A diaphragm wall slotting verticality dynamic management and control method and system based on BIM

Abstract

The application discloses a kind of based on BIM's diaphragm wall trenching verticality dynamic control method and system, it is related to underground traffic hub new construction engineering control technical field, this method includes: establishing underground traffic hub diaphragm wall BIM model, along the depth of trench section division monitoring section, determine each section design depth and corresponding stratum normalised geological resistance index.By BIM integration multi-source monitoring, real-time acquisition horizontal deviation, mud density, grab swing angle frequency, groundwater level fluctuation and adjacent load additional lateral pressure.Calculation normalised verticality deviation index;Combined with geological resistance, swing angle frequency and mud density, according to dynamic prediction model, predicted verticality deviation index is obtained.Based on deviation error and change rate, from smooth closed-loop control law generation correction signal, drive guide mechanism adjusts grab posture.According to water level fluctuation, additional lateral pressure and verticality deviation, according to risk index model, calculate multi-factor dynamic risk index, when over early warning threshold, linkage BIM triggers corresponding grade early warning.

Description

Technical Field

[0001] This invention belongs to the field of control technology for new underground transportation hub construction projects, and particularly relates to a BIM-based method and system for dynamic control of the verticality of diaphragm wall trenching. Background Technology

[0002] Construction of new underground transportation hubs (such as urban rail transit underground integrated transportation hubs) typically requires the simultaneous progress of multiple work fronts, including deep foundation pit excavation, demolition and reconstruction of transfer halls, addition of safety exits and ventilation shafts, and renovation of existing passageways, while also involving trenching construction of the diaphragm walls for the new structures. These projects have the following characteristics: First, the hub project area experiences high pedestrian and vehicle traffic, necessitating zoned enclosure for construction, resulting in a short trenching window. Second, the hub project area retains existing operational facilities such as subway stations and transfer passageways, as well as underground pipelines; exceeding the verticality limit during trenching can easily lead to instability of adjacent structures or damage to pipelines. Third, construction proceeds continuously in sections and segments along the perimeter of the diaphragm wall foundation pit or along the route (i.e., the direction of construction), with the geological conditions, mud properties, and groundwater level of each trench section dynamically changing with the direction and depth of construction.

[0003] Existing methods for controlling the verticality of diaphragm wall trenching mainly rely on manual periodic readings of inclinometer readings or the inclination angle display on the trenching machine, using experience to determine whether correction is needed. This approach has the following shortcomings: First, there is a significant time lag between measurement and correction, making continuous closed-loop adjustment of the trenching machine's grab bucket posture difficult. Second, it fails to uniformly model multiple dynamic factors such as geological resistance, mud density fluctuations, groundwater level fluctuations, and loads from adjacent operational facilities, resulting in low accuracy in verticality trend prediction. Third, it lacks a two-way data link with the BIM model, preventing real-time reflection of on-site measured data in the digital twin model, hindering collaborative verification among design, supervision, and construction parties. Fourth, it lacks a risk warning mechanism coupled with the construction progress direction, leaving blind spots in the protection of adjacent operational facilities and underground pipelines within underground transportation hubs. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a BIM-based method for dynamic control of the verticality of diaphragm wall trenching, comprising: Step S1: Establish a BIM model of the diaphragm wall of the new underground transportation hub, divide several monitoring sections along the design depth direction of the trench section, and number and sort each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding stratum. Step S2: During the trenching construction process, the multi-source monitoring device integrated into the BIM model collects the measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals, and synchronizes them to the BIM model in real time. Step S3: Calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section; Step S4: Based on the normalized geological resistance index, the grab bucket swing angular frequency and mud density, calculate the predicted verticality deviation index according to the preset dynamic deviation prediction model. Step S5: Calculate the real-time correction control signal based on the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the trenching machine guide correction mechanism to adjust the grab bucket posture. Step S6: Based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, calculate the multi-factor dynamic risk index according to the preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, trigger the corresponding level of early warning by linking the BIM model.

[0005] Further, in step S7: based on the normalized verticality deviation index and its confidence weight, and the multi-factor dynamic risk index, the BIM digital twin verticality deviation index is calculated according to the preset time decay weighted fusion update model, and synchronously written into the component attributes of the corresponding trench segment of the BIM model along the construction progress direction, for use as a dynamic verification basis at the next monitoring time.

[0006] Further, step S8: Repeat steps S1-S7 along the depth direction of the trench segment until the total design depth is reached. Determine whether the BIM digital twin verticality deviation index meets the preset allowable deviation limit. If it meets the limit, proceed to the next trench segment along the construction progress direction. If it does not meet the limit, output correction suggestions and pause the transfer to the next trench segment until the verification is qualified and a continuous BIM acceptance record of diaphragm wall verticality along the construction progress direction is generated.

[0007] Furthermore, dynamic bias prediction models include:

[0008] in, For the first Each monitoring section at time The predicted verticality deviation index, For the first The design depth of each monitoring section The reference deviation coefficient, The deep saturation attenuation coefficient, For the first The normalized geological resistance index of the strata where each monitoring section is located It is the power exponent of the geological resistance index. For a moment Measured value of the grab bucket swing angular frequency. The swing angular frequency of the grab bucket is designed. The power exponent of the grab swing frequency ratio. For a moment Measured mud density Design a baseline density for the mud. This is the penalty coefficient for mud density deviation.

[0009] Furthermore, the calculated real-time correction control signals include:

[0010] in, For the first Each monitoring section at time The output real-time correction control signal, To guide the correction mechanism to its maximum output correction stroke, For the bias error sensitivity gain, For the first Each monitoring section at time The deviation error amount, This is the sensitivity steepness coefficient for the rate of change. For the first Rate of change of deviation error of each monitoring section The threshold for the rate of change of deviation that triggers correction.

[0011] Furthermore, calculate the first... Each monitoring section at time Deviation error include:

[0012] Calculate the first Rate of change of deviation error at each monitoring section include:

[0013] in, For the first Each monitoring section at time The normalized verticality deviation index, The time interval between adjacent monitoring times. For the first Each monitoring section at time The normalized verticality deviation index.

[0014] Furthermore, risk index models include:

[0015] in, For the first Each monitoring section at time A multi-factor dynamic risk index. For a moment The amplitude of groundwater level fluctuations This serves as the benchmark amplitude for early warning of groundwater level fluctuations. For a moment Additional lateral pressure, To allow for lateral pressure limits, To allow for verticality deviation limits, This is the first risk index model. This is the second risk index model. This is the third risk index model. The first fractional power exponent. It is the second fractional power exponent. For normalized roots, This is the sensitivity coefficient for the deviation smoothing term.

[0016] Furthermore, the calculation of the BIM digital twin verticality deviation index includes:

[0017] in, For the first Each monitoring section at time Integrate the updated vertical deviation index of BIM digital twin. This represents the time decay coefficient for historical data. For the first Each monitoring section at a historical moment The confidence weight of the measured data, To adjust the fusion gain for risk correction, For symbolic functions, For the first Each monitoring section at time The predicted verticality deviation index, For the first Each monitoring section at time A multi-factor dynamic risk index. For the first Each monitoring section at time The normalized verticality deviation index, For the first Each monitoring section at a historical moment The normalized verticality deviation index.

[0018] This invention also proposes a BIM-based dynamic control system for the verticality of diaphragm wall trenching, comprising: The BIM model building module is used to build a BIM model of the diaphragm wall of the new underground transportation hub. It divides the trench into several monitoring sections along the design depth direction and numbers and sorts each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding strata. The synchronization module is used to collect measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals during the trenching construction process through a multi-source monitoring device integrated into the BIM model, and synchronize them to the BIM model in real time. The calculation module is used to calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section. The prediction module is used to calculate the predicted verticality deviation index based on the normalized geological resistance index, the grab bucket swing angular frequency, and the mud density, according to a preset dynamic deviation prediction model. The control module is used to calculate the real-time correction control signal according to the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the guide correction mechanism of the trenching machine to adjust the attitude of the grab bucket. The early warning module is used to calculate a multi-factor dynamic risk index based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, according to a preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, the BIM model is linked to trigger an early warning of the corresponding level.

[0019] Furthermore, the verification module is used to calculate the BIM digital twin verticality deviation index based on the normalized verticality deviation index and its confidence weight, the multi-factor dynamic risk index, and a preset time decay weighted fusion update model, and synchronously write the index into the component attributes of the corresponding trench segment in the BIM model along the construction progress direction, so as to be called as a dynamic verification basis at the next monitoring time.

[0020] Compared with the prior art, the present invention has the following advantages and technical effects: 1) By establishing a dynamic deviation prediction model, multiple dynamic factors such as geological resistance, grab bucket swing frequency, and mud density deviation are coupled in a unified manner to achieve early quantitative prediction of the verticality deviation trend of the diaphragm wall trench, which can significantly detect the deviation trend earlier than traditional manual experience judgment. 2) By using real-time correction control signals, smooth and continuous closed-loop control of the trenching machine's guide correction mechanism is achieved, avoiding frequent reciprocating movements and local over-excavation caused by traditional threshold-triggered correction. 3) By constructing a risk index model, three heterogeneous indicators—groundwater level fluctuation, load of adjacent operating facilities of the hub, and verticality deviation—are uniformly mapped into comparable dimensionless risk indices, thereby enabling graded early warning of retained operating facilities and underground pipelines within underground transportation hubs. 4) By using the BIM digital twin verticality deviation index, the monitoring data from each monitoring session are dynamically weighted and integrated according to reliability and timeliness, and risk correction items are added. This allows the digital twin field of diaphragm wall verticality in the BIM model to be updated in real time along with the construction progress, providing a unified and traceable collaborative data foundation for design, supervision, and construction parties. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0024] Example 1 like Figure 1 As shown in the figure, this embodiment proposes a BIM-based method for dynamic control of the verticality of diaphragm wall trenching, including: Step S1: Establish a BIM model of the diaphragm wall of the new underground transportation hub, divide several monitoring sections along the design depth direction of the trench section, and number and sort each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding stratum. Preferably, a BIM model of the diaphragm wall of the newly constructed underground transportation hub is established, and a BIM model of the geology of the hub project area is established in conjunction with the survey data. Each trench segment is divided into sections along the design depth direction. Each monitoring section is numbered and sorted along the construction progress direction to determine the design depth of each monitoring section. ( ) and the corresponding normalized geological resistance index of the strata ,in, The standard penetration test blow count and the characteristic values ​​of the compressive strength of the natural strata, obtained by the exploration unit from the drilling layer measurements of each borehole during the geological exploration stage, are assigned values ​​by the BIM geological model according to the depth of the monitoring section, and then weighted and normalized according to the following formula. Furthermore, during the trenching construction stage, the assigned values ​​in the BIM geological model can be dynamically corrected based on the stratigraphic data revealed by the trenching.

[0025] in, For the first The measured number of standard penetration tests (SPT) blows (in blows) of the strata where each monitoring section is located. This is the normalized reference upper limit (blows) for the number of standard penetration tests (SPT) blows within the geological exploration area of ​​the project. For the first The characteristic value of the natural compressive strength (kPa) of the strata where each monitoring section is located. This is the normalized reference upper limit (kPa) for the compressive strength characteristic value within the geological exploration area of ​​the project. , To meet The weighting coefficients are set to 1 when the calculated result is greater than 1, to ensure... .

[0026] Step S2: During the trenching construction process, the multi-source monitoring device integrated into the BIM model collects the measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals, and synchronizes them to the BIM model in real time. Preferably, the multi-source monitoring devices include: laser inclinometer, mud density meter, grab bucket attitude sensor, groundwater level gauge and load monitoring equipment for adjacent operating facilities of the hub, etc.

[0027] Step S3: Calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section; Calculate the first Each monitoring section at time Normalized verticality deviation index include:

[0028] in, For a moment The measured value of the horizontal deviation.

[0029] Step S4: Based on the normalized geological resistance index, the grab bucket swing angular frequency and mud density, calculate the predicted verticality deviation index according to the preset dynamic deviation prediction model. Specifically, dynamic bias prediction models include:

[0030] in, For the first Each monitoring section at time The predicted verticality deviation index, For the first The design depth of each monitoring section The reference deviation coefficient, with examples of possible values. ‰, This is the deep saturation decay coefficient; examples of possible values. , For the first The normalized geological resistance index of the strata where each monitoring section is located This is the power exponent of the geological resistance index; examples of its values ​​are provided. , For a moment Measured value of the grab bucket swing angular frequency. The swing angular frequency of the grab bucket is designed. The power exponent of the grab swing frequency ratio is given by the following example values. , For a moment Measured mud density Design a baseline density for the mud. This is the penalty coefficient for mud density deviation; examples of possible values. .

[0031] Step S5: Calculate the real-time correction control signal based on the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the trenching machine guide correction mechanism to adjust the grab bucket posture. Specifically, the calculated real-time correction control signals include:

[0032] in, For the first Each monitoring section at time The output real-time correction control signal, To guide the correction mechanism to its maximum output correction stroke, For the bias error sensitivity gain, here are some examples of values. =50, dimensionless (1 / ‰) For the first Each monitoring section at time The deviation error amount, The rate of change sensitivity steepness coefficient, with example values. , For the first Rate of change of deviation error of each monitoring section The threshold for the rate of change of deviation that triggers correction.

[0033] Specifically, calculate the first Each monitoring section at time Deviation error include:

[0034] Calculate the first Rate of change of deviation error at each monitoring section include:

[0035] in, For the first Each monitoring section at time The normalized verticality deviation index, The time interval between adjacent monitoring times. For the first Each monitoring section at time The normalized verticality deviation index.

[0036] Step S6: Based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, calculate the multi-factor dynamic risk index according to the preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, trigger the corresponding level of early warning by linking the BIM model.

[0037] Specifically, the risk index model includes:

[0038] in, For the first Each monitoring section at time A multi-factor dynamic risk index. For a moment The amplitude of groundwater level fluctuations This serves as the benchmark amplitude for early warning of groundwater level fluctuations. For a moment Additional lateral pressure, To allow for lateral pressure limits, To allow for verticality deviation limits, This is the first risk index model. This is the second risk index model. Example values ​​for the third risk index model. , , , The first fractional power exponent, with examples of possible values. , The second fractional power exponent, with examples of possible values. , Examples of normalized root values , The sensitivity coefficient for the deviation smoothing term; example values. .

[0039] Preferably, in step S6, the "preset graded early warning threshold" is set according to... The value range is pre-divided into five levels, and the corresponding handling measures for each level are pre-set and entered into the BIM platform's early warning and linkage rules, as shown in the following example: Risk Level - Response Measures Table

[0040] The above level classification and the corresponding levels The interval, warning color, and handling measures are pre-set and stored in the BIM platform based on the diaphragm wall design and acceptance specifications and the protection level of adjacent operational facilities of the hub before project implementation. Step S6 calculates the results. Then, it is compared with the preset interval level by level, triggering the corresponding level of early warning linkage.

[0041] Step S7: Based on the normalized verticality deviation index and its confidence weight, and the multi-factor dynamic risk index, calculate the BIM digital twin verticality deviation index according to the preset time decay weighted fusion update model, and synchronously write it into the component attributes of the corresponding trench segment of the BIM model along the construction progress direction, so as to be called as the basis for dynamic verification at the next monitoring time.

[0042] Specifically, calculating the verticality deviation index of BIM digital twins includes:

[0043] in, For the first Each monitoring section at time Integrate the updated vertical deviation index of BIM digital twin. This represents the time decay coefficient for historical data. For the first Each monitoring section at a historical moment The confidence weight of the measured data, To adjust the fusion gain for risk correction, For symbolic functions, For the first Each monitoring section at time The predicted verticality deviation index, For the first Each monitoring section at time A multi-factor dynamic risk index. For the first Each monitoring section at time The normalized verticality deviation index, For the first Each monitoring section at a historical moment The normalized verticality deviation index.

[0044] Parameter example: , ‰; for 5 consecutive monitoring times , Weighted fusion, take , When ‰ (with the sign of negative one) is calculated, the result is... ‰, meets the allowable deviation limit. The requirement is ‰ (corresponding to the standard limit of 1 / 300).

[0045] Step S8: Repeat steps S1-S7 along the depth direction of the trench segment until the total design depth is reached. Determine whether the BIM digital twin verticality deviation index meets the preset allowable deviation limit. If it meets the limit, proceed to the next trench segment along the construction progress direction. If it does not meet the limit, output correction suggestions and pause the transfer to the next trench segment until the verification is qualified and a continuous BIM acceptance record of diaphragm wall verticality along the construction progress direction is generated.

[0046] Example 2 like Figure 2 As shown, this embodiment proposes a BIM-based dynamic control system for the verticality of diaphragm wall trenching, including: The BIM model building module is used to build a BIM model of the diaphragm wall of the new underground transportation hub. It divides the trench into several monitoring sections along the design depth direction and numbers and sorts each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding strata. The synchronization module is used to collect measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals during the trenching construction process through a multi-source monitoring device integrated into the BIM model, and synchronize them to the BIM model in real time. The calculation module is used to calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section. The prediction module is used to calculate the predicted verticality deviation index based on the normalized geological resistance index, the grab bucket swing angular frequency, and the mud density, according to a preset dynamic deviation prediction model. The control module is used to calculate the real-time correction control signal according to the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the guide correction mechanism of the trenching machine to adjust the attitude of the grab bucket. The early warning module is used to calculate a multi-factor dynamic risk index based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, according to a preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, the BIM model is linked to trigger an early warning of the corresponding level.

[0047] Since Example 2 is based on Example 1, it will not be described again.

[0048] Example 3 This invention also proposes a storage medium storing multiple instructions, which are used to implement the BIM-based dynamic control method for the verticality of diaphragm wall trenching.

[0049] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0050] Optionally, in this embodiment, the storage medium is configured to store program code for performing the method steps of Embodiment 1.

[0051] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0052] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0054] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0055] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0056] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A BIM-based method for dynamic control of the verticality of diaphragm wall trenches, characterized in that, include: Step S1: Establish a BIM model of the diaphragm wall of the new underground transportation hub, divide several monitoring sections along the design depth direction of the trench section, and number and sort each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding stratum. Step S2: During the trenching construction process, the multi-source monitoring device integrated into the BIM model collects the measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals, and synchronizes them to the BIM model in real time. Step S3: Calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section; Step S4: Based on the normalized geological resistance index, the grab bucket swing angular frequency and mud density, calculate the predicted verticality deviation index according to the preset dynamic deviation prediction model. Step S5: Calculate the real-time correction control signal based on the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the trenching machine guide correction mechanism to adjust the grab bucket posture. Step S6: Based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, calculate the multi-factor dynamic risk index according to the preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, trigger the corresponding level of early warning by linking the BIM model.

2. The method for dynamic control of verticality of diaphragm wall trenching based on BIM as described in claim 1, characterized in that, Step S7: Based on the normalized verticality deviation index and its confidence weight, and the multi-factor dynamic risk index, calculate the BIM digital twin verticality deviation index according to the preset time decay weighted fusion update model, and synchronously write it into the component attributes of the corresponding trench segment of the BIM model along the construction progress direction, so as to be called as the basis for dynamic verification at the next monitoring time.

3. The BIM-based dynamic control method for verticality of diaphragm wall trenching as described in claim 2, characterized in that, Step S8: Repeat steps S1-S7 along the depth direction of the trench segment until the total design depth is reached. Determine whether the BIM digital twin verticality deviation index meets the preset allowable deviation limit. If it meets the limit, proceed to the next trench segment along the construction progress direction. If it does not meet the limit, output correction suggestions and pause the transfer to the next trench segment until the verification is qualified and a continuous BIM acceptance record of diaphragm wall verticality along the construction progress direction is generated.

4. The method for dynamic control of verticality of diaphragm wall trenching based on BIM as described in claim 1, characterized in that, Dynamic bias prediction models include: ， in, For the first Each monitoring section at time The predicted verticality deviation index, For the first The design depth of each monitoring section The reference deviation coefficient, The deep saturation attenuation coefficient, For the first The normalized geological resistance index of the strata where each monitoring section is located It is the power exponent of the geological resistance index. For a moment Measured value of the grab bucket swing angular frequency. The swing angular frequency of the grab bucket is designed. The power exponent of the grab swing frequency ratio. For a moment Measured mud density Design a baseline density for the mud. This is the penalty coefficient for mud density deviation.

5. The method for dynamic control of verticality of diaphragm wall trenching based on BIM as described in claim 4, characterized in that, The calculated real-time correction control signals include: ， in, For the first Each monitoring section at time The output real-time correction control signal, To guide the correction mechanism to its maximum output correction stroke, For the bias error sensitivity gain, For the first Each monitoring section at time The deviation error amount, This is the sensitivity steepness coefficient for the rate of change. For the first Rate of change of deviation error of each monitoring section The threshold for the rate of change of deviation that triggers correction.

6. The method for dynamic control of verticality of diaphragm wall trenching based on BIM as described in claim 5, characterized in that, Calculate the first Each monitoring section at time Deviation error include: ， Calculate the first Rate of change of deviation error at each monitoring section include: ， in, For the first Each monitoring section at time The normalized verticality deviation index, The time interval between adjacent monitoring times. For the first Each monitoring section at time The normalized verticality deviation index.

7. The BIM-based dynamic control method for verticality of diaphragm wall trenching as described in claim 4, characterized in that, Risk index models include: ， in, For the first Each monitoring section at time A multi-factor dynamic risk index. For a moment The amplitude of groundwater level fluctuations This serves as the benchmark amplitude for early warning of groundwater level fluctuations. For a moment Additional lateral pressure, To allow for lateral pressure limits, To allow for verticality deviation limits, This is the first risk index model. This is the second risk index model. This is the third risk index model. The first fractional power exponent. It is the second fractional power exponent. For normalized roots, This is the sensitivity coefficient for the deviation smoothing term.

8. A method for dynamic control of verticality of diaphragm wall trenching based on BIM as described in claim 2, characterized in that, The calculation of the BIM digital twin verticality deviation index includes: ， in, For the first Each monitoring section at time Integrate the updated vertical deviation index of BIM digital twin. This represents the time decay coefficient for historical data. For the first Each monitoring section at a historical moment The confidence weight of the measured data, To adjust the fusion gain for risk correction, For symbolic functions, For the first Each monitoring section at time The predicted verticality deviation index, For the first Each monitoring section at time A multi-factor dynamic risk index. For the first Each monitoring section at time The normalized verticality deviation index, For the first Each monitoring section at a historical moment The normalized verticality deviation index.

9. A BIM-based dynamic control system for the verticality of diaphragm wall trenches, characterized in that, include: The BIM model building module is used to build a BIM model of the diaphragm wall of the new underground transportation hub. It divides the trench into several monitoring sections along the design depth direction and numbers and sorts each trench section along the construction progress direction to determine the design depth coordinates of each monitoring section and the normalized geological resistance index of the corresponding strata. The synchronization module is used to collect measured values ​​of horizontal deviation, mud density, grab bucket swing angular frequency, groundwater level fluctuation amplitude, and additional lateral pressure caused by the load of adjacent operating facilities of the hub at fixed time intervals during the trenching construction process through a multi-source monitoring device integrated into the BIM model, and synchronize them to the BIM model in real time. The calculation module is used to calculate the normalized verticality deviation index based on the measured value of the horizontal deviation and the design depth of the corresponding monitoring section. The prediction module is used to calculate the predicted verticality deviation index based on the normalized geological resistance index, the grab bucket swing angular frequency, and the mud density, according to a preset dynamic deviation prediction model. The control module is used to calculate the real-time correction control signal based on the deviation error between the normalized verticality deviation index and the predicted verticality deviation index and its rate of change, and output it to the trenching machine guide correction mechanism to adjust the grab bucket posture. The early warning module is used to calculate a multi-factor dynamic risk index based on the groundwater level fluctuation amplitude, the additional lateral pressure, and the normalized verticality deviation index, according to a preset risk index model. When the multi-factor dynamic risk index exceeds the preset graded early warning threshold, the BIM model is linked to trigger an early warning of the corresponding level.

10. A BIM-based dynamic control system for the verticality of diaphragm wall trenching as described in claim 9, characterized in that, The verification module is used to calculate the BIM digital twin verticality deviation index based on the normalized verticality deviation index and its confidence weight, the multi-factor dynamic risk index, and the preset time decay weighted fusion update model. The BIM digital twin verticality deviation index is then synchronously written into the component attributes of the corresponding trench segment in the BIM model along the construction progress direction, and is used as a dynamic verification basis for the next monitoring time.

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