Bridge hoisting assembly dynamic deformation monitoring and shaping parameter optimization method

Abstract

The application discloses a bridge hoisting and assembling dynamic deformation monitoring and shaping parameter optimization method, relates to the technical field of bridge assembling, and comprises the following steps: acquiring a sequence bridge section, and acquiring deformation feature points of the sequence bridge section and a first assembling mode based on a digital twin model; acquiring a standard stress array based on the first assembling mode; and performing dynamic deformation monitoring and shaping parameter optimization on the bridge based on the standard stress array; the application is used for solving the problem in the prior art that the dynamic deformation monitoring and shaping parameter optimization method for bridge hoisting and assembling cannot optimize the deployment position of a sensor based on the deformation characteristics of a bridge section to be assembled during assembling, so that redundant data exist in the data collected by the sensor, the data processing time is prolonged, and the assembling optimization efficiency is reduced.

Description

Technical Field

[0001] This invention relates to the field of bridge hoisting technology, specifically to a method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly. Background Technology

[0002] Bridge hoisting and assembly is a construction method that uses hoisting equipment to assemble prefabricated components into the main body of a bridge on site. It is mainly used in the construction of prefabricated bridges. Bridge hoisting and assembly is suitable for projects with tight schedules and good transportation conditions, such as long-span cable-stayed bridges and urban viaducts. It can significantly improve construction efficiency and reduce the amount of wet work on site.

[0003] Existing methods for dynamic deformation monitoring and shaping parameter optimization in bridge hoisting and assembly typically involve placing sensors within the bridge based on the construction assembly sequence and the structural form of the beam segments. The data collected by these sensors is then used to control the bridge structure during assembly. While this improved method can construct a structural control method based on sensor data, it cannot optimize the sensor deployment based on the deformation characteristics of the bridge segments during assembly. This results in redundant data in the sensor collection, preventing all collected data from providing sufficient support for deformation monitoring and shaping parameter optimization. This leads to increased data processing time and reduced assembly optimization efficiency. For example, patent application CN111272135A discloses an automatic measurement and control method for the alignment of precast beam bridges during construction. The proposed solution involves deploying wireless strain sensors along the bridge direction on the top and bottom slabs of each precast beam segment, based on the construction assembly sequence and the structural form of the beam segments. By building a neural network model using the collected strain information, the structural alignment of the precast continuous beam bridge can be controlled. However, other improvements to dynamic deformation monitoring and shaping parameter optimization methods for bridge hoisting and assembly typically focus on optimizing the selection of sensor types. These methods still cannot optimize the sensor deployment based on the deformation characteristics of the bridge segments during assembly, resulting in redundant data in the sensor collection. This prevents all collected data from providing data support for deformation monitoring and shaping parameter optimization during bridge segment assembly, leading to increased data processing time and reduced assembly optimization efficiency. Therefore, it is necessary to improve existing methods for dynamic deformation monitoring and shaping parameter optimization in bridge hoisting and assembly. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by proposing a method for dynamic deformation monitoring and shaping parameter optimization in bridge hoisting and assembly. This method addresses the issue that existing methods for dynamic deformation monitoring and shaping parameter optimization in bridge hoisting and assembly cannot optimize the sensor deployment based on the deformation characteristics of the bridge segment to be assembled during assembly. This results in redundant data collected by the sensors, preventing all collected data from providing data support for deformation monitoring and shaping parameter optimization of the bridge segment during assembly. Consequently, this increases data processing time and reduces assembly optimization efficiency.

[0005] To achieve the above objectives, this application provides a method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly, comprising the following steps:

[0006] The sequence of bridge segments is obtained based on the assembly order of prefabricated bridge segments during bridge hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, the corresponding digital twin model is obtained using digital twin, and the deformation feature points and first assembly method of each sequence of bridge segments are obtained using the bridge segment assembly analysis method based on the digital twin model.

[0007] Based on the deformation feature points of each sequence bridge segment, wireless stress sensors are placed in the unassembled bridge, and the standard stress array of each sequence bridge segment is obtained based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensors.

[0008] When assembling a sequence of bridge segments from an unassembled bridge, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the sequence of bridge segments. Based on the real-time stress array and the standard stress array corresponding to the sequence of bridge segments, dynamic deformation monitoring and shaping parameter optimization of the bridge are performed.

[0009] Furthermore, based on the assembly sequence of prefabricated bridge segments during bridge hoisting, a sequence of bridge segments is obtained; based on the dimensional data of the sequence of bridge segments and the unassembled bridge sections, a corresponding digital twin model is obtained using digital twin technology, including:

[0010] Based on the assembly sequence of prefabricated bridge segments during bridge hoisting, all prefabricated bridge segments are sequentially denoted as sequence segment XQ1 to sequence segment XQ. n The three-dimensional coordinates of points on the bridge surface are determined using a drone equipped with a lidar sensor, and corresponding lidar point cloud data is generated based on the three-dimensional coordinates of the bridge surface points, which is recorded as the bridge size data.

[0011] Based on the bridge dimension data, a digital twin model corresponding to the bridge is obtained using digital twin technology and denoted as the bridge twin model. For any sequence of bridge segments: the dimension data of the sequence of bridge segments is obtained, and a digital twin model corresponding to the sequence of bridge segments is obtained using digital twin technology based on the dimension data of the sequence of bridge segments and denoted as the segment twin model of the sequence of bridge segments.

[0012] Furthermore, obtain the bridge segment twin models corresponding to all sequence bridge segments. The bridge segment assembly analysis method includes:

[0013] For any given bridge segment sequence: place the bridge twin model and the segment twin models of the sequence bridge segments in the same spatial coordinate system, and use AI to obtain the different assembly methods for hoisting and assembling the sequence bridge segments into the bridge twin model, and denot them as Assembleable Method KP1 to Assembleable Method KP respectively. t ;

[0014] Based on AI, all key stress locations of the bridge in the bridge twin model are obtained and recorded as bridge stress locations; wireless stress sensors are deployed at all stress locations of the bridge in the bridge twin model and recorded as bridge stress sensors.

[0015] Furthermore, the bridge segment assembly analysis method also includes:

[0016] For any assembly method: use the assembly method to assemble the bridge segment twin model into the bridge twin model, and during the assembly process, all apex angles in contact between the bridge segment twin model and the bridge twin model are recorded as the stress apex angles of the bridge segment;

[0017] The bridge segment twin model is removed from the bridge twin model, and wireless stress sensors are placed at the stress apex of all bridge segments in the bridge segment twin model, denoted as bridge segment stress sensors. The bridge segment twin model is then reassembled into the bridge twin model using a modular assembly method, and the sensing data of all bridge segment stress sensors and all bridge stress sensors are acquired. The time when the bridge twin model and the bridge segment twin model begin to have contact when the modular assembly method is reassembled is denoted as t1, and the time when the bridge segment twin model is stably located within the bridge twin model is denoted as t2.

[0018] Furthermore, the bridge segment assembly analysis method also includes:

[0019] Establish a Cartesian coordinate system with units of min and Pa for the X and Y axes, respectively, denoted as the stress analysis coordinate system; for any bridge segment stress sensor: based on the data detected by the bridge segment stress sensor during the process from t1 to t2, plot the relationship curve between time and stress in the bridge segment stress sensor, and denot it as the bridge segment stress curve; denote the curves with non-zero slopes in the bridge segment stress curve as effective monitoring curves, and denote all peaks and troughs in the effective monitoring curves as extractable points;

[0020] Obtain the ordinates of all extractable points, and record the difference between the maximum and minimum values ​​of all ordinates as the standard stress difference of the bridge stress sensor.

[0021] Furthermore, the bridge segment assembly analysis method also includes:

[0022] Based on the stress analysis coordinate system, the standard stress difference corresponding to all bridge segment stress sensors and all bridge stress sensors is obtained. The minimum value among the standard stress differences of all bridge segment stress sensors is recorded as the bridge segment judgment value, and the sum of the standard stress differences of all bridge segment stress sensors is recorded as the bridge segment stress difference.

[0023] Bridge stress sensors whose standard stress difference is greater than or equal to the bridge segment judgment value are recorded as bridge usable sensors. The sum of the standard stress differences of all bridge usable sensors is recorded as the bridge stress difference. The sum of the bridge segment stress difference and the bridge stress difference is recorded as the total stress difference.

[0024] Furthermore, the bridge segment assembly analysis method also includes:

[0025] Obtain the total stress difference of all possible assembly methods, and record the assembly method with the smallest total stress difference as the first assembly method of the sequence segment;

[0026] The positions of all bridge segment stress sensors and bridge stress sensors corresponding to the first assembly method of the sequence bridge segments are recorded as the deformation feature points of the sequence bridge segments.

[0027] Furthermore, based on the deformation feature points of each sequence bridge segment, placing wireless stress sensors in the unassembled bridge includes:

[0028] For any given bridge segment: the bridge segment is assembled and tested. The assembly test includes: placing wireless stress sensors at all deformation feature points on the unassembled bridge and within the bridge segment; assembling the bridge segment using the first assembly method.

[0029] Once the assembly test begins, based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially designated as sequence sensor XC1 to sequence sensor XC2. e , where e is the number of deformation feature points.

[0030] Furthermore, based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensor, the standard stress array for each sequence bridge segment is obtained, including:

[0031] After the assembly test is completed, obtain the maximum stress monitored by all sequence sensors during the assembly test and record it as the maximum sensing force; establish an array of length e, denoted as the standard stress array, and sequentially connect sequence sensors XC1 to XC... e The maximum sensing force is filled into the standard stress array, where the r-th value from left to right in the standard stress array is the value of the sequence sensor XC. r The maximum sensing force, r is a positive integer less than or equal to e and greater than or equal to 1;

[0032] Obtain the standard stress array for all sequence bridge segments.

[0033] Furthermore, when assembling unassembled bridge segments into a sequence, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the segment. Based on this real-time stress array and the standard stress array corresponding to the segment, dynamic deformation monitoring and shaping parameter optimization of the bridge are performed, including:

[0034] When assembling unassembled bridge segments in sequence, for any segment being assembled: the first assembly method is used to assemble the segment into the unassembled bridge. Wireless stress sensors are placed at all deformation feature points corresponding to the segment. Based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially designated as real-time sensor SC1 to real-time sensor SC2. e ;

[0035] Within the spatial coordinate system of the bridge twin model and the corresponding bridge segment twin model, mark the real-time sensors and sequence sensors at all deformation feature points corresponding to the sequence bridge segment; for any real-time sensor SC at a deformation feature point... α and sequence sensor XC β When α and β are equal, the deformation feature point is recorded as a normal deformation point; when α and β are not equal, the deformation feature point is recorded as an abnormal deformation point.

[0036] Based on the method of obtaining the standard stress array from the sequential sensor, the array corresponding to the real-time sensor is obtained and recorded as the real-time stress array; the array obtained by subtracting the real-time stress array from the standard stress array is recorded as the stress difference array. When there is a value γ in the stress difference array whose absolute value is greater than the stress tolerance threshold, the deformation feature point where the real-time sensor corresponding to γ ​​is located is recorded as the parameter anomaly point.

[0037] A drone equipped with a lidar sensor is used to scan all abnormal deformation points, and a model is generated based on the obtained point cloud data to monitor the deformation of abnormal deformation points in real time.

[0038] Change the force direction of the abnormal points in the first assembly method of the sequence bridge segment, and re-acquire the real-time stress array based on the assembly test after the change, until no abnormal points are acquired during the assembly test. Record the first assembly method at this time as the optimized first assembly method. Use the optimized first assembly method to assemble the sequence bridge segment into other bridges that have not been assembled.

[0039] The beneficial effects of this invention are as follows: First, this application obtains a sequence of bridge segments based on the assembly sequence of prefabricated bridge segments during bridge hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, a digital twin model is obtained using digital twins, and based on the digital twin model, a bridge segment assembly analysis method is used to obtain the deformation feature points and the first assembly method of each sequence of bridge segments. The advantage of this is that by establishing a digital twin model and obtaining the deformation feature points and the first assembly method of each sequence of bridge segments, the assembly method with the lowest stress generated during the assembly of the sequence of bridge segments into the bridge can be obtained, namely the first assembly method. The deformation feature points obtained through the first assembly method are the feature points with large stress changes generated when the sequence of bridge segments are assembled, so that the assembly can be accurately and effectively optimized by collecting data at the deformation feature points using wireless stress sensors in the subsequent process, while avoiding the problem of redundant data in the data collected by the sensors affecting the efficiency of assembly optimization.

[0040] This application also places wireless stress sensors in the unassembled bridge based on the deformation feature points of each sequence bridge segment, and obtains a standard stress array for each sequence bridge segment based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensors. Finally, when assembling the sequence bridge segments in the unassembled bridge, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the sequence bridge segments. Based on the real-time stress array and the standard stress array corresponding to the sequence bridge segments, dynamic deformation monitoring and shaping parameter optimization of the bridge are performed. The advantage of this is that by obtaining the standard stress array of each sequence bridge segment, a standard can be obtained to judge and analyze the stress data collected at the deformation feature points during assembly when the sequence bridge segments are assembled into the bridge. This allows for the identification of abnormal deformation points and parameter anomalies in the bridge based on the difference between the real-time stress array and the standard stress array after obtaining the real-time stress array, thereby enabling precise dynamic deformation monitoring and shaping parameter optimization of the bridge. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0042] Figure 2 This is a schematic diagram illustrating the extractable points and the acquisition of standard stress differences according to the present invention.

[0043] Figure 3This is a schematic diagram of obtaining abnormal deformation points according to the present invention.

[0044] Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0045] 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.

[0046] Example 1, please refer to Figure 1 As shown, this application provides a method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly, including the following steps:

[0047] Step S1: Obtain the sequence of bridge segments based on the assembly order of prefabricated bridge segments during bridge hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, use digital twin to obtain the corresponding digital twin model, and use the bridge segment assembly analysis method based on the digital twin model to obtain the deformation feature points and the first assembly method of each sequence of bridge segments.

[0048] Step S1 includes: Step S101, based on the assembly sequence of the precast bridge segments during bridge hoisting, all precast bridge segments are sequentially denoted as sequence segment XQ1 to sequence segment XQ. n The three-dimensional coordinates of points on the bridge surface are determined using a drone equipped with a lidar sensor, and corresponding lidar point cloud data is generated based on the three-dimensional coordinates of the bridge surface points, which is recorded as the bridge size data.

[0049] Step S102: Based on the bridge size data, use digital twin to obtain the digital twin model corresponding to the bridge, and denot it as the bridge twin model; For any sequence of bridge segments: obtain the size data of the sequence of bridge segments, and based on the size data of the sequence of bridge segments, use digital twin to obtain the digital twin model corresponding to the sequence of bridge segments, and denot it as the bridge segment twin model of the sequence of bridge segments.

[0050] In the specific implementation process, by constructing bridge twin models and bridge segment twin models based on digital twins, it is possible to conduct assembly tests of sequential bridge segments in the subsequent spatial coordinate system using the data synchronization of digital twins. This saves testing time and avoids safety hazards caused by using physical objects for assembly tests.

[0051] Obtain the bridge segment twin models corresponding to all sequence bridge segments. The bridge segment assembly analysis method includes: Step V1, for any sequence bridge segment: place the bridge twin model and the segment twin model of the sequence bridge segment in the same spatial coordinate system, and use AI to obtain the different assembly methods for hoisting and assembling the sequence bridge segments into the bridge twin model, and denot them as Assembleable Method KP1 to Assembleable Method KP respectively. t ;

[0052] In this embodiment, the criterion for determining whether the assembly methods are different is: if there is a step in the assembly process where the assembly direction of the sequence bridge segments is different, then the two assembly methods can be considered to be different. For example, in actual assembly, the sequence bridge segments need to be assembled from the placement site A to the assembly point B. For the two assembly methods C1 and C2, the lifting method of the sequence bridge segments is exactly the same for both assembly methods C1 and C2. However, when assembling the sequence bridge segments into the assembly point B, assembly method C1 fills them from the southeast to the northwest direction; while assembly method C2 fills them into the assembly point B from the due east to the due west direction. Therefore, assembly methods C1 and C2 can be considered to be two different assembly methods.

[0053] Step V2: Based on AI, obtain all key stress locations of the bridge in the bridge twin model and record them as bridge stress locations; deploy wireless stress sensors at all stress locations of the bridge in the bridge twin model and record them as bridge stress sensors.

[0054] In practical implementation, key stress locations on the bridge can include the main beam and related parts of the beam body, the piers and abutments, connection and stress transfer points, and other stress-bearing points. Specifically, the main beam and related parts can include the mid-span, supports, webs, flanges, top and bottom plates of the box girder, beam segment splices, prestressed anchorage ends, and areas near expansion joints. For the piers and abutments, specific areas include the pier base, pier cross-section changes, pier cap beams, abutment backs, abutment truss walls, and the top surface of the foundation. For connection and stress transfer points, specific areas include bearing pads, cable anchorages, stay cable anchorages, truss nodes, steel box girder welds, arch feet, and arch crowns. In practical applications, the stress locations on the bridge can be more specifically set based on locations susceptible to stress within the bridge, ensuring that bridge stress sensors can comprehensively monitor these locations when the bridge segment twin model is assembled into the bridge twin model.

[0055] Step V3, for any assembly method: use the assembly method to assemble the bridge segment twin model into the bridge twin model, and during the assembly process, record the apex angles in contact between the bridge segment twin model and the bridge twin model as the stress apex angles of the bridge segment;

[0056] Step V4: Remove the bridge segment twin model from the bridge twin model and place wireless stress sensors at the stress-bearing apex of all bridge segments in the bridge segment twin model, denoted as bridge segment stress sensors; reassemble the bridge segment twin model into the bridge twin model using the assembly method, and acquire the sensing data of all bridge segment stress sensors and all bridge stress sensors. The time when the bridge twin model and the bridge segment twin model begin to have contact when the assembly method is reused is denoted as t1, and the time when the bridge segment twin model is stably located within the bridge twin model is denoted as t2.

[0057] Step V5: Establish a Cartesian coordinate system with units of min and Pa for the X-axis and Y-axis, respectively, denoted as the stress analysis coordinate system; for any bridge segment stress sensor: based on the data detected by the bridge segment stress sensor from t1 to t2, plot the relationship curve between time and stress in the bridge segment stress sensor, and denot it as the bridge segment stress curve; denote the curves with non-zero slopes in the bridge segment stress curve as effective monitoring curves, and denote all peaks and troughs in the effective monitoring curves as extractable points;

[0058] In the specific implementation process, for example, during a data analysis, the obtained bridge segment stress curve is as follows: Figure 2 As shown by curve QY, curve YJ within curve QY can be recorded as the effective monitoring curve, and points KT1 to KT5 in curve YJ can be recorded as extractable points; through data calculation, the standard stress difference of the bridge segment stress sensor is 50MPa;

[0059] Step V6: Obtain the ordinates of all extractable points, and record the difference between the maximum and minimum values ​​of all ordinates as the standard stress difference of the bridge stress sensor.

[0060] Step V7: Based on the stress analysis coordinate system, obtain the standard stress difference corresponding to all bridge segment stress sensors and all bridge stress sensors, and record the minimum value among the standard stress differences of all bridge segment stress sensors as the bridge segment judgment value, and record the sum of the standard stress differences of all bridge segment stress sensors as the bridge segment stress difference.

[0061] In the specific implementation process, by obtaining the standard stress difference and further obtaining the bridge segment stress difference, we can obtain the stress change corresponding to the stress monitoring data of the bridge segment stress sensor during the process of assembling the bridge segment twin model into the bridge twin model. The larger the bridge segment stress difference, the greater the stress change at each stress point within the bridge segment during the process of assembling the bridge segment twin model into the bridge twin model. Similarly, the subsequently obtained bridge stress difference can reflect the stress change at each stress point within the bridge during the process of assembling the bridge segment twin model into the bridge twin model. Therefore, to prevent large deformation of the sequential bridge segments during assembly, in the subsequent analysis, the assembly method with the smallest total stress difference should be selected as the first assembly method, and the sequential bridge segments should be assembled accordingly.

[0062] Step V8: Bridge stress sensors whose standard stress difference is greater than or equal to the bridge segment judgment value are recorded as bridge usable sensors. The sum of the standard stress differences of all bridge usable sensors is recorded as the bridge stress difference. The sum of the bridge segment stress difference and the bridge stress difference is recorded as the total stress difference.

[0063] Step V9: Obtain the total stress difference of all possible assembly methods, and record the assembly method with the smallest total stress difference as the first assembly method of the sequence segment;

[0064] In the specific implementation process, if the number of first assembly methods is greater than 1 during actual execution, the assembly method with the smallest total stress difference and the smallest stress difference of the bridge segment, or the smallest total stress difference and the smallest stress difference of the bridge segment, can be recorded as the first assembly method of the sequence bridge segment according to the actual stability state of the bridge and the bridge segment.

[0065] Step V10: Record the positions of all bridge segment stress sensors and bridge stress sensors corresponding to the first assembly method of the sequence bridge segments as the deformation feature points of the sequence bridge segments.

[0066] Step S2: Based on the deformation feature points of each sequence bridge segment, place wireless stress sensors in the unassembled bridge, and obtain the standard stress array of each sequence bridge segment based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensors.

[0067] Step S2 includes: Step S201, for any sequence bridge segment: perform an assembly test on the sequence bridge segment, the assembly test includes: placing wireless stress sensors at all deformation feature points on the unassembled bridge and within the sequence bridge segment; assembling the sequence bridge segment using the first assembly method;

[0068] Step S202: After the assembly test begins, based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially recorded as sequence sensor XC1 to sequence sensor XC2. e, where e is the number of deformation feature points.

[0069] Step S203: After the assembly test is completed, obtain the maximum stress monitored by all sequence sensors during the assembly test and record it as the maximum sensing force; establish an array of length e, denoted as the standard stress array, and sequentially connect sequence sensors XC1 to XC... e The maximum sensing force is filled into the standard stress array, where the r-th value from left to right in the standard stress array is the value of the sequence sensor XC. r The maximum sensing force, r is a positive integer less than or equal to e and greater than or equal to 1;

[0070] In the specific implementation process, the assembly tests are all performed using bridge twin models and bridge segment twin models in a spatial coordinate system; for example, in a data analysis, if the number of deformation feature points corresponding to the first assembly method of a sequence of bridge segments is 7, then the value of e is 7; after the assembly test is completed, the maximum sensing forces of the sequence sensors XC1 to XC7 are obtained as 200MPa, 220MPa, 230MPa, 210MPa, 220MPa and 200MPa respectively, then the standard stress array should be The stress values ​​are [200MPa, 220MPa, 230MPa, 210MPa, 220MPa, 200MPa]. By obtaining the standard stress array, a standard can be obtained to judge and analyze the stress data collected at deformation feature points during the assembly of the sequence bridge segments into the bridge. This allows for the identification of abnormal deformation points and parameter anomalies in the bridge based on the difference between the real-time stress array and the standard stress array after obtaining the real-time stress array. This enables precise dynamic deformation monitoring and shaping parameter optimization of the bridge.

[0071] Step S204: Obtain the standard stress array for all sequence bridge segments.

[0072] Step S3: When assembling the unassembled bridge segments, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the segment. Based on the real-time stress array and the standard stress array corresponding to the segment, dynamic deformation monitoring and shaping parameter optimization are performed on the bridge.

[0073] Step S3 includes: Step S301, when assembling sequential bridge segments of an unassembled bridge, for any sequential bridge segment being assembled: the sequential bridge segment is assembled into the unassembled bridge using the first assembly method of the sequential bridge segment; wireless stress sensors are placed at all deformation feature points corresponding to the sequential bridge segment; and based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially recorded as real-time sensor SC1 to real-time sensor SC2. e ;

[0074] Step S302: In the spatial coordinate system where the bridge twin model and the bridge segment twin model corresponding to the sequence bridge segment are located, mark the real-time sensors and sequence sensors at all deformation feature points corresponding to the sequence bridge segment; for any real-time sensor SC at a deformation feature point... α and sequence sensor XC β When α and β are equal, the deformation feature point is recorded as a normal deformation point; when α and β are not equal, the deformation feature point is recorded as an abnormal deformation point.

[0075] In the specific implementation process, for example, during a data analysis, the bridge twin model corresponding to a certain sequence bridge segment is obtained as follows: Figure 3 As shown, all the points where the center of the circles are located are deformation feature points, and XC1 to XC4 are the positions corresponding to the sequence sensors XC1 to XC4 respectively, and SC1 to SC4 are the positions corresponding to the real-time sensors SC1 to SC4 respectively. Through analysis, it can be found that the sequence sensors corresponding to the deformation feature points where SC2 and SC3 are located are XC3 and XC2 respectively. This indicates that during the process of assembling the bridge twin model into the unassembled bridge, the force sequence of the deformation feature points where SC2 and SC3 are located is abnormal. Therefore, the deformation feature points where SC2 and SC3 are located should be recorded as abnormal deformation points and real-time deformation monitoring should be carried out in the future.

[0076] Step S303: Based on the method of obtaining the standard stress array from the sequence sensor, obtain the array corresponding to the real-time sensor and record it as the real-time stress array; subtract the real-time stress array from the standard stress array and record it as the stress difference array; when there is a value γ in the stress difference array whose absolute value is greater than the stress tolerance threshold, record the deformation feature point where the real-time sensor corresponding to γ ​​is located as the parameter anomaly point.

[0077] In the specific implementation process, the specific value of the stress tolerance threshold can be determined according to the actual bridge's tolerance to stress changes and the judgment criteria for the risk of stress changes. In this embodiment, the stress tolerance threshold is set to 15MPa. For example, in a data analysis, the standard stress array of a sequence of bridge segments to be assembled should be [200MPa, 220MPa, 230MPa, 210MPa, 220MPa, 200MPa], and the real-time stress array is [200MPa, 190MPa, 220MPa, 210MPa, 220MPa, 200MPa]. Through analysis, the stress difference array is [0, 30MPa, 10MPa, 0, 0, 0]. The stress difference array has a value with an absolute value greater than 15MPa, that is, 30MPa in the stress difference array. Therefore, the deformation feature point where the real-time sensor SC2 is located corresponding to 30MPa should be recorded as a parameter anomaly point.

[0078] Step S304: Use a drone equipped with a lidar sensor to scan all abnormal deformation points, and use a model generated based on the obtained point cloud data to monitor the deformation of abnormal deformation points in real time.

[0079] Step S305: Change the force direction of the abnormal point in the first assembly method of the sequence bridge segment, and re-acquire the real-time stress array based on the assembly test after the change, until no abnormal point is acquired during the assembly test. Record the first assembly method at this time as the optimized first assembly method. Use the optimized first assembly method to assemble the sequence bridge segment into other bridges that have not been assembled.

[0080] Example 2, please refer to Figure 4 As shown, Figure 4The example illustrates the structure of an electronic device, which may include a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, which the processor can call. When the processor executes these instructions, it runs steps such as those in the method for dynamic deformation monitoring and shaping parameter optimization during bridge hoisting and assembly, to achieve the following functions: First, a sequence of bridge segments is obtained based on the assembly order of prefabricated bridge segments during hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, a corresponding digital twin model is obtained using digital twins, and the deformation feature points and first assembly method of each sequence of bridge segments are obtained using the bridge segment assembly analysis method based on the digital twin model; then, based on the deformation feature points of each sequence of bridge segments, wireless stress sensors are placed in the unassembled bridge, and a standard stress array for each sequence of bridge segments is obtained based on the first assembly method of the sequence of bridge segments and the sensing results of the wireless stress sensors; finally, when assembling the unassembled bridge segments, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the sequence of bridge segments, and dynamic deformation monitoring and shaping parameter optimization of the bridge are performed based on the real-time stress array and the standard stress array corresponding to the sequence of bridge segments.

[0081] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the bridge hoisting and assembly dynamic deformation monitoring and shaping parameter optimization method provided by the above methods. This method includes: firstly, obtaining a sequence of bridge segments based on the assembly sequence of prefabricated bridge segments during bridge hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, using digital twins to obtain corresponding digital twin models, and using bridge segment assembly based on the digital twin models. The method involves using an assembly analysis approach to obtain the deformation feature points and the first assembly method for each bridge segment in a sequence. Then, based on these feature points, wireless stress sensors are placed in the unassembled sections of the bridge. A standard stress array for each segment is obtained based on the first assembly method and the sensor results from the wireless stress sensors. Finally, when assembling the unassembled bridge segments, a real-time stress array is obtained based on the sensor results from all wireless stress sensors corresponding to each segment. Based on this real-time stress array and the standard stress array corresponding to each segment, dynamic deformation monitoring and shaping parameter optimization are performed on the bridge.

[0083] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it runs the steps of the above-mentioned method for dynamic deformation monitoring and shaping parameter optimization during bridge hoisting and assembly to achieve the following functions: First, based on the assembly sequence of prefabricated bridge segments during bridge hoisting, a sequence of bridge segments is obtained; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, a corresponding digital twin model is obtained using digital twins, and based on the digital twin model, the deformation feature points and first assembly method of each sequence of bridge segments are obtained using the bridge segment assembly analysis method; then, based on the deformation feature points of each sequence of bridge segments, wireless stress sensors are placed in the unassembled bridge, and a standard stress array for each sequence of bridge segments is obtained based on the first assembly method of the sequence of bridge segments and the sensing results of the wireless stress sensors; finally, when assembling the unassembled bridge segments, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the sequence of bridge segments, and dynamic deformation monitoring and shaping parameter optimization of the bridge are performed based on the real-time stress array and the standard stress array corresponding to the sequence of bridge segments.

[0084] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0085] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly, characterized in that, Includes the following steps: The sequence of bridge segments is obtained based on the assembly order of prefabricated bridge segments during bridge hoisting; based on the dimensional data of the sequence of bridge segments and the unassembled bridge, the corresponding digital twin model is obtained using digital twin, and the deformation feature points and first assembly method of each sequence of bridge segments are obtained using the bridge segment assembly analysis method based on the digital twin model. Based on the deformation feature points of each sequence bridge segment, wireless stress sensors are placed in the unassembled bridge, and the standard stress array of each sequence bridge segment is obtained based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensors. When assembling a sequence of bridge segments that have not yet been assembled, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the sequence of bridge segments. Based on the real-time stress array and the standard stress array corresponding to the sequence of bridge segments, dynamic deformation monitoring and shaping parameter optimization of the bridge are performed. Obtain the bridge segment twin models corresponding to all sequence bridge segments. The bridge segment assembly analysis method includes: For any given bridge segment sequence: place the bridge twin model and the segment twin models of the sequence bridge segments in the same spatial coordinate system, and use AI to obtain the different assembly methods for hoisting and assembling the sequence bridge segments into the bridge twin model, and denot them as Assembleable Method KP1 to Assembleable Method KP respectively. t ; Based on AI, all key stress locations of the bridge in the bridge twin model are obtained and recorded as bridge stress locations; wireless stress sensors are deployed at all bridge stress locations in the bridge twin model and recorded as bridge stress sensors. For any assembly method: use the assembly method to assemble the bridge segment twin model into the bridge twin model, and during the assembly process, all apex angles in contact between the bridge segment twin model and the bridge twin model are recorded as the stress apex angles of the bridge segment; The bridge segment twin model is removed from the bridge twin model, and wireless stress sensors are placed at the stress apex of all bridge segments in the bridge segment twin model, denoted as bridge segment stress sensors. The bridge segment twin model is then reassembled into the bridge twin model using a modular assembly method, and the sensing data of all bridge segment stress sensors and all bridge stress sensors are acquired. The time when the bridge twin model and the bridge segment twin model begin to have contact when the modular assembly method is reassembled is denoted as t1, and the time when the bridge segment twin model is stably located within the bridge twin model is denoted as t2. Establish a Cartesian coordinate system with units of min and Pa for the X and Y axes, respectively, denoted as the stress analysis coordinate system; for any bridge segment stress sensor: based on the data detected by the bridge segment stress sensor during the process from t1 to t2, plot the relationship curve between time and stress in the bridge segment stress sensor, and denot it as the bridge segment stress curve; denote the curves with non-zero slopes in the bridge segment stress curve as effective monitoring curves, and denote all peaks and troughs in the effective monitoring curves as extractable points; Obtain the ordinates of all extractable points, and record the difference between the maximum and minimum values ​​of all ordinates as the standard stress difference of the bridge stress sensor. Based on the stress analysis coordinate system, the standard stress difference corresponding to all bridge segment stress sensors and all bridge stress sensors is obtained. The minimum value among the standard stress differences of all bridge segment stress sensors is recorded as the bridge segment judgment value, and the sum of the standard stress differences of all bridge segment stress sensors is recorded as the bridge segment stress difference. Bridge stress sensors whose standard stress difference is greater than or equal to the bridge segment judgment value are recorded as bridge usable sensors. The sum of the standard stress differences of all bridge usable sensors is recorded as the bridge stress difference. The sum of the bridge segment stress difference and the bridge stress difference is recorded as the total stress difference. Obtain the total stress difference of all possible assembly methods, and record the assembly method with the smallest total stress difference as the first assembly method of the sequence segment; The positions of all bridge segment stress sensors and bridge stress sensors corresponding to the first assembly method of the sequence bridge segments are recorded as the deformation feature points of the sequence bridge segments.

2. The method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly according to claim 1, characterized in that, Sequence segments are obtained based on the assembly sequence of prefabricated bridge segments during bridge hoisting; Based on the dimensional data of the sequential bridge segments and the unassembled bridge, the corresponding digital twin model is obtained using digital twins, including: Based on the assembly sequence of prefabricated bridge segments during bridge hoisting, all prefabricated bridge segments are sequentially denoted as sequence segment XQ1 to sequence segment XQ. n The three-dimensional coordinates of points on the bridge surface are determined using a drone equipped with a lidar sensor, and corresponding lidar point cloud data is generated based on the three-dimensional coordinates of the bridge surface points, which is recorded as the bridge size data. Based on the bridge dimension data, a digital twin model corresponding to the bridge is obtained using digital twin technology and denoted as the bridge twin model. For any sequence of bridge segments: the dimension data of the sequence of bridge segments is obtained, and a digital twin model corresponding to the sequence of bridge segments is obtained using digital twin technology based on the dimension data of the sequence of bridge segments and denoted as the segment twin model of the sequence of bridge segments.

3. The method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly according to claim 2, characterized in that, Based on the deformation feature points of each sequence bridge segment, placing wireless stress sensors in the unassembled bridge includes: For any given bridge segment: the bridge segment is assembled and tested. The assembly test includes: placing wireless stress sensors at all deformation feature points on the unassembled bridge and within the bridge segment; assembling the bridge segment using the first assembly method. Once the assembly test begins, based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially designated as sequence sensor XC1 to sequence sensor XC2. e , where e is the number of deformation feature points.

4. The method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly according to claim 3, characterized in that, Based on the first assembly method of the sequence bridge segments and the sensing results of the wireless stress sensor, the standard stress array for each sequence bridge segment is obtained, including: After the assembly test is completed, obtain the maximum stress monitored by all sequence sensors during the assembly test and record it as the maximum sensing force; establish an array of length e, denoted as the standard stress array, and sequentially connect sequence sensors XC1 to XC... e The maximum sensing force is filled into the standard stress array, where the r-th value from left to right in the standard stress array is the value of the sequence sensor XC. r The maximum sensing force, r is a positive integer less than or equal to e and greater than or equal to 1; Obtain the standard stress array for all sequence bridge segments.

5. The method for monitoring dynamic deformation and optimizing shaping parameters during bridge hoisting and assembly according to claim 4, characterized in that, When assembling unassembled bridge segments, a real-time stress array is obtained based on the sensing results of all wireless stress sensors corresponding to the segment. Based on this real-time stress array and the standard stress array corresponding to the segment, dynamic deformation monitoring and shaping parameter optimization of the bridge are performed, including: When assembling unassembled bridge segments in sequence, for any segment being assembled: the first assembly method is used to assemble the segment into the unassembled bridge. Wireless stress sensors are placed at all deformation feature points corresponding to the segment. Based on the order in which the wireless stress sensors at all deformation feature points detect stress, the wireless stress sensors at all deformation feature points are sequentially designated as real-time sensor SC1 to real-time sensor SC2. e ; Within the spatial coordinate system of the bridge twin model and the corresponding bridge segment twin model, mark the real-time sensors and sequence sensors at all deformation feature points corresponding to the sequence bridge segment; for any real-time sensor SC at a deformation feature point... α and sequence sensor XC β When α and β are equal, the deformation feature point is recorded as a normal deformation point; when α and β are not equal, the deformation feature point is recorded as an abnormal deformation point. Based on the method of obtaining the standard stress array from the sequence sensor, the array corresponding to the real-time sensor is obtained and recorded as the real-time stress array; the array obtained by subtracting the real-time stress array from the standard stress array is recorded as the stress difference array. When there is a value γ in the stress difference array whose absolute value is greater than the stress fault tolerance threshold, the deformation feature point where the real-time sensor corresponding to γ ​​is located is recorded as the parameter anomaly point. The drone equipped with a lidar sensor scans all abnormal deformation points, and the model generated based on the obtained point cloud data is used to monitor the deformation of the abnormal deformation points in real time. Change the force direction of the abnormal points in the first assembly method of the sequence bridge segment, and re-acquire the real-time stress array based on the assembly test after the change, until no abnormal points are acquired during the assembly test. Record the first assembly method at this time as the optimized first assembly method. Use the optimized first assembly method to assemble the sequence bridge segment into other bridges that have not been assembled.

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