A smart construction site monitoring method and system based on TEKLA modeling of pipe racks

Abstract

The present invention provides a smart construction site monitoring method and system based on TEKLA modeling of a conductor frame, using TEKLA modeling technology to establish a conductor frame model, importing a task management information flow into the model; deploying a sensor network at various preset locations on the construction site to monitor sampling data in real time; importing the conductor frame model into an interactive device, updating the conductor frame model using the sampling data, retrieving the task nodes in the task management information flow, building a real three-dimensional construction site model, and displaying it in the interactive device. The present invention imports the task management information flow in the TEKLA modeling stage, so that the conductor frame model can display a three-dimensional model according to the task nodes in the task management information flow, and uses the sampling data monitored in real time on site to update the three-dimensional model, so that the task nodes can be optionally directly displayed in the interactive device. The real three-dimensional construction site model is improved, and the monitoring intensity during the construction process is improved, and the interaction effect with the constructors is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of conductor frame construction, and in particular relates to a smart construction site monitoring method and system based on conductor frame TEKLA modeling. Background Art

[0002] With the continuous growth of global energy demand, offshore oil extraction has become the top priority of global oil development. However, the easily exploitable oil and gas resources have gradually been exhausted. In the future, the oil and gas reserves that can be exploited in China are mainly unconventional, deep-layer, deep-water and other "two deep and one non-" difficult-to-exploit oil and gas reserves. Therefore, the exploitation of oil and gas resources will gradually shift to the deep-water field, which puts higher requirements on the design of the jacket and also brings huge challenges to the construction field.

[0003] In the actual construction process of the conductor rack, although the existing BIM model can provide a visual three-dimensional model, it is based on a static theoretical model after completion. The construction period of the conductor rack is long, and the construction progress and quality should be monitored in real time during the construction process for timely adjustment and optimization. The existing technology cannot monitor and feedback the actual construction progress of the conductor rack and the problems encountered in the process in real time. The linkage between the model and the actual construction is poor. In addition to the need to invest a lot of manpower to track the progress on site, when problems arise in the construction, the design party and the construction party still need to spend a lot of communication time to solve the construction problems. At the same time, the construction period of the conductor rack is long and there are many task nodes. Now it is necessary to follow up and record the construction progress manually, and it is impossible to combine the current construction progress with the three-dimensional model, and the intelligence level is low. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a smart construction site monitoring method and system based on TEKLA modeling of the conductor frame, which is mainly used to solve the problems of inadequate monitoring and insufficient interaction with constructors during the construction of the conductor frame in the prior art.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a smart construction site monitoring method based on jacket TEKLA modeling, comprising:

[0007] A jacket model is established by using TEKLA modeling technology, and a task management information flow is imported into the model; wherein, the task management information flow is formulated, the construction sequence of the jacket is determined, and the jacket is subdivided into various task nodes, and the task nodes are configured to have their names including construction steps and slice names; a task list is established, and all task nodes that need to be completed are listed in the construction sequence, and a status mark is configured for each task node in the task list; each task node is associated with a modeling step in the TEKLA modeling process, and a jacket model is established based on the construction sequence of the jacket;

[0008] Deploy a sensor network at various preset locations on the construction site to monitor sampling data in real time;

[0009] Importing the jacket model into the interactive device, updating the jacket model using the sampled data, retrieving the task node in the task management information flow, building a real three-dimensional construction site model, and displaying it in the interactive device;

[0010] Among them, when updating the jacket model using the sampling data, calling the task nodes in the task management information flow, and building a real three-dimensional construction site model, it includes:

[0011] Using virtual simulation technology, the sampling data is combined with the TEKLA modeling of the jacket to form a first model, wherein the sampling data includes monitoring data obtained by actually sampling the jacket at the current task node, and the monitoring data includes attitude data, stress data, temperature data, vibration data, and load data of the jacket;

[0012] Update the jacket model, call the current task node from the task list, and change the status mark of the current task node to completed;

[0013] The three-dimensional model of the jacket corresponding to the current task node is retrieved and defined as the second model. The Hausdorff distance and point cloud alignment parameters of the first model and the second model are evaluated. If both are lower than the set threshold, the first model is judged to have high confidence, and the first model is used to construct a real three-dimensional construction site model.

[0014] After the model error evaluation is completed at the first task node, the first model is bound to the first task node, and the local jacket model corresponding to the subsequent second task node is built on the basis of the first model, and so on;

[0015] When evaluating the Hausdorff distance between the first model and the second model, point set A is used to represent the first model, point set B is used to represent the second model, and the maximum distance is calculated using the following formula:

[0016] H ( A , B )=max{ d ( a , b ), d ( a , b )}

[0017] d ( a ,b ) represents the Euclidean distance between point a and point b; the threshold used to evaluate the Hausdorff distance is the first threshold, and the first threshold is 0.5 meters;

[0018] When evaluating the point cloud alignment parameters of the first model and the second model, the point cloud P = {p i} Represents the first model, using the point cloud Q = {q j} Representing the second model, the ICP algorithm is used to find the rigid body transformation parameters between P and Q, including the rotation matrix R and the translation vector t, so that the distance error between the transformed point cloud P' and Q is minimized;

[0019] The transformed point cloud P' is expressed as:

[0020] P'={ R i + t}

[0021] Through iterative optimization, R and t are gradually solved to minimize the following objective function:

[0022]

[0023] After the iteration converges, the obtained R and t are the alignment parameters between the two point clouds; the thresholds used to evaluate the point cloud alignment parameters are the second threshold and the third threshold, the second threshold is 0.2 meters, which is used to evaluate the translation error; the third threshold is 5 degrees, which is used to evaluate the rotation error.

[0024] In some embodiments, when presenting in the interactive device, it includes:

[0025] Receive a display instruction, and parse out a target task node, where the target task node is a current task node in the task list or any task node before the current task node;

[0026] Retrieving a corresponding target three-dimensional construction site model according to the target task node, wherein the target three-dimensional construction site model is configured as a three-dimensional construction site model updated according to the sampled data stored in the task node;

[0027] The target three-dimensional construction site model is displayed on the interactive device.

[0028] In some embodiments, when the sampling data is combined with the TEKLA modeling of the catheter rack to form a first model, the method includes:

[0029] The Kalman filter is used to realize the fusion of sampling data and TEKLA modeling of the jacket. The mathematical model expression is:

[0030] x k =x k∣k−1 +K k (z k −H k x k∣k−1 )

[0031] in, x k represents the fused data, x k∣k−1 TEKLA modeling for the jacket, K k is the Kalman gain, z k is the sampling data, H k is the observation matrix.

[0032] In some embodiments, when the sampling data is combined with the TEKLA modeling of the catheter rack to form a first model, the method includes:

[0033] The sampling data includes several types of monitoring data, and the timing of the monitoring data is analyzed;

[0034] If the monitoring data is missing, it is processed using interpolation polynomials, and its mathematical model expression is:

[0035]

[0036] Where P(x) is the Lagrange interpolation polynomial, y i is the value of the known data point, x i is the corresponding position coordinate.

[0037] In some embodiments, simulation analysis is performed based on a real three-dimensional construction site model to simulate changes in different construction scenarios, where the construction scenarios are configured as construction scenarios constructed based on different environmental data;

[0038] The optimization method is used to find the best solution, and the resource allocation is optimized through the linear programming model. The mathematical model expression of the linear programming model is:

[0039] MinimizeZ=c T x

[0040] Subject to:

[0041] A x ≤ b

[0042] x≥0

[0043] Among them, Z represents the optimized objective function, c is the coefficient vector of the objective function, x is the optimization variable vector, and A and b are the coefficient matrix and vector of the constraint conditions.

[0044] In some embodiments, the sampled data is recorded on a blockchain to form a continuously growing chain of data blocks, each data block being configured with a hash value associated with a task node;

[0045] The model data and modification records related to the task node are stored in the corresponding data block. When the sampling data changes, the data block is automatically updated and the TEKLA model is updated synchronously.

[0046] Encryption technology is used to encrypt data transmission and storage.

[0047] In some embodiments, data analysis and prediction are performed using sampled data and preset warning thresholds to predict potential risk factors;

[0048] Establish intelligent algorithm models to generate solution strategies based on monitoring data and potential risk factors;

[0049] The solution strategy is visualized to display monitoring data and early warning information in interactive devices.

[0050] In a second aspect, the present invention provides a smart construction site monitoring system based on jacket TEKLA modeling, which uses the above-mentioned smart construction site monitoring method based on jacket TEKLA modeling, including:

[0051] A TEKLA modeling module is configured to build a jacket model using TEKLA modeling technology and import task management information flow into the model;

[0052] The sampling module is configured to deploy a sensor network at various preset locations on the construction site to monitor sampling data in real time;

[0053] A processing module is configured to import the jacket model into the interactive device, update the jacket model using the sampled data, retrieve the task node in the task management information flow, and construct a real three-dimensional construction site model;

[0054] The interactive device is configured to display a three-dimensional site model and / or a jacket model.

[0055] Compared with the prior art, the present invention has at least the following beneficial effects:

[0056] The task management information flow is imported into the TEKLA modeling stage, so that the jacket model can display the three-dimensional model according to the task nodes in the task management information flow, and update the three-dimensional model using the sampling data of real-time monitoring on site, so that the task nodes can be optionally directly displayed in the interactive device to improve the monitoring intensity during the construction process and optimize the interaction effect with the constructors.

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0059] Figure 1 The present invention is a flowchart of a smart construction site monitoring method based on TEKLA modeling of a conductor frame under an embodiment.

[0060] Figure 2 It is a flow chart of a smart construction site monitoring method based on TEKLA modeling of a conductor frame under another embodiment.

[0061] Figure 3 It is a flowchart of a smart construction site monitoring method based on TEKLA modeling of a conductor frame under another embodiment.

[0062] Figure 4 It is a schematic diagram of a smart construction site monitoring system based on TEKLA modeling of a conductor frame under an embodiment. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0064] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0065] In the description of the present invention, when it is described that a specific device is located between a first device and a second device, there may or may not be an intermediate device between the specific device and the first device or the second device. When it is described that a specific device is connected to other devices, the specific device may be directly connected to the other devices without an intermediate device, or may not be directly connected to the other devices but have an intermediate device.

[0066] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0067] First, refer to Figure 1 The present invention provides a smart construction site monitoring method based on TEKLA modeling of a jacket, comprising:

[0068] Use TEKLA modeling technology to build a jacket model and import task management information flow into the model;

[0069] Deploy a sensor network at various preset locations on the construction site to monitor sampling data in real time;

[0070] The jacket model is imported into the interactive device, the jacket model is updated using the sampled data, the task node in the task management information flow is retrieved, a real three-dimensional construction site model is constructed, and displayed in the interactive device.

[0071] It should be noted that TEKLA modeling technology is a building information management software based on BIM (Building Information Modeling), which can be used for various building structure design and construction tasks. It can provide high-quality model visualization and simulation functions to help designers and constructors better understand and evaluate the performance of buildings, and can help designers create and manage various workflows.

[0072] Therefore, when using TEKLA modeling technology to build a jacket model, we also define task nodes, specifically, define model status, construction time, role responsibilities, construction steps, construction materials and other information to form a task management information flow, and import the task management information flow into the model. When a specific task node is selected, such as the chassis installation node, the jacket model will only display the model that has been completed to the chassis installation according to the construction steps, while the models associated with the task nodes that have not yet been constructed will not be displayed;

[0073] The sensor network deployed in advance at the construction site is used to monitor the sampling data in real time. The sampling data includes information such as the stress condition, size distance, deformation condition, temperature change, humidity change, etc. of the conductor rack. The conductor rack model is updated using these real-time sampling data. The conductor rack model is updated in sequence according to the order of the task nodes in the task management information flow. A real three-dimensional construction site model can be constructed based on the retrieved task nodes and displayed in the interactive device.

[0074] Preferably, the interactive device is a display screen. As an implementation method, the display screen is set at the construction site, and the construction workers can intuitively see the three-dimensional construction site model on the display screen, which is associated with the actual construction status of the conductor rack; as an implementation method, the display screen is set in the control room and monitored in real time by supervisors.

[0075] Through the above implementation methods, the task management information flow can be combined with the model, and the jacket model can be updated using the sampling data of real-time monitoring, so that the constructed three-dimensional construction site model is more realistic and the interactive effect is increased.

[0076] Reference Figure 2 As an implementation method, when establishing a jacket model using TEKLA modeling technology and importing task management information flow into the model, it includes:

[0077] Formulate a task management information flow, determine the construction order of the jacket, and subdivide it into various task nodes, wherein the task nodes are configured to have their names including construction steps and slice names; for example, when constructing a jacket, there are 8 construction steps, so there are also 8 task nodes, each of which is arranged in order and has its own construction step and slice name;

[0078] Create a task list, list all task nodes that need to be completed in the order of construction, and configure a status mark for each task node in the task list. The status marks include "completed", "in progress" and "uncompleted";

[0079] Each task node is associated with the modeling step in the TEKLA modeling process, and the jacket model is established based on the jacket construction sequence.

[0080] In each task node, the modeling steps of each part will be defined. For example, in the first task node, the constructed local jacket model involves 10 parts, and these 10 parts are built in sequence according to the modeling steps; when entering the second task node, the constructed local jacket model involves 15 parts, and these 15 parts are built on the basis of the 10 parts in the previous construction step. Therefore, when the jacket model of the second task node is called, a jacket model with 25 parts will be finally presented.

[0081] Reference Figure 3 As an implementation method, when updating the jacket model using the sampled data, retrieving the task node in the task management information flow, and building a real three-dimensional construction site model, it includes:

[0082] Using virtual simulation technology, the sampling data is combined with the TEKLA modeling of the jacket to form a first model. The sampling data includes monitoring data obtained by actually sampling the jacket at the current task node, and the monitoring data includes the jacket's posture data, stress data, temperature data, vibration data, and load data. Since there may be errors between the jacket parameters in the design state and the actual state, such as the length and size of the steel pipe, which may affect the jacket's posture, stress, etc., it is necessary to re-update and calibrate the model every time a task node is completed to combine the actual sampling data into the jacket model.

[0083] Update the jacket model, call the current task node from the task list, and change the status mark of the current task node to completed;

[0084] The three-dimensional model of the jacket corresponding to the current task node is retrieved and defined as the second model. The first model is a three-dimensional model combined with actual sampling data, which is equivalent to the actual model; and the second model is the jacket model obtained in the TEKLA modeling stage, which is equivalent to the ideal model; in order to evaluate the gap between the first model and the second model, the Hausdorff distance and point cloud alignment parameters of the first model and the second model are calculated, where the Hausdorff distance is used to measure the maximum difference between the two models; the point cloud alignment parameters measure the difference between them by aligning the point cloud data of the two models, and the error after alignment can be calculated; the Hausdorff distance and the point cloud alignment parameters have their own set thresholds. If they are both lower than their corresponding set thresholds, it is judged that the first model has high confidence, and the first model is used to build a real three-dimensional construction site model.

[0085] It should be noted that after the above-mentioned model error evaluation is completed at the first task node, the first model is bound to the first task node, and the local jacket model corresponding to the subsequent second task node is built on the basis of this first model. In other words, as the construction steps proceed, the jacket model is continuously updated with sampling data, and the jacket model is gradually iterated and updated from the ideal model to the actual model in the order of the task nodes. When all the construction steps are completed, the real three-dimensional site model finally presented is composed of the first models one by one. The above method is conducive to reflecting the accumulated errors and actively taking strategies to eliminate the errors.

[0086] More specifically, when evaluating the Hausdorff distance between the first model and the second model, point set A is used to represent the first model, point set B is used to represent the second model, and the maximum distance is calculated by the following formula:

[0087] H ( A , B )=max{ d ( a , b ), d ( a , b )}

[0088] d ( a , b ) represents the Euclidean distance between point a and point b; the threshold used to evaluate the Hausdorff distance is the first threshold, and the first threshold is 0.5 meters;

[0089] When evaluating the point cloud alignment parameters of the first model and the second model, the point cloud P = {p i} Represents the first model, using the point cloud Q = {q j} Representing the second model, the ICP algorithm is used to find the rigid body transformation parameters between P and Q, including the rotation matrix R and the translation vector t, so that the distance error between the transformed point cloud P' and Q is minimized;

[0090] The transformed point cloud P' is expressed as:

[0091] P'={ R i + t}

[0092] Through iterative optimization, R and t are gradually solved to minimize the following objective function:

[0093]

[0094] After the iteration converges, the obtained R and t are the alignment parameters between the two point clouds; the thresholds used to evaluate the point cloud alignment parameters are the second threshold and the third threshold, the second threshold is 0.2 meters, which is used to evaluate the translation error; the third threshold is 5 degrees, which is used to evaluate the rotation error.

[0095] Through the above two calculation methods, the Hausdorff distance and point cloud alignment parameters of the first model and the second model are calculated respectively. When calculating the Hausdorff distance, it is compared with the first threshold; when calculating the point cloud alignment parameters, it is compared with the second threshold and the third threshold. Only when the three thresholds are met can it be proved that the first model has high confidence and the first model is used to build a real three-dimensional construction site model. Therefore, through the above calculation verification and evaluation method, high requirements are placed on the accuracy of the first model, and at the same time, the accuracy of the final model formed through continuous iterative updates is guaranteed.

[0096] As an implementation manner, when displaying in the interactive device, it includes:

[0097] Receive a display instruction, and parse out a target task node, where the target task node is a current task node in the task list or any task node before the current task node;

[0098] Retrieving a corresponding target three-dimensional construction site model according to the target task node, wherein the target three-dimensional construction site model is configured as a three-dimensional construction site model updated according to the sampled data stored in the task node;

[0099] The target three-dimensional construction site model is displayed on the interactive device.

[0100] The operator can call out the target three-dimensional construction site model corresponding to the task node in the interactive device. This is convenient for setting up a smart construction site on site, allowing the construction workers or supervisors to intuitively see the current construction progress. It can also trace back to every construction detail.

[0101] As an implementation mode, when the sampling data is combined with the TEKLA modeling of the jacket to form the first model, it includes:

[0102] The Kalman filter is used to realize the fusion of sampling data and TEKLA modeling of the jacket. The mathematical model expression is:

[0103] x k =x k∣k−1 +K k (z k −H k x k∣k−1 )

[0104] in, x k represents the fused data, xk∣k−1 TEKLA modeling for the jacket, K k is the Kalman gain, z k is the sampling data, H k is the observation matrix.

[0105] In the process of TEKLA modeling of the jacket, the Kalman filter can be used to fuse the real-time sampling data with the modeling data. The sampling data is used as the observation value of the Kalman filter, while the modeling data is used as a priori estimation. Through the iterative process of the Kalman filter, the data can be optimized and fused, making the modeling data closer to the real-time monitoring data, thereby realizing the timely update of the 3D site model.

[0106] As an implementation mode, when the sampling data is combined with the TEKLA modeling of the jacket to form the first model, it includes:

[0107] The sampling data includes several types of monitoring data, and the time series of the monitoring data is analyzed; the monitoring data includes various parameters of the construction site such as temperature, pressure, stress, vibration, etc. These data are collected through the sensor network and have a certain time series;

[0108] If the monitoring data is missing, it is processed using interpolation polynomials, and its mathematical model expression is:

[0109]

[0110] Where P(x) is the Lagrange interpolation polynomial, y i is the value of the known data point, x i is the corresponding position coordinate.

[0111] Since building a detailed three-dimensional construction site model requires processing complex three-dimensional structures and various monitoring data, and the monitoring data may be missing, but it is impossible to go back and re-measure, this embodiment uses interpolation polynomials to handle the incompleteness of real-time monitoring data, and can infer data values ​​at other locations based on known data points to ensure the integrity and accuracy of the model. The missing parts of the monitoring data can be inferred through interpolation polynomials, and the missing data points can be supplemented to make the three-dimensional construction site model more complete. In this way, after the modeling data is combined with the real-time data, it can more accurately reflect the actual situation of the construction site.

[0112] As an implementation method, simulation analysis is performed based on a real three-dimensional construction site model to simulate changes in different construction scenarios, where the construction scenarios are configured as construction scenarios constructed based on different environmental data. Since construction scenarios are diverse, different environmental data may result in different results. Through optimization algorithms, optimization of construction processes, refinement of resource allocation, and improvement of safety management measures can be achieved to improve construction efficiency and quality.

[0113] Specifically, the optimization method is used to find the best solution, and the linear programming model is used to optimize resource allocation to achieve the minimum cost or the most efficient construction site management solution. The mathematical model expression of the linear programming model is:

[0114] MinimizeZ=c T x

[0115] Subject to:

[0116] A x ≤ b

[0117] x≥0

[0118] Among them, Z represents the optimized objective function, c is the coefficient vector of the objective function, x is the optimization variable vector, and A and b are the coefficient matrix and vector of the constraint conditions.

[0119] In the process of TEKLA modeling of the jacket, the linear programming model can be used to optimize resource allocation and construction process. Through the linear programming model, the resource allocation problem can be transformed into a mathematical programming problem, with minimizing cost or maximizing efficiency as the optimization goal. In the modeling process, the linear programming model is combined with the 3D site model, and the resource allocation plan is adjusted according to the model results to optimize the construction process and resource allocation, thereby improving construction efficiency and safety management level.

[0120] As an implementation method, in order to achieve the security and integrity of the monitoring data during the construction of the jacket, blockchain technology is used in this embodiment. Blockchain is a decentralized, tamper-proof and highly secure data storage and transmission technology, which is suitable for improving the credibility and security of monitoring data. Specifically:

[0121] The sampled data is recorded on the blockchain to form a growing chain of data blocks. Each data block is configured with a hash value associated with the task node, making the modification of the data irreversible.

[0122] The model data and modification records related to the task node are stored in the corresponding data block. When the sampled data changes, the data block is automatically updated and the TEKLA model is updated synchronously. All blockchain participants can verify the consistency of the data to ensure that the data has not been tampered with. When the data is modified, the blockchain network will automatically update and broadcast to all nodes to ensure data consistency.

[0123] Encryption technology is used to encrypt data transmission and storage, and an authority control mechanism is set up to ensure that only authorized personnel can modify the data, thereby improving data confidentiality.

[0124] As an implementation method, in order to identify construction risks in advance, based on big data detection, the data of historical failure cases can be used as warning thresholds. For example, the stress value of a certain type of steel pipe or the deflection value of a wire rope reaches a certain value, which causes the material to fail easily or interfere with other structures. Therefore, big data analysis can be used, specifically:

[0125] Use sampled data and preset warning thresholds to analyze and predict potential risk factors; identify potential problems and trends in advance and provide decision-making basis for managers;

[0126] Establish intelligent algorithm models to generate solutions based on monitoring data and potential risk factors, helping managers make decisions quickly and optimize construction processes;

[0127] Visualize the solution strategy and display monitoring data and warning information on interactive devices to help managers fully understand the current situation and make more effective decisions.

[0128] Reference Figure 4 In a second aspect, the present invention provides a smart construction site monitoring system based on TEKLA modeling of a jacket, and applies a smart construction site monitoring method based on TEKLA modeling of a jacket as described in the above embodiment, including:

[0129] The TEKLA modeling module is configured to establish a jacket model using TEKLA modeling technology and import task management information flow into the model; and to realize the construction of an ideal model in the TEKLA modeling module;

[0130] The sampling module is configured to deploy a sensor network at various preset locations on the construction site to monitor the sampling data in real time; the sampling module includes but is not limited to a stress sensor, a length measurement sensor, a temperature and humidity sensor, and a vibration sensor;

[0131] A processing module is configured to import the jacket model into the interactive device, update the jacket model using the sampled data, retrieve the task node in the task management information flow, and construct a real three-dimensional construction site model;

[0132] The interactive device is configured to display a three-dimensional construction site model and / or a conductor rack model. The interactive device can be used as a large billboard for the construction site. The task manager can be called up on the interactive device to view the tasks of the day or history. The tasks contain information related to construction such as materials, processes, and personnel. After completing a task node, the completed task node in the task management information flow can be called up to display the target three-dimensional construction site model.

[0133] In summary, compared with the prior art, the above embodiments provide a smart construction site monitoring method and system based on TEKLA modeling of the conductor rack, which imports the task management information flow in the TEKLA modeling stage, so that the conductor rack model can display the three-dimensional model according to the task nodes in the task management information flow, and use the sampling data of real-time monitoring on site to update the three-dimensional model, so that the task node can be optionally directly displayed in the interactive device. The real three-dimensional construction site model is improved, the monitoring intensity during the construction process is improved, and the interaction effect with the constructors is optimized.

[0134] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A smart construction site monitoring method based on TEKLA modeling of jackets, characterized in that: include: A jacket model is established by using TEKLA modeling technology, and a task management information flow is introduced into the model; wherein the task management information flow is formulated, the construction sequence of the jacket is determined, and the jacket is subdivided into various task nodes, and the task nodes are configured with names including construction steps and slice names; Create a task list, list all task nodes that need to be completed in the order of construction, and configure a status mark for each task node in the task list; associate each task node with the modeling step in the TEKLA modeling process, and build a jacket model based on the jacket construction order; deploy a sensor network at various preset locations on the construction site to monitor the sampled data in real time; Importing the jacket model into the interactive device, updating the jacket model using the sampled data, retrieving the task node in the task management information flow, building a real three-dimensional construction site model, and displaying it in the interactive device; Among them, when updating the jacket model with the sampled data, retrieving the task nodes in the task management information flow, and building a real three-dimensional construction site model, it includes: Using virtual simulation technology, the sampling data is combined with the TEKLA modeling of the jacket to form a first model, wherein the sampling data includes monitoring data obtained by actually sampling the jacket at the current task node, and the monitoring data includes attitude data, stress data, temperature data, vibration data, and load data of the jacket; Update the jacket model, call the current task node from the task list, and change the status mark of the current task node to completed; The three-dimensional model of the jacket corresponding to the current task node is retrieved and defined as the second model. The Hausdorff distance and point cloud alignment parameters of the first model and the second model are evaluated. If both are lower than the set threshold, the first model is judged to have high confidence, and the first model is used to construct a real three-dimensional construction site model. After the model error evaluation is completed at the first task node, the first model is bound to the first task node, and the local jacket model corresponding to the subsequent second task node is built on the basis of the first model, and so on; When evaluating the Hausdorff distance between the first model and the second model, point set A is used to represent the first model, point set B is used to represent the second model, and the maximum distance is calculated using the following formula: d(a,b) represents the Euclidean distance between point a and point b; the threshold used to evaluate the Hausdorff distance is the first threshold, which is 0.5 meters; When evaluating the point cloud alignment parameters of the first model and the second model, the point cloud Represents the first model, using point cloud Representing the second model, the ICP algorithm is used to find the rigid body transformation parameters between P and Q, including the rotation matrix R and the translation vector t, so that the distance error between the transformed point cloud P' and Q is minimized; The transformed point cloud P' is expressed as: Through iterative optimization, R and t are gradually solved to minimize the following objective function: After the iteration converges, the obtained R and t are the alignment parameters between the two point clouds; the thresholds used to evaluate the point cloud alignment parameters are the second threshold and the third threshold, the second threshold is 0.2 meters, which is used to evaluate the translation error; the third threshold is 5 degrees, which is used to evaluate the rotation error.

2. The intelligent construction site monitoring method based on TEKLA modeling of the jacket as claimed in claim 1, characterized in that: When displaying in the interactive device, it includes: Receive a display instruction, and parse out a target task node, where the target task node is a current task node in the task list or any task node before the current task node; Retrieving a corresponding target three-dimensional construction site model according to the target task node, wherein the target three-dimensional construction site model is configured as a three-dimensional construction site model updated according to the sampled data stored in the task node; The target three-dimensional construction site model is displayed on the interactive device.

3. The intelligent construction site monitoring method based on TEKLA modeling of the jacket as claimed in claim 2, characterized in that: When combining the sampling data with the jacket TEKLA modeling to form the first model, it includes: The Kalman filter is used to realize the fusion of sampling data and TEKLA modeling of the jacket. The mathematical model expression is: x k =x k∣k-1 +K k (z k -H k x k∣k-1 ) Among them, x k represents the fused data, x k∣k-1 Modeling the TEKLA model for the jacket k is the Kalman gain, z k is the sampling data, H k is the observation matrix.

4. The intelligent construction site monitoring method based on TEKLA modeling of the jacket as claimed in claim 3, characterized in that: When combining the sampling data with the jacket TEKLA modeling to form the first model, it includes: The sampling data includes several types of monitoring data, and the timing of the monitoring data is analyzed; If the monitoring data is missing, it is processed using interpolation polynomials, and its mathematical model expression is: Where P(x) is the Lagrange interpolation polynomial, y i is the value of the known data point, x i is the corresponding position coordinate.

5. The intelligent construction site monitoring method based on TEKLA modeling of the jacket as claimed in claim 4, characterized in that: Based on the real three-dimensional construction site model, simulation analysis is performed to simulate changes in different construction scenarios, where the construction scenarios are configured as construction scenarios constructed based on different environmental data; The optimization method is used to find the best solution, and the resource allocation is optimized through the linear programming model. The mathematical model expression of the linear programming model is: Minimize Z=c T x Subject to: A x ≤b x≥0 Among them, Z represents the optimized objective function, c is the coefficient vector of the objective function, x is the optimization variable vector, and A and b are the coefficient matrix and vector of the constraint conditions.

6. A smart construction site monitoring method based on jacket TEKLA modeling as claimed in any one of claims 1 to 5, characterized in that: The sampled data is recorded on the blockchain to form a growing chain of data blocks, each of which is configured with a hash value associated with the task node; The model data and modification records related to the task node are stored in the corresponding data block. When the sampling data changes, the data block is automatically updated and the TEKLA model is updated synchronously. Encryption technology is used to encrypt data transmission and storage.

7. The intelligent construction site monitoring method based on TEKLA modeling of the jacket as claimed in claim 6, characterized in that: Use sampled data and preset warning thresholds to perform data analysis and forecasting to predict potential risk factors; Establish intelligent algorithm models to generate solution strategies based on monitoring data and potential risk factors; The solution strategy is visualized to display monitoring data and early warning information in interactive devices.

8. A smart construction site monitoring system based on TEKLA modeling of a jacket, using a smart construction site monitoring method based on TEKLA modeling of a jacket as claimed in any one of claims 1 to 7, characterized in that: include: A TEKLA modeling module is configured to build a jacket model using TEKLA modeling technology and import task management information flow into the model; The sampling module is configured to deploy a sensor network at various preset locations on the construction site to monitor sampling data in real time; A processing module is configured to import the jacket model into the interactive device, update the jacket model using the sampled data, retrieve the task node in the task management information flow, and construct a real three-dimensional construction site model; The interactive device is configured to display a three-dimensional site model and / or a jacket model.

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