A collaborative design optimization method for water supply and drainage BIM forward design

Abstract

The application relates to the technical field of water supply and drainage collaborative design, in particular to a collaborative design optimization method for water supply and drainage BIM forward design, which comprises the following steps: analyzing the difference between the clustering mean of each clustering cluster and the maximum clustering mean in all clustering clusters, determining the clustering weight of each clustering cluster at each moment, and combining the conflict probability to determine a soft conflict risk index; based on the soft conflict risk index at all moments within a preset time length before each moment and the heating and ventilation pipeline vibration frequency, a decision tree algorithm is used to obtain urgency, and a multi-objective genetic algorithm scoring is used to determine a treatment efficiency ratio, so that the water supply and drainage BIM forward design at the current moment is subjected to collaborative design optimization. The application solves the problem that cross-professional logic conflicts are difficult to be covered by a single rule engine due to the lack of a unified data correlation framework, and improves the multi-professional collaborative efficiency and resource utilization rate in the collaborative design optimization of the water supply and drainage BIM forward design.

Description

Technical Field

[0001] This application relates to the field of collaborative design technology for water supply and drainage, specifically to a collaborative design optimization method for BIM-based forward design of water supply and drainage. Background Technology

[0002] Collaborative design in BIM-based forward design for water supply and drainage is an important practice in the digital transformation of the construction industry. In the traditional two-dimensional design stage, information from various disciplines is isolated and coordination efficiency is low, which can easily lead to problems such as pipeline collisions and construction rework. With the increasing complexity of building projects and the increasing requirements for green and energy-saving technologies, forward design with BIM technology at its core has become an inevitable trend. Forward design emphasizes full-discipline collaboration based on a three-dimensional model from the scheme stage. It breaks down professional barriers through information integration, and collaborative design is the core support for achieving efficient collaboration among multiple disciplines. Through model information sharing, collaborative design enables precise connection between water supply and drainage systems and building structures, HVAC, and other disciplines, which can improve space utilization and reduce construction costs.

[0003] In the collaborative design optimization of water supply and drainage BIM-based forward design, the insufficient accuracy and depth of conflict detection manifests itself in the fact that traditional tools are relatively mature in detecting geometric hard collisions, but weak in identifying logical soft collisions. These soft collisions include regulatory compliance, system performance inconsistencies, and cross-disciplinary logical conflicts. Existing technologies mainly address these issues through parametric association and dynamic feedback. However, cross-disciplinary logical conflicts are difficult to cover with a single rule engine due to the lack of a unified data association framework. Consequently, this reduces the efficiency and resource utilization of multi-disciplinary collaboration in the collaborative design optimization of water supply and drainage BIM-based forward design. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a collaborative design optimization method for water supply and drainage BIM-based forward design, thereby resolving the existing issues.

[0005] The collaborative design optimization method for water supply and drainage BIM-based forward design in this application adopts the following technical solution:

[0006] One embodiment of this application provides a collaborative design optimization method for water supply and drainage BIM-based forward design, the method comprising the following steps:

[0007] Real-time acquisition of fluid pipeline slope, flow velocity, support sharing rate, construction cost, and HVAC pipeline vibration frequency during the BIM forward design process of water supply and drainage;

[0008] The pipeline slope and the pipeline flow rate at each time form a two-tuple, all two-tuples within a preset time period before each time are clustered, the cluster mean of each cluster is determined based on the average distribution of all elements in the cluster center of each cluster, and the cluster weight of each cluster at each time is determined by analyzing the difference between the cluster mean of each cluster and the maximum cluster mean in all cluster means.

[0009] Based on the difference between the pipeline slope and the preset standard slope in each two-tuple in each cluster at each time, and the difference between the pipeline flow rate and the preset standard flow rate, the conflict probability of each cluster at each time is obtained by using logistic regression analysis, and the soft conflict risk index of the fluid pipeline at each time is determined in combination with the cluster weight.

[0010] Based on the soft conflict risk index at each time within a preset time period before each time, the support sharing rate and the construction cost, the multi-objective genetic algorithm score of the fluid pipeline at each time is obtained by using a multi-objective genetic algorithm, and the urgency is obtained by using a decision tree algorithm based on the soft conflict risk index at each time within a preset time period before each time and the vibration frequency of the heating and ventilation pipeline, and the treatment efficiency ratio of the fluid pipeline at each time is determined in combination with the multi-objective genetic algorithm score.

[0011] The difference between the treatment efficiency ratio of the fluid pipeline at the current time and the maximum treatment efficiency ratio within a preset time period before the current time is compared to determine the priority reconstruction value of the fluid pipeline at the current time, so as to optimize the collaborative design of the drainage BIM forward design at the current time.

[0012] Preferably, the distance in the clustering process is the Euclidean distance between the two-tuples.

[0013] Preferably, the cluster mean of each cluster is the mean of the normalized pipeline slope and the normalized pipeline flow rate in the cluster center of each cluster.

[0014] Preferably, the cluster weight of each cluster at each time is the ratio of the cluster mean of each cluster at each time to the maximum cluster mean in all cluster means.

[0015] Preferably, the conflict probability of each cluster at each time is obtained by:

[0016] In each cluster at each time, the difference between the pipeline slope and the preset standard slope in each two-tuple, and the difference between the pipeline flow rate and the preset standard flow rate are calculated respectively, and are denoted as the first deviation and the second deviation respectively, the first deviation and the second deviation of all two-tuples in each cluster are taken as the input of the logistic regression analysis algorithm, and the output probability value is taken as the conflict probability of each cluster at each time.

[0017] Preferably, the determination method of the soft conflict risk index of the fluid pipeline at each time is:

[0018] The product of the clustering weight and the conflict probability of each cluster at each time is calculated, and the accumulation of the products of all cluster at each time is taken as the soft conflict risk index of the fluid pipeline at each time.

[0019] Preferably, the urgency acquisition comprises:

[0020] The historical soft conflict risk index and the historical HVAC pipeline vibration frequency are acquired and taken as the input of the decision tree, wherein the priority labels are set as urgent and postponable, and the decision tree is trained.

[0021] The soft conflict risk index and the HVAC pipeline vibration frequency of all times within a preset time period before each time are taken as the input of the trained decision tree, the proportion of the number of all urgent label leaf nodes in all leaf nodes is counted, and the proportion is taken as the urgency of the fluid pipeline at each time.

[0022] Preferably, the governance potency ratio of the fluid pipeline at each time is the ratio of the multi-objective genetic algorithm score of the fluid pipeline at each time to the urgency.

[0023] Preferably, the expression of the priority reconstruction value of the fluid pipeline at the current time is: In the formula, Y represents the priority reconstruction value of the fluid pipeline at the current time; B represents the governance potency ratio of the fluid pipeline at the current time; B max represents the maximum value of all governance potency ratios of the fluid pipeline within a preset period before the current time.

[0024] Preferably, the collaborative design optimization of the current time drainage BIM forward design comprises:

[0025] If the priority reconstruction value of the fluid pipeline at the current time is greater than a preset first threshold value, the pipeline routing is reconstructed.

[0026] If the priority reconstruction value of the fluid pipeline at the current time is between a preset second threshold value and a preset first threshold value, or the priority reconstruction value is equal to the preset second threshold value, or the priority reconstruction value is equal to the preset first threshold value, the cross-professional parameter linkage is executed, wherein the preset second threshold value is less than the preset first threshold value.

[0027] If the priority reconstruction value of the fluid pipeline at the current time is less than the preset second threshold value, the rule base is iteratively updated.

[0028] The present application has at least the following beneficial effects:

[0029] The present application aims at the problem of insufficient quantification of logical soft conflict in traditional methods. By using fuzzy cluster analysis and logistic regression, the present application maps parameters such as pipeline slope and pipeline flow rate to risk level, constructs a soft conflict risk index, quantifies the comprehensive risk caused by parameter deviation in collaborative design, solves the problem of difficult accurate identification of hidden soft conflict, reduces construction rework and operation hidden danger, thereby improving the multi-specialty collaboration efficiency and resource utilization rate in collaborative design optimization of water supply and drainage BIM forward design; further, the present application aims at the problem of difficult balance between cross-specialty logical conflict and multi-objective constraint. By using multi-objective genetic algorithm to quantify the comprehensive optimization potential of design scheme, combining with decision tree analysis to evaluate problem urgency, and finally calculating governance cost-effectiveness ratio, the present application excludes the limitation of single objective optimization, reflects the global optimization potential under unit improvement cost, realizes scientific evaluation of optimization resource input benefit, effectively solves the problem that traditional methods are difficult to balance multi-objective constraint, quantify optimization potential and make priority, thereby improving collaborative design efficiency and quality, and further improving the multi-specialty collaboration efficiency and resource utilization rate in collaborative design optimization of water supply and drainage BIM forward design; further, the present application compares the current governance cost-effectiveness ratio with the historical optimal value, quantitatively evaluates the urgency and potential of design improvement, and dynamically adjusts the optimization strategy accordingly, realizes data-driven and adaptive collaborative optimization decision, effectively solves the problem that traditional methods rely on single rule and lack dynamic adjustment basis, significantly improves design efficiency and problem solving ability, and further improves the multi-specialty collaboration efficiency and resource utilization rate in collaborative design optimization of water supply and drainage BIM forward design. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 A step flow chart of a collaborative design optimization method for water supply and drainage BIM forward design provided by an embodiment of the present application is shown in the figure.

[0032] Figure 2 A governance cost-effectiveness ratio extraction process schematic diagram provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0033] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object of the application, the following describes in detail the specific implementation, structure, features and effects of a collaborative design optimization method for water supply and drainage BIM forward design according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0035] The specific scheme of the collaborative design optimization method for water supply and drainage BIM forward design provided by the present application is described below in combination with the accompanying drawings.

[0036] The collaborative design optimization method for water supply and drainage BIM forward design provided by one embodiment of the present application specifically provides a collaborative design optimization method for water supply and drainage BIM forward design as follows. Please refer to Figure 1 The method includes the following steps:

[0037] Step S1: Real-time acquisition of pipe slope, pipe flow rate, support sharing rate, construction cost and HVAC pipe vibration frequency of fluid pipe in the water supply and drainage BIM forward design process.

[0038] In the water supply and drainage BIM forward design process, RevitAPI secondary development is used to collect pipe slope, EPANET dynamic link library is used to generate pipe flow rate, IFC standard analysis engine is used to extract support sharing rate, construction cost is calculated according to the bill of quantities and BIM model parameter mapping, and HVAC sensor data interface HVAC pipe vibration frequency is obtained. All data acquisition frequencies are set to f, and all data acquisition is real-time and synchronous. Further, in order to eliminate the influence of data dimension, the five types of collected data are normalized respectively.

[0039] It should be noted that the value of the data acquisition frequency f is artificially set. In the present embodiment, the value of the data acquisition frequency f is 1 min / time. In actual application, as other implementation manners, the implementer can also set it himself according to the specific circumstances, and the present embodiment does not make special limitation.

[0040] In addition, it should be noted that there are many commonly used normalization methods. In the present embodiment, z-score standardization method is used for data normalization. In actual application, as other implementation manners, the implementer can also use maximum and minimum value normalization method and other normalization methods according to the specific circumstances. The present embodiment does not make special limitation on the selection of normalization method.

[0041] Wherein, the z-score standardization method is a known technology, and the specific process of normalizing the data will not be repeated.

[0042] It is further explained that the following content is analyzed using normalized data values, and the names remain consistent with the original data names. In this embodiment, any content involving normalization is based on the z-score standardization method.

[0043] Step S2: Forming a two-tuple of the pipeline slope and the pipeline flow rate at each time, clustering all two-tuples within a preset time length before each time, determining the cluster mean of each cluster based on the average distribution of all elements within the cluster center of each cluster, analyzing the difference between the cluster mean of each cluster and the maximum cluster mean in all clusters to determine the cluster weight of each cluster at each time, and using logistic regression analysis to obtain the conflict probability of each cluster at each time based on the difference between the pipeline slope and the preset standard slope, and the difference between the pipeline flow rate and the preset standard flow rate in each two-tuple in each cluster at each time, and determining the soft conflict risk index of the fluid pipeline at each time in combination with the cluster weight.

[0044] In the collaborative optimization process of water supply and drainage BIM forward design, due to the excessive dependence of traditional methods on geometric hard collision detection, there is a lack of quantitative analysis of logical soft conflicts such as specification compliance, e.g., insufficient drainage slope leading to flow rate lower than self-cleaning capacity, and system performance contradictions, e.g., pipeline vibration conduction risk, which makes it difficult to accurately identify hidden risks at the design stage and easily causes construction rework and operation and maintenance risks.

[0045] Therefore, in order to quantitatively analyze and identify some difficult-to-catch logical soft conflicts and reduce operation and maintenance risks of construction rework, in this embodiment, a two-tuple of the pipeline slope and the pipeline flow rate at each time is formed, all two-tuples within a preset time length before each time are clustered, the cluster mean of each cluster is determined based on the average distribution of all elements within the cluster center of each cluster, the difference between the cluster mean of each cluster and the maximum cluster mean in all clusters is analyzed to determine the cluster weight of each cluster at each time, and the conflict probability of each cluster at each time is obtained by using logistic regression analysis based on the difference between the pipeline slope and the preset standard slope, and the difference between the pipeline flow rate and the preset standard flow rate in each two-tuple in each cluster at each time, and the soft conflict risk index of the fluid pipeline at each time is determined in combination with the cluster weight. Specifically,

[0046] (1) In this embodiment, all two-tuples within a preset time length before each time are clustered, and the cluster mean of each cluster is determined based on the average distribution of all elements within the cluster center of each cluster, specifically:

[0047] As an implementation form, in the embodiment, the pipeline slope and the pipeline flow rate at each time form a two-tuple, and all two-tuples in a preset time period before each time are clustered, where the distance is the Euclidean distance between two-tuples. In the embodiment, in order to correspond to high-risk, medium-risk, and low-risk levels, the number of clustering clusters k is set to 3, and three clustering clusters are output to identify a pipe segment cluster with similar soft conflict characteristics.

[0048] It should be noted that there are many commonly used clustering algorithms. In the embodiment, the k-means clustering algorithm is used to cluster two-tuples. In actual application, as other implementation forms, the implementer can also select other clustering algorithms such as fuzzy C-means clustering according to specific conditions. The selection of the clustering algorithm is not specially limited in the embodiment.

[0049] The k-means clustering algorithm and the calculation method of the Euclidean distance are both known technologies. The specific process of clustering two-tuples by the k-means clustering algorithm and the calculation process of the Euclidean distance are not repeated in the embodiment.

[0050] It should be noted that the value of the preset time period is set by humans. In the embodiment, the value of the preset time period is 30 days. In actual application, as other implementation forms, the implementer can also set it by himself according to specific conditions. The embodiment does not specially limit it.

[0051] (2) Further, based on the average distribution of all elements in the clustering center of each clustering cluster, the clustering mean of each clustering cluster is determined, the difference between the clustering mean of each clustering cluster and the maximum clustering mean in all clustering clusters is analyzed, and the clustering weight of each clustering cluster at each time is determined. Specifically,

[0052] In the embodiment, the average of the pipeline slope normalized value and the pipeline flow rate normalized value in the clustering center of each clustering cluster is taken as the clustering mean of the clustering cluster.

[0053] According to the clustering mean of each clustering cluster, it can be understood that the slope and flow rate of a single pipeline are key parameters for measuring whether the design is reasonable. The slope affects the drainage capacity, the flow rate affects the hydraulic efficiency and pipeline wear, and direct one-sided judgment using the pipeline slope or the pipeline flow rate is difficult to grasp the overall trend. Therefore, by calculating the average of the pipeline slope normalized value and the pipeline flow rate normalized value in each two-tuple in each clustering cluster, a feature for representing the overall design status of the clustering cluster is used. The larger the clustering mean is, the greater the degree of deviation of the design parameters of the pipeline in the clustering cluster from the ideal state or the specification requirement as a whole, thereby reflecting the overall severity of the potential soft conflict risk or design defect of the corresponding clustering cluster.

[0054] Further, the ratio of the cluster mean of each cluster at each time to the maximum cluster mean in all cluster is taken as the cluster weight of each cluster at each time.

[0055] According to the cluster weight of each cluster at each time, it can be understood that the cluster weight reflects the overall severity or representativeness of the pipe segment cluster of a specific risk level relative to the pipe segment cluster of the highest risk level. The larger the cluster mean, the higher the risk represented by the cluster. Therefore, in this embodiment, the cluster with the largest cluster mean is recorded as a high-risk cluster, the cluster with the smallest cluster mean is recorded as a low-risk cluster, and the remaining cluster is a medium-risk cluster. The closer the ratio of the cluster mean of the current cluster to the maximum cluster mean in all clusters to 1, the higher the risk level of the current cluster, and therefore the larger the corresponding cluster weight, indicating that the overall severity of the current pipe segment cluster relative to the pipe segment cluster of the highest risk level is greater.

[0056] On the contrary, the farther the ratio of the cluster mean of the current cluster to the maximum cluster mean in all clusters from 1, i.e. the smaller the ratio between the cluster means, the lower the risk level of the current cluster, and therefore the smaller the corresponding cluster weight, indicating that the overall severity of the current pipe segment cluster relative to the pipe segment cluster of the highest risk level is smaller.

[0057] (3) Further, in this embodiment, based on the difference between the pipeline slope and the preset standard slope and the difference between the pipeline flow rate and the preset standard flow rate in each tuple in each cluster at each time, a logistic regression analysis is used to obtain the conflict probability of each cluster at each time, specifically:

[0058] In this embodiment, in each cluster at each time, the difference between the pipeline slope and the preset standard slope and the difference between the pipeline flow rate and the preset standard flow rate are calculated respectively, and are recorded as the first deviation and the second deviation respectively. The first deviation and the second deviation of all tuples in each cluster are taken as the input of the logistic regression analysis algorithm, wherein the regularization coefficient is set to 1.0. The output probability value is taken as the conflict probability of each cluster at each time, which is used to represent the possibility of single pipe segment soft conflict. The larger the conflict probability, the greater the possibility of single pipe segment soft conflict. On the contrary, the smaller the conflict probability, the smaller the possibility of single pipe segment soft conflict.

[0059] It should be noted that the values of the preset standard slope and the preset standard flow rate are artificially set. In this embodiment, the value of the preset standard slope is 25°, and the value of the preset standard flow rate is 0.75 m / s. In actual application, as other implementation manners, the implementer can also set it according to the specific situation, which is not specially limited in this embodiment.

[0060] The logistic regression analysis algorithm is a known technology, and the specific process of predicting probability will not be described again.

[0061] (4) Further, the embodiment determines a soft conflict risk index of the fluid pipeline at each time point based on the conflict probability and the cluster weight, specifically:

[0062] The product of the cluster weight and the conflict probability of each cluster at each time point is calculated, and the accumulation of the products of all cluster groups at each time point is taken as the soft conflict risk index of the fluid pipeline at each time point.

[0063] According to the soft conflict risk index of the fluid pipeline at each time point, it can be understood that the soft conflict risk index is used to represent the overall severity of the conflict between the design parameter deviation of the water supply and drainage and the system performance. The greater the soft conflict risk index, the more likely there are multiple high-probability soft conflicts in the entire water supply and drainage system, the higher the possibility of design deviation from the standard and optimal performance, and the higher the attention and optimization required. Therefore, the greater the cluster weight of the current cluster, the more important the cluster occupies in the soft conflict risk. The greater the cluster weight, the closer the slope and flow rate of the current cluster to the high-risk state, and therefore the greater the corresponding soft conflict risk index. At the same time, the greater the conflict probability of the current cluster, the higher the possibility of soft conflict in the water supply and drainage system, and therefore the greater the corresponding soft conflict risk index.

[0064] On the contrary, the smaller the cluster weight of the current cluster, the less important the cluster occupies in the soft conflict risk. The design parameters such as the pipeline slope and flow rate are relatively closer to the safe or standard state, and therefore the contribution to the overall risk index is relatively small, and therefore the corresponding soft conflict risk index is smaller. At the same time, the smaller the conflict probability of the current cluster, the lower the possibility of soft conflict in the cluster, and even if the design parameters have some deviation, the risk of actually causing problems is relatively low, and therefore the corresponding soft conflict risk index is smaller. Both of these situations reflect that the design of the cluster is relatively more in line with the standard requirements, and the risk of serious soft conflict is relatively low, and therefore its priority is relatively low in the overall risk assessment and optimization sorting.

[0065] So far, the embodiment quantitatively identifies the soft conflict risk that is difficult to capture by traditional methods by clustering analysis of the binary group of pipeline slope and flow rate, combining with the conflict probability evaluation of deviation by logistic regression, and introducing the weight to distinguish the risk level, thereby improving the design compliance and system performance, reducing the construction rework and operation hidden danger, and improving the multi-specialty collaboration efficiency and resource utilization rate in the collaborative design optimization of water supply and drainage BIM positive design.

[0066] Step S3: based on the soft conflict risk index at all time points within the preset time period before each time point, the support sharing rate and the construction cost, a multi-objective genetic algorithm is used to obtain the multi-objective genetic algorithm score of the fluid pipeline at each time point; based on the soft conflict risk index at all time points within the preset time period before each time point and the vibration frequency of the heating and ventilation pipeline, a decision tree algorithm is used to obtain the urgency, and the multi-objective genetic algorithm score is combined to determine the treatment efficiency ratio of the fluid pipeline at each time point.

[0067] Because of the cross-professional logic conflicts in the collaborative optimization of water supply and drainage BIM forward design, such as: when the pipe and the heating and ventilation pipeline share the support, the vibration of the heating and ventilation equipment may be transmitted to the water supply and drainage pipeline, affecting its stability or function, and these cross-professional logic conflicts and multiple goals that need to be met in design, such as reducing components, saving space, ensuring function or composite specifications, are difficult to be effectively balanced and processed by traditional methods, so that the traditional methods cannot quantitatively optimize the potential and make priorities.

[0068] Therefore, in order to quantitatively evaluate the improvement space of the design scheme, the embodiment obtains the multi-objective genetic algorithm score of the fluid pipeline at each time point based on the soft conflict risk index at all time points within the preset time period before each time point, the support sharing rate and the construction cost; based on the soft conflict risk index at all time points within the preset time period before each time point and the vibration frequency of the heating and ventilation pipeline, a decision tree algorithm is used to obtain the urgency, and the multi-objective genetic algorithm score is combined to determine the treatment efficiency ratio of the fluid pipeline at each time point, specifically:

[0069] Firstly, the embodiment obtains the multi-objective genetic algorithm score of the fluid pipeline at each time point based on the soft conflict risk index at all time points within the preset time period before each time point, the support sharing rate and the construction cost, specifically:

[0070] In the embodiment, the soft conflict risk index at all time points within the preset time period before each time point, the support sharing rate and the construction cost are used as the input of the multi-objective genetic algorithm, wherein the population size is set to 200, the iteration number is set to 500, the crossover rate is set to 0.8, and the mutation rate is set to 0.1, and the multi-objective genetic algorithm score is output. The higher the multi-objective genetic algorithm score is, the greater the comprehensive optimization potential is.

[0071] Wherein, the multi-objective genetic algorithm is a known technology, and its specific principle will not be repeated.

[0072] Further, the embodiment obtains the urgency based on the soft conflict risk index at all time points within the preset time period before each time point and the vibration frequency of the heating and ventilation pipeline, specifically:

[0073] In the embodiment, the historical soft conflict risk index and the historical warm and air pipeline vibration frequency are acquired and taken as inputs of the decision tree, wherein the maximum depth is set as 5, the minimum sample split is set as 20, the information gain criterion is set as the Gini coefficient, the priority labels are set as urgent and postponable, the urgent label is set as 1, and the postponable label is set as 0, and the decision tree is trained.

[0074] The soft conflict risk index and the warm and air pipeline vibration frequency of all time points within a preset time length before each time point are taken as inputs of the trained decision tree, the proportion of the number of all urgent label leaf nodes in all leaf nodes is counted, and the proportion is taken as the urgency of the fluid pipeline at each time point.

[0075] The training of the decision tree and the decision tree algorithm are all known technologies, and the specific training process and principle are not described in detail.

[0076] Further, based on the urgency and the multi-objective genetic algorithm score, the treatment efficiency ratio of the fluid pipeline at each time point is determined, specifically:

[0077] In the embodiment, the ratio of the multi-objective genetic algorithm score of the fluid pipeline at each time point to the urgency is taken as the treatment efficiency ratio of the fluid pipeline at each time point.

[0078] Preferably, the treatment efficiency ratio extraction process diagram provided by the embodiment is as shown in Figure 2 .

[0079] According to the treatment efficiency ratio of the fluid pipeline at each time point, it can be understood that the treatment efficiency ratio is used to measure the comprehensive benefits that can be brought by the investment of optimization resources to solve the current urgent problem; the larger the multi-objective genetic algorithm score of the fluid pipeline at the current time point, the higher the comprehensive optimization potential of the current design scheme, and a better balance can be achieved in conflict risk control, cost saving and space utilization, and greater overall benefits can be obtained, so the corresponding treatment efficiency ratio is higher; at the same time, if the urgency is smaller, it reflects that the problem faced by the current pipe section or design scheme is not urgent and can be postponed, in this case, even if the multi-objective genetic algorithm score is not high, the treatment efficiency ratio is still high due to the low urgency, which indicates that this is an ideal optimization object with high optimization potential and low risk.

[0080] Conversely, if the multi-objective genetic algorithm score of the fluid pipeline at the current time is smaller, it means that the comprehensive optimization potential of the current design scheme is lower, the balance in multiple objectives such as conflict risk control, cost saving and space utilization is poorer, and the overall benefit brought by the current design scheme is limited, so the corresponding governance efficiency ratio is lower. At the same time, if the urgency is greater, it reflects that the problem faced by the current pipe section or design scheme is very urgent and needs to be processed in priority. In this case, even if the multi-objective genetic algorithm score is acceptable, the denominator is increased due to the high urgency, so the corresponding governance efficiency ratio is still low, which means that this is an optimization object with relatively low optimization potential and high risk urgency, which needs to be prioritized to invest resources to reduce its risk, rather than pursuing its potential optimization benefit.

[0081] So far, by quantifying the comprehensive optimization potential of the design scheme by the multi-objective genetic algorithm, combining the decision tree analysis to evaluate the problem urgency, and finally calculating the governance efficiency ratio, the embodiment realizes the scientific evaluation of the optimization resource investment benefit, effectively solves the problem that the traditional method is difficult to balance multiple objective constraints, quantify optimization potential and make priority, thereby improving the collaborative design efficiency and quality, and further improving the multi-specialty collaboration efficiency and resource utilization rate in the collaborative design optimization of the water supply and drainage BIM forward design.

[0082] Step S4: comparing the difference between the governance efficiency ratio of the fluid pipeline at the current time and the maximum governance efficiency ratio in the preset period before the current time, determining the priority reconstruction value of the fluid pipeline at the current time, and performing collaborative design optimization on the water supply and drainage BIM forward design at the current time.

[0083] Due to the lack of cross-specialty data association framework in the water supply and drainage BIM forward design, and the insufficient accuracy of logical soft conflict identification, the collaborative design efficiency is low and the construction rework rate is high. The traditional method relies on a single rule engine, which is difficult to cover multiple optimization requirements, and lacks quantitative basis for dynamic adjustment, resulting in frequent problems such as pipeline co-arch conflict, insufficient drainage slope, etc., which seriously affects the project cost and construction period.

[0084] Therefore, the governance efficiency ratio B of the fluid pipeline needs to be designed to improve the strategy. The embodiment compares the difference between the governance efficiency ratio of the fluid pipeline at the current time and the maximum governance efficiency ratio in the preset period before the current time, determines the priority reconstruction value of the fluid pipeline at the current time, and performs collaborative design optimization on the water supply and drainage BIM forward design at the current time, which is specifically:

[0085] As an implementation manner, in the embodiment, the expression of the priority reconstruction value of the fluid pipeline at the current time is: In the formula, Y represents the priority reconstruction value of the fluid pipeline at the current time; B represents the governance efficiency ratio of the fluid pipeline at the current time; B maxrepresents the maximum value of all governance efficiency ratios of the fluid pipeline within the preset time period before the current time.

[0086] It should be noted that the value of the preset time period length is artificially set, and in the embodiment, the value of the preset time period length is 30 days. In actual application, as another implementation manner, the implementer can set it by himself according to the specific situation, and the embodiment does not make special limitation.

[0087] Further, if the priority reconstruction value of the fluid pipeline at the current time is greater than the preset first threshold value, the pipeline routing is reconstructed.

[0088] If the priority reconstruction value of the fluid pipeline at the current time is between the preset second threshold value and the preset first threshold value, or the priority reconstruction value is equal to the preset second threshold value, or the priority reconstruction value is equal to the preset first threshold value, the cross-professional parameter linkage is performed, wherein the preset second threshold value is less than the preset first threshold value.

[0089] If the priority reconstruction value of the fluid pipeline at the current time is less than the preset second threshold value, the rule base is iteratively updated, wherein the rule base is a business rule set for storing drainage design compliance standards, cross-professional coordination logic and optimization strategies. In the embodiment, when the priority reconstruction value is low, it indicates that the current design state is optimal, and the model does not need to be directly modified, but the rule base is updated, such as adding or adjusting rules, to accumulate knowledge for subsequent optimization.

[0090] It should be noted that the values of the preset first threshold value and the preset second threshold value are artificially set, and in the embodiment, the value of the preset first threshold value is 0.8, and the value of the preset second threshold value is 0.5. In actual application, as another implementation manner, the implementer can set it by himself according to the specific situation, and the embodiment does not make special limitation.

[0091] So far, the embodiment compares the current governance efficiency ratio with the historical optimal value, quantitatively evaluates the urgency and potential of design improvement, and dynamically adjusts the optimization strategy accordingly, realizes the data-driven and adaptive collaborative optimization decision, effectively solves the problem that the traditional method relies on a single rule and lacks dynamic adjustment basis, significantly improves the design efficiency and problem solving ability, and further improves the multi-professional collaboration efficiency and resource utilization rate in the collaborative design optimization of the drainage BIM forward design.

[0092] It should be noted that the above sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the specific embodiments of the present description are described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0093] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments.

[0094] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; the technical solutions recorded in the foregoing embodiments are modified, or some technical features are replaced equivalently, and the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, which should be included in the protection scope of the present application.

Claims

1. A collaborative design optimization method for water supply and drainage BIM forward design, characterized in that, The method comprises the following steps: Real-time acquisition of fluid pipe slope, pipe flow rate, support sharing rate, construction cost and heating and ventilation pipe vibration frequency in the water supply and drainage BIM forward design process; The pipe slope and the pipe flow rate at each time are combined into a two-tuple, all two-tuples within a preset time period before each time are clustered, the average distribution of all elements in the cluster center of each cluster is determined based on the average distribution of all elements in the cluster center of each cluster, and the cluster mean of each cluster is determined; the difference between the cluster mean of each cluster and the maximum cluster mean in all cluster means is analyzed to determine the cluster weight of each cluster at each time; Based on the difference between the pipe slope and the preset standard slope in each two-tuple in each cluster at each time, and the difference between the pipe flow rate and the preset standard flow rate, the conflict probability of each cluster at each time is obtained by using logistic regression analysis, and the cluster weight is combined to determine the soft conflict risk index of the fluid pipe at each time; Based on the soft conflict risk index, the support sharing rate and the construction cost at all times within a preset time period before each time, a multi-objective genetic algorithm is used to obtain a multi-objective genetic algorithm score of the fluid pipe at each time; based on the soft conflict risk index and the heating and ventilation pipe vibration frequency at all times within a preset time period before each time, a decision tree algorithm is used to obtain urgency, and the multi-objective genetic algorithm score is combined to determine the treatment efficiency ratio of the fluid pipe at each time; The difference between the treatment efficiency ratio of the fluid pipe at the current time and the maximum treatment efficiency ratio within a preset time period before the current time is compared to determine the priority reconstruction value of the fluid pipe at the current time, so as to perform collaborative design optimization on the water supply and drainage BIM forward design at the current time; The cluster mean of each cluster is the mean of the normalized value of the pipe slope and the normalized value of the pipe flow rate in the cluster center of each cluster; The urgency includes: Obtain historical soft conflict risk index and historical heating and ventilation pipe vibration frequency and use them as inputs of the decision tree, wherein the priority label is set as urgent and postponable, and the decision tree is trained; The soft conflict risk index and the heating and ventilation pipe vibration frequency at all times within a preset time period before each time are used as inputs of the trained decision tree, the proportion of the number of leaf nodes of all urgent labels in all leaf nodes is counted, and the proportion is used as the urgency of the fluid pipe at each time.

2. The collaborative design optimization method for water supply and drainage BIM forward design according to claim 1, wherein, The distance in the clustering process is the Euclidean distance between two-tuples.

3. The collaborative design optimization method for water supply and drainage BIM forward design according to claim 1, characterized in that, The cluster weight of each cluster at each time is the ratio of the cluster mean of each cluster at each time to the maximum cluster mean in all cluster means.

4. The collaborative design optimization method for water supply and drainage BIM forward design of claim 1, wherein, The conflict probability of each cluster at each time is obtained by: In each cluster at each time, the difference between the pipe slope and the preset standard slope in each two-tuple is calculated, and the difference between the pipe flow rate and the preset standard flow rate is calculated, which are respectively denoted as the first deviation and the second deviation, the first deviation and the second deviation of all two-tuples in each cluster are used as inputs of the logistic regression analysis algorithm, and the output probability value is used as the conflict probability of each cluster at each time.

5. The collaborative design optimization method for water supply and drainage BIM forward design of claim 1, wherein, The determination method of the soft conflict risk index of the fluid pipe at each time is: The product of the clustering weight and the conflict probability of each clustering cluster at each time is calculated, and the accumulation of the products of all clustering clusters at each time is taken as the soft conflict risk index of the fluid pipeline at each time.

6. The collaborative design optimization method for water supply and drainage BIM forward design of claim 1, wherein, The governance potency ratio of the fluid pipeline at each time is the ratio of the multi-objective genetic algorithm score of the fluid pipeline at each time to the urgency.

7. The collaborative design optimization method for water supply and drainage BIM forward design of claim 1, wherein, The expression of the priority reconstruction value of the fluid pipeline at the current time is: ; wherein, represents the priority reconstruction value of the fluid pipeline at the current time; represents the governance potency ratio of the fluid pipeline at the current time; represents the maximum value of all the governance potency ratios of the fluid pipeline within the preset time period before the current time.

8. The collaborative design optimization method for water supply and drainage BIM forward design of claim 1, wherein, The collaborative design optimization of the forward design of the water and wastewater BIM at the current time includes: If the priority reconstruction value of the fluid pipeline at the current time is greater than a preset first threshold, the pipeline routing is reconstructed; If the priority reconstruction value of the fluid pipeline at the current time is between a preset second threshold and a preset first threshold, or the priority reconstruction value is equal to the preset second threshold, or the priority reconstruction value is equal to the preset first threshold, a cross-professional parameter linkage is performed, wherein the preset second threshold is less than the preset first threshold; If the priority reconstruction value of the fluid pipeline at the current time is less than the preset second threshold, the rule base is iteratively updated.

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