A method and device for coordinating and managing the construction period of municipal roads based on BIM

Abstract

The present invention relates to the technical field of construction period management, a method and device for coordinating and managing the construction period of municipal roads based on BIM, comprising: calculating an initial factor vector sequence, constructing a target period prediction coordinate model according to the initial factor vector sequence, obtaining a current factor value sequence, predicting multiple sets of current node periods, calculating the current prediction accuracy, judging whether the current prediction accuracy is greater than a prediction accuracy threshold, if not, updating the initial number of influencing factors, if greater, judging whether it meets the standard node period set, if not, identifying the target factor connection vector in the target period prediction coordinate model, and adjusting construction resources according to the target factor connection vector. The present invention can solve the problems of high risk of construction progress delay and low prediction accuracy in the current municipal road construction process.

Description

Technical Field

[0001] The present invention relates to the technical field of construction period management, and in particular to a method and device for coordinating and managing the construction period of a municipal road based on BIM. Background Art

[0002] With the acceleration of urbanization, municipal roads, as an important part of urban infrastructure, are increasingly being built. When traditional construction management methods are applied to municipal road projects, there are often problems such as information islands and unreasonable resource allocation, which lead to frequent construction delays and cost overruns. Therefore, efficient construction period coordination management methods are particularly important.

[0003] BIM (Building Information Modeling) technology is a digital model technology that integrates information from the entire life cycle of building design, construction, and operation and maintenance. It digitally expresses the physical and functional characteristics of construction projects by creating a three-dimensional visual model, and supports information sharing, transmission, and collaborative work.

[0004] Currently, in the process of managing the construction period of municipal roads, the construction progress is usually monitored and adjusted by building a BIM model. The BIM model can help managers understand the construction dynamics and progress deviations in real time. However, when using the BIM model for construction management, the construction influencing factors and historical construction data are not effectively combined, resulting in the current municipal road construction process. There is a high risk of construction progress delays and low prediction accuracy. Summary of the invention

[0005] The present invention provides a method and device for coordinating and managing the construction period of a municipal road based on BIM, the main purpose of which is to solve the problems of high risk of construction progress delay and low prediction accuracy in the current municipal road construction process.

[0006] To achieve the above object, the present invention provides a method for coordinating and managing the construction period of municipal roads based on BIM, comprising:

[0007] Acquire a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set;

[0008] According to a preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is p and the number of the initial influencing factors is q;

[0009] The pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain an initial factor vector sequence, wherein the vector angle formula is as follows:

[0010]

[0011] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i represents the initial factor value of the i-th initial influencing factor, where i represents the sequence number of the initial influencing factor;

[0012] Constructing an initial factor connection vector in a pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence;

[0013] Identify the positive horizontal axis reference intersection of the initial factor connection vector, extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration;

[0014] Summarize the initial factor connection vectors, the positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences to obtain the target construction period prediction coordinate model;

[0015] Acquire a current BIM construction model, extract current construction data from the current BIM construction model, and extract a current factor value sequence from the current construction data according to the initial influencing factor sequence;

[0016] According to the current factor value sequence, the target construction period prediction coordinate model is used to predict multiple groups of current node construction periods, and the current prediction accuracy is calculated according to the multiple groups of current node construction periods;

[0017] Determining whether the current prediction accuracy is greater than a preset prediction accuracy threshold;

[0018] If the current prediction accuracy is not greater than the prediction accuracy threshold, the initial number of influencing factors is updated and the process returns to the above step of obtaining the historical construction data set;

[0019] If the current prediction accuracy is greater than the prediction accuracy threshold, determining whether the multiple sets of current node durations meet a preset set of standard node durations;

[0020] If the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model;

[0021] If the multiple groups of current node durations do not conform to the standard node duration set, a target factor connection vector is identified in the target duration prediction coordinate model, and construction resources are adjusted according to the target factor connection vector.

[0022] Optionally, constructing an initial factor connection vector in a pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence includes:

[0023] According to the initial number of influencing factors and the preset unit vector modulus, the negative value of the vertical axis is calculated using the pre-constructed starting point longitudinal distance formula, wherein the starting point longitudinal distance formula is as follows:

[0024] y=q×l

[0025] Among them, y represents the negative value of the vertical axis, l represents the unit vector modulus, and q represents the number of initial influencing factors;

[0026] Determine the starting point coordinates of the vector in the original construction period prediction coordinate model according to the negative value of the vertical axis, wherein the starting point coordinates of the vector are (0, -y);

[0027] extracting a first factor vector from the initial factor vector sequence;

[0028] Identify a first vector end point of the first factor vector and a next factor vector, and extract a next vector start point of the next factor vector, wherein the first vector end point refers to the vector end point of the first factor vector, and the next vector start point refers to the vector start point of the next factor vector;

[0029] The next factor vector is translated according to the next vector starting point and the first vector end point to obtain an iterative connection vector, wherein the next vector starting point in the iterative connection vector coincides with the first vector end point;

[0030] Identifying a vector connection number of the iterative connection vector, and determining whether the vector connection number is equal to the initial number of influencing factors;

[0031] If the vector connection number is not equal to the initial influencing factor number, the first factor vector is updated using the next factor vector, and the process returns to the step of identifying the first vector end point and the next factor vector of the first factor vector;

[0032] If the vector connection number is equal to the initial influencing factor number, a target connection vector is obtained;

[0033] Identify the starting point of the target connection vector, translate the target connection vector according to the starting point of the connection vector and the coordinates of the vector starting point, and obtain the original factor connection vector, wherein the starting point of the connection vector in the original factor connection vector coincides with the coordinates of the vector starting point;

[0034] The original factor connection vector is vector-scaled to obtain the initial factor connection vector.

[0035] Optionally, performing vector scaling on the original factor connection vector to obtain the initial factor connection vector includes:

[0036] Identify the connection vector end point of the original factor connection vector and the positive horizontal axis of the original construction period prediction coordinate model;

[0037] Determine whether the end point of the connection vector is on the positive horizontal axis of the original construction period prediction coordinate model;

[0038] If the end point of the connection vector is not on the positive horizontal axis of the original construction period prediction coordinate model, constructing the original factor synthesis vector according to the starting point and the end point of the connection vector;

[0039] The telescopic distance between the original factor synthesis vector and the positive horizontal axis is calculated, and the telescopic ratio is calculated using a pre-constructed telescopic formula according to the telescopic distance and the original factor synthesis vector, wherein the telescopic formula is as follows:

[0040]

[0041] Among them, k represents the scaling ratio, represents the original factor synthesis vector, d is equal to the telescopic distance, represents a unit synthetic vector, the unit synthetic vector has the same vector direction as the original factor synthetic vector, |*| represents the modulus symbol, and the stretching distance is a negative number or a positive number;

[0042] scaling each initial factor vector in the initial factor vector sequence according to the scaling ratio to obtain a scaled factor vector sequence;

[0043] An initial factor concatenation vector is constructed according to the scaling factor vector sequence.

[0044] Optionally, determining a plurality of groups of initial construction period coordinate points in the original construction period prediction coordinate model according to the forward horizontal axis reference intersection point and the target node construction period includes:

[0045] Determine the initial construction period coordinate point in the original construction period prediction coordinate model according to the forward horizontal axis reference intersection point and the target node construction period;

[0046] The initial construction period coordinate points of the construction period of each target node are summarized to obtain multiple groups of initial construction period coordinate points of the positive horizontal axis reference intersection points.

[0047] Optionally, predicting multiple groups of current node durations using the target duration prediction coordinate model according to the current factor value sequence includes:

[0048] Constructing a current factor vector sequence according to the current factor value sequence, and constructing a current factor connection vector according to the current factor vector sequence;

[0049] Taking the initial factor connection vectors corresponding to each group of initial factor value sequences in the target construction period prediction coordinate model as the to-be-compared factor connection vectors, and obtaining a to-be-compared factor connection vector set;

[0050] Extracting the connection vectors of the factors to be compared in sequence from the connection vector set of the factors to be compared, and identifying the positive horizontal axis comparison intersection points of the connection vectors of the factors to be compared;

[0051] Identify the current intersection point of the positive horizontal axis of the current factor connection vector;

[0052] Determine whether the current factor connection vector and the to-be-compared factor connection vector have a mid-section intersection;

[0053] If there is a mid-section intersection between the current factor connection vector and the to-be-compared factor connection vector, the mid-section intersections are summarized to obtain a connection vector intersection set;

[0054] Constructing a connection vector polygon set according to the vector starting point coordinates, the connection vector intersection set, the positive horizontal axis comparison intersection point and the positive horizontal axis current intersection point;

[0055] Calculating the total area of ​​the connection vector polygon set, and using the total area as the connection vector difference value;

[0056] If the current factor connection vector and the to-be-compared factor connection vector do not have a mid-section intersection, construct a connection vector polygon according to the vector starting point coordinates, the positive horizontal axis comparison intersection and the positive horizontal axis current intersection;

[0057] Calculating the area of ​​the connection vector polygon and using the area as the connection vector difference value;

[0058] Summarizing the connection vector difference values ​​of the connection vectors of all factors to be compared to obtain a connection vector difference value set;

[0059] Extracting a minimum connection vector difference value from the connection vector difference value set, and identifying a similarity factor connection vector corresponding to the minimum connection vector difference value;

[0060] The similar intersection points on the positive horizontal axis of the similar factor connection vectors are identified, and multiple groups of current node durations corresponding to the similar intersection points on the positive horizontal axis are extracted.

[0061] Optionally, the calculating the current prediction accuracy according to multiple groups of current node durations includes:

[0062] Extracting a cluster of adjacent predicted node durations from the multiple groups of current node durations according to a preset duration threshold;

[0063] Sequentially extracting the upcoming prediction node durations in the upcoming prediction node duration cluster;

[0064] Obtaining the actual node duration corresponding to the adjacent predicted node duration;

[0065] According to the preset accuracy formula, the current prediction accuracy is calculated using the upcoming predicted node duration and the actual node duration, wherein the accuracy formula is as follows:

[0066]

[0067] Among them, ε represents the current prediction accuracy, α represents the accuracy node working period weight, t 1 Indicates the duration of the first upcoming forecast node, t' 1 represents the actual node duration corresponding to the first adjacent predicted node duration, t j represents the duration of the jth near-forecast node, t' j represents the actual node duration corresponding to the jth predicted node duration, t J represents the duration of the Jth nearest predicted node, t' J It represents the actual node duration corresponding to the Jth adjacent predicted node duration, j represents the sequence number of the adjacent predicted node duration, and J represents the total number of adjacent predicted node durations.

[0068] Optionally, the updating of the initial number of influencing factors includes:

[0069] According to the current prediction accuracy and the prediction accuracy threshold, the initial factor update number is calculated using a pre-built update formula, wherein the update formula is as follows:

[0070]

[0071] Among them, q' represents the initial factor update number, e represents the natural constant, β represents the accuracy adjustment index, δ represents the prediction accuracy threshold, q represents the initial number of influencing factors, and ε represents the current prediction accuracy;

[0072] The initial number of influencing factors is updated using the initial factor update number.

[0073] Optionally, identifying the target factor connection vector in the target construction period prediction coordinate model includes:

[0074] Receiving multiple sets of node duration time limits input by a user, and selecting multiple initial duration coordinate sets in the target duration prediction coordinate model according to the multiple sets of node duration time limits;

[0075] Identify a plurality of undetermined factor connection vectors corresponding to the plurality of initial duration coordinate groups;

[0076] A target factor connection vector selected by a user from among the plurality of pending factor connection vectors is received.

[0077] Optionally, adjusting the construction resources according to the target factor connection vector includes:

[0078] Extracting target factor vectors in sequence from the target factor connection vector, calculating target factor values ​​corresponding to the target factor vectors, and obtaining a target factor value set;

[0079] The construction resources are adjusted according to the target factor value set.

[0080] To achieve the above object, the present invention also provides a BIM-based municipal road construction period coordination management device, comprising:

[0081] The initial factor connection vector construction module is used to obtain a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set; according to a preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences, and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is p, and the number of initial influencing factors is q; using a pre-constructed vector angle formula, the initial factor vector of each initial influencing factor in the initial influencing factor sequence is calculated according to the initial factor value sequence to obtain an initial factor vector sequence, wherein the vector angle formula is as follows:

[0082]

[0083] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i represents the initial factor value of the i-th initial influencing factor, i represents the sequence number of the initial influencing factor; constructs the initial factor connection vector in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence;

[0084] The target duration prediction coordinate model building module is used to identify the positive horizontal axis reference intersection of the initial factor connection vector, sequentially extract the target node duration corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration; summarize the initial factor connection vectors, positive horizontal axis reference intersections and multiple groups of initial duration coordinate points corresponding to each group of initial factor value sequences to obtain the target duration prediction coordinate model;

[0085] A module for judging the standardization of multiple sets of current node durations, used to obtain the current BIM construction model, extract the current construction data from the current BIM construction model, and extract the current factor value sequence from the current construction data according to the initial influencing factor sequence; predict multiple sets of current node durations using the target duration prediction coordinate model according to the current factor value sequence, and calculate the current prediction accuracy according to the multiple sets of current node durations; determine whether the current prediction accuracy is greater than a preset prediction accuracy threshold; if the current prediction accuracy is not greater than the prediction accuracy threshold, update the initial number of influencing factors and return to the above step of obtaining the historical construction data set; if the current prediction accuracy is greater than the prediction accuracy threshold, determine whether the multiple sets of current node durations meet the preset standard node duration set; if the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model;

[0086] The construction resource adjustment module is used to identify the target factor connection vector in the target construction period prediction coordinate model if the multiple groups of current node construction periods do not meet the standard node construction period set, and adjust the construction resources according to the target factor connection vector.

[0087] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0088] A memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned BIM-based municipal road construction schedule coordination management method.

[0089] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned BIM-based municipal road construction schedule coordination management method.

[0090] The present invention is to solve the problem described in the background technology. The present invention determines whether to adjust the construction resources by constructing a target construction period prediction coordinate model to determine whether multiple groups of current node construction periods meet the standard node construction period set. When the construction resources need to be adjusted, the target factor connection vector is identified in the target construction period prediction coordinate model, and the construction resources can be adjusted according to the target factor connection vector. When constructing the target construction period prediction coordinate model, it is constructed based on the historical construction data set, so it is necessary to obtain the historical construction data set first. Since each historical construction data is independent, it is necessary to extract the historical construction data in sequence from the historical construction data set, and then select the initial influencing factor sequence, multiple groups of influencing factors from the historical construction data according to the preset initial influencing factor number. Initial factor value sequence and multiple groups of initial node duration sequences. Since the initial factor value sequence needs to be converted into an initial factor vector sequence, the pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain the initial factor vector sequence. Then, the initial factor connection vector is constructed in the pre-constructed original duration prediction coordinate model according to the initial factor vector sequence. At this time, the target duration prediction coordinate model can be constructed. First, the positive horizontal axis reference intersection of the initial factor connection vector needs to be identified, and the target node duration corresponding to the positive horizontal axis reference intersection is extracted in turn in the initial node duration sequence. Then, the target node duration corresponding to the positive horizontal axis reference intersection is extracted according to the positive horizontal axis reference intersection and the target node duration. The construction period determines multiple groups of initial construction period coordinate points in the original construction period prediction coordinate model, and finally summarizes the initial factor connection vectors, positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences. The target construction period prediction coordinate model is constructed according to the corresponding relationship between the initial factor connection vectors, the positive horizontal axis reference intersection points and the multiple groups of initial construction period coordinate points. Since the current prediction accuracy of the target construction period prediction coordinate model needs to be evaluated, when judging whether the current prediction accuracy is qualified, the current BIM construction model can be obtained first, and the current construction data can be extracted from the current BIM construction model. Then, the current factor value sequence can be extracted from the current construction data according to the initial influencing factor sequence. At this time, the target construction period prediction coordinate model can be obtained according to the corresponding relationship between the initial factor connection vectors, the positive horizontal axis reference intersection points and the multiple groups of initial construction period coordinate points. Take a value sequence, use the target construction period prediction coordinate model to predict multiple groups of current node construction periods, and then calculate the current prediction accuracy based on the multiple groups of current node construction periods. If the current prediction accuracy is not greater than the prediction accuracy threshold, it means that the current prediction accuracy of the target construction period prediction coordinate model is unqualified, and it is necessary to update the initial number of influencing factors and return to the step of obtaining the historical construction data set. If the current prediction accuracy is greater than the prediction accuracy threshold, it means that the current prediction accuracy of the target construction period prediction coordinate model is qualified. At this time, it can be determined whether the multiple groups of current node construction periods meet the standard node construction period set. If the multiple groups of current node construction periods meet the standard node construction period set, then return to the above step of obtaining the current BIM construction model. If the multiple groups of current node construction periods do not meet the standard node construction period set,Then, the target factor connection vector is identified in the target construction period prediction coordinate model, and the construction resources are adjusted according to the target factor connection vector. Therefore, the present invention can solve the problems of high risk of construction progress delay and low prediction accuracy in the current municipal road construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A flow chart of a method for coordinating and managing the construction period of a municipal road based on BIM provided in one embodiment of the present invention;

[0092] Figure 2 A functional module diagram of a BIM-based municipal road construction period coordination management device provided in one embodiment of the present invention;

[0093] Figure 3 A schematic diagram of the structure of an electronic device for implementing the BIM-based municipal road construction schedule coordination management method provided in one embodiment of the present invention.

[0094] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0095] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0096] The embodiment of the present application provides a method for coordinating and managing the construction period of municipal roads based on BIM. The execution subject of the method for coordinating and managing the construction period of municipal roads based on BIM includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for coordinating and managing the construction period of municipal roads based on BIM can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0097] Reference Figure 1 FIG. 1 is a flow chart of a method for coordinating and managing the construction period of municipal roads based on BIM according to an embodiment of the present invention. In this embodiment, the method for coordinating and managing the construction period of municipal roads based on BIM includes:

[0098] S1. Obtain a historical construction data set, and extract historical construction data in sequence from the historical construction data set.

[0099] In detail, the historical construction data set refers to a collection of historical construction data corresponding to a single municipal road construction project in history, and the historical construction data refers to the construction data of a single municipal road construction project in history. The historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set.

[0100] Furthermore, the historical influencing factor set refers to the set of factors that affect the construction period of a municipal road construction project at a certain construction moment in history, such as: construction progress, project cycle, number of construction personnel, weather, construction temperature, soil geology, etc. The historical factor value set refers to the specific numerical value set corresponding to each historical influencing factor of a construction project at a certain construction moment in history, such as: the construction progress of the project after 1 year is 40%, the project cycle is 2 years, the number of construction personnel is 500, the weather is poor (values ​​need to be assigned), etc. The historical node duration set refers to the remaining completion time set of each engineering node of a construction project at a certain construction moment in history, for example: when the construction project is 3 months, the construction data of a municipal road construction project is: the remaining completion time of the foundation pit excavation engineering node is 2 months; the remaining completion time of the drainage pipe installation engineering node is 3 months; the remaining completion time of the roadbed filling engineering node is 6 months; the remaining completion time of the pavement structure construction engineering node is 8 months, etc.

[0101] S2. Selecting an initial influencing factor sequence, multiple groups of initial factor value sequences and multiple groups of initial node duration sequences from the historical construction data according to a preset number of initial influencing factors.

[0102] It can be understood that the initial number of influencing factors refers to the number of influencing factors used when extracting construction data from the historical construction data. The initial factor value sequence refers to a sequence of specific values ​​corresponding to each influencing factor extracted from the historical construction data. The initial node duration sequence refers to a sequence of the remaining completion time of each engineering node corresponding to the initial factor value sequence.

[0103] In detail, the number of groups of the initial factor value sequence is p, and the number of initial influencing factors is q.

[0104] For example, when the initial number of influencing factors is 6, the first group of initial factor value sequences are: the construction progress of the project after 1 year of construction is 40%, the project cycle is 2 years, the number of construction personnel is 500, the weather is poor (can be assigned a value of 1), the construction temperature is 30°C, and the soil geology is good (can be assigned a value of 2); the second group of initial factor value sequences are: the construction progress of the project after 1 year and 10 days of construction is 42%, the project cycle is 2 years, the number of construction personnel is 509, the weather is good (assigned a value of 2), the construction temperature is 28°C, and the soil geology is good (assigned a value of 2), and so on. The value time intervals of each group of initial factor value sequences can be fixed, which is 10 days here.

[0105] Furthermore, when the time interval for taking values ​​is 10 days, the initial node duration sequence of the taking value time points of the first group of initial factor value sequences can be: the remaining completion time of the foundation pit excavation project node is 2 months, the remaining completion time of the drainage pipe installation project node is 3 months, the remaining completion time of the roadbed filling project node is 6 months, the remaining completion time of the pavement structure construction project node is 8 months, and so on; the initial node duration sequence of the taking value time points of the second group of initial factor value sequences can be: the remaining completion time of the foundation pit excavation project node is 50 days, the remaining completion time of the drainage pipe installation project node is 80 days, the remaining completion time of the roadbed filling project node is 170 days, the remaining completion time of the pavement structure construction project node is 230 days, and so on.

[0106] S3. Using a pre-constructed vector angle formula, the initial factor vector of each initial influencing factor in the initial influencing factor sequence is calculated according to the initial factor value sequence to obtain an initial factor vector sequence.

[0107] It is understandable that the initial factor vector refers to a unit vector representing the value of the initial influencing factor. The magnitude of the initial influencing factor value can be represented by the direction of the unit vector. The initial factor vector sequence is a sequence of the initial factor vectors arranged in a certain order.

[0108] It can be understood that the vector angle formula is as follows:

[0109]

[0110] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i It represents the initial factor value of the i-th initial influencing factor, and i represents the sequence number of the initial influencing factor.

[0111] It can be explained that when the initial influencing factor is the construction progress, the value range of the influencing factor can be [0, 100%]; when the initial influencing factor is the project cycle, the value range of the influencing factor can be [0, 10 years]; when the initial influencing factor is the construction personnel, the value range of the influencing factor can be [1, 1000]. When the construction progress of the project at a certain moment in the construction process is 40%, the initial factor vector angle of the construction progress is 90°×0.4, and the specific calculation method is When the construction progress of the project is 40%, the factor vector angle of the corresponding initial factor vector is 36°, so the construction progress of the project is converted into the factor vector angle to facilitate subsequent analysis.

[0112] S4. Construct an initial factor connection vector in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence.

[0113] Furthermore, the original construction period prediction coordinate model refers to a coordinate axis storing each historical construction data in the form of an initial factor vector, and the vertical axis of the original construction period prediction coordinate model represents the remaining time of the project node. The initial factor connection vector refers to a vector that completes the vector expansion after connecting each initial factor vector end to end according to the order of the initial factor vectors in the initial factor vector sequence.

[0114] In the embodiment of the present invention, the initial factor connection vector is constructed in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence, including:

[0115] According to the initial number of influencing factors and the preset unit vector modulus, the negative value of the vertical axis is calculated using the pre-constructed starting point longitudinal distance formula, wherein the starting point longitudinal distance formula is as follows:

[0116] y=q×l

[0117] Among them, y represents the negative value of the vertical axis, l represents the unit vector modulus, and q represents the number of initial influencing factors;

[0118] Determine the starting point coordinates of the vector in the original construction period prediction coordinate model according to the negative value of the vertical axis, wherein the starting point coordinates of the vector are (0, -y);

[0119] extracting a first factor vector from the initial factor vector sequence;

[0120] Identify a first vector end point of the first factor vector and a next factor vector, and extract a next vector start point of the next factor vector, wherein the first vector end point refers to the vector end point of the first factor vector, and the next vector start point refers to the vector start point of the next factor vector;

[0121] The next factor vector is translated according to the next vector starting point and the first vector end point to obtain an iterative connection vector, wherein the next vector starting point in the iterative connection vector coincides with the first vector end point;

[0122] Identifying a vector connection number of the iterative connection vector, and determining whether the vector connection number is equal to the initial number of influencing factors;

[0123] If the vector connection number is not equal to the initial influencing factor number, the first factor vector is updated using the next factor vector, and the process returns to the step of identifying the first vector end point and the next factor vector of the first factor vector;

[0124] If the vector connection number is equal to the initial influencing factor number, a target connection vector is obtained;

[0125] Identify the starting point of the target connection vector, translate the target connection vector according to the starting point of the connection vector and the coordinates of the vector starting point, and obtain the original factor connection vector, wherein the starting point of the connection vector in the original factor connection vector coincides with the coordinates of the vector starting point;

[0126] The original factor connection vector is vector-scaled to obtain the initial factor connection vector.

[0127] It can be understood that the modulus of the unit vector can be 1, and the negative value of the vertical axis refers to the distance value from the starting point of the initial factor connection vector to the origin of the original construction period prediction coordinate model. The first factor vector refers to the initial factor vector with sequence number 1 in the initial factor vector sequence. The next factor vector refers to the next initial factor vector of the first factor vector in the initial factor vector sequence. The vector connection number refers to the number of initial factor vectors in the iterative connection vector. The original factor connection vector refers to the target connection vector whose starting point of the connection vector is located at the vector starting point coordinate, and the vector scaling refers to scaling the modulus of each initial factor vector in the original factor connection vector.

[0128] Furthermore, the vector scaling of the original factor connection vector to obtain the initial factor connection vector includes:

[0129] Identify the connection vector end point of the original factor connection vector and the positive horizontal axis of the original construction period prediction coordinate model;

[0130] Determine whether the end point of the connection vector is on the positive horizontal axis of the original construction period prediction coordinate model;

[0131] If the end point of the connection vector is not on the positive horizontal axis of the original construction period prediction coordinate model, constructing the original factor synthesis vector according to the starting point and the end point of the connection vector;

[0132] The telescopic distance between the original factor synthesis vector and the positive horizontal axis is calculated, and the telescopic ratio is calculated using a pre-constructed telescopic formula according to the telescopic distance and the original factor synthesis vector, wherein the telescopic formula is as follows:

[0133]

[0134] Among them, k represents the scaling ratio, represents the original factor synthesis vector, d is equal to the telescopic distance, represents a unit synthetic vector, the unit synthetic vector has the same vector direction as the original factor synthetic vector, |*| represents the modulus symbol, and the stretching distance is a negative number or a positive number;

[0135] scaling each initial factor vector in the initial factor vector sequence according to the scaling ratio to obtain a scaled factor vector sequence;

[0136] An initial factor concatenation vector is constructed according to the scaling factor vector sequence.

[0137] It can be explained that the end point of the connection vector refers to the vector end point of the initial factor connection vector, the positive horizontal axis refers to the positive part of the horizontal axis of the original construction period forecast coordinate model (that is, the horizontal axis to the right of the origin), and the original factor composite vector refers to a single vector obtained by taking the starting point and the end point of the connection vector as the vector starting point and the vector end point respectively. The telescopic distance refers to the distance between the vector end point of the original factor composite vector and the positive horizontal axis in the direction of the original factor composite vector. For example: when the vector end point of the original factor composite vector is (50, -10), and the vector direction of the original factor composite vector is 45°, the telescopic distance is When the vector end point of the original factor synthesis vector is (50, 10) and the vector direction of the original factor synthesis vector is 45°, the telescopic distance is also The unit composite vector refers to a unit vector having the same direction as the vector of the original factor composite vector.

[0138] It can be understood that the method of constructing the initial factor connection vector according to the scaling factor vector sequence is consistent with the method of constructing the initial factor connection vector according to the initial factor vector sequence, which will not be described in detail here.

[0139] S5. Identify the positive horizontal axis reference intersection of the initial factor connection vector, extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence in sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model based on the positive horizontal axis reference intersection and the target node duration.

[0140] It can be understood that the positive horizontal axis reference intersection refers to the intersection of the initial factor connection vector and the positive horizontal axis, that is, the vector end point of the initial factor connection vector. The target node duration refers to a single initial node duration in the initial node duration sequence corresponding to the initial factor connection vector. Since there are multiple groups of initial node durations in a set of initial factor value sequences, the initial factor connection vector has multiple groups of initial duration coordinate points.

[0141] In the embodiment of the present invention, the determining of multiple groups of initial construction period coordinate points in the original construction period prediction coordinate model according to the positive horizontal axis reference intersection point and the target node construction period includes:

[0142] Determine the initial construction period coordinate point in the original construction period prediction coordinate model according to the forward horizontal axis reference intersection point and the target node construction period;

[0143] The initial construction period coordinate points of the construction period of each target node are summarized to obtain multiple groups of initial construction period coordinate points of the positive horizontal axis reference intersection points.

[0144] It can be understood that the horizontal coordinate of the initial construction period coordinate point is the positive horizontal axis reference intersection, and the vertical coordinate is the target node construction period. Since the horizontal coordinates of multiple groups of initial construction period coordinate points are all positive horizontal axis reference intersections, all initial construction period coordinate points are arranged vertically, and each group of initial construction period coordinate points corresponds to the remaining completion time of a project node.

[0145] S6. Summarize the initial factor connection vectors, the positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences to obtain a target construction period prediction coordinate model.

[0146] It can be understood that the target construction period prediction coordinate model refers to a coordinate model that establishes the correspondence between multiple groups of initial factor connection vectors and positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points, and the number of such correspondences is the same as the number of groups of the initial factor value sequence.

[0147] S7. Acquire the current BIM construction model, extract current construction data from the current BIM construction model, and extract a current factor value sequence from the current construction data according to the initial influencing factor sequence.

[0148] It is understandable that the current BIM construction model refers to a BIM model constructed based on the current municipal road construction data. The BIM model is a prior art and will not be described in detail here. The current construction data refers to the construction data of the current municipal road construction project. The current construction data includes: a current set of influencing factors, a current set of factor values, and multiple sets of current node durations. The current factor value sequence refers to the numerical sequence obtained after sorting the current factor value set. For example, the current factor value sequence can be: the construction progress is 40%, the project cycle is 2 years, the construction personnel are 500, the weather is poor (can be assigned a value of 1), the construction temperature is 30°C, the soil geology is good (can be assigned a value of 2), and the current construction data can be construction data one year from the start of construction, and so on.

[0149] S8. According to the current factor value sequence, the target duration prediction coordinate model is used to predict multiple groups of current node durations, and the current prediction accuracy is calculated according to the multiple groups of current node durations.

[0150] It can be understood that the multiple groups of current node durations refer to multiple groups of node durations matched in the target duration prediction coordinate model according to the current factor value sequence. The current prediction accuracy refers to the prediction accuracy of the current node duration.

[0151] In the embodiment of the present invention, the method of predicting the duration of multiple current nodes according to the current factor value sequence and using the target duration prediction coordinate model includes:

[0152] Constructing a current factor vector sequence according to the current factor value sequence, and constructing a current factor connection vector according to the current factor vector sequence;

[0153] Taking the initial factor connection vectors corresponding to each group of initial factor value sequences in the target construction period prediction coordinate model as the to-be-compared factor connection vectors, and obtaining a to-be-compared factor connection vector set;

[0154] Extracting the connection vectors of the factors to be compared in sequence from the connection vector set of the factors to be compared, and identifying the positive horizontal axis comparison intersection points of the connection vectors of the factors to be compared;

[0155] Identify the current intersection point of the positive horizontal axis of the current factor connection vector;

[0156] Determine whether the current factor connection vector and the to-be-compared factor connection vector have a mid-section intersection;

[0157] If there is a mid-section intersection between the current factor connection vector and the to-be-compared factor connection vector, the mid-section intersections are summarized to obtain a connection vector intersection set;

[0158] Constructing a connection vector polygon set according to the vector starting point coordinates, the connection vector intersection set, the positive horizontal axis comparison intersection point and the positive horizontal axis current intersection point;

[0159] Calculating the total area of ​​the connection vector polygon set, and using the total area as the connection vector difference value;

[0160] If the current factor connection vector and the to-be-compared factor connection vector do not have a mid-section intersection, construct a connection vector polygon according to the vector starting point coordinates, the positive horizontal axis comparison intersection and the positive horizontal axis current intersection;

[0161] Calculating the area of ​​the connection vector polygon and using the area as the connection vector difference value;

[0162] Summarizing the connection vector difference values ​​of the connection vectors of all factors to be compared to obtain a connection vector difference value set;

[0163] Extracting a minimum connection vector difference value from the connection vector difference value set, and identifying a similarity factor connection vector corresponding to the minimum connection vector difference value;

[0164] The similar intersection points on the positive horizontal axis of the similar factor connection vectors are identified, and multiple groups of current node durations corresponding to the similar intersection points on the positive horizontal axis are extracted.

[0165] It can be understood that the current factor vector sequence refers to the factor vector sequence representing the current factor value sequence. The current factor connection vector refers to the vector obtained by connecting the current factor vectors end to end according to the order of the current factor vectors in the current factor vector sequence. The positive horizontal axis comparison intersection refers to the intersection of the connection vector of the factor to be compared and the positive horizontal axis, that is, the vector end point of the connection vector of the factor to be compared. The positive horizontal axis current intersection refers to the intersection of the current factor connection vector and the positive horizontal axis, that is, the vector end point of the current factor connection vector.

[0166] Furthermore, there may be two situations where the current factor connection vector intersects with the connection vector of the factor to be compared. The first is that only the current factor connection vector intersects with the vector starting point (vector starting point coordinate) of the connection vector of the factor to be compared. The second is that not only the vector starting points intersect, but the current factor connection vector also intersects with the connection vector of the factor to be compared at other positions. The middle section intersection refers to the vector middle section intersection (non-vector starting point coordinate) of the current factor connection vector and the connection vector of the factor to be compared. Therefore, the connection vector polygon set refers to the set of polygons enclosed by the current factor connection vector and the connection vector of the factor to be compared. The connection vector difference value refers to the degree of vector difference between the current factor connection vector and the connection vector of the factor to be compared. The similar factor connection vector refers to the connection vector of the factor to be compared that has the smallest connection vector difference value with the current factor connection vector in the connection vector set of the factors to be compared. The positive horizontal axis similarity intersection refers to the vector end point of the similar factor connection vector (the intersection of the similar factor connection vector and the positive horizontal axis).

[0167] In the embodiment of the present invention, the current prediction accuracy is calculated according to multiple groups of current node durations, including:

[0168] Extracting a cluster of adjacent predicted node durations from the multiple groups of current node durations according to a preset duration threshold;

[0169] Sequentially extracting the upcoming prediction node durations in the upcoming prediction node duration cluster;

[0170] Obtaining the actual node duration corresponding to the adjacent predicted node duration;

[0171] According to the preset accuracy formula, the current prediction accuracy is calculated using the upcoming predicted node duration and the actual node duration, wherein the accuracy formula is as follows:

[0172]

[0173] Among them, ε represents the current prediction accuracy, α represents the accuracy node working period weight, t 1 Indicates the duration of the first upcoming forecast node, t' 1 represents the actual node duration corresponding to the first adjacent predicted node duration, t j represents the duration of the jth nearest forecast node, t' j represents the actual node duration corresponding to the jth predicted node duration, t J represents the duration of the Jth nearest predicted node, t' J It represents the actual node duration corresponding to the Jth adjacent predicted node duration, j represents the sequence number of the adjacent predicted node duration, and J represents the total number of adjacent predicted node durations.

[0174] It is understandable that the construction period threshold refers to a preset period for extracting the construction period of the adjacent prediction node, for example, it can be 5 months. The adjacent prediction node construction period cluster refers to a set of adjacent prediction node construction periods. The adjacent prediction node construction period refers to the current node construction period in multiple groups of current node construction periods whose time difference with the current time is less than or equal to the construction period threshold. For example, when the multiple groups of current node construction periods are: the remaining completion time of the foundation pit excavation project node is 2 months; the remaining completion time of the drainage pipe installation project node is 3 months; the remaining completion time of the roadbed filling project node is 6 months; the remaining completion time of the pavement structure construction project node is 8 months, the adjacent prediction node construction period cluster is: the remaining completion time of the foundation pit excavation project node is 2 months; the remaining completion time of the drainage pipe installation project node is 3 months.

[0175] Furthermore, the actual node duration refers to the difference between the actual completion time of the engineering node corresponding to the near predicted node duration and the current time. The current prediction accuracy refers to the prediction accuracy of the near predicted node duration cluster.

[0176] S9: Determine whether the current prediction accuracy is greater than a preset prediction accuracy threshold.

[0177] If the current prediction accuracy is not greater than the prediction accuracy threshold, S10 is executed to update the initial number of influencing factors.

[0178] It is understandable that when the current prediction accuracy is not greater than the prediction accuracy threshold, it means that the prediction accuracy of the target construction period prediction coordinate model is unqualified, so it is necessary to adjust the target construction period prediction coordinate model in real time. Since the larger the number of initial influencing factors, the more initial factor values ​​need to be obtained, and the more initial factor values ​​are obtained, the more data affecting the current construction progress is obtained. When more data affecting the current construction progress is obtained, the current prediction accuracy will also be improved. Therefore, when it is necessary to improve the current prediction accuracy, it is only necessary to increase the number of initial influencing factors according to certain rules, so it is necessary to update the number of initial influencing factors. The number of groups of the initial factor value sequence is generally fixed, that is, according to the standard that the time interval of the value of each group of initial factor value sequence is 10 days. Of course, the number of groups of the initial factor value sequence can also be expanded, that is, the time interval of the value of each group of initial factor value sequence is shortened to increase the amount of data and improve the current prediction accuracy. I will not go into details here.

[0179] Furthermore, the updating method of the initial influencing factors may be to reclassify the initial influencing factors, for example, the number of construction personnel may be reclassified into the number of technicians, the number of material transport personnel, etc., and the weather may be reclassified into sunny days, cloudy days, and rainy days, etc., so as to refine the initial influencing factors and make the data affecting the construction progress more refined, so as to improve the current prediction accuracy. It is also possible to extract multiple construction influencing factors from the pre-built construction influencing factor set.

[0180] In the embodiment of the present invention, the updating of the initial number of influencing factors includes:

[0181] According to the current prediction accuracy and the prediction accuracy threshold, the initial factor update number is calculated using a pre-built update formula, wherein the update formula is as follows:

[0182]

[0183] Among them, q' represents the initial factor update number, e represents the natural constant, β represents the accuracy adjustment index, δ represents the prediction accuracy threshold, q represents the initial number of influencing factors, and ε represents the current prediction accuracy;

[0184] The initial number of influencing factors is updated using the initial factor update number.

[0185] Return to the above steps to obtain the historical construction dataset.

[0186] It is understandable that after the initial number of influencing factors is updated, it is necessary to return to the step of obtaining the historical construction data set and perform prediction again.

[0187] If the current prediction accuracy is greater than the prediction accuracy threshold, S11 is executed to determine whether the multiple groups of current node durations meet the preset standard node duration set.

[0188] It can be explained that the standard node duration set refers to the standard remaining completion time of each current engineering node. For example, when the municipal road project has been started for 3 months, the construction period of the foundation pit excavation engineering node is 4 months, and the construction period of the drainage pipe installation engineering node is 5 months, then the standard node construction period of the foundation pit excavation engineering node is 1 month, and the standard node construction period of the drainage pipe installation engineering node is 2 months.

[0189] If the multiple groups of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model.

[0190] It is understandable that if multiple sets of current node durations meet the standard node duration set, there is no need to adjust the construction resources and re-acquire the current BIM construction model.

[0191] If the multiple groups of current node durations do not conform to the standard node duration set, S12 is executed to identify the target factor connection vector in the target duration prediction coordinate model, and to adjust the construction resources according to the target factor connection vector.

[0192] It is understandable that the target factor connection vector refers to the initial factor connection vector selected by the user that best matches the current construction progress. The construction resource adjustment refers to adjusting the values ​​of the influencing factors in the current construction process, for example: increasing the number of construction personnel by 59, extending the daily construction time by 5 hours, etc. It should be noted that some initial factor values ​​cannot be adjusted, such as: construction progress, weather, soil geology, etc.

[0193] In the embodiment of the present invention, the step of identifying the target factor connection vector in the target construction period prediction coordinate model includes:

[0194] Receiving multiple sets of node duration time limits input by a user, and selecting multiple initial duration coordinate sets in the target duration prediction coordinate model according to the multiple sets of node duration time limits;

[0195] Identify a plurality of undetermined factor connection vectors corresponding to the plurality of initial duration coordinate groups;

[0196] A target factor connection vector selected by a user from among the plurality of pending factor connection vectors is received.

[0197] It is understandable that the node construction period refers to the completion time set for each engineering node before construction. The multiple initial construction period coordinate groups refer to multiple initial node construction period sequences that meet the multiple groups of node construction period time limits. The undetermined factor connection vector refers to the initial factor connection vector corresponding to the initial construction period coordinate group.

[0198] In the embodiment of the present invention, the construction resource adjustment according to the target factor connection vector includes:

[0199] Extracting target factor vectors in sequence from the target factor connection vector, calculating target factor values ​​corresponding to the target factor vectors, and obtaining a target factor value set;

[0200] The construction resources are adjusted according to the target factor value set.

[0201] It can be understood that the target factor value refers to the initial factor value corresponding to the target factor vector, for example: there are 100 construction workers, the construction time is 15 hours per day, etc. Finally, the construction resources can be adjusted according to the target factor value set.

[0202] The present invention is to solve the problem described in the background technology. The present invention determines whether to adjust the construction resources by constructing a target construction period prediction coordinate model to determine whether multiple groups of current node construction periods meet the standard node construction period set. When the construction resources need to be adjusted, the target factor connection vector is identified in the target construction period prediction coordinate model, and the construction resources can be adjusted according to the target factor connection vector. When constructing the target construction period prediction coordinate model, it is constructed based on the historical construction data set, so it is necessary to obtain the historical construction data set first. Since each historical construction data is independent, it is necessary to extract the historical construction data in sequence from the historical construction data set, and then select the initial influencing factor sequence, multiple groups of influencing factors from the historical construction data according to the preset initial influencing factor number. Initial factor value sequence and multiple groups of initial node duration sequences. Since the initial factor value sequence needs to be converted into an initial factor vector sequence, the pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain the initial factor vector sequence. Then, the initial factor connection vector is constructed in the pre-constructed original duration prediction coordinate model according to the initial factor vector sequence. At this time, the target duration prediction coordinate model can be constructed. First, the positive horizontal axis reference intersection of the initial factor connection vector needs to be identified, and the target node duration corresponding to the positive horizontal axis reference intersection is extracted in turn in the initial node duration sequence. Then, the target node duration corresponding to the positive horizontal axis reference intersection is extracted according to the positive horizontal axis reference intersection and the target node duration. The construction period determines multiple groups of initial construction period coordinate points in the original construction period prediction coordinate model, and finally summarizes the initial factor connection vectors, positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences. The target construction period prediction coordinate model is constructed according to the corresponding relationship between the initial factor connection vectors, the positive horizontal axis reference intersection points and the multiple groups of initial construction period coordinate points. Since the current prediction accuracy of the target construction period prediction coordinate model needs to be evaluated, when judging whether the current prediction accuracy is qualified, the current BIM construction model can be obtained first, and the current construction data can be extracted from the current BIM construction model. Then, the current factor value sequence can be extracted from the current construction data according to the initial influencing factor sequence. At this time, the target construction period prediction coordinate model can be obtained according to the corresponding relationship between the initial factor connection vectors, the positive horizontal axis reference intersection points and the multiple groups of initial construction period coordinate points. Take a value sequence, use the target construction period prediction coordinate model to predict multiple groups of current node construction periods, and then calculate the current prediction accuracy based on the multiple groups of current node construction periods. If the current prediction accuracy is not greater than the prediction accuracy threshold, it means that the current prediction accuracy of the target construction period prediction coordinate model is unqualified, and it is necessary to update the initial number of influencing factors and return to the step of obtaining the historical construction data set. If the current prediction accuracy is greater than the prediction accuracy threshold, it means that the current prediction accuracy of the target construction period prediction coordinate model is qualified. At this time, it can be determined whether the multiple groups of current node construction periods meet the standard node construction period set. If the multiple groups of current node construction periods meet the standard node construction period set, then return to the above step of obtaining the current BIM construction model. If the multiple groups of current node construction periods do not meet the standard node construction period set,Then, the target factor connection vector is identified in the target construction period prediction coordinate model, and the construction resources are adjusted according to the target factor connection vector. Therefore, the present invention can solve the problems of high risk of construction progress delay and low prediction accuracy in the current municipal road construction process.

[0203] like Figure 2 , which is a functional module diagram of a BIM-based municipal road construction schedule coordination management device provided by an embodiment of the present invention.

[0204] The BIM-based municipal road construction period coordination management device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the BIM-based municipal road construction period coordination management device 100 can include an initial factor connection vector construction module 101, a target period prediction coordinate model construction module 102, a plurality of current node period standardization judgment modules 103 and a construction resource adjustment module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0205] The initial factor connection vector construction module 101 is used to obtain a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set; select an initial influencing factor sequence, multiple groups of initial factor value sequences, and multiple groups of initial node duration sequences from the historical construction data according to a preset number of initial influencing factors, wherein the number of groups of the initial factor value sequence is p, and the number of initial influencing factors is q; use a pre-constructed vector angle formula to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence, and obtain an initial factor vector sequence, wherein the vector angle formula is as follows:

[0206]

[0207] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i represents the initial factor value of the i-th initial influencing factor, i represents the sequence number of the initial influencing factor; constructs the initial factor connection vector in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence;

[0208] The target duration prediction coordinate model building module 102 is used to identify the positive horizontal axis reference intersection of the initial factor connection vector, sequentially extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration; summarize the initial factor connection vectors, positive horizontal axis reference intersections and multiple groups of initial duration coordinate points corresponding to each group of initial factor value sequences to obtain the target duration prediction coordinate model;

[0209] The module 103 for judging the standardization of the multiple sets of current node durations is used to obtain the current BIM construction model, extract the current construction data from the current BIM construction model, and extract the current factor value sequence from the current construction data according to the initial influencing factor sequence; predict the multiple sets of current node durations using the target duration prediction coordinate model according to the current factor value sequence, and calculate the current prediction accuracy according to the multiple sets of current node durations; judge whether the current prediction accuracy is greater than a preset prediction accuracy threshold; if the current prediction accuracy is not greater than the prediction accuracy threshold, update the initial number of influencing factors and return to the above step of obtaining the historical construction data set; if the current prediction accuracy is greater than the prediction accuracy threshold, judge whether the multiple sets of current node durations meet the preset standard node duration set; if the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model;

[0210] The construction resource adjustment module 104 is used to identify the target factor connection vector in the target construction period prediction coordinate model if the multiple groups of current node construction periods do not meet the standard node construction period set, and adjust the construction resources according to the target factor connection vector.

[0211] In detail, each module in the BIM-based municipal road construction schedule coordination management device 100 in the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means are the same as the BIM-based municipal road construction schedule coordination management method described in, and can produce the same technical effects, so I will not go into details here.

[0212] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a method for coordinating and managing the construction schedule of a municipal road based on BIM, provided by one embodiment of the present invention.

[0213] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a BIM-based municipal road construction schedule coordination management method program.

[0214] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 can be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 can also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the municipal road construction period coordination management method program based on BIM, but also can be used to temporarily store data that has been output or is to be output.

[0215] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the municipal road construction schedule coordination management method program based on BIM, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0216] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0217] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0218] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0219] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0220] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0221] The BIM-based municipal road construction schedule coordination management method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0222] Acquire a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set;

[0223] According to a preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is p and the number of the initial influencing factors is q;

[0224] The pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain an initial factor vector sequence, wherein the vector angle formula is as follows:

[0225]

[0226] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i represents the initial factor value of the i-th initial influencing factor, where i represents the sequence number of the initial influencing factor;

[0227] Constructing an initial factor connection vector in a pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence;

[0228] Identify the positive horizontal axis reference intersection of the initial factor connection vector, extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration;

[0229] Summarize the initial factor connection vectors, the positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences to obtain the target construction period prediction coordinate model;

[0230] Acquire a current BIM construction model, extract current construction data from the current BIM construction model, and extract a current factor value sequence from the current construction data according to the initial influencing factor sequence;

[0231] According to the current factor value sequence, the target construction period prediction coordinate model is used to predict multiple groups of current node construction periods, and the current prediction accuracy is calculated according to the multiple groups of current node construction periods;

[0232] Determining whether the current prediction accuracy is greater than a preset prediction accuracy threshold;

[0233] If the current prediction accuracy is not greater than the prediction accuracy threshold, the initial number of influencing factors is updated and the process returns to the above step of obtaining the historical construction data set;

[0234] If the current prediction accuracy is greater than the prediction accuracy threshold, determining whether the multiple sets of current node durations meet a preset set of standard node durations;

[0235] If the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model;

[0236] If the multiple sets of current node durations do not meet the standard node duration set, the target factor connection vector is identified in the target duration prediction coordinate model, and the construction resources are adjusted according to the target factor connection vector. Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0237] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0238] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0239] Acquire a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set;

[0240] According to a preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is p and the number of the initial influencing factors is q;

[0241] The pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain an initial factor vector sequence, wherein the vector angle formula is as follows:

[0242]

[0243] Among them, θ i Indicates the factor vector angle of the i-th initial influencing factor, u i represents the influence factor range of the i-th initial influence factor, x i represents the initial factor value of the i-th initial influencing factor, where i represents the sequence number of the initial influencing factor;

[0244] Constructing an initial factor connection vector in a pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence;

[0245] Identify the positive horizontal axis reference intersection of the initial factor connection vector, extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration;

[0246] Summarize the initial factor connection vectors, the positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences to obtain the target construction period prediction coordinate model;

[0247] Acquire a current BIM construction model, extract current construction data from the current BIM construction model, and extract a current factor value sequence from the current construction data according to the initial influencing factor sequence;

[0248] According to the current factor value sequence, the target construction period prediction coordinate model is used to predict multiple groups of current node construction periods, and the current prediction accuracy is calculated according to the multiple groups of current node construction periods;

[0249] Determining whether the current prediction accuracy is greater than a preset prediction accuracy threshold;

[0250] If the current prediction accuracy is not greater than the prediction accuracy threshold, the initial number of influencing factors is updated and the process returns to the above step of obtaining the historical construction data set;

[0251] If the current prediction accuracy is greater than the prediction accuracy threshold, determining whether the multiple sets of current node durations meet a preset set of standard node durations;

[0252] If the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model;

[0253] If the multiple groups of current node durations do not conform to the standard node duration set, a target factor connection vector is identified in the target duration prediction coordinate model, and construction resources are adjusted according to the target factor connection vector.

[0254] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the above-described apparatus embodiments are only illustrative, and actual implementation may have other division methods.

[0255] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0256] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0257] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0258] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for coordinating and managing the construction period of municipal roads based on BIM, characterized in that: The method comprises: Acquire a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set; According to the preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is , the initial number of influencing factors is ; The pre-constructed vector angle formula is used to calculate the initial factor vector of each initial influencing factor in the initial influencing factor sequence according to the initial factor value sequence to obtain an initial factor vector sequence, wherein the vector angle formula is as follows: in, represents the factor vector angle of the i-th initial influencing factor, represents the influencing factor value range of the i-th initial influencing factor, represents the initial factor value of the i-th initial influencing factor, where i represents the sequence number of the initial influencing factor; Constructing an initial factor connection vector in a pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence; Identify the positive horizontal axis reference intersection of the initial factor connection vector, extract the target node durations corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration; Summarize the initial factor connection vectors, the positive horizontal axis reference intersection points and multiple groups of initial construction period coordinate points corresponding to each group of initial factor value sequences to obtain the target construction period prediction coordinate model; Acquire a current BIM construction model, extract current construction data from the current BIM construction model, and extract a current factor value sequence from the current construction data according to the initial influencing factor sequence; According to the current factor value sequence, the target construction period prediction coordinate model is used to predict multiple groups of current node construction periods, and the current prediction accuracy is calculated according to the multiple groups of current node construction periods; Determining whether the current prediction accuracy is greater than a preset prediction accuracy threshold; If the current prediction accuracy is not greater than the prediction accuracy threshold, the initial number of influencing factors is updated and the process returns to the above step of obtaining the historical construction data set; If the current prediction accuracy is greater than the prediction accuracy threshold, determining whether the multiple sets of current node durations meet a preset set of standard node durations; If the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model; If the multiple groups of current node durations do not conform to the standard node duration set, identifying the target factor connection vector in the target duration prediction coordinate model, and adjusting the construction resources according to the target factor connection vector; The initial factor connection vector is constructed in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence, including: According to the initial number of influencing factors and the preset unit vector modulus, the negative value of the vertical axis is calculated using the pre-constructed starting point longitudinal distance formula, wherein the starting point longitudinal distance formula is as follows: in, Indicates negative values ​​on the vertical axis. represents the unit vector modulus, represents the initial number of influencing factors; Determine the starting point coordinates of the vector in the original construction period prediction coordinate model according to the negative value of the vertical axis, wherein the starting point coordinates of the vector are (0, -y); extracting a first factor vector from the initial factor vector sequence; Identify a first vector end point of the first factor vector and a next factor vector, and extract a next vector start point of the next factor vector, wherein the first vector end point refers to the vector end point of the first factor vector, and the next vector start point refers to the vector start point of the next factor vector; The next factor vector is translated according to the next vector starting point and the first vector end point to obtain an iterative connection vector, wherein the next vector starting point in the iterative connection vector coincides with the first vector end point; Identifying a vector connection number of the iterative connection vector, and determining whether the vector connection number is equal to the initial number of influencing factors; If the vector connection number is not equal to the initial influencing factor number, the first factor vector is updated using the next factor vector, and the process returns to the step of identifying the first vector end point and the next factor vector of the first factor vector; If the vector connection number is equal to the initial influencing factor number, a target connection vector is obtained; Identify the starting point of the target connection vector, translate the target connection vector according to the starting point of the connection vector and the coordinates of the vector starting point, and obtain the original factor connection vector, wherein the starting point of the connection vector in the original factor connection vector coincides with the coordinates of the vector starting point; Perform vector expansion and contraction on the original factor connection vector to obtain the initial factor connection vector; The step of performing vector expansion and contraction on the original factor connection vector to obtain the initial factor connection vector includes: Identify the connection vector end point of the original factor connection vector and the positive horizontal axis of the original construction period prediction coordinate model; Determine whether the end point of the connection vector is on the positive horizontal axis of the original construction period prediction coordinate model; If the end point of the connection vector is not on the positive horizontal axis of the original construction period prediction coordinate model, constructing the original factor synthesis vector according to the starting point and the end point of the connection vector; The telescopic distance between the original factor synthesis vector and the positive horizontal axis is calculated, and the telescopic ratio is calculated using a pre-constructed telescopic formula according to the telescopic distance and the original factor synthesis vector, wherein the telescopic formula is as follows: in, Indicates the scaling ratio. represents the original factor synthesis vector, Equal to the telescopic distance, represents a unit synthetic vector, the unit synthetic vector has the same vector direction as the original factor synthetic vector, represents the modulus symbol, and the telescopic distance is a negative number or a positive number; scaling each initial factor vector in the initial factor vector sequence according to the scaling ratio to obtain a scaled factor vector sequence; An initial factor concatenation vector is constructed according to the scaling factor vector sequence.

2. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 1, characterized in that: The step of determining a plurality of groups of initial construction period coordinate points in the original construction period prediction coordinate model according to the positive horizontal axis reference intersection point and the target node construction period includes: Determine the initial construction period coordinate point in the original construction period prediction coordinate model according to the forward horizontal axis reference intersection point and the target node construction period; The initial construction period coordinate points of the construction period of each target node are summarized to obtain multiple groups of initial construction period coordinate points of the positive horizontal axis reference intersection points.

3. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 1, characterized in that: The method of predicting the duration of multiple current nodes according to the current factor value sequence and using the target duration prediction coordinate model comprises: Constructing a current factor vector sequence according to the current factor value sequence, and constructing a current factor connection vector according to the current factor vector sequence; Taking the initial factor connection vectors corresponding to each group of initial factor value sequences in the target construction period prediction coordinate model as the to-be-compared factor connection vectors, and obtaining a to-be-compared factor connection vector set; Extracting the connection vectors of the factors to be compared in sequence from the connection vector set of the factors to be compared, and identifying the positive horizontal axis comparison intersection points of the connection vectors of the factors to be compared; Identify the current intersection point of the positive horizontal axis of the current factor connection vector; Determine whether the current factor connection vector and the to-be-compared factor connection vector have a mid-section intersection; If there is a mid-section intersection between the current factor connection vector and the to-be-compared factor connection vector, the mid-section intersections are summarized to obtain a connection vector intersection set; Constructing a connection vector polygon set according to the vector starting point coordinates, the connection vector intersection set, the positive horizontal axis comparison intersection point and the positive horizontal axis current intersection point; Calculating the total area of ​​the connection vector polygon set, and using the total area as the connection vector difference value; If the current factor connection vector and the to-be-compared factor connection vector do not have a mid-section intersection, construct a connection vector polygon according to the vector starting point coordinates, the positive horizontal axis comparison intersection and the positive horizontal axis current intersection; Calculating the area of ​​the connection vector polygon and using the area as the connection vector difference value; Summarizing the connection vector difference values ​​of the connection vectors of all factors to be compared to obtain a connection vector difference value set; Extracting a minimum connection vector difference value from the connection vector difference value set, and identifying a similarity factor connection vector corresponding to the minimum connection vector difference value; The similar intersection points on the positive horizontal axis of the similar factor connection vectors are identified, and multiple groups of current node durations corresponding to the similar intersection points on the positive horizontal axis are extracted.

4. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 3, characterized in that: The calculating of the current prediction accuracy according to the multiple groups of current node durations includes: Extracting a cluster of adjacent predicted node durations from the multiple groups of current node durations according to a preset duration threshold; Sequentially extracting the upcoming prediction node durations in the upcoming prediction node duration cluster; Obtaining the actual node duration corresponding to the adjacent predicted node duration; According to the preset accuracy formula, the current prediction accuracy is calculated using the upcoming predicted node duration and the actual node duration, wherein the accuracy formula is as follows: in, represents the current prediction accuracy, Indicates the precision node working period weight, Indicates the duration of the first upcoming forecast node. represents the actual node duration corresponding to the first adjacent predicted node duration, represents the duration of the jth upcoming forecast node, represents the actual node duration corresponding to the jth adjacent predicted node duration, represents the duration of the Jth upcoming forecast node, It represents the actual node duration corresponding to the Jth adjacent predicted node duration, j represents the sequence number of the adjacent predicted node duration, and J represents the total number of adjacent predicted node durations.

5. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 4, characterized in that: The updating of the initial number of influencing factors includes: According to the current prediction accuracy and the prediction accuracy threshold, the initial factor update number is calculated using a pre-constructed update formula, wherein the update formula is as follows: in, represents the number of initial factor updates, represents a natural constant, Represents the precision adjustment index, represents the prediction accuracy threshold, represents the initial number of influencing factors, Indicates the current prediction accuracy; The initial number of influencing factors is updated using the initial factor update number.

6. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 3, characterized in that: The step of identifying the target factor connection vector in the target construction period prediction coordinate model includes: Receiving multiple sets of node duration time limits input by a user, and selecting multiple initial duration coordinate sets in the target duration prediction coordinate model according to the multiple sets of node duration time limits; Identify a plurality of undetermined factor connection vectors corresponding to the plurality of initial duration coordinate groups; A target factor connection vector selected by a user from among the plurality of pending factor connection vectors is received.

7. The method for coordinating and managing the construction period of municipal roads based on BIM as claimed in claim 6, characterized in that: The construction resource adjustment according to the target factor connection vector includes: Extracting target factor vectors in sequence from the target factor connection vector, calculating target factor values ​​corresponding to the target factor vectors, and obtaining a target factor value set; The construction resources are adjusted according to the target factor value set.

8. A BIM-based municipal road construction period coordination management device using the method as claimed in claim 1, characterized in that: The device comprises: The initial factor connection vector construction module is used to obtain a historical construction data set, and extract historical construction data in the historical construction data set in sequence, wherein the historical construction data includes: a historical influencing factor set, a historical factor value set, and a historical node duration set; according to a preset number of initial influencing factors, an initial influencing factor sequence, multiple groups of initial factor value sequences, and multiple groups of initial node duration sequences are selected from the historical construction data, wherein the number of groups of the initial factor value sequence is , the initial number of influencing factors is ; Using the pre-constructed vector angle formula, the initial factor vector of each initial influencing factor in the initial influencing factor sequence is calculated according to the initial factor value sequence to obtain the initial factor vector sequence, wherein the vector angle formula is as follows: in, represents the factor vector angle of the i-th initial influencing factor, represents the influencing factor value range of the i-th initial influencing factor, represents the initial factor value of the i-th initial influencing factor, i represents the sequence number of the initial influencing factor; constructs the initial factor connection vector in the pre-constructed original construction period prediction coordinate model according to the initial factor vector sequence; The target duration prediction coordinate model building module is used to identify the positive horizontal axis reference intersection of the initial factor connection vector, sequentially extract the target node duration corresponding to the positive horizontal axis reference intersection in the initial node duration sequence, and determine multiple groups of initial duration coordinate points in the original duration prediction coordinate model according to the positive horizontal axis reference intersection and the target node duration; summarize the initial factor connection vectors, positive horizontal axis reference intersections and multiple groups of initial duration coordinate points corresponding to each group of initial factor value sequences to obtain the target duration prediction coordinate model; A module for judging the standardization of multiple sets of current node durations, used to obtain the current BIM construction model, extract the current construction data from the current BIM construction model, and extract the current factor value sequence from the current construction data according to the initial influencing factor sequence; predict multiple sets of current node durations using the target duration prediction coordinate model according to the current factor value sequence, and calculate the current prediction accuracy according to the multiple sets of current node durations; determine whether the current prediction accuracy is greater than a preset prediction accuracy threshold; if the current prediction accuracy is not greater than the prediction accuracy threshold, update the initial number of influencing factors and return to the above step of obtaining the historical construction data set; if the current prediction accuracy is greater than the prediction accuracy threshold, determine whether the multiple sets of current node durations meet the preset standard node duration set; if the multiple sets of current node durations meet the standard node duration set, return to the above step of obtaining the current BIM construction model; The construction resource adjustment module is used to identify the target factor connection vector in the target construction period prediction coordinate model if the multiple groups of current node construction periods do not meet the standard node construction period set, and adjust the construction resources according to the target factor connection vector.

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