Prefabricated building component prestress intelligent tensioning method and device

Abstract

The invention provides an intelligent prestress tensioning method and device for a fabricated building component, relates to the technical field of stress detection and analysis, and aims to perform three-dimensional simulation testing and visual attention monitoring to determine basic tensioning information, train a self-correction module, control an intelligent tensioning pump station and realize intelligent prestress tensioning of the fabricated building component. The device is used for assisting an upper industrial personal computer in numerical control management of a jack tensioning full period, synchronously monitoring and receiving sensing data to carry out deviation positioning and deviation correction decision making, and generating a deviation correction strategy to carry out numerical control compensation management of a feedback target, and is used for solving the problems that in the prior art, tensioning intelligence and flexibility are insufficient; the technical problems that the adaptive capacity under the complex working condition is poor, and full-period effective operation conforming to the actual operation condition cannot be achieved are solved. Effective construction information is determined through simulation and attention analysis, stress sensing layout is carried out, dynamic and static analysis and decision making are carried out in combination with a self-correction module, the intelligence and flexibility of tensioning control are improved, and reasonable and effective operation is carried out according to the actual operation condition.

Description

Technical Field

[0001] The present invention relates to the technical field of stress detection and analysis, and in particular to a method and a device for intelligent prestressing of assembled building components. Background Art

[0002] Prefabricated buildings have received widespread attention in recent years due to their high efficiency and sustainability, and the application of prestressed technology has become one of its key links. Prestressed intelligent tensioning technology improves the bearing capacity and durability of components by applying prestress to them. However, existing tensioning methods mostly rely on experience and manual operation, and lack a systematic monitoring and feedback mechanism. Traditional methods usually rely on only a single stress sensor during the tensioning process, and are unable to monitor the coordinated work of multiple jacks in real time, making it difficult to ensure tensioning accuracy. In addition, existing technical means are often unable to handle data deviations in a timely manner, resulting in potential safety hazards and structural risks.

[0003] These deficiencies make it difficult to control prestress in complex construction environments. Common problems include unclear stress fluctuations and elongation relationships during the tensioning process, making it difficult to effectively assess risk characteristics. At the same time, the lack of correction capabilities for static and dynamic errors makes it impossible to achieve ideal CNC management in actual construction, increasing construction costs and time. Therefore, there is an urgent need for a more intelligent tensioning method that can comprehensively consider multiple factors, provide real-time feedback and adjustments, in order to improve the overall performance and safety of prefabricated building components.

[0004] In summary, the existing technology has the technical problems of insufficient tensioning intelligence and flexibility, poor adaptability to complex working conditions, and inability to achieve full-cycle effective operation that conforms to actual operating conditions. Summary of the invention

[0005] The present application provides a method and device for intelligent prestressing of prefabricated building components, which is used to solve the technical problems existing in the prior art of insufficient intelligence and flexibility in prestressing, poor adaptability to complex working conditions, and inability to achieve full-cycle effective operation that conforms to actual working conditions.

[0006] In view of the above problems, the present application provides a method and device for intelligent prestressing of prefabricated building components.

[0007] In a first aspect, the present application provides a prestressed intelligent tensioning method for assembled building components, the method comprising: Interactive tensioning component configuration, three-dimensional simulation test of multi-top coordinated tensioning and visual attention monitoring, determine basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationship, and the visual attention target is stress fluctuation; Combined with the basic tensioning information, the static single point error and the dynamic trend error are used as the benchmark to perform data-driven training on the self-correction module; The tensioning numerical control parameters respond to the intelligent tensioning pump station, assisting the upper industrial computer to perform numerical control management of the full cycle of jack tensioning, and the intelligent tensioning pump station is connected to at least one jack; Synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; The deviation correction strategy responds to the upper-level industrial computer in the intelligent tension pump station to perform numerical control compensation management of the feedback target.

[0008] In a second aspect, the present application provides a prestressed intelligent tensioning device for assembled building components, the device comprising: An information determination unit, the information determination unit is used for interactive tensioning component configuration, performing three-dimensional simulation testing of multi-top coordinated tensioning and visual attention monitoring, and determining basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationships, and the visual attention target is stress fluctuation; A module training unit, the module training unit is used to combine the basic tensioning information, take the static single point error deviation and the dynamic trend deviation as the benchmark, and perform data-driven training on the self-correction module; A tensioning control unit, wherein the tensioning numerical control parameter responds to the intelligent tensioning pump station, and assists the upper industrial computer in numerical control management of the entire tensioning cycle of the jack, and the intelligent tensioning pump station is connected to at least one jack; A feedback correction unit, the feedback correction unit is used to synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; A feedback compensation unit is used for the correction strategy to respond to the upper industrial computer in the intelligent tension pump station to perform numerical control compensation management of the feedback target.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: The prestressed intelligent tensioning method of prefabricated building components provided in the embodiment of the present application, interactive tensioning component configuration, three-dimensional simulation test and visual attention monitoring of multi-top collaborative tensioning, determine basic tensioning information, and train the self-correction module based on static single-point error deviation and dynamic trend deviation. The tensioning numerical control parameters respond to the intelligent tensioning pump station, assist the upper industrial control computer to perform numerical control management of the entire cycle of jack tensioning, synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, generate correction strategies and respond to the upper industrial control computer in the intelligent tensioning pump station, and perform numerical control compensation management of feedback targets, which is used to solve the technical problems of insufficient tensioning intelligence and flexibility in the prior art, poor adaptability under complex working conditions, and inability to achieve full-cycle effective operation in line with actual working conditions. Through simulation and attention analysis to determine effective construction information, and perform stress sensing layout, combined with the self-correction module to perform dynamic and static analysis and decision-making, improve the intelligence and flexibility of tensioning control, and perform reasonable and effective operations in line with actual working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process for intelligent prestressing of prefabricated building components is provided for this application; Figure 2 A structural schematic diagram of a prestressed intelligent tensioning device for assembled building components is provided for this application.

[0011] Explanation of the accompanying drawings: information determination unit 11, module training unit 12, tensioning control unit 13, feedback correction unit 14, feedback compensation unit 15. DETAILED DESCRIPTION

[0012] The present application provides a method and device for intelligent prestressing of prefabricated building components, conducts three-dimensional simulation testing and visual attention monitoring to determine basic tensioning information and train a self-correcting module, controls an intelligent tensioning pump station, assists a host industrial computer in CNC management of the entire jack tensioning cycle, synchronously monitors and receives sensor data for deviation positioning and correction decisions, generates a correction strategy for CNC compensation management of feedback targets, and is used to solve the technical problems existing in the prior art of insufficient tensioning intelligence and flexibility, poor adaptability to complex working conditions, and inability to achieve full-cycle effective operation that matches actual operating conditions.

[0013] Embodiment 1 like Figure 1 As shown, the present application provides a prestressed intelligent tensioning method for assembled building components, the method comprising: S1: Interactive tensioning component configuration, three-dimensional simulation test of multi-top coordinated tensioning and visual attention monitoring, determine basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationship, and the visual attention target is stress fluctuation; Firstly, the tension member configuration is a physical configuration for field operations, including basic physical information such as building assemblies, jacks, stress tendons, etc., and three-dimensional reconstruction is performed in combination with the tension member configuration to produce a three-dimensional simulation model of the component. Exemplarily, computer-aided design and finite element analysis software are used to create a three-dimensional model of the prefabricated building component, and dynamic control principles are introduced to generate three-dimensional virtual mapping of the physical entity.

[0014] Further based on the three-dimensional simulation model, the initial positions and force application methods of multiple jacks are set, and various tensioning conditions are tested through simulation. According to the preliminary analysis results, the jack positions and force application modes are adjusted, and multiple rounds of simulation are performed to determine the optimal configuration. Simultaneously, by monitoring the dynamic test data, during the tensioning process, the force application of each jack and the stress state of the component are monitored in real time and the data is recorded. The tensioning CNC parameters are collected, including the force and time applied by each jack, and the tensioning elongation relationship is analyzed to establish a mathematical relationship between the tensioning force and the deformation of the component. With stress as the visual attention target, the prestressed points are determined. These points are key stress concentration locations, usually weak points in the component, and are integrated as the basic tensioning information. The collected tension simulation data is analyzed to evaluate the tension effect and make adjustments based on the stress characteristics. If stress fluctuations are found to exceed the preset range, the tension strategy is immediately adjusted to correct the deviation.

[0015] Through systematic three-dimensional simulation testing and efficient monitoring methods, the tensioning process of prefabricated building components is ensured to be more scientific, accurate and safe, so as to improve the fit with the actual tensioning scene. By accurately controlling the prestress, it is ensured that the components have good bearing capacity and durability after construction, reducing the need for subsequent maintenance.

[0016] Furthermore, for determining the basic tensioning information, step S1 of the present application also includes: performing three-dimensional simulation of the tensioning component configuration, determining the target tensioning configuration by adjusting the collaborative jacks and the tensioning mode, wherein synchronous symmetrical tensioning and multi-step symmetrical tensioning are the tensioning modes; locking the target tensioning configuration, performing tensioning evolution by monitoring the force characteristics, and determining the tensioning simulation data; and determining the basic tensioning information by analyzing the tensioning simulation data.

[0017] Specifically, in the process of determining the basic tensioning information, it is first necessary to conduct a detailed three-dimensional simulation test on the tensioning component configuration. Based on the three-dimensional simulation model, different jack configurations and tensioning modes are simulated to find the optimal target tensioning configuration. The tensioning mode selection includes synchronous symmetrical tensioning and multi-step symmetrical tensioning, which are designed to ensure the coordination and balance between the jacks during the force application process.

[0018] Among them, the jack configuration is the number and distribution position characteristics of the configuration jacks for performing assembly construction. Based on the single-step and multi-step coordination, evolution tests are performed separately to determine the optimal tensioning configuration. The synchronous symmetrical tensioning is a one-time construction, and multiple jacks perform symmetrical tensioning synchronously to complete the assembly construction. The multi-step symmetrical construction is divided into multiple continuous steps, and the synchronous symmetrical construction of some jacks is performed step by step until the assembly component is completed. Among them, a tensioning pump station can control multiple jacks to tension synchronously and symmetrically at the same time, realizing a multi-jack synchronous tensioning process and improving construction efficiency.

[0019] Once the target tensioning configuration is determined, the next step is to lock the configuration and observe the evolution of the tensioning process by real-time monitoring of the stress characteristics. The stress data applied to the component can be collected in real time, reflecting the dynamic changes of the tensioning process. By analyzing these stress characteristics, detailed tensioning simulation data can be generated, which not only records the specific process of tensioning, but also reveals the impact of the force applied by each jack on the overall stress state of the component.

[0020] Finally, through in-depth analysis of the collected tension simulation data, the basic tension information can be accurately determined, including tension CNC parameters, prestress points, and tension-elongation relationships. This series of steps ensures precise control of the tension process, laying a solid foundation for subsequent self-correction and dynamic adjustment, thereby improving the overall performance and safety of prefabricated building components.

[0021] Furthermore, by analyzing the tensioning simulation data, the basic tensioning information is determined. Step S1 of the present application also includes: traversing the tensioning simulation data to determine the tensioning CNC parameters of the entire tensioning cycle; using the key stress points and stress fluctuation exceeding limit points in the prestressed tendons as the prestressed points; and using the tensioning distribution parameters-prestress distribution-prestressed tendon elongation characteristics to explore the tensioning-elongation relationship.

[0022] The tension simulation data is tension simulation data of a target tension configuration acquired based on the dynamic evolution of the three-dimensional simulation model, which is traversed and integrated in a positive time sequence based on the full tension cycle.

[0023] By directly identifying the tensioning simulation data, the jack control parameters within the entire tensioning cycle are extracted, and the timestamp identification of synchronous control based on simultaneity is used as the tensioning numerical control parameters, which are feasible after simulation testing.

[0024] Furthermore, in the data analysis, the key stress points in the prestressed tendons and the points where stress fluctuations exceed the limit are focused on. These key points are the areas where stress concentration or overload is most likely to occur during the tensioning process of the component, so they are specially marked and monitored as prestressed points. By identifying these prestressed points, the behavior of the component under stress can be better understood, providing an important basis for subsequent tensioning adjustments. At the same time, the determined prestressed points are the targets for subsequent construction monitoring, and tensioning feedback adjustment is performed by monitoring stress data.

[0025] In addition, combined with the relative relationship between tension distribution parameters, prestress distribution and elongation characteristics of prestressed tendons, combined with the tension simulation data, the influence of the force applied by the jack on the deformation of the entire component is deeply explored. Based on the relative relationship between the data, the mathematical relationship between the tension force and the deformation of the component is established as a benchmark. It is used to guide the subsequent actual construction. This information provides a theoretical basis for achieving more accurate tension control and optimization, making the entire tension process more scientific and reasonable.

[0026] Furthermore, after determining the tensioning simulation data, step S1 of the present application also includes: traversing the tensioning simulation data to determine the risk characteristics based on tensioning stability and safety; based on the risk characteristics, performing time and space node identification on the tensioning CNC parameters; introducing the principle of automatic balance, slow release, pressure relief and tensioning force closed-loop feedback to perform operational compensation of the tensioning CNC parameters.

[0027] After determining the tension simulation data, the stability and safety of the tension process are evaluated to identify risk characteristics. By systematically analyzing the force applied by each jack and the stress state of the component, potential risk factors can be found, such as stress concentration or uneven distribution, which are key factors affecting component safety.

[0028] Based on the identified risk characteristics, the time phase nodes of each risk characteristic in the whole tensioning cycle and the spatial position of the jack to which it belongs are determined as the time-space nodes. Next, the tensioning CNC parameters are marked with time-space nodes, that is, the points where risks may exist are marked at specific times and locations, so as to achieve accurate monitoring and management of the tensioning process. At the same time, this mark is the key monitoring target in subsequent actual construction. This time-space mark helps to quickly respond to potential safety hazards and ensure the efficiency and safety of the construction process.

[0029] Finally, the principle of automatic balancing slow-release pressure relief and tension force closed-loop feedback is introduced to compensate for the tensioning CNC parameters. The automatic balancing slow-release pressure relief technology can be used to adaptively adjust the output parameters of the jack to prevent beam slippage and avoid impact on the clip, ensuring the safety and stability of the tensioning process. The principle of tension force closed-loop feedback is used to monitor feedback and correct deviations during real-time construction.

[0030] In summary, the force of the jack during construction can be adjusted in real time, making the tensioning process more stable and reliable, and making adaptive adjustments according to the actual scene conditions. When facing risks, the system can automatically execute compensation strategies to reduce stress fluctuations and ensure that the overall components remain stable within the predetermined range. This series of measures significantly improves the control accuracy of the tensioning process and the safety of the components, providing a strong guarantee for the construction quality of prefabricated buildings.

[0031] S2: combining the basic tensioning information, taking the static single point error and the dynamic trend error as the benchmark, performing data-driven training on the self-correction module; Furthermore, the training of the self-correcting module, step S2 of the present application also includes: supervising the training of the static correction block with the global prestressed balance error and the single-point deviation error of the single-phase node; supervising the training of the dynamic correction block with the global prestressed balance deviation and the single-point deviation error of the multi-phase node; and laterally connecting the static correction block and the dynamic correction block to generate the self-correcting module.

[0032] In the embodiment of the present application, based on the basic tensioning information, the tensioning standard under standard construction conditions is determined, and sample data collection and training are performed based on the static single-point error deviation and the dynamic trend deviation to construct the self-correcting module.

[0033] Specifically, we searched engineering big data, called up the tensioning data records, and conducted in-depth analysis to identify the static single-point deviation that occurred during the tensioning process, that is, the force deviation of a certain jack at a certain moment, the stress deviation of a certain prestressed point, etc. At the same time, we must also pay attention to the dynamic trend deviation, which reflects the trend and pattern of force changes during the entire tensioning process. Based on the dynamic and static integration of the tensioning data records, we determined the correction framework and conducted sample-driven training.

[0034] In the data-driven training process, for example, machine learning principles will be used to establish a self-correction model based on the above-mentioned static and dynamic errors. Through training with a large amount of historical data, the model can learn the best correction strategy in different situations to cope with future tensioning tasks. The goal of this self-correction module is to monitor and evaluate the deviations in the tensioning process in real time and make timely adjustments to ensure the accuracy of force application and the stability of the component.

[0035] In an embodiment of the present application, the correction architecture is a dual-channel network structure, including a static correction block based on a single time node, and a dynamic correction time zone based on multiple time nodes, and a lateral interaction channel is established between the static correction block and the dynamic correction block. The lateral interaction channel is used to perform dual-block analysis and interaction during the decision-making correction process to improve the accuracy of decision-making.

[0036] Sample-driven training is performed for the correction architecture. Specifically, in the process of building the self-correction module, the global prestress balance error and single-point deviation error of a single-phase node are first used as supervisory signals, that is, the deviation of a single position and the global position balance fluctuation deviation under the coordination of multiple jacks, to train the static correction block. By analyzing the prestressed state at different time nodes, the model can identify the deviation of the force applied by each jack and generate the corresponding correction strategy as the underlying logic, and sample-driven training is performed to generate a static correction block. The static correction mechanism focuses on adjusting the force at a specific moment to ensure that the force applied by each jack is consistent with the preset target.

[0037] At the same time, the dynamic correction block will be trained for the global prestress equilibrium deflection and single-point deflection error of multi-phase nodes. The training process focuses on analyzing the trends and changes in the tensioning process, and adjusting the force strategy in real time to adapt to the changes that may occur during the entire tensioning cycle. For example, by combining the data of the upper time node for trend change analysis, the dynamic correction block can quickly respond to the change of force through the learning of sample data, ensuring that the component can maintain a good prestressed state at all stages.

[0038] Finally, the static correction block is connected to the dynamic correction block laterally to form a complete self-correction module. Through this integration, the system can fully monitor and adjust the tensioning process at both static and dynamic levels to achieve real-time correction. It can effectively improve the accuracy and safety of the tensioning process and provide more solid technical support for the intelligent construction of prefabricated buildings.

[0039] S3: The tensioning numerical control parameters respond to the intelligent tensioning pump station, assisting the upper industrial computer to perform numerical control management of the full cycle of jack tensioning, and the intelligent tensioning pump station is connected to at least one jack; In the embodiment of the present application, the tensioning numerical control parameters are managed by an intelligent tensioning pump station, which is responsible for controlling and adjusting the force of at least one connected jack. The pump station feeds back data to the upper industrial computer based on the tensioning numerical control parameters collected in real time to ensure accurate control of the entire tensioning process.

[0040] During the whole tensioning cycle, the intelligent tensioning pump station cooperates with the upper industrial computer to implement numerical control management. From the initial force application to the final tensioning, the intelligent tensioning pump station controls the force of the connected jacks, and the upper industrial computer performs upper-level overall management of multiple jacks to achieve orderly and rapid response. Through the integrated management based on the upper working condition machine, data tracing and positioning are facilitated.

[0041] The working status and stress of each jack can be monitored in real time. Through automated control, the system can adjust the force in time and optimize the tensioning effect according to the preset tensioning target and real-time feedback.

[0042] The integrated control mechanism in the embodiment of the present application not only improves the accuracy of the tensioning operation, but also effectively reduces the need for manual intervention and reduces the risks caused by human factors. Ultimately, the collaborative work of the intelligent tensioning pump station and the industrial computer ensures that the prefabricated building components reach the optimal prestressed state during the construction process, improving the overall construction efficiency and safety.

[0043] S4: synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; In the embodiment of the present application, stress sensors are respectively configured at each prestressing point to monitor the overall stress during the construction process to determine the force equilibrium state. Displacement sensors are installed on each jack to monitor the elongation and displacement of the prestressed tendons during the construction process.

[0044] As the tensioning construction progresses, the stress sensing data and elongation sensing data are monitored synchronously to achieve a comprehensive assessment of the component status. The stress sensing data is collected in real time by the stress sensors configured at the prestressing points. These sensors can accurately reflect the stress changes generated by the components during the tensioning process. At the same time, the elongation sensing data is collected by the displacement sensors installed on each jack to monitor the deformation of the component caused by the force applied by the jack.

[0045] By integrating these two types of data, the system can locate deviations and make correction decisions in the self-correction module. By analyzing the relationship between stress and displacement data, it can identify whether there are excessive stress fluctuations or elongation deviations. Once a deviation is detected, the system will generate a corresponding correction strategy and adjust the force parameters of the jack in time to restore the prestressed state of the component.

[0046] The deviation correction mechanism based on real-time data not only improves the stability and safety of the tensioning process, but also effectively copes with various uncertainties that arise during the construction process, thereby ensuring the overall performance and reliability of prefabricated building components. Through precise deviation management, efficient and safe construction goals can ultimately be achieved.

[0047] S5: The deviation correction strategy responds to the upper-level industrial computer in the intelligent tension pump station to perform numerical control compensation management of the feedback target.

[0048] During this process, the upper industrial computer determines the corresponding adjustment jacks and adjustment parameters according to the correction instructions generated by the self-correction module, and makes real-time adjustments to the force applied by each jack. The feedback mechanism ensures that the system can respond quickly to the monitored deviations and optimize the accuracy and stability of the tensioning process.

[0049] Specifically, by identifying stress sensing and displacement sensing data, as well as the identified additional stress and deviation trends, combined with the tension-stretching relationship, the required compensation amount is calculated, the corresponding control instructions are generated, and these instructions are transmitted to the connected jacks through the intelligent tension pump station.

[0050] Through this CNC compensation management, the system can not only achieve dynamic adjustment during the tensioning process, but also effectively respond to various changes in the construction environment to ensure that the prestressed state of the components is always within a safe and stable range. The implementation of this strategy has significantly improved the level of intelligence and flexibility of construction, and adaptive feedback correction has been carried out according to working conditions, reducing the need for manual intervention and improving overall construction efficiency and safety.

[0051] Furthermore, before generating the deviation correction strategy, step S4 of the present application also includes: setting a preset adjustment window; based on the static deviation correction block, making a deviation over-limit judgment and generating static deviation data; identifying the upper-level adjustment time node, if the time span based on the upper-level adjustment time node is less than the preset adjustment window, combining with the dynamic deviation correction block, identifying the dynamic deviation and making a favorable direction judgment; if the deviation trend is in a favorable direction, no deviation correction feedback instruction is generated; if the time span is greater than the preset adjustment window, or the deviation trend is in a bad direction, a deviation correction feedback instruction is generated.

[0052] In the embodiment of the present application, the preset adjustment window is a self-set time interval for adjusting the response, which can be self-set based on expert experience, so as to effectively monitor and manage the deviations in the tensioning process. Based on the static deviation correction block, the stress sensing data and the elongation sensing data are analyzed for single-point deviation over-limit analysis, balanced fluctuation over-limit analysis between stress points, and balanced over-limit analysis of the elongation data to generate static deviation data. These data are used to identify whether there is a significant force deviation at a specific moment, and help evaluate the stability of the current tensioning state.

[0053] When there is a static deviation of a single time node, a dynamic trend determination is further performed. For example, if there is an adjustment of the upper time node and the current time is in the response time period, even if there is a static deviation, as long as the deviation trend is a favorable trend towards recovery, it is considered a normal operating state and no adjustment is required.

[0054] Next, identify the upper-level adjustment time node and analyze the tensioning trend. If the time span based on the upper-level adjustment time node is less than the preset adjustment window, it is still in the response time zone of the upper-level adjustment. In this case, the system identifies the dynamic deviation and makes a favorable direction judgment. If the deviation trend is judged to be favorable, it means that the stability of the tensioning process is good, and the system will not generate a correction feedback instruction to avoid unnecessary intervention.

[0055] On the contrary, if the time span is greater than the preset adjustment window, it indicates that it is in a normal operating state, and there is a need for adjustment, and feedback adjustment of the current static deviation is required. When the time span is less than the preset adjustment window and the deviation trend is judged to be in a bad direction, it indicates that although it is in the response stage of the upper adjustment, the response state is abnormal, and a correction feedback instruction is generated. The above instructions are used to adjust the force of the jack in real time to restore the prestressed state of the component and ensure construction safety. Through this flexible adjustment mechanism, the system can efficiently respond to various challenges in a dynamic construction environment and ensure the precise control and reliability of the tensioning process.

[0056] Furthermore, after receiving the stress sensing data, step S4 of the present application also includes: based on the stress sensing data, identifying additional stress with prestress balance as the baseline condition; if additional stress exists, tracing the source and predicting the stress source to determine an additional prediction trend; and performing tension adjustment management based on the additional prediction trend.

[0057] Specifically, in the further operation of the self-correcting module, through the stress sensing data, with the prestress balance as the baseline condition, it is determined whether there is external intervening stress that affects the current tension balance state, that is, whether additional stress concentration occurs under the normal prestressed state, which may cause structural instability or damage, and it is determined as additional stress.

[0058] If additional stress is identified, source location and stress source prediction will be performed. Not only the size of the additional stress needs to be determined, but also its source needs to be analyzed to find out the specific cause of the additional stress. The impact of the stress source can be controlled by tracing back the additional stress source. For example, data flow tracking can be performed through retrospective analysis of historical data to predict the possible impact of additional stress. If the additional stress source cannot be effectively controlled, it is necessary to predict the subsequent impact of the stress source. Specifically, the characteristics of the stress source itself can be mined and analyzed, such as historical record data, to determine the additional prediction trend.

[0059] Since it is impossible to control by adjusting the additional stress source, compensatory adjustments are required from the tension control aspect. Tension adjustment management is performed based on the identified additional prediction trend. This management strategy will adjust the force of the jack in time according to the predicted stress changes to cope with the impact of the additional stress and ensure that the components are always maintained in a safe prestressed state. This adjustment mechanism based on real-time data and predictive analysis has significantly improved the construction safety and structural stability of prefabricated buildings.

[0060] The prestressed intelligent tensioning method for assembled building components provided in this application has the following technical effects: 1. Conduct three-dimensional simulation tests and visual attention monitoring to determine basic tensioning information and train the self-correction module, control the intelligent tensioning pump station, assist the upper industrial computer in CNC management of the entire cycle of jack tensioning, synchronously monitor and receive sensor data for deviation positioning and correction decisions, generate correction strategies for CNC compensation management of feedback targets, and solve the technical problems of insufficient tensioning intelligence and flexibility in existing technologies, poor adaptability to complex working conditions, and inability to achieve full-cycle effective operations that match the actual working conditions. Determine effective construction information through simulation and attention analysis, and conduct stress sensing layout, combine with the self-correction module for dynamic and static analysis and decision-making, improve the intelligence and flexibility of tensioning control, and perform reasonable and effective operations in accordance with the actual working conditions.

[0061] 2. Carry out three-dimensional simulation testing and visual attention monitoring of multi-top collaborative tensioning, determine the underlying layout mechanism based on the tensioning CNC parameters, prestressing points and tensioning elongation relationship, and the initial reasonable planning lays a solid foundation for subsequent work feedback.

[0062] 3. Modeling is done based on static single-point deviation and dynamic trend deviation, combining static and dynamic methods to accurately measure the actual stress field conditions and avoid redundant or invalid feedback.

[0063] 4. Assist the upper industrial computer to carry out the overall management of multiple jacks to achieve targeted and orderly control. At the same time, it can quickly trace and analyze data flow to improve control and decision-making efficiency.

[0064] Embodiment 2 Based on the same inventive concept as the prestressed intelligent tensioning method of assembled building components in the aforementioned embodiment, Figure 2 As shown, the present application provides a prestressed intelligent tensioning device for assembled building components, the device comprising: An information determination unit 11, the information determination unit 11 is used for interactive tensioning component configuration, performing three-dimensional simulation testing and visual attention monitoring of multi-top coordinated tensioning, and determining basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationships, and the visual attention target is stress fluctuation; A module training unit 12, the module training unit 12 is used to combine the basic tensioning information, take the static single point error deviation and the dynamic trend deviation as the benchmark, and perform data-driven training on the self-correction module; A tension control unit 13, the tension control unit 13 is used for the tension numerical control parameters to respond to the intelligent tension pump station, and assist the upper industrial computer to perform numerical control management of the full cycle of jack tensioning, and the intelligent tension pump station is connected to at least one jack; A feedback correction unit 14, the feedback correction unit 14 is used to synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; The feedback compensation unit 15 is used for the correction strategy to respond to the upper industrial computer in the intelligent tension pump station and perform numerical control compensation management of the feedback target.

[0065] Among them, the information determination unit 11 is also used to execute the following steps: perform three-dimensional simulation of the tensioning component configuration, determine the target tensioning configuration by adjusting the collaborative jack and the tensioning mode, wherein synchronous symmetrical tensioning and multi-step symmetrical tensioning are tensioning modes; lock the target tensioning configuration, perform tensioning evolution by monitoring the force characteristics, and determine the tensioning simulation data; determine the basic tensioning information by analyzing the tensioning simulation data.

[0066] Among them, the information determination unit 11 is also used to perform the following steps: traverse the tensioning simulation data to determine the tensioning CNC parameters of the entire tensioning cycle; use the key stress points and stress fluctuation excess points in the prestressed tendons as the prestressed points; and use the tensioning distribution parameters-prestress distribution-prestressed tendon elongation characteristics to explore the tensioning and elongation relationship.

[0067] Among them, the information determination unit 11 is also used to perform the following steps: traverse the tensioning simulation data to determine the risk characteristics based on tensioning stability and safety; based on the risk characteristics, perform time and space node identification on the tensioning CNC parameters; introduce the principle of automatic balance, slow release, pressure relief and tensioning force closed-loop feedback to perform operational compensation of the tensioning CNC parameters.

[0068] Among them, the module training unit 12 is also used to perform the following steps: supervise the training of the static correction block with the global prestress balance error and single-point deviation error of the single-phase node; supervise the training of the dynamic correction block with the global prestress balance deviation and single-point deviation error of the multi-phase node; laterally connect the static correction block and the dynamic correction block to generate the self-correction module.

[0069] Among them, the feedback correction unit 14 is also used to perform the following steps: setting a preset adjustment window; based on the static correction block, making a deviation over-limit judgment and generating static deviation data; identifying the upper-level adjustment time node, if the time span based on the upper-level adjustment time node is less than the preset adjustment window, combining with the dynamic correction block, identifying the dynamic deviation and making a favorable direction judgment; if the deviation trend is in a favorable direction, no correction feedback instruction is generated; if the time span is greater than the preset adjustment window, or the deviation trend is in a bad direction, a correction feedback instruction is generated.

[0070] Among them, the feedback correction unit 14 is also used to perform the following steps: based on the stress sensing data, with prestress balance as the baseline condition, identify additional stress; if additional stress exists, perform source tracing and stress source prediction to determine additional prediction trends; based on the additional prediction trends, perform tension adjustment management.

[0071] Through the above-mentioned detailed description of the prestressed intelligent tensioning method for prefabricated building components in this specification, those skilled in the art can clearly understand the prefabricated building component prestressed intelligent tensioning method and device in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0072] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent prestressing method for assembled building components, characterized in that: The method comprises: Interactive tensioning component configuration, three-dimensional simulation test of multi-top coordinated tensioning and visual attention monitoring, determine basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationship, and the visual attention target is stress fluctuation; Combined with the basic tensioning information, the static single point error and the dynamic trend error are used as the benchmark to perform data-driven training on the self-correction module; The tensioning numerical control parameters respond to the intelligent tensioning pump station, assisting the upper industrial computer to perform numerical control management of the full cycle of jack tensioning, and the intelligent tensioning pump station is connected to at least one jack; Synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; The deviation correction strategy responds to the upper-level industrial computer in the intelligent tension pump station to perform numerical control compensation management of the feedback target.

2. The prestressed intelligent tensioning method for assembled building components according to claim 1, characterized in that: The determining of basic tensioning information includes: Performing three-dimensional simulation on the tensioning component configuration, and determining the target tensioning configuration by adjusting the coordinated jack and the tensioning mode, wherein synchronous symmetrical tensioning and multi-step symmetrical tensioning are tensioning modes; The target tension configuration is locked, tension evolution is performed by monitoring the stress characteristics, and tension simulation data is determined; The basic tension information is determined by analyzing the tension simulation data.

3. The prestressed intelligent tensioning method for assembled building components according to claim 2, characterized in that: Determining the basic tensioning information by analyzing the tensioning simulation data includes: Traversing the tensioning simulation data to determine the tensioning numerical control parameters of the full tensioning cycle; The key stress points and stress fluctuation exceeding limit points in the prestressed tendons are used as the prestressing points; The tension-stretching relationship is explored based on the tension distribution parameters-prestress distribution-prestressing tendon elongation characteristics.

4. The prestressed intelligent tensioning method for assembled building components according to claim 3, characterized in that: After determining the tension simulation data, including: Traversing the tension simulation data to determine risk characteristics based on tension stability and safety; Based on the risk characteristics, the tensioning numerical control parameters are marked with time and space nodes; The principle of automatic balanced slow-release pressure relief and tension force closed-loop feedback is introduced to carry out operational compensation of tension CNC parameters.

5. The prestressed intelligent tensioning method for assembled building components according to claim 1, characterized in that: The training self-correction module comprises: The global prestress equilibrium error and single-point deviation error of the single-phase node are used to supervise the training of the static deviation correction block. The dynamic deviation correction block is supervised and trained based on the global prestress equilibrium deviation and single-point deviation error of multi-phase nodes. The static deflection correction block and the dynamic deflection correction block are laterally connected to generate the self-deflection correction module.

6. The prestressed intelligent tensioning method for assembled building components according to claim 5, characterized in that: Before generating a correction strategy, include: Set the preset adjustment window; Based on the static deviation correction block, a deviation over-limit determination is performed to generate static deviation data; Identify the upper adjustment time node, and if the time span based on the upper adjustment time node is less than the preset adjustment window, identify the dynamic deviation and make an optimal direction determination in combination with the dynamic deviation correction block; If the deviation trend is in the favorable direction, no correction feedback instruction will be generated; If the time span is greater than the preset adjustment window, or the deviation trend is unfavorable, a correction feedback instruction is generated.

7. The prestressed intelligent tensioning method for assembled building components according to claim 1, characterized in that: After receiving the stress sensing data, it includes: Based on the stress sensing data, the additional stress is identified with the prestress balance as a baseline condition; If additional stress exists, trace the source and predict the stress source to determine the additional prediction trend; Based on the additional predicted trends, tension adjustment management is performed.

8. Prestressed intelligent tensioning device for assembled building components, characterized in that: The device is used to implement the prestressed intelligent tensioning method of assembled building components according to any one of claims 1 to 7, comprising: An information determination unit, the information determination unit is used for interactive tensioning component configuration, performing three-dimensional simulation testing of multi-top coordinated tensioning and visual attention monitoring, and determining basic tensioning information, wherein the basic tensioning information includes tensioning numerical control parameters, prestressing points and tensioning elongation relationships, and the visual attention target is stress fluctuation; A module training unit, the module training unit is used to combine the basic tensioning information, take the static single point error deviation and the dynamic trend deviation as the benchmark, and perform data-driven training on the self-correction module; A tensioning control unit, wherein the tensioning numerical control parameter responds to the intelligent tensioning pump station, and assists the upper industrial computer in numerical control management of the entire tensioning cycle of the jack, and the intelligent tensioning pump station is connected to at least one jack; A feedback correction unit, the feedback correction unit is used to synchronously monitor and receive stress sensing data and elongation sensing data, perform deviation positioning and correction decision-making in the self-correction module, and generate a correction strategy, wherein the stress sensing data is collected based on the stress sensor configured at the prestressing point, and the elongation sensing data is collected based on the displacement sensor installed on each jack; A feedback compensation unit is used for the correction strategy to respond to the upper industrial computer in the intelligent tension pump station to perform numerical control compensation management of the feedback target.

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