Building engineering plan intelligent management method and system

Abstract

The invention provides a building engineering plan intelligent management method and system, and relates to the technical field of building engineering management, and the method comprises the steps: obtaining a plurality of projects, each project containing a plurality of work packages; based on the information of each project and the work package, a building engineering plan intelligent scheduling model is constructed by minimizing the total construction period and the resource consumption variance; optimizing the model through an adaptive NSGA-II optimization algorithm, and outputting a plurality of alternative scheduling schemes; according to the real-time data of the construction site, the prediction completion progress of each work package is output through the fuzzy neural network prediction model; calculating a progress deviation index of each work package, and calculating a total progress deviation index of each alternative scheduling scheme in combination with the key work package node; judging whether the minimum value in the total progress deviation indexes of the alternative scheduling schemes is greater than a threshold value or not; if yes, returning and re-executing; otherwise, executing the current alternative scheduling scheme, and completing the management of the construction engineering plan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering management, and particularly relates to a construction engineering plan intelligent management method and system. BACKGROUND

[0002] With the rapid development of the construction industry, traditional engineering management methods have been unable to meet the increasingly complex and efficient project requirements. The management of construction engineering involves a large number of resource scheduling, progress monitoring, quality control, and cost management tasks. How to reduce costs and avoid risks while ensuring quality and construction period has become an important challenge in construction project management. In order to address this problem, intelligent management methods have gradually become a core trend in construction engineering.

[0003] Currently, some intelligent technologies have been applied in the field of construction engineering management. First, real-time monitoring systems based on the Internet of Things have been widely used in construction sites. With the help of sensors, real-time monitoring of construction materials, equipment, and environmental factors is ensured to optimize resource utilization during the construction process. In addition, big data analysis technology has also been applied to project cost and progress management. By analyzing historical project data, project managers can make more accurate decisions. Second, artificial intelligence (AI) is used for progress optimization and risk prediction, which can intelligently schedule resources and predict potential schedule and cost problems based on historical data and current progress of the project.

[0004] However, existing Internet of Things and sensors can obtain a large amount of real-time data on site, but these data are often scattered among different systems and platforms, making it difficult to integrate and analyze them uniformly. This makes it impossible to quickly and accurately feedback the real-time progress of the construction site, thereby affecting the decision-making efficiency of project management. Existing progress prediction models usually rely on historical data for calculation, making it difficult to adapt to various sudden changes during the construction process, resulting in insufficient prediction accuracy. SUMMARY

[0005] In view of the above deficiencies of the prior art, the purpose of the embodiments of the present application is to provide a construction engineering plan intelligent management method, which can solve the technical problems that existing Internet of Things and sensors can obtain a large amount of real-time data on site, but these data are often scattered among different systems and platforms, making it difficult to integrate and analyze them uniformly. This makes it impossible to quickly and accurately feedback the real-time progress of the construction site, thereby affecting the decision-making efficiency of project management. Existing progress prediction models usually rely on historical data for calculation, making it difficult to adapt to various sudden changes during the construction process, resulting in insufficient prediction accuracy.

[0006] The first aspect of the embodiments of the present application proposes a construction engineering plan intelligent management method, comprising:

[0007] S1: extracting a plurality of project information about a construction project plan from a construction project plan book, each of the project information containing a plurality of work packages;

[0008] S2: constructing an intelligent scheduling model of the construction project plan based on the information of each project and work package, aiming to minimize the total duration and resource consumption variance;

[0009] S3: optimizing the intelligent scheduling model of the construction project plan by using an adaptive NSGA-II optimization algorithm, and outputting a plurality of alternative scheduling schemes of the construction project plan;

[0010] S4: determining a key work package node in the construction project plan;

[0011] S5: acquiring real-time data of a construction site;

[0012] S6: outputting a predicted completion progress of each of the work packages by using a pre-constructed fuzzy neural network prediction model according to the real-time data of the construction site;

[0013] S7: calculating a progress deviation index between the predicted completion progress of each of the work packages and a planned completion progress in the alternative scheduling scheme;

[0014] S8: calculating a total progress deviation index of each of the alternative scheduling schemes according to each of the progress deviation indexes in combination with the key work package node;

[0015] S9: judging whether a minimum value in the total progress deviation indexes of each of the alternative scheduling schemes is greater than a preset total progress deviation index; if yes, returning to step S1; otherwise, executing the alternative scheduling scheme with the minimum total progress deviation index to complete the management of the construction project plan.

[0016] The second aspect of the embodiment of the present application provides an intelligent management system of a construction project plan, comprising a processor and a memory.

[0017] The memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the intelligent management method of the construction project plan according to the first aspect.

[0018] The third aspect of the embodiment of the present application provides a readable storage medium, and the readable storage medium stores programs or instructions, and the programs or instructions are executed by a processor to implement the steps of the intelligent management method of the construction project plan according to the first aspect.

[0019] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0020] In the embodiment of the present application, by using the fuzzy neural network model, the construction progress can be predicted and the on-site sudden situation can be quickly responded by dynamically adjusting according to the real-time construction data, so that the progress prediction is no longer simply dependent on historical data, but can be adjusted in real time according to the changes on site, thereby improving the accuracy of prediction. By calculating the progress deviation index of each work package, the predicted progress is compared with the actual progress, and the weighted sum is obtained, which helps to identify the progress lag or advance problem in time, and through real-time data prediction and dynamic adjustment, the adaptability and accuracy of the progress prediction model are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0022] Figure 1 FIG. 1 is a flowchart of a building engineering plan intelligent management method according to an embodiment of the present application;

[0023] Figure 2 FIG. 2 is a structural diagram of a building engineering plan intelligent management system according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order for those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0025] The building engineering plan intelligent management method provided by the embodiments of the present application will be described in detail below in conjunction with the drawings and specific embodiments and application scenarios.

[0026] Reference is made to the accompanying drawings Figure 1 FIG. 1 is a flowchart of a building engineering plan intelligent management method according to an embodiment of the present application;

[0027] The building engineering plan intelligent management method provided by the embodiments of the present application can include the following steps:

[0028] S1: extracting a plurality of project information about the construction project plan from the construction project plan, each project containing a plurality of work packages.

[0029] Among them, the work package (Work Package) is a basic unit in project management, usually refers to the task or activity that can be independently planned, executed, monitored and controlled in the project.

[0030] In a possible implementation, the information of the work package specifically includes: the logical dependency relationship of each work package, the resource quota and the job duration.

[0031] S2: based on the information of each project and work package, constructing a construction project plan intelligent scheduling model with the goal of minimizing the total duration and resource consumption variance.

[0032] In a possible implementation, S2 specifically includes:

[0033] S201: calculating the target duration of each work package according to the earliest start time and the latest end time of each work package.

[0034] S202: determining the priority of each project.

[0035] It should be noted that the priority reflects the "management importance", rather than the "progress criticality", for example: the customer acceptance node may have high priority, but it is not necessarily a progress critical point.

[0036] S203: according to the target duration of each work package and the priority of each project, the total duration target function is obtained by weighted summation:

[0037]

[0038] Among them, f1 represents the total duration target function, N represents the total number of projects, ω i represents the priority of the i-th project, D i represents the duration of the i-th project, max represents the maximum value, S i,j represents the actual start time of the j-th work package in the i-th project, d i,j represents the duration of the j-th work package in the i-th project, min represents the minimum value.

[0039] S204: calculating the average consumption of each resource according to the resource consumption of each work package.

[0040] S205: according to each average consumption, calculating the resource consumption difference of each work package to each resource in each time unit.

[0041] S206: Accumulate the square of each resource consumption difference and divide by the total duration to obtain the resource consumption variance objective function:

[0042]

[0043] wherein f2 represents the resource consumption variance objective function, D represents the total duration, M represents the total number of resource types, n i represents the total number of work packages, t represents the index of the time unit, i represents the index of the project, j represents the index of the work package, k represents the index of the resource type, r i,j,k (t) represents the consumption of the kth resource type in the jth work package of the ith project in the tth time unit, represents the average consumption of the kth resource in the entire project.

[0044] In the embodiments of the present application, by calculating the duration, priority, resource consumption and difference of the work package, the engineering scheduling plan and resource allocation can be better optimized, the resource consumption variance and total duration are minimized, resource conflicts and project schedule delays are avoided, and the smoothness of project execution is improved. At the same time, by constructing the objective function, the project manager can more accurately control the project progress and resource consumption, timely adjust the strategy, and ensure the smooth progress of the project according to the plan.

[0045] S207: Set the constraint condition.

[0046] In a possible implementation, the constraint condition specifically includes:

[0047] In each time unit, the total amount of resources consumed by each work package cannot exceed the maximum supply of the resource:

[0048] r k (t)≤R k (t),t∈[1,D],k∈[1,M]

[0049] wherein r k (t) represents the consumption of the kth resource in the tth time unit, R k (t) represents the maximum supply of the kth resource in the tth time unit, t represents the index of the time unit, D represents the total duration, and k represents the index of the resource type. M represents the total number of resource types.

[0050] The total duration cannot exceed the mandatory duration:

[0051] D≤T

[0052] wherein T represents the mandatory duration limit of the engineering project.

[0053] The start time of each work package cannot be earlier than the earliest start time:

[0054]

[0055] wherein S i,j denotes the actual start time of the jth work package in the ith project, TE i,j denotes the earliest start time of the jth work package in the ith project, denotes any symbol.

[0056] The order between the work packages complies with the logical dependency relationship, and the subsequent work package needs to start after the completion of the preceding work package:

[0057]

[0058] wherein d i,j denotes the duration of the jth work package in the ith project, S i,j+1 denotes the actual start time of the j+1th work package in the ith project.

[0059] S208: An intelligent scheduling model of the construction engineering plan is constructed according to the total duration target function, the resource consumption variance target function and the constraint conditions.

[0060] In the embodiments of the present application, the scheduling scheme of the construction engineering is ensured to comply with the realistic restrictions and requirements by setting and utilizing the constraint conditions. These constraint conditions include the supply limit of resources, the duration limit, the time constraint of work packages and the order of work packages. The introduction of these constraint conditions helps to optimize the scheduling of the construction engineering and ensures that the project can proceed smoothly and meet all necessary conditions.

[0061] S3: The intelligent scheduling model of the construction engineering plan is optimized by the adaptive NSGA-II optimization algorithm, and multiple alternative scheduling schemes of the construction engineering plan are output.

[0062] wherein the adaptive NSGA-II algorithm (Adaptive NSGA-II) is a multi-objective optimization algorithm improved on the basis of the classical NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm II). NSGA-II is one of the classical evolutionary algorithms and is widely used to solve multi-objective optimization problems. The adaptive NSGA-II introduces an adaptive mechanism on this basis, optimizes the exploration ability in the search space, and improves the efficiency and performance of the algorithm.

[0063] In a possible implementation, S3 specifically includes:

[0064] S301: The reciprocal of the sum of the total duration and the resource consumption variance is taken as the fitness function of the adaptive NSGA-II optimization algorithm.

[0065] S302: generating a random start time between the earliest start time and the latest end time of each work package according to the logical relationship between the work packages, to form a first schedule set containing multiple schedules:

[0066] S' i,j = TE i,j + (TL i,j - TE i,j - d i,j ) · rand

[0067] wherein S' i,j represents the random start time of the jthwork package in the ithproject, TE i,,j represents the earliest start time of the jthwork package in the ithproject, TL i,,j represents the latest start time of the jthwork package in the ithproject, d i,j represents the duration of the jthwork package in the ithproject, and rand represents a random number between 0 and 1.

[0068] In the embodiments of the present application, by randomly selecting the start time within the range of the earliest and latest times of each work package, multiple different scheduling schemes can be generated, thereby providing diversified options for subsequent optimization. At the same time, the randomized start time can ensure that the work package starts within the legal time interval (i.e. between the earliest start time and the latest end time). This avoids the bias that may be caused by manually setting a fixed start time, making the scheduling scheme more flexible.

[0069] S303: calculating the fitness values of the various schedules, non-dominantly sorting the various schedules in the first schedule set in descending order of the fitness values, and dividing the first schedule set into a first level, a second level and a third level.

[0070] S304: selecting individuals from the first level of the first schedule set, and using an adaptive crossover and mutation probability adjustment mechanism to perform crossover and mutation operations, to obtain a second schedule set containing multiple schedules.

[0071]

[0072] wherein P c represents the crossover probability, P c,max represents the maximum probability of the crossover operation, P c,min represents the minimum probability of the crossover operation, P m represents the mutation probability, P m,max represents the maximum probability of the mutation operation, and P m,minThe minimum probability of mutation is represented by cr, a constant is represented by gr, an algebraic adjustment factor is represented by cont, and e represents the current algebra. () This represents an exponential function.

[0073] It should be noted that in the early stages of the algorithm, the crossover and mutation probabilities are relatively high, meaning the search space can be broader. As iterations progress, the crossover probability gradually decreases, which helps the algorithm converge.

[0074] In this embodiment of the invention, as the number of algebras increases, the adaptive adjustment gradually reduces the crossover and mutation probabilities. This helps the algorithm converge to a local optimum in the later stages of the optimization process, thereby avoiding over-search and accelerating the convergence of the final solution. Lower crossover and mutation probabilities make the solution more stable in the search space, allowing for finer adjustment of the final solution. Furthermore, in multi-objective optimization, the complexity of the problem and the structure of the solution space may change as the search progresses. The adaptive crossover and mutation probability adjustment mechanism can dynamically adapt to changes in the problem, ensuring efficient optimization at each stage.

[0075] S305: Calculate the fitness value of each schedule in the second schedule set.

[0076] S306: Merge the fitness values ​​of each schedule in the second schedule set into the first level, and repeat steps S304 to S306 until the maximum number of iterations is reached, to obtain the final target schedule set.

[0077] S307: Collect multiple schedule outputs that meet the constraints of the construction project and use them as multiple alternative scheduling schemes.

[0078] Specifically, the alternative scheduling schemes include:

[0079] Work Package Scheduling: Start and End Times: Each work package has a predetermined start and end time, which are scheduled based on the overall project objectives and the dependencies between work packages. Duration: The estimated time required to complete each work package, typically determined by the complexity of the task, resource requirements, and past experience.

[0080] Work Package Order and Dependencies: Logical Dependencies: In a project, not all work packages can be executed in parallel. Some work packages must be completed before they can begin; the relationships between these work packages are usually reflected in the scheduling scheme. Prerequisites and Subsequent Tasks: Each work package may have prerequisites (tasks that must begin before it is completed) and subsequent tasks.

[0081] Resource allocation: Alternative scheduling plans will take into account the resources required for each work package (such as personnel, equipment, materials, etc.).

[0082] The alternative scheduling scheme also sets a target duration, usually estimated based on the time schedule and resource allocation of the work packages, to estimate the completion time of the overall project.

[0083] S4: Determine the critical work package nodes in the construction project plan.

[0084] The critical work package nodes refer to those work packages or task nodes that have significant impact on the overall progress, resource consumption, risk control, etc. of the project. They are the focus of project scheduling, usually requiring more attention, resource support and management intervention to ensure the smooth progress and timely delivery of the project

[0085] In one possible implementation, S4 specifically includes:

[0086] S401: Calculate the total float impact value of each work package according to the latest end time of the preceding work package and the earliest start time of the subsequent work package in the alternative scheduling scheme:

[0087]

[0088] Wherein, represents the total float impact value of the jth work package, E j represents the latest end time of the j-1th work package, S j represents the earliest start time of the j+1th work package.

[0089] S402: Calculate the resource sensitivity value of each work package according to the resource quota and resource consumption peak of each work package in the alternative scheduling scheme:

[0090]

[0091] Wherein, C j represents the resource sensitivity value of the jth work package, C d represents the resource consumption peak, C b represents the resource quota.

[0092] S403: Calculate the comprehensive critical score of each work package by combining the total float impact value and the resource sensitivity value:

[0093]

[0094] Wherein, G j represents the comprehensive critical score of the jth work package, D represents the total duration, C j represents the resource sensitivity value of the jth work package.

[0095] S404: Sort the comprehensive critical scores in descending order, and select the top pre-set number of work packages as critical work package nodes.

[0096] It should be noted that the size of the pre-set number can be set by the person skilled in the art as needed, and the present application does not limit it.

[0097] Specifically, first, according to the alternative scheduling scheme, the total float impact value of each work package is calculated, that is, the impact on the progress is evaluated by the difference between the latest end time of the preceding work package and the earliest start time of the subsequent work package. Then, according to the resource quota and consumption peak, the resource sensitivity value of each work package is calculated, reflecting the demand and dependence of the work package on resources. Then, combined with the total float impact value, the resource sensitivity value and the management priority, the comprehensive critical score of each work package is calculated, so as to measure its importance in the project; finally, the top pre-set number of work packages are selected as critical work package nodes in descending order of comprehensive score, which are crucial to the progress and resource management of the project and need to be prioritized and scheduled.

[0098] In the embodiments of the present application, by calculating the total float impact value and resource sensitivity value of each work package, those work packages that have a greater impact on project progress and resource consumption can be effectively identified. This helps to prioritize tasks that have a significant impact on overall progress and reduce the risk of delay. At the same time, by calculating the comprehensive critical score, work packages that have a significant impact on project progress, resource consumption and risk management can be identified and prioritized, ensuring that these tasks receive adequate attention and support, thereby effectively improving project success rate, reducing risk, optimizing resource allocation and ensuring timely delivery.

[0099] S5: Obtain real-time data of the construction site.

[0100] Specifically, the real-time data includes worker attendance rate: records the attendance of each worker or construction team, including the number of workers actually present and the number of absentees. Material usage: real-time tracking of material consumption at the construction site, such as the amount of cement, steel, wood, equipment, etc. Weather conditions: including temperature, humidity, precipitation, wind speed, etc. Weather is an important factor in the construction process. The current amount of work completed or progress of the construction site reflects the actual completion degree of the work package. The actual start time of each work package provides the actual start time of the work package, which helps to determine whether it is started on schedule and has an impact on subsequent progress.

[0101] S6: According to the real-time data of the construction site, the predicted completion progress of each work package is output through the pre-constructed fuzzy neural network prediction model.

[0102] It is to be noted that the predicted completion progress of the work package includes the start time and end time of each work package and the duration of the work package. It is estimated according to the current site progress, historical progress and real-time data through fuzzy logic.

[0103] Specifically, at the fuzzy inference layer, the system infers the input real-time data according to the pre-set fuzzy rules, and outputs the fuzzy prediction result of each work package. After these fuzzy results are de-fuzzified, they are converted into specific prediction progress, thereby providing a scientific decision basis for construction project management. This method can handle the uncertainty and fuzziness in the data, providing reliable support for accurately predicting the completion progress of each work package, helping managers to adjust resource allocation and optimize construction plans in a timely manner.

[0104] In the embodiment of the present application, the fuzzy neural network prediction model (Fuzzy Neural Network Prediction Model) combines the advantages of fuzzy logic and neural network, and is used to handle complex prediction problems, especially widely used in environments with high uncertainty and fuzziness. The model uses the inference mechanism of fuzzy systems and the learning ability of neural networks to handle the fuzziness and uncertainty in the data, providing an efficient solution for prediction tasks.

[0105] S7: Calculate the progress deviation index between the predicted completion progress of each work package and the planned completion progress in the alternative scheduling scheme.

[0106] The schedule deviation index is an index for measuring the difference between the actual progress and the planned progress of each work package or task in a construction project. It is used to evaluate the deviation between the actual progress and the expected planned progress during the execution of the construction project, helping project managers to discover and correct progress problems in a timely manner, and ensuring that the project can be completed on time.

[0107] In one possible implementation, S7 specifically includes:

[0108] S701: Obtain the target progress plan of each work package in each alternative scheduling scheme;

[0109] S702: Calculate the progress deviation index of each work package according to each target progress plan and the predicted completion progress:

[0110]

[0111] wherein δ j represents the progress deviation index of the jth work package, represents the start time of the jth work package in the target progress plan, denotes the start time of the jth work package in the target schedule, denotes the end time of the jth work package in the target schedule, denotes the end time of the jth work package in the predicted schedule.

[0112] Specifically, first, the target schedule of each work package in each alternative scheduling scheme is obtained. Then, according to the target schedule and the predicted completion schedule, the progress deviation index of each work package is calculated. The progress deviation index is calculated by comparing the start time and the end time in the target schedule and the predicted schedule. In this way, the progress deviation of each work package can be quantified, helping to evaluate the difference between the plan and the actual execution.

[0113] In the embodiments of the present application, by calculating the progress deviation index of the work package, the project manager can more accurately grasp the progress of each work package, so as to make timely adjustments, optimize resource allocation, identify potential risks, and evaluate the effect of different scheduling schemes. This approach brings transparency, refinement and scientificity to project progress management, making the entire project execution process more controllable.

[0114] S8: According to the progress deviation index of each work package, the total progress deviation index of each alternative scheduling scheme is calculated in combination with the key work package nodes.

[0115] In one possible implementation, the calculation method of the total progress deviation index is specifically as follows:

[0116]

[0117] wherein δ W denotes the total progress deviation index, J key denotes the number of key work package nodes, w1 denotes the weight of the key work package node, and w2 denotes the weight of the remaining work package.

[0118] Optionally, w1=0.9, w1=0.1.

[0119] In the embodiments of the present application, by calculating the progress deviation index and weighted sum to obtain the total progress deviation index, the project manager can quantify the progress health status of the project, effectively identify and correct the progress deviation. The weighting strategy enables the key work package to be given priority, thereby improving the accuracy of resource allocation. Therefore, the application of the progress deviation index not only improves the efficiency and accuracy of project scheduling, but also helps to improve the on-time delivery rate of the project, reduce risks, and optimize the decision-making process, ultimately promoting the smooth completion of the project.

[0120] S9: Determine whether the minimum value in the total progress deviation index of each alternative scheduling scheme is greater than the preset total progress deviation index. If yes, return to step S1. Otherwise, execute the alternative scheduling scheme with the minimum total progress deviation index, and complete the management of the construction project plan.

[0121] Specifically, by screening the smallest total progress deviation index, the project manager can identify the scheduling scheme that best meets the actual progress requirements. If the minimum progress deviation index is still greater than the preset target deviation index, it indicates that there is still a large deviation in the project progress, and the system will return to S1 for further scheduling optimization. Only when the minimum total progress deviation index meets the preset target, the scheme will be executed as the final scheduling scheme, ensuring that the construction project can be successfully completed according to the optimized progress. This process effectively guarantees the dynamic adjustment of project management, helps the project to maintain on the optimal progress track, and ensures timely delivery.

[0122] It should be noted that the size of the preset total progress deviation index can be set by the person skilled in the art according to actual needs, and the present application does not limit it.

[0123] In one possible implementation, after S9, it further includes:

[0124] S10: After a preset time, re-enter step S1 to update the alternative scheduling scheme.

[0125] Optionally, the preset time can be one week.

[0126] Specifically, after a period of time, the system will automatically re-enter step S1 to update the scheduling scheme. This cyclic process ensures that the project can adapt to changing actual conditions, such as resource changes, progress delays, or other unforeseen factors. By regularly updating the scheduling scheme, the system can continuously optimize the execution progress of the project, ensuring optimal progress and resource utilization efficiency throughout the project life cycle.

[0127] In the embodiments of the present application, various changes may occur during project execution, such as resource fluctuations, worker attendance rate changes, material supply delays, or weather influences. By regularly updating the scheduling scheme, the system can automatically adjust according to these changes to ensure that the project is always in the best execution state, avoiding progress lag or resource waste.

[0128] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0129] In the embodiment of the present application, by using the fuzzy neural network model, the construction progress can be predicted and quickly respond to the sudden situation on site according to the real-time construction data, which makes the progress prediction no longer simply rely on historical data, but can be adjusted in real time according to the changes on site, improving the accuracy of prediction. By calculating the progress deviation index of each work package, the predicted progress is compared with the actual progress, and the weighted sum is obtained, which helps to identify the problem of progress lag or advance in time, and through real-time data prediction and dynamic adjustment, the adaptability and accuracy of the progress prediction model are effectively improved.

[0130] With reference to the accompanying drawings Figure 2 The structure schematic diagram of the building engineering plan intelligent management system provided by the embodiment of the present application is shown.

[0131] The embodiment of the present application provides a building engineering plan intelligent management system 20, which comprises a processor 201 and a memory 202.

[0132] The memory 202 stores programs or instructions that can run on the processor 201, and the programs or instructions are executed by the processor 201 to realize the steps of the building engineering plan intelligent management method described above, and can achieve the same technical effect. To avoid repetition, the present application will not be described again.

[0133] It should be understood that the processor 201 in the embodiment of the present application can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0134] It should also be understood that the memory 202 in embodiments of the present application can be volatile or nonvolatile memory, or can include both volatile and nonvolatile memory. Nonvolatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as external cache. By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DR RAM).

[0135] The above-described embodiments can be implemented in part or in whole through software, hardware (e.g., circuitry), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When loaded and executed by a computer, the computer instructions or computer programs cause the computer to perform the processes or functions described above according to the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, such as from a website, a computer, a server, or a data center to another website, computer, server, or data center through a wired (e.g., infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium or a collection of medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more medium. The medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0136] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0137] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, apparatuses and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0139] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other manners. For example, the embodiments of the apparatus described above are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0140] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0141] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit.

[0142] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts of the technical solutions that make contributions to the prior art, or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0143] The embodiments of the present application provide a readable storage medium, which includes: a program or instructions stored on the readable storage medium, the program or instructions are executed by a processor to implement the steps of the building engineering plan intelligent management method described above, and the same technical effects can be achieved. To avoid repetition, the present application will not be described again.

[0144] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application.

Claims

1. A method for intelligent management of construction engineering plans, characterized in that, The method comprises the following steps: S1: extracting multiple project information about a construction project plan from a construction project plan book, each project containing multiple work packages; S2: constructing an intelligent scheduling model for the construction project plan based on the information of each project and work package, aiming to minimize the total duration and resource consumption variance; S3: optimizing the intelligent scheduling model for the construction project plan through an adaptive NSGA-II optimization algorithm, and outputting multiple alternative scheduling schemes for the construction project plan; S4: determining key work package nodes in the construction project plan; S5: obtaining real-time data of a construction site; S6: outputting the predicted completion progress of each work package through a pre-constructed fuzzy neural network prediction model according to the real-time data of the construction site; S7: calculating the progress deviation index between the predicted completion progress of each work package and the planned completion progress in the alternative scheduling scheme; S8: calculating the total progress deviation index of each alternative scheduling scheme according to each progress deviation index in combination with the key work package nodes; S9: determining whether the minimum value of the total progress deviation index of each alternative scheduling scheme is greater than a preset total progress deviation index; if yes, returning to step S1; otherwise, executing the alternative scheduling scheme with the minimum total progress deviation index to complete the management of the construction project plan.

2. The construction work plan intelligent management method according to claim 1, characterized in that, The information of the work package specifically comprises the logical dependency relationship, resource quota and operation duration of each work package.

3. The construction plan intelligent management method according to claim 1, wherein, The S2 specifically comprises: S201: calculating the target duration of each work package according to the earliest start time and the latest end time of each work package; S202: determining the priority of each project; S203: obtaining a total duration target function through weighted summation according to the target duration of each work package and the priority of each project; S204: calculating the average consumption of each resource according to the resource consumption of each work package; S205: calculating the resource consumption difference of each resource within each time unit according to each average consumption; S206: accumulating the square of each resource consumption difference and dividing by the total duration to obtain a resource consumption variance target function; S207: setting a constraint condition; S208: constructing the intelligent scheduling model for the construction project plan according to the total duration target function, the resource consumption variance target function and the constraint condition.

4. The construction plan intelligent management method according to claim 3, wherein, The constraint condition specifically comprises: In each time unit, the total amount of resources consumed by each work package cannot exceed the maximum supply of the resource; The total duration cannot exceed the mandatory duration; The start time of each work package cannot be earlier than the earliest start time; The order between each work package complies with the logical dependency relationship, and the subsequent work package needs to start after the preceding work package is completed.

5. The intelligent management method of construction engineering plan according to claim 1, characterized in that, The S3 specifically comprises: S301: taking the reciprocal of the sum of the total duration and the resource consumption variance as the fitness function of the adaptive NSGA-II optimization algorithm; S302: generating a random start time between the earliest start time and the latest end time of each work package according to the logical relationship between the work packages, to form a first schedule set containing multiple schedules; S303: calculating the fitness value of each schedule, non-dominantly sorting each schedule in the first schedule set in descending order of the fitness value, and dividing the first schedule set into a first level, a second level, and a third level; S304: selecting individuals from the first level of the first schedule set, and performing crossover and mutation operations using an adaptive crossover and mutation probability adjustment mechanism to obtain a second schedule set containing multiple schedules; S305: calculating the fitness value of each schedule in the second schedule set; S306: merging the fitness values of each schedule in the second schedule set into the first level, and repeating steps S304 to S306 until a maximum number of iterations is reached to stop, to obtain a final target schedule set; S307: outputting multiple schedules in the target schedule set that meet the construction engineering constraints as multiple candidate scheduling schemes.

6. The construction plan intelligent management method according to claim 1, wherein, The S4 specifically includes: S401: calculating the total float impact value of each work package according to the latest end time of the preceding work package and the earliest start time of the subsequent work package in the candidate scheduling scheme; S402: calculating the resource sensitivity value of each work package according to the resource quota and the resource consumption peak of each work package in the candidate scheduling scheme; S403: combining the total float impact value and the resource sensitivity value to calculate the comprehensive critical score of each work package; S404: sorting each comprehensive critical score in descending order, and selecting the top pre-set number of work packages as the critical work package nodes.

7. The construction plan intelligent management method according to claim 1, wherein, The S7 specifically includes: S701: obtaining the target schedule of each work package in each candidate scheduling scheme; S702: calculating the schedule deviation index of each work package according to each target schedule and the predicted completion schedule.

8. The construction plan intelligent management method according to claim 1, wherein, The calculation method of the total schedule deviation index is specifically: wherein δ W represents the total progress deviation index, J key represents the number of critical work package nodes, w1 represents the weight of the critical work package nodes, δ j represents the progress deviation index of the jth work package, and w2 represents the weight of the non-critical work package nodes.

9. The construction plan intelligent management method according to claim 1, wherein, After the S9, there is further included: S10: after a pre-set time, re-entering step S1 to update the candidate scheduling scheme.

10. A construction engineering plan intelligent management system, characterized in that, It includes: a processor and a memory; The memory stores programs or instructions that can be run on the processor, and the programs or instructions are executed by the processor to implement the steps of the construction engineering plan intelligent management method according to any one of claims 1 to 9.

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