Intelligent construction and loss management system for composite foam cement insulation board

Abstract

The invention discloses an intelligent construction and loss management system for a composite foam cement insulation board, and the system comprises the following steps: S1, collecting the geometric characteristic information of a construction region, including irregular edge information and window surrounding information, and obtaining the construction environment condition information, including a high-wind-pressure region or a humid environment; s2, on the basis of historical construction projects, collecting construction process data related to the II-type composite foam cement insulation board, including the cutting size of the board, cutting errors, material loss records, an anchor arrangement mode and the influence of the anchor arrangement mode on the stability of the board, and establishing a data storage library, according to the method, a multi-objective optimization mechanism is introduced in a construction scheme generation process by fusing a particle swarm intelligent optimization algorithm, and a comprehensive optimization model is constructed by considering multi-dimensional objectives of construction efficiency, material loss and construction quality at the same time; according to the optimization algorithm, material loss minimization, anchoring effect optimization and construction efficiency maximization in the construction process are ensured.

Description

Technical Field

[0001] The present invention relates to the fields of civil engineering, mechanical equipment and steel structure, and in particular to an intelligent construction and loss management system for composite foamed cement insulation boards. Background Art

[0002] With the development of intelligent building technology, some intelligent construction auxiliary systems have begun to be applied to the field of exterior wall insulation. The application of exterior wall insulation systems has become an important means to improve the energy-saving effect of buildings. Among them, composite foamed cement insulation boards have gradually become the preferred material for exterior wall insulation projects due to their superior insulation performance and construction convenience. However, the construction process of composite foamed cement insulation boards is complicated and affected by many factors. The problems of material waste and low construction efficiency have long plagued the construction industry. Against this background, how to optimize the construction process and reduce material loss through intelligent means has become a research hotspot.

[0003] Currently, most exterior wall insulation construction still relies on traditional manual cutting and anchor placement methods. Construction workers cut insulation panels based on experience and use a fixed anchor placement density to complete installation. Although manual cutting and anchor placement has certain operability, it has obvious limitations in actual application:

[0004] First, the cutting process of the insulation boards relies heavily on the experience of the construction workers, making it difficult to achieve an accurate match between the board size and the geometric characteristics of the exterior wall, resulting in a large amount of waste of scraps.

[0005] Secondly, the anchor arrangement method usually adopts a uniform distribution, ignoring the environmental differences such as wind pressure and humidity in different areas of the exterior wall, which can easily lead to insufficient anchoring effect in high-loss risk areas and increase the possibility of material falling off and waste.

[0006] In addition, the real-time dynamic adjustment capability during the construction process is weak. Once encountering complex geometric shapes or special environmental conditions, traditional methods are often difficult to make timely optimization adjustments, resulting in low construction efficiency. Summary of the Invention

[0007] This invention aims to address the significant shortcomings of existing technologies in terms of intelligent construction processes, material loss management, and optimization of high-loss risk areas, making it difficult to meet the requirements of modern construction projects for efficient, precise, and energy-efficient construction. Therefore, this application proposes an intelligent construction and loss management system that comprehensively considers multiple construction factors, minimizes material loss, and maximizes construction efficiency to address these issues.

[0008] The present invention proposes an intelligent construction and loss management system for composite foamed cement insulation boards, comprising the following steps:

[0009] S1: Collects geometric characteristics of the construction area, including irregular edge information and window surrounding information, and obtains construction environmental conditions information, including high wind pressure areas or humid environments;

[0010] S2: Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation boards, including board cutting dimensions, cutting errors, material loss records, anchor placement methods and their impact on board stability, and establish a data repository;

[0011] S3: A loss prediction model for Type II composite foamed cement insulation boards is established using the geometric characteristics of the construction area, construction environmental conditions, and construction process data. The loss prediction model comprehensively considers the matching degree between the board cutting size and the geometric characteristics of the construction area, the impact of the anchor arrangement on the board stability, and the potential impact of the construction environment on the board shedding and loss.

[0012] S4: Substituting the geometric characteristics of the construction area into the loss prediction model to dynamically analyze the material loss risk at different locations within the construction area and identify high-loss risk areas, including areas with irregular edges, areas around windows, and areas with high wind pressure or high humidity.

[0013] S5: Dynamically adjust the anchor placement strategy based on the analysis results of the loss prediction model, including the number, distribution density, and layout of anchors, prioritizing the optimization of anchor stability in areas with high loss risk to ensure anchoring effectiveness while reducing material loss.

[0014] S6: Adjust the cutting strategy of the foamed cement insulation board based on the geometric characteristics of the construction area and the distribution characteristics of the high-loss risk areas. This includes optimizing the cutting size, cutting sequence, and waste material utilization of the board to ensure that the cut board has the best match with the geometric characteristics of the construction area.

[0015] S7: Apply the optimized anchor placement strategy and insulation board cutting plan to the construction process, dynamically monitor the board usage and loss level during construction, and fine-tune the construction plan based on real-time feedback;

[0016] S8: Evaluate the overall installation effect of the insulation board after optimized construction to verify whether its stability, loss level and construction quality meet the expected standards. If not, return to S3 to adjust the parameters of the loss prediction model or the optimization algorithm and re-execute the subsequent steps.

[0017] As a preferred solution of the present invention, S1 includes the following steps:

[0018] S11, collect geometric characteristic information of the construction area to build a geometric characteristic data set G, the geometric characteristic data set includes the contour data G of the construction area contour, edge irregular data G edge and window surrounding information G window , the geometric characteristic information is obtained by laser scanning or photogrammetry technology and represented as three-dimensional point cloud data P(x,y,z):

[0019] x, y, z are the three-dimensional coordinates of a point in the point cloud, representing the local features in the geometric structure of the construction area;

[0020] G contour The overall outline information of the exterior wall of the construction area, including the boundary shape and the area of each area;

[0021] G edge The geometric characteristics of irregular edges in the construction area, including bending radius and inclination angle;

[0022] G window The geometric characteristics around the windows in the construction area, including the window size and the intersection characteristics with the wall;

[0023] S12, obtain construction environment condition information to build an environment condition data set E, the environment condition data set includes high wind pressure area data E wind and humid environment data E humidity ;

[0024] S13. Based on the collected geometric characteristic information dataset G and environmental condition dataset E, a comprehensive data matrix M is established:

[0025] M=[G contour G edge G window E wind E humidity ];

[0026] Each column corresponds to the geometric or environmental feature information of a different area.

[0027] As a preferred solution of the present invention, S2 comprises the following steps:

[0028] S21. Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation board to construct a construction process dataset D. The construction process dataset D includes the cutting size data D of the board. cut , clipping error data D error , Material loss record data D loss , Anchor arrangement data D anchor Data D on the influence of anchor arrangement on plate stability stability :

[0029] Cutting size data D of the plate cut ={l i , wi , t i}, represents the length l of different plates during the historical construction process i 、Width w i , thickness t i ;

[0030] Clipping error data D error Indicates the error in length, width and thickness during the cutting process of the board;

[0031] Material loss record data D loss The actual loss of the plate recorded during construction, expressed in unit volume V loss express;

[0032] Anchor arrangement data D anchor ={n a , d a , p a} represents the number of anchors n a , Anchor spacing d a and the arrangement position of the anchor a ;

[0033] Data D on the influence of anchor arrangement on plate stability stability The comprehensive effect of anchor arrangement on plate stability is expressed by combining anchor arrangement data and plate dimensional characteristics;

[0034] S22. Establish a construction data repository S based on the collected construction process data set D D The contents of the repository include data on the matching between the cutting size of the plate and the target size, material loss records, and data on the partitioned impact of anchor arrangement on plate stability.

[0035] As a preferred solution of the present invention, S3 includes the following steps:

[0036] S31. Using the construction area's geometric characteristics dataset G, the environmental conditions dataset E, and the construction process dataset D, a loss prediction model L for type II composite foamed cement insulation panels is established. The loss prediction model comprehensively considers the matching degree between the panel cutting dimensions and the construction area's geometric characteristics, the effect of the anchor arrangement on the panel's stability, and the potential impact of the construction environment on panel shedding and loss.

[0037] S32. Construct the loss function F of the matching degree between the plate cutting size and the geometric characteristics of the construction area match :

[0038]

[0039] Among them, n is the total number of plates that need to be cut, A waste，i A represents the waste area remaining after cutting the i-th plate.board，i is the original area of the i-th plate, κ is the loss nonlinear coefficient, reflecting the nonlinear relationship between the loss rate and the waste ratio, W material is the weight of the plate per unit area, φ i is the complexity coefficient of the i-th plate in the construction area, and its value range is 0<φ i ≤1, the more complex the construction area, the i The closer it is to 1, the greater the impact of complex geometry on clipping loss;

[0040] S33. Construct the loss function F of the effect of anchor arrangement on plate stability stability :

[0041]

[0042] Where m is the number of zones in the construction area, S stability,j represents the stability coefficient of the plate in the jth partition, η j is the environmental correction factor, A panel,j is the total area of the plate in the jth partition;

[0043] S34. Construct the loss function F of the influence of construction environment on the shedding and loss of plate env :

[0044]

[0045] Among them, A total is the total area of the exterior walls of the construction area, E wind is the actual wind pressure value in the construction area, E wind，max is the maximum wind pressure value allowed in the design, λ is the wind pressure influence index, E humidity is the actual relative humidity in the construction area, ranging from 0 to E humidity ≤1, E humidity，max The maximum relative humidity allowed by the design, the value range is 0<E humidity，max ≤1, μ is the humidity influence index, reflecting the nonlinear influence of humidity on plate loss;

[0046] S35, integrate the loss functions of S32-S34 to establish the total loss prediction model L total :

[0047] L total =ω zatch ×F match +ω stability ×F stability +ω env ×F env ;

[0048] Among them, ω match 、ωstability 、ω env It is the loss weight coefficient, which is used to balance the contribution of different loss factors to the total loss and is set according to the actual needs of the project.

[0049] As a preferred solution of the present invention, S4 includes the following steps:

[0050] S41, substitute the geometric characteristic dataset G, environmental condition dataset E and construction process dataset D of the construction area into the loss prediction model L total Calculate the material loss risk R at each location within the construction area loss (x,y):

[0051] S42. Based on the loss risk function R loss (x,y) calculates the average loss risk R of each sub-area within the construction area avg，k :

[0052]

[0053] Among them, Ω k is the two-dimensional integral domain of the kth sub-area in the construction area, A k is the area of the kth subregion, R loss (x, y) is the loss risk of each point in the sub-area;

[0054] S43. Identify high loss risk areas Ω high , the construction area is divided into two categories: high loss risk area and ordinary area. The high loss risk area meets the following conditions:

[0055] Ω high ={(x,y)∈Ω|R loss (x,y)≥R threshold};

[0056] Among them, R threshold is the loss risk threshold, Ω is the entire construction area;

[0057] S44. Segment specific high-risk scenarios based on the distribution of high-loss risk areas:

[0058] Irregular edge regions: Based on the complexity of irregular edges in the geometric feature dataset, we can identify high-loss areas where cropping becomes more difficult due to bending or tilting.

[0059] The area around the window, based on the size of the window and the characteristics of its intersection with the wall in the geometric characteristic data set, identifies high-loss areas due to high cutting accuracy requirements or large installation complexity;

[0060] High wind pressure areas: Based on the spatial distribution of wind pressure values in the environmental condition dataset, identify high-loss areas where wind pressure can cause material shedding or anchor failure.

[0061] High humidity areas, based on the spatial distribution of humidity values in the environmental condition dataset, identify high-loss areas where material performance deteriorates or construction becomes difficult due to high humidity.

[0062] As a preferred solution of the present invention, S5 includes the following steps:

[0063] S51. Calculate the anchor placement demand function R at each location within the construction area based on the analysis results of the loss prediction model. anchor (x, y):

[0064]

[0065] Among them, R anchor (x, y) is the anchor arrangement requirement value at position (x, y), A panel，(x，y) is the plate coverage area f at position (x,y) anchor is the fixing force of a single anchor, β is the incremental coefficient of loss risk, which reflects the weighted impact of loss risk on the demand for anchors;

[0066] S52, according to the anchor arrangement demand function R anchor (x,y) Determine the number of anchors n in each sub-area within the construction area anchor，k :

[0067]

[0068] Among them, n anchor,k is the number of anchors in the kth sub-region, Ω k is the two-dimensional integration domain of the kth subregion, d a is the average spacing between anchors;

[0069] S53, Dynamically adjust the anchor arrangement density ρ anchor，k , giving priority to optimizing anchor stability in areas with high loss risk;

[0070] S54. Dynamically adjust the anchor placement strategy, including the specific location and placement of the anchors, to improve the anchoring effect in areas with high loss risk.

[0071] As a preferred solution of the present invention, S6 includes the following steps:

[0072] S61, based on the geometric characteristic dataset G of the construction area and the distribution characteristics Ω of the high loss risk area high Optimize the cutting size D of foamed cement insulation board cut ;

[0073] S62. Determine the cutting order π of the insulation board based on the optimized cutting size:

[0074]

[0075] Among them, Γ is the optimization objective function of the cutting order, which represents the total time cost in the cutting process, T move，i is the moving time of the i-th plate, which is related to the distance matrix of the cutting order π, T cut，i is the cutting time of the i-th plate, calculated based on the cutting complexity;

[0076] S63, according to the high loss risk area Ω high And the waste material utilization strategy to optimize the reuse of waste materials after cutting:

[0077]

[0078] Among them, U waste is the residual material utilization objective function, m is the total number of residual material fragments, A reuse，j ,j} is the reusable area of the jth piece of waste material, A waste，j is the total area of the jth piece of waste material, θ j is the surplus material adaptation coefficient, which considers the matching degree between the surplus material and the geometric characteristics in the construction area;

[0079] S64, combined with cutting size D cut , cutting order π and the way to use the leftover material, generate the optimized cutting strategy, including:

[0080] Cutting size of each board and corresponding waste distribution;

[0081] Cutting sequence and distribution of panels within the construction area;

[0082] Residue recycling plan and its matching areas.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] 1. The present invention establishes a dynamic loss prediction model and comprehensively considers the geometric characteristics, environmental conditions and construction process data of the construction area to achieve accurate identification of high loss risk areas. Traditional methods mostly rely on fixed rules or empirical judgments and cannot respond to changes in complex construction environments in real time. The present invention constructs a multi-factor coupled loss risk function to dynamically quantify material losses in irregular edge areas, areas around windows, and areas with high wind pressure and high humidity, making the identification of high loss risk areas more efficient and accurate. Through this technology, it is possible to significantly reduce material waste caused by the construction environment and geometric complexity, providing a scientific basis for subsequent construction optimization.

[0085] 2. The present invention has achieved intelligent upgrades in the optimization of anchor arrangement and plate cutting. The number, density and specific arrangement of anchors are dynamically adjusted through the anchor arrangement demand function based on the loss prediction model, and the anchoring effect of high-loss risk areas is optimized first to ensure the overall stability of construction while reducing the risk of material loss. In addition, the present invention optimizes the cutting strategy based on the distribution characteristics of high-loss areas. By improving the plate cutting size, cutting sequence and waste material utilization method, the matching degree between the cut plate and the geometric characteristics of the construction area is greatly improved, avoiding the resource loss caused by improper cutting or waste of waste materials in traditional cutting methods.

[0086] 3. This invention incorporates a multi-objective optimization mechanism into the construction plan generation process by integrating a particle swarm intelligent optimization algorithm. This model simultaneously considers the multi-dimensional objectives of construction efficiency, material loss, and construction quality to construct a comprehensive optimization model. Traditional methods are typically single-objective oriented and struggle to balance multiple factors. However, this invention's optimization algorithm generates a globally optimal construction plan based on multi-objective constraints, ensuring that material loss is minimized, anchoring effects are optimized, and construction efficiency is maximized during the construction process. Through the dynamic adjustment of intelligent construction plans, the system's adaptability and robustness in complex construction scenarios are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a flow chart of the present invention;

[0088] Figure 2 This is a flow chart for the construction of the comprehensive loss prediction model and the analysis of high-loss areas in the present invention. DETAILED DESCRIPTION

[0089] The present invention is further described below with reference to the accompanying drawings and embodiments. It is obvious that the described embodiments are only part of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without any creative work are all within the scope of protection of the present invention.

[0090] like Figure 1-2 As shown, this embodiment proposes an intelligent construction and loss management system for composite foamed cement insulation boards, including the following steps:

[0091] S1: Collects geometric characteristics of the construction area, including irregular edge information and window surrounding information, and obtains construction environmental conditions information, including high wind pressure areas or humid environments;

[0092] S2: Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation boards, including board cutting dimensions, cutting errors, material loss records, anchor placement methods and their impact on board stability, and establish a data repository;

[0093] S3: A loss prediction model for Type II composite foamed cement insulation boards is established using the geometric characteristics of the construction area, construction environmental conditions, and construction process data. The loss prediction model comprehensively considers the matching degree between the board cutting size and the geometric characteristics of the construction area, the impact of the anchor arrangement on the board stability, and the potential impact of the construction environment on the board shedding and loss.

[0094] S4: Substituting the geometric characteristics of the construction area into the loss prediction model to dynamically analyze the material loss risk at different locations within the construction area and identify high-loss risk areas, including areas with irregular edges, areas around windows, and areas with high wind pressure or high humidity.

[0095] S5: Dynamically adjust the anchor placement strategy based on the analysis results of the loss prediction model, including the number, distribution density, and layout of anchors, prioritizing the optimization of anchor stability in areas with high loss risk to ensure anchoring effectiveness while reducing material loss.

[0096] S6: Adjust the cutting strategy of the foamed cement insulation board based on the geometric characteristics of the construction area and the distribution characteristics of the high-loss risk areas. This includes optimizing the cutting size, cutting sequence, and waste material utilization of the board to ensure that the cut board has the best match with the geometric characteristics of the construction area.

[0097] S7: Apply the optimized anchor placement strategy and insulation board cutting plan to the construction process, dynamically monitor the board usage and loss level during construction, and fine-tune the construction plan based on real-time feedback;

[0098] S8: Evaluate the overall installation effect of the insulation board after optimized construction to verify whether its stability, loss level and construction quality meet the expected standards. If not, return to S3 to adjust the parameters of the loss prediction model or the optimization algorithm and re-execute the subsequent steps.

[0099] As a preferred solution of the present invention, S1 includes the following steps:

[0100] S11, collect geometric characteristic information of the construction area to build a geometric characteristic data set G, the geometric characteristic data set includes the contour data G of the construction area contour , edge irregular data G edge and window surrounding information G window , the geometric characteristic information is obtained by laser scanning or photogrammetry technology and represented as three-dimensional point cloud data P(x,y,z):

[0101] x, y, z are the three-dimensional coordinates of a point in the point cloud, representing the local features in the geometric structure of the construction area;

[0102] G contourThe overall outline information of the exterior wall of the construction area, including the boundary shape and the area of each area;

[0103] G edge The geometric characteristics of irregular edges in the construction area, including bending radius and inclination angle;

[0104] G window The geometric characteristics around the windows in the construction area, including the window size and the intersection characteristics with the wall;

[0105] S12, obtain construction environment condition information to build an environment condition data set E, the environment condition data set includes high wind pressure area data E wind and humid environment data E humidity ;

[0106] S13. Based on the collected geometric characteristic information dataset G and environmental condition dataset E, a comprehensive data matrix M is established:

[0107] M=[G contour G edge G window E wind E humidity ];

[0108] Each column corresponds to the geometric or environmental feature information of a different area.

[0109] As a preferred solution of the present invention, S2 comprises the following steps:

[0110] S21. Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation board to construct a construction process dataset D. The construction process dataset D includes the cutting size data D of the board. cut , clipping error data D error , Material loss record data D loss , Anchor arrangement data D anchor Data D on the influence of anchor arrangement on plate stability stablity :

[0111] Cutting size data D of the plate cut ={l i ,w i ,t i}, represents the length l of different plates during the historical construction process i 、Width w i , thickness t i ;

[0112] Clipping error data D error Indicates the error in length, width and thickness during the cutting process of the board;

[0113] Material loss record data D loss The actual loss of the plate recorded during construction, expressed in unit volume V loss express;

[0114] Anchor arrangement data D anchor ={n a , d a , p a} represents the number of anchors n a , Anchor spacing d a and the arrangement position of the anchor a ;

[0115] Data D on the influence of anchor arrangement on plate stability stability The comprehensive effect of anchor arrangement on plate stability is expressed by combining anchor arrangement data and plate dimensional characteristics;

[0116] S22. Establish a construction data repository S based on the collected construction process data set D D The contents of the repository include data on the matching between the cutting size of the plate and the target size, material loss records, and data on the partitioned impact of anchor arrangement on plate stability.

[0117] As a preferred solution of the present invention, S3 includes the following steps:

[0118] S31. Using the construction area's geometric characteristics dataset G, the environmental conditions dataset E, and the construction process dataset D, a loss prediction model L for type II composite foamed cement insulation panels is established. The loss prediction model comprehensively considers the matching degree between the panel cutting dimensions and the construction area's geometric characteristics, the effect of the anchor arrangement on the panel's stability, and the potential impact of the construction environment on panel shedding and loss.

[0119] S32. Construct the loss function F of the matching degree between the plate cutting size and the geometric characteristics of the construction area match :

[0120]

[0121] Among them, n is the total number of plates that need to be cut, A waste，i A represents the waste area remaining after cutting the i-th plate. board，i is the original area of the i-th plate, κ is the loss nonlinear coefficient, reflecting the nonlinear relationship between the loss rate and the waste ratio, W material is the weight of the plate per unit area, φ i is the complexity coefficient of the i-th plate in the construction area, and its value range is 0<φ i ≤1, the more complex the construction area, the i The closer it is to 1, the greater the impact of complex geometry on clipping loss;

[0122] S33. Construct the loss function F of the effect of anchor arrangement on plate stability stability :

[0123]

[0124] Where m is the number of zones in the construction area, S stability，j represents the stability coefficient of the plate in the jth partition, η j is the environmental correction factor, A panel，j is the total area of the plate in the jth partition;

[0125] S34. Construct the loss function F of the influence of construction environment on the shedding and loss of plate env :

[0126]

[0127] Among them, A total is the total area of the exterior walls of the construction area, E wind is the actual wind pressure value in the construction area, E wind，max is the maximum wind pressure value allowed in the design, λ is the wind pressure influence index, E humidity is the actual relative humidity in the construction area, ranging from 0 to E humidity ≤1, E humidity，max The maximum relative humidity allowed by the design, the value range is 0<E humidity，max ≤1, μ is the humidity influence index, reflecting the nonlinear influence of humidity on plate loss;

[0128] S35, integrate the loss functions of S32-S34 to establish the total loss prediction model L total :

[0129] L total =ω match ×F match +ω stability ×F stability +ω env ×F env ;

[0130] Among them, ω match 、ω stability 、ω env It is the loss weight coefficient, which is used to balance the contribution of different loss factors to the total loss and is set according to the actual needs of the project.

[0131] As a preferred solution of the present invention, S4 includes the following steps:

[0132] S41, substitute the geometric characteristic dataset G, environmental condition dataset E and construction process dataset D of the construction area into the loss prediction model L total Calculate the material loss risk R at each location within the construction area loss (x, y):

[0133] S42. Based on the loss risk function R loss (x, y) calculates the average loss risk R of each sub-area within the construction area avg，k :

[0134]

[0135] Among them, Ω k is the two-dimensional integral domain of the kth sub-area in the construction area, A k is the area of the kth subregion, R loss (x, y) is the loss risk of each point in the sub-area;

[0136] S43. Identify high loss risk areas Ω high , the construction area is divided into two categories: high loss risk area and ordinary area. The high loss risk area meets the following conditions:

[0137] Ω high ={(x,y)∈Ω|R loss (x,y)≥R threshold};

[0138] Among them, R threshold is the loss risk threshold, Ω is the entire construction area;

[0139] S44. Segment specific high-risk scenarios based on the distribution of high-loss risk areas:

[0140] Irregular edge regions: Based on the complexity of irregular edges in the geometric feature dataset, we can identify high-loss areas where cropping becomes more difficult due to bending or tilting.

[0141] The area around the window, based on the size of the window and the characteristics of its intersection with the wall in the geometric characteristic data set, identifies high-loss areas due to high cutting accuracy requirements or large installation complexity;

[0142] High wind pressure areas: Based on the spatial distribution of wind pressure values in the environmental condition dataset, identify high-loss areas where wind pressure can cause material shedding or anchor failure.

[0143] High humidity areas, based on the spatial distribution of humidity values in the environmental condition dataset, identify high-loss areas where material performance deteriorates or construction becomes difficult due to high humidity.

[0144] As a preferred solution of the present invention, S5 includes the following steps:

[0145] S51. Calculate the anchor placement demand function R at each location within the construction area based on the analysis results of the loss prediction model. anchor (x, y):

[0146]

[0147] Among them, R anchor (x, y) is the anchor arrangement requirement value at position (x, y), A panel，(x，y) is the plate coverage area f at position (x,y) anchor is the fixing force of a single anchor, β is the incremental coefficient of loss risk, which reflects the weighted impact of loss risk on the demand for anchors;

[0148] S52, according to the anchor arrangement demand function R anchor (x, y) determines the number of anchors n in each sub-area within the construction area anchor，k :

[0149]

[0150] Among them, n anchor，k is the number of anchors in the kth sub-region, Ω k is the two-dimensional integration domain of the kth subregion, d a is the average spacing between anchors;

[0151] S53, Dynamically adjust the anchor arrangement density ρ anchor，k , giving priority to optimizing anchor stability in areas with high loss risk;

[0152] S54. Dynamically adjust the anchor placement strategy, including the specific location and placement of the anchors, to improve the anchoring effect in areas with high loss risk.

[0153] As a preferred solution of the present invention, S6 includes the following steps:

[0154] S61, based on the geometric characteristic dataset G of the construction area and the distribution characteristics Ω of the high loss risk area high Optimize the cutting size D of foamed cement insulation board cut ;

[0155] S62. Determine the cutting order π of the insulation board based on the optimized cutting size:

[0156]

[0157] Among them, Γ is the optimization objective function of the cutting order, which represents the total time cost in the cutting process, T move，i is the moving time of the i-th plate, which is related to the distance matrix of the cutting order π, T cut，iis the cutting time of the i-th plate, calculated based on the cutting complexity;

[0158] S63, according to the high loss risk area Ω high And the waste material utilization strategy to optimize the reuse of waste materials after cutting:

[0159]

[0160] Among them, U waste is the residual material utilization objective function, m is the total number of residual material fragments, A reuse,j ,j} is the reusable area of the jth piece of waste material, A waste,j is the total area of the jth piece of waste material, θ j is the surplus material adaptation coefficient, which considers the matching degree between the surplus material and the geometric characteristics in the construction area;

[0161] S64, combined with cutting size D cut , cutting order π and the way to use the leftover material, generate the optimized cutting strategy, including:

[0162] Cutting size of each board and corresponding waste distribution;

[0163] Cutting sequence and distribution of panels within the construction area;

[0164] Residue recycling plan and its matching areas.

[0165] The following are the operating steps and construction cases of the present invention:

[0166] In a previous construction case, the complex had a construction area of 28,000 square meters and a total exterior wall area of 13,000 square meters. The winter climate was cold, with the lowest temperature reaching -25°C and high wind speeds around the building, with an average wind speed of 14m / s. The owner required reducing the building's heating energy consumption, while also minimizing construction material waste and optimizing the construction process to ensure the overall thermal insulation performance and structural stability of the exterior wall after construction was completed.

[0167] The project implementation team decided to adopt the particle swarm intelligence optimization-based exterior wall insulation thickness distribution design method of the present invention and apply it to the design optimization of the north-south main walls and the window-wall connection areas on the east and west sides of the complex.

[0168] On December 10, the implementation team used 3D laser scanning equipment to conduct a comprehensive scan of the building's exterior walls and generated a high-precision point cloud model. In the point cloud model, it was found that the area of the south wall was 6,500 square meters, the area of the north wall was 5,500 square meters, and the window-wall connections were distributed on the east and west walls, with a total area of approximately 1,000 square meters.

[0169] Environmental monitoring equipment recorded the climate conditions in the construction area during three consecutive days of data collection:

[0170] The maximum wind speed was 16 m / s, mainly concentrated in the south wall near the corner area;

[0171] The average humidity on the north wall is 85%, which is significantly higher than other areas;

[0172] The average daytime temperature is -10℃ and the lowest nighttime temperature is -25℃.

[0173] The material data of Type II composite foamed cement insulation board collected during the construction process showed that the original size of each board was 1200*800*600, and the average loss rate of the board after cutting was 18%.

[0174] During the data analysis phase on December 15, the construction team input the aforementioned geometric characteristic data, environmental condition data, and construction process data into the loss prediction model. The model analysis revealed the following key issues:

[0175] 1. High wind pressure area: Due to high wind speeds, the area near the southeast corner of the south wall had a potential risk score of 85 points (out of a total of 100 points), significantly exceeding the safety threshold of 70 points.

[0176] 2. High humidity area: Due to high humidity in the north wall near the window, the insulation material loss risk score due to moisture is 78 points, also exceeding the threshold;

[0177] 3. Cutting loss: The irregular edge area of the south wall has a cutting waste area of 32 square meters. The model predicts that if the cutting strategy is not optimized, the cutting loss rate will be as high as 20% after construction is completed.

[0178] After analyzing the results, the team immediately generated a distribution map of high-loss risk areas and marked them on the 3D building model.

[0179] On December 17, the team used the particle swarm optimization algorithm of this invention to optimize the thickness distribution of the insulation layer in the above-mentioned high-loss risk areas. The optimization process used the following parameters:

[0180] The thickness of the south wall foundation insulation layer is 60mm, and the thickness of the north wall foundation insulation layer is 50mm;

[0181] The thickness adjustment range in high wind pressure areas is 80-100mm;

[0182] The thickness adjustment range for high humidity areas is 70-90mm.

[0183] The optimized thickness distribution results are:

[0184] The insulation thickness of the high wind pressure area of the south wall is adjusted to 90mm, and the middle ordinary area is 60mm;

[0185] The thickness of the north wall near the window area is adjusted to 80mm, and the thickness of other areas is adjusted to 50mm;

[0186] The window-wall connection is adjusted to 70mm to reduce heat loss.

[0187] On February 20, the team began optimizing the material cutting strategy, focusing on high-loss areas. The optimized cutting plan included:

[0188] Intelligent cutting equipment was used on the irregular edge areas of the south wall to reduce the generation of large pieces of waste;

[0189] The trimmed leftovers are reused to fill in the small areas where windows and walls meet, with a total utilization rate of 92%.

[0190] By optimizing the cutting sequence and the way the remaining materials are used, the team reduced the cutting waste rate from 20% to 5%.

[0191] During the construction process on December 25, the construction team dynamically adjusted the density of anchor bolts according to the optimized design plan:

[0192] The anchor density in the high wind pressure area of the south wall is adjusted to 12 per square meter, while the density in the general area remains at 8 per square meter.

[0193] The density of anchor nails in the window area of the north wall is adjusted to 10 / ㎡, and in the rest of the area it is 6 / ㎡.

[0194] At the same time, the on-site construction management system monitors the cutting waste rate and material loss in real time, and finds that the cutting error during the construction process is controlled within 2mm.

[0195] After the construction was completed, the implementation team evaluated the effectiveness of the optimized solution and compared it with the traditional method. The results are as follows:

[0196]

[0197]

[0198] The implementation results show that the method of the present invention significantly reduces the loss of construction materials, optimizes the thickness distribution of the insulation layer, and reduces construction time and labor costs. Taking the south wall as an example, by dynamically adjusting the thickness of the insulation layer and the anchor layout strategy, the material shedding risk score in the high wind pressure area is reduced from 85 points to 40 points, and the material loss risk score in the high humidity area of the north wall is reduced from 78 points to 35 points. The final insulation effect is significantly better than the traditional method, and the annual building energy consumption is reduced by 30%.

[0199] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent construction and loss management system for composite foamed cement insulation boards, characterized in that: The following steps are involved: S1: Collects geometric characteristics of the construction area, including irregular edge information and window surrounding information, and obtains construction environmental conditions information, including high wind pressure areas or humid environments; S2: Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation boards, including board cutting dimensions, cutting errors, material loss records, anchor placement methods and their impact on board stability, and establish a data repository; S3: A loss prediction model for Type II composite foamed cement insulation boards is established using the geometric characteristics of the construction area, construction environmental conditions, and construction process data. The loss prediction model comprehensively considers the matching degree between the board cutting size and the geometric characteristics of the construction area, the impact of the anchor arrangement on the board stability, and the potential impact of the construction environment on the board shedding and loss. S4: Substituting the geometric characteristics of the construction area into the loss prediction model to dynamically analyze the material loss risk at different locations within the construction area and identify high-loss risk areas, including areas with irregular edges, areas around windows, and areas with high wind pressure or high humidity. S5: Dynamically adjust the anchor placement strategy based on the analysis results of the loss prediction model, including the number, distribution density, and layout of anchors, prioritizing the optimization of anchor stability in areas with high loss risk to ensure anchoring effectiveness while reducing material loss. S6: Adjust the cutting strategy of the foamed cement insulation board based on the geometric characteristics of the construction area and the distribution characteristics of the high-loss risk areas. This includes optimizing the cutting size, cutting sequence, and waste material utilization of the board to ensure that the cut board has the best match with the geometric characteristics of the construction area. S7: Apply the optimized anchor placement strategy and insulation board cutting plan to the construction process, dynamically monitor the board usage and loss level during construction, and fine-tune the construction plan based on real-time feedback; S8: Evaluate the overall installation effect of the insulation board after optimized construction to verify whether its stability, loss level and construction quality meet the expected standards. If not, return to S3 to adjust the parameters of the loss prediction model or the optimization algorithm and re-execute the subsequent steps.

2. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S1 includes the following steps: S11, collect geometric characteristic information of the construction area to build a geometric characteristic data set G, the geometric characteristic data set includes the contour data G of the construction area contour , edge irregular data G edge and window surrounding information G window , the geometric characteristic information is obtained by laser scanning or photogrammetry technology and represented as three-dimensional point cloud data P(x,y,z): x, y, z are the three-dimensional coordinates of a point in the point cloud, representing the local features in the geometric structure of the construction area; G contour The overall outline information of the exterior wall of the construction area, including the boundary shape and the area of each area; G edge The geometric characteristics of irregular edges in the construction area, including bending radius and inclination angle; G window The geometric characteristics around the windows in the construction area, including the window size and the intersection characteristics with the wall; S12, obtain construction environment condition information to build an environment condition data set E, the environment condition data set includes high wind pressure area data E wind and humid environment data E humidity ; S13. Based on the collected geometric characteristic information dataset G and environmental condition dataset E, a comprehensive data matrix M is established: M=[G contour G edge G window E win E humidity ]; Each column corresponds to the geometric or environmental feature information of a different area.

3. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S2 comprises the following steps: S21. Based on historical construction projects, collect construction process data related to Type II composite foamed cement insulation board to construct a construction process dataset D. The construction process dataset D includes the cutting size data D of the board. cut , clipping error data D error , Material loss record data D loss , Anchor arrangement data D anchor Data D on the influence of anchor arrangement on plate stability stability : Cutting size data D of the plate cut ={l i , w i , t i }, represents the length l of different plates during the historical construction process i 、Width w i , thickness t i ; Clipping error data D error Indicates the error in length, width and thickness during the cutting process of the board; Material loss record data D loss The actual loss of the plate recorded during construction, expressed in unit volume V loss express; Anchor arrangement data D anchor ={n a , d a , p a } represents the number of anchors n a , Anchor spacing d a and the arrangement position of the anchor a ; Data D on the influence of anchor arrangement on plate stability stability The comprehensive effect of anchor arrangement on plate stability is expressed by combining anchor arrangement data with plate dimensional characteristics. S22. Establish a construction data repository S based on the collected construction process data set D D The contents of the repository include data on the matching between the cutting size of the plate and the target size, material loss records, and data on the partitioned impact of anchor arrangement on plate stability.

4. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S3 includes the following steps: S31. Using the construction area's geometric characteristics dataset G, the environmental conditions dataset E, and the construction process dataset D, a loss prediction model L for type II composite foamed cement insulation panels is established. The loss prediction model comprehensively considers the matching degree between the panel cutting dimensions and the construction area's geometric characteristics, the effect of the anchor arrangement on the panel's stability, and the potential impact of the construction environment on panel shedding and loss. S32. Construct the loss function F of the matching degree between the plate cutting size and the geometric characteristics of the construction area match : Among them, n is the total number of plates that need to be cut, A waste，i A represents the remaining waste area after cutting the i-th plate. board，i is the original area of the i-th plate, κ is the loss nonlinear coefficient, reflecting the nonlinear relationship between the loss rate and the waste ratio, W material is the weight of the plate per unit area, φ i is the complexity coefficient of the i-th plate in the construction area, and its value range is 0<φ i ≤1, the more complex the construction area, the i The closer it is to 1, the greater the impact of complex geometry on clipping loss; S33. Construct the loss function F of the effect of anchor arrangement on plate stability stability : Where m is the number of zones in the construction area, S stability，j represents the stability coefficient of the plate in the jth partition, η j is the environmental correction factor, A panel，j is the total area of the plate in the jth partition; S34. Construct the loss function F of the influence of construction environment on the shedding and loss of plate env : Among them, A total is the total area of the exterior walls of the construction area, E wind is the actual wind pressure value in the construction area, E wind，max is the maximum wind pressure value allowed in the design, λ is the wind pressure influence index, E humidity is the actual relative humidity in the construction area, ranging from 0 to E humidity ≤1, E humidity，max The maximum relative humidity allowed by the design, the value range is 0<E humidity，max ≤1, μ is the humidity influence index, reflecting the nonlinear influence of humidity on plate loss; S35, integrate the loss functions of S32-S34 to establish the total loss prediction model L total : L total =ω match ×F match +oh stabilty ×F stability +oh env ×F env ; Among them, ω match 、ω stability 、ω env It is the loss weight coefficient, which is used to balance the contribution of different loss factors to the total loss and is set according to the actual needs of the project.

5. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S4 includes the following steps: S41, substitute the geometric characteristic dataset G, environmental condition dataset E and construction process dataset D of the construction area into the loss prediction model L total Calculate the material loss risk R at each location within the construction area loss (x, y): S42. Based on the loss risk function R loss (x, y) calculates the average loss risk R of each sub-area within the construction area avg，k : Among them, Ω k is the two-dimensional integral domain of the kth sub-area in the construction area, A k is the area of the kth subregion, R loss (x, y) is the loss risk of each point in the sub-area; S43. Identify high loss risk areas Ω hight , the construction area is divided into two categories: high loss risk area and ordinary area. The high loss risk area meets the following conditions: Ω high ={(x,y)∈Ω|R loss (x,y)≥R threshold }; Among them, R threshold is the loss risk threshold, Ω is the entire construction area; S44. Segment specific high-risk scenarios based on the distribution of high-loss risk areas: Irregular edge regions: Based on the complexity of irregular edges in the geometric feature dataset, we can identify high-loss areas where cropping becomes more difficult due to bending or tilting. The area around the window, based on the size of the window and the characteristics of its intersection with the wall in the geometric characteristic data set, identifies high-loss areas due to high cutting accuracy requirements or large installation complexity; High wind pressure areas: Based on the spatial distribution of wind pressure values in the environmental condition dataset, identify high-loss areas where wind pressure can cause material shedding or anchor failure. High humidity areas, based on the spatial distribution of humidity values in the environmental condition dataset, identify high-loss areas where material performance deteriorates or construction becomes difficult due to high humidity.

6. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S5 includes the following steps: S51. Calculate the anchor placement demand function R at each location within the construction area based on the analysis results of the loss prediction model. anchor (x, y): Among them, R anchor (x, y) is the anchor arrangement requirement value at position (x, y), A panel，(x，y) is the plate coverage area f at position (x,y) anchor is the fixing force of a single anchor, β is the incremental coefficient of loss risk, which reflects the weighted impact of loss risk on the demand for anchors; S52, according to the anchor arrangement demand function R anchor (x,y) Determine the number of anchors n in each sub-area within the construction area anchor,k : Among them, n anchor,k is the number of anchors in the kth sub-region, Ω k is the two-dimensional integration domain of the kth subregion, d a is the average spacing between anchors; S53, Dynamically adjust the anchor arrangement density ρ anchor，k , giving priority to optimizing anchor stability in areas with high loss risk; S54. Dynamically adjust the anchor placement strategy, including the specific location and placement of the anchors, to improve the anchoring effect in areas with high loss risk.

7. The intelligent construction and loss management system for composite foamed cement insulation boards according to claim 1, characterized in that: The S6 includes the following steps: S61, based on the geometric characteristic dataset G of the construction area and the distribution characteristics Ω of the high loss risk area high Optimize the cutting size D of foamed cement insulation board cut ; S62. Determine the cutting order π of the insulation board based on the optimized cutting size: Among them, Γ is the optimization objective function of the cutting order, which represents the total time cost in the cutting process, T move,i is the moving time of the i-th plate, which is related to the distance matrix of the cutting order π, T cut，i is the cutting time of the i-th plate, calculated based on the cutting complexity; S63, according to the high loss risk area Ω high And the waste material utilization strategy to optimize the reuse of waste materials after cutting: Among them, U waste is the residual material utilization objective function, m is the total number of residual material fragments, A reuse，j ,j} is the reusable area of the jth piece of waste material, A waste，j is the total area of the jth piece of waste material, θ j is the surplus material adaptation coefficient, which considers the matching degree between the surplus material and the geometric characteristics in the construction area; S64, combined with cutting size D cut , cutting order π and the way to use the leftover material, generate the optimized cutting strategy, including: Cutting size of each board and corresponding waste distribution; Cutting sequence and distribution of panels within the construction area; Residue recycling plan and its matching areas.

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