Construction resource optimization method and system based on cloud computing

Abstract

The invention relates to the technical field of resource optimization, in particular to a construction resource optimization method and system based on cloud computing, and the method comprises the following steps: collecting and preprocessing the real-time data of a construction site, updating a decision model, adjusting a decision tree, and dynamically matching manpower and material resources; the mechanical configuration influence is evaluated by applying ensemble learning, resource allocation and maintenance are optimized, and project efficiency and cost effectiveness are improved. According to the method, real-time data preprocessing and resource demand prediction are utilized, the dynamic allocation capacity of construction resources is remarkably improved, the construction progress and environment change are monitored in real time, resource allocation is adjusted in real time, material and manpower configuration is optimized, prediction precision is improved, and excessive use and waste of resources are reduced; synchronous improvement of project progress and quality is ensured, the influence of mechanical equipment configuration is analyzed by adopting integrated learning, the cost-benefit ratio is further optimized, and the flexibility and response speed of project management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource optimization, and in particular to a construction resource optimization method and system based on cloud computing. Background Art

[0002] The field of resource optimization technology focuses on improving the efficiency and cost-effectiveness of various industrial and commercial activities through efficient resource management and allocation. In the building and construction industry, this involves the rational allocation of human, mechanical, material and financial resources to achieve optimal workflow and cost management. This technology field uses a variety of strategies, such as algorithmic models, data analysis and real-time monitoring systems, to predict resource needs, optimize inventory management and improve work site layout. Through these methods, waste can be significantly reduced and project progress can be accelerated while ensuring construction quality and safety standards.

[0003] Construction resource optimization methods refer to the application of management strategies in building and construction projects to ensure the most efficient use of resources. The main purpose of these methods is to reduce project costs, shorten construction time, and improve project quality and efficiency. By accurately allocating materials, equipment, and personnel, construction resource optimization can have a positive impact on the overall cost and schedule of the project, while also helping to enhance the sustainability and environmental friendliness of the project.

[0004] Existing technologies rely on preset models for resource management and are not responsive enough to real-time changes, resulting in inefficient resource allocation when encountering emergencies. For example, in traditional methods, resource allocation is not flexible enough and often cannot respond immediately to situations such as sudden weather changes or mechanical failures, which can easily cause construction delays and increased costs. In addition, existing technologies fail to make full use of real-time environmental and geological data, resulting in inaccurate resource allocation decisions and increasing project uncertainty and environmental risks. These problems limit the economy and efficiency of projects and affect the sustainability and environmental friendliness of construction activities. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a construction resource optimization method and system based on cloud computing.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a construction resource optimization method based on cloud computing, comprising the following steps:

[0007] S1: Collect real-time construction site data based on the cloud environment, collect project progress updates, machinery and equipment utilization rates, and real-time weather conditions, and summarize environmental and geological information. Preprocess the data, clean up outliers and noise, calculate resource consumption rates and environmental impact values, and obtain a resource demand forecast table.

[0008] S2: using the resource demand forecast table, updating the decision model in real time, adjusting the decision tree nodes and path weights according to the real-time construction site data, recalculating the optimal matching solution of human resources and material resources, and obtaining a dynamic resource matching solution;

[0009] S3: Starting from the dynamic resource matching scheme, the impact of the mechanical equipment combination on the project progress is evaluated, and the delay risk and cost-effectiveness under the differentiated configuration are analyzed using the integrated learning method. The efficiency and maintenance cost of the configuration are calculated, and the resource allocation and maintenance time are adjusted to form a resource optimization plan.

[0010] As a further solution of the present invention, the step of calculating the resource consumption rate is specifically:

[0011] S111: Collect real-time mechanical equipment utilization data and instant weather data, remove noise and outliers from the data preprocessing stage, and use the formula:

[0012]

[0013] Calculate the resource consumption of multiple devices and obtain the resource consumption table of each device;

[0014] Among them, R t Represents the total resource consumption rate, U i represents the equipment utilization rate, C i Represents the resource consumption coefficient, P i represents the equipment performance adjustment factor;

[0015] S112: Combine the resource consumption table of each device and refer to the influence of weather conditions on resource consumption, and use the formula:

[0016] R corrected =R t ×(1+W a ×F t )

[0017] Calculate and generate adjusted resource consumption rate;

[0018] Among them, R corrected Represents the adjusted resource consumption rate, W a Represents the weather condition adjustment factor, F t represents the time factor adjustment coefficient;

[0019] S113: Using the adjusted resource consumption rate, combined with the environmental protection coefficient and the project progress coefficient, the formula is adopted:

[0020]

[0021] Calculate the resource consumption rate;

[0022] Among them, R final Represents the resource consumption rate, E a Represents the environmental protection factor, G a Represents the project progress coefficient.

[0023] As a further solution of the present invention, the step of calculating the environmental impact value is specifically:

[0024] S121: Collect environmental and geological information through target sensors to obtain air quality and soil stability data using the formula:

[0025] E base =A q ×S s ×(1+D e )

[0026] Calculate basic environmental impact values ​​and generate basic environmental impact values;

[0027] Among them, E base Represents the basic environmental impact value, A q Represents the air quality index, S s represents soil stability index, D e represents the environmental fluctuation factor;

[0028] S122: Using the basic environmental impact value, referring to the project progress and environmental protection regulations, the formula is adopted:

[0029] E adjusted =E base ×(1+R p -E p )

[0030] Calculate and generate adjusted environmental impact values;

[0031] Among them, E adjusted represents the adjusted environmental impact value, R p Represents the environmental adjustment factor, E p represents the regulatory penalty coefficient;

[0032] S123: Adjust according to the adjusted environmental impact value, using the formula:

[0033]

[0034] Calculate and generate environmental impact values;

[0035] Among them, E final Represents the environmental impact value, I f represents the construction impact adjustment index, P f Represents the public opinion feedback factor.

[0036] As a further solution of the present invention, the step of obtaining the resource demand forecast table is specifically as follows:

[0037] S131: Integrate the resource consumption rate and the environmental impact value, using the formula:

[0038]

[0039] Evaluate preliminary resource requirements and generate preliminary resource requirement forecasts;

[0040] Among them, D initial Represents the preliminary resource requirements forecast table;

[0041] S132: Based on the preliminary resource demand forecast table, the real-time weather condition impact coefficient and the geological data update coefficient are introduced, and the formula is adopted:

[0042]

[0043] Adjust resource requirements and generate an adjusted resource requirement forecast table;

[0044] Among them, D adjusted represents the adjusted resource demand forecast table, W c Represents the weather condition influence coefficient, G u represents the geological data update coefficient;

[0045] S133: Using the adjusted resource demand forecast table, combined with real-time mechanical equipment efficiency and engineering progress changes, the formula is adopted:

[0046]

[0047] Determine and generate a resource demand forecast table;

[0048] Among them, D final represents the resource demand forecast table, M e Represents the mechanical equipment efficiency coefficient, P c Represents the coefficient of variation of project progress.

[0049] As a further solution of the present invention, the steps of obtaining the dynamic resource matching solution are specifically as follows:

[0050] S211: Starting from the resource demand forecast table, integrating the real-time data of the construction site, using the formula:

[0051]

[0052] Initialize the decision-making model, calculate the initial human resource and material resource matching, and generate a basic resource matching plan;

[0053] Among them, S realtime Represents real-time construction data, M base Represents the basic resource matching scheme;

[0054] S212: Analyze the basic resource matching solution, adjust the decision tree nodes and path weights of the decision model, and apply the dynamic weight adjustment formula:

[0055]

[0056] and

[0057] M adjusted =M base ×W adjust

[0058] Perform updates and generate updated resource matching solutions;

[0059] Among them, ΔW represents the weight adjustment value, W adjust Represents the adjusted weight, W current Represents the current weight, M adjusted Represents the updated resource matching plan;

[0060] S213: According to the updated resource matching solution, the formula is adopted:

[0061]

[0062] Calculate and generate dynamic resource matching solutions;

[0063] Among them, S efficiency represents the resource utilization efficiency index, P d Represents the project progress coefficient, M final Represents a dynamic resource matching scheme.

[0064] As a further solution of the present invention, the steps of obtaining the resource optimization solution are specifically as follows:

[0065] S311: Starting from the dynamic resource matching scheme, an integrated learning method is used to evaluate the impact of the combination of mechanical equipment on the project progress, using the formula:

[0066]

[0067] Calculate the project schedule impact and generate a project schedule impact assessment;

[0068] Among them, I progress Stands for Project Schedule Impact Assessment, D risk represents the delay risk factor;

[0069] S312: Analyze the impact assessment of the project schedule and evaluate the delay risk and cost-effectiveness under the differentiated configuration, using the formula:

[0070]

[0071] Calculate the efficiency and maintenance costs of the configuration to generate a cost-benefit assessment;

[0072] Among them, R risk stands for cost-effectiveness evaluation, C budget represents the budget cost, C var represents the coefficient of variation of cost;

[0073] S313: Adjust resource allocation and maintenance time based on the cost-benefit evaluation, using the formula:

[0074]

[0075] Generate resource optimization solutions;

[0076] Among them, S opt represents the resource optimization solution, T maintain Indicates the estimated maintenance period.

[0077] A cloud computing-based construction resource optimization system, the cloud computing-based construction resource optimization system is used to execute the cloud computing-based construction resource optimization method, the system comprises:

[0078] The data collection and preprocessing module collects real-time data from the construction site, including project progress, mechanical equipment usage and instant weather data, integrates environmental and geological information, screens data, and calculates resource consumption rate and environmental impact value to form a resource demand forecast table;

[0079] The real-time decision adjustment module uses the resource demand forecast table to update the node and path weights of the decision tree, re-evaluate the matching of manpower and materials, update the decision model, and output a dynamic resource matching solution;

[0080] The risk and cost-benefit analysis module evaluates the combination of mechanical equipment in the dynamic resource matching scheme, analyzes the impact on the project schedule, calculates the efficiency and maintenance cost of the configuration, and outputs the configuration efficiency and maintenance cost results;

[0081] The resource allocation optimization module optimizes the configuration of mechanical equipment and human resources according to the configuration efficiency and maintenance cost results, adjusts resource allocation and maintenance time, and forms a resource optimization plan.

[0082] Compared with the prior art, the advantages and positive effects of the present invention are:

[0083] In the present invention, real-time data preprocessing and resource demand forecasting are used to significantly improve the dynamic allocation capability of construction resources. By real-time monitoring of construction progress and environmental changes, resource allocation is adjusted immediately to optimize the configuration of materials and manpower. The improvement of prediction accuracy reduces the overuse and waste of resources and ensures the simultaneous improvement of project progress and quality. The use of integrated learning to analyze the impact of mechanical equipment configuration further optimizes the cost-effectiveness ratio and improves the flexibility and response speed of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0085] Figure 2 A flow chart of the steps for calculating resource consumption rate of the present invention;

[0086] Figure 3 A flow chart of the steps for calculating the environmental impact value of the present invention;

[0087] Figure 4 A flow chart of the steps for obtaining a resource demand forecast table of the present invention;

[0088] Figure 5 A flowchart of the acquisition steps of the dynamic resource matching solution of the present invention;

[0089] Figure 6 This is a flow chart of the steps for obtaining the resource optimization solution of the present invention. DETAILED DESCRIPTION

[0090] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0091] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0092] Embodiment 1

[0093] See also Figure 1 The present invention provides a technical solution: a construction resource optimization method based on cloud computing, comprising the following steps:

[0094] S1: Collect real-time construction site data based on the cloud environment, collect project progress updates, machinery and equipment utilization rates, and real-time weather conditions, and summarize environmental and geological information. Preprocess the data, clean up outliers and noise, calculate resource consumption rates and environmental impact values, and obtain a resource demand forecast table.

[0095] S2: Use the resource demand forecast table to update the decision model in real time, adjust the decision tree nodes and path weights according to the real-time construction site data, recalculate the optimal matching plan of human resources and material resources, and obtain a dynamic resource matching plan;

[0096] S3: Starting from the dynamic resource matching plan, evaluate the impact of the machinery and equipment combination on the project progress, use the integrated learning method to analyze the delay risk and cost-effectiveness under the differentiated configuration, calculate the efficiency and maintenance cost of the configuration, adjust the resource allocation and maintenance time, and form a resource optimization plan.

[0097] The resource demand forecast table specifically includes resource consumption rate and environmental impact value. The dynamic resource matching plan includes manpower allocation and material allocation. The resource optimization plan specifically includes delay risk, cost-effectiveness, configuration efficiency, and maintenance cost.

[0098] See also Figure 2 , the steps for calculating resource consumption rate are as follows:

[0099] S111: Collect real-time mechanical equipment utilization data and instant weather data, remove noise and outliers from the data preprocessing stage, and use the formula:

[0100]

[0101] Calculate the resource consumption of multiple devices and obtain the resource consumption table of each device;

[0102] Among them, R t Represents the total resource consumption rate, U i represents the equipment utilization rate, C i Represents the resource consumption coefficient, P i represents the equipment performance adjustment factor;

[0103] S112: Combined with the resource consumption table of each device and referring to the impact of weather conditions on resource consumption, the formula is adopted:

[0104] R corrected =R t ×(1+W a ×F t )

[0105] Calculate and generate adjusted resource consumption rate;

[0106] Among them, R corrected Represents the adjusted resource consumption rate, W a Represents the weather condition adjustment factor, F t represents the time factor adjustment coefficient;

[0107] S113: Use the adjusted resource consumption rate, combined with the environmental protection factor and the project progress factor, and adopt the formula:

[0108]

[0109] Calculate the resource consumption rate;

[0110] Among them, R final Represents the resource consumption rate, E a Represents the environmental protection factor, G a Represents the project progress coefficient.

[0111] formula:

[0112]

[0113] in:

[0114] U i : Utilization rate of device i

[0115] C i : Resource consumption coefficient of device i

[0116] P i : Performance adjustment factor for device i

[0117] R t : Total resource consumption rate

[0118] Calculation process:

[0119] Determine the number of devices n. Assume n=3.

[0120] Determine the utilization rate U of each device separately i , resource consumption coefficient C i , and the performance adjustment factor P i .

[0121] Device 1: U1=0.8, C1=0.5, P1=0.1

[0122] Device 2: U2=0.6, C2=0.7, P2=0.2

[0123] Device 3: U3=0.9, C3=0.4, P3=0.15

[0124] Substitute the formula to calculate the resource consumption of each device and add up the total resource consumption:

[0125] Resource consumption of device 1: 0.8×0.5×(1+0.1)=0.44

[0126] Resource consumption of device 2: 0.6×0.7×(1+0.2)=0.504

[0127] Resource consumption of device 3: 0.9×0.4×(1+0.15)=0.414

[0128] Total resource consumption: R t =0.44+0.504+0.414=1.358

[0129] Result description:

[0130] Total resource consumption rate R t =1.358 represents the total resource consumption of all devices after reference performance adjustment.

[0131] formula:

[0132] R corrected =R t ×(1+W a ×F t )

[0133] in:

[0134] R t : Total resource consumption rate

[0135] W a : Adjustment coefficient of weather conditions on resource consumption

[0136] F t : Time factor adjustment coefficient

[0137] R corrected : Adjusted resource consumption rate

[0138] Calculation process:

[0139] R t =1.358.

[0140] Assuming weather condition adjustment factor W a =0.05 and time factor adjustment coefficient F t =0.1.

[0141] Substitute into the formula to calculate the adjusted resource consumption rate:

[0142] R corrected =1.358×(1+0.05×0.1)=1.358×1.005=1.3649

[0143] Result description:

[0144] Adjusted resource consumption rate R corrected =1.3649 reflects the impact of weather and time factors on total resource consumption.

[0145] formula:

[0146]

[0147] in:

[0148] R corrected : Adjusted resource consumption rate

[0149] E a : Environmental protection factor

[0150] G a :Project progress coefficient

[0151] R final : Resource consumption rate

[0152] Calculation process:

[0153] R corrected =1.3649.

[0154] Assuming the environmental protection factor E a =0.95 and engineering progress factor G a =0.9.

[0155] Substitute the formula to calculate the resource consumption rate:

[0156]

[0157] Result description:

[0158] Resource consumption rate R final =1.596 represents the actual resource consumption rate per unit time after referring to environmental protection and project progress factors.

[0159] See also Figure 3 , the specific steps for calculating the environmental impact value are:

[0160] S121: Collect environmental and geological information through target sensors to obtain air quality and soil stability data using the formula:

[0161] E base =A q ×S s ×(1+D e )

[0162] Calculate basic environmental impact values ​​and generate basic environmental impact values;

[0163] Among them, E base Represents the basic environmental impact value, A q Represents the air quality index, S s represents soil stability index, D e represents the environmental fluctuation factor;

[0164] S122: Using the basic environmental impact value, referring to the project progress and environmental protection regulations, the formula is adopted:

[0165] E adjusted =E base ×(1+R p -E p )

[0166] Calculate and generate adjusted environmental impact values;

[0167] Among them, E adjusted represents the adjusted environmental impact value, R p Represents the environmental adjustment factor, E p represents the regulatory penalty coefficient;

[0168] S123: Make adjustments based on the adjusted environmental impact value using the formula:

[0169]

[0170] Calculate and generate environmental impact values;

[0171] Among them, E final Represents the environmental impact value, I f represents the construction impact adjustment index, P f Represents the public opinion feedback factor.

[0172] formula:

[0173] E base =A q ×S s ×(1+D e )

[0174] Parameter details:

[0175] A q : Air quality index. Assuming it is 80, it represents the daily air quality of a general city.

[0176] S s : Soil stability index. Assuming it is 0.9, it means the soil is relatively stable.

[0177] D e : Environmental fluctuation factor. Assumed to be 0.05, indicating slight environmental changes.

[0178] Derivation process:

[0179] Assumptions:

[0180] A q =80

[0181] S s =0.9

[0182] D e =0.05

[0183] Calculate E base :

[0184] E base =80×0.9×(1+0.05)=80×0.9×1.05=75.6

[0185] This represents a base environmental impact value of 75.6, a value that reflects the combined effects of air quality and soil stability, adjusted slightly higher to reflect environmental fluctuations.

[0186] formula:

[0187] E adjusted =E base ×(1+R p -E p )

[0188] Parameter details:

[0189] E base : Basic environmental impact value.

[0190] R p : Environmental protection adjustment coefficient. Assumed to be 0.1, it represents the coefficient increased due to the implementation of environmental protection projects.

[0191] E p : Regulatory penalty coefficient. Assumed to be 0.03, it indicates the penalty introduced for non-compliance with certain environmental regulations.

[0192] Derivation process:

[0193] E base =75.6

[0194] R p =0.1

[0195] E p =0.03

[0196] Calculate E adjusted :

[0197] E adjusted =75.6×(1+0.1-0.03)=75.6×1.07=80.892

[0198] This means that the adjusted environmental impact value is 80.892, referring to the positive impact of environmental protection projects and the negative impact of regulations.

[0199] formula:

[0200]

[0201] Parameter details:

[0202] E adjusted : Adjusted environmental impact value.

[0203] I f : Construction impact adjustment index. Assumed to be 0.02, it represents the impact of construction activities on the environment.

[0204] P f : Public opinion feedback factor. Assumed to be 0.01, it reflects the public’s sensitivity to the environmental impact of the project.

[0205] Derivation process:

[0206] E adjusted =80.892,

[0207] I f =0.02

[0208] P f =0.01

[0209] Calculate E final :

[0210]

[0211] This represents an environmental impact value of 80.871, a small reduction reflecting the minor impacts of construction and public feedback.

[0212] See also Figure 4 , the specific steps for obtaining the resource demand forecast table are:

[0213] S131: Integrate resource consumption rate and environmental impact value, using the formula:

[0214]

[0215] Evaluate preliminary resource requirements and generate preliminary resource requirement forecasts;

[0216] Among them, D initial Represents the preliminary resource requirements forecast table;

[0217] S132: Based on the preliminary resource demand forecast table, the real-time weather condition impact coefficient and geological data update coefficient are introduced, and the formula is adopted:

[0218]

[0219] Adjust resource requirements and generate an adjusted resource requirement forecast table;

[0220] Among them, D adjusted represents the adjusted resource demand forecast table, W c Represents the weather condition influence coefficient, G u represents the geological data update coefficient;

[0221] S133: Using the adjusted resource demand forecast table, combined with real-time mechanical equipment efficiency and engineering progress changes, the formula is:

[0222]

[0223] Determine and generate a resource demand forecast table;

[0224] Among them, D final represents the resource demand forecast table, M e Represents the mechanical equipment efficiency coefficient, P c Represents the coefficient of variation of project progress.

[0225] formula:

[0226]

[0227] Assumed values:

[0228] R final =100 (unit: unit resource consumption rate)

[0229] E final =50 (Unit: Environmental Impact Value)

[0230] Derivation process:

[0231] Calculating molecules

[0232] Calculate the denominator R final +E final =100+50=150

[0233] Apply the formula to calculate

[0234] Analysis:

[0235] D initial Represents the preliminary resource demand forecast, and the calculated result is 3333.33, which means that based on the current resource consumption rate and environmental impact value, the predicted resource demand is 3333.33 units.

[0236] formula:

[0237]

[0238] Assumed values:

[0239] D initial =3333.33

[0240] W c =0.1 (Weather condition influence coefficient)

[0241] G u =0.05 (geological data update coefficient)

[0242] Derivation process:

[0243] Calculate the sum of impact factors

[0244] Apply the formula to calculate D adjusted =3333.33×1.0125≈3375

[0245] Analysis:

[0246] D adjusted Represents the adjusted resource demand forecast, which is calculated to be 3375, meaning that after taking into account weather and geological data, resource demand has increased slightly.

[0247] formula:

[0248]

[0249] Assumed values:

[0250] D adjusted =3375

[0251] M e =0.95 (mechanical equipment efficiency coefficient)

[0252] P c =0.9 (project progress variation coefficient)

[0253] Derivation process:

[0254] The product of computing efficiency and progress plus one M e ·P c +1=0.95×0.9+1=0.855+1=1.855

[0255] Apply the formula to calculate

[0256] Analysis:

[0257] D finalIt represents the predicted resource demand, and the calculated result is 1820, which means that after referring to the machine efficiency and engineering progress, the predicted resource demand has been reduced, reflecting the actual amount of resources needed.

[0258] See also Figure 5 , the specific steps for obtaining the dynamic resource matching solution are:

[0259] S211: Starting from the resource demand forecast table, integrating the real-time data of the construction site, using the formula:

[0260]

[0261] Initialize the decision-making model, calculate the initial human resource and material resource matching, and generate a basic resource matching plan;

[0262] Among them, S realtime Represents real-time construction data, M base Represents the basic resource matching scheme;

[0263] S212: Analyze the basic resource matching solution, adjust the decision tree nodes and path weights of the decision model, and apply the dynamic weight adjustment formula:

[0264]

[0265] and

[0266] M adjusted =M base ×W adjust

[0267] Perform updates and generate updated resource matching solutions;

[0268] Among them, ΔW represents the weight adjustment value, W adjust Represents the adjusted weight, W current Represents the current weight, M adjusted Represents the updated resource matching plan;

[0269] S213: According to the updated resource matching scheme, the formula is adopted:

[0270]

[0271] Calculate and generate dynamic resource matching solutions;

[0272] Among them, S efficiency represents the resource utilization efficiency index, P d Represents the project progress coefficient, M final Represents a dynamic resource matching scheme.

[0273] formula:

[0274]

[0275] Parameter Description:

[0276] D final : The value of the resource demand forecast table.

[0277] S realtime : The numerical value of real-time construction data.

[0278] Assumed parameters:

[0279] D final =100 (unit: unit resource demand)

[0280] S realtime =2 (unit: real-time data coefficient)

[0281] Calculation process:

[0282] calculate

[0283] calculate

[0284] Calculate the denominator

[0285] calculate

[0286] Analysis of calculation results:

[0287] M base =44.44 means that the estimated value of the initial resource matching solution adjusted based on real-time construction data is 44.44 units of resources.

[0288] formula:

[0289]

[0290] and

[0291] M adjusted =M base ×W adjust

[0292] Parameter Description:

[0293] W current : Current weight value.

[0294] ΔW: weight adjustment value.

[0295] M base : The value of the basic matching scheme.

[0296] Assumed parameters:

[0297] Wcurrent =1.5

[0298] ΔW=0.5

[0299] M base =44.44

[0300] Calculation process:

[0301] calculate

[0302] Calculating molecules

[0303] Calculate the denominator 1+ΔW 2 =1+0.5 2 =1.25

[0304] calculate

[0305] Calculate M adjusted =44.44×1.576≈70.06

[0306] Analysis of calculation results:

[0307] M adjusted =70.06 means that after the reference weight adjustment, the estimated value of the resource matching solution is 70.06 units of resources.

[0308] formula:

[0309]

[0310] Parameter Description:

[0311] S efficiency : Resource utilization efficiency index.

[0312] P d : Project progress coefficient.

[0313] M adjusted : The value of the adjusted resource matching solution.

[0314] Assumed parameters:

[0315] S efficiency =0.2

[0316] P d =3

[0317] M adjusted =70.06

[0318] Calculation process:

[0319] Calculate S efficiency ×P d=0.2×3=0.6

[0320] Calculate e -0.6 ≈0.5488

[0321] Calculate the denominator 1+e -0.6 =1+0.5488=1.5488

[0322] calculate

[0323] Analysis of calculation results:

[0324] M final =45.23 means that after adjusting with reference to resource efficiency and project progress, the estimated value of the dynamic resource matching solution is 45.23 units of resources.

[0325] See also Figure 6 , the specific steps for obtaining the resource optimization plan are:

[0326] S311: Starting from the dynamic resource matching scheme, the ensemble learning method is used to evaluate the impact of mechanical equipment combination on project progress, using the formula:

[0327]

[0328] Calculate the project schedule impact and generate a project schedule impact assessment;

[0329] Among them, I progress Stands for Project Schedule Impact Assessment, D risk represents the delay risk factor;

[0330] S312: Analyze the impact assessment of project schedule and evaluate the delay risk and cost-effectiveness under differentiated configurations using the formula:

[0331]

[0332] Calculate the efficiency and maintenance costs of the configuration to generate a cost-benefit assessment;

[0333] Among them, R risk stands for cost-effectiveness evaluation, C budget represents the budget cost, C var represents the coefficient of variation of cost;

[0334] S313: Adjust resource allocation and maintenance time based on cost-benefit assessment using the formula:

[0335]

[0336] Generate resource optimization solutions;

[0337] Among them, S optrepresents the resource optimization solution, T maintain Indicates the estimated maintenance period.

[0338] formula:

[0339]

[0340] Parameter interpretation and derivation:

[0341] M final : The efficiency of the dynamic resource matching scheme is assumed to be 0.85 (dimensionless, indicating the degree of optimization of resource allocation).

[0342] D risk : Delay risk coefficient, assumed to be 0.2 (dimensionless, indicating the possibility of project delay).

[0343] Calculation process:

[0344] Using the above parameter values, calculate I progress as follows:

[0345]

[0346] Results I progress =0.542 means that after the project is affected by dynamic resource allocation, the probability that the progress reaches the ideal state is 54.2%, reflecting a medium level of progress assurance.

[0347] formula:

[0348]

[0349] Parameter interpretation and derivation:

[0350] I progress : is 0.542.

[0351] C bidanet : Budget cost, assumed to be RMB 10 million.

[0352] C var : Cost variation coefficient, assumed to be 500,000 yuan.

[0353] Calculation process:

[0354]

[0355] Results risk =0.075 means that after referring to the progress and cost, the risk assessment index of the project is 0.075, which is relatively low, indicating that the risk of the project is controllable under the current configuration.

[0356] formula:

[0357]

[0358] Parameter interpretation and derivation:

[0359] R risk : is 0.075.

[0360] T maintain : Estimated maintenance period, assumed to be 24 months.

[0361] Calculation process:

[0362]

[0363] Results opt =0.013 means that after the reference maintenance cycle, the efficiency of the resource optimization solution is very low, and iterative optimization strategies are needed to improve the efficiency.

[0364] A cloud computing-based construction resource optimization system, which is used to execute the cloud computing-based construction resource optimization method, includes:

[0365] The data collection and preprocessing module collects real-time data from the construction site, including project progress, mechanical equipment usage and instant weather data, integrates environmental and geological information, screens data, and calculates resource consumption rate and environmental impact value to form a resource demand forecast table;

[0366] The real-time decision adjustment module uses the resource demand forecast table to update the node and path weights of the decision tree, re-evaluate the matching of manpower and materials, update the decision model, and produce a dynamic resource matching plan;

[0367] The risk and cost-benefit analysis module evaluates the combination of mechanical equipment in the dynamic resource matching plan, analyzes the impact on the project schedule, calculates the efficiency and maintenance cost of the configuration, and outputs the configuration efficiency and maintenance cost results;

[0368] The resource allocation optimization module optimizes the configuration of mechanical equipment and human resources according to the configuration efficiency and maintenance cost results, adjusts resource allocation and maintenance time, and forms a resource optimization plan.

[0369] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Construction resource optimization method based on cloud computing, It is characterized in that The following steps are involved: Collect real-time construction site data based on the cloud environment, collect project progress updates, machinery and equipment utilization rates, and real-time weather conditions, and summarize environmental and geological information. Preprocess the data, clean up outliers and noise, calculate resource consumption rates and environmental impact values, and obtain resource demand forecasts. Using the resource demand forecast table, the decision model is updated in real time, the decision tree nodes and path weights are adjusted according to the real-time construction site data, the optimal matching solution of human resources and material resources is recalculated, and a dynamic resource matching solution is obtained; Starting from the dynamic resource matching scheme, the impact of the combination of mechanical equipment on the project progress is evaluated, and the delay risk and cost-effectiveness under differentiated configurations are analyzed using ensemble learning methods. The efficiency and maintenance cost of the configuration are calculated, resource allocation and maintenance time are adjusted, and a resource optimization plan is formed.

2. According to the cloud computing-based construction resource optimization method of claim 1, It is characterized in that The step of calculating the resource consumption rate is specifically as follows: Collect real-time mechanical equipment utilization data and instant weather data, remove noise and outliers from the data preprocessing stage, and use the formula: Calculate the resource consumption of multiple devices and obtain the resource consumption table of each device; Among them, R t Represents the total resource consumption rate, U i represents the equipment utilization rate, C i Represents the resource consumption coefficient, P i represents the equipment performance adjustment factor; Combined with the resource consumption table of each device, and referring to the impact of weather conditions on resource consumption, the formula is adopted: R corrected =R t ×(1+W a ×F t ) Calculate and generate adjusted resource consumption rate; Among them, R corrected Represents the adjusted resource consumption rate, W a Represents the weather condition adjustment factor, F t represents the time factor adjustment coefficient; Using the adjusted resource consumption rate, combined with the environmental protection factor and the project progress factor, the formula is adopted: Calculate the resource consumption rate; Among them, R final Represents the resource consumption rate, E a Represents the environmental protection factor, G a Represents the project progress coefficient.

3. According to the cloud computing-based construction resource optimization method of claim 2, It is characterized in that The steps of calculating the environmental impact value are specifically as follows: Collect environmental and geological information through target sensors to obtain air quality and soil stability data using the formula: E base =A q ×S s ×(1+D e ) Calculate basic environmental impact values ​​and generate basic environmental impact values; Among them, E base Represents the basic environmental impact value, A q Represents the air quality index, S s represents soil stability index, D e represents the environmental fluctuation factor; Using the basic environmental impact value, referring to the project progress and environmental protection regulations, the formula is adopted: AND adjusted =And base ×(1+R p -AND p ) Calculate and generate adjusted environmental impact values; Among them, E adjusted represents the adjusted environmental impact value, R p Represents the environmental adjustment factor, E p represents the regulatory penalty coefficient; According to the adjusted environmental impact value, adjustments are made using the formula: Calculate and generate environmental impact values; Among them, E final Represents the environmental impact value, I f represents the construction impact adjustment index, P f Represents the public opinion feedback factor.

4. The cloud computing-based construction resource optimization method according to claim 3, It is characterized in that The steps for obtaining the resource demand forecast table are specifically as follows: Integrate the resource consumption rate and the environmental impact value, using the formula: Evaluate preliminary resource requirements and generate preliminary resource requirement forecasts; Among them, D initial Represents the preliminary resource requirements forecast table; Based on the preliminary resource demand forecast table, the real-time weather condition impact coefficient and geological data update coefficient are introduced, and the formula is adopted: Adjust resource requirements and generate an adjusted resource requirement forecast table; Among them, D adjusted represents the adjusted resource demand forecast table, W c Represents the weather condition influence coefficient, G u represents the geological data update coefficient; Using the adjusted resource demand forecast table, combined with real-time mechanical equipment efficiency and engineering progress changes, the formula is adopted: Determine and generate a resource demand forecast table; Among them, D final represents the resource demand forecast table, M e Represents the mechanical equipment efficiency coefficient, P c Represents the coefficient of variation of project progress.

5. According to the cloud computing-based construction resource optimization method of claim 4, It is characterized in that The steps for obtaining the dynamic resource matching solution are specifically as follows: Starting from the resource demand forecast table, integrating the real-time data of the construction site, the formula is adopted: Initialize the decision-making model, calculate the initial human resource and material resource matching, and generate a basic resource matching plan; Among them, S realtime Represents real-time construction data, M base Represents the basic resource matching scheme; The basic resource matching scheme is analyzed, the decision tree nodes and path weights of the decision model are adjusted, and the dynamic weight adjustment formula is applied: and M adjusted =M base ×W adjust Perform updates and generate updated resource matching solutions; Among them, ΔW represents the weight adjustment value, W adjust Represents the adjusted weight, W current Represents the current weight, M adjusted Represents the updated resource matching plan; According to the updated resource matching scheme, the formula is adopted: Calculate and generate dynamic resource matching solutions; Among them, S efficiency represents the resource utilization efficiency index, P d Represents the project progress coefficient, M final Represents a dynamic resource matching scheme.

6. The cloud computing-based construction resource optimization method according to claim 5, It is characterized in that The steps for obtaining the resource optimization solution are specifically as follows: Based on the dynamic resource matching scheme, the ensemble learning method is used to evaluate the impact of mechanical equipment combination on project progress, using the formula: Calculate the project schedule impact and generate a project schedule impact assessment; Among them, I progress Stands for Project Schedule Impact Assessment, D risk represents the delay risk factor; The project schedule impact assessment is analyzed to evaluate the delay risk and cost-effectiveness under differentiated configurations using the formula: Calculate the efficiency and maintenance costs of the configuration to generate a cost-benefit assessment; Among them, R risk stands for cost-effectiveness evaluation, C budget represents the budget cost, C var represents the coefficient of variation of cost; Based on the cost-benefit assessment, adjust resource allocation and maintenance time using the formula: Generate resource optimization solutions; Among them, S opt represents the resource optimization solution, T maintain Indicates the estimated maintenance period.

7. Construction resource optimization system based on cloud computing, It is characterized in that According to the cloud computing-based construction resource optimization method according to any one of claims 1 to 6, the system comprises: The data collection and preprocessing module collects real-time data from the construction site, including project progress, mechanical equipment usage and instant weather data, integrates environmental and geological information, screens data, and calculates resource consumption rate and environmental impact value to form a resource demand forecast table; The real-time decision adjustment module uses the resource demand forecast table to update the node and path weights of the decision tree, re-evaluate the matching of manpower and materials, update the decision model, and output a dynamic resource matching solution; The risk and cost-benefit analysis module evaluates the combination of mechanical equipment in the dynamic resource matching scheme, analyzes the impact on the project schedule, calculates the efficiency and maintenance cost of the configuration, and outputs the configuration efficiency and maintenance cost results; The resource allocation optimization module optimizes the configuration of mechanical equipment and human resources according to the configuration efficiency and maintenance cost results, adjusts resource allocation and maintenance time, and forms a resource optimization plan.

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