Method and device for monitoring whole construction process of offshore open caisson and readable medium

Abstract

The invention discloses an offshore open caisson construction whole process monitoring method and device and a readable medium, and the method comprises the steps: obtaining each monitoring item data corresponding to each monitoring type under the corresponding working condition in the construction process collected by a sensor at the current moment, judging whether to give an early warning or not, and if yes, giving an early warning and adjusting a construction scheme; otherwise, construction continues to be conducted according to the construction scheme, correction is conducted on the basis of the initial finite element model, and a corrected finite element model and a finite element analysis result are obtained; inputting the finite element analysis result of the corresponding working condition in the construction process at the current moment, the environment information and the monitoring item data into a trained monitoring item data prediction model, and predicting to obtain monitoring item data at the next moment; whether early warning is conducted or not is judged according to the predicted monitoring item data at the next moment, if yes, early warning is conducted, the construction scheme is adjusted, and the two steps are repeated, and if not, construction continues to be conducted according to the construction scheme. The method can predict the data of each monitoring item at the future moment, and predicts the risk in advance.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction, and in particular to a method, a device and a readable medium for monitoring the entire process of offshore caisson construction. Background Art

[0002] With the rapid development of civil engineering construction at home and abroad, caissons, as important structures, have gradually developed from land to water, and the size of caissons has gradually tended to be larger. The construction of water caissons, especially offshore caissons, has attracted much attention in engineering construction in recent years. Offshore caissons are generally more common in offshore bridge construction, and the cross-section of caissons is mainly circular and rectangular. Offshore caisson structures are divided into steel caissons and reinforced concrete caissons, which are generally prefabricated and assembled, and there are also cast-in-place construction methods.

[0003] At present, in the process of offshore caisson construction using the prefabricated assembly method, the offshore environment has a huge impact on the safety of caisson construction from assembly, floating, positioning, and sinking. Most of the existing caisson monitoring systems are based on land caisson construction monitoring. The land caisson construction monitoring process is relatively simple, mainly focusing on the caisson sinking process, and the focus is on the relationship between the caisson sinking process and the surrounding soil. There are also some water caisson monitoring systems, but they mainly focus on a certain link in caisson construction.

[0004] There are the following problems in monitoring the construction of offshore caissons: First, offshore construction is generally an all-offshore construction operation, and the parties involved in the construction are distributed on different ships. There is a physical isolation between the ships, and the convenience of information exchange is lower than that of onshore operations; second, the floating distance is long, and the distance between the caisson processing and the positioning below is far. After the processing and prefabrication, it is floated to the designated area at sea. There are many uncontrollable factors in the whole process. At present, the current status of the caisson can only be judged by checking the monitoring data of the process; and the floating transportation requires the participation of multiple tugboats, and the caisson needs to be towed along the established route during the process. The stability of the caisson needs to be guaranteed; third, the positioning accuracy is high and the difficulty is great. The caisson is large in size and heavy in weight. The operation is difficult, and it needs to be lowered to the designated location under the joint operation of multiple cable force control equipment; fourth, there are many factors affecting the hydrological and meteorological environment, including wind speed, wind direction, tide level, seawater flow rate, seawater waves, and weather factors; fifth, the communication signal at sea is poor, data transmission is delayed and missing, the caisson is out of the dock and floating distance is long, the construction area is at sea, and the signal base station coverage is weaker than on land, making communication difficult; sixth, at present, finite element calculation and analysis is mainly carried out based on the working conditions of the design state, and then construction monitoring is guided, which has a certain lag. If the actual working conditions change greatly compared with the design working conditions, re-analysis is required, which affects the progress of construction. The above problems have become the key factors restricting the development of the offshore caisson construction field. How to solve the above problems efficiently and scientifically is the most urgent thing at present. Summary of the invention

[0005] The purpose of this application is to propose a method, device and readable medium for monitoring the entire process of offshore caisson construction in response to the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a method for monitoring the entire process of offshore caisson construction, comprising the following steps:

[0007] Obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish the initial finite element model of the caisson structure and determine the key positions of the caisson structure, and arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model;

[0008] Obtain the data of each monitoring item corresponding to each monitoring type under the corresponding working condition in the construction process collected by the sensor at the current moment and determine whether an early warning is issued. If so, issue an early warning and adjust the construction plan. Otherwise, continue to construct according to the construction plan and make corrections based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working condition in the construction process at the current moment;

[0009] Obtaining environmental information and monitoring item data of the working conditions corresponding to the current construction process, inputting the finite element analysis results, environmental information and monitoring item data of the working conditions corresponding to the current construction process into the trained monitoring item data prediction model, and predicting the monitoring item data at the next moment;

[0010] Determine whether to issue an early warning based on the predicted monitoring item data at the next moment. If so, issue an early warning and adjust the construction plan and repeat the above two steps. Otherwise, continue construction according to the construction plan.

[0011] Preferably, the monitoring types include caisson environment, caisson geometry, water levels inside and outside the caisson, structural stress, anchor cable tension, offshore hydrological environment, dock tide level and compartment water level monitoring; the monitoring data corresponding to the caisson environment include video and wind speed and direction collected above the caisson structure; the monitoring data corresponding to the caisson geometry include plane position, caisson sinking depth, torsion angle and inclination, the inclination includes the caisson top inclination collected at the midpoints of the four sides above the caisson structure and the caisson bottom inclination collected at the midpoints of the four sides below the caisson structure; the monitoring data corresponding to the water levels inside and outside the caisson include the middle compartment water level, side compartment water level and wellbore water level collected at the bottom of the middle compartment, the bottom of the side compartment and the bottom of the wellbore respectively. water level; the monitoring data corresponding to the structural stress include the caisson structure stress collected at the midpoints of the four upper and lower sides of the caisson structure, the cofferdam support stress collected at the cofferdam support end and the support stress collected above the supporting column; the monitoring data corresponding to the anchor cable tension include the anchor cable tension collected at the top of the jack; the monitoring data corresponding to the offshore hydrological environment include the seawater flow velocity, seawater waves, wind speed and direction and tide information collected on the platform in the construction area; the monitoring data corresponding to the dock tide include the tide information collected from the caisson undocking area; the monitoring data corresponding to the bulkhead water level monitoring include the video collected from the top of the bulkhead, and the environmental information and monitoring data are the data after data filtering.

[0012] As a preference, the calculation process of the plane position is as follows:

[0013] The transverse direction of the caisson structure is taken as the X-axis direction of the rectangular coordinate system, and the longitudinal direction of the caisson structure is taken as the Y-axis direction of the rectangular coordinate system. The coordinates of the midpoints of the four sides of the top surface of the caisson structure are measured in the rectangular coordinate system (x1, y1), (x2, y2), (x3, y3), (x4, y4), and the plane coordinates of the center point of the top surface of the caisson structure (x5, y5) are calculated using the following formula:

[0014]

[0015]

[0016] Determine the design coordinates (x'5, y'5) of the center point of the top surface of the caisson structure, and calculate the offset of the plane position of the center point of the caisson structure by the following formula:

[0017] Δx0=x5-x'5;

[0018] Δy0=y5-y'5;

[0019] Among them, Δx0 and Δy0 are the offsets of the plane position of the center point of the caisson structure on the X-axis and Y-axis respectively;

[0020] The calculation process of the caisson sinking depth is as follows:

[0021] Get the Z-axis coordinates z1, z2, z3, z4, z5 of the four midpoints and the center of the top surface of the caisson structure in the direction perpendicular to the top surface of the caisson structure. The sinking depth z0 of the caisson is calculated using the following formula:

[0022]

[0023] The following formula is used to calculate the deviation Δz0 of the caisson sinking depth:

[0024] Δz0=z0-z'0;

[0025] Among them, z'0 is the design elevation of the center point of the top surface of the caisson structure;

[0026] The calculation process of the inclination is as follows:

[0027] The following formula is used to calculate the inclination Δh along the bridge: y :

[0028] Δh y =h2-h4;

[0029] The following formula is used to calculate the inclination angle θ along the bridge direction: y :

[0030]

[0031] The following formula is used to calculate the transverse bridge inclination Δh x :

[0032] Δh x =h1-h3;

[0033] The following formula is used to calculate the transverse bridge tilt angle θ x :

[0034]

[0035] The following formula is used to calculate the coordinates of the center point of the caisson bottom surface (x xo ,y xo ):

[0036] x xo =x5+h×tanθ x ;

[0037] y xo =y5+h×tanθ y ;

[0038] Among them, h1, h2, h3, and h4 are the elevations of the four corner points of the caisson structure respectively; a and b are the horizontal distances between the measuring points along the bridge and transverse to the bridge at the initial equilibrium position respectively; h is the height of the caisson; x5 and y5 are the horizontal and vertical coordinates of the center point of the top surface of the caisson structure;

[0039] The calculation process of the torsion angle is as follows:

[0040] The following formula is used to calculate the angle β1 between the line connecting the coordinates of the four midpoints of the top surface of the caisson structure (x1, y1) and (x3, y3) and the bridge axis, and the angle β2 between the line connecting (x4, y4) and (x2, y2) and the bridge axis:

[0041]

[0042] The average of β1 and β2 is taken as the torsion angle β of the caisson structure:

[0043]

[0044] Preferably, the corresponding working condition in the construction process at the current moment is the working condition corresponding to any moment in any one of the links of floating, sinking into place to implantation, water injection into the steel shell, pouring concrete, bottom sealing and core filling. The environmental information includes hydrological and meteorological information, unplanned external loads and environmental data in the affected area. The design data includes survey data, geometric dimensions, material properties, structural analysis data, design specifications and standard data, construction process data, structural detail data, safety data and cost data. The survey data includes topography, geological data, hydrological and meteorological data, environmental data and survey specification data.

[0045] Preferably, the monitoring item data prediction model includes a feedforward neural network or a BP neural network.

[0046] As a preferred method, determining whether to issue an early warning specifically includes:

[0047] The monitoring item data is compared with the threshold intervals of different levels. If the monitoring item data is within the corresponding threshold interval, an early warning of the corresponding level is issued. If the monitoring item data is within the normal data interval, no early warning is issued.

[0048] In a second aspect, the present invention provides a device for monitoring the entire process of offshore caisson construction, comprising:

[0049] The model building module is configured to obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish an initial finite element model of the caisson structure and determine the key positions of the caisson structure, and arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model;

[0050] The finite element analysis module is configured to obtain the data of each monitoring item corresponding to each monitoring type under the corresponding working condition in the construction process collected by the sensor at the current moment and determine whether to issue an early warning. If so, issue an early warning and adjust the construction plan. Otherwise, continue to construct according to the construction plan and make corrections based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working condition in the construction process at the current moment;

[0051] The data prediction module is configured to obtain the environmental information and monitoring item data of the working condition corresponding to the current construction process, input the finite element analysis results, environmental information and monitoring item data of the working condition corresponding to the current construction process into the trained monitoring item data prediction model, and predict the monitoring item data at the next moment;

[0052] The early warning module is configured to determine whether to issue an early warning based on the predicted monitoring item data at the next moment. If so, an early warning is issued and the construction plan is adjusted and the above two steps are repeated. Otherwise, construction continues according to the construction plan.

[0053] In a third aspect, the present invention provides an electronic device comprising one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0055] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) The method for monitoring the entire process of offshore caisson construction proposed in the present invention monitors the entire process of caisson construction, including the caisson launching, floating, sinking to the bed, water injection into the steel shell, pouring concrete, and caisson bottom sealing and core filling. The data of each monitoring item in the process is analyzed in real time, and the overall safety status of the caisson can be grasped from point to surface. At the same time, the monitoring item data prediction model is used to predict the data of each monitoring item at the next moment, guide the next step of construction, and further improve the controllability of caisson construction.

[0058] (2) The method for monitoring the entire process of offshore caisson construction proposed in the present invention first constructs an initial finite element model based on the design data and survey data, and then performs finite element analysis based on the real-time collected monitoring data. The initial finite element model is corrected by verifying the design data with the actual monitoring data. The finite element analysis results obtained at the same time can also be used as a basis for prediction to determine whether to make dynamic adjustments to the construction. At the same time, the stress conditions of the parts where sensors are not deployed can be discovered through analysis of the revised finite element model, thereby discovering potential risks.

[0059] (3) The method for monitoring the entire process of offshore caisson construction proposed in the present invention can input the finite element analysis results, environmental information and monitoring item data of the corresponding working conditions in the current construction process into a trained monitoring item data prediction model, and predict the monitoring item data at the next moment, so as to avoid risks in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A flow chart of a method for monitoring the entire process of offshore caisson construction according to an embodiment of the present application;

[0062] Figure 2 A construction flow chart of a method for monitoring the entire process of offshore caisson construction according to an embodiment of the present application;

[0063] Figure 3 A schematic diagram of the arrangement of caisson geometric posture monitoring points of a method for monitoring the entire process of offshore caisson construction according to an embodiment of the present application;

[0064] Figure 4 A schematic diagram of calculating the inclination of a caisson in the whole process monitoring method of offshore caisson construction according to an embodiment of the present application;

[0065] Figure 5 A schematic diagram of calculating the torsion angle of a caisson in a method for monitoring the entire process of offshore caisson construction according to an embodiment of the present application;

[0066] Figure 6 A logic block diagram of a method for monitoring the entire process of offshore caisson construction according to an embodiment of the present application;

[0067] Figure 7 This is a structural diagram of an intelligent monitoring and prediction system for the entire offshore caisson construction process according to an embodiment of the present application;

[0068] Figure 8 A schematic diagram of a whole-process monitoring device for offshore caisson construction according to an embodiment of the present application;

[0069] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Figure 1 A method for monitoring the entire process of offshore caisson construction provided by an embodiment of the present application is shown, comprising the following steps:

[0072] S1, obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish the initial finite element model of the caisson structure and determine the key positions of the caisson structure, arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model.

[0073] In a specific embodiment, the monitoring types include caisson environment, caisson geometry, water levels inside and outside the caisson, structural stress, anchor cable tension, offshore hydrological environment, dock tide level and compartment water level monitoring; the monitoring data corresponding to the caisson environment include video and wind speed and direction collected above the caisson structure; the monitoring data corresponding to the caisson geometry include plane position, caisson sinking depth, torsion angle and inclination, the inclination includes the caisson top inclination collected at the midpoints of the four sides above the caisson structure and the caisson bottom inclination collected at the midpoints of the four sides below the caisson structure; the monitoring data corresponding to the water levels inside and outside the caisson include the middle compartment water level, side compartment water level and Wellbore water level; the monitoring data corresponding to the structural stress include the caisson structure stress collected at the midpoints of the four upper and lower sides of the caisson structure, the cofferdam support stress collected at the cofferdam support end and the support stress collected above the supporting column; the monitoring data corresponding to the anchor cable tension include the anchor cable tension collected at the top of the jack; the monitoring data corresponding to the offshore hydrological environment include the seawater flow velocity, seawater waves, wind speed and direction and tide information collected on the platform in the construction area; the monitoring data corresponding to the dock tide include the tide information collected in the caisson undocking area; the monitoring data corresponding to the compartment water level monitoring include the video collected from the top of the compartment, and the environmental information and monitoring data are the data after data filtering.

[0074] Specifically, the monitoring items during the caisson structure construction process are shown in Table 1.

[0075] Table 1

[0076]

[0077]

[0078] In a specific embodiment, the calculation process of the plane position is as follows:

[0079] The transverse direction of the caisson structure is taken as the X-axis direction of the rectangular coordinate system, and the longitudinal direction of the caisson structure is taken as the Y-axis direction of the rectangular coordinate system. The coordinates of the midpoints of the four sides of the top surface of the caisson structure are measured in the rectangular coordinate system (x1, y1), (x2, y2), (x3, y3), (x4, y4), and the plane coordinates of the center point of the top surface of the caisson structure (x5, y5) are calculated using the following formula:

[0080]

[0081] Determine the design coordinates (x'5, y'5) of the center point of the top surface of the caisson structure, and calculate the offset of the plane position of the center point of the caisson structure by the following formula:

[0082] Δx0=x5-x'5;

[0083] Δy0=y5-y'5;

[0084] Among them, Δx0 and Δy0 are the offsets of the plane position of the center point of the caisson structure on the X-axis and Y-axis respectively;

[0085] The calculation process of the caisson sinking depth is as follows:

[0086] Get the Z-axis coordinates z1, z2, z3, z4, z5 of the four midpoints and the center of the top surface of the caisson structure in the direction perpendicular to the top surface of the caisson structure. The sinking depth z0 of the caisson is calculated using the following formula:

[0087]

[0088] The following formula is used to calculate the deviation Δz0 of the caisson sinking depth:

[0089] Δz0=z0-z'0;

[0090] Among them, z'0 is the design elevation of the center point of the top surface of the caisson structure;

[0091] The calculation process of the inclination is as follows:

[0092] The following formula is used to calculate the inclination Δh along the bridge: y :

[0093] Δh y =h2-h4;

[0094] The following formula is used to calculate the inclination angle θ along the bridge direction: y :

[0095]

[0096] The following formula is used to calculate the transverse bridge inclination Δh x :

[0097] Δh x =h1-h3;

[0098] The following formula is used to calculate the transverse bridge tilt angle θ x :

[0099]

[0100] The following formula is used to calculate the coordinates of the center point of the caisson bottom surface (x xo ,y xo ):

[0101] x xo =x5+h×tanθ x ;

[0102] y xo =y5+h×tanθ y ;

[0103] Among them, h1, h2, h3, and h4 are the elevations of the four corner points of the caisson structure respectively; a and b are the horizontal distances between the measuring points along the bridge and transverse to the bridge at the initial equilibrium position respectively; h is the height of the caisson; x5 and y5 are the horizontal and vertical coordinates of the center point of the top surface of the caisson structure;

[0104] The calculation process of the torsion angle is as follows:

[0105] The following formula is used to calculate the angle β1 between the line connecting the coordinates of the four midpoints of the top surface of the caisson structure (x1, y1) and (x3, y3) and the bridge axis, and the angle β2 between the line connecting (x4, y4) and (x2, y2) and the bridge axis:

[0106]

[0107] The average of β1 and β2 is taken as the torsion angle β of the caisson structure:

[0108]

[0109] In a specific embodiment, the monitoring item data prediction model includes a feedforward neural network or a BP neural network.

[0110] refer to Figure 2In the early stage of construction, simulation calculations are carried out through the data of design and survey materials. Specifically, finite element software is used to establish the initial finite element model of the caisson structure. The caisson construction process is simulated and analyzed through the initial finite element model, and the key positions of the caisson structure are calculated. Sensors are arranged at the key positions of the caisson structure to guide the installation of sensors.

[0111] The data of each monitoring item is collected and processed in real time through sensors. Caisson geometric posture monitoring calculation description: GNSS is used to monitor the three-dimensional geometric coordinates of five key points in the caisson structure in real time and perform calculations, so as to grasp the change information of the geometric posture such as inclination, horizontal displacement and horizontal torsion of the caisson structure in real time.

[0112] (1) Planar position of the center point of the caisson structure

[0113] Through real-time monitoring, the analysis results of the measurement data around the caisson structure are compared with the theoretical results to obtain the x, y coordinate difference of each monitoring point, and the point displacement is calculated. The coordinates of the center point of the caisson structure are calculated using the coordinates of the monitoring points around the caisson structure, and then compared with the design coordinates of the center point of the caisson structure, the offset of the plane position of the center point of the caisson structure can be obtained.

[0114] by Figure 3 For example, the X-axis direction is the transverse direction of the bridge, and the Y-axis direction is the longitudinal direction of the bridge. Point 5→2 is defined as the positive direction of the X-axis; using the left-hand rule, point 5→1 is defined as the positive direction of the Y-axis. The plane coordinates of the center point 5 on the top surface of the caisson structure can be calculated from points 1, 3, 2 and 4. The coordinate of point 5 in the X-axis direction is expressed by the average value of the X-axis coordinates of points 1 and 3. The coordinate of point 5 in the Y-axis direction is expressed by the average value of the Y-axis coordinates of points 2 and 4. The plane coordinates of the center point 5 on the top surface of the caisson structure are then checked with the design coordinates of point 5.

[0115] (2) The difference in the plane distribution of the caisson sinking depth is the most important monitoring indicator during the caisson sinking process. Monitoring the sinking depth and its uniformity in the plane distribution is the most important monitoring content during the caisson construction process, and is also the focus of this design monitoring. By monitoring the sinking depth and its differences in the caisson structure, it is possible to timely and intuitively understand the construction status of the caisson during the production and sinking process of the caisson and provide a basis for the caisson sinking construction and correction. The caisson sinking depth is expressed by the vertical coordinates of the top surface of the caisson structure, and its coordinate value is the average of the vertical coordinates of the five monitoring points.

[0116] (3)Reference Figure 4 The inclination of the caisson is determined by comparing the elevation difference and distance of the four control points on the side of the caisson to calculate the inclination of the caisson. The data is then directly measured and verified with the inclinometer installed at the same measuring point of the elevation control to ensure the accuracy of the inclination test.

[0117] (4)Reference Figure 5 , according to the coordinates of the four corner control points 1', 2' or 3', 4' measured by real-time monitoring, the angle between them and the bridge axis is calculated, which is the torsion angle of the caisson structure. In order to eliminate the measurement error, the average value of the calculation results of β1 and β2 is taken as the torsion angle of the caisson structure. For the deflection and rotation of the caisson structure during the sinking process, according to the reasons for the deflection and rotation of the caisson structure, the cable force and cable length are reasonably adjusted to correct the deviation.

[0118] Construct a monitoring item data prediction model, which includes but is not limited to a neural network model of machine learning, such as a feedforward neural network or a BP neural network. Select an existing network model and targeted training data to perform regular training on it. The specific network structure and training process will not be repeated here. The monitoring item data prediction model is used to predict the monitoring item data at future times.

[0119] S2, obtains the data of each monitoring item corresponding to each monitoring type under the corresponding working conditions in the construction process collected by the sensor at the current moment and determines whether an early warning is issued. If so, an early warning is issued and the construction plan is adjusted. Otherwise, construction continues according to the construction plan, and corrections are made based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working conditions in the construction process at the current moment.

[0120] In a specific embodiment, the corresponding working conditions in the construction process at the current moment are the working conditions corresponding to any moment in any one of the links of floating, sinking into place to implantation, water injection into the steel shell, pouring concrete, bottom sealing and core filling. The environmental information includes hydrological and meteorological information, unplanned external loads and environmental data in the affected area. The design data includes survey data, geometric dimensions, material properties, structural analysis data, design specifications and standard data, construction process data, structural detail data, safety data and cost data. The survey data includes topography, geological data, hydrological and meteorological data, environmental data and survey specification data.

[0121] Specifically, refer to Figure 6According to the actual situation on site, the calculation finite element model is adjusted in time, the stress and deformation characteristics of the whole process of caisson sinking are grasped, and the structural verification and review are carried out for the working conditions such as caisson floating, implantation, and concrete pouring in the steel shell. Specifically, the finite element analysis of the actual working conditions on site is compared with the simulation calculation of the previous construction process, and the initial finite element model is corrected to obtain the corrected finite element model and analyze the finite element analysis results, so as to provide more accurate results for the next working condition analysis and data prediction. For potential abnormal working conditions (such as uneven base, uneven concrete pouring in the steel shell, uneven settlement of the caisson base, etc.), the monitoring item data prediction model is used to predict and analyze the data of each monitoring item of the caisson in advance, predict risks in advance, and take corresponding control strategies to provide a control basis for the positioning, sinking, and implantation of the caisson. Environmental data refers to the surrounding scene environment including above and below ground, such as buildings around the construction area, underground pipelines or structures, etc. Structural analysis data refers to the index data of structural stability, deformation, and mechanics obtained by calculation programs; structural detail data refers to the detailed design of key parts, such as the connection form of key parts, structural style, construction convenience, process selection, etc.

[0122] The significance of caisson construction monitoring is to dynamically control the safety of caisson construction based on on-site monitoring data. First, finite element calculation and analysis is performed on the caisson sinking process to understand the stress characteristics of the caisson structure and the influence of each monitoring data on the caisson construction process, so as to implement the arrangement of sensors according to the specific characteristics of the structure of this project and the experience of similar projects. During the construction process, through comprehensive analysis and prediction of monitoring data, effective guidance opinions are timely put forward for caisson construction to ensure construction safety. Once abnormal monitoring data is found, relevant construction plans are immediately implemented to ensure the safety of the caisson structure. At the same time, according to the monitoring data and prediction results, it can be checked whether the design of the caisson structure and the sinking construction calculation mode are consistent with the actual situation. Combined with theoretical analysis, the finite element model is continuously revised to provide accurate theoretical analysis and prediction algorithms for subsequent similar projects.

[0123] S3, obtain the environmental information and monitoring item data of the working conditions corresponding to the current construction process, input the finite element analysis results, environmental information and monitoring item data of the working conditions corresponding to the current construction process into the trained monitoring item data prediction model, and predict the monitoring item data at the next moment.

[0124] Specifically, according to the monitoring types at the current moment (caisson environment, caisson geometry, water levels inside and outside the caisson, structural stress, anchor cable tension, offshore hydrological environment, dock tide level, compartment water level monitoring), the data of each monitoring item are combined with the environmental information and analysis of the construction conditions to obtain the finite element analysis results, and the trained monitoring item data prediction model is used to predict the data of each monitoring item at the next moment, and early warning judgment is made based on the predicted data of each monitoring item to guide construction and avoid risks in advance.

[0125] S4, judging whether to issue an early warning based on the predicted monitoring item data at the next moment, if so, issue an early warning and adjust the construction plan and repeat the above two steps, otherwise continue construction according to the construction plan.

[0126] In a specific embodiment, determining whether to issue an early warning specifically includes:

[0127] The monitoring item data is compared with the threshold intervals of different levels. If the monitoring item data is within the corresponding threshold interval, an early warning of the corresponding level is issued. If the monitoring item data is within the normal data interval, no early warning is issued.

[0128] Specifically, for each monitoring item data during the caisson construction process, by comparing the measured data or predicted data with different level thresholds of the equipment, the measured data or predicted data will give an alarm of the corresponding level within the corresponding threshold range, and no alarm will be given within the normal data range.

[0129] 1. The construction monitoring of caisson mainly achieves the following purposes:

[0130] (1) Overall attitude control of caisson:

[0131] During the sinking process of the caisson, the caisson's posture must be strictly controlled and predicted to avoid deviation.

[0132] (2) Caisson sinking control:

[0133] During the sinking process of the caisson, the water injection into the compartments and the water level inside and outside the well are adjusted in real time to control the sinking speed of the caisson.

[0134] (3) Safety control of caisson structure:

[0135] During the floating and implantation processes of the caisson, the real-time monitoring system is used to reflect the stress state of the caisson structure in real time, ensuring that the caisson components are within a reasonable stress range.

[0136] (4) Information monitoring and prediction:

[0137] Through automated real-time monitoring, we can obtain data on various monitoring items during the caisson sinking process, issue real-time warnings, and ensure the quality of caisson construction. At the same time, we can use predictive methods to predict risks in advance and ensure that the caisson sinking process is measurable, visible, and controllable.

[0138] Corresponding to the method for monitoring the entire process of offshore caisson construction proposed in the embodiment of the present application, reference is made to Figure 7The present application provides an intelligent monitoring and prediction system for the whole process of offshore caisson construction, including: intelligent sensor module, data acquisition and transmission module, database storage management module, comprehensive warning module, real-time finite element analysis module, prediction module, and user interaction module. The intelligent sensor module is installed on the top, side wall and inside of the caisson structure, and is used to fully intelligently monitor the physical mechanics and state information of the caisson structure; the data acquisition and transmission module is installed above the caisson structure, and is connected to the intelligent sensor module by wired and wireless forms, and is used to read various data of the intelligent sensor module and perform edge computing processing on the data; the database storage management module, the comprehensive warning module, and the real-time finite element analysis module are deployed in the server, and the database storage management module is connected by wired and / or wireless forms to receive the data of the data acquisition and transmission module and classify and store it; the comprehensive warning module sets the design data by classification and grading, and according to the analysis and comparison of the current data and the design data, an alarm is given after exceeding the design data range; the real-time finite element analysis module interacts with the database storage management module through the data interface, and analyzes the overall structure of the caisson through the finite element program; the prediction module is used to predict the data of each monitoring item of the caisson.

[0139] The intelligent sensing module includes split and integrated sensors, and the arrangement of sensors is guided by finite element analysis and calculation of the caisson structure. The split sensor is a monitoring element that only has the function of sensing the physical mechanics and status information of the caisson structure, while the integrated sensor has the function of sensing the physical mechanics and status information of the caisson structure and has communication transmission function.

[0140] The data acquisition and transmission module is connected to the split sensor in the intelligent sensing module through wired form, and is connected to the integrated sensor in the intelligent sensing module through wireless form; the data acquisition and transmission module uniformly collects the data of the intelligent sensing module and performs edge computing processing; to ensure the validity of the data, edge computing performs certain algorithm processing on the data, removes invalid data, and improves data transmission efficiency.

[0141] The database storage management module is the data center of the intelligent monitoring system. It receives data from the data acquisition and transmission module, and classifies, stores and manages the received data, and interacts at various data levels according to the set interface rules.

[0142] The comprehensive early warning module targets various monitoring factors during the caisson construction process. By comparing the measured data or predicted data with the different level thresholds of the equipment, the measured data or predicted data will be alarmed at the corresponding level within the corresponding threshold range, and no alarm will be given in the normal data range.

[0143] The real-time finite element analysis module interacts with the database storage management module through the data interface by implanting and calling the calculation engine. According to the set data sampling frequency, the finite element analysis of the caisson structure as a whole is performed. By verifying the design parameters and actual parameters, it is determined whether to dynamically adjust the construction plan. The real-time finite element analysis module can be used to discover the stress conditions of parts where sensors are not installed and identify potential risks.

[0144] The prediction module uses the data of various monitoring items of the current monitoring types (caisson environment, caisson geometry, water levels inside and outside the caisson, structural stress, anchor cable tension, offshore hydrological environment, dock tide level, and compartment water level monitoring) in combination with the construction conditions, and adopts neural network models including but not limited to machine learning to predict the data of various monitoring items at future moments, and make early warning judgments based on the predicted data of each monitoring item.

[0145] The user interaction module is a window displayed locally and in the cloud, including mobile and web terminals.

[0146] Further references Figure 8 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a monitoring device for the entire process of offshore caisson construction. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0147] The present application embodiment provides a device for monitoring the entire process of offshore caisson construction, including:

[0148] The model building module 1 is configured to obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish an initial finite element model of the caisson structure and determine the key positions of the caisson structure, and arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model;

[0149] The finite element analysis module 2 is configured to obtain the data of each monitoring item corresponding to each monitoring type under the corresponding working condition in the construction process collected by the sensor at the current moment and determine whether an early warning is issued. If so, an early warning is issued and the construction plan is adjusted. Otherwise, construction continues according to the construction plan and corrections are made based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working condition in the construction process at the current moment;

[0150] The data prediction module 3 is configured to obtain the environmental information and monitoring item data of the working condition corresponding to the current construction process, input the finite element analysis results, environmental information and monitoring item data of the working condition corresponding to the current construction process into the trained monitoring item data prediction model, and predict the monitoring item data at the next moment;

[0151] The early warning module 4 is configured to determine whether to issue an early warning based on the predicted monitoring item data at the next moment. If yes, issue an early warning and adjust the construction plan and repeat the above two steps. Otherwise, continue construction according to the construction plan.

[0152] Fig. 9 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. Fig. 9 As shown, the electronic device of this embodiment includes: a processor 901 and a memory 902; wherein the memory 902 is used to store computer-executable instructions; the processor 901 is used to execute the computer-executable instructions stored in the memory to implement the various steps performed by the electronic device in the above embodiment. For details, please refer to the relevant description in the above method embodiment.

[0153] Optionally, the memory 902 may be independent or integrated with the processor 901 .

[0154] When the memory 902 is independently provided, the electronic device further includes a bus 903 for connecting the memory 902 and the processor 901 .

[0155] The embodiment of the present invention further provides a computer storage medium, in which computer execution instructions are stored. When the processor 901 executes the computer execution instructions, the above method is implemented.

[0156] The embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by the processor 901, the above method is implemented.

[0157] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0158] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0159] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned module-composed unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0160] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor 901 to perform some steps of the methods of various embodiments of the present application.

[0161] It should be understood that the processor 901 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or the processor 901 may be any conventional processor 901, etc. The steps of the method disclosed in the invention may be directly embodied in the hardware processor 901 for execution, or may be executed by a combination of hardware and software modules in the processor 901.

[0162] The memory 902 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

[0163] The bus 903 may be an Industry Standard Architecture (ISA), a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 903 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus 903 in the drawings of the present application is not limited to only one bus 903 or one type of bus 903.

[0164] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0165] An exemplary storage medium is coupled to the processor 901, so that the processor 901 can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor 901. The processor 901 and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor 901 and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0166] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the entire process of offshore caisson construction, characterized in that: The following steps are involved: Obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish the initial finite element model of the caisson structure and determine the key positions of the caisson structure, and arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model; Obtain the data of each monitoring item corresponding to each monitoring type under the corresponding working condition in the construction process collected by the sensor at the current moment and determine whether an early warning is issued. If so, issue an early warning and adjust the construction plan. Otherwise, continue to construct according to the construction plan, and make corrections based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working condition in the construction process at the current moment; Obtaining environmental information and monitoring item data of the working conditions corresponding to the current construction process, inputting the finite element analysis results, environmental information and monitoring item data of the working conditions corresponding to the current construction process into the trained monitoring item data prediction model, and predicting the monitoring item data at the next moment; Determine whether to issue an early warning based on the predicted monitoring item data at the next moment. If so, issue an early warning and adjust the construction plan and repeat the above two steps. Otherwise, continue construction according to the construction plan.

2. The method for monitoring the entire process of offshore caisson construction according to claim 1, characterized in that: The monitoring types include caisson environment, caisson geometry, water levels inside and outside the caisson, structural stress, anchor cable tension, offshore hydrological environment, dock tide level and compartment water level monitoring; wherein the monitoring item data corresponding to the caisson environment include video and wind speed and direction collected above the caisson structure; the monitoring item data corresponding to the caisson geometry include plane position, caisson sinking depth, torsion angle and inclination, and the inclination includes the caisson top inclination collected from the midpoints of the four sides above the caisson structure and the caisson bottom inclination collected from the midpoints of the four sides below the caisson structure; the monitoring item data corresponding to the water levels inside and outside the caisson include the middle compartment water level, side compartment water level and wellbore water level collected at the bottom of the middle compartment, the bottom of the side compartment and the bottom of the wellbore respectively. The monitoring item data corresponding to the structural stress include the caisson structure stress collected at the midpoints of the four upper and lower sides of the caisson structure, the cofferdam support stress collected at the cofferdam support end and the support stress collected above the supporting column; the monitoring item data corresponding to the anchor cable tension include the anchor cable tension collected at the top of the jack, the monitoring item data corresponding to the offshore hydrological environment include the seawater flow velocity, seawater waves, wind speed and direction and tide level information collected on the platform in the construction area; the monitoring item data corresponding to the dock tide level include the tide level information collected from the caisson undocking area; the monitoring item data corresponding to the compartment water level monitoring include the video collected from the top of the compartment, and the environmental information and monitoring item data are data after data filtering.

3. The method for monitoring the entire process of offshore caisson construction according to claim 2 is characterized in that: The calculation process of the plane position is as follows: The transverse direction of the caisson structure is taken as the X-axis direction of the rectangular coordinate system, and the longitudinal direction of the caisson structure is taken as the Y-axis direction of the rectangular coordinate system. The coordinates of the midpoints of the four sides of the top surface of the caisson structure are measured in the rectangular coordinate system (x1, y1), (x2, y2), (x3, y3), and (x4, y4). The plane coordinates (x5, y5) of the center point of the top surface of the caisson structure are calculated using the following formula: Determine the design coordinates (x'5, y'5) of the center point of the top surface of the caisson structure, and calculate the offset of the plane position of the center point of the caisson structure by the following formula: Δx0=x5-x'5; Δy0=y5-y'5; Among them, Δx0 and Δy0 are the offsets of the plane position of the center point of the caisson structure on the X-axis and Y-axis respectively; The calculation process of the sinking depth of the caisson is as follows: Get the Z-axis coordinates z1, z2, z3, z4, z5 of the four midpoints and the center point of the top surface of the caisson structure in the direction perpendicular to the top surface of the caisson structure. The sinking depth z0 of the caisson is calculated using the following formula: The following formula is used to calculate the deviation Δz0 of the caisson sinking depth: Δz0=z0-z'0; Among them, z'0 is the design elevation of the center point of the top surface of the caisson structure; The calculation process of the inclination is as follows: The following formula is used to calculate the inclination Δh along the bridge: y : Δh y =h2-h4; The following formula is used to calculate the inclination angle θ along the bridge direction: y : The following formula is used to calculate the transverse bridge inclination Δh x : Δh x =h1-h3; The following formula is used to calculate the transverse bridge tilt angle θ x : The following formula is used to calculate the coordinates of the center point of the caisson bottom surface (x xo ,y xo ): <h2 style=";text-align:left;direction:ltr">x<h2 style=";text-align:left;direction:ltr"> xo <h2 style=";text-align:left;direction:ltr"> = x5 + h × tanθ<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> ; and xo =y5+h×tanθ y ; Among them, h1, h2, h3, and h4 are the elevations of the four corner points of the caisson structure respectively; a and b are the horizontal distances between the measuring points along the bridge and transverse to the bridge at the initial equilibrium position respectively; h is the height of the caisson; x5 and y5 are the horizontal and vertical coordinates of the center point of the top surface of the caisson structure; The calculation process of the torsion angle is as follows: The following formula is used to calculate the angle β1 between the line connecting the coordinates of the four midpoints of the top surface of the caisson structure (x1, y1) and (x3, y3) and the bridge axis, and the angle β2 between the line connecting (x4, y4) and (x2, y2) and the bridge axis: The average of β1 and β2 is taken as the torsion angle β of the caisson structure:

4. The method for monitoring the entire process of offshore caisson construction according to claim 1 is characterized in that: The corresponding working conditions in the construction process at the current moment are the working conditions corresponding to any moment in any one of the links of floating, sinking into place to implantation, water injection into the steel shell, pouring concrete, bottom sealing and core filling. The environmental information includes hydrological and meteorological information, unplanned external loads and environmental data in the affected area. The design data includes survey data, geometric dimensions, material properties, structural analysis data, design specifications and standard data, construction process data, structural detail data, safety data and cost data. The survey data includes topography, geological data, hydrological and meteorological data, environmental data and survey specification data.

5. The method for monitoring the entire process of offshore caisson construction according to claim 1 is characterized in that: The monitoring item data prediction model includes a feedforward neural network or a BP neural network.

6. The method for monitoring the entire process of offshore caisson construction according to claim 1, characterized in that: The determination of whether to issue an early warning specifically includes: The monitoring item data is compared with threshold intervals of different levels. If the monitoring item data is within the corresponding threshold interval, an early warning of the corresponding level is issued. If the monitoring item data is within the normal data interval, no early warning is issued.

7. A monitoring device for the entire process of offshore caisson construction, characterized in that: include: The model building module is configured to obtain the design data and survey data in the early stage of the construction process and perform finite element analysis, establish an initial finite element model of the caisson structure and determine the key positions of the caisson structure, and arrange sensors at the key positions of the caisson structure; construct and train a monitoring item data prediction model to obtain a trained monitoring item data prediction model; The finite element analysis module is configured to obtain the data of each monitoring item corresponding to each monitoring type under the corresponding working condition in the construction process collected by the sensor at the current moment and determine whether to issue an early warning. If so, issue an early warning and adjust the construction plan. Otherwise, continue to construct according to the construction plan and make corrections based on the initial finite element model to obtain the corrected finite element model and the finite element analysis results of the corresponding working condition in the construction process at the current moment; A data prediction module is configured to obtain environmental information and monitoring item data of the working condition corresponding to the construction process at the current moment, input the finite element analysis results, environmental information and monitoring item data of the working condition corresponding to the construction process at the current moment into the trained monitoring item data prediction model, and predict the monitoring item data at the next moment; The early warning module is configured to determine whether to issue an early warning based on the predicted monitoring item data at the next moment. If so, an early warning is issued and the construction plan is adjusted and the above two steps are repeated. Otherwise, construction continues according to the construction plan.

8. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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