Synchronous Improvement of Construction Human-Computer Interaction Methods, Systems, and Media Based on Cloud Computing

Abstract

The present application relates to a kind of cloud computing-based synchronous lifting construction man-machine interaction method, system, medium, interaction method includes: the displacement value of at least one slave synchronous lifting oil cylinder is collected to main synchronous lifting oil cylinder, for the slave synchronous lifting oil cylinder, the control parameter of displacement error minimum is obtained by using particle swarm algorithm iterative calculation;The data of each sensor in synchronous lifting construction scene is obtained, based on the relationship between the stress of dangerous point and influence factor obtained in advance, the stress of dangerous point of component in synchronous lifting process is calculated;The displacement value of main synchronous lifting oil cylinder and slave synchronous lifting oil cylinder is exported to visual terminal, and the stress of dangerous point of the component is exported to visual terminal.Compared with prior art, the safety of the present application further improves synchronous lifting technology, and the monitoring visualization degree is combined with information technology and synchronous lifting construction, which promotes the development of digital transformation in synchronous lifting process.

Description

Technical Field

[0001] This invention relates to the field of integrated control technology, and in particular to a cloud computing-based synchronous lifting construction human-machine interaction method, system, and medium. Background Technology

[0002] Nowadays, with the development and progress of the field of architecture, modern buildings are becoming increasingly tall and large-scale, making the construction process increasingly difficult. To solve these problems, simultaneous lifting technology is being increasingly applied in the construction of large-scale buildings.

[0003] In the design phase before synchronous lifting construction, most projects still rely on traditional safety calculations combined with the experience of construction workers. This approach is increasingly ill-suited to the ever-complexing scenarios of synchronous lifting construction. During the construction process, PLCs or dedicated industrial control computers are typically used to monitor the operation of various construction equipment. While this monitoring method can ensure the correct operation of the equipment to some extent, its low level of informatization makes it difficult to collect, record, and analyze data from modern construction processes. This hinders the collection and utilization of critical information by construction workers, restricting the rapid development of modern construction technology and making it difficult to adapt to the clustering and networking trends of synchronous lifting technology. Synchronous lifting construction urgently needs to integrate with increasingly mature information technology and cloud computing technology to significantly improve its computing capabilities.

[0004] The invention patent application "A Cloud Computing-Based Bridge Construction Alignment Control System" (Publication No.: CN115962803A) proposes a cloud computing-based bridge construction alignment control system, including a measurement data cloud processing module, a structural calculation and analysis module, an intelligent early warning module, a construction data cloud processing module, and a BIM and GIS webpage display module. This application can be used to filter and process data generated by various data acquisition devices at the construction site and automatically calculate and analyze the bridge structure. It can consider the influence of multiple factors on the bridge structure alignment to predict the alignment under completed bridge conditions and use existing data for intelligent early warning.

[0005] The invention patent application "An Internet of Things-Based Shield Tunneling Construction Information Monitoring System" (Publication No.: CN115801839A) proposes an Internet of Things-based shield tunneling construction information monitoring system, including shield tunneling construction data, a PLC, an IoT remote module, a cloud server, a monitoring cloud platform, and a user terminal. After collecting the shield tunneling construction data, the PLC transmits the data to the IoT remote module. The IoT remote module connects to the cloud server via a network and transmits the data to the cloud server. The cloud server connects to the monitoring cloud platform and the user terminal. The monitoring cloud platform obtains data from the cloud server or achieves remote monitoring and management of construction data and shield tunneling equipment through the user terminal.

[0006] While various control technologies combined with cloud computing are currently used in different industries, a cloud platform technology specifically for synchronous lifting construction is still lacking. Furthermore, current synchronous lifting construction methods have several shortcomings. For example, the hydraulic control parameters are selected based on the experience of the lifting technicians, which cannot guarantee the synchronicity of the lifting process to the greatest extent possible. The information technology level of construction monitoring is low, failing to monitor uneven loads caused by different displacements during the lifting process, which can easily affect construction accuracy and even lead to safety accidents. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a cloud computing-based human-computer interaction method, system, and medium for synchronous lifting construction, so as to intuitively display the stress at dangerous points of components and the synchronization of hydraulic cylinders during synchronous lifting operations.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] One aspect of the present invention provides a cloud computing-based synchronous improvement construction human-computer interaction method, applied on a server side, the interaction method comprising:

[0010] The displacement values ​​of the main synchronous lifting cylinder and at least one slave synchronous lifting cylinder are collected. For the slave synchronous lifting cylinder, the control parameters with the minimum displacement error are obtained by iterative calculation using the particle swarm optimization algorithm.

[0011] Data from various sensors in the synchronous lifting construction scenario is acquired, and the stress at the critical point of the component is calculated based on the pre-obtained relationship between the stress at the critical point and the influencing factor during the synchronous lifting process.

[0012] The displacement values ​​of the main synchronous lifting cylinder and the slave synchronous lifting cylinder, as well as the stress at the critical point of the component, are output to the visualization terminal.

[0013] As a preferred technical solution, the process of obtaining the relationship between the stress at the critical point and the influencing factor includes the following steps:

[0014] The influencing factors and their corresponding value ranges that cause stress changes at critical points of the lifting components during the lifting process are obtained. A dataset is constructed based on the value range, and the relationship between the stress at critical points and the influencing factors is obtained by fitting.

[0015] As a preferred technical solution, stress analysis is used to obtain the influencing factors that cause stress changes at dangerous points of the lifting component during the lifting process.

[0016] As a preferred technical solution, the experimental conditions are selected within the range of values ​​using the diffusion experimental design method, and the data set is obtained by calculating the stress at the critical point using the finite element method.

[0017] As a preferred technical solution, the relationship between the stress at the critical point and the influencing factor is obtained by fitting the response surface methodology.

[0018] As a preferred technical solution, the data from each sensor in the synchronous lifting construction scenario includes cylinder piston position data, hydraulic lifter anchor status data, hydraulic lifter oil pressure data, and component height difference data.

[0019] As a preferred technical solution, the control parameters that minimize displacement error are obtained by iterative calculation using the particle swarm optimization algorithm, including the following steps:

[0020] The displacement value of the main synchronous lifting cylinder is taken as the expected displacement, and the displacement value of the secondary synchronous lifting cylinder is taken as the actual displacement to calculate the displacement error.

[0021] Based on the displacement error between the main synchronous lifting cylinder and the slave synchronous lifting cylinder, the fitness is calculated. The fitness is used as the input of the particle swarm algorithm to obtain the optimized control parameters. The PID controller is then configured, the displacement error is updated, and this step is repeated iteratively to obtain the final control parameters.

[0022] As a preferred technical solution, before performing iterative calculations using the particle swarm optimization algorithm, the following steps are also included:

[0023] Obtain the dead zone threshold and integral separation threshold of the PID controller, and set the PID controller used to control the synchronous lifting cylinder.

[0024] Another aspect of the present invention is a cloud computing-based synchronous construction human-machine interaction system, comprising:

[0025] Edge equipment includes a main synchronous lifting cylinder and at least one piston position sensor, a lifting anchor status sensor, a hydraulic pressure sensor, and a component height difference sensor from the synchronous lifting cylinder.

[0026] The main control module is used to collect the displacement value of the hydraulic cylinder and sensor data from the edge device;

[0027] The synchronous lifting control module is used to iteratively calculate the control parameters that minimize the displacement error of the synchronous lifting cylinder using the particle swarm optimization algorithm.

[0028] The real-time stress calculation module is used to calculate the stress at the critical point of the component during synchronous lifting based on the sensor data and the pre-obtained relationship between the stress at the critical point and the influencing factor.

[0029] The human-computer interaction module is used to convert and output the displacement values ​​of the main synchronous lifting cylinder and the slave synchronous lifting cylinder, as well as the stress at the critical point of the component, in graphical form based on ThingJS.

[0030] In another aspect, a computer-readable storage medium is characterized by comprising one or more programs executable by one or more processors of an electronic device, said one or more programs including instructions for performing the above-described cloud-based synchronous lifting construction human-machine interaction method.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] (1) Improve the intuitiveness of synchronous operation: This application intuitively displays the stress of dangerous points of components and the synchronization of cylinders in synchronous lifting operations, so that operators can easily grasp the status of synchronous lifting cylinders and components in operation, and realize precise control and real-time monitoring of synchronous lifting construction process.

[0033] (2) High real-time performance of calculation: Based on the influence factors that cause stress changes at dangerous points of the lifting components during the lifting process and the corresponding value range, this application constructs a dataset according to the value range and combines the response surface method to fit the relationship between the stress at dangerous points and the influence factors, thereby improving the real-time performance of the calculation.

[0034] (3) Wide range of applications: This application analyzes the open-loop transfer function of a single hydraulic lifter, constructs a control parameter optimization method under the displacement synchronization control strategy, and establishes a simulation platform using parametric modeling. The model library based on the MySQL relational database can query the characteristic parameters of different models of hydraulic lifters using user data returned from the front end. By combining the two through hybrid programming technology, the system can be applied in different scenarios.

[0035] (4) Convenient human-computer interaction: This application combines ThingJS technology with B / S structure to display various data in the synchronous lifting construction process using a combination of two-dimensional charts and three-dimensional animations, and provides construction personnel with an interactive window for optimizing control parameters before construction, thereby reducing the learning cost of the system. Attached Figure Description

[0036] Figure 1This is a schematic diagram of the human-computer interaction process for synchronous improvement construction based on cloud computing in the embodiment.

[0037] Figure 2 This is a schematic diagram of the cloud server structure in the embodiment;

[0038] Figure 3 This is a schematic diagram of a cloud-based synchronous lifting construction human-machine interaction system in the embodiment.

[0039] Figure 4 A schematic diagram of the human-computer interaction interface for improving component strain calculation program;

[0040] Figure 5 This is a schematic diagram of a PID control algorithm;

[0041] Figure 6 A schematic diagram illustrating the iterative process of optimizing PID to improve the particle swarm optimization algorithm;

[0042] Figure 7 This is the optimal individual fitness curve obtained from the experiment. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] See Figure 1 To address the problems existing in the aforementioned technologies, this embodiment provides a cloud-based synchronous lifting construction human-machine interaction system. The system hardware includes: edge devices, a cloud server backend, and a frontend program. The control module includes: collecting data from various sensors in the pump station electrical module, the hydraulic cylinder piston position detection module, and the laser rangefinder measurement module; encapsulating the collected data and uploading it to the cloud server; calculating the optimal control parameters during construction using the collected data and parameters set by the construction personnel; calculating real-time stress data of the stress danger points of the lifting components based on the on-site construction sensor data uploaded by the edge computing layer; and visualizing the existing data. This control technology enables precise control and real-time monitoring of the synchronous lifting construction process.

[0046] See Figure 3 The software component includes a data acquisition program, a data calculation program, a synchronous lifting control optimization program, a lifting component strain calculation program, and a visualization program. Among these:

[0047] The data acquisition and calculation programs collect on-site data through sensors deployed at the construction site, including cylinder piston position sensors (for measuring the position of the hydraulic lifter cylinder piston), anchorage status sensors (for measuring the status of the upper and lower anchorages of the hydraulic lifter), oil pressure sensors (for measuring the working oil pressure of the large chamber of each hydraulic lifter), and height difference sensors (for measuring the height of the lifting components). The data is then processed, packaged, and transmitted to the cloud server via Ethernet.

[0048] The synchronous lifting control optimization program first analyzes the control system of the synchronous lifting process, obtaining the open-loop transfer function of a single hydraulic lifter. Then, an improved particle swarm optimization algorithm is used to optimize the control parameters under the displacement synchronous control strategy. The improved algorithm has a higher optimization speed and better optimization performance. Next, the open-loop transfer function is parametrically modeled in MATLAB / Simulink, a mathematical model library of hydraulic lifters is established, and the synchronous lifting control optimization algorithm is integrated into a cloud server using Java / MATLAB hybrid programming, realizing the function of optimizing synchronous lifting control parameters according to different engineering scenarios.

[0049] In the application scenario of this embodiment, there are multiple synchronous lifting control cylinders, one of which is designated as the master cylinder and the rest as slave cylinders. The control strategy adopted is a displacement synchronization control strategy, that is, the displacement of the piston of the master cylinder is used as a control signal to output to the other slave cylinders. The slave cylinders use this as a target to adjust their own displacement, and finally achieve the purpose of synchronization.

[0050] In the practical control strategy, the transfer function model of the hydraulic control system is first established, and an improved PID control algorithm is used as the control model. An improved particle swarm optimization algorithm is then applied to optimize the PID control parameters. The model structure is as follows: Figure 5 As shown.

[0051] The fitness calculation is performed by taking the difference between the expected displacement value and the actual displacement value output by the current PID control parameters as input. The fitness is then used as input for the improved particle swarm optimization algorithm. The optimized PID parameters are then reused in the PID controller. This process is repeated until an optimal set of PID control parameters is found.

[0052] The optimal control parameters obtained by the improved particle swarm optimization algorithm are the control parameters K of the PID controller. p ,K i ,K d The improved particle swarm optimization algorithm optimizes the iterative process of PID control, as follows: Figure 6 As described above, a maximum number of iterations is set during the loop process. The optimal PID control parameters are determined to be obtained when the optimal individual fitness obtained by the improved particle swarm optimization algorithm converges or exceeds the maximum number of iterations. The experimentally obtained optimal individual fitness curve is shown below. Figure 7 As shown.

[0053] The strain calculation program for lifting components first performs a stress analysis on the lifting components to obtain the variables that cause stress changes at critical points during the lifting process. Based on actual engineering requirements, the range of values ​​for each variable is determined. The center diffusion test design method is used to select experimental conditions within the range of values, and the finite element method is used to calculate the stress magnitude at critical points under the conditions to form an initial dataset. Then, the response surface methodology is used to fit the relationship between the stress at critical points and the variables to obtain an approximate finite element model of stress.

[0054] The visualization program first uses ThingJS technology to build a visual model of the construction site, lifting equipment, and lifting components. Then, it uses a combination of 2D charts and 3D animations to display various data during the synchronous lifting construction process. Finally, it designs the interface of the synchronous lifting construction cloud platform, which provides construction personnel with an interactive window for optimizing control parameters before construction and monitoring personnel with an interactive window for real-time monitoring of construction during the construction process through a B / S structure.

[0055] See Figure 2 The edge devices and the cloud server backend use a TCP connection, while the cloud server backend and the frontend use a WebSocket connection. Data exchange uses JSON format. The frontend uses the ThingsJS architecture and visualizes the data using Echarts charts, numerical models, and user interaction design. The final visualization effect is as follows: Figure 4 As stated above.

[0056] Example 2

[0057] Building upon Example 1, this example provides a cloud-based human-machine interaction method for synchronous lifting construction, involving edge devices, a cloud server backend, and a frontend program. Edge devices collect, process, and encapsulate data from various sensors on-site before uploading it to the cloud server. Based on the displacement synchronization control strategy, the open-loop transfer function of a single hydraulic lifter is analyzed, and a control parameter optimization algorithm based on an improved particle swarm optimization algorithm is used to optimize control parameters before construction. By analyzing the forces acting on the lifting components during the lifting process, the influencing factors causing stress changes in the lifting components are obtained. Based on this, experimental conditions are selected, and finite element stress calculations are performed. The relationship between the stress of the lifting components and the influencing factors is regressed using a response surface methodology, forming a real-time monitoring function for the stress of the lifting components during construction. This, together with the synchronous lifting control optimization program, constitutes a cloud computing program. A visualization model of the construction scenario is built using ThingJS technology. Then, two-dimensional charts are used in conjunction with a three-dimensional model to visualize the data before and during synchronous lifting construction. Finally, a construction cloud platform interface is designed based on a B / S architecture, forming a user-level human-machine interaction function. This invention further improves the safety and monitoring visualization of synchronous lifting technology, combines information technology with synchronous lifting construction, and promotes the development of digital transformation of the synchronous lifting process.

[0058] The present invention has the following beneficial effects:

[0059] (1) Solve the problem of difficulty in selecting control parameters during synchronous lifting construction caused by different hydraulic lifters.

[0060] (2) Solve the problem that the stress at dangerous points is difficult to monitor in real time due to the asynchronous displacement of each lifting point during the lifting process.

[0061] (3) The human-computer interaction interface is adopted to display various data in a visual form, which greatly facilitates the comprehensive monitoring of the construction process.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cloud computing-based synchronous lifting construction human-computer interaction method, characterized in that, The interaction method is applied to a server and comprises the following steps: Collecting displacement values of a master synchronous lifting oil cylinder and at least one slave synchronous lifting oil cylinder, and iteratively calculating control parameters with the smallest displacement error of the slave synchronous lifting oil cylinder by using a particle swarm algorithm; Obtaining data of various sensors in a synchronous lifting construction scene, and calculating stresses of dangerous points of a component in a synchronous lifting process based on a previously obtained relationship between the stresses of the dangerous points and influence factors; Outputting the displacement values of the master synchronous lifting oil cylinder and the slave synchronous lifting oil cylinder and the stresses of the dangerous points of the component to a visual terminal, The process of obtaining the relationship between the stresses of the dangerous points and the influence factors comprises the following steps: Obtaining influence factors causing changes in stresses of dangerous points of a lifting component in a lifting process and corresponding value ranges, constructing a data set based on the value ranges, and fitting the relationship between the stresses of the dangerous points and the influence factors, Obtaining the influence factors causing the changes in the stresses of the dangerous points of the lifting component in the lifting process through stress analysis, Selecting test conditions in the value ranges by using a diffusion experimental design method, and calculating the sizes of the stresses of the dangerous points by using a finite element method to obtain the data set, The steps of iteratively calculating the control parameters with the smallest displacement error by using the particle swarm algorithm comprise the following steps: Taking the displacement value of the master synchronous lifting oil cylinder as an expected displacement and taking the displacement value of the slave synchronous lifting oil cylinder as an actual displacement, and calculating a displacement error; Based on the displacement error between the master synchronous lifting oil cylinder and the slave synchronous lifting oil cylinder, calculating a fitness, taking the fitness as an input of the particle swarm algorithm, obtaining optimized control parameters, configuring a PID controller, updating the displacement error, repeating the step to iteratively calculate, and obtaining final control parameters, Before iteratively calculating by using the particle swarm algorithm, the following steps are further included: Obtaining a dead zone threshold and an integral separation threshold of the PID, and setting a PID controller for controlling the synchronous lifting oil cylinder.

2. The cloud computing-based synchronous lifting construction human-computer interaction method according to claim 1, characterized in that, The relationship between the stresses of the dangerous points and the influence factors is fitted by using a response surface method.

3. The cloud computing-based synchronous lifting construction human-computer interaction method according to claim 1, characterized in that, The data of various sensors in the synchronous lifting construction scene comprises oil cylinder piston position data, hydraulic lifter anchor state data, hydraulic lifter oil pressure data, and component height difference data.

4. A cloud computing-based synchronous lifting construction human-computer interaction system, characterized in that, A system for implementing the cloud computing-based synchronous lifting construction human-computer interaction method according to any one of claims 1-3 comprises: An edge device comprising a piston position sensor of a master synchronous lifting oil cylinder and at least one slave synchronous lifting oil cylinder, a lifter anchor state sensor, an oil pressure sensor, and a component height difference sensor; A master control module for collecting displacement values of the oil cylinder and sensor data from the edge device; A synchronous lifting control module for iteratively calculating control parameters with the smallest displacement error of the slave synchronous lifting oil cylinder by using a particle swarm algorithm; A real-time stress calculation module for calculating stresses of dangerous points of a component in a synchronous lifting process based on the sensor data and based on a previously obtained relationship between the stresses of the dangerous points and influence factors; A human-computer interaction module for converting the displacement values of the master synchronous lifting oil cylinder and the slave synchronous lifting oil cylinder and the stresses of the dangerous points of the component into a chart form and outputting the chart form based on ThingJS.

5. A computer readable storage medium, characterized in that, One or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing the cloud-computing-based synchronization-lifting construction human-machine interaction method of any one of claims 1-3.

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