BIM-based building construction progress management method and system

Abstract

The invention relates to the technical field of data processing, and provides a BIM-based building construction progress management method and system, and the method comprises the steps: carrying out the decomposition of a BIM model into a component set according to the construction process of a building, and building the construction plan mapping of each component; the method comprises the following steps: collecting construction site data of a building, and preprocessing the construction site data to obtain preprocessed data; processing the preprocessed data through a progress recognition model to obtain the actual construction progress of the building; calculating the deviation between the actual construction progress and the planned construction progress of the building; according to the deviation between the actual construction progress and the planned construction progress of the building, a multi-objective optimization model is combined to adjust the construction plan to obtain a target construction plan; and performing construction progress management on the building based on the target construction plan. According to the invention, the accuracy and the intelligent degree of building construction progress management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a BIM-based construction progress management method, system, and electronic device. Background Art

[0002] Nowadays, in current construction management, construction progress management usually relies on traditional Gantt charts, schedules, and manual records. These tools express construction plans and actual progress through two-dimensional planes, facilitating project managers to grasp the construction situation.

[0003] However, these methods lack an intuitive expression of the complex spatial relationships at the construction site and are difficult to accurately correspond the construction plan with the actual progress. Secondly, the data collection of construction plans and actual progress mostly relies on manual labor, and it is easy to have delayed, incomplete, or even incorrect information records, thus affecting the accuracy of decision-making. Summary of the Invention

[0004] The present invention aims at the technical problems existing in the prior art, and provides a BIM-based construction progress management method, system, and electronic device that can improve the accuracy and intelligence of construction progress management.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a BIM-based construction progress management method, and the method includes:

[0007] Decompose the BIM model into a set of components according to the construction process of the building, and establish a construction plan mapping for each component;

[0008] Collect the construction site data of the building, and preprocess the construction site data to obtain preprocessed data;

[0009] Process the preprocessed data through a progress recognition model to obtain the actual construction progress of the building;

[0010] Calculate the deviation between the actual construction progress and the planned construction progress of the building;

[0011] According to the deviation between the actual construction progress and the planned construction progress of the building, combine a multi-objective optimization model to adjust the construction plan to obtain a target construction plan;

[0012] Based on the target construction plan, perform a construction progress management method on the building.

[0013] Further, the decomposing the BIM model into a set of components according to the construction process of the building and establishing a construction plan mapping for each component includes:

[0014] Extract each of the components from the set of components;

[0015] Extract the planned start time, planned end time, set of prerequisite tasks, and required resource vector of the construction from the construction plan of the building;

[0016] Establish the mapping relationship between each of the components and its corresponding planned start time, planned end time, set of prerequisite tasks, and required resource vector to obtain the construction plan mapping of each of the components.

[0017] Furthermore, the preprocessing of the construction site data to obtain preprocessed data includes:

[0018] Perform noise reduction and filtering preprocessing on the collected construction site data to obtain intermediate processed data;

[0019] Extract the feature information of each of the components from the intermediate processed data;

[0020] Align the construction site data collected from different data sources according to the data source weights corresponding to the data sources of the construction site data, and perform fusion and standardization processing according to the data source weights and the corresponding feature information to obtain the preprocessed data.

[0021] Furthermore, the preprocessed data is expressed as:

[0022] P(D(t)) = ∑.ω i ·F i (D(t)) / +b;

[0023] where P(D(t)) is the preprocessed data, D(t) is the construction site data, ω i is the data source weight of the i-th data source, F i is the feature information of each component, and b is the bias term.

[0024] Furthermore, the construction site data includes:

[0025] The point cloud data, image data, sensor data, and construction record data collected manually at the construction site.

[0026] Furthermore, the calculation of the deviation between the actual construction progress and the planned construction progress of the building includes:

[0027] Determine the initial deviation between the actual construction progress and the planned construction progress according to the actual construction progress and the planned construction progress of the building;

[0028] Obtain the importance weight of each of the components and the time decay function used to represent the change of the initial deviation over time;

[0029] Process the initial deviation according to the importance weight of each component and the time decay function to obtain the deviation between the actual construction progress and the planned construction progress of the building.

[0030] Further, the deviation between the actual construction progress and the planned construction progress of the building is expressed as:

[0031] δ(t) = ∑(α i ·|C(t) i - M(e i ) t |)·β(t);

[0032] where δ(t) is the deviation between the actual construction progress and the planned construction progress, C(t) i is the actual construction progress of the i-th component at time t output by the progress recognition model, M(e i ) t is the construction plan mapping reflecting the planned construction progress of the i-th component at time t, α i is the importance weight of the i-th component, and β(t) is the time decay function.

[0033] Further, adjusting the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building and combining with the multi-objective optimization model to obtain the target construction plan includes:

[0034] Determine the resource cost value of the resource allocation cost for the building construction;

[0035] Determine the construction risk value of the risk control assessment for the building construction;

[0036] Construct the upper limit constraint of resource usage, the construction period requirement constraint, and the construction quality constraint for the building construction;

[0037] Construct the multi-objective optimization model according to the deviation between the actual construction progress and the planned construction progress of the building, the resource cost value, the construction risk value, the upper limit constraint of resource usage, the construction period requirement constraint, and the construction quality constraint for the building construction;

[0038] Determine the optimized objective function value based on the multi-objective optimization model, and adjust the construction plan based on the optimized objective function value to obtain the target construction plan.

[0039] Further, the method further includes:

[0040] Determine the actual construction progress sequence determined by the progress recognition model at multiple historical times;

[0041] Determine the deviation sequence between the actual construction progress and the planned construction progress at multiple historical moments;

[0042] Process the actual construction progress sequence and the deviation sequence through an LSTM network to predict the construction progress of the building at future moments.

[0043] The present invention also provides a BIM-based building construction progress management system, which includes:

[0044] A plan mapping module, configured to decompose the BIM model into a component set according to the construction process of the building and establish a construction plan mapping for each component;

[0045] A data processing module, configured to collect the construction site data of the building and preprocess the construction site data to obtain preprocessed data;

[0046] A progress prediction module, configured to process the preprocessed data through a progress identification model to obtain the actual construction progress of the building;

[0047] A progress deviation module, configured to calculate the deviation between the actual construction progress and the planned construction progress of the building;

[0048] A plan adjustment module, configured to adjust the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building and combine a multi-objective optimization model to obtain a target construction plan;

[0049] A progress management module, configured to manage the construction progress of the building based on the target construction plan.

[0050] The beneficial effects of the present invention are:

[0051] (1) By combining the BIM model with machine learning technology, the present invention can accurately identify the actual progress of each component during the construction process and compare it with the original plan. Traditional manual progress management is easily affected by human factors and information lag, while the present invention can achieve accurate and real-time construction progress tracking based on real-time data collection and automated progress identification, thereby improving the accuracy and efficiency of progress management.

[0052] (2) The present invention collects the data of the construction site in real time through devices such as drones and sensors, and monitors the progress in real time through a deep learning model (such as an LSTM network). Through the dynamic analysis and prediction of progress deviation, potential problems during the construction process (such as progress lag, insufficient resources, etc.) can be discovered in a timely manner, and dynamic adjustment can be made according to the actual situation, which can ensure that the construction progress management is more flexible and efficient.

[0053] (3) The present invention uses an LSTM network to train historical data and can accurately predict future construction progress changes. By calculating the deviation between the actual progress and the planned progress and combining the trend of historical data, it can provide more accurate progress predictions for project managers and identify potential construction risks in advance. The progress prediction function is crucial for the long-term planning and resource allocation of the project and can effectively reduce cost waste and project delays caused by lagging or advancing progress.

[0054] In summary, the present invention realizes precise management, real-time monitoring, intelligent prediction, and multi-objective optimization of construction progress by combining BIM technology and machine learning. The application of these technologies brings higher construction efficiency, accuracy, and flexibility, reduces manual intervention and management costs, reduces construction risks, and improves the quality and progress controllability of the project. Through intelligent progress management and optimization, it promotes the digital and intelligent transformation of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a scenario diagram of a BIM-based building construction progress management method provided by the present invention;

[0056] Figure 2 It is a flowchart of a BIM-based building construction progress management method provided by the present invention;

[0057] Figure 3 It is a schematic structural diagram of a BIM-based building construction progress management system provided by the present invention;

[0058] Figure 4 It is a schematic hardware structure diagram of a possible electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] Please refer to Figure 1 , Figure 1 It is a scenario diagram of a BIM-based building construction progress management method provided by the present invention. As Figure 1As shown in the figure, the terminal is connected to the server through a network, such as a wired or wireless network connection, etc. Among them, the terminal may include, but is not limited to, portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. Among them, the server provides various business services for users, including service push servers, user recommendation servers, etc.

[0061] It should be noted that Figure 1 The scenario diagram of a BIM-based building construction progress management method shown is only an example. The terminals, servers, and application scenarios described in the embodiments of the present invention are for more clearly explaining the technical solutions of the embodiments of the present invention, and do not generate limitations on the technical solutions provided by the embodiments of the present invention. Those of ordinary skill in the art know that with the evolution of the system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0062] Among them, the terminal can be used for:

[0063] Decompose the BIM model into a set of components according to the construction process of the building, and establish a construction plan mapping for each component;

[0064] Collect the construction site data of the building, and preprocess the construction site data to obtain preprocessed data;

[0065] Process the preprocessed data through a progress recognition model to obtain the actual construction progress of the building;

[0066] Calculate the deviation between the actual construction progress and the planned construction progress of the building;

[0067] According to the deviation between the actual construction progress and the planned construction progress of the building, combine a multi-objective optimization model to adjust the construction plan to obtain a target construction plan;

[0068] Based on the target construction plan, perform a construction progress management method for the building.

[0069] Please refer to Figure 2 , which provides a flowchart of a BIM-based building construction progress management method of the present invention, including the following steps:

[0070] Step 201: Decompose the BIM model into a set of components according to the construction process of the building, and establish a construction plan mapping for each component.

[0071] In some embodiments, step 201 may include:

[0072] Extract each of the components from the set of components;

[0073] Extract the planned start time, planned end time, set of predecessor tasks, and required resource vector of the construction from the construction plan of the building;

[0074] Establish the mapping relationship between each of the components and its corresponding planned start time, planned end time, set of predecessor tasks, and required resource vector, to obtain the construction plan mapping of each of the components.

[0075] The set of components is E = {e1, e2,..., e n}, and the construction plan mapping is M(e i ) = {t start , t end , p pre , r req}.

[0076] Among them, E is the set of components of the construction process, e i is the i-th component, M(e i ) is the construction plan mapping, t start is the planned start time, t end is the planned end time, p pre is the set of predecessor tasks, and r req is the required resource vector.

[0077] Specifically, E is a representation form of a mathematical set, used to describe all the components obtained after decomposing the BIM model according to the construction process in the intelligent management method for building construction progress based on BIM and machine learning, and is represented by the symbol "E".

[0078] Each element e i in the set represents the i-th component. Here, i is a positive integer, and its value range is from 1 to n. That is to say, e1 is the first component after decomposition, e2 is the second component, and so on, e n is the n-th component. In this way, all the components related to the construction process in the BIM model are included in this set E, which is convenient for subsequent analysis and management of the attributes and construction plans of each component.

[0079] M(e i ) is a mapping function M for each component e i in the set E. It corresponds each component e i to a set containing specific construction plan information, and this set contains four key pieces of information:

[0080] t start represents the planned start time of the component e i . This is the time point at which the component is expected to start construction in the entire construction plan, and is used to arrange the construction process and coordinate resources.

[0081] t end represents the planned end time of the component e i It clarifies the expected completion time of the construction of the component, and together with t start determines the time span of the construction of the component, which is an important basis for construction progress management.

[0082] p pre refers to the set of prerequisite tasks. It includes all the prerequisite tasks that must be completed before starting the construction of component e i The completion status of these prerequisite tasks directly affects whether component e i can start construction on time. By clarifying the set of prerequisite tasks, the construction sequence can be better planned to ensure the continuity and rationality of the construction process.

[0083] r req represents the required resource vector. It describes the situation of various resources required for the construction of component e i such as manpower, materials, equipment, etc. Each element in the vector corresponds to a specific resource, and its value represents the demand for that resource. By determining r req resource allocation and preparation can be carried out in advance to ensure the smooth progress of the construction.

[0084] The mapping relationship M of the present invention assigns detailed construction plan information to each component ei, providing basic data and reference standards for subsequent construction progress management, deviation analysis, and optimization and adjustment, etc.

[0085] Step 202, collect the construction site data of the building, and preprocess the construction site data to obtain preprocessed data.

[0086] In some embodiments, step 202 may include:

[0087] Perform noise reduction and filtering preprocessing on the collected construction site data to obtain intermediate processed data;

[0088] Extract the feature information of each component from the intermediate processed data;

[0089] Align the construction site data collected from different data sources according to the data source weights corresponding to the data sources of the construction site data, and perform fusion and standardization processing according to the data source weights and the corresponding feature information to obtain the preprocessed data.

[0090] In some embodiments, the preprocessed data is represented as:

[0091] P(D(t)) = ∑.ω i ·F i(D(t)) / +b;

[0092] where P(D(t)) is the preprocessed data, D(t) is the construction site data, ω i is the data source weight of the i-th data source, F i is the feature information of each component, and b is the bias term.

[0093] In some embodiments, the construction site data may include point cloud data, image data, sensor data, and construction record data collected manually at the construction site.

[0094] Specifically, D(t) represents the construction site data, which is a data set that changes with time t and contains various types of information such as point cloud data and images collected by devices such as drones and sensors. These data reflect the actual situation of the construction site at different times and are an important basis for subsequent construction progress identification and analysis.

[0095] P(D(t)) represents the data after preprocessing. Preprocessing the original construction site data is to transform it into a form more suitable for machine learning models to improve the accuracy and efficiency of the models.

[0096] ω i is the weight coefficient of the i-th data source. Since the construction site data may come from multiple different data sources (such as different types of sensors, drone shots from different angles, etc.), the importance of the information provided by each data source for the final analysis may be different. ω i is a parameter used to measure the relative importance of each data source. The larger its value, the higher the weight of the corresponding data source in the data preprocessing process and the greater its impact on the final preprocessing result.

[0097] F i (D(t)) is a feature extraction function for each component. Its role is to extract the feature information related to the i-th component from the original data D(t). These feature information can reflect various attributes and states of the component at the construction site, such as the shape, position, and surface condition of the component. Different components may require different types of features to be extracted. Through F i the original data can be transformed into a feature vector with practical significance.

[0098] ∑.ω i ·F i (D(t)) / represents the weighted sum of the feature information extracted from all data sources. That is, multiplying the weight coefficient ω i of each data source by the corresponding feature extraction result F i(D(t)) are multiplied, and then all the products are added together. The purpose of doing this is to comprehensively consider the information provided by each data source and obtain a comprehensive vector that fully reflects the data characteristics of the construction site.

[0099] b is the bias term, which is a constant term. During the data preprocessing process, the role of the bias term is to fine-tune the result of the weighted sum, so that the final preprocessed data P(D(t)) can better meet the requirements of the subsequent machine learning model. The bias term can help the model better fit the data and improve the generalization ability and prediction accuracy of the model.

[0100] In summary, through the weight assignment, feature extraction, and bias adjustment of different data sources, the present invention realizes the effective preprocessing of the original construction site data D(t), laying a foundation for the subsequent training and analysis of the construction progress recognition model based on these data.

[0101] Step 203: Process the preprocessed data through the progress recognition model to obtain the actual construction progress of the building.

[0102] In some embodiments, the progress recognition model can deeply analyze and extract features from the preprocessed data, and identify key features and patterns related to the construction progress. These features may include image features of different construction stages, the intensity of construction activities monitored by sensors, the usage of construction materials, etc. The model learns the mapping relationship between these features and the actual construction progress through a large amount of training data.

[0103] Based on the extracted features and the learned mapping relationship, the progress recognition model will perform calculations and inferences to obtain the actual construction progress of each component or the entire building at the current moment. For each component, the model may predict its current completion ratio or the construction stage it is in according to its specific features and historical progress data; for the entire building, the model will comprehensively consider the progress of each component and their logical relationships to give an overall estimated value of the actual construction progress.

[0104] After being processed and calculated by the model, the final output is the actual construction progress of the building, usually represented in the form of specific progress values, completion ratios, construction stages, etc. For example, a certain component has been completed 70%, or the entire building is in the middle stage of the main structure construction, etc. These output results can provide accurate progress information for construction management personnel, so that they can timely discover progress deviations and take corresponding measures for adjustment and management.

[0105] Step 204: Calculate the deviation between the actual construction progress and the planned construction progress of the building.

[0106] In some embodiments, step 204 may include:

[0107] Determine an initial deviation between the actual construction progress and the planned construction progress of the building according to the actual construction progress and the planned construction progress of the building;

[0108] Obtain the importance weight of each component and a time decay function for representing the change of the initial deviation over time;

[0109] Process the initial deviation according to the importance weight of each component and the time decay function to obtain the deviation between the actual construction progress and the planned construction progress of the building.

[0110] In some embodiments, the deviation between the actual construction progress and the planned construction progress of the building is expressed as:

[0111] δ(t) = ∑(α i · |C(t) i - M(e i ) t |) · β(t);

[0112] Where δ(t) is the deviation between the actual construction progress and the planned construction progress, C(t) i is the actual construction progress of the i-th component at time t output by the progress recognition model, M(e i ) t is the construction plan mapping reflecting the planned construction progress of the i-th component at time t, α i is the importance weight of the i-th component, and β(t) is the time decay function.

[0113] Specifically, α i is the importance weight of the i-th component. In a building project, different components have different degrees of importance to the overall project. For example, the basic structure components of a building may be more important than some decorative components, and their progress deviations have a greater impact on the overall project. α i is a parameter used to measure this importance difference. The larger the value, the more important the component, and the higher its weight in calculating the progress deviation.

[0114] |C(t) i - M(e i ) t | is to perform a weighted sum of the progress deviations of all components according to their importance weights. In this way, the importance of different components and their respective progress deviation situations are comprehensively considered to obtain a preliminary overall progress deviation value.

[0115] β(t) is a time decay function. It reflects the impact of time factors on schedule deviation. During the construction process, the same magnitude of schedule deviation may have different impacts on the project at different time points. For example, a certain schedule deviation at the initial stage of the project may leave more time and opportunities for adjustment and compensation; while the same schedule deviation at the later stage of the project may have a more serious impact on the delivery time, cost, etc. of the entire project. β(t) adjusts this degree of impact according to the change of time t. Usually, as time goes by, the impact of the deviation gradually increases (or has a specific change rule according to the actual situation).

[0116] ∑(α i ·|C(t) i -M(e i ) t |)·β(t) is to multiply the sum of the weighted schedule deviations of each component by the time decay function β(t) to obtain the total deviation δ(t) between the actual construction progress and the planned construction progress at time t, considering the importance of components and time factors. This deviation value provides an important basis for subsequent schedule analysis, prediction, and optimization adjustment, helping construction managers accurately understand the project schedule and take corresponding measures in a timely manner.

[0117] In some embodiments, the method of the present invention may further include:

[0118] Determining a sequence of actual construction progress determined by the progress recognition model at multiple historical moments;

[0119] Determining a sequence of deviations between the actual construction progress and the planned construction progress at the multiple historical moments;

[0120] Processing the actual construction progress sequence and the deviation sequence through an LSTM network to predict the construction progress of the building at a future moment.

[0121] The construction progress at a future moment can be expressed as:

[0122] P(t+Δt)=LSTM([C(t-k),...,C(t),δ(t-k),...,δ(t)]);

[0123] where P(t+Δt) is the predicted value of the construction progress at the future time t+Δt, C(t-k),...,C(t) is the sequence of historical progress recognition results, and δ(t-k),...,δ(t) is the sequence of historical schedule deviations.

[0124] Specifically, P(t + Δt) is the predicted target value, representing the predicted construction progress value at the future time (t + Δt). Construction managers can make advance preparations for resource allocation, progress planning, etc. based on this predicted value to cope with possible progress changes.

[0125] LSTM is the Long Short-Term Memory network, a special type of Recurrent Neural Network (RNN). It can effectively handle the long-term dependence problems in time series data and overcome the problems of vanishing gradients or exploding gradients existing in traditional RNNs. In construction progress prediction, the LSTM model can learn the patterns and rules in historical construction progress and deviation data and use this information to predict future construction progress.

[0126] C(t - k),..., C(t) is the sequence of historical progress recognition results. Among them, C(t) is the progress recognition result at the current time t, C(t - 1) is the progress recognition result at the previous time t - 1, and so on until C(t - k), that is, the progress recognition result at time t - k. These historical progress recognition results reflect the actual progress of the construction process at different past times and are important bases for the model to learn the changing trend of construction progress. By analyzing these historical data, the LSTM model can capture the dynamic change patterns of construction progress, such as changes in construction speed, construction characteristics at different stages, etc.

[0127] δ(t - k),..., δ(t) is the sequence of historical progress deviations. δ(t) is the deviation between the actual construction progress and the planned construction progress at the current time t, δ(t - 1) is the progress deviation at the previous time t - 1, and so on until δ(t - k). The progress deviation reflects the differences between the actual construction situation and the plan, and these differences may include the influence of various factors, such as weather, resource supply, construction technology, etc. Taking the historical progress deviations as inputs, the LSTM model can learn the influence rules of these deviations on construction progress, thereby predicting future construction progress more accurately. For example, if the progress deviation shows a certain specific trend in the past period, the model can adjust the prediction of future progress according to this trend.

[0128] In the present invention, by taking the historical progress recognition results and historical progress deviations as the inputs of the LSTM model, the model can comprehensively consider the historical evolution of construction progress and the deviation from the plan, and make a relatively accurate prediction of the construction progress at the future time t + Δt, providing valuable decision-making support for construction management.

[0129] Step 205: Adjust the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building, and combine with a multi-objective optimization model to obtain a target construction plan.

[0130] In some embodiments, step 205 may include:

[0131] Determining the resource cost value of the resource allocation cost for the building construction;

[0132] Determining the construction risk value of the risk control assessment for the building construction;

[0133] Constructing the upper limit constraint of resource usage, the construction period requirement constraint, and the construction quality constraint for the building construction;

[0134] Constructing the multi-objective optimization model according to the deviation between the actual construction progress and the planned construction progress of the building, the resource cost value, the construction risk value, the upper limit constraint of resource usage, the construction period requirement constraint, and the construction quality constraint for the building construction;

[0135] Determining the optimized objective function value based on the multi-objective optimization model, and adjusting the construction plan based on the optimized objective function value to obtain the target construction plan.

[0136] In some embodiments, the optimized objective function value can be expressed as:

[0137] minF = λ1∑δ(t) + λ2∑Cost(r) + λ3∑Risk(s);

[0138] Wherein, minF is the optimized objective function value, λ1 is the first weight, λ2 is the second weight, λ3 is the third weight, Cost(r) is the resource cost value, and Risk(s) is the construction risk value.

[0139] Specifically, minF represents the optimized objective function value, and the "min" here indicates that the objective function F needs to be minimized. In building construction management, this means finding a set of optimal decision variables (such as construction progress arrangement, resource allocation, etc.) to minimize the value of the objective function F. This is the core goal of the entire optimization process, achieving comprehensive optimization by adjusting various factors.

[0140] λ1 is the first weight, which is used to measure the importance of the deviation between the actual construction progress and the planned construction progress in the objective function. Different weight settings reflect different degrees of emphasis on the progress deviation by construction managers.

[0141] ∑δ(t) is the summation of the progress deviations at each moment t. Excessive progress deviation may lead to delays in subsequent construction links, resource waste, etc., so it needs to be considered and minimized in the objective function.

[0142] λ2 is the second weight, which is used to reflect the relative importance of resource cost in the objective function. Different projects have different sensitivities to resource cost. By adjusting this weight, the emphasis on cost control in the project can be reflected.

[0143] ∑Cost(r) sums up the costs Cost(r) of all resources. Resource cost is an important economic factor in construction, including labor cost, material cost, equipment rental cost, etc. Controlling resource cost is one of the important goals of project management.

[0144] λ3 is the third weight, which is used to balance the importance of construction risk in the objective function. Different projects face different degrees of risk. Construction managers can adjust this weight according to the actual situation to focus on and reduce risks.

[0145] ∑Ris□(s) sums up all construction risks Risk(s). There are various risks during the construction process, such as safety risks, quality risks, weather risks, etc. These risks may cause losses to the project. Therefore, they need to be considered in the objective function and the risk value should be minimized as much as possible.

[0146] Overall, the objective function F comprehensively considers the three important factors of schedule deviation, resource cost, and construction risk through weighted summation. By adjusting the weights, the relationship between these three factors can be balanced according to the specific requirements and emphases of the project to achieve the overall optimal effect.

[0147] In some embodiments, the constraint conditions can be expressed as:

[0148] s.t.R(t)≤R max ,T end ≤T deadline ,∏Quality(e i )≥Q min ;

[0149] Among them, R(t) is the resource usage, R max is the upper limit of resource usage, T end is the planned construction end time, T deadline is the construction deadline, Quality(e i ) is the quality value of each component, Q min is the minimum quality requirement value.

[0150] Specifically, R(t)≤R max is the constraint condition of resource usage. R(t) represents the resource usage at time t, and R maxIt is the upper limit of resource usage. This constraint ensures that during the construction process, the resource usage at any given time does not exceed the pre-set upper limit, avoiding problems such as supply shortages or cost overruns caused by excessive resource usage.

[0151] T end ≤T deadline is a constraint on the construction time, where T end is the planned end time of construction, and T deadline is the construction deadline. This constraint requires that the planned end time of construction does not exceed the specified deadline, ensuring that the project can be delivered on time and meet the time requirements of the project.

[0152] ∏Quality(e i )≥Q min is a constraint on quality, where Quality(e i ) is the quality value of each component e i , and Q min is the minimum quality requirement value. This constraint ensures that the quality of the entire construction project meets the minimum standard by imposing requirements on the product of the quality values of all components, guaranteeing the safety and reliability of the building.

[0153] The constraints of the present invention jointly limit the feasible region of the optimization, ensuring that in the process of finding the minimum value of the objective function, the obtained solutions are feasible and reasonable in terms of resource usage, construction time, and quality, meeting the requirements of the actual construction project.

[0154] Step 206: Based on the target construction plan, implement a construction progress management method for the building.

[0155] It can be understood that by optimizing the objective function on the premise of meeting these constraints, the present invention formulates a reasonable construction progress adjustment plan, ensuring that the construction progress can not only meet the planned requirements but also achieve a balance in terms of resource cost, construction risk, and quality, ultimately realizing the efficient, high-quality, and safe progress of building construction.

[0156] In summary, with the target construction plan as the core, the present invention realizes the refined management of building construction progress through comprehensive monitoring, analysis, and optimization of the construction process, improving the controllability and success rate of the project.

[0157] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a BIM-based building construction progress management system provided by the present invention.

[0158] As Figure 3 shown, a BIM-based building construction progress management system proposed in an embodiment of the present invention includes:

[0159] The plan mapping module 301 is used to decompose the BIM model into a set of components according to the construction process of the building and establish the construction plan mapping for each component;

[0160] The data processing module 302 is used to collect the on-site construction data of the building and preprocess the on-site construction data to obtain preprocessed data;

[0161] The progress prediction module 303 is used to process the preprocessed data through a progress recognition model to obtain the actual construction progress of the building;

[0162] The progress deviation module 304 is used to calculate the deviation between the actual construction progress and the planned construction progress of the building;

[0163] The plan adjustment module 305 is used to adjust the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building, and combine a multi-objective optimization model to obtain a target construction plan;

[0164] The progress management module 306 is used to manage the construction progress of the building based on the target construction plan.

[0165] Please refer to Figure 4 , Figure 4 which is a schematic diagram of an embodiment of the electronic device provided by the embodiment of the present invention. As Figure 4 shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0166] Decompose the BIM model into a set of components according to the construction process of the building and establish the construction plan mapping for each component;

[0167] Collect the on-site construction data of the building and preprocess the on-site construction data to obtain preprocessed data;

[0168] Process the preprocessed data through a progress recognition model to obtain the actual construction progress of the building;

[0169] Calculate the deviation between the actual construction progress and the planned construction progress of the building;

[0170] Adjust the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building, and combine a multi-objective optimization model to obtain a target construction plan;

[0171] Manage the construction progress of the building based on the target construction plan.

[0172] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0173] Those skilled in the art will understand that the embodiments of the present invention may provide a method, a system, or a computer program product. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.

Claims

1. A BIM-based construction progress management method, characterized in that, The method includes: Decomposing the BIM model into a set of components according to the construction process of the building, and establishing a construction plan mapping for each component; Collecting the construction site data of the building, and preprocessing the construction site data to obtain preprocessed data; Processing the preprocessed data through a progress recognition model to obtain the actual construction progress of the building; Calculating the deviation between the actual construction progress and the planned construction progress of the building; Adjusting the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building, and combining with a multi-objective optimization model to obtain a target construction plan; Based on the target construction plan, a construction progress management method for the building.

2. The method for managing the construction progress of a building based on BIM according to claim 1, wherein, The decomposing the BIM model into a set of components according to the construction process of the building, and establishing a construction plan mapping for each component includes: Extracting each of the components from the set of components; Extracting the planned start time, planned end time, set of prerequisite tasks, and required resource vector of the construction from the construction plan of the building; Establishing a mapping relationship between each of the components and its corresponding planned start time, planned end time, set of prerequisite tasks, and required resource vector to obtain a construction plan mapping for each of the components.

3. The method for managing the construction progress of a building based on BIM according to claim 2, wherein, The preprocessing the construction site data to obtain preprocessed data includes: Performing noise reduction and filtering preprocessing on the collected construction site data to obtain intermediate processed data; Extracting the feature information of each of the components from the intermediate processed data; Aligning the construction site data collected from different data sources according to the time stamp according to the data source weight corresponding to the data source of the construction site data, and performing fusion and standardization processing according to the data source weight and the corresponding feature information to obtain the preprocessed data.

4. The method for managing the construction progress of a building based on BIM according to claim 3, wherein, The construction site data includes: The point cloud data, image data, sensor data, and construction record data manually collected at the construction site.

5. A BIM-based construction progress management method according to claim 4, characterized in that The calculating the deviation between the actual construction progress and the planned construction progress of the building includes: Determining an initial deviation between the actual construction progress and the planned construction progress according to the actual construction progress and the planned construction progress of the building; Obtaining the importance weight of each of the components, and a time decay function for representing the change of the initial deviation over time; Processing the initial deviation according to the importance weight of each of the components and the time decay function to obtain the deviation between the actual construction progress and the planned construction progress of the building.

6. The method for managing the construction progress of a building based on BIM according to claim 5, characterized in that, The adjusting the construction plan according to the deviation between the actual construction progress and the planned construction progress of the building, and combining with a multi-objective optimization model to obtain a target construction plan includes: Determining the resource cost value of the resource allocation cost of the building construction; Determining the construction risk value of the risk control assessment of the building construction.

7. A BIM-based construction progress management method according to claim 6, characterized in that, Constructing the upper limit constraint of resource use, the construction period requirement constraint, and the construction quality constraint for the building construction; Constructing the multi-objective optimization model according to the deviation between the actual construction progress and the planned construction progress of the building, the resource cost value of the building construction, the construction risk value, the upper limit constraint of resource use, the construction period requirement constraint, and the construction quality constraint; Determine the optimized objective function value based on the multi-objective optimization model, and adjust the construction plan based on the optimized objective function value to obtain the target construction plan.

8. The method for managing the construction progress of a building based on BIM according to claim 7, characterized in that The method further includes: Determine the actual construction progress sequences determined by the progress identification model at multiple historical moments; Determine the deviation sequences between the actual construction progress and the planned construction progress at multiple historical moments; Process the actual construction progress sequences and the deviation sequences through an LSTM network to predict the construction progress of the building at future moments.

9. A BIM-based building construction progress management system, which implements the method described in claim 1, characterized in that, The system includes: A plan mapping module, configured to decompose the BIM model into a component set according to the construction process of the building, and establish a construction plan mapping for each component; A data processing module, configured to collect the construction site data of the building, and preprocess the construction site data to obtain preprocessed data; A progress prediction module, configured to process the preprocessed data through a progress identification model to obtain the actual construction progress of the building; A progress deviation module, configured to calculate the deviation between the actual construction progress and the planned construction progress of the building; A plan adjustment module, configured to adjust the construction plan in combination with a multi-objective optimization model according to the deviation between the actual construction progress and the planned construction progress of the building to obtain a target construction plan; A progress management module, configured to manage the construction progress of the building based on the target construction plan.

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