A method and system for collecting and analyzing construction information of intelligent buildings based on BIM

Abstract

This invention discloses a BIM-based intelligent building construction information collection and analysis method and system. The method includes the following steps: constructing a BIM-based building information management platform; evaluating the green construction level of a building project using the Vague similarity aggregation method based on index weights; proposing corresponding green construction improvement measures and carrying out the project construction; identifying and correcting collected abnormal construction information; and importing the corrected construction information of the building project into the BIM-based building information management platform for visualization. This invention also discloses a BIM-based intelligent building construction information collection and analysis system. This invention not only reduces resource and energy consumption and achieves sustainable construction development, but also effectively avoids the impact of abnormal data on the visualization of construction information, enabling project managers to better understand the project's construction status.

Description

Technical Field

[0001] The present invention relates to the field of building technology, and in particular to a method and system for collecting and analyzing construction information of intelligent buildings based on BIM. Background Art

[0002] BIM (Building Information Modeling) technology is a digital tool used in engineering design, construction, and management. By integrating digital and information models of buildings, it is shared and transmitted throughout the entire life cycle of project planning, operation, and maintenance. It enables engineering and technical personnel to correctly understand and efficiently respond to various building information, provides a basis for collaborative work for design teams and all construction entities including construction and operation units, and plays an important role in improving production efficiency, saving costs, and shortening construction periods.

[0003] At present, in order to standardize and unify the information management level of construction projects and improve the sharing and consistency of the successful informatization of various construction projects, it is an important technical means to collect or effectively store construction project information. For example, information collection terminal equipment can be used to collect information on construction projects. For example, in order to monitor the progress of construction projects, video monitoring can be performed to obtain corresponding construction progress videos. In addition, it is also possible to determine whether the construction process is compliant based on the construction progress video. Therefore, in order to enable all parties to better understand the construction status of the construction project, the present invention proposes a method and system for collecting and analyzing intelligent building construction information based on BIM.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes a method and system for collecting and analyzing intelligent building construction information based on BIM to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] According to one aspect of the present invention, a method for collecting and analyzing construction information of an intelligent building based on BIM is provided, the method comprising the following steps:

[0008] S1. Build a BIM-based building information management platform to obtain and organize project information of construction projects;

[0009] S2. Use Vague similarity summary method based on index weights and project information of construction projects to evaluate the green construction level of construction projects;

[0010] S3. Propose corresponding green construction improvement measures based on the evaluation results, and carry out engineering construction according to the green construction improvement measures;

[0011] S4. According to the progress of the construction project, the construction information of the construction project is gradually collected, and the abnormal construction information collected is identified and corrected by using the improved K-means clustering analysis algorithm;

[0012] S5. Convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visual display.

[0013] Furthermore, the construction information of the construction project includes construction cost information, construction progress information and construction quality information.

[0014] Furthermore, the method of evaluating the green construction level of a construction project by using the Vague similarity summary method based on indicator weights in combination with the project information of the construction project includes the following steps:

[0015] S21. Obtaining engineering overview information and design document information from the construction project information, and analyzing to obtain information data related to the green construction index system of the construction project;

[0016] S22, using the hierarchical analysis method to calculate the factor index weights, and determine the Vague value of each expert for the secondary evaluation index according to the factor index weights and the corresponding Vague values;

[0017] S23, calculating the consistency matrix according to the Vague values ​​of each expert for the secondary evaluation indicators, and determining the expert objective weight and the expert comprehensive weight;

[0018] S24, summarizing the expert evaluation opinions according to the expert comprehensive weight and the Vague value of the secondary evaluation index to obtain the group aggregate evaluation value of the primary evaluation index;

[0019] S25, using the scoring function to calculate the first-level evaluation index score, and determine the importance level of the evaluation index according to the first-level evaluation index score;

[0020] S26. Use the normalized score to determine the weight of the first-level evaluation index, and determine the green construction level of the project based on the calculated weighted Vague value of the first-level evaluation index.

[0021] Furthermore, the step of calculating the factor index weights by using the hierarchical analysis method and determining the Vague value of each expert for the secondary evaluation index according to the factor index weights and the corresponding Vague values ​​includes the following steps:

[0022] S221, the linguistic fuzzy variables of the factor indicators are given by experts and converted into Vague values ​​according to the preset conversion rules;

[0023] S222. Determine the indicator weights using the analytic hierarchy process and calculate the weighted Vague value in combination with the converted Vague value to determine the Vague value of each expert for the secondary evaluation indicator.

[0024] The calculation formula of the weighted Vague value is: ;

[0025] In the formula, represents the composite weighted Vague value of the evaluation index considered by the kth expert, ω ij Indicates evaluation index c ij The weight of The range of Vague values ​​representing the transformation of linguistic fuzzy variables, represents the tensor product, and m represents the number of evaluation indicators.

[0026] Furthermore, the calculation of the consistency matrix according to the Vague values ​​of the secondary evaluation indicators of each expert and the determination of the expert objective weight and the expert comprehensive weight include the following steps:

[0027] S231. Determine the weight of each expert based on the expert's knowledge, experience and cognitive ability of green construction evaluation to obtain the expert's subjective weight, where the calculation formula of the expert's subjective weight is: ;

[0028] In the formula, r i represents the expert's subjective weight, Z k represents the expert weight, k =1,2,3…,m.

[0029] S232, Utilization and Represents the fuzzy evaluation information of the kth expert and the uth expert on the i-th indicator, and calculates and The similarity of represents the composite weighted Vague value of the evaluation index considered by the kth expert, It represents the composite weighted Vague value of the evaluation index considered by the u-th expert. The calculation formula is: In the formula, S(k,u) represents the expert E k and expert E u The evaluation similarity of Indicates expert Ek The range of Vague values, Indicates expert E u The range of Vague values, Represents experts E k and expert E u For indicators i The degree of hesitation;

[0030] S233, obtaining a similarity matrix between the evaluation values ​​of the expert groups based on the similarity, calculating the average consistency of the experts based on the similarity matrix, and calculating the objective weight and comprehensive weight of the experts based on the average consistency of the experts;

[0031] The calculation formula for the average expert agreement is: ;

[0032] The objective weight is calculated as follows: ;

[0033] The calculation formula of comprehensive weight is: ;

[0034] In the formula, A(E k ) represents the average agreement of experts, RAD k represents the objective weight of the expert, AC k represents the comprehensive weight of the experts, r k Indicates k The subjective weight of the experts, β Represents a parameter, with a value of 0.5.

[0035] Furthermore, the calculation formula of the group aggregation evaluation value is: ;

[0036] The formula of the scoring function is: ;

[0037] In the formula, V i represents the group aggregation evaluation value, S represents the first-level indicator score, t x Represents the weighted Vague value of the first-level evaluation index, f x It represents the unknowability of the first-level evaluation indicators, that is, the degree of missing knowledge and information corresponding to the first-level evaluation indicators.

[0038] Furthermore, the step of gradually collecting the construction information of the construction project according to the progress of the construction project, and using the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information includes the following steps:

[0039] S41, gradually collecting construction information of the construction project according to the progress of the construction project, and converting the construction information data into a construction curve;

[0040] S42, calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points;

[0041] S43, classifying the points in the abnormal suspicious point set according to the principle that the distance between two adjacent points does not exceed a first preset distance value;

[0042] S44, based on the classified abnormal suspicious point set, using the translation replacement method in combination with the corresponding K-means clustering center curve to translate and replace the corresponding points of the construction curve, to obtain a first sequence;

[0043] S45, making a relative difference sequence between the first sequence obtained by replacement and the actual value, and recording the data in the first sequence whose value is greater than the sequence preset value, to form a new set;

[0044] S46, dividing the new set according to the principle that the distance between two adjacent points does not exceed the second distance preset value, and using the translation replacement method in S44 to replace the corresponding points to obtain a second sequence, then making a relative difference sequence between the second sequence and the actual value, and taking the points in the second sequence with values ​​greater than the preset value as abnormal suspicious points;

[0045] S47, repeatedly executing S46 until the new set no longer changes and the number of iterations does not exceed the preset number, and using the curve obtained by the last iteration as the final correction curve to correct the abnormal construction information data.

[0046] Furthermore, the step of calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points includes the following steps:

[0047] S421, calculating the moving average of the construction curve to obtain a new smooth curve, and calculating a relative difference sequence between the smooth curve and the original construction curve;

[0048] S422, judging whether the relative difference sequence between the smooth curve and the original construction curve is greater than a preset sequence value, if so, taking the point data as an abnormal suspicious point, and recording the point data to form an abnormal suspicious point set.

[0049] Furthermore, the calculation formula of the translation replacement method is: ;

[0050] In the formula, t 1 =a -1, t 2 = b +1, interval [ a,b ] indicates suspicious data segment, suspicious point l ∈[ a,b ], X tz ( l ) represents the cluster center curve of the class to which the construction curve belongs. l The value of the point, X tz ( p ) represents the cluster center curve of the class to which the construction curve belongs. p The value of the point, V(p) Indicates the smooth curve p The value of the point, K ( l ) represents the point after replacement.

[0051] According to one aspect of the present invention, there is provided a BIM-based intelligent building construction information collection and analysis system, the system comprising a platform construction module, a green construction grade assessment module, an improvement measure proposal module, an abnormal construction information identification and correction module and a visual display module;

[0052] The platform construction module is used to construct a BIM-based building information management platform to obtain and organize project information of construction projects;

[0053] The green construction level assessment module is used to assess the green construction level of the construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project;

[0054] The improvement measure proposing module is used to propose corresponding green construction improvement measures based on the evaluation results, and to carry out engineering construction according to the green construction improvement measures;

[0055] The abnormal construction information identification and correction module is used to gradually collect the construction information of the construction project according to the progress of the construction project, and use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information;

[0056] The visualization display module is used to convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visualization display.

[0057] The beneficial effects of the present invention are:

[0058] 1) The present invention can not only evaluate the green construction level of the construction project by using the Vague similarity summary method based on indicator weights in combination with the project information of the construction project, and propose corresponding green construction improvement measures based on the evaluation results, so that construction personnel can carry out green construction according to the green construction improvement measures, thereby effectively reducing the adverse effects of construction activities on the site and the surrounding environment, reducing the consumption of resources and energy, and realizing sustainable construction, but also can use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information, so that the corrected construction information can be imported into the BIM-based building information management platform for visual display, thereby effectively avoiding the influence of abnormal data on the visualization of construction information, so that project managers can better understand the project construction status.

[0059] 2) The present invention realizes the evaluation of the green construction level of the construction project by utilizing the Vague similarity aggregation method based on indicator weights, so that not only can the decision be made on the target under multi-level indicators, but also the individual evaluation opinions of experts can be aggregated based on the secondary indicator weights and expert weights, so that the calculation result of the scoring function can be used as the weight of the indicator, and the weighted Vague value can be used to determine the green construction level of the construction project.

[0060] 3) The present invention uses an improved K-means cluster analysis algorithm, which can not only use the moving average method to identify suspicious points in construction information, but also use the K-means cluster analysis method to identify suspicious points and abnormal data between suspicious points. Therefore, the combination of the two methods can effectively eliminate abnormal data in engineering construction information, better realize the collection and processing of construction information in construction projects, and effectively avoid the influence of abnormal data on the visualization of construction information. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 The present invention is a flowchart of a method for collecting and analyzing intelligent building construction information based on BIM according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0064] According to an embodiment of the present invention, a method and system for collecting and analyzing construction information of an intelligent building based on BIM are provided.

[0065] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a method for collecting and analyzing intelligent building construction information based on BIM is provided, and the method comprises the following steps:

[0066] S1. Build a BIM-based building information management platform to obtain and organize project information of construction projects.

[0067] Among them, the BIM-based construction information management platform provides cloud services for survey and design, supervision, construction, equipment, materials, operations and other units. The platform uses WebServices to build an SOA architecture, which can provide full life cycle management such as planning, design, bidding, projects, contracts, progress, quality, investment, personnel, materials, equipment, risks, drawings and documents, and completion settlement.

[0068] S2. Evaluate the green construction level of the construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project. The method of evaluating the green construction level of the construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project includes the following steps:

[0069] S21. Obtaining engineering overview information and design document information from the construction project information, and analyzing to obtain information data related to the green construction index system of the construction project;

[0070] S22. Calculate the factor index weights using the hierarchical analysis method, and derive the weighted Vague value based on the factor index weights and the corresponding Vague value to determine the Vague value of each expert for the secondary evaluation index.

[0071] Specifically, the method of calculating the factor index weights by using the hierarchical analysis method, and obtaining the weighted Vague value according to the factor index weights and the corresponding Vague value, and determining the Vague value of each expert for the secondary evaluation index includes the following steps:

[0072] S221. The linguistic fuzzy variables of the evaluation factors are given by experts and converted into Vague values ​​according to the preset conversion rules (as shown in Table 1 below).

[0073] Table 1 Conversion rules for Vague values

[0074] S222. Determine the weights of the evaluation indicators using the analytic hierarchy process and calculate the weighted Vague values ​​in combination with the converted Vague values ​​to determine the Vague values ​​of each expert for the secondary evaluation indicators.

[0075] The calculation formula of the weighted Vague value is: ;

[0076] In the formula, represents the composite weighted Vague value of the evaluation index considered by the kth expert, ω ij Indicates evaluation index c ij The weight of The range of Vague values ​​representing the transformation of linguistic fuzzy variables, represents the tensor product, and m represents the number of evaluation indicators.

[0077] S23. Calculate the consistency matrix based on the Vague values ​​of each expert for the secondary evaluation indicators, and determine the expert objective weight and expert comprehensive weight.

[0078] Specifically, the calculation of the consistency matrix according to the Vague values ​​of the secondary evaluation indicators of each expert and the determination of the expert objective weight and the expert comprehensive weight include the following steps:

[0079] S231. Determine the weight of each expert based on the expert's knowledge, experience and cognitive ability of green construction evaluation to obtain the expert's subjective weight, where the calculation formula of the expert's subjective weight is: ;

[0080] In the formula, r i represents the expert's subjective weight, Z k represents the expert weight, k =1,2,3…,m.

[0081] S232, Utilization and Represents the fuzzy evaluation information of the kth expert and the uth expert on the i-th (i=1,2,…,n)th indicator, and calculates and The similarity of represents the composite weighted Vague value of the evaluation index considered by the kth expert, It represents the composite weighted Vague value of the evaluation index considered by the u-th expert. The calculation formula is: ;

[0082] In the formula, S(k,u) represents the expert E k and expert E u The evaluation similarity of Indicates expert E k The range of Vague values, Indicates expert E u The range of Vague values, Represents experts E k and expert E u For indicators i The degree of hesitation.

[0083] If expert E k The greater the similarity with other experts’ evaluations, the higher the support from other experts. k The greater the weight it carries.

[0084] S233, obtaining a similarity matrix between the evaluation values ​​of the expert groups based on the similarity, calculating the average consistency of the experts based on the similarity matrix, and calculating the objective weight and comprehensive weight of the experts based on the average consistency of the experts;

[0085] The calculation formula for the average expert agreement is: ;

[0086] The objective weight is calculated as follows: ;

[0087] The calculation formula of comprehensive weight is: ;

[0088] In the formula, A(E k ) represents the average agreement of experts, RAD k represents the objective weight of the expert, AC k represents the comprehensive weight of the experts, r k Indicates k The subjective weight of the experts, β Represents a parameter, with a value of 0.5.

[0089] S24, summarizing the expert evaluation opinions according to the expert comprehensive weight and the Vague value of the secondary evaluation index to obtain the group aggregate evaluation value of the primary evaluation index;

[0090] S25. Calculate the score of the first-level evaluation indicator using the scoring function, and determine the importance level of the evaluation indicator according to the score of the first-level evaluation indicator.

[0091] Specifically, the calculation formula of the group aggregation evaluation value is: ;

[0092] The formula of the scoring function is: ;

[0093] In the formula, V i represents the group aggregation evaluation value, S represents the first-level indicator score, t x Represents the weighted Vague value of the first-level evaluation index, f x It represents the unknowability of the first-level evaluation indicators, that is, the degree of missing knowledge and information corresponding to the first-level evaluation indicators.

[0094] S26. Use the normalized score to determine the weight of the first-level evaluation index, and determine the green construction level of the project based on the calculated weighted Vague value of the first-level evaluation index.

[0095] In addition, the green construction index system for building projects in this embodiment includes a target layer, primary indicators, secondary indicators and factors;

[0096] Among them, the target layer includes green construction C;

[0097] The first-level indicators (i.e. all the first-level indicators constitute the target layer) include:

[0098] Reducing ecological and environmental load C1, saving and utilizing resources and energy C2, green technology and management C3, and the impact of construction on the comprehensive performance of the entire life cycle C4;

[0099] Secondary indicators (i.e. all secondary indicators constitute primary indicators) include:

[0100] Utilization and conservation of water resources 11 、Air Pollution ControlC 12 , Solid Waste Control and Treatment C 13 、Ecological restoration C 14 , Protection of underground space and resources 15 , Material saving and utilization C 21 , Land conservation and utilization C 22 , water resources conservation and utilization C 23 , Energy conservation and utilization C 24 , Green Planning and Organization C31 , On-site personnel health and safety management C 32 、Green Culture C 33 , Comprehensive cost of the whole life cycle C 41 And use function C 42 ,

[0101] Factors (i.e. all factors that constitute the secondary indicators) include:

[0102] Utilization of traditional water sources 111 , the use of water-saving equipment C 112 , Comprehensive Utilization of Rainwater and SewageC 113 , Dust Control C 121 , Toxic Substance Control C 122 , Exhaust Control C 123 , reduction C 131 , Recycling C 132 , Impact on the original landform C 141 , Impact on surface runoff C 142 , protection and improvement of the original ecological environment 143 , Protection of underground municipal pipe network C 151 , Protection of groundwater resources 152 , Protection of underground soil resources 153 , Use of green building materials 211 , Saving and utilization of building materials C 212 , Construction site planning C 221 , Construction Road Planning C 222 、Temporary land protection C 223 , Water use planning C 231 , Reasonable use of rainwater 232 , Rational use of water-saving equipment C 233 , Water safety C 234 、Conservation of traditional energy 241 , Utilization of clean energy 242 , Utilization of renewable energy 243 , Green construction organization design C 311 , Green construction technology selection C 312 , Green Construction Organization C 313 , Selection of environmentally friendly equipment C 314 , Safety of surrounding areas C 321 , protection of on-site cultural relics and ancient trees 322 , All staff participate in green construction C 323 、Construction workers' health 324 , Promotion of green management culture 331 , Regional Historic Landscape and Protection C 332 , the combination of regional humanities 333 、Satisfaction of nearby residents with the construction334 , Green Construction Cost Control C 411 , Green operation cost control C 412 、Engineering function realization C 421 And the engineering condition 422 .

[0103] S3. Propose corresponding green construction improvement measures based on the evaluation results, and carry out engineering construction according to the green construction improvement measures;

[0104] S4. According to the progress of the construction project, the construction information of the construction project is gradually collected, and the improved K-means clustering analysis algorithm is used to identify and correct the collected abnormal construction information.

[0105] The construction information of the construction project includes construction cost information, construction progress information, construction quality information and the like.

[0106] In specific applications, the moving average method can better transform the original construction curve into a smooth curve, and has a good ability to identify single mutation points, but it is powerless for patches of bad data. In general, when patches of bad data appear, the edges of the patches of bad data are generally steeper. At this time, the moving average method can identify these steep edges, but it is powerless to determine whether the points between these points are bad data.

[0107] K-means cluster analysis is better for identifying data in pieces, but it is prone to misidentification and missed identification. In view of this situation, the present invention proposes an improved method combining the moving average method and the K-means cluster analysis method: first, the moving average method is used to identify suspicious points, and then the K-means cluster analysis method is used to identify whether the suspicious points and the points between the suspicious points are bad data, so that the bad data can be more effectively identified and corrected.

[0108] Specifically, the step of gradually collecting the construction information of the construction project according to the progress of the construction project, and using the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information includes the following steps:

[0109] S41, gradually collecting construction information of the construction project according to the progress of the construction project, and converting the construction information data into a construction curve;

[0110] S42, calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points;

[0111] Specifically, the step of calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points includes the following steps:

[0112] S421, calculating the moving average of the construction curve to obtain a new smooth curve, and calculating a relative difference sequence between the smooth curve and the original construction curve;

[0113] S422, determining whether the relative difference sequence between the smooth curve and the original construction curve is greater than a preset sequence value (the preset value may be 0.02), if so, taking the point data as an abnormal suspicious point, and recording the point data to form an abnormal suspicious point set.

[0114] S43, classifying the points in the abnormal suspicious point set according to the principle that the distance between two adjacent points does not exceed a first distance preset value (the first distance may be 50);

[0115] S44. Based on the classified abnormal suspicious point set, the corresponding points of the construction curve are replaced by the translation replacement method in combination with the corresponding K-means clustering center curve to obtain the first sequence.

[0116] Specifically, the calculation formula of the translation replacement method is: ;

[0117] In the formula, t 1 = a -1, t 2 = b +1, interval [ a,b ] indicates suspicious data segment, suspicious point l ∈[ a,b ], X tz ( l ) represents the cluster center curve of the class to which the construction curve belongs. l The value of the point, X tz ( p ) represents the cluster center curve of the class to which the construction curve belongs. p The value of the point, V(p) Indicates the smooth curve p The value of the point, K ( l ) represents the point after replacement.

[0118] S45, making a relative difference sequence between the first sequence obtained by replacement and the actual value, and recording the data in the sequence whose value is greater than a preset value of the sequence (the preset value may be 0.02), to form a new set;

[0119] S46, dividing the new set according to the principle that the distance between two adjacent points does not exceed a second distance preset value (the first distance may be 5), and replacing the corresponding points using the translation replacement method in S44 to obtain a second sequence, then making a relative difference sequence between the second sequence and the actual value, and taking the points in the second sequence whose values ​​are greater than a preset value (the preset value may be 0.02) as abnormal suspicious points;

[0120] S47, repeatedly executing S46 until the new set does not change any more and the number of iterations does not exceed a preset number (the preset number may be 10), and using the curve obtained by the last iteration as the final correction curve to correct the abnormal construction information data.

[0121] S5. Convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visual display.

[0122] According to another embodiment of the present invention, there is provided a BIM-based intelligent building construction information collection and analysis system, the system comprising a platform construction module, a green construction grade assessment module, an improvement measure proposal module, an abnormal construction information identification and correction module, and a visualization display module;

[0123] The platform construction module is used to construct a BIM-based building information management platform to obtain and organize project information of construction projects;

[0124] The green construction level assessment module is used to assess the green construction level of the construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project;

[0125] The improvement measure proposing module is used to propose corresponding green construction improvement measures based on the evaluation results, and to carry out engineering construction according to the green construction improvement measures;

[0126] The abnormal construction information identification and correction module is used to gradually collect the construction information of the construction project according to the progress of the construction project, and use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information;

[0127] The visualization display module is used to convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visualization display.

[0128] In summary, with the help of the above-mentioned technical scheme of the present invention, the present invention can not only evaluate the green construction level of the construction project by utilizing the Vague similarity aggregation method based on indicator weights in combination with the project information of the construction project, and propose corresponding green construction improvement measures based on the evaluation results, so that construction personnel can carry out green engineering construction according to the green construction improvement measures, thereby effectively reducing the adverse effects of construction activities on the site and the surrounding environment, reducing the consumption of resources and energy, and realizing sustainable construction, but also can use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information, so that the corrected engineering construction information can be imported into the BIM-based building information management platform for visualization, thereby effectively avoiding the influence of abnormal data on the visualization of construction information, so that project managers can better understand the project construction status.

[0129] In addition, the present invention realizes the evaluation of the green construction level of the construction project by utilizing the Vague similarity aggregation method based on indicator weights, so that not only can the goal be decided under multi-level indicators, but also the individual evaluation opinions of experts can be aggregated based on the secondary indicator weights and expert weights, so that the calculation result of the scoring function can be used as the weight of the indicator, and the weighted Vague value can be used to determine the green construction level of the construction project.

[0130] In addition, the present invention uses an improved K-means cluster analysis algorithm, so that not only can the moving average method be used to identify suspicious points in construction information, but also the K-means cluster analysis method can be used to identify suspicious points and abnormal data between suspicious points. Therefore, the combination of the two methods can effectively eliminate abnormal data in engineering construction information, better realize the collection and processing of construction information in construction projects, and effectively avoid the influence of abnormal data on the visualization of construction information.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for collecting and analyzing construction information of intelligent buildings based on BIM, characterized in that: include: S1. Build a BIM-based building information management platform to obtain and organize project information of construction projects; S2. Use Vague similarity summary method based on index weights and project information of construction projects to evaluate the green construction level of construction projects; S3. Propose corresponding green construction improvement measures based on the evaluation results, and carry out engineering construction according to the green construction improvement measures; S4. Gradually collect construction information of the construction project according to the progress of the construction project, and use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information; specifically including: S41, gradually collecting construction information of the construction project according to the progress of the construction project, and converting the construction information data into a construction curve; S42, calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points; S43, classifying the points in the abnormal suspicious point set according to the principle that the distance between two adjacent points does not exceed a first preset distance value; S44, based on the classified abnormal suspicious point set, using the corresponding K-means clustering center curve to translate and replace the corresponding points of the construction curve, to obtain a first sequence; S45, making a relative difference sequence between the first sequence obtained by replacement and the actual value, and recording the data in the sequence whose value is greater than the preset value to form a new set; S46, dividing the new set according to the principle that the distance between two adjacent points does not exceed the second distance preset value, and using the translation replacement method in S44 to replace the corresponding points to obtain a second sequence, then making a relative difference sequence between the second sequence and the actual value, and taking the points in the second sequence with values ​​greater than the preset value as abnormal suspicious points; S47, repeatedly executing S46 until the new set does not change any more and the number of iterations does not exceed a preset number, and using the curve obtained by the last iteration as the final correction curve to correct the abnormal construction information data; The calculation formula of the translation replacement method is: ; In the formula, t 1 = a -1, t 2 = b +1, interval [ a,b ] indicates suspicious data segment, suspicious point l ∈[ a,b ], X tz ( l ) represents the cluster center curve of the class to which the construction curve belongs. l The value of the point, X tz ( p ) represents the cluster center curve of the class to which the construction curve belongs. p The value of the point, V ( p ) represents the first p The value of the point, K ( l ) represents the point after replacement; S5. Convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visual display.

2. The method for collecting and analyzing construction information of intelligent buildings based on BIM according to claim 1 is characterized in that: The construction information of the construction project includes construction cost information, construction progress information and construction quality information.

3. The method for collecting and analyzing construction information of intelligent buildings based on BIM according to claim 1 is characterized in that: The method of evaluating the green construction level of a construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project comprises the following steps: S21. Obtaining engineering overview information and design document information from the construction project information, and analyzing to obtain information data related to the green construction index system of the construction project; S22, using the hierarchical analysis method to calculate the factor index weights, and determine the Vague value of each expert for the secondary evaluation index according to the factor index weights and the corresponding Vague values; S23, calculating the consistency matrix according to the Vague values ​​of each expert for the secondary evaluation indicators, and determining the expert objective weight and the expert comprehensive weight; S24, summarizing the expert evaluation opinions according to the expert comprehensive weight and the Vague value of the secondary evaluation index to obtain the group aggregate evaluation value of the primary evaluation index; S25, using the scoring function to calculate the first-level evaluation index score, and determine the importance level of the evaluation index according to the first-level evaluation index score; S26. Use the normalized score to determine the weight of the first-level evaluation index, and determine the green construction level of the project based on the calculated weighted Vague value of the first-level evaluation index.

4. The method for collecting and analyzing construction information of intelligent buildings based on BIM according to claim 3 is characterized in that: The method of calculating the factor index weights by using the hierarchical analysis method and determining the Vague value of each expert for the secondary evaluation index according to the factor index weights and the corresponding Vague values ​​includes the following steps: S221, the linguistic fuzzy variables of the factor indicators are given by experts, and converted into Vague values ​​according to the preset conversion rules; S222. Determine the weights of factor indicators using the analytic hierarchy process and calculate the weighted Vague value in combination with the converted Vague value to determine the Vague value of each expert for the secondary evaluation indicator.

5. The method for collecting and analyzing intelligent building construction information based on BIM according to claim 3 is characterized in that: The calculation of the consistency matrix according to the Vague values ​​of the secondary evaluation indicators of each expert and the determination of the expert objective weight and the expert comprehensive weight include the following steps: S231. Determine the weight of each expert based on the expert's knowledge, experience and cognitive ability of green construction evaluation to obtain the expert's subjective weight; S232, Utilization and represents the kth expert and the uth expert's i The fuzzy evaluation information of the indicators is calculated and The similarity of represents the composite weighted Vague value of the evaluation index considered by the kth expert, represents the composite weighted Vague value of the evaluation index considered by the u-th expert; S233. A similarity matrix between the evaluation values ​​of the expert groups is obtained based on the similarity, the average consistency of the experts is calculated based on the similarity matrix, and the objective weight and comprehensive weight of the experts are calculated based on the average consistency of the experts.

6. The method for collecting and analyzing intelligent building construction information based on BIM according to claim 3 is characterized in that: The calculation formula of the group aggregation evaluation value is: ; The formula of the scoring function is: ; In the formula, AC k represents the comprehensive weight of experts, m represents the number of experts, represents the tensor product, represents the composite weighted Vague value of the evaluation index considered by the kth expert, S represents the first-level index score, t x Represents the weighted Vague value of the first-level evaluation index, f x It represents the unknowability of the first-level evaluation indicators, that is, the degree of missing knowledge and information corresponding to the first-level evaluation indicators.

7. The method for collecting and analyzing construction information of intelligent buildings based on BIM according to claim 1 is characterized in that: The step of calculating the moving average of the construction curve to obtain a smooth curve, and comparing and analyzing the smooth curve with the original construction curve to obtain a set of abnormal suspicious points includes the following steps: S421, calculating the moving average of the construction curve to obtain a new smooth curve, and calculating a relative difference sequence between the smooth curve and the original construction curve; S422, judging whether the relative difference sequence between the smooth curve and the original construction curve is greater than a preset sequence value, if so, taking the point data as an abnormal suspicious point, and recording the point data to form an abnormal suspicious point set.

8. A BIM-based intelligent building construction information collection and analysis system, used to implement the steps of the BIM-based intelligent building construction information collection and analysis method described in any one of claims 1 to 7, characterized in that: The system includes a platform construction module, a green construction grade assessment module, an improvement measure proposal module, an abnormal construction information identification and correction module, and a visual display module; The platform construction module is used to construct a BIM-based building information management platform to obtain and organize project information of construction projects; The green construction level assessment module is used to assess the green construction level of the construction project by using the Vague similarity summary method based on indicator weights combined with the project information of the construction project; The improvement measure proposal module is used to propose corresponding green construction improvement measures based on the evaluation results, and to carry out engineering construction according to the green construction improvement measures; The abnormal construction information identification and correction module is used to gradually collect the construction information of the construction project according to the progress of the construction project, and use the improved K-means clustering analysis algorithm to identify and correct the collected abnormal construction information; The visualization display module is used to convert the revised construction information of the building project into digital information and import it into the BIM-based building information management platform for visualization display.

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