, and the torque application strategy is adjusted through feedback control to optimize the locking process.

2. A method for monitoring substation frame assembly construction according to claim 1, characterized in that: Based on finite element analysis, the key stress nodes and displacement areas of the steel structure frame are determined, sensors are arranged, and quaternion is used for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors. The specific process is as follows: Before assembling the steel structure frame of the substation, determine the spatial distribution of key stress nodes and potential displacement points based on structural mechanics analysis; The stress conditions of the steel structure frame are simulated through finite element analysis to determine the stress concentration area and the displacement area; According to the simulation results, the displacement sensors are arranged and the initial position and initial attitude of the sensors are calibrated; Use quaternion representation to calibrate the initial posture of the sensor, calculate the initial angle of the sensor to the reference plane, and correct the displacement data according to the initial angle; After the sensor is installed and the initial posture is determined, initial calibration is performed and the Kalman filter algorithm is used to correct the sensor data in real time.

3. A method for monitoring substation frame assembly construction according to claim 1, characterized in that: According to the sensor layout and calibration results, formulate the data acquisition protocol, set the sampling frequency and data format, use the phase-locked loop and timestamp technology to synchronize the multi-sensor data and align the time sequence and space. The specific process is as follows: Determine the initial angle and attitude adjustment parameters of the sensor setting according to the initial position and attitude data of the sensor; Perform real-time data acquisition on the calibrated displacement sensor and set the data acquisition protocol, which includes setting the data sampling frequency, data format, transmission method and error correction mechanism; The phase-locked loop and timestamp synchronization technology are used to perform timing synchronization and data alignment on the collected data.

4. A method for monitoring substation frame assembly construction according to claim 1, characterized in that: According to the collected and synchronized sensor data, the stress-displacement state space is constructed, key variables are extracted for nonlinear mapping and dimensionality reduction, and the state equation is established. The specific process is as follows: Perform displacement and stress correlation analysis on the data collected by the calibrated and set-up sensors; Construct the stress-displacement state space and select key variables for constructing the stress-displacement state space, including different dimensional characteristics of displacement and stress; In the construction of state space, the displacement and stress states of the structure in different directions are determined based on the linear displacement and stress components; Determine the local stress concentration and energy accumulation phenomenon based on the strain energy density and local bending stress; Perform nonlinear feature extraction and transformation on the selected state variables to map the variables to a new state space; Nonlinear feature extraction uses the kernel function method to map high-dimensional features into a high-dimensional reproducing kernel Hilbert space; After the high-dimensional state space is constructed, principal component analysis combined with singular value decomposition is used for compression and optimization, and the state equation describing the dynamic behavior of the system is established for the state space after dimensionality reduction and optimization.

5. A method for monitoring substation frame assembly construction according to claim 4, characterized in that: Analyze the trajectory and detect singular points to evaluate the dynamic behavior of the structure, and use the analysis results to monitor the gradual locking process. The specific process is as follows: Generate trajectories in the state space based on the constructed state space and determine the evolutionary behavior in the time dimension; The trajectory in the state space includes the time variation of stress and displacement. The state equation is solved using the numerical integration method to obtain the trajectory curve that varies with time. Analyze the state space trajectory, calculate the velocity and acceleration of the trajectory, and identify the points where the trajectory changes suddenly or drastically; The singular points in the state space trajectory are detected and the sensitivity analysis of the singular points is performed. Based on the distribution of the singular points and the sensitivity analysis results, an early warning strategy is formulated.

6. A method for monitoring substation frame assembly construction according to claim 5, characterized in that: During the gradual locking process, the torque and displacement data of each node are collected in real time, and combined with the displacement-stress correlation analysis results, the structural stress distribution is dynamically evaluated, and the torque application strategy is adjusted through feedback control to optimize the locking process. The specific process is as follows: During the step-by-step locking process, the moment and displacement data of each locking node are collected in real time as the basic input for displacement-stress correlation analysis; Perform dynamic correlation analysis of the moment-displacement curve, combine the real-time collected moment and displacement data with the displacement-stress correlation analysis results in the state space, and evaluate the impact of the locking process on the overall structural stress distribution; Calculate the slope of the torque displacement curve to determine the stable state of the node behavior during the locking process; Based on the real-time torque and displacement data during the locking process, combined with displacement-stress correlation analysis, the stress distribution of the structure is dynamically evaluated, potential danger areas are identified, and early warning signals are generated; According to the results of dynamic stress evaluation, real-time feedback control is implemented to optimize the locking process.

7. A method for monitoring substation frame assembly construction according to claim 6, characterized in that: According to the results of dynamic stress assessment, real-time feedback control is implemented to optimize the locking process. The specific steps are as follows: Formulate feedback control strategies based on the magnitude and changing trend of stress deviation; If the stress value of a node deviates from the reference stress value, the torque applied to the node is adjusted immediately, and the speed or sequence of torque application is adjusted through a differential feedback mechanism; Based on the feedback control strategy, the torque applied to each locking node is automatically adjusted, including increasing or decreasing the applied torque, changing the order of force application, and adjusting the locking speed; Each time the feedback control strategy is adjusted, new stress and displacement data are monitored and the next round of stress assessment and feedback control is carried out.

8. A substation frame assembly construction monitoring system, used to implement a substation frame assembly construction monitoring method according to any one of claims 1 to 7, characterized in that: include: The sensor calibration module determines the key stress nodes and displacement areas of the steel structure frame based on finite element analysis, arranges sensors, and uses quaternions for attitude calibration and Kalman filtering to correct the attitude and measurement errors of the sensors; The data processing module is used to formulate data acquisition protocols, set sampling frequency and data format according to sensor layout and calibration results, and use phase-locked loop and time stamp technology to synchronize the timing and align the multi-sensor data in space; The state analysis module is used to construct the stress-displacement state space based on the collected and synchronized sensor data, extract key variables for nonlinear mapping and dimensionality reduction, establish state equations, analyze trajectories and detect singular points, evaluate the dynamic behavior of the structure, and use the analysis results for monitoring the step-by-step locking process; The locking control module is used to collect the torque and displacement data of each node in real time during the gradual locking process, and dynamically evaluate the structural stress distribution in combination with the displacement-stress correlation analysis results, adjust the torque application strategy through feedback control, and optimize the locking process.

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