Environmental design work display system

Abstract

The present application relates to the technical field of design work display, and specifically relates to an environmental design work display system, which comprises a model extraction module, a three-dimensional laser scanner is used to scan a design work model, point cloud data is obtained, and a digital twin model is generated; an intelligent detection module is used to detect the digital twin model and identify design defect areas; a dynamic optimization module generates a plurality of parameter adjustment schemes for each defect area; and an interactive display module synchronously displays an original design, optimization schemes and key index comparison data in a side-by-side view, and supports a user to select and apply an optimization scheme in real time. The present application generates a digital twin model through three-dimensional laser scanning, automatically detects key parameters such as fire passage width and energy-saving heat transfer coefficient in combination with a specification database, and calculates defect severity scores, so as to realize the precision of problem positioning and the priority of sorting.

Description

Environmental Design Works Display System Technical Field

[0001] This invention relates to the field of design work display technology, specifically to an environmental design work display system. Background Technology

[0002] In traditional environmental design portfolio presentations, designers typically rely on two-dimensional drawings or static three-dimensional models to showcase their solutions. However, these methods have the following limitations:

[0003] Defect detection is inefficient; manual inspection of whether the design complies with fire protection, energy conservation and other standards is time-consuming and prone to omissions, especially hidden problems in complex spatial structures; optimization solutions are limited, and design adjustments rely heavily on experience, lacking comprehensive evaluation with quantitative indicators, making it difficult to balance the needs of multiple parties; interactivity is insufficient, users cannot participate in solution optimization in real time, and the display format lacks dynamic comparison, resulting in long decision-making cycles and poor user experience.

[0004] While digital twin technology has been applied in the construction field, it is mostly focused on construction monitoring or operation and maintenance. A systematic solution has not yet been formed for automated defect repair and multi-objective optimization in the design phase. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an environmental design works display system, which can effectively solve the problems mentioned in the existing technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides an environmental design work display system, comprising:

[0008] The model extraction module uses a 3D laser scanner to scan the design model, obtain point cloud data, and generate a digital twin model.

[0009] The intelligent detection module is used to detect digital twin models, identify areas of design defects, calculate the defect severity score S for each defect area, and set a threshold for the defect severity score. When the calculated defect severity score S is greater than the severity score threshold Dynamically repair the design work at that time;

[0010] The dynamic optimization module generates several parameter adjustment schemes for each defect area and calculates the correction cost of each scheme. Environmental enhancement value Matching user preferences with the degree of P, and adjusting costs Environmental enhancement value The user preference matching degree P is normalized to generate a decision matrix, including the adjusted cost score. Environmental improvement score User preference rating The optimal solution is obtained by combining the decision matrix;

[0011] The interactive display module synchronously displays the original design, optimized solutions, and key indicator comparison data in a side-by-side view, allowing users to select and apply optimized solutions in real time.

[0012] Furthermore, the intelligent detection module includes:

[0013] Standardize database units and adopt a database sharding architecture storage design standard, which includes a first threshold. Second threshold The first threshold is the fire lane width threshold, and the second threshold is the energy-saving heat transfer coefficient threshold.

[0014] The width of the fire lane is obtained from the digital twin model data, and the energy-saving heat transfer coefficient is obtained by the following formula:

[0015]

[0016] in, For energy-saving heat transfer coefficient, For the glass area, Let be the heat transfer coefficient of the glass. For the area of ​​non-transparent walls, The heat transfer coefficient of the non-transparent wall. This refers to the total area of ​​both glass and opaque walls;

[0017] Defect identification unit: In real time, compare the parameter values ​​of the digital twin model with the design standards stored in the specification database unit. When the width of the fire lane is less than the first threshold or the energy-saving heat transfer coefficient is greater than the second threshold, mark the fire lane area or wall area of ​​the digital twin model as a defect area.

[0018] Calculate the defect severity score S for the defective region:

[0019]

[0020] in, , These are the weighting coefficients. , This refers to the actual width of the fire lane. This represents the actual energy-saving heat transfer coefficient.

[0021] When the defect severity score S is greater than the severity score threshold At that time, the design work is optimized.

[0022] Furthermore, the dynamic optimization module generates the adjustment scheme in the following manner:

[0023] Call the parametric design template library to match the adjustment strategy corresponding to the defect type, including:

[0024] When the width of the fire lane is less than the first threshold, the following solution is generated:

[0025]

[0026] in, This refers to the width of the fire escape after the wall has been moved. This is the distance the wall needs to be moved. The number of additional escape routes required. This is the total length of the fire lane. This represents the minimum number of escape routes.

[0027] When the energy-saving heat transfer coefficient is greater than the second threshold, the following scheme is generated:

[0028]

[0029] A genetic algorithm is used to iteratively generate parameter combinations that satisfy multiple constraints.

[0030] Furthermore, the specific steps for iteratively generating parameter combinations that satisfy multiple constraints using a genetic algorithm include:

[0031] Construct a chromosome that includes fire escape routes and energy-saving heat transfer adjustment parameters;

[0032] Optimize the objective function:

[0033]

[0034] in, Let be the objective function. and As weight, , For the normalization correction cost, For normalized environmental improvement value, Normalized user preference matching degree;

[0035] Iteratively perform the selection-crossover-mutation operation until convergence, and output the Pareto optimal solution set;

[0036] The optimal solution set is the final optimized solution.

[0037] Furthermore, several parameter adjustment schemes are generated for each defective region, and the correction cost of each scheme is calculated. and environmental improvement value The specific steps include:

[0038] The correction cost The calculation formula is:

[0039]

[0040] in, Let be the unit adjusted cost coefficient for the i-th type of scheme. Let i be the adjustment amount for the i-th type of scheme. For the complexity factor of the construction environment, For fixed costs;

[0041] The environmental improvement value The calculation formula is:

[0042]

[0043] in, As weight, For the actual parameter values ​​of the materials used in the i-th type of scheme, The industry benchmark value for materials used in the i-th type of scheme.

[0044] Furthermore, the user preference matching degree P is obtained by collecting users' historical operation data, acquiring the frequency of users' historical material selections, the design style selections in the past, and the increase in the average acceptance cost in the past. The material similarity is obtained by weighting and summing the frequency of users' historical material selections. The calculation formula is as follows:

[0045]

[0046] in, For material similarity, For materials The percentage of usage in the current plan, Let be a function representing the material in the user's historical preference set. The value is 1 if it is in the middle, and 0 otherwise. This is the user's historical preference set, i.e., the set of materials that the user uses frequently;

[0047] Next, user preference style vectors are obtained by analyzing user history and design style selections. Style similarity is then calculated using the vector space cosine similarity formula:

[0048]

[0049] in, For style similarity, For user preference style vectors, This represents the style vector for the current scheme;

[0050] Next, cost sensitivity is calculated using the historical average increase in user acceptance cost, as shown in the formula:

[0051]

[0052] in, Due to cost sensitivity, It is a natural exponential function. To correct costs, This represents the increase in the historical average cost of acceptance for users. Accepting standard deviation of fluctuations in user costs;

[0053] Finally, the user preference matching degree is calculated using the following formula:

[0054]

[0055] in, For user preference matching degree, and As weight, .

[0056] Furthermore, the interactive display module includes:

[0057] Three-dimensional difference units, using red-yellow-green scales to identify regions of defect severity score S from high to low;

[0058] The solution comparison unit is used to display the cost change rate before and after optimization in real time;

[0059] The voice interaction unit is used to respond to optimization instructions and update the model synchronously. The optimization instructions include selecting scheme A, selecting scheme B, selecting scheme C, and selecting scheme D.

[0060] A method for displaying environmental design works, including:

[0061] S1. Use a 3D laser scanner to scan the design work, obtain point cloud data, build a digital twin model based on the point cloud data, and extract spatial structure parameters and material property parameters;

[0062] S2. Detect the digital twin model, identify design defect areas, calculate the defect severity score S for each defect area, and set a defect severity score threshold. When the calculated defect severity score S is greater than the severity score threshold Dynamically repair the design work at that time;

[0063] S3. Generate several parameter adjustment schemes for each defect area and calculate the correction cost of each scheme. Environmental enhancement value Matching degree P with user preferences;

[0064] S4, Adjustment Costs Environmental enhancement value The matching degree P with user preferences is normalized to obtain the decision matrix, and the optimal solution is obtained by combining the decision matrix.

[0065] S5 displays the original design, optimized solutions, and key indicator comparison data in a side-by-side view, allowing users to select and apply optimized solutions in real time.

[0066] Furthermore, the generation of the optimization scheme includes:

[0067] When the width of the fire escape route is insufficient, two adjustment options are generated: moving the wall or adding an escape route.

[0068] When the energy-saving heat transfer coefficient is found to be substandard, two adjustment schemes are generated: changing the glass type and reducing the glass area.

[0069] Furthermore, it also includes:

[0070] Record the optimization schemes selected by users and the paths that are manually modified;

[0071] Update the user preference model based on the recorded data to improve the accuracy of subsequent recommendations.

[0072] The technical solution provided by this invention has the following advantages compared with the known prior art:

[0073] 1. This invention generates a digital twin model through three-dimensional laser scanning, and automatically detects key parameters such as fire lane width and energy-saving heat transfer coefficient by combining it with a standard database, and calculates the severity score of defects to achieve accurate problem location and priority ranking.

[0074] 2. Generate multiple adjustment solutions for defects, such as moving the wall or changing the glass type, and optimize the objective function based on the genetic algorithm. Take into account the correction cost, environmental improvement value and user preference matching degree, and output the Pareto optimal solution set to ensure the balance between the economic efficiency, environmental protection and personalized needs of the solution.

[0075] 3. By comparing the original design and the optimized solution through side-by-side views, and using red-yellow-green scales to indicate the severity of defects, the system supports real-time adjustment of the model via voice commands, reducing the user's operational threshold and improving decision-making efficiency. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0077] Figure 1 is a schematic diagram of the modules of the present invention;

[0078] Figure 2 is a schematic diagram of the process of the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0080] The present invention will be further described below with reference to embodiments.

[0081] Example 1: Referring to Figure 1, an environmental design work display system includes:

[0082] The model extraction module uses a 3D laser scanner to scan the design model, obtain point cloud data, and generate a digital twin model.

[0083] The intelligent detection module is used to detect digital twin models, identify areas of design defects, calculate the defect severity score S for each defect area, and set a threshold for the defect severity score. When the calculated defect severity score S is greater than the severity score threshold Dynamically repair the design work at that time;

[0084] The dynamic optimization module generates several parameter adjustment schemes for each defect area and calculates the correction cost of each scheme. Environmental enhancement value Matching user preferences with the degree of P, and adjusting costs Environmental enhancement value The user preference matching degree P is normalized to generate a decision matrix, including the adjusted cost score. Environmental improvement score User preference rating The optimal solution is obtained by combining the decision matrix;

[0085] The interactive display module synchronously displays the original design, optimized solutions, and key indicator comparison data in a side-by-side view, allowing users to select and apply optimized solutions in real time.

[0086] In one specific embodiment, a 3D laser scanner is used to scan the lobby of a commercial complex to obtain point cloud data including the glass curtain wall, fire exits, and decorative columns, generating a digital twin model and extracting parameters such as the actual width of the fire exits. Standardized threshold glass area Non-transparent wall area The current heat transfer coefficients of glass and walls are calculated, and the areas to be optimized, such as fire escape routes (red) and glass curtain walls (yellow), are marked in the visualization model.

[0087] Furthermore, the intelligent detection module includes:

[0088] Standardize database units and adopt a database sharding architecture storage design standard, which includes a first threshold. Second threshold The first threshold is the fire lane width threshold, and the second threshold is the energy-saving heat transfer coefficient threshold.

[0089] The width of the fire lane is obtained from the digital twin model data, and the energy-saving heat transfer coefficient is obtained using the following formula:

[0090]

[0091] in, For energy-saving heat transfer coefficient, For the glass area, Let be the heat transfer coefficient of the glass. For the area of ​​non-transparent walls, The heat transfer coefficient of the non-transparent wall. This refers to the total area of ​​both glass and opaque walls;

[0092] Defect identification unit: In real time, compare the parameter values ​​of the digital twin model with the design standards stored in the specification database unit. When the width of the fire lane is less than the first threshold or the energy-saving heat transfer coefficient is greater than the second threshold, mark the fire lane area or wall area of ​​the digital twin model as a defect area.

[0093] Calculate the defect severity score S for the defective area:

[0094]

[0095] in, , These are the weighting coefficients. , This refers to the actual width of the fire lane. This represents the actual energy-saving heat transfer coefficient.

[0096] When the defect severity score S is greater than the severity score threshold At that time, the design work is optimized.

[0097] In one specific embodiment, the calculated energy-saving heat transfer coefficient meets the threshold standard and is qualified; however, the fire lane width is 1.2m < 1.5m, which is marked as a defect.

[0098] Calculate the defect severity score S, weights Fire safety has a higher weight. The formula is:

[0099] ;

[0100] Severity scoring threshold ,because The system prompts an optimization request: "Fire lane width is insufficient and needs optimization."

[0101] Furthermore, the dynamic optimization module generates adjustment plans in the following ways:

[0102] Call the parametric design template library to match the adjustment strategy corresponding to the defect type, including:

[0103] When the width of the fire lane is less than the first threshold, the following solution is generated:

[0104]

[0105] in, This refers to the width of the fire escape after the wall has been moved. This is the distance the wall needs to be moved. The number of additional escape routes required. This is the total length of the fire lane. This represents the minimum number of escape routes.

[0106] When the energy-saving heat transfer coefficient is greater than the second threshold, the following scheme is generated:

[0107]

[0108] A genetic algorithm is used to iteratively generate parameter combinations that satisfy multiple constraints.

[0109] Furthermore, the specific steps for iteratively generating parameter combinations that satisfy multiple constraints using a genetic algorithm include:

[0110] Construct a chromosome that includes fire escape routes and energy-saving heat transfer adjustment parameters;

[0111] Optimize the objective function:

[0112]

[0113] in, Let be the objective function. and As weight, , For the normalization correction cost, For normalized environmental improvement value, Normalized user preference matching degree;

[0114] Iteratively perform the selection-crossover-mutation operation until convergence, and output the Pareto optimal solution set;

[0115] The optimal solution set is the final optimized solution.

[0116] In one specific embodiment, an adjustment plan is generated to address the fire escape route issue, including:

[0117] Option A: Move the wall. , Correction costs Including construction complexity factor ;

[0118] Option B: Add an escape route. Correction costs ;

[0119] Regarding the fire escape route issue, although it met the standards, the user requested improvements, resulting in an adjustment plan including:

[0120] Option C: Replace the glass with vacuum glass. , 20,000 yuan;

[0121] Option D: Reduce the glass area by 10%. , 12,000 yuan.

[0122] Furthermore, several parameter adjustment schemes are generated for each defective area, and the correction cost of each scheme is calculated. and environmental improvement value The specific steps include:

[0123] Correction costs The calculation formula is:

[0124]

[0125] in, Let be the unit adjusted cost coefficient for the i-th type of scheme. Let i be the adjustment amount for the i-th type of scheme. For the complexity factor of the construction environment, For fixed costs;

[0126] Environmental Enhancement Value The calculation formula is:

[0127]

[0128] in, As weight, For the actual parameter values ​​of the materials used in the i-th type of scheme, The industry benchmark value for materials used in the i-th type of scheme.

[0129] Furthermore, the user preference matching degree P is obtained by collecting users' historical operation data, acquiring the frequency of users' historical material selections, the design style selections in their history, and the increase in users' historical average acceptance cost. The material similarity is then obtained by weighting and summing the frequency of users' historical material selections. The calculation formula is as follows:

[0130]

[0131] in, For material similarity, For materials The percentage of usage in the current plan, Let be a function representing the material in the user's historical preference set. The value is 1 if it is in the middle, and 0 otherwise. This is the user's historical preference set, i.e., the set of materials that the user uses frequently;

[0132] Next, user preference style vectors are obtained by analyzing user history and design style selections. Style similarity is then calculated using the vector space cosine similarity formula:

[0133]

[0134] in, For style similarity, For user preference style vectors, This represents the style vector for the current scheme;

[0135] Next, cost sensitivity is calculated using the historical average increase in user acceptance cost, as shown in the formula:

[0136]

[0137] in, Due to cost sensitivity, It is a natural exponential function. To correct costs, This represents the increase in the historical average cost of acceptance for users. Accepting standard deviation of fluctuations in user costs;

[0138] Finally, the user preference matching degree is calculated using the following formula:

[0139]

[0140] in, For user preference matching degree, and As weight, .

[0141] In one specific embodiment, multi-objective decision-making is performed, user preference data is obtained, and historical selections prioritize "low cost". , Their preferred style is "modern minimalist". ;

[0142] Calculate the matching degree of schemes B+D:

[0143] Material similarity The solution uses 60% of commonly used aluminum plates;

[0144] Style similarity Scheme vector ;

[0145] Cost sensitivity , Yuan, total cost increase ;

[0146] User preference matching degree The genetic algorithm outputs the optimal solution: solution B+D, with a comprehensive score. The system displayed the recommended solution: "Move the wall and reduce the glass area", which increased the total cost by 17,000 yuan, met fire safety standards, and reduced the heat transfer coefficient to 1.98 W / (m²·K).

[0147] Furthermore, the interactive display module includes:

[0148] Three-dimensional difference units, using red-yellow-green scales to identify regions of defect severity score S from high to low;

[0149] The solution comparison unit is used to display the cost change rate before and after optimization in real time;

[0150] The voice interaction unit is used to respond to optimization commands and update the model synchronously. The optimization commands include selecting scheme A, selecting scheme B, selecting scheme C, and selecting scheme D.

[0151] In one specific embodiment, the interactive display module includes:

[0152] 3D comparison: Original model: fire escape route marked in red (defect), glass curtain wall marked in yellow (optimization recommended); Optimized model: fire escape route widened to 1.5m (green), areas with reduced glass area highlighted;

[0153] Data panel: Real-time display of cost changes, +17,000 yuan (+14%); Environmental improvement value. ;

[0154] Voice interaction: User command, "View Solution A+C", the system switches the display and updates the cost, +28,000 yuan; final command, "Apply Solution B+D", the system saves the selection and updates the user preference library.

[0155] Example 2: Referring to Figure 2, a method for displaying environmental design works includes:

[0156] S1. Use a 3D laser scanner to scan the design work, obtain point cloud data, build a digital twin model based on the point cloud data, and extract spatial structure parameters and material property parameters;

[0157] S2. Detect the digital twin model, identify design defect areas, calculate the defect severity score S for each defect area, and set a defect severity score threshold. When the calculated defect severity score S is greater than the severity score threshold Dynamically repair the design work at that time;

[0158] S3. Generate several parameter adjustment schemes for each defect area and calculate the correction cost of each scheme. Environmental enhancement value Matching degree P with user preferences;

[0159] S4, Adjustment Costs Environmental enhancement value The matching degree P with user preferences is normalized to obtain the decision matrix, and the optimal solution is obtained by combining the decision matrix.

[0160] S5 displays the original design, optimized solutions, and key indicator comparison data in a side-by-side view, allowing users to select and apply optimized solutions in real time.

[0161] Furthermore, the optimization scheme generation includes:

[0162] When the width of the fire escape route is insufficient, two adjustment options are generated: moving the wall or adding an escape route.

[0163] When the energy-saving heat transfer coefficient is found to be substandard, two adjustment schemes are generated: changing the glass type and reducing the glass area.

[0164] Furthermore, it also includes:

[0165] Record the optimization schemes selected by users and the paths that are manually modified;

[0166] Update the user preference model based on the recorded data to improve the accuracy of subsequent recommendations.

[0167] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. An environmental design work display system, characterized in that, include: The model extraction module uses a 3D laser scanner to scan the design model, obtain point cloud data, and generate a digital twin model. The intelligent detection module is used to detect digital twin models, identify areas of design defects, calculate the defect severity score S for each defect area, and set a threshold for the defect severity score. When the calculated defect severity score S is greater than the severity score threshold In this process, the design work is dynamically repaired; the dynamic optimization module generates several parameter adjustment schemes for each defective area and calculates the correction cost of each scheme. Environmental enhancement value Matching user preferences with the degree of P, and adjusting costs Environmental enhancement value The user preference matching degree P is normalized to generate a decision matrix, including the adjusted cost score. Environmental improvement score User preference rating The optimal solution is obtained by integrating the decision matrix; wherein, the correction cost The calculation formula is: in, Let be the unit adjusted cost coefficient for the i-th type of scheme. Let i be the adjustment amount for the i-th type of scheme. For the complexity factor of the construction environment, For fixed costs; the environmental improvement value The calculation formula is: in, As weight, For the actual parameter values ​​of the materials used in the i-th type of scheme, The industry benchmark value for materials used in the i-th type of solution. The formula for calculating the user preference matching degree P is: in, For material similarity, For style similarity, Due to cost sensitivity, and As weight, And will adjust costs. Environmental enhancement value The matching degree P with user preferences is normalized to generate a decision matrix, including a revised cost score. Environmental improvement score User preference rating The comprehensive decision matrix yields the optimal solution; the interactive display module displays the original design, optimized solution, and key indicator comparison data in a side-by-side view, allowing users to select and apply the optimized solution in real time.

2. The environmental design work display system according to claim 1, characterized in that, The intelligent detection module includes: a standardized database unit, adopting a database sharding architecture storage design standard, the design standard including a first threshold. Second threshold The first threshold is the fire lane width threshold, and the second threshold is the energy-saving heat transfer coefficient threshold; the fire lane width is obtained from digital twin model data, and the energy-saving heat transfer coefficient is obtained by the following formula: in, For energy-saving heat transfer coefficient, For the glass area, Let be the heat transfer coefficient of the glass. For the area of ​​non-transparent walls, The heat transfer coefficient of the non-transparent wall. The total area of ​​glass and non-transparent walls; Defect identification unit: Real-time comparison of parameter values ​​of the digital twin model with design standards stored in the specification database unit; when the width of the fire lane is less than the first threshold or the energy-saving heat transfer coefficient is greater than the second threshold, the fire lane area or wall area of ​​the digital twin model is marked as a defect area; Calculate the defect severity score S of the defect area: in, 、 These are the weighting coefficients. ， This refers to the actual width of the fire lane. This represents the actual energy-saving heat transfer coefficient; when the defect severity score S is greater than the severity score threshold... At that time, the design work is optimized.

3. The environmental design work display system according to claim 1, characterized in that, The dynamic optimization module generates adjustment schemes by calling the parametric design template library and matching the adjustment strategies corresponding to the defect types, including: generating a scheme when the width of the fire lane is less than the first threshold. in, This refers to the width of the fire escape after the wall has been moved. This is the distance the wall needs to be moved. The number of additional escape routes required. This is the total length of the fire lane. The minimum number of escape routes; when the energy-saving heat transfer coefficient is greater than the second threshold, the following scheme is generated: A genetic algorithm is used to iteratively generate parameter combinations that satisfy multiple constraints.

4. The environmental design work display system according to claim 3, characterized in that, The specific steps for iteratively generating parameter combinations that satisfy multiple constraints using a genetic algorithm include: constructing a chromosome containing parameters for fire escape routes and energy-saving heat transfer adjustment; and optimizing the objective function. in, Let be the objective function. and As weight, ， For the normalization correction cost, For normalized environmental improvement value, Normalized user preference matching degree; iteratively execute selection-crossover-mutation operation until convergence, and output Pareto optimal solution set; the optimal solution set is the final optimized scheme.

5. The environmental design work display system according to claim 1, characterized in that, The process involves generating several parameter adjustment schemes for each defective region and calculating the correction cost of each scheme. and environmental improvement value The specific steps include: the revised cost The calculation formula is: in, Let be the unit adjusted cost coefficient for the i-th type of scheme. Let i be the adjustment amount for the i-th type of scheme. For the complexity factor of the construction environment, For fixed costs; the environmental improvement value The calculation formula is: in, As weight, For the actual parameter values ​​of the materials used in the i-th type of scheme, The industry benchmark value for materials used in the i-th type of scheme.

6. The environmental design work display system according to claim 1, characterized in that, The user preference matching degree P is obtained by collecting users' historical operation data, acquiring the frequency of users' historical material selections, the design style selections, and the increase in users' historical average acceptance cost, and then weighting and summing the similarity of materials by the frequency of users' historical material selections. The calculation formula is as follows: in, For material similarity, For materials The percentage of usage in the current plan, Let be a function representing the material in the user's historical preference set. The value is 1 if it is in the middle, and 0 otherwise. The user's historical preference set is the set of materials frequently used by the user. Then, a user preference style vector is obtained by analyzing the user's historical design style selections. Style similarity is calculated using the vector space cosine similarity formula: in, For style similarity, For user preference style vectors, The style vector for the current solution is used; then, the cost sensitivity is calculated using the historical average increase in user acceptance cost, as shown in the formula: in, Due to cost sensitivity, It is a natural exponential function. To correct costs, This represents the increase in the historical average cost of acceptance for users. Accept the standard deviation of user cost fluctuations; finally, calculate the user preference matching degree using the following formula: in, For user preference matching degree, and As weight, 。 7. The environmental design work display system according to claim 1, characterized in that, The interactive display module includes: a three-dimensional difference unit, which uses red-yellow-green levels to mark the areas of defect severity score S from high to low; a scheme comparison unit, which is used to display the cost change rate before and after optimization in real time; and a voice interaction unit, which is used to respond to optimization commands and update the model synchronously. The optimization commands include selecting scheme A, selecting scheme B, selecting scheme C, and selecting scheme D.

8. A method for displaying environmental design works, characterized in that, include: S1. Scan the design using a 3D laser scanner to acquire point cloud data. Construct a digital twin model based on the point cloud data and extract spatial structural parameters and material property parameters. S2. Inspect the digital twin model, identify design defect areas, calculate the defect severity score S for each defect area, and set a threshold for the defect severity score. When the calculated defect severity score S is greater than the severity score threshold In S3, dynamically repair the design; generate several parameter adjustment schemes for each defective area and calculate the correction cost of each scheme. Environmental enhancement value Matching degree P with user preferences; S4, Adjustment Costs Environmental enhancement value The matching degree P with user preferences is normalized to obtain a decision matrix, and the optimal solution is obtained by combining the decision matrix; S5, the original design, the optimized solution and the comparison data of key indicators are displayed in a side-by-side view, and users can select and apply the optimized solution in real time.

9. The method according to claim 8, characterized in that, The optimization scheme generation includes: when the width of the fire escape is insufficient, generating two adjustment schemes: moving the wall and adding an escape hatch; when the energy-saving heat transfer coefficient is not up to standard, generating two adjustment schemes: changing the glass type and reducing the glass area.

10. The method according to claim 8, characterized in that, Also includes: Record the optimization schemes selected by users and the paths manually modified; update the user preference model based on the recorded data to improve the accuracy of subsequent recommendations.

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