Tunnel pouring progress collaborative management method and system based on digital twinning

Abstract

The application provides a tunnel pouring progress collaborative management method and system based on digital twinning, and relates to the technical field of intelligent tunnel construction management. The method comprises the following steps: acquiring pouring progress data of a tunnel construction site, wherein the pouring progress data comprises pouring positions, time tags and material consumption data; acquiring preset pouring plan data and environmental monitoring data; constructing a digital twinning model of tunnel construction based on the pouring progress data; determining progress deviation data based on the digital twinning model, the preset pouring plan data and the environmental monitoring data; calculating equipment adjustment parameters based on the progress deviation data; and generating control instructions based on the equipment adjustment parameters to control on-site construction equipment. The application improves the precision, efficiency and automation level of tunnel pouring construction.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for tunnel construction, and in particular to a collaborative management method and system for tunnel pouring progress based on digital twins. Background Technology

[0002] With the rapid development of infrastructure construction in my country, the scale and complexity of tunnel projects are constantly increasing. Tunnel concrete pouring, as a crucial step in tunnel construction, directly affects the quality of the tunnel lining structure and the construction progress. Under the traditional tunnel pouring construction management model, monitoring of construction progress mainly relies on manual inspections and experience-based judgment. Management personnel grasp the pouring progress through on-site observation and regular recording. This method suffers from problems such as delayed information collection, inaccurate data, and difficulty in reflecting the overall construction situation.

[0003] Currently, some tunnel construction projects have introduced sensor monitoring and information management systems to assist in construction management. For example, environmental and structural data are collected by deploying temperature and humidity sensors, displacement sensors, and other equipment at the construction site, and then displayed through an information platform. However, existing technical solutions still have the following shortcomings: First, there is a lack of effective correlation and comparative analysis mechanisms between the data collected at the construction site and the construction plan, making it difficult to quickly and accurately identify deviations in construction progress; second, when a progress deviation is detected, adjustments to equipment parameters still rely on human experience for decision-making and operation, resulting in slow response speed and low adjustment accuracy; third, there is a lack of complete closed-loop management capabilities from data acquisition and deviation analysis to equipment control, with each stage of the construction process being isolated and unable to achieve coordinated operation.

[0004] Therefore, how to use digital twin technology to map and accurately analyze the progress of tunnel pouring construction, and on this basis realize the automated and collaborative control of construction equipment, has become a technical problem that urgently needs to be solved in the field of intelligent management of tunnel construction. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method and system for collaborative management of tunnel pouring progress based on digital twins, so as to solve the problems of inaccurate monitoring of tunnel pouring construction progress, untimely response to deviations, and low degree of automation of equipment control in the prior art.

[0006] In a first aspect, the present invention discloses a collaborative management method for tunnel pouring progress based on digital twins, comprising: acquiring pouring progress data at the tunnel construction site, the pouring progress data including pouring location, time stamp, and material consumption data; acquiring preset pouring plan data and environmental monitoring data; constructing a digital twin model of tunnel construction based on the pouring progress data; determining progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data; calculating equipment adjustment parameters based on the progress deviation data; generating control commands based on the equipment adjustment parameters, and controlling on-site construction equipment based on the control commands.

[0007] Optionally, acquiring the pouring progress data at the tunnel construction site includes: acquiring concrete surface height data and the movement trajectory data of the pouring equipment in the target construction area of ​​the tunnel; determining the spatial location data of the pouring equipment based on the movement trajectory data; acquiring the material consumption rate of the pouring equipment; calculating the concrete filling volume of the target construction area based on the concrete surface height data and preset tunnel outline data; and integrating the spatial location data, the material consumption rate, the time stamp, and the change in the concrete filling volume to generate the pouring progress data.

[0008] Optionally, obtaining the preset pouring plan data and environmental monitoring data includes: obtaining preset segmented pouring coordinates, material ratio parameters, and equipment speed thresholds, and using the segmented pouring coordinates, material ratio parameters, and equipment speed thresholds as the preset pouring plan data; obtaining temperature and humidity data and surrounding rock deformation data of the tunnel construction working face; calculating the environmental impact coefficient based on the temperature and humidity data, and converting the surrounding rock deformation data into spatial displacement data; associating the segmented pouring coordinates with the spatial displacement data, and combining the material ratio parameters with the environmental impact coefficient to obtain the environmental monitoring data.

[0009] Optionally, the step of constructing a digital twin model of tunnel construction based on the pouring progress data includes: generating a concrete state change function and an equipment operation schedule based on the time tags and material consumption data in the pouring progress data; generating time constraints based on the setting time parameter in the concrete state change function; dividing the tunnel construction area into multiple spatial blocks based on the pouring location in the pouring progress data; associating the material consumption data with the corresponding spatial blocks to obtain a target spatial block; and constructing the digital twin model based on the time constraints, the equipment operation schedule, and the target spatial block.

[0010] Optionally, determining the progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data includes: generating a planned construction trajectory in the digital twin model based on the segmented pouring coordinates in the preset pouring plan data; correcting the planned construction trajectory based on the spatial displacement data in the environmental monitoring data to obtain a baseline construction trajectory; acquiring the boundary coordinate set and pouring rate of the concrete distribution pattern in the digital twin model; calculating the positional overlap between the boundary coordinate set and the baseline construction trajectory; calculating the volume difference based on the pouring rate and the material mix parameters in the preset pouring plan data; and using the positional overlap and the volume difference as the progress deviation data.

[0011] Optionally, the step of calculating equipment adjustment parameters based on the schedule deviation data includes: obtaining the material strength variation curve in the preset pouring plan data; calculating the equipment safety margin based on the equipment movement path constraints in the environmental monitoring data and the position overlap in the schedule deviation data, wherein the equipment safety margin includes a horizontal movement threshold and a vertical lifting range; obtaining temperature gradient data in the digital twin model and superimposing the temperature gradient data with the environmental impact coefficient in the environmental monitoring data to obtain an environmental interference coefficient; calculating the product of the material strength variation curve and the volume difference in the schedule deviation data to obtain a pouring compensation coefficient; determining a first movement priority based on the horizontal movement threshold and calculating the difference between the vertical lifting range and the position overlap to obtain a second movement priority; calculating the product of the environmental interference coefficient and the solidification time parameter in the preset pouring plan data to obtain a time adjustment coefficient; and combining the pouring compensation coefficient, the first movement priority, the second movement priority, and the time adjustment coefficient to obtain the equipment adjustment parameters.

[0012] Optionally, the step of generating control commands based on the equipment adjustment parameters and controlling the on-site construction equipment based on the control commands includes: proportionally converting the pouring compensation coefficient in the equipment adjustment parameters to obtain a pumping pressure adjustment coefficient; determining a spatial movement weight based on the first movement priority and the second movement priority in the equipment adjustment parameters, and generating the movement target coordinates of the pouring equipment based on the spatial movement weight; calculating the product of the time adjustment coefficient in the equipment adjustment parameters and a preset solidification time parameter to obtain a vibration time offset; generating the control commands based on the pumping pressure adjustment coefficient, the movement target coordinates, and the vibration time offset, wherein the control commands include pressure control data, position coordinate data, and time control data; sending the pressure control data to the pump truck's pressure controller, sending the position coordinate data to the pump truck's navigation device, and sending the time control data to the vibrator's timing controller to control the pump truck and the vibrator.

[0013] Secondly, this invention discloses a collaborative management system for tunnel pouring progress based on digital twins, comprising: a first acquisition module for acquiring pouring progress data at the tunnel construction site, the pouring progress data including pouring location, time stamp, and material consumption data; a second acquisition module for acquiring preset pouring plan data and environmental monitoring data; a construction module for constructing a digital twin model of the tunnel construction based on the pouring progress data; a determination module for determining progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data; a calculation module for calculating equipment adjustment parameters based on the progress deviation data; and a control module for generating control commands based on the equipment adjustment parameters and controlling the on-site construction equipment based on the control commands.

[0014] Thirdly, the present invention discloses a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to realize the aforementioned collaborative management method for tunnel pouring progress based on digital twins.

[0015] Fourthly, the present invention discloses a computer storage medium storing a computer program, which, when executed by a computer, implements the aforementioned method for collaborative management of tunnel pouring progress based on digital twins.

[0016] In this invention, the pouring progress data of the tunnel construction site can be obtained, as well as the preset pouring plan data and environmental monitoring data; a digital twin model of the tunnel construction can be constructed based on the pouring progress data; progress deviation data can be determined based on the digital twin model, the preset pouring plan data and the environmental monitoring data; equipment adjustment parameters can be calculated based on the progress deviation data; and control commands can be generated based on the equipment adjustment parameters to control the on-site construction equipment.

[0017] Therefore, the method of this invention can fuse multi-source data from the tunnel pouring construction site to construct a digital twin model, realizing the mapping of the physical construction scene in virtual space. This allows construction managers to comprehensively and accurately grasp the pouring progress. Simultaneously, by comprehensively comparing the digital twin model with the preset pouring plan and environmental data, progress deviations can be quickly and accurately identified, and corresponding equipment adjustment parameters can be automatically calculated. Ultimately, control commands that can be directly issued to on-site construction equipment such as pump trucks and vibrators are generated, thus achieving closed-loop management of the entire process from data acquisition, model construction, deviation analysis to equipment control. This significantly improves the accuracy and efficiency of tunnel pouring construction, reduces quality problems caused by delays in human judgment and operational errors, lowers construction costs, and enhances the overall safety and reliability of tunnel projects. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a collaborative management method for tunnel pouring progress based on digital twins disclosed in this invention; Figure 2 This is a schematic diagram of a pouring progress data acquisition process disclosed in this invention; Figure 3 This is a schematic diagram of a digital twin model construction process disclosed in this invention; Figure 4 This is a schematic diagram of a schedule deviation determination and equipment adjustment process disclosed in this invention; Figure 5 This is a schematic diagram of a collaborative management system for tunnel pouring progress based on digital twins disclosed in this invention. Figure 6 This is a structural diagram of a computing device disclosed in this invention. Detailed Implementation

[0020] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Currently, tunnel pouring construction management mainly relies on manual inspections and traditional information systems for progress monitoring. This results in problems such as a lack of effective correlation between data collection and construction plans, slow response to deviations due to reliance on manual experience, and a lack of closed-loop management capabilities. Consequently, the construction accuracy and efficiency are insufficient to meet the ever-increasing quality requirements of modern tunnel engineering.

[0022] To overcome the aforementioned technical problems, this invention discloses a method and system for collaborative management of tunnel pouring progress based on digital twins. It can map the construction progress by constructing a digital twin model, and achieve automated collaborative control of construction equipment through automatic analysis of progress deviations and intelligent calculation of equipment adjustment parameters, thereby significantly improving the accuracy, efficiency and safety of tunnel pouring construction.

[0023] See Figure 1 As shown in the figure, this invention discloses a collaborative management method for tunnel pouring progress based on digital twins, including: Step S101: Obtain the pouring progress data at the tunnel construction site. The pouring progress data includes the pouring location, time tag, and material consumption data.

[0024] In this embodiment, it is first necessary to obtain the pouring progress data at the tunnel construction site in order to fully understand the current actual progress of the construction. For example... Figure 2 Specifically, it is necessary to obtain data on the concrete surface height of the target construction area of ​​the tunnel and the movement trajectory data of the pouring equipment. The concrete surface height data can be obtained by using equipment such as laser scanners or depth cameras deployed above the construction area; this data reflects the actual height changes of the surface during concrete pouring. The movement trajectory data of the pouring equipment can be obtained by using positioning devices installed on the equipment, such as UWB ultra-wideband positioning systems or BeiDou / GPS positioning modules, to acquire the equipment's movement trajectory in the construction space.

[0025] Furthermore, it is necessary to determine the spatial location data of the pouring equipment based on the movement trajectory data. Specifically, coordinate transformation and filtering can be performed on the movement trajectory data to convert the original positioning data into spatial location data consistent with the tunnel construction coordinate system, and to remove positioning noise and outliers, thereby obtaining the precise spatial location of the pouring equipment at each point in time.

[0026] Simultaneously, it is necessary to obtain the material consumption rate of the pouring equipment. The material consumption rate can be obtained through flow sensors installed on the concrete pumping pipeline or by monitoring the relationship between the discharge rate of the mixing plant and time. This data characterizes the actual amount of concrete consumed per unit time and is an important parameter for evaluating pouring efficiency and predicting completion time.

[0027] Furthermore, based on concrete surface height data and preset tunnel profile data, the concrete filling volume of the target construction area needs to be calculated. The preset tunnel profile data refers to the standard cross-sectional data of the tunnel design, including the tunnel's designed outer and inner profile information. By spatially comparing the actually collected concrete surface height data with the tunnel profile data, the actual concrete filling volume of the target construction area can be calculated using a volume integration method. For example, the construction area can be discretized into multiple sections along the tunnel's longitudinal direction, the actual concrete filling area can be calculated at each section, and then the total filling volume can be obtained by integrating along the longitudinal direction.

[0028] Finally, spatial location data, material consumption rates, time stamps, and changes in concrete filling volume are integrated to generate pouring progress data. Specifically, various data types are aligned and merged according to a unified time stamp to form a comprehensive dataset containing pouring location, time stamps, and material consumption data, serving as the foundational data source for building a digital twin model. In this way, through the fusion of multi-source data, the actual progress of the pouring construction can be comprehensively reflected from multiple dimensions such as spatial location, material consumption, and volume changes, laying a data foundation for subsequent precise analysis and intelligent control.

[0029] Step S102: Obtain preset pouring plan data and environmental monitoring data.

[0030] In this embodiment, it is necessary to obtain preset pouring plan data and environmental monitoring data for comparison and analysis with actual construction data. Specifically, it is necessary to obtain preset segmented pouring coordinates, material proportioning parameters, and equipment speed thresholds. The segmented pouring coordinates are the start and end coordinates of each pouring segment predetermined according to the tunnel construction design scheme, representing the spatial planning of the pouring process. The material proportioning parameters include the water-cement ratio, aggregate ratio, and admixture dosage of the concrete, which determine the workability and strength development characteristics of the concrete. The equipment speed thresholds are the maximum and minimum allowable movement speeds of the pouring equipment during construction, and are important constraints to ensure construction quality and safety. The aforementioned segmented pouring coordinates, material proportioning parameters, and equipment speed thresholds are used as the preset pouring plan data.

[0031] Simultaneously, it is necessary to obtain temperature and humidity data and surrounding rock deformation data at the tunnel construction face. Temperature and humidity data can be collected by temperature and humidity sensors deployed near the tunnel construction face. This data reflects the temperature and humidity conditions of the construction environment and has a significant impact on the setting time and strength development of concrete. Surrounding rock deformation data can be collected by equipment such as strain sensors, convergence meters, or multi-point displacement meters installed on the tunnel perimeter. This data reflects the deformation of the surrounding rock during construction.

[0032] Furthermore, it is necessary to calculate the environmental impact coefficient based on temperature and humidity data. Specifically, based on the empirical relationship or theoretical model between temperature and humidity data and concrete setting characteristics, the degree of influence of current environmental conditions on concrete setting rate, strength development, and other properties can be calculated and expressed in the form of quantified coefficients. For example, when the temperature is low, the environmental impact coefficient will indicate the degree of prolongation of setting time; when the humidity is low, the environmental impact coefficient will indicate the degree of adverse impact of accelerated moisture evaporation on concrete strength. At the same time, it is necessary to convert the surrounding rock deformation data into spatial displacement data, that is, to convert the strain or deformation of each measuring point into a displacement vector in three-dimensional space, so that it can be directly used in the digital twin model.

[0033] Furthermore, it is necessary to correlate the segmented pouring coordinates with spatial displacement data to account for the impact of surrounding rock deformation on the spatial location of the pouring plan in subsequent analyses. Simultaneously, the material mix design parameters and environmental impact coefficients should be combined to assess the actual performance of the concrete under current environmental conditions. The correlated and combined data will then be used as environmental monitoring data. In this way, by effectively integrating the pre-set plan with environmental data, a dynamic reference benchmark that considers environmental factors can be established, providing a more accurate and reliable basis for comparison in subsequent deviation analysis.

[0034] Step S103: Based on the pouring progress data, construct a digital twin model of the tunnel construction.

[0035] In this embodiment, a digital twin model of the tunnel construction needs to be built based on the pouring progress data to map the physical construction scene in virtual space. For example... Figure 3 As shown, specifically, the first step is to generate a concrete state change function and an equipment operation schedule based on the time stamps and material consumption data in the pouring progress data. The concrete state change function describes the entire process of concrete state evolution from pouring to final solidification, including the time nodes and state characteristics of each stage such as initial flow dynamics, initial setting state, and final setting state. The input parameters for this function include mix proportion information from the material consumption data and factors such as ambient temperature. The equipment operation schedule records the actual working time, idle time, and maintenance time of each pouring device, providing a basis for subsequent scheduling optimization.

[0036] Furthermore, time constraints need to be generated based on the setting time parameter in the concrete state change function. The setting time parameter characterizes the time required for concrete to reach its initial and final setting after pouring. These time constraints require that subsequent pours be completed before the previous layer reaches its initial setting to ensure good interlayer bonding; simultaneously, vibration must be completed before the final setting to ensure effective compaction. These time constraints are crucial boundary conditions for digital twin models in simulating and optimizing construction processes.

[0037] Simultaneously, based on the pouring locations in the pouring progress data, the tunnel construction area needs to be divided into multiple spatial blocks. Specifically, the construction area can be divided into regular three-dimensional spatial blocks along the longitudinal and circumferential directions of the tunnel, according to the working range of the pouring equipment and the pouring sequence of the concrete. Each spatial block corresponds to a relatively independent pouring unit, with clear spatial boundaries and pouring sequence.

[0038] Furthermore, it is necessary to link the material consumption data to the corresponding spatial blocks to obtain the target spatial blocks. Specifically, based on the spatial location data of the pouring equipment at each time point and the material consumption data at the same moment, the concrete filling volume and filling rate of each spatial block in each time period can be determined. After linking the material consumption data with the spatial blocks, the resulting target spatial blocks not only contain spatial geometric information but also the corresponding historical material consumption records, thereby achieving data fusion of the spatial and material dimensions.

[0039] Finally, a digital twin model is constructed based on time constraints, equipment operation schedules, and target spatial blocks. This digital twin model establishes a one-to-one digital mirror image of the physical construction scenario in virtual space, including spatial division information of the construction area, concrete status and filling progress information of each block, equipment operation status and location information, and various time constraints. This model can receive data updates from the construction site, maintaining synchronization with the physical scenario and providing a virtual simulation platform for subsequent deviation analysis and decision optimization.

[0040] Step S104: Based on the digital twin model, the preset pouring plan data, and the environmental monitoring data, determine the progress deviation data.

[0041] In this embodiment, it is necessary to determine the progress deviation data based on the digital twin model, preset pouring plan data, and environmental monitoring data, in order to quantify the difference between the actual construction progress and the plan. For example... Figure 4As shown, specifically, the first step is to generate a planned construction trajectory in the digital twin model based on the segmented pouring coordinates in the preset pouring plan data. The planned construction trajectory is an ideal construction path generated in the three-dimensional space of the digital twin model according to the segmented pouring coordinates, representing the spatial trajectory that the pouring equipment should follow according to the construction plan.

[0042] Furthermore, the planned construction trajectory needs to be corrected based on spatial displacement data from environmental monitoring data to obtain a baseline construction trajectory. Since the surrounding rock may deform during tunnel construction, causing deviations between the actual construction space and the design space, the original planned construction trajectory needs to be corrected using spatial displacement data generated by surrounding rock deformation. The corrected baseline construction trajectory takes into account the influence of surrounding rock deformation, more realistically reflects the ideal pouring path under the current construction environment, and can serve as a reliable reference benchmark for schedule deviation analysis.

[0043] Simultaneously, it is necessary to obtain the set of boundary coordinates and pouring rate of the concrete distribution pattern in the digital twin model. The set of boundary coordinates is the set of three-dimensional boundary coordinate points of the actual concrete pouring range in the digital twin model, reflecting the actual spatial distribution pattern of the concrete. The pouring rate is the rate of change of the actual poured volume of concrete at the current moment.

[0044] Furthermore, it is necessary to calculate the positional overlap between the boundary coordinate set and the baseline construction trajectory. Positional overlap characterizes the degree of spatial agreement between the actual concrete distribution area and the planned construction trajectory. When the actual pouring proceeds exactly as planned, the positional overlap should approach its maximum value; when the actual pouring deviates from the plan, the positional overlap will decrease accordingly. The positional overlap can be calculated using methods such as the proportion of spatial overlap area or the three-dimensional intersection-union ratio.

[0045] Simultaneously, the volume difference needs to be calculated based on the pouring rate and the material proportion parameters in the preset pouring plan data. The volume difference represents the deviation between the actual pouring volume and the planned pouring volume under the current pouring rate and material proportion conditions. Specifically, the theoretically achievable pouring volume can be calculated based on the material proportion parameters, and then compared with the pouring volume estimated based on the actual pouring rate to obtain the volume difference.

[0046] Finally, the positional overlap and volume difference are used as schedule deviation data. Positional overlap reflects deviation in the spatial dimension, while volume difference reflects deviation in the material dimension. Together, they constitute a comprehensive schedule deviation assessment index, providing multi-dimensional deviation input for subsequent equipment adjustment parameter calculations.

[0047] Step S105: Calculate the equipment adjustment parameters based on the schedule deviation data.

[0048] In this embodiment, it is necessary to calculate equipment adjustment parameters based on schedule deviation data to determine the amount of equipment operation adjustment required to eliminate the deviation. Specifically, it is first necessary to obtain the material strength variation curve from the preset pouring plan data. The material strength variation curve describes the strength development law of concrete at different ages. This curve is affected by factors such as material ratio and curing conditions, and is an important basis for assessing whether the concrete has reached the strength required for construction at a specific time.

[0049] Furthermore, it is necessary to calculate the equipment safety margin based on the equipment movement path constraints in the environmental monitoring data and the positional overlap in the schedule deviation data. The equipment safety margin includes a horizontal movement threshold and a vertical lifting range, which characterize the spatial range within which the pouring equipment can be safely moved under the current construction conditions. The horizontal movement threshold limits the maximum distance the equipment can move in the horizontal direction, and the vertical lifting range limits the maximum lifting amplitude of the equipment in the vertical direction. These limitations must take into account both the physical constraints of the tunnel space and the bearing capacity and condition of the already poured concrete.

[0050] Simultaneously, it is necessary to acquire temperature gradient data from the digital twin model and superimpose this data with the environmental impact coefficient from environmental monitoring data to obtain the environmental interference coefficient. The temperature gradient data reflects the temperature distribution differences between the interior and surface of the poured concrete; excessively large temperature gradients may lead to concrete cracking. The environmental interference coefficient comprehensively reflects the overall impact of current environmental conditions and temperature gradients on the construction process.

[0051] Furthermore, it is necessary to calculate the product of the material strength variation curve and the volume difference in the schedule deviation data to obtain the pouring compensation coefficient. The pouring compensation coefficient represents the amount of additional pouring required to eliminate the volume deviation, and takes into account the constraints of material strength development factors on the timing and rate of compensation pouring.

[0052] Simultaneously, a first movement priority is determined based on a horizontal movement threshold, and a second movement priority is obtained by calculating the difference between the vertical lifting range and the positional overlap. The first and second movement priorities describe the adjustment priority and adjustment amount of the pouring equipment in the horizontal and vertical directions, respectively. When the positional deviation mainly occurs in the horizontal direction, the first movement priority is higher; when the positional deviation mainly occurs in the vertical direction, the second movement priority is higher.

[0053] Furthermore, the time adjustment coefficient is obtained by multiplying the environmental interference coefficient by the setting time parameter in the preset pouring plan data. The time adjustment coefficient represents the amount of adjustment that needs to be made to the time arrangement of each construction procedure under the current environmental conditions. For example, in a low-temperature environment, it may be necessary to appropriately extend the vibration time to ensure the compactness of the concrete.

[0054] Finally, the pouring compensation coefficient, the first movement priority, the second movement priority, and the time adjustment coefficient are combined to obtain the equipment adjustment parameters. These parameters comprehensively describe the adjustments required for the construction equipment from three dimensions: pouring volume compensation, spatial position adjustment, and time rhythm adjustment, providing a complete parameter basis for the subsequent generation of control commands.

[0055] Step S106: Generate control commands based on the equipment adjustment parameters, and control the on-site construction equipment based on the control commands.

[0056] In this embodiment, control commands that can be directly issued to the construction equipment need to be generated based on the equipment adjustment parameters, and the automated control of the on-site construction equipment is achieved through these control commands. Specifically, the pouring compensation coefficient in the equipment adjustment parameters first needs to be proportionally converted to obtain the pumping pressure adjustment coefficient. The pumping pressure adjustment coefficient represents the proportion by which the pumping pressure of the concrete pump truck needs to be adjusted relative to the current value. By adjusting the pumping pressure, the concrete delivery rate and pouring volume can be changed, thereby achieving compensation for volume deviation.

[0057] Furthermore, it is necessary to determine the spatial movement weights based on the first and second movement priorities in the equipment adjustment parameters, and then generate the target coordinates of the pouring equipment based on these weights. The spatial movement weights determine the proportion of movement allocated to the equipment in both the horizontal and vertical directions, while the target coordinates represent the precise three-dimensional spatial position the pouring equipment needs to reach next. By rationally allocating the movement weights, it can be ensured that the equipment moves along the optimal path to the target position required to eliminate spatial deviations.

[0058] Simultaneously, it is necessary to calculate the product of the time adjustment coefficient in the equipment adjustment parameters and the preset setting time parameter to obtain the vibration time offset. The vibration time offset represents the amount by which the working time of the vibrator needs to be increased or decreased relative to the standard working time under the current environmental and construction conditions. For example, when the ambient temperature is low, causing the concrete to set more slowly, the vibration time offset is positive, indicating that the vibration time needs to be appropriately extended to ensure the compaction effect of the concrete.

[0059] Furthermore, control commands need to be generated based on the pumping pressure adjustment coefficient, the coordinates of the moving target, and the vibration time offset. The control commands consist of three parts: pressure control data, position coordinate data, and time control data. The pressure control data includes the target value and adjustment rate of the pumping pressure; the position coordinate data includes the three-dimensional coordinates and speed of the moving target; and the time control data includes the start and stop times and duration of the vibrator.

[0060] Finally, the pressure control data is sent to the pump truck's pressure controller, the position coordinate data is sent to the pump truck's navigation equipment, and the time control data is sent to the vibrator's timing controller to control the pump truck and vibrator. This multi-device collaborative control method allows for synchronized optimization and adjustment of the pouring volume, pouring position, and vibration time, ensuring consistent coordination between all devices.

[0061] In this embodiment, by acquiring pouring progress data from the tunnel construction site and constructing a digital twin model, the construction progress is mapped in a virtual space. By comparing the digital twin model with preset plans and environmental data to determine progress deviations, rapid and accurate identification of deviations is achieved. Furthermore, by automatically calculating equipment adjustment parameters based on deviation data and generating control commands, intelligent collaborative control of the construction equipment is realized. This forms a complete closed-loop management chain from data acquisition, model construction, deviation analysis to equipment control, significantly improving the accuracy, efficiency, and automation level of tunnel pouring construction.

[0062] As a preferred embodiment, in determining schedule deviation data, in addition to calculating positional overlap and volume difference, a time-dimensional deviation analysis can be further introduced. Specifically, the actual completion time of each spatial block in the digital twin model can be compared with the planned completion time to calculate the time deviation value. When the time deviation value exceeds a preset time deviation threshold, the system automatically increases the adjustment priority of the corresponding block to ensure that the construction progress on the critical path is not affected. This supplementary time-dimensional analysis makes the deviation assessment more comprehensive and helps to make more reasonable control decisions in complex construction scenarios.

[0063] As a preferred embodiment, a safety verification can be performed after the equipment adjustment parameters are calculated but before generating control commands. Specifically, the calculated equipment adjustment parameters can be virtually simulated and verified in a digital twin model, simulating the construction state of the equipment after the adjustment parameters are executed, and checking for safety risks such as equipment collisions, concrete overflow, and excessive structural stress. Only equipment adjustment parameters that pass the safety verification will be converted into control commands and issued to the field equipment; otherwise, the system will recalculate the adjustment parameters or reduce the adjustment range until the safety requirements are met. This virtual pre-simulation mechanism can effectively prevent safety accidents caused by improper equipment adjustment and further improve the safety of the construction process.

[0064] As a preferred embodiment, to improve the system's ability to respond to emergencies, this method can also set up an anomaly early warning mechanism. When the surrounding rock deformation data in the environmental monitoring data exceeds a preset safe deformation threshold, or when the rate of change of temperature and humidity data exceeds a preset rate of change threshold, the system automatically enters an early warning mode. In early warning mode, the system will pause the current equipment adjustment parameter calculation process, reassess construction safety based on the latest environmental data, generate an emergency shutdown command if necessary, issue it to all construction equipment, and send alarm information to management personnel. After the abnormal situation is eliminated, the system recalculates the equipment adjustment parameters based on the latest environmental data and construction status, and resumes the normal collaborative management process. This anomaly early warning mechanism can minimize the impact of abnormal situations on the construction progress while ensuring construction safety.

[0065] See Figure 5 As shown in the figure, an embodiment of the present invention discloses a collaborative management system for tunnel pouring progress based on digital twins, comprising: The first acquisition module 51 is used to acquire pouring progress data at the tunnel construction site. The pouring progress data includes pouring location, time tag and material consumption data.

[0066] The second acquisition module 52 is used to acquire preset pouring plan data and environmental monitoring data.

[0067] Module 53 is used to construct a digital twin model of tunnel construction based on the pouring progress data.

[0068] The determination module 54 is used to determine the progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data.

[0069] The calculation module 55 is used to calculate the equipment adjustment parameters based on the schedule deviation data.

[0070] The control module 56 is used to generate control commands based on the device adjustment parameters, and to control the on-site construction equipment based on the control commands.

[0071] In this embodiment, through the collaborative work of various modules, the entire process of tunnel pouring construction can be managed in a closed loop. From data acquisition to deviation analysis to equipment control, each link is interconnected and executed automatically, which significantly improves the accuracy, efficiency and automation level of tunnel pouring construction.

[0072] In some embodiments, the first acquisition module 51 may specifically include: a surface height acquisition unit, used to acquire concrete surface height data and movement trajectory data of the pouring equipment in the target construction area of ​​the tunnel; a spatial location determination unit, used to determine the spatial location data of the pouring equipment based on the movement trajectory data; a material consumption acquisition unit, used to acquire the material consumption rate of the pouring equipment; a filling volume calculation unit, used to calculate the concrete filling volume of the target construction area based on the concrete surface height data and preset tunnel contour data; and a data integration unit, used to integrate the spatial location data, the material consumption rate, the time stamp, and the change in the concrete filling volume to generate the pouring progress data.

[0073] In some embodiments, the construction module 53 may specifically include: a function generation unit, used to generate a concrete state change function and an equipment operation schedule based on the time stamp and material consumption data in the pouring progress data; a constraint generation unit, used to generate time constraints based on the setting time parameter in the concrete state change function; a block division unit, used to divide the tunnel construction area into multiple spatial blocks based on the pouring position in the pouring progress data; a data association unit, used to associate the material consumption data with the corresponding spatial blocks to obtain a target spatial block; and a model construction unit, used to construct the digital twin model based on the time constraints, the equipment operation schedule, and the target spatial block.

[0074] In some embodiments, the control module 56 may specifically include: a pressure conversion unit, used to proportionally convert the pouring compensation coefficient in the equipment adjustment parameters to obtain a pumping pressure adjustment coefficient; a coordinate generation unit, used to determine the spatial movement weight based on the first movement priority and the second movement priority and generate the movement target coordinates of the pouring equipment; a time calculation unit, used to calculate the product of the time adjustment coefficient and the preset solidification time parameter to obtain the vibration time offset; an instruction generation unit, used to generate control instructions based on the pumping pressure adjustment coefficient, the movement target coordinates and the vibration time offset; and an instruction issuing unit, used to send the pressure control data, position coordinate data and time control data to the controller of the corresponding equipment respectively.

[0075] Furthermore, embodiments of the present invention also disclose a computing device. Figure 6 This is a structural diagram of a computing device 60 according to an exemplary embodiment. The contents of the diagram should not be construed as limiting the scope of the invention.

[0076] Figure 6This is a schematic diagram of a computing device 60 provided in an embodiment of the present invention. The computing device 60 specifically includes: at least one processor 61, at least one memory 62, a power supply 63, a communication interface 64, an input / output interface 65, and a communication bus 66. The memory 62 stores a computer program, which is loaded and executed by the processor 61 to implement the relevant steps in the tunnel pouring progress collaborative management method based on digital twins disclosed in any of the foregoing embodiments. Furthermore, the computing device 60 in this embodiment can specifically be an industrial control computer or an edge computing server.

[0077] In this embodiment, the power supply 63 is used to provide operating voltage for each hardware device on the computing device 60; the communication interface 64 can create a data transmission channel between the computing device 60 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this invention, and is not specifically limited here; the input / output interface 65 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0078] In addition, the memory 62, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored thereon can include an operating system 621, computer programs 622, etc., and the storage method can be temporary storage or permanent storage.

[0079] The operating system 621 is used to manage and control the various hardware devices on the computing device 60 and the computer program 622, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the digital twin-based tunnel pouring progress collaborative management method executed by the computing device 60 as disclosed in any of the foregoing embodiments, the computer program 622 may further include computer programs capable of performing other specific tasks.

[0080] Furthermore, the present invention also discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed method for collaborative management of tunnel pouring progress based on digital twins. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0081] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0082] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory, memory, read-only memory, electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any storage medium of any form known in the art.

[0084] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A collaborative management method for tunnel pouring progress based on digital twins, characterized in that, include: Obtain pouring progress data at the tunnel construction site, including pouring location, time stamp, and material consumption data; Obtain pre-set pouring plan data and environmental monitoring data; Based on the pouring progress data, a digital twin model of the tunnel construction is constructed; Based on the digital twin model, the preset pouring plan data, and the environmental monitoring data, the progress deviation data is determined; Calculate equipment adjustment parameters based on the aforementioned schedule deviation data; Based on the device's adjusted parameters, control commands are generated, and the on-site construction equipment is controlled based on these control commands.

2. The method according to claim 1, characterized in that, The acquisition of pouring progress data at the tunnel construction site includes: Acquire data on the concrete surface height of the target construction area of ​​the tunnel and the movement trajectory data of the pouring equipment; Based on the movement trajectory data, the spatial location data of the pouring equipment is determined; Obtain the material consumption rate of the pouring equipment; Based on the concrete surface height data and the preset tunnel outline data, the concrete filling volume of the target construction area is calculated; The spatial location data, the material consumption rate, the time stamp, and the change in concrete filling volume are integrated to generate the pouring progress data.

3. The method according to claim 1, characterized in that, The acquisition of preset pouring plan data and environmental monitoring data includes: Obtain the preset segmented pouring coordinates, material ratio parameters, and equipment speed threshold, and use the segmented pouring coordinates, material ratio parameters, and equipment speed threshold as the preset pouring plan data; Obtain temperature and humidity data and surrounding rock deformation data at the tunnel construction face; The environmental impact coefficient is calculated based on the temperature and humidity data, and the surrounding rock deformation data is converted into spatial displacement data. The segmented casting coordinates are correlated with the spatial displacement data, and the material proportioning parameters are combined with the environmental impact coefficient to obtain the environmental monitoring data.

4. The method according to claim 1, characterized in that, The construction of a digital twin model of tunnel construction based on the pouring progress data includes: Based on the time tags and material consumption data in the pouring progress data, a concrete state change function and an equipment operation schedule are generated. Based on the setting time parameter in the concrete state change function, time constraints are generated. Based on the pouring location in the pouring progress data, the tunnel construction area is divided into multiple spatial blocks; The material consumption data is associated with the corresponding spatial blocks to obtain the target spatial block; The digital twin model is constructed based on the time constraints, the equipment operation schedule, and the target spatial block.

5. The method according to claim 1, characterized in that, The determination of progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data includes: Based on the segmented pouring coordinates in the preset pouring plan data, a planned construction trajectory is generated in the digital twin model; The planned construction trajectory is corrected based on the spatial displacement data in the environmental monitoring data to obtain the baseline construction trajectory; Obtain the set of boundary coordinates and pouring rate of the concrete distribution pattern in the digital twin model; Calculate the positional overlap between the boundary coordinate set and the reference construction trajectory; The volume difference is calculated based on the pouring rate and the material ratio parameters in the preset pouring plan data. The positional overlap and the volume difference are used as the progress deviation data.

6. The method according to claim 1, characterized in that, The calculation of equipment adjustment parameters based on the schedule deviation data includes: Obtain the material strength variation curve from the preset pouring plan data; Based on the equipment movement path constraints in the environmental monitoring data and the position overlap in the schedule deviation data, the equipment safety margin is calculated, which includes a horizontal movement threshold and a vertical lifting range. The temperature gradient data in the digital twin model is obtained, and the temperature gradient data is superimposed with the environmental impact coefficient in the environmental monitoring data to obtain the environmental interference coefficient. The casting compensation coefficient is obtained by multiplying the material strength variation curve with the volume difference in the progress deviation data. A first movement priority is determined based on the horizontal movement threshold, and a second movement priority is obtained by calculating the difference between the vertical lifting range and the position overlap. The time adjustment coefficient is obtained by multiplying the environmental interference coefficient by the solidification time parameter in the preset pouring plan data. The equipment adjustment parameters are obtained by combining the pouring compensation coefficient, the first movement priority, the second movement priority, and the time adjustment coefficient.

7. The method according to claim 6, characterized in that, The process of generating control commands based on the device's adjusted parameters, and controlling the on-site construction equipment based on the control commands, includes: The pouring compensation coefficient in the equipment adjustment parameters is proportionally converted to obtain the pumping pressure adjustment coefficient; Based on the first movement priority and the second movement priority in the equipment adjustment parameters, a spatial movement weight is determined, and the target coordinates of the pouring equipment are generated based on the spatial movement weight. The product of the time adjustment coefficient in the equipment adjustment parameters and the preset solidification time parameter is calculated to obtain the vibration time offset. Based on the pumping pressure adjustment coefficient, the moving target coordinates, and the vibration time offset, the control command is generated, and the control command includes pressure control data, position coordinate data, and time control data. The pressure control data is sent to the pressure controller of the pump truck, the position coordinate data is sent to the navigation device of the pump truck, and the time control data is sent to the timing controller of the vibrator to control the pump truck and the vibrator.

8. A collaborative management system for tunnel pouring progress based on digital twins, characterized in that, include: The first acquisition module is used to acquire pouring progress data at the tunnel construction site. The pouring progress data includes pouring location, time tag and material consumption data. The second acquisition module is used to acquire preset pouring plan data and environmental monitoring data; A construction module is used to build a digital twin model of the tunnel construction based on the pouring progress data; The determination module is used to determine the progress deviation data based on the digital twin model, the preset pouring plan data, and the environmental monitoring data; The calculation module is used to calculate the equipment adjustment parameters based on the schedule deviation data; The control module is used to generate control commands based on the device's adjustment parameters, and to control the on-site construction equipment based on the control commands.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a tunnel pouring progress collaborative management method based on digital twin as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a collaborative management method for tunnel pouring progress based on digital twins as described in any one of claims 1 to 7.

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