Control system for stabilizing equipment foundation embedded part mounting device

Abstract

The invention relates to a control system for stabilizing an equipment foundation embedded part mounting device. Comprising the following steps that a dynamic sensing network is arranged on an embedded part mounting device, comprises a tilt angle sensor, a pressure sensor and a displacement sensor and is used for monitoring the dynamic balance state of the embedded part in real time and obtaining monitoring data; the monitoring data are integrated through the central processing unit to form real-time three-dimensional attitude and gravity center position feedback of the equipment foundation; constructing a posture adjustment algorithm based on real-time monitoring data, simulating mechanical changes in the installation process of the embedded part by utilizing a finite element dynamic analysis model, and calculating an adjustment angle and a position deviation; the inclination angle and height of the mounting platform are finely adjusted through an electric servo device, and self-adaptive posture regulation and control of the embedded part are controlled; a partition pressure control technology is adopted, the bottom of the mounting device is divided into a plurality of mechanical unit areas, and the supporting force of each area is adjusted in real time through a distributed pressure regulator; and dynamic balance monitoring data is combined.

Description

Technical Field

[0001] The invention relates to the control of an embedded component installation device, in particular to a control system of an embedded component installation device for a stable equipment foundation. Background Art

[0002] The Chinese invention patent in the comparative document, patent number 2022113402125, describes a method for installing building buckling restraint support embedded parts. Its core technical point is to achieve the installation of embedded parts by optimizing the position model, using positioning devices and slide rail installation devices, and combining BIM technology for simulation and inspection. Although this method has certain advantages, it still has obvious shortcomings and disadvantages when facing a wider range of equipment foundation embedded parts installation scenarios, which are mainly reflected in the following aspects:

[0003] First, the scheme focuses on the installation and positioning of building components, and lacks adaptability to the complex construction scenarios of equipment foundation embedded parts. In actual engineering, equipment foundation embedded parts are usually subjected to multi-directional mechanical effects, and their posture and mechanical stability need to be ensured under complex foundation conditions. The comparative document only completes the installation by optimizing the relative position of column reinforcement and embedded parts and using a slide rail device, but does not solve the dynamic adjustment problem of embedded parts in a multi-dimensional force environment, and cannot cope with complex construction conditions such as uneven foundation and vibration impact. For example, when there is local settlement of the foundation or vibration caused by construction machinery, this method lacks real-time monitoring and adjustment functions, resulting in tilting, deformation or increased installation errors of embedded parts, which directly affects the construction quality and the safety of subsequent equipment operation. Secondly, the installation accuracy of this scheme is highly dependent on the optimization of the static model and the prior simulation inspection, without considering the real-time dynamic response capability during the construction process. Although BIM technology is used to detect the collision between column reinforcement and embedded parts and optimize them in the model stage, this technology can only solve static position conflicts, and cannot solve installation errors caused by external force interference or other unforeseen factors during construction. In addition, the slide rail installation device can only realize the transportation and positioning of embedded parts, and lacks dynamic correction function. In actual construction, due to factors such as uneven soil density, foundation settlement or on-site operation errors, embedded parts deviate from the target position during installation, and the method of comparative documents lacks the ability to monitor the inclination, displacement and pressure distribution in real time, and does not propose targeted corrective measures.

[0004] Thirdly, the solution described in the comparative document does not propose an effective control strategy for the complexity of the construction environment. For example, in a construction site with a sloping foundation, soft soil environment or mechanical vibration, the installation of embedded parts faces not only positioning problems, but also challenges in supporting force balance and posture stability. The column reinforcement positioning device and slide rail installation device in the comparative document are both static structures that cannot dynamically adapt to changes in the external environment. This installation solution that lacks dynamic response causes uneven force on the embedded parts, further causing foundation instability or reduced bearing capacity. In addition, the optimization method of the comparative document is limited to model adjustment of the installation position, and does not consider the potential impact of external vibration and impact force on the installation quality during construction, which is unacceptable for scenarios with high precision requirements such as embedded parts for mechanical equipment foundations.

[0005] At the same time, this method also has obvious defects in mechanical control and quality management. The comparative document does not involve pressure monitoring and regulation mechanisms, and ignores the problem of balanced distribution of supporting forces in multi-unit areas. In the installation of large-scale equipment foundations, the force uniformity of each supporting point directly affects the long-term stability of embedded parts. The method of the comparative document cannot monitor and optimize the distribution of supporting forces in real time, especially when the foundation at the bottom of the supporting device is uneven or the local pressure is too large. This lack of mechanical regulation leads to gradual displacement or damage of the foundation during equipment operation. In addition, the solution of the comparative document lacks a closed-loop control mechanism, and cannot provide feedback and adjust the installation process based on real-time monitoring data, nor can it generate mechanical distribution data or quality reports during the construction process. This not only increases the risk of construction, but also lays hidden dangers for subsequent quality management and operation and maintenance. Summary of the invention

[0006] The object of the present invention is to provide a control system for a device for installing embedded parts of a stable equipment foundation, thereby solving some of the drawbacks and deficiencies pointed out in the background technology.

[0007] The present invention solves the above-mentioned technical problems by adopting the following technical solution: comprising the following steps:

[0008] S1. Use multi-dimensional dynamic balance monitoring module:

[0009] S1.1. A dynamic sensor network is arranged on the embedded component installation device, including an inclination sensor, a pressure sensor and a displacement sensor, to monitor the dynamic balance state of the embedded component in real time and obtain monitoring data;

[0010] S1.2, the monitoring data is integrated by the central processing unit to form real-time three-dimensional posture and center of gravity position feedback of the equipment;

[0011] S2. Development of attitude adaptive control algorithm:

[0012] S2.1. Construct a posture adjustment algorithm based on real-time monitoring data, use the finite element dynamic analysis model to simulate the mechanical changes during the installation of embedded parts, and calculate the adjustment angle and position deviation;

[0013] S2.2, fine-tune the inclination angle and height of the installation platform through the electric servo device to control the adaptive posture control of the embedded parts;

[0014] S3, Pressure balance distribution and adjustment mechanism:

[0015] S3.1. Use zoned pressure control technology to divide the bottom of the installation device into several mechanical unit areas, and use distributed pressure regulators to adjust the support force of each area in real time;

[0016] S3.2, combined with dynamic balance monitoring data, calculate the pressure difference in real time and compensate through closed-loop feedback;

[0017] S4, vibration suppression and impact buffer control module:

[0018] S4.1. Design a vibration suppressor and impact buffer at the bottom of the installation device, analyze the vibration frequency and impact intensity in real time through an intelligent control algorithm, and dynamically adjust the buffer strength;

[0019] S4.2. Combine multi-dimensional dynamic balancing data to identify the vibration source during installation and suppress it in time;

[0020] S5, Mechanical state closed-loop control:

[0021] Construct a closed-loop feedback system for the mechanical state to compare and analyze the real-time collected pressure, displacement and vibration data with the preset installation target parameters; when deviating from the target parameters, activate the control strategy and make corrections by adjusting the pressure distribution, inclination angle and depth parameters.

[0022] Furthermore, the posture adaptive control algorithm development method includes:

[0023] By laying out inclination sensors, pressure sensors and displacement sensors on the base, lever arm and support points of the installation device, the three-dimensional inclination, support force and displacement data of the embedded parts during installation are collected in real time; the adaptive Kalman filter algorithm is used to denoise the noise signal, and the filtered data is described by a dynamic weight function:

[0024]

[0025] Where: X f (t) represents the data signal after filtering; X(t) is the original signal collected in real time; N(t) is the noise interference; is the time-dependent dynamic weight function; τ nis the filtering time constant, which indicates the attenuation characteristics of the data signal in the time dimension; Δτ is the time interval of data acquisition, which reflects the time accuracy of signal changes; exp(-Δτ / τ n ) is the time decay factor.

[0026] Furthermore, the posture adaptive control algorithm development method includes:

[0027] Based on real-time monitoring data, a mechanical finite element dynamic analysis model is constructed to simulate the posture and force of embedded parts during installation. The finite element model defines boundary conditions, including the friction factor between the base and the ground and mechanical parameters including vertical force and lateral force loading. The mechanical behavior is simulated by nonlinear dynamic equilibrium equations:

[0028]

[0029] Among them: M is the mass matrix, which describes the mass distribution characteristics of the installation device and embedded parts; C is the damping matrix, which reflects the energy dissipation of the installation device to vibration or dynamic impact; K is the stiffness matrix, which is used to describe the resistance of the structure to external forces; is the acceleration vector, which represents the motion response of the embedded part under the external force F(t); is the velocity vector, which indicates the dynamic rate of change of the installation device; u(t) is the displacement vector, which indicates the spatial position of the embedded part; F(t) is the external force in the form of a time function, including vertical force and lateral force, which drives the generation of dynamic response.

[0030] Furthermore, the posture adaptive control algorithm development method includes:

[0031] Based on the results of finite element analysis, a real-time posture adjustment algorithm is introduced to correct the inclination deviation and position deviation during the installation process; the adjustment path is calculated by optimizing the control strategy, and the angle and position correction amount are adjusted; the calculation formula of the adjustment amount P(t) is:

[0032]

[0033] Where: P(t) is the dynamic adjustment amount, which indicates the angle or displacement that the servo drive system needs to adjust; Δx = x target x current Indicates the difference between the target position and the current actual position, used to correct the horizontal offset of the installation; Δθ = θ target θ current It represents the difference between the target tilt angle and the current tilt angle, which is used to correct the inclination of the installation; α is the adjustment gain coefficient, which is used to control the adjustment strength and response speed; β is the attitude error weight factor, which represents the influence of the tilt angle deviation on the adjustment amount; t0 and t are the initial time and the current time respectively, and the adjustment process is dynamically integrated to the current time; exp(-β·Δθ2 ) is the exponential decay function of the inclination deviation.

[0034] Furthermore, the pressure balance distribution and adjustment mechanism includes:

[0035] The bottom of the installation device is divided into several mechanical unit areas. After the area division, a pressure sensor network is deployed to collect the support force data F in real time in each unit area. i (t), and integrate the support forces of all unit areas to form the support force distribution function P(x,y,t):

[0036]

[0037] Where: P(x,y,t) is the distribution function of the bottom support force, which represents the contribution of the support force of each area at a specific time t to the overall system; F i (t) is the support force value of the i-th mechanical unit area at time t; φ i (x, y) is the spatial influence function of the i-th unit area, which represents the force range and distribution characteristics of area i; N is the total number of mechanical unit areas.

[0038] Furthermore, the pressure balance distribution and adjustment mechanism includes:

[0039] A distributed pressure controller is installed in each mechanical unit area to control the pressure output through the servo drive unit; the distributed controller combines the target support force The actual support force F i (t), calculate the pressure error ΔF i (t); the error represents the difference between the current support force and the target support force, which is described by the following formula:

[0040]

[0041] Where: ΔF i (t) is the pressure error of the i-th unit area at time t; is the target support force of the ith unit area at time t; F i (t) is the actual support force of the ith unit area at time t.

[0042] Furthermore, the pressure balance distribution and adjustment mechanism includes:

[0043] When the pressure error exceeds the preset threshold, the pressure regulator will generate an adjustment value A based on the real-time error. i (t); then a dynamic adjustment formula based on integral and exponential weighting is adopted:

[0044]

[0045] Among them: A i (t) is the dynamic adjustment amount of the ith unit area at time t; γ is the adjustment gain coefficient, which controls the sensitivity of the adjustment output; t0 and t represent the initial time and current time of the adjustment process respectively; τ is the integral time variable, which represents the cumulative effect of the adjustment amount over time; β is the error weight factor, which is used to suppress the interference of excessive errors on the system; is the error weighting factor.

[0046] Furthermore, the pressure balance distribution and adjustment mechanism includes:

[0047] Combined with the real-time inclination data θ(t) of the installation device, the overall center of gravity position (x c ,y c ,z c ) and external environmental parameters including the unevenness of the foundation, the zoned pressure control system dynamically optimizes the support force distribution of each unit area; the support force distribution P(x,y,t) is adjusted through the following dynamic equilibrium equation:

[0048] ∫ Ω [P(x,y,t)·h(x,y)-ρgh(x,y)]dxdy=0

[0049] Where: Ω is the total area of ​​the bottom of the support device; h(x, y) is the height function of the foundation, which represents the height distribution of the foundation in the x, y coordinates; ρgh(x, y) is the reaction force density of the foundation, which is determined by combining the material density ρ and the gravitational acceleration g.

[0050] The control system of the embedded parts installation device for the stabilizing equipment foundation of the present invention significantly improves the accuracy, stability and adaptability of the embedded parts installation by integrating modules such as multi-dimensional dynamic monitoring, attitude adaptive control, pressure balance distribution and adjustment, and vibration suppression, and has the following beneficial effects:

[0051] The system combines real-time sensor data and advanced posture adjustment algorithms to detect and correct the inclination deviation and position deviation of embedded parts in real time during installation. By introducing the finite element dynamic analysis model, the system simulates and predicts the mechanical imbalance that occurs during installation, quickly generates an optimized adjustment path, ensures that the posture of embedded parts always meets the design requirements, and greatly improves installation accuracy.

[0052] Through the pressure balance distribution and adjustment mechanism, a distributed pressure regulator is used to control the bottom of the installation device in different zones. The system monitors the support force of each unit area in real time, and optimizes the support force distribution through dynamic balance equations to eliminate pressure differences. Even in uneven foundations or complex construction environments, the system can ensure mechanical balance and avoid tilting or instability of the installation device.

[0053] The system combines inclination data, center of gravity position and external environmental parameters (such as foundation height and density distribution) for comprehensive analysis, and can adapt to non-uniform foundations or dynamic load changes. By dynamically optimizing the support force distribution and adjusting the posture in real time, the system can still maintain the stability and safety of embedded parts installation under complex construction conditions such as inclined foundations and soft soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention is a control system flow chart of the embedded parts installation device for the stabilizing equipment foundation.

[0055] Figure 2 The present invention is a flow chart of the posture adaptive control algorithm development method.

[0056] Figure 3 It is a system flow chart of the pressure balancing distribution and adjustment mechanism of the present invention. DETAILED DESCRIPTION

[0057] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0058] Combined with Figure 1 Flowchart, control system of embedded parts installation device for stable equipment foundation: The first step of the system is to adopt a multi-dimensional dynamic balance monitoring module, that is, to lay out a dynamic sensor network on the embedded parts installation device, including inclination sensors, pressure sensors and displacement sensors, which are used to monitor the dynamic balance state of embedded parts in real time and obtain monitoring data. Specifically, the inclination sensor can detect the inclination angle of the embedded parts during the installation process to ensure that it remains within the predetermined posture range; the pressure sensor is used to measure the pressure in all directions on the embedded parts to help evaluate the force uniformity and stability; the displacement sensor monitors the spatial position change of the embedded parts to prevent position offset. The dynamic sensor network composed of these sensors can capture the real-time state of the embedded parts during the installation process and provide comprehensive mechanical and kinematic data for the system. The monitoring data is integrated through the central processing unit, which is responsible for collecting and processing data from various sensors and using data fusion technology to comprehensively analyze multi-source information. Through efficient algorithm operation, the system can generate real-time three-dimensional posture and center of gravity position feedback of the equipment foundation, accurately reflecting the spatial positioning and force conditions of the embedded parts.

[0059] The second step of the system is to develop an attitude adaptive control algorithm. First, the attitude adjustment algorithm is constructed based on real-time monitoring data. The algorithm is based on the inclination angle, pressure and displacement data provided by the dynamic sensor network. The deviation between the current attitude of the embedded parts and the target attitude is analyzed through efficient calculation to ensure that the adjustment has a scientific basis. In order to improve the control accuracy and dynamic adaptability, the system introduces a finite element dynamic analysis model. By simulating the mechanical changes during the installation of embedded parts, including key parameters such as load distribution, stress concentration and support stability, the adjustment angle and position deviation are dynamically calculated. The finite element model can update input parameters such as sensor data and environmental characteristics in real time, and generate detailed stress distribution and displacement prediction results for determining the adjustment path and optimization strategy. The adjustment data output by the attitude control algorithm is directly used to drive the electric servo device, which fine-tunes the inclination angle and height of the installation platform. Specifically, the servo device receives the adjustment signal calculated by the system and accurately adjusts the height change of the support point or the inclination of the platform to achieve adaptive attitude control of the embedded parts. The entire control process is achieved through closed-loop feedback. When the control causes the posture to change, the sensor network will update the new status data in real time, and the system will recalculate and optimize the adjustment amount until the deviation converges within the allowable range.

[0060] The third step of the system is to realize the pressure balance distribution and adjustment mechanism. By adopting the zone pressure control technology, the bottom of the installation device is divided into several mechanical unit areas. The division of each area is based on the results of mechanical analysis and structural design to ensure that each unit can independently sense the external load and bear the corresponding mechanical effect. Inside each unit area, a distributed pressure regulator is arranged, which can adjust the support force in the area in real time and control the output pressure through the servo drive unit to meet the mechanical requirements and design goals of the system. In order to achieve dynamic pressure adjustment, the system combines real-time dynamic balance monitoring data with information such as pressure, inclination and displacement collected by the sensor network to conduct a comprehensive analysis of the support force of each unit area. The central processing unit compares the current pressure distribution data with the target distribution parameters and calculates the pressure difference of each area. Specifically, the pressure difference reflects the degree of deviation between the current support force and the target support force, which is the core basis of pressure regulation. When the pressure difference exceeds the threshold allowed by the system, the distributed pressure regulator dynamically compensates for the deviation through a closed-loop feedback mechanism. The closed-loop control ensures the real-time and stability of the regulation. Through repeated iterative pressure adjustments, the system gradually balances the supporting force in each area, thereby optimizing the mechanical state of the entire device and avoiding structural tilt or instability caused by uneven distribution of supporting force.

[0061] The fourth step of the system is to implement the vibration suppression and impact buffering control module, by designing a vibration suppressor and impact buffering device at the bottom of the installation device to cope with the vibration and impact effects that occur during the construction process. These devices use intelligent control algorithms to analyze the vibration frequency, impact intensity and dynamic balance state data provided by the system sensor network in real time, thereby dynamically adjusting the buffering strength. Specifically, the vibration suppressor mainly targets high-frequency vibrations, and intervenes in the vibration transmission path through the built-in damping adjustment mechanism to reduce the degree of vibration energy transmitted to the main body of the installation device; while the impact buffering device focuses on alleviating the transient stress concentration caused by large impact forces to avoid structural damage to embedded parts and installation devices. The intelligent control algorithm calculates the vibration frequency characteristics and impact intensity thresholds in real time through fast Fourier transform (FFT) and time domain analysis, identifies the location and nature of the vibration source, and predicts its impact in combination with multi-dimensional dynamic balance data. When the system detects abnormal vibration or impact behavior, the control module will respond quickly and start the corresponding suppression or buffering plan. For example, for vibration, the system weakens vibration transmission by increasing the stiffness of the damper or adjusting the elastic properties of the support points; for impact, the buffer device disperses stress concentration by changing the dynamic stiffness of the built-in material or enabling additional buffering mechanisms.

[0062] The fifth step of the system is to build a closed-loop control module for the mechanical state, and ensure the stability and accuracy of the embedded parts installation process through real-time data collection and dynamic feedback control. Specifically, the system first relies on the dynamic sensor network to collect real-time bottom pressure, embedded parts displacement and vibration data in the construction environment, which serve as the mechanical feedback basis for the current installation state. Subsequently, the central processing unit compares and analyzes these real-time data with the system's preset installation target parameters, which include key indicators such as the uniform distribution of pressure, the position and depth accuracy of embedded parts, and vibration interference that should be avoided during installation. Through error analysis, the system can accurately identify the deviation between the current state and the target parameters and convert it into a dynamic adjustment signal. When the error from the target parameter exceeds the allowable range, the system immediately starts the preset control strategy and implements dynamic correction through a closed-loop feedback mechanism. The control strategy covers three core aspects: first, adjusting the pressure distribution. Based on the real-time pressure error data, the system uses the distributed pressure controller to dynamically compensate the support force of each mechanical unit to make the pressure uniform again; second, optimizing the inclination angle. The system uses the servo drive device to accurately fine-tune the inclination angle of the installation platform to ensure that the embedded parts remain within the designed posture range; third, correcting the installation depth. Combined with the buried depth deviation data fed back by the displacement sensor, the propulsion speed and direction of the platform are adjusted to make the embedded parts reach the ideal depth position. In the entire closed-loop control process, real-time data collection, error analysis and control actions form a dynamic closed loop, and the control results are fed back to the sensor network again, forming a new round of error comparison and correction iterations until the system parameters are fully converged to the target range.

[0063] Embodiment 1:

[0064] Combined with Figure 2 Flowchart, a construction site is installing embedded parts for large equipment. The construction environment is complex, the foundation is slightly tilted and accompanied by periodic mechanical vibration. In order to ensure the installation accuracy and stability of the embedded parts, the construction team introduced the control system of the stable equipment foundation embedded parts installation device of the present invention, and dynamically adjusted the real-time data during the installation process based on the attitude adaptive control algorithm.

[0065] In actual application, the construction team installed inclination sensors, pressure sensors and displacement sensors on the base, lever arm and support points of the installation device to collect the three-dimensional inclination, support force and displacement data of the embedded parts in real time. The acquisition frequency is 100 times per second, and the original data stream X(t) output by the sensor network includes the following characteristics: the inclination data fluctuates between -1.5° and +1.5°, the support force monitored by the pressure sensor is 1800N±50N in each unit area, and the error recorded by the displacement sensor is within the range of ±5mm. However, the mechanical vibration noise N(t) accompanying the site interferes with the data accuracy, causing high-frequency fluctuations in the original signal.

[0066] In order to improve data quality, the system uses an adaptive Kalman filter algorithm to denoise the noise signal N(t). The filtered data X f (t) is described by the dynamic weight function and the time decay factor. The specific formula is:

[0067]

[0068] In this formula:

[0069] X f (t) represents the filtered data signal, which is used for subsequent posture adjustment.

[0070] X(t) is the original signal (e.g., inclination, pressure, and displacement) collected by the sensor in real time.

[0071] N(t) is the noise interference signal, and the noise amplitude caused by vibration is 0.1 to 0.5.

[0072] It is a time-dependent dynamic weight function with a value range of 0.8 to 1.0, which is used to assign weights to different sensors.

[0073] τ n is the filtering time constant, ranging from 0.05 seconds to 0.2 seconds, which represents the attenuation characteristics of the signal in the time dimension.

[0074] Δτ is the data acquisition time interval, which is 0.01 seconds here, reflecting the time accuracy of signal sampling.

[0075] Time decay factor exp(-Δτ / τ n )Short-term dynamic response of the control signal.

[0076] In the actual filtering process, the tilt angle signal X(t) is set to 1.2° and the noise N(t) is set to 0.4. τ n =0.1s, Δτ=0.01s. Calculated by filtering formula:

[0077]

[0078] Simplified to:

[0079] X f (t)=0.9·0.8·exp(-0.1)≈0.648·0.9048≈0.586 inclination

[0080] This filtered value greatly reduces the impact of noise compared to the original signal, providing more accurate posture data for subsequent calculations.

[0081] Next, the system uses the filtered three-dimensional inclination, support force and displacement data to dynamically calculate the deviation between the current posture and the target posture through the posture adjustment algorithm. For example, if the target inclination is 0° and the current filtered data is 0.586°, the inclination deviation that needs to be adjusted is -0.586°. Based on this deviation, the electric servo device starts the fine-tuning mechanism to reduce the tilt by adjusting the height of the base support point. The servo device completes each adjustment within 0.1 seconds and feeds back the new status data in real time to enter the next round of iteration.

[0082] During the complete 10-second installation process, after about 1,000 iterations, the system adjusted the inclination angle to within the target range of ±0.01°, the support force deviation to within 5N, and the displacement error to within ±1mm. Ultimately, high-precision positioning of embedded parts installation was achieved, and the stability of the entire process was ensured through a dynamic closed-loop feedback mechanism.

[0083] The finite element dynamic analysis model based on real-time monitoring data is used to simulate the posture and force of the embedded parts during the installation process. Specifically, the real-time monitoring data of the installation device include the inclination angle θ(t), the support force F i These data are transmitted to the central processing unit through the dynamic sensor network and used to establish the mechanical model and predict the dynamic behavior of embedded parts under complex construction conditions.

[0084] First, the finite element model is initialized through the geometric structure and material properties of the installation device and embedded parts. The mass matrix M is defined as a mathematical description of the mass distribution characteristics of the embedded parts. Here, the total mass of the embedded parts is set to 500kg, evenly distributed on the installation platform. The mass matrix M = 500kg·I, where I is the unit matrix. The damping matrix C reflects the energy dissipation of the installation device to vibration or dynamic impact. The value range is C = 100Ns / m to 200Ns / m, and the actual setting is C = 150Ns / m. The stiffness matrix K is used to describe the structure's resistance to external forces. The total stiffness of the installation device is set to K = 1.2×10 5 N / m.

[0085] In a dynamic environment, the external force F(t) consists of vertical force and lateral force. The vertical force is the total weight F of the embedded parts and the installation device. v =500kg·9.8m / s 2 =4900N, the lateral force is caused by environmental vibration and is set to a periodic change F h (t) = 300sin(2πt)N. In the finite element model, the mechanical behavior of the system is simulated by the following nonlinear dynamic equilibrium equation:

[0086]

[0087] in:

[0088] M = 500 kg·I, describing the mass distribution characteristics of the embedded parts; C = 150 Ns / m, the damping matrix of the system; K = 1.2 × 10 5 N / m, is the stiffness matrix; is the acceleration vector of the embedded part; is the velocity vector; u(t) is the displacement vector; F(t) = F v +F h (t), is the combination of vertical and lateral forces.

[0089] Through numerical solution methods (such as Newmark-beta method), the system calculates the displacement response u(t), velocity and acceleration Under the initial conditions, the displacement u(0) of the embedded part is set to 0, the speed The dynamic response result 1 second after the external force is applied is:

[0090] Vertical displacement u y (1) = 0.0041m;

[0091] Vertical speed

[0092] Transverse displacement u x (1) = 0.0023 m.

[0093] The analysis results show that the displacement deviation of the embedded parts in the lateral and vertical directions are within the acceptable range. By adjusting the damping coefficient C and stiffness matrix K of the system, the installation team further optimized the dynamic response characteristics and controlled the lateral deviation caused by vibration within ±0.001m. Combined with the real-time adjustment function, the electric servo device fine-tunes the support point height according to the displacement deviation data, thereby ensuring that the embedded parts posture always remains within the design target.

[0094] At the above construction site, after the current posture and force of the embedded parts were obtained through finite element dynamic analysis, the system introduced a real-time posture adjustment algorithm to correct the inclination deviation and position deviation that occurred during the installation process to ensure installation accuracy. The goal of the construction site is to control the horizontal position deviation of the embedded parts within ±1mm and the inclination deviation within ±0.05°.

[0095] First, based on the real-time monitoring data, the system finds that the current horizontal position deviation Δx = x target -x current =2.5mm, which exceeds the target deviation range; at the same time, the inclination deviation Δθ=θ target -θ current=-0.1°, which also exceeds the allowable value. To achieve dynamic adjustment, the system uses an optimization control strategy to calculate the adjustment path and determines the dynamic adjustment amount P(t) that the servo drive system needs to apply through the following formula:

[0096]

[0097] in:

[0098] Δx=x target -x current =2.5mm, which is the current horizontal position deviation;

[0099] Δθ=θ target -θ current =-0.1°, which is the current inclination deviation;

[0100] α=0.8mm / s, adjustment gain coefficient, used to control the intensity and speed of adjustment;

[0101] β=50rad -2 , is the attitude error weight factor, which indicates the influence of the inclination angle on the adjustment amount;

[0102] t0=0s, initial time;

[0103] t=1s, current time.

[0104] Substitute the above values ​​to calculate the adjustment amount P(t):

[0105]

[0106] Among them, exp(-50·(-0.1) 2 )=exp(-0.5)≈0.6065.

[0107] therefore:

[0108]

[0109] Based on the calculation results, the servo drive system adjusts the support point of the installation device to the target position by 1.213mm. After the adjustment is completed, the system collects data again and performs error correction, and finds that the new horizontal position deviation Δx = 1.287mm and the inclination deviation Δθ = -0.06°. The system repeatedly calculates the adjustment amount P(t) and continuously iterates and adjusts until the deviation converges to the allowable range.

[0110] After 5 adjustments, the final deviation converged to Δx = 0.8mm, Δθ = -0.03°, both meeting the construction requirements. Through the implementation of this algorithm, the servo drive system achieved rapid response and precise adjustment, ensuring the installation stability and high-precision positioning of the embedded parts.

[0111] This example verifies the feasibility and effectiveness of the attitude adaptive control algorithm under complex construction conditions. The value range of the adjustment gain coefficient α is usually between 0.5mm / s and 1.0mm / s, which is used to control the adjustment speed; and the recommended range of the attitude error weight factor β is 30rad -2 Up to 100rad -2 , used to ensure that the inclination error is moderately sensitive to adjustment.

[0112] Embodiment 2:

[0113] Combined with Figure 3 Flowchart, at the construction site, in order to ensure the installation stability of the embedded parts of the foundation of large equipment, the construction team adopted the pressure balance distribution and adjustment mechanism of the present invention. The foundation conditions on site are complex, the soil density in some areas is low, and the supporting force is unevenly distributed. In order to optimize the supporting force at the bottom of the installation device, the system divides the bottom into 4 independent mechanical unit areas, i=1, 2, 3, 4, respectively. The force range of each unit is designed according to the geometric characteristics and force requirements.

[0114] After the area division was completed, the team deployed a high-precision pressure sensor network in each unit area to collect support force data in real time. i (t). Set the initial collected support force data as follows:

[0115] F1(t) = 1800N, indicating the current support force in the first unit area;

[0116] F2(t)=1700N;

[0117] F3(t)=2000N;

[0118] F4(t)=1500N.

[0119] At the same time, the spatial influence function φ of each unit area i (x,y) is defined according to the region geometry:

[0120] μ1(x,y)=0.25, covering the first area at the bottom;

[0121] φ2(x,y)=0.20, covering the second area at the bottom;

[0122] φ3(x,y)=0.30, covering the third area at the bottom;

[0123] φ4(x,y)=0.25, covering the 4th area at the bottom.

[0124] Combining the above data, the system integrates the support forces of all unit areas to form the support force distribution function P(x,y,t) at the bottom:

[0125]

[0126] Substitute the actual data into:

[0127] P(x,y,t)=1800·0.25+1700·0.20+2000·0.30+1500·0.25

[0128] Calculated:

[0129] P(x,y,t)=450+340+600+375=1765N

[0130] This means that the overall support force distribution of the bottom is 1765 N. According to the design goal, the system requires the overall support force to reach 1800 N, and the support force distribution of each unit area should be as balanced as possible.

[0131] To achieve this goal, the system adjusts each unit area through a distributed pressure controller. First, according to the target distribution F target =1800N, calculate the deviation of each unit:

[0132] Region 1: ΔF1=18001800=0N;

[0133] Area 2: ΔF2 = 18001700 = +100N;

[0134] Region 3: ΔF3 = 18002000 = -200N;

[0135] Area 4: ΔF4=18001500=+300N.

[0136] According to the deviation data, the system starts the pressure regulator and adjusts the support point through the servo drive unit. For example, if the support force of the third area is reduced by 200N, the servo drive unit will sink the support point of this area by 1.67mm (set the unit stiffness k = 120N / mm, then the displacement Δx = ΔF / k = 200 / 120 = 1.67"{mm}). Similarly, if the support force of the fourth area is increased by 300N, the servo drive unit will raise the support point by 2.5mm.

[0137] Through continuous adjustment and real-time monitoring, the system adjusts the support force of all units to a balanced state. The new support force data is:

[0138] F1(t)=1800N;

[0139] F2(t)=1800N;

[0140] F3(t)=1800N;

[0141] F4(t)=1800N.

[0142] The support force distribution function is updated as follows:

[0143] P(x,y,t)=1800·0.25+1800·0.20+1800·0.30+1800·0.25=1800N

[0144] Ultimately, the system successfully adjusted the support force to the target value of 1800N, achieved balanced distribution in each unit area, optimized the mechanical properties of the bottom, and verified the feasibility and accuracy of the pressure balance distribution and adjustment mechanism.

[0145] At the above-mentioned construction site, the system has optimized the support force distribution function P(x, y, t) through the pressure balance distribution and adjustment mechanism. Next, in order to further accurately control the support force of each unit area, the construction team started the distributed pressure controller and adjusted the pressure output in real time through the servo drive unit.

[0146] In the set construction environment, the bottom of the installation device is divided into 4 mechanical unit areas, and the actual support force F initially collected i (t) and target support as follows:

[0147] Area 1: F1(t) = 1750N, target

[0148] Area 2: F2(t) = 1900N, target

[0149] Area 3: F3(t) = 1600N, target

[0150] Area 4: F4(t) = 2000N, target

[0151] Calculate the pressure error ΔF in each area according to the formula i (t):

[0152]

[0153] Region 1: ΔF1(t)=18001750=+50N;

[0154] Region 2: ΔF2(t)=18001900=-100N;

[0155] Region 3: ΔF3(t)=18001600=+200N;

[0156] Region 4: ΔF4(t)=18002000=-200N.

[0157] According to the pressure error data, the distributed controller starts the servo drive unit to adjust the pressure output of each area. Set the unit stiffness coefficient of each area to k = 100N / mm, then adjust the required displacement Δx i Calculated by the following formula:

[0158]

[0159] Region 1: (Lift the support point 0.5mm);

[0160] Area 2: (lower the support point by 1.0mm);

[0161] Region 3: (Lift the support point 2.0mm);

[0162] Region 4: (Lower the support point by 2.0mm).

[0163] The servo drive unit gradually performs the adjustment action and monitors the adjusted support force in real time through the pressure sensor, and enters the next round of closed-loop feedback control. For example, in the first area, the new support force after adjustment is:

[0164] F′1(t)=F1(t)+k·Δx1=1750+100·0.5=1800N

[0165] Similarly, other regions are adjusted to obtain:

[0166] F'2(t)=1900100·1.0=1800N;

[0167] F'3(t)=1600+100·2.0=1800N;

[0168] F′4(t)=2000100·2.0=1800N.

[0169] After one adjustment, the support force of all areas reaches the target value of 1800N. The support force distribution function P(x,y,t) is updated as:

[0170]

[0171] Set the spatial influence function φ i(x, y) is consistent with the previous one, and the overall support force distribution calculated by the system is completely balanced, achieving the design goal. This example shows that the distributed pressure regulator, combined with real-time monitoring data and dynamic adjustment functions, can effectively eliminate the pressure errors in each unit area and optimize the mechanical response of the bottom. In practical applications, the stiffness coefficient k usually ranges from 80N / mm to 150N / mm, and the displacement adjustment accuracy of the servo drive unit is ±0.1mm. This dynamic control mechanism can adapt to real-time changes in complex construction environments and significantly improve the accuracy of embedded parts installation and the overall stability of the system.

[0172] At the construction site, in order to further optimize the dynamic response of the support force adjustment, the system applies the dynamic adjustment formula in the pressure balance distribution and adjustment mechanism. i (t) When the preset threshold (e.g. ±10N) is exceeded, the pressure regulator generates the adjustment value A through real-time error calculation. i (t). The following takes the Unit 3 area as an example to explain in detail the application and adjustment process of the dynamic adjustment formula.

[0173] Assuming that at the initial time t0=0s, the target support force of the third area is The actual support force F3(t) is 1600N. Calculate the initial pressure error:

[0174]

[0175] When the system detects that the error exceeds the preset threshold, it starts the dynamic adjustment formula to calculate the adjustment amount A3(t). The adjustment amount is described by the following formula:

[0176]

[0177] Assume that the adjustment gain coefficient γ = 0.01mm / N and the error weight factor β = 0.001N -2 In the time interval [t0, t], the set pressure error ΔF3(t) gradually decreases, satisfying the following change rule:

[0178] At t = 0.5s, ΔF3(0.5) = 150N;

[0179] At t=1.0s, ΔF3(1.0)=100N.

[0180] Substituting into the formula, the integration process of the adjustment amount is as follows:

[0181]

[0182] Calculate the weighted values ​​within the integration interval with a step length of 0.5 seconds:

[0183] At t = 0.5s, exp(-0.001·200 2 )=exp(-40)≈4.25×10 -18 ,Due to the large error, the contribution to the adjustment amount after weighting is close to 0;

[0184] At t = 1.0s, exp(-0.001·150 2 )=exp(-22.5)≈1.77×10 -10 , the contribution is still small, but the adjustment gain is gradually increasing.

[0185] The system continues to integrate until t = 2.0s, at which point the error is further reduced to ΔF3(2.0) = 50N. Recalculate the weights:

[0186] exp(-0.001·50 2 )=exp(-2.5)≈0.0821

[0187] The amount of adjustment has increased significantly:

[0188] A3(t)=0.01·[200·4.25×10 -18 +150 1.77 × 10 -10 +50 · 0.0821]

[0189] A3(t)≈0.01·(0+2.66×10 -8 +4.105)=0.04105mm

[0190] The servo drive unit performs an adjustment action, raising the support point in the third area by A3(t) = 0.04105 mm, and then updates the support force in real time:

[0191] F′3(t)=F3(t)+k·A3(t)

[0192] Set the unit stiffness k = 100N / mm, then:

[0193] F′3(t)=1600+100·0.04105=1604.105N

[0194] The system repeatedly adjusted and monitored, and finally completed 10 iterations within 5 seconds, adjusting the actual support force of the third area to the target support force of 1800N. At the same time, the adjustment amount A of the first, second, and fourth areas i (t) Calculated using the same formula, convergence is completed within the preset time to ensure balanced distribution of the overall support force.

[0195] This dynamic adjustment mechanism can effectively reduce the interference of large errors on the system through integration and weighted control, and gradually optimize the support force of each unit, so that the construction system can achieve precise pressure regulation and installation stability in complex environments. The recommended value range of the gain coefficient γ is 0.005mm / N to 0.02mm / N, and the error weight factor β is usually set at 0.0005N. -2 Up to 0.002N -2 to balance sensitivity and stability.

[0196] At the construction site, in order to optimize the stability of the embedded parts installation device of the large equipment foundation, the system further combines the real-time inclination data θ(t), the overall center of gravity position (x c ,y c ,z c ) and external environmental parameters of foundation unevenness, dynamically adjust the support force distribution P(x,y,t). Site conditions show that the foundation has a height variation h(x,y). The height distribution within the x,y coordinate range is obtained from topographic survey data, with a maximum height difference of 20mm and a material density of ρ = 2000kg / m 3 , gravitational acceleration g = 9.8 m / s 2 .

[0197] The total area of ​​the bottom of the installation device is set to a rectangular area Ω, with a length of 2m and a width of 1.5m. The system divides the bottom into four mechanical unit areas, namely Ω1, Ω2, Ω3, and Ω4. In the initial state, the support force distribution P(x, y, t) and foundation reaction force density ρgh((x, y)) of each unit are as follows:

[0198] Unit 1 area: P1(t) = 1800N, foundation height h1(x, y) = 0.018m;

[0199] Unit 2 area: P2(t) = 1600N, foundation height h2(x,y) = 0.015m;

[0200] Unit 3 area: P3(t) = 2000N, foundation height h3(x,y) = 0.020m;

[0201] Unit 4 area: P4(t)=1400N, foundation height h4(x,y)=0.017m.

[0202] The calculation formula of the reaction force density of the foundation is:

[0203] ρgh(x,y)=2000·9.8·h(x,y)

[0204] Substituting the data, we can get the reaction force density of each unit area:

[0205] Unit 1 area: ρgh1(x,y)=2000·9.8·0.018=352.8N / m 2 ;

[0206] Unit 2 area: ρgh2(x,y)=2000·9.8·0.015=294.0N / m 2 ;

[0207] Unit 3 area: ρgh3(x,y)=2000·9.8·0.020=392.0N / m 2 ;

[0208] Unit 4 area: ρgh4(x,y)=2000·9.8·0.017=333.2N / m 2 .

[0209] According to the dynamic equilibrium equation:

[0210] ∫ Ω [P(x,y,t)·h(x,y)ρgh(x,y)]dxdy=0

[0211] The system integrates the difference between the support force distribution P(x, y, t) and the foundation reaction force ρgh(x, y) to adjust the support force. The adjustment gain coefficient is set to κ = 0.1, and the adjustment formula is:

[0212]

[0213] Calculate the adjustment for each unit area:

[0214] Unit 1 area: ΔP1(t) = 0.1 · [352.81800 · 0.018] = 0.1 · (352.832.4) = +32.04N;

[0215] Unit 2 area: ΔP2(t) = 0.1·[294.01600·0.015] = 0.1·(294.024.0) = +27.00N;

[0216] Unit 3 area: ΔP3(t) = 0.1 · [392.02000 · 0.020] = 0.1 · (392.040.0) = +35.20N;

[0217] Unit area 4: ΔP4(t)=0.1·[333.21400·0.017]=0.1·(333.223.8)=+30.94N.

[0218] The system dynamically adjusts the support force of each unit area based on the calculation results:

[0219] Unit 1 area: P′1(t)=P1(t)+ΔP1(t)=1800+32.04=1832.04N;

[0220] Unit 2 area: P′2(t)=P2(t)+ΔP2(t)=1600+27.00=1627.00N;

[0221] Unit 3 area: P′3(t)=P3(t)+ΔP3(t)=2000+35.20=2035.20N;

[0222] Unit area 4: P′4(t)=P4(t)+ΔP4(t)=1400+30.94=1430.94N.

[0223] Through dynamic adjustment, the system gradually optimizes the support force distribution, making it more accurately matched with the foundation reaction force density, thus significantly improving the stability and force uniformity of the installation device. Ultimately, the construction system achieves dynamic balance, ensuring the safe and reliable installation of embedded parts under complex foundation conditions. The recommended range of the adjustment gain coefficient κ is 0.05 to 0.15, which is used to balance the adjustment response speed and stability to ensure real-time controllability during construction.

[0224] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The control system of the embedded parts installation device for the stable equipment foundation is characterized by The following steps are involved: S1. Use multi-dimensional dynamic balance monitoring module: S1.

1. A dynamic sensor network is arranged on the embedded component installation device, including an inclination sensor, a pressure sensor and a displacement sensor, to monitor the dynamic balance state of the embedded component in real time and obtain monitoring data; S1.2, the monitoring data is integrated by the central processing unit to form real-time three-dimensional posture and center of gravity position feedback of the equipment; S2. Development of attitude adaptive control algorithm: S2.

1. Construct a posture adjustment algorithm based on real-time monitoring data, use the finite element dynamic analysis model to simulate the mechanical changes during the installation of embedded parts, and calculate the adjustment angle and position deviation; S2.2, fine-tune the inclination angle and height of the installation platform through the electric servo device to control the adaptive posture control of the embedded parts; S3, Pressure balance distribution and adjustment mechanism: S3.

1. Use zoned pressure control technology to divide the bottom of the installation device into several mechanical unit areas, and use distributed pressure regulators to adjust the support force of each area in real time; S3.2, combined with dynamic balance monitoring data, calculate the pressure difference in real time and compensate through closed-loop feedback; S4, vibration suppression and impact buffer control module: S4.

1. Design a vibration suppressor and impact buffer at the bottom of the installation device, analyze the vibration frequency and impact intensity in real time through an intelligent control algorithm, and dynamically adjust the buffer strength; S4.

2. Combine multi-dimensional dynamic balancing data to identify the vibration source during installation and suppress it in time; S5, Mechanical state closed-loop control: Construct a closed-loop feedback system for the mechanical state to compare and analyze the real-time collected pressure, displacement and vibration data with the preset installation target parameters; when deviating from the target parameters, activate the control strategy and make corrections by adjusting the pressure distribution, inclination angle and depth parameters.

2. The control system of the embedded parts installation device for the stable equipment foundation according to claim 1 is characterized in that The method for developing the attitude adaptive control algorithm comprises: By laying out inclination sensors, pressure sensors and displacement sensors on the base, lever arm and support points of the installation device, the three-dimensional inclination, support force and displacement data of the embedded parts during installation are collected in real time; the adaptive Kalman filter algorithm is used to denoise the noise signal, and the filtered data is described by a dynamic weight function: Where: X f (t) represents the data signal after filtering; X(t) is the original signal collected in real time; N(t) is the noise interference; is the time-dependent dynamic weight function; τ n is the filtering time constant, which indicates the attenuation characteristics of the data signal in the time dimension; Δτ is the time interval of data acquisition, which reflects the time accuracy of signal changes; exp(-Δτ / τ n ) is the time decay factor.

3. The control system of the embedded component installation device for the stable equipment foundation according to claim 2 is characterized in that The method for developing an attitude adaptive control algorithm includes: constructing a mechanical finite element dynamic analysis model based on real-time monitoring data to simulate the attitude and force conditions of embedded parts during installation; the finite element model defines boundary conditions, including the friction factor between the base and the ground and mechanical parameters including vertical force and lateral force loading.

4. The control system of the embedded component installation device for the stable equipment foundation according to claim 3 is characterized in that The method for developing an attitude adaptive control algorithm includes: introducing a real-time attitude adjustment algorithm based on finite element analysis results to correct the inclination angle deviation and position deviation during the installation process; calculating the adjustment path by optimizing the control strategy, and adjusting the angle and position correction amount.

5. The control system of the embedded component installation device for the stable equipment foundation according to claim 1 is characterized in that The pressure balance distribution and adjustment mechanism includes: The bottom of the installation device is divided into several mechanical unit areas. After the area division, a pressure sensor network is deployed to collect the support force data F in real time in each unit area. i (t), and integrate the support forces of all unit areas to form the support force distribution function P(x,y,t): Where: P(x,y,t) is the distribution function of the bottom support force, which represents the contribution of the support force of each area at a specific time t to the overall system; F i (t) is the support force value of the i-th mechanical unit area at time t; φ i (x, y) is the spatial influence function of the i-th unit area, which represents the force range and distribution characteristics of area i; N is the total number of mechanical unit areas.

6. The control system of the device for installing embedded parts of a stable equipment foundation according to claim 5, characterized in that The pressure balance distribution and adjustment mechanism includes: A distributed pressure controller is installed in each mechanical unit area to control the pressure output through the servo drive unit; the distributed controller combines the target support force The actual support force F i (t), calculate the pressure error ΔF i (t); the error represents the difference between the current support force and the target support force, which is described by the following formula: Where: ΔF i (t) is the pressure error of the i-th unit area at time t; is the target support force of the ith unit area at time t; F i (t) is the actual support force of the ith unit area at time t.

7. The control system of the embedded component installation device for the stable equipment foundation according to claim 6 is characterized in that The pressure balance distribution and adjustment mechanism includes: When the pressure error exceeds the preset threshold, the pressure regulator will generate an adjustment value A based on the real-time error. i (t); then a dynamic adjustment formula based on integral and exponential weighting is adopted: Among them: A i (t) is the dynamic adjustment amount of the ith unit area at time t; γ is the adjustment gain coefficient, which controls the sensitivity of the adjustment output; t0 and t represent the initial time and current time of the adjustment process respectively; τ is the integral time variable, which represents the cumulative effect of the adjustment amount over time; β is the error weight factor, which is used to suppress the interference of excessive errors on the system; is the error weighting factor.

8. The control system of the embedded component installation device for the stable equipment foundation according to claim 7, characterized in that The pressure balance distribution and adjustment mechanism includes: Combined with the real-time inclination data θ(t) of the installation device, the overall center of gravity position (x c ,y c ,z c ) and external environmental parameters including the unevenness of the foundation, the zoned pressure control system dynamically optimizes the support force distribution of each unit area; the support force distribution P(x,y,t) is adjusted through the following dynamic equilibrium equation: ∫ Ω [P(x,y,t)·h(x,y)-ρgh(x,y)]dxdy=0 Where: Ω is the total area of ​​the bottom of the support device; h(x, y) is the height function of the foundation, which represents the height distribution of the foundation in the x, y coordinates; ρgh(x, y) is the reaction force density of the foundation, which is determined by combining the material density ρ and the gravitational acceleration g.

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