Curve skew bridge efficient and accurate construction system based on intelligent sensing and cooperative control

Abstract

The invention discloses a curve skew bridge efficient and accurate construction system based on intelligent sensing and cooperative control, and the system comprises a construction module which is used for constructing an all-dimensional intelligent sensing network in the construction process of a curve skew bridge, and collecting a plurality of groups of historical construction data through the all-dimensional intelligent sensing network; the training module is used for performing analysis training based on multiple groups of historical construction data through a deep learning algorithm, and establishing a parameter optimization model for curve skew bridge construction; the prediction module is used for predicting optimal construction parameters of the curve skew bridge through the model and determining efficiency monitoring indexes according to the optimal construction parameters; and the monitoring module is used for accurately monitoring the construction process of the curve skew bridge based on the efficiency monitoring index and feeding back stage work progress. The construction of the omnibearing intelligent sensing network can provide accurate data support for the construction process, guarantees the objectivity and precision of the data, and guarantees the collaborative operation effect and working efficiency among the construction equipment to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering construction, and particularly relates to an efficient and precise construction system for a curved skew bridge based on intelligent perception and collaborative control. Background Art

[0002] Due to its unique geometric shape and mechanical properties, a curved skew bridge faces many challenges during prefabrication and installation. The traditional construction mode relies on manual experience and conventional measurement means, making it difficult to accurately control various parameters during the construction process, resulting in poor coordination and low efficiency among subsequent construction equipment. For example, during the hoisting and installation of precast beams, the lack of real-time position and attitude monitoring leads to positioning deviations; there is a lack of effective coordination among construction links, and the operation efficiency of equipment such as bridge erecting machines and beam transporting vehicles is low, and the construction period is long. Moreover, the traditional construction method is difficult to adapt to complex and changeable construction environments, such as the construction of curved skew bridges in mountainous areas with complex terrains, where the construction difficulty is high and the quality is difficult to guarantee. Summary of the Invention

[0003] In view of the problems shown above, the present invention provides an efficient and precise construction system for a curved skew bridge based on intelligent perception and collaborative control to solve the problems in the background art that the traditional construction mode relies on manual experience and conventional measurement means, making it difficult to accurately control various parameters during the construction process, resulting in poor coordination and low efficiency among subsequent construction equipment.

[0004] An efficient and precise construction system for a curved skew bridge based on intelligent perception and collaborative control, the system includes:

[0005] A construction module, used to construct an all-round intelligent perception network during the construction process of the curved skew bridge, and collect multiple groups of historical construction data through the all-round intelligent perception network;

[0006] A training module, used to analyze and train based on multiple groups of historical construction data through deep learning algorithms to establish a parameter optimization model for the construction of the curved skew bridge;

[0007] A prediction module, used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model, and determine the efficiency monitoring index according to the optimal construction parameters;

[0008] A monitoring module, used to accurately monitor the construction process of the curved skew bridge based on the efficiency monitoring index and provide phased work progress feedback.

[0009] Preferably, the construction module includes:

[0010] A first determination sub-module, used to determine the work process of the curved skew bridge construction, and determine key material components, key operation media, and key operation equipment according to the work process;

[0011] The second determination sub-module is used to determine the respective state changes of key material components, key operation media, and key operation equipment during the construction process, and select different types of sensors according to the state changes;

[0012] The construction sub-module is used to construct an all-round intelligent perception network during the construction process of the curve skew bridge through various types of sensors and the communication unit;

[0013] The data acquisition and sorting sub-module is used to collect and sort the historical construction data during the construction of multiple curve skew bridges under project by using the all-round intelligent perception network.

[0014] Preferably, the key material component is a precast beam, the key operation medium is a bridge pier, and the key operation equipment is a bridge erecting machine;

[0015] Among them, the sensors corresponding to the precast beam are a high-precision three-dimensional laser displacement sensor and an inclination sensor. The three-dimensional laser displacement sensor is used to monitor the spatial position change of the precast beam during hoisting and installation, and the inclination sensor is used to measure the inclination angle of the precast beam during hoisting and installation;

[0016] The sensors corresponding to the bridge pier are stress and strain sensors, which are used to monitor the stress state of the bridge pier structure during the construction process;

[0017] The sensors corresponding to the bridge erecting machine are stress and strain sensors and inclination sensors. The inclination sensor is used to measure the inclination angle of the legs of the bridge erecting machine, and the strain sensor is used to monitor the stress state of the main structure of the bridge erecting machine during the construction process.

[0018] Preferably, the training module includes:

[0019] The preprocessing sub-module is used to perform data cleaning and standardization preprocessing on multiple groups of historical construction data to obtain the preprocessed historical construction data;

[0020] The design sub-module is used to extract the construction stage features and environmental impact features corresponding to the preprocessed historical construction data through a deep learning algorithm, and select a deep learning model and design the model architecture based on the construction stage features and environmental impact features;

[0021] The training sub-module is used to divide the preprocessed historical construction data into a training set, a validation set, and a test set, and train the deep learning model through the inspection set to establish a trained model;

[0022] The first generation sub-module is used to verify and test the accuracy and performance of the trained model through the validation set and the test set. After the verification and test are passed, a parameter optimization model for the construction of the curve skew bridge is generated.

[0023] Preferably, the prediction module includes:

[0024] A first acquisition sub-module, configured to obtain target design parameters of the current curve skew bridge project, collect environmental parameters of the construction site, and real-time construction data;

[0025] A prediction sub-module, configured to input the target design parameters, environmental parameters of the construction site, and real-time construction data into a parameter optimization model to predict the best construction parameters;

[0026] A third determination sub-module, configured to determine the collaborative operation parameters between each construction equipment based on the best construction parameters, and determine the scheduling control parameters for each construction equipment according to the collaborative operation parameters;

[0027] A fourth determination sub-module, configured to determine the matching efficiency index type according to the scheduling control parameters for each construction equipment and the construction target, obtain the quantization index of the matching efficiency index type, and determine the quantization index as the monitoring index of each type of efficiency index.

[0028] Preferably, the monitoring module includes:

[0029] A second acquisition sub-module, configured to determine the reference data system for each efficiency monitoring index, and collect the corresponding data of the construction process of the curve skew bridge according to the reference data system;

[0030] A fifth determination sub-module, configured to calculate the efficiency index of each efficiency monitoring index according to the collected corresponding data, determine the construction qualification according to the efficiency index, and perform corresponding optimization;

[0031] A sixth determination sub-module, configured to determine the phased target of each efficiency monitoring index in each construction stage, and determine the phased actual construction progress according to the phased target and real-time construction data;

[0032] A second generation sub-module, configured to determine the deviation between the phased actual construction progress and the theoretical progress, and determine the low-efficiency construction links to generate a phased work progress feedback report.

[0033] Preferably, the system is further configured to:

[0034] Detect abnormal environmental parameters and abnormal event parameters at the construction site, and determine risk construction equipment and potential safety hazards according to the abnormal environmental parameters and abnormal event parameters;

[0035] Based on the risk construction equipment and potential safety hazards, determine the recommended suspension link of the construction operation mechanism, and deploy and adjust the relevant construction equipment configuration and manpower configuration of the recommended suspension link;

[0036] Re-predict and adapt construction parameters through a parameter optimization model based on abnormal environmental parameters, and adjust the optimal construction parameters based on the adapted construction parameters.

[0037] Preferably, the installation position of the stress sensor on the pier is determined by the following method:

[0038] Obtain the volume parameter of the pier, determine the stress area of the pier according to the volume parameter, and determine the stress area within the stress area according to the operation structure of the precast beam and the stress area of the pier;

[0039] Calculate the stress area according to the stress area of the pier, and determine the stress uniformity of the pier based on the stress area and the overlapping characteristics of the stress area and the stress area of the pier;

[0040] Determine the stress-biased direction and the non-stress-biased direction according to the stress uniformity, set the stress sensor position at the first height on the first pier side in the stress-biased direction, and set the stress sensor position at the second height on the second pier side in the non-stress-biased direction;

[0041] Wherein, the distance from the second height to the vertex position of the pier is 1.5 times the distance from the first height to the vertex position of the pier.

[0042] Preferably, determine the collaborative operation parameters between each construction equipment based on the optimal construction parameters, including:

[0043] Determine the pre-construction conditions of each construction equipment, and determine the construction task parameters of each construction equipment based on the pre-construction conditions;

[0044] Determine the construction target according to the construction task parameters, determine the data state change rule before and after construction according to the construction target, and determine the state critical value of collaborative operation according to the optimal construction parameters and the data state change rule before and after construction of each construction equipment;

[0045] Determine the synchronous data change parameters of the collaborative operation of each construction equipment based on the state critical value of the collaborative operation of each construction equipment, and determine the collaborative operation parameters between each construction equipment according to the synchronous data change parameters.

[0046] Preferably, the system is also used for:

[0047] Determine multiple reserved installation positions of the precast beam according to the target design parameters of the current curved skew bridge project, and obtain the bearing installation area of each reserved installation position;

[0048] Determine the best docking posture between the precast beam and the bearing at each reserved installation position according to the bearing installation area of each reserved installation position and the lithology factor of the reserved installation position;

[0049] Determine the docking aerial operation form of the precast beam at each reserved installation position according to the optimal docking posture, and determine the required rotation angle of the outriggers of the bridge erecting machine based on the docking aerial operation form;

[0050] Generate an improvement plan based on the required rotation angle of the outriggers and the current outrigger structure of the bridge erecting machine, and perform technical structure improvement on the current outrigger structure based on the improvement plan;

[0051] Generate the construction drawings and material dimension drawings of the current curved skew bridge project according to the target design parameters, and review the construction drawings through the BIM model to determine the unreasonable processes for feedback;

[0052] Based on the material dimension drawings, conduct dimensional inspection and engineering requirement matching evaluation through BIM technology modeling;

[0053] Obtain the organic soil environment parameters of the construction site, determine the soil water component content according to the organic soil environment parameters, and determine the water stability of the filler according to the water component content;

[0054] Determine the water resistance requirements based on the water stability of the filler, and select the target variety of curing agent according to the water resistance requirements in combination with the reaction parameters of the curing agent, clay minerals and organic matter;

[0055] Determine the soil looseness according to the soil environment parameters, determine the soil consolidation performance according to the soil looseness, and confirm whether the soil consolidation performance meets the construction requirements;

[0056] If not, generate a new structure according to the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton and the microscopic network structure composition of the soil;

[0057] Determine the mixing structure ratio of inorganic construction waste recycled aggregate and soil according to the new structure, and optimize the soil according to the mixing structure ratio.

[0058] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0059] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0060] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0061] Figure 1Schematic diagram of a high-efficiency and precise construction system for curved skew bridges based on intelligent perception and collaborative control provided by the present invention;

[0062] Figure 2 Schematic diagram of the construction module in a high-efficiency and precise construction system for curved skew bridges based on intelligent perception and collaborative control provided by the present invention;

[0063] Figure 3 Schematic diagram of the prediction module in a high-efficiency and precise construction system for curved skew bridges based on intelligent perception and collaborative control provided by the present invention. Detailed implementation manners

[0064] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0065] Due to its unique geometric shape and force characteristics, curved skew bridges face many challenges during prefabrication and installation. The traditional construction mode relies on manual experience and conventional measurement means, making it difficult to accurately control various parameters during the construction process, resulting in poor coordination and low efficiency among subsequent construction equipment. For example, during the hoisting and installation of precast beams, there is a lack of real-time position and attitude monitoring, leading to positioning deviations; there is a lack of effective coordination among construction links, and the operation efficiency of equipment such as bridge erecting machines and beam transporting vehicles is low, and the construction period is long. Moreover, the traditional construction method is difficult to adapt to complex and changeable construction environments, such as the construction of curved skew bridges in mountainous areas with complex terrains, where the construction difficulty is high and the quality is difficult to guarantee. To solve the above problems, this embodiment discloses a high-efficiency and precise construction system for curved skew bridges based on intelligent perception and collaborative control.

[0066] A high-efficiency and precise construction system for curved skew bridges based on intelligent perception and collaborative control, as Figure 1 shown, the system includes:

[0067] A construction module 101, configured to construct an omni-directional intelligent perception network during the construction process of a curved skew bridge, and collect multiple groups of historical construction data through the omni-directional intelligent perception network;

[0068] A training module 102, configured to perform analysis and training based on multiple groups of historical construction data through a deep learning algorithm, and establish a parameter optimization model for the construction of curved skew bridges;

[0069] The prediction module 103 is used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model, and determine the efficiency monitoring index according to the optimal construction parameters;

[0070] The monitoring module 104 is used to accurately monitor the construction process of the curved skew bridge based on the efficiency monitoring index and provide feedback on the progress of each stage of work.

[0071] In this embodiment, the optimal construction parameters can perfectly control the construction timing of each construction equipment. For example, during the hoisting process of the precast beam, the beam carrier transports the precast beam accurately under the bridge erecting machine. The bridge erecting machine automatically adjusts the lifting position and angle according to the precast beam position information fed back by the intelligent perception network to achieve precise docking.

[0072] The working principle of the above technical solution is as follows: A number of historical construction data during the construction process of the curved skew bridge are collected through an all-round intelligent perception network. Here, the historical construction data are standard and complete construction data. The intelligent sensor detection method is used to replace manual detection to improve accuracy and work efficiency. Then, through deep learning algorithms, deep learning is performed on multiple sets of construction data to determine the working parameters for the coordinated synchronous work between each construction equipment, and a parameter optimization model for the construction of the curved skew bridge is constructed. Then, the engineering parameters of the current curved skew bridge are obtained, and the operation parameters of each construction equipment are determined according to the engineering parameters. Then, based on the calibrated operation parameters and the real-time operation parameters of each construction equipment collected in real time, the optimal construction parameters, that is, the coordinated and efficient construction parameters between each construction equipment, are determined through the model. During the construction process, the construction efficiency is monitored in real time by determining the efficiency monitoring index. The summary and feedback of the phased completion progress of the project are carried out according to the monitoring results.

[0073] The beneficial effects of the above technical solution are as follows: By constructing an all-round intelligent perception network, accurate data support can be provided for the construction process, ensuring the objectivity and accuracy of the data. Further, by constructing a parameter optimization model for the construction of the curved skew bridge to predict the optimal construction parameters, the coordinated operation effect and work efficiency between each construction equipment can be maximally guaranteed, improving stability and practicability, and solving the problems in the prior art that the traditional construction mode relies on manual experience and conventional measurement means, and it is difficult to accurately control various parameters during the construction process, resulting in poor coordination and low efficiency of the subsequent work of each construction equipment.

[0074] In one embodiment, as Figure 2 shown, the construction module 101 includes:

[0075] The first determination sub-module 1011 is used to determine the work process of the curved skew bridge construction, and determine the key material components, key operation media, and key operation equipment according to the work process;

[0076] The second determination sub-module 1012 is configured to determine the respective state changes of key material components, key working media, and key operating equipment during the construction process, and select different types of sensors according to the state changes.

[0077] The construction sub-module 1013 is configured to construct an all-round intelligent perception network during the construction process of the curved skew bridge through various types of sensors and the communication unit.

[0078] The data acquisition and arrangement sub-module 1014 is configured to collect and arrange historical construction data during the construction of multiple curved skew bridges under project using the all-round intelligent perception network.

[0079] The beneficial effects of the above technical solution are as follows: determining relevant components and equipment according to the construction work process can ensure the reliability of the construction process. Further, selecting sensors according to the state changes of relevant components and equipment, constructing an all-round intelligent perception network, and collecting historical construction data can ensure the accuracy and precision of the data, and at the same time, improve the efficiency of data acquisition.

[0080] In one embodiment, the key material component is a precast beam, the key working medium is a bridge pier, and the key operating equipment is a bridge erecting machine.

[0081] Among them, the sensors corresponding to the precast beam are a high-precision three-dimensional laser displacement sensor and an inclination sensor. The three-dimensional laser displacement sensor is used to monitor the spatial position change of the precast beam during hoisting and installation, and the inclination sensor is used to measure the inclination angle of the precast beam during hoisting and installation.

[0082] The sensors corresponding to the bridge pier are stress and strain sensors, which are used to monitor the stress state of the bridge pier structure during the construction process.

[0083] The sensors corresponding to the bridge erecting machine are stress and strain sensors and an inclination sensor. The inclination sensor is used to measure the inclination angle of the legs of the bridge erecting machine, and the strain sensor is used to monitor the stress state of the main structure of the bridge erecting machine during the construction process.

[0084] In one embodiment, the training module includes:

[0085] The preprocessing sub-module is configured to perform data cleaning and standardization preprocessing on multiple groups of historical construction data to obtain preprocessed historical construction data.

[0086] The design sub-module is configured to extract the construction stage features and environmental impact features corresponding to the preprocessed historical construction data through a deep learning algorithm, and select a deep learning model and design a model architecture based on the construction stage features and environmental impact features.

[0087] A training sub-module for dividing the preprocessed historical construction data into a training set, a validation set, and a test set, training a deep learning model through the inspection set, and establishing a trained model;

[0088] A first generation sub-module for verifying and testing the accuracy and performance of the trained model through the validation set and the test set, and generating a parameter optimization model for the construction of a curved skew bridge after passing the verification and test.

[0089] The beneficial effects of the above technical solution are as follows: Preprocessing the historical data can ensure the consistency of the data. Further, extracting the construction stage features and environmental impact features corresponding to the historical construction data, and training and validating the model can improve the performance and accuracy of the model, enhance the reliability of the model. Further, generating a parameter optimization model for the construction of a curved skew bridge can improve the accuracy and quality of the construction process and reduce construction errors.

[0090] In one embodiment, as Figure 3 shown, the prediction module 103 includes:

[0091] A first acquisition sub-module 1031 for obtaining the target design parameters of the current curved skew bridge project, collecting the environmental parameters and real-time construction data at the construction site;

[0092] A prediction sub-module 1032 for inputting the target design parameters, the environmental parameters at the construction site, and the real-time construction data into the parameter optimization model to predict the best construction parameters;

[0093] A third determination sub-module 1033 for determining the collaborative operation parameters between each construction equipment based on the best construction parameters, and determining the scheduling control parameters for each construction equipment according to the collaborative operation parameters;

[0094] A fourth determination sub-module 1034 for determining the matching efficiency index type according to the scheduling control parameters and construction objectives of each construction equipment, obtaining the quantization index of the matching efficiency index type, and determining the quantization index as the monitoring index of each type of efficiency index.

[0095] The beneficial effects of the above technical solution are as follows: Predicting the best construction parameters according to the environmental parameters and real-time construction data at the construction site, thereby determining the collaborative operation parameters between each construction equipment and the scheduling control parameters of each construction equipment, can ensure the efficient cooperation between the equipment, reduce the construction quality problems caused by unreasonable equipment scheduling, adjust the scheduling strategy in time to ensure the construction quality. Further, determining the quantization index of the matching efficiency index type can clarify the key links and bottlenecks in the construction process, so as to optimize them pertinently. At the same time, it can clearly understand the construction progress and resource utilization situation, and then optimize the construction process to improve the efficiency.

[0096] In one embodiment, the monitoring module includes:

[0097] A second acquisition sub-module, configured to determine a reference data system for each efficiency monitoring index, and acquire corresponding data of the construction process of the skew bridge according to the reference data system;

[0098] A fifth determination sub-module, configured to calculate the efficiency index of each efficiency monitoring index according to the acquired corresponding data, determine the construction compliance according to the efficiency index and perform corresponding optimization;

[0099] A sixth determination sub-module, configured to determine the phased target of each efficiency monitoring index in each construction stage, and determine the actual phased construction progress according to the phased target and the real-time construction data;

[0100] A second generation sub-module, configured to determine the deviation between the actual phased construction progress and the theoretical progress, and determine the low-efficiency construction links to generate a phased work progress feedback report.

[0101] The beneficial effects of the above technical solution are as follows: The efficiency index of each efficiency monitoring index is determined according to the corresponding data of the construction process, and the construction compliance is evaluated and optimized, which can accurately locate the weak links in the construction process and optimize them pertinently to improve the construction quality. Further, the low-efficiency links are determined according to the deviation between the actual phased construction progress and the theoretical progress and a report is generated, which can adjust the low-efficiency links pertinently, improve the construction efficiency of each link, and at the same time, ensure the visualization of the construction progress.

[0102] In one embodiment, the system is further configured to:

[0103] Detect abnormal environmental parameters and abnormal event parameters at the construction site, and determine risk construction equipment and potential safety hazards according to the abnormal environmental parameters and abnormal event parameters;

[0104] Based on the risk construction equipment and potential safety hazards, determine the proposed suspension links of the construction operation mechanism, and deploy and adjust the relevant construction equipment configuration and manpower configuration of the proposed suspension links;

[0105] Based on the abnormal environmental parameters, re-predict and adapt the construction parameters through a parameter optimization model, and adjust the optimal construction parameters based on the adapted construction parameters.

[0106] The beneficial effects of the above technical solution are as follows: By determining potential safety hazards and recommended suspension links based on abnormal environmental parameters and abnormal event parameters during construction, the redeployment of manpower and equipment can be carried out, thereby improving construction safety. At the same time, the benefits of equipment and manpower can be maximized. Further, by determining and adjusting the construction parameters adapted to the construction, the construction process can be accurately controlled, while improving construction quality and efficiency. On the one hand, the multi-device collaborative control system makes emergency adjustments to construction equipment to ensure construction safety; on the other hand, the construction parameter optimization model based on artificial intelligence recalculates according to the new situation and generates adjusted construction parameters, and construction personnel continue construction based on the adjusted parameters to ensure the continuity and accuracy of construction.

[0107] In one embodiment, the installation positions of the stress sensors on the bridge pier are determined in the following manner:

[0108] Obtain the volume parameters of the bridge pier, determine the stress area of the bridge pier according to the volume parameters, and determine the stress area within the stress area based on the operation structure of the precast beam and the stress area of the bridge pier;

[0109] Calculate the stress area based on the stress area of the stress area, and determine the stress uniformity of the bridge pier based on the stress area and the overlapping characteristics of the stress area and the stress area;

[0110] Determine the stress-biased direction and the non-stress-biased direction according to the stress uniformity, set the stress sensor position at the first height on the first side of the bridge pier in the stress-biased direction, and set the stress sensor position at the second height on the second side of the bridge pier in the non-stress-biased direction;

[0111] Wherein, the distance from the second height to the vertex position of the bridge pier is 1.5 times the distance from the first height to the vertex position of the bridge pier.

[0112] The beneficial effects of the above technical solution are as follows: By setting the sensor positions according to the stress distribution, the stress change data of the bridge pier can be detected in all directions, so as to comprehensively understand the stress change of the bridge pier, ensuring the accuracy, reliability and integrity of the data. At the same time, it potentially avoids safety accidents such as the collapse of precast beams caused by uneven stress of the bridge pier, improving safety.

[0113] In one embodiment, based on the optimal construction parameters, the collaborative operation parameters between construction equipment are determined, including:

[0114] Determine the pre-construction conditions of each construction equipment, and determine the construction task parameters of each construction equipment based on the pre-construction conditions;

[0115] Determine the construction objectives based on the construction task parameters, determine the data state change law before and after construction according to the construction objectives, and determine the state critical value of collaborative operation according to the best construction parameters and the data state change law of each construction equipment before and after construction;

[0116] Determine the synchronous data change parameters of the collaborative operation of each construction equipment based on the state critical value of the collaborative operation of each construction equipment, and determine the collaborative operation parameters between each construction equipment according to the synchronous data change parameters.

[0117] The beneficial effects of the above technical solution are: by determining the pre-construction conditions of each construction equipment and then determining the reference data, it can be intuitively determined which data each construction equipment refers to for operation. At the same time, the synchronous state change of the collaborative work of each construction equipment during the operation is used to determine the collaborative operation parameters, which is more rapid and intuitive.

[0118] In one embodiment, the system is further configured to:

[0119] Determine multiple reserved installation positions of the precast beam according to the target design parameters of the current curved skew bridge project, and obtain the bearing installation area of each reserved installation position;

[0120] Determine the best docking posture between the precast beam and the bearing at each reserved installation position according to the bearing installation area of each reserved installation position and the lithology factor of the reserved installation position;

[0121] Determine the docking aerial operation form of the precast beam at each reserved installation position according to the best docking posture, and determine the required rotation angle of the outriggers of the bridge erecting machine based on the docking aerial operation form;

[0122] Generate an improvement plan according to the required rotation angle of the outriggers and the current outrigger structure of the bridge erecting machine, and perform technical structure improvement on the current outrigger structure based on the improvement plan;

[0123] Generate the construction drawing and material dimension drawing of the current curved skew bridge project according to the target design parameters, and feedback the unreasonable process by auditing the construction drawing through the BIM model;

[0124] Perform dimension inspection and engineering requirement matching evaluation based on the material dimension drawing through BIM technology modeling;

[0125] Obtain the organic soil environment parameters of the construction site, determine the soil water component content according to the organic soil environment parameters, and determine the water stability of the filler according to the water component content;

[0126] Determine the water resistance requirement based on the water stability of the filler, and select the target variety of curing agent according to the water resistance requirement in combination with the reaction parameters of the curing agent, clay minerals and organic matter;

[0127] Determine the soil looseness based on soil environmental parameters, determine the soil consolidation performance based on the soil looseness, and confirm whether the soil consolidation performance meets the construction requirements;

[0128] If not, generate a new structure according to the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton and the composition of the soil's microscopic network structure;

[0129] Determine the mixing structure ratio of inorganic construction waste recycled aggregate and soil according to the new structure, and optimize the soil according to the mixing structure ratio.

[0130] The beneficial effects of the above technical solution are as follows: By generating an improved solution and then improving the technical structure of the current leg structure based on the improved solution, it can not only ensure the stability of precast beam handling, but also achieve the precise posture docking between the precast beam and the support, as well as the precise positioning of the precast box girder of the large-angle skewed curved bridge, improving the positioning accuracy, docking stability and working efficiency. Further, by selecting the type of curing agent and optimizing the soil treatment, the problem of poor water stability of the filler can be solved, and at the same time, the durability of the soil body can be increased, improving the practicality and the compatibility with the construction conditions.

[0131] Those skilled in the art should understand that the first and second in the present invention refer to different application stages.

[0132] After considering the specification and practicing the disclosure herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only considered exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0133] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. An efficient and accurate construction system for curved skew bridges based on intelligent perception and collaborative control, characterized in that: The system includes: A construction module is used to construct an all-round intelligent perception network during the construction of a curved skew bridge, and collect multiple sets of historical construction data through the all-round intelligent perception network; The training module is used to analyze and train multiple sets of historical construction data using a deep learning algorithm to establish a parameter optimization model for the construction of curved skew bridges; The prediction module is used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model and determine the efficiency monitoring index according to the optimal construction parameters; The monitoring module is used to accurately monitor the construction process of the curved skew bridge based on efficiency monitoring indicators and provide phased work progress feedback.

2. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1 is characterized in that: The building blocks include: The first determination submodule is used to determine the workflow of the curved skew bridge construction, and determine the key material components, key operating media and key operating equipment according to the workflow; The second determination submodule is used to determine the state changes of key material components, key operating media and key operating equipment during the construction process, and select different types of sensors according to the state changes; Construction submodules are used to build a comprehensive intelligent perception network during the construction of curved skew bridges through various types of sensors and communication units; The data collection and collation submodule is used to collect and organize historical construction data during the construction of multiple curved skew bridge projects using an all-round intelligent perception network.

3. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 2 is characterized in that: The key material component is a prefabricated beam, the key operating medium is a bridge pier, and the key operating equipment is a bridge erection machine; Among them, the sensors corresponding to the prefabricated beams are high-precision three-dimensional laser displacement sensors and inclination sensors. The three-dimensional laser displacement sensors are used to monitor the spatial position changes of the prefabricated beams during the lifting and installation process, and the inclination sensors are used to measure the inclination angles of the prefabricated beams during the lifting and installation process. The sensors corresponding to the bridge piers are stress-strain sensors, which are used to monitor the stress state of the bridge pier structure during the construction process; The corresponding sensors of the bridge-building machine are stress-strain sensors and inclination sensors. The inclination sensors are used to measure the inclination angles of the bridge-building machine's legs, and the strain sensors are used to monitor the stress state of the main structure of the bridge-building machine during the construction process.

4. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1 is characterized in that: The training module comprises: A preprocessing submodule is used to perform data cleaning and standardization preprocessing on multiple groups of historical construction data to obtain preprocessed historical construction data; Design submodule, which is used to extract the construction stage characteristics and environmental impact characteristics corresponding to the pre-processed historical construction data through deep learning algorithm, select the deep learning model based on the construction stage characteristics and environmental impact characteristics, and design the model architecture; The training submodule is used to divide the pre-processed historical construction data into a training set, a validation set, and a test set, train the deep learning model through the inspection set, and establish the trained model; The first generation submodule is used to verify and test the model accuracy and performance of the trained model through a verification set and a test set. After the verification test is passed, a parameter optimization model for the construction of a curved skew bridge is generated.

5. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1 is characterized in that: The prediction module comprises: The first acquisition submodule is used to obtain the target design parameters of the current curved skew bridge project, collect environmental parameters of the construction site and real-time construction data; The prediction submodule is used to input the target design parameters, the environmental parameters of the construction site and the real-time construction data into the parameter optimization model to predict the optimal construction parameters; The third determination submodule is used to determine the collaborative operation parameters between the various construction equipment based on the optimal construction parameters, and determine the scheduling control parameters for each construction equipment according to the collaborative operation parameters; The fourth determination submodule is used to determine the matching efficiency index type according to the scheduling control parameters and construction objectives of each construction equipment, obtain the quantitative index of the matching efficiency index type, and determine the quantitative index as the monitoring index of each type of efficiency index.

6. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1 is characterized in that: The monitoring module comprises: The second collection submodule is used to determine the reference data system of each efficiency monitoring indicator, and collect the corresponding data of the construction process of the curved skew bridge according to the reference data system; The fifth determination submodule is used to calculate the efficiency index of each efficiency monitoring indicator according to the collected corresponding data, determine the construction qualification according to the efficiency index and perform corresponding optimization; The sixth determination submodule is used to determine the phased target of each efficiency monitoring indicator in each construction phase, and determine the actual progress of the phased construction according to the phased target and real-time construction data; The second generation submodule is used to determine the deviation between the actual progress and theoretical progress of the phased construction and to identify the inefficient construction links to generate a phased work progress feedback report.

7. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1 is characterized in that: The system is also used to: Detect abnormal environmental parameters and abnormal event parameters at the construction site, and determine risky construction equipment and potential safety hazards based on the abnormal environmental parameters and abnormal event parameters; Determine the recommended suspension phases of the construction operation mechanism based on risky construction equipment and potential safety hazards, and deploy and adjust the configuration of relevant construction equipment and manpower in the recommended suspension phases; Based on the abnormal environmental parameters, the parameter optimization model is used to re-predict the adaptive construction parameters, and the optimal construction parameters are adjusted based on the adaptive construction parameters.

8. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 3 is characterized in that: The location of the stress sensor on the bridge pier is determined by: Obtaining the volume parameters of the bridge pier, determining the area of ​​the stress-bearing region of the bridge pier according to the volume parameters, and determining the stress area in the stress-bearing region according to the operating structure of the precast beam and the stress-bearing region of the bridge pier; The area of ​​the stress region is calculated according to the area of ​​the stress region, and the force uniformity of the pier is determined based on the area of ​​the stress region and the overlapping characteristics of the stress region and the stress region; Determine the biased force direction and the non-biased force direction according to the uniformity of the force, set the stress sensor position at a first height on the side of the first pier in the biased force direction, and set the stress sensor position at a second height on the side of the second pier in the non-biased force direction; The distance between the second height and the apex of the bridge pier is 1.5 times the distance between the first height and the apex of the bridge pier.

9. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 5 is characterized in that: Determine the collaborative operation parameters between various construction equipment based on the optimal construction parameters, including: Determine the pre-construction conditions for each construction equipment, and determine the construction task parameters for each construction equipment based on the pre-construction conditions; Determine the construction target according to the construction task parameters, determine the data status change law before and after the construction according to the construction target, and determine the state critical value of the collaborative operation according to the optimal construction parameters and the data status change law before and after the construction of each construction equipment; Based on the critical state value of the collaborative operation of each construction equipment, the synchronous data change parameter of the collaborative operation of each construction equipment is determined, and the collaborative operation parameters between the various construction equipment are determined according to the synchronous data change parameter.

10. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 5 is characterized in that: The system is also used to: Determine multiple reserved installation positions of precast beams according to the target design parameters of the current curved skew bridge project, and obtain the support installation area of ​​each reserved installation position; Determine the best docking posture between the precast beam and the support at each reserved installation position according to the support installation area of ​​each reserved installation position and the lithology factors of the reserved installation position; Determine the docking aerial operation form of the prefabricated beam at each reserved installation position according to the optimal docking posture, and determine the required rotation angle of the legs of the bridge erecting machine based on the docking aerial operation form; An improvement plan is generated according to the required outrigger rotation angle and the current outrigger structure of the bridge erecting machine, and the technical structure of the current outrigger structure is improved based on the improvement plan; Generate construction drawings and material dimension drawings of the current curved skew bridge project based on the target design parameters, and review the construction drawings through the BIM model to identify unreasonable processes and provide feedback; Based on the material size drawing, BIM technology modeling is used to conduct dimensional inspection and engineering demand matching assessment; Obtain the organic soil environmental parameters of the construction site, determine the soil water content based on the organic soil environmental parameters, and determine the water stability of the filler based on the water content; Determine the water resistance requirements based on the water stability of the filler, and select the target variety of curing agent based on the water resistance requirements and the reaction parameters of the curing agent, clay minerals and organic matter; Determine soil looseness based on soil environmental parameters, determine soil consolidation performance based on soil looseness, and confirm whether the soil consolidation performance meets construction requirements; If not, a new framework is generated based on the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton and the microscopic network structure of the soil; The mixing structure ratio of inorganic construction waste recycled aggregate and soil is determined according to the new framework, and the soil is optimized according to the mixing structure ratio.

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