Building design collaboration method and system based on virtual reality and consensus model

Abstract

The invention relates to the technical field of building environment design, in particular to a building design collaboration method and system based on virtual reality and a consensus model, and the method comprises the steps: constructing a fuzzy decision matrix according to the preference attitude of each stakeholder, and calculating the complementary preference relationship of each stakeholder through a preference relationship membership function, calculating the preference similarity and preference proximity between each stakeholder and the group to obtain the consensus degree of each current stakeholder; when the consensus degrees of all stakeholders are greater than or equal to the lowest satisfaction threshold value, outputting a target design scheme; and when the consensus degree of the stakeholder is less than the lowest satisfaction threshold, generating a modification suggestion by using an automatic feedback mechanism. The virtual reality technology is used for eliminating potential errors or conflicts, effective cooperation and negotiation can be promoted, development of acceptable building environment design is promoted, and the efficiency and quality of collaborative design results are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment design, and in particular to a building design collaboration method and system based on virtual reality and consensus model. Background Art

[0002] In recent years, in order to overcome the decision-making problems in building environment design, promote all stakeholders to reach a consensus on design solutions and standards, and achieve a design solution that satisfies each stakeholder, building environment design usually chooses to implement collaborative decision-making with multiple stakeholders. One of the most important issues in the field of building environment design is how to achieve the optimal group decision-making among multiple stakeholders.

[0003] Currently, the commonly used collaborative decision-making methods include: brainstorming, design review meetings, and concept development, etc. These methods improve the quality and efficiency of decision-making by promoting communication and consensus among multiple stakeholders, and ensure the transparency and fairness of the decision-making process. However, there are various problems in design collaboration, such as the lack of clarity in the negotiation process, the lack of sufficient guidance or different explanations, and the herd mentality bias, etc., which lead to the collaboration in building environment design being very time-consuming.

[0004] To solve these problems, researchers have implemented various measures, and a relatively effective measure is to implement the consensus model. This strategy is driven by decision-making methods with multiple criteria, aiming to encourage effective discussion and reduce inconsistencies. In group decision-making, the consensus-based method takes into account the requirements of individuals and groups, ensuring that the views of the minority are not ignored. However, in the face of different backgrounds, different professional knowledge, and different communication methods of stakeholders, the facilitator still needs to make great efforts, and ineffective negotiations may exacerbate the bias in the consensus formation process.

[0005] Therefore, although the consensus model of group decision-making has potential, it still has understanding and communication barriers in the context of building environment design cooperation involving different stakeholders, reducing the efficiency of collaboration in building environment design and making it difficult to achieve decision-making balance. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problems that the consensus model of group decision-making in the prior art still has understanding and communication barriers in the context of building environment design cooperation involving different stakeholders, reducing the efficiency of collaboration in building environment design and making it difficult to achieve decision-making balance.

[0007] To solve the above technical problem, the present invention provides a building design collaboration method based on virtual reality and consensus model, including:

[0008] S1: Obtain the design elements of the building environment and the alternative solutions composed thereof;

[0009] S2: Build the initial scene of the building environment in the virtual reality environment and import each alternative solution into the VR system in a visual way;

[0010] S3: Each stakeholder expresses their preference attitude towards different design elements in each alternative solution in the form of linguistic variables, and obtains the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative solution;

[0011] S4: According to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative solution, construct the fuzzy decision matrix of each stakeholder;

[0012] S5: Calculate the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each alternative solution according to the fuzzy decision matrix of each stakeholder, and then calculate the complementary preference relationship of each stakeholder by using the membership function of the preference relationship; Based on the fuzzy decision matrix and the complementary preference relationship of each stakeholder, calculate the preference similarity and preference proximity between each stakeholder and the group, and obtain the consensus degree of each stakeholder;

[0013] S6: If the consensus degrees of all stakeholders are greater than or equal to the minimum satisfaction threshold, then calculate the group preference value of each alternative solution according to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative solution, the preset weight of the design element, and the importance degree of the stakeholder, and sort all alternative solutions, and select the alternative solution with the highest group preference value as the target design solution, and terminate this collaborative decision-making.

[0014] Preferably, after obtaining the consensus degree of each stakeholder, it further includes:

[0015] If there is a stakeholder whose consensus degree is less than the minimum satisfaction threshold, then mark this stakeholder as an "inconsistent stakeholder";

[0016] Judge whether the number of consensus degree updates reaches the preset maximum iteration number, or whether the change of the consensus degree of the "inconsistent stakeholder" is less than the tolerance value in two consecutive iterations;

[0017] If not, then obtain the design elements that do not meet the consensus for the stakeholder marked as "inconsistent stakeholder", and calculate the recommended preference value of the design elements that do not meet the consensus; According to the difference between the preference attitude of the "inconsistent stakeholder" towards the design elements that do not meet the consensus and the recommended preference value, use the VR system to provide the stakeholder with preference adjustment suggestions; After the "inconsistent stakeholder" adjusts the preference attitude towards the design elements that do not meet the consensus, return to execute S4 to update the consensus degree of all stakeholders;

[0018] If so, select the alternative with the highest group preference value as the target design solution and terminate the current collaborative decision-making.

[0019] Preferably, in S4, construct the fuzzy decision matrix of each stakeholder, including:

[0020]

[0021] where g h represents the fuzzy decision matrix of the h-th stakeholder, n represents the number of alternatives, and m represents the number of design elements; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the m-th design element in the n-th alternative, and l', m', and u' represent the lower limit, middle value, and upper limit of the triangular fuzzy number respectively; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the m-th design element in the 1st alternative; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the 1st design element in the n-th alternative.

[0022] Preferably, in S5, calculate the triangular fuzzy number of the preference attitude of each stakeholder towards each alternative according to the fuzzy decision matrix of each stakeholder, and then calculate the complementary preference relationship of each stakeholder using the preference relationship membership function, including:

[0023] According to the fuzzy decision matrix of each stakeholder, calculate the triangular fuzzy number of the preference attitude of each stakeholder towards each alternative; among them, the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the i-th alternative is The triangular fuzzy number of the preference attitude of the h-th stakeholder towards the k-th alternative is ξ j is the preset weight of the j-th design element;

[0024] Then calculate the complementary preference relationship of each stakeholder using the preference relationship membership function

[0025] where P h represents the complementary preference relationship of the h-th stakeholder; represents the preference degree of the h-th stakeholder towards the i-th alternative relative to the k-th alternative;

[0026] μ P represents the preference relationship membership function, and the formula is expressed as:

[0027]

[0028] Preferably, in S5, based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, calculate the preference similarity between each stakeholder and the group, including:

[0029] Calculate the preference similarity between two different stakeholders for each design element in each alternative solution according to the fuzzy decision matrix of each stakeholder. The formula is:

[0030] i = 1, 2, …, n, j = 1, 2, …, m, h, l = 1, 2, …, H and l ≠ h

[0031] Wherein, represents the preference similarity between the hth stakeholder and the lth stakeholder for the jth design element in the ith alternative solution, and d(·) represents the distance function. and respectively represent the triangular fuzzy numbers of the preference attitudes of the hth stakeholder and the lth stakeholder towards the jth design element in the ith alternative solution; n represents the number of alternative solutions, m represents the number of design elements; H represents the number of stakeholders.

[0032] Calculate the preference similarity between each stakeholder and the group for each design element in each alternative solution. The formula is:

[0033]

[0034] Wherein, represents the preference similarity between the hth stakeholder and the group for the jth design element in the ith alternative solution.

[0035] Calculate the preference similarity between two different stakeholders for each pair of alternative solutions according to the complementary preference relationship of each stakeholder. The formula is:

[0036] i, k = 1, 2, …, n, h, l = 1, 2, …, H and l ≠ h

[0037] Wherein, represents the preference similarity between the hth stakeholder and the lth stakeholder for the pair of alternative solutions (x i , x k ), x i and x k respectively represent the ith alternative solution and the kth alternative solution, k = 1, 2, …, n and k ≠ i; and respectively represent the preference degrees of the hth stakeholder and the lth stakeholder for the ith alternative solution relative to the kth alternative solution.

[0038] Calculate the preference similarity between each stakeholder and the group for each pair of alternative solutions. The formula is:

[0039]

[0040] where represents the preference similarity between the h-th stakeholder and the group for the pair of alternative solutions (x i , x k );

[0041] Calculate the preference similarity between each stakeholder and the group for the alternative solutions. The formula is:

[0042]

[0043] where represents the preference similarity between the h-th stakeholder and the group for the i-th alternative solution;

[0044] Calculate the preference similarity between each stakeholder and the group. The formula is:

[0045]

[0046] where SD h represents the preference similarity between the h-th stakeholder and the group.

[0047] Preferably, in S5, based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, calculate the preference proximity between each stakeholder and the group, including:

[0048] Calculate the relative similarity between each stakeholder and the group according to the preference similarity between each stakeholder and the group. The formula is:

[0049]

[0050] where RSD h represents the relative similarity between the h-th stakeholder and the group; SD h and SD l respectively represent the preference similarities between the h-th stakeholder and the l-th stakeholder and the group;

[0051] Calculate the importance degree of each stakeholder according to the relative preference similarity between each stakeholder and the group. The formula is:

[0052] w h = η · AID h + (1 - η) · RSD h , h = 1, 2, …, H

[0053] Among them, w h represents the importance degree of the h-th stakeholder, η represents the importance degree coefficient of the stakeholders, and AID h represents the relevant importance degree of the h-th stakeholder;

[0054] Calculate the collective decision matrix according to the fuzzy decision matrix of each stakeholder. The formula is:

[0055]

[0056] Among them, represents the triangular fuzzy number of the preference attitude of the j-th design element in the i-th alternative plan in the collective decision matrix, represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the j-th design element in the i-th alternative plan;

[0057] Calculate the group preference relationship according to the complementary preference relationship of each stakeholder The formula is:

[0058]

[0059] Among them, represents the preference degree of the group for the i-th alternative plan relative to the k-th alternative plan, w H represents the importance degree of the H-th stakeholder, represents the preference degree of the H-th stakeholder for the i-th alternative plan relative to the k-th alternative plan;

[0060] Calculate the preference proximity between each stakeholder and the group for each design element in each alternative plan. The formula is:

[0061]

[0062] Among them, represents the preference proximity between the h-th stakeholder and the group for the j-th design element in the i-th alternative plan;

[0063] Calculate the preference proximity between each stakeholder and the group for each pair of alternative plans. The formula is:

[0064]

[0065] Among them, represents the preference proximity between the h-th stakeholder and the group for the pair of alternative plans (x i , x k ); Indicates the preference degree of the h-th stakeholder for the i-th alternative relative to the k-th alternative;

[0066] Calculate the preference proximity between each stakeholder and the group for the alternatives, and the formula is:

[0067]

[0068] where, Indicates the preference proximity of the h-th stakeholder to the group for the i-th alternative; n represents the number of alternatives;

[0069] Calculate the preference proximity between each stakeholder and the group, and the formula is:

[0070]

[0071] where, PD h Indicates the preference proximity between the h-th stakeholder and the group.

[0072] Preferably, according to the preference similarity and preference proximity between each stakeholder and the group, obtain the consensus degree of each current stakeholder, and the formula is:

[0073] CL h = ψ·SD h +(1 - ψ)·PD h , h = 1, 2, …, H

[0074] where, CL h Indicates the consensus degree of the h-th stakeholder; ψ represents the weight parameter of the consensus level; SD h and PD h respectively represent the preference similarity and preference proximity between the h-th stakeholder and the group.

[0075] Preferably, for the stakeholders marked as "inconsistent stakeholders", obtain the design elements that do not meet the consensus, including:

[0076] Calculate the consensus degree of each stakeholder for the design elements, and the formula is:

[0077]

[0078] where, Indicates the consensus level of the h-th stakeholder for the j-th design element in the i-th alternative; and respectively represent the preference similarity and preference proximity between the h-th stakeholder and the group for the j-th design element in the i-th alternative; ψ represents the weight parameter of the consensus level; n represents the number of alternatives, m represents the number of design elements; H represents the number of stakeholders;

[0079] Calculate the consensus degree of each stakeholder for the alternatives, and the formula is:

[0080]

[0081] where, represents the consensus level of the h-th stakeholder for the i-th alternative; and respectively represent the preference similarity and preference proximity between the h-th stakeholder and the group for the i-th alternative;

[0082] "Inconsistent stakeholders" are defined as IND = {h|CL h <γ}, where CL h represents the consensus degree of the h-th stakeholder, and γ represents the minimum satisfaction threshold;

[0083] The alternatives that do not meet the consensus are defined as

[0084] The design elements that do not meet the consensus are defined as

[0085] Preferably, calculate the recommended preference value of the design elements that do not meet the consensus, and the formula is:

[0086]

[0087] where, represents the recommended preference value of the design elements that do not meet the consensus, δ represents the feedback mechanism parameter; APS represents the design elements that do not meet the consensus; represents the triangular fuzzy number of the preference attitude of the j-th design element in the i-th alternative in the collective decision matrix, represents the triangular fuzzy number of the preference attitude of the h-th stakeholder for the j-th design element in the i-th alternative.

[0088] Preferably, use the minimum adjustment optimization model to calculate the feedback mechanism parameter δ;

[0089] The minimum adjustment optimization model is expressed as:

[0090]

[0091] where, APS represents the design elements that do not meet the consensus, and respectively represent the triangular fuzzy number of the original preference attitude and the adjusted preference attitude of the h-th stakeholder towards the j-th design element in the i-th alternative; CL h' represents the adjusted consensus degree of the h-th stakeholder, CL s represents the consensus degree of other stakeholders.

[0092] The present invention also provides a building design collaboration system based on virtual reality and consensus model, including:

[0093] An alternative acquisition module, configured to acquire design elements of a building environment and alternatives constituted thereby;

[0094] A visualization module, configured to build an initial scene of the building environment in a virtual reality environment and import each alternative into the VR system in a visual manner;

[0095] A preference attitude acquisition module, configured to enable each stakeholder to express preference attitudes towards different design elements in each alternative in the form of linguistic variables;

[0096] A fuzzy decision matrix construction module, configured to construct a fuzzy decision matrix of each stakeholder according to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative;

[0097] A consensus degree calculation module, configured to calculate the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each alternative according to the fuzzy decision matrix of each stakeholder, and then calculate the complementary preference relationships of each stakeholder by using the preference relation membership function; based on the fuzzy decision matrix and complementary preference relationships of each stakeholder, calculate the preference similarity and preference proximity between each stakeholder and the group to obtain the consensus degree of each stakeholder;

[0098] A target design scheme output module, configured to, if the consensus degrees of all stakeholders are greater than or equal to the minimum satisfaction threshold, calculate the group preference value of each alternative according to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative, the preset weight of the design element, and the importance degree of the stakeholder, and sort all alternatives, and select the alternative with the highest group preference value as the target design scheme to terminate the current collaborative decision-making.

[0099] The above technical solutions of the present invention have the following beneficial effects compared with the prior art:

[0100] The present invention relates to a collaborative method for architectural design based on virtual reality and consensus model, which uses a consensus model enhanced by virtual reality to solve the problem of group decision-making in architectural environment design collaboration. A fuzzy decision matrix is constructed according to the preference attitudes of each stakeholder, and the complementary preference relationships of each stakeholder are calculated using the membership function of the preference relationship. Furthermore, the preference similarity and preference proximity between each stakeholder and the group are calculated to obtain the consensus degree of each current stakeholder. When the consensus degree of all stakeholders is greater than or equal to the minimum satisfaction threshold, the target design scheme is output according to the group preference value of the alternative scheme. When the consensus degree of a stakeholder is less than the minimum satisfaction threshold, an automatic feedback mechanism is used to calculate the recommended preference value of the design elements that do not meet the consensus for feedback, guiding the stakeholders with insufficient consensus to make modifications until all stakeholders reach a consensus. The present invention uses virtual reality technology to create a multi-sensory three-dimensional environment, enhancing the user experience and helping stakeholders visually examine the design scheme from all angles. It can eliminate potential errors or conflicts, greatly reduce the communication cost caused by imagination deviation, and improve the satisfaction of end-users. In addition, the collaborative decision-making method adopted by the present invention can promote effective cooperation and negotiation, facilitate the development of acceptable architectural environment designs, and improve the efficiency and quality of collaborative design results. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to make the content of the present invention easier to be clearly understood, the following further details the present invention according to specific embodiments of the present invention in combination with the accompanying drawings, where:

[0102] Figure 1 is a flowchart of a collaborative method for architectural design based on virtual reality and consensus model of the present invention;

[0103] Figure 2 is an illustration of the system interface when stakeholders log in and prefer design element options in the second embodiment;

[0104] Figure 3 is a schematic diagram of the graphical user interface during the consensus analysis and automatic feedback mechanism in the second embodiment, where Figure 3 in (a) is a schematic diagram of the interface for identifying the current consensus level, Figure 3 in (b) is a schematic diagram of the interface for reviewing specific design opinions, Figure 3 in (c) is a schematic diagram of the interface for accessing design knowledge and information, Figure 3 in (d) is a schematic diagram of the interface for accessing the design alternatives of "inconsistent stakeholders";

[0105] Figure 4 is a schematic diagram of re-evaluating design element options in the VR environment in the second embodiment, where Figure 4In (a) is a schematic diagram showing the layout of design element D1 changed from a long corridor type to a U shape. Figure 4 In (b) is a schematic diagram showing the floor of design element D4 changed from tiles to wood.

[0106] Figure 5 is a schematic diagram of a collaborative design solution generated by the virtual reality enhanced consensus model system in the second embodiment, where Figure 5 in (a) is a schematic diagram of alternative 71. Figure 5 in (b) is a schematic diagram of alternative 47. Figure 5 in (c) is a schematic diagram of alternative 72. Specific implementation manners

[0107] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the embodiments given are not intended to limit the present invention.

[0108] Embodiment 1

[0109] Referring to Figure 1 as shown, the present invention provides a building design collaboration method based on virtual reality and consensus model, including:

[0110] S1: Conduct a demand survey on the building design process, obtain the design elements of the building environment and the alternative solutions composed thereof, such as different layouts, styles, materials, equipment selections, etc.; and construct a consensus measurement model to provide a unified evaluation framework for subsequent group decision-making.

[0111] S2: Build an initial scene of the building environment in a virtual reality environment, and import the alternative solutions composed of each design element into the VR system in a visual manner. This environment allows each stakeholder to view and operate different combinations of design elements in a three-dimensional space, providing an immersive experience for collaborative decision-making.

[0112] S3: Each stakeholder expresses their preference attitudes towards different design elements in each alternative solution in the form of linguistic variables such as "like", "dislike", "acceptable", etc., and obtains the triangular fuzzy numbers (TFNs) of each stakeholder's preference attitudes towards each design element in each alternative solution. The immersion and operation flexibility of the VR environment enable them to more intuitively understand the characteristics and potential impacts of each option.

[0113] S4: According to the triangular fuzzy numbers of each stakeholder's preference attitudes towards each design element in each alternative solution, construct a fuzzy decision matrix for each stakeholder to quantitatively reflect the satisfaction and uncertainty of each stakeholder towards each design element option.

[0114] Specifically, the fuzzy decision matrix of each stakeholder is constructed as follows:

[0115]

[0116] where g h represents the fuzzy decision matrix of the h-th stakeholder, n represents the number of alternative solutions, and m represents the number of design elements; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the m-th design element in the n-th alternative solution, and l', m', and u' represent the lower limit, middle value, and upper limit of the triangular fuzzy number respectively; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the m-th design element in the 1st alternative solution; represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the 1st design element in the n-th alternative solution.

[0117] Meanwhile, the preset weights of different design elements can be set according to their importance in the overall building design.

[0118] S5: Calculate the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each alternative solution based on the fuzzy decision matrix of each stakeholder, and then use the preference relation membership function to calculate the complementary preference relations of each stakeholder to describe the relative preferences of "who is superior to whom, who is equivalent to whom, or who is inferior to whom". Based on the fuzzy decision matrix and complementary preference relations of each stakeholder, calculate the preference similarity and preference proximity between each stakeholder and the group to obtain the consensus degree of each stakeholder.

[0119] The specific steps are as follows:

[0120] S501: Calculate the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each alternative solution based on the fuzzy decision matrix of each stakeholder.

[0121] The triangular fuzzy number of the preference attitude of the h-th stakeholder towards the i-th alternative solution is The triangular fuzzy number of the preference attitude of the h-th stakeholder towards the k-th alternative solution is ξ j is the preset weight of the j-th design element;

[0122] S502: Then use the preference relation membership function to calculate the complementary preference relations of each stakeholder

[0123] where P h represents the complementary preference relation of the h-th stakeholder; represents the preference degree of the h-th stakeholder for the i-th alternative relative to the k-th alternative, and

[0124] μ P represents the membership function of the preference relationship, and is expressed by the formula:

[0125]

[0126] After obtaining the complementary preference relationships of all stakeholders, the consensus measurement model will calculate the similarity and proximity of each stakeholder relative to the group preference at four levels: design elements, alternatives, alternative pairs, and preference relationships to estimate the current consensus state.

[0127] S503: Based on the fuzzy decision matrices and complementary preference relationships of all stakeholders, calculate the preference similarity between each stakeholder and the group. The preference similarity evaluates the relative similarity between stakeholders. The calculation steps are as follows:

[0128] S503-1: Calculate the preference similarity of each design element in each alternative between two different stakeholders according to the fuzzy decision matrices of all stakeholders. The formula is:

[0129] i = 1, 2, …, n, j = 1, 2,..., m, h, l = 1, 2, …, H and l ≠ h

[0130] Among them, represents the preference similarity of the j-th design element in the i-th alternative between the h-th stakeholder and the l-th stakeholder, and d(·) represents the distance function, which is used to measure and the standardized distance between; and are the elements in the fuzzy decision matrices g h and g l respectively, and represent the triangular fuzzy numbers of the preference attitudes of the h-th stakeholder and the l-th stakeholder towards the j-th design element in the i-th alternative; n represents the number of alternatives, and m represents the number of design elements.

[0131] Calculate the preference similarity of each design element in each alternative between each stakeholder and the group. The formula is:

[0132]

[0133] Among them, denotes the preference similarity between the \(h\)th stakeholder and the group for the \(j\)th design element in the \(i\)th alternative; \(H\) represents the number of stakeholders.

[0134] S503-2: Calculate the preference similarity between two different stakeholders for each pair of alternatives according to the complementary preference relationships of the stakeholders. The formula is:

[0135] i, k = 1, 2, …, n, h, l = 1, 2, …, H and l ≠ h

[0136] where denotes the preference similarity between the \(h\)th stakeholder and the \(l\)th stakeholder for the pair of alternatives \((x i , x k ), \(x i and \(x k represent the \(i\)th alternative and the \(k\)th alternative respectively, k = 1, 2, …, n and k ≠ i; and are the elements in the complementary preference relationships \(P h and \(P l respectively, representing the preference degrees of the \(h\)th stakeholder and the \(l\)th stakeholder for the \(i\)th alternative relative to the \(k\)th alternative.

[0137] Calculate the preference similarity between each stakeholder and the group for each pair of alternatives. The formula is:

[0138]

[0139] where denotes the preference similarity between the \(h\)th stakeholder and the group for the pair of alternatives \((x i , x k ).

[0140] S503-3: Calculate the preference similarity between each stakeholder and the group for an alternative. The formula is:

[0141]

[0142] where denotes the preference similarity between the \(h\)th stakeholder and the group for the \(i\)th alternative.

[0143] S503-4: Calculate the preference similarity between each stakeholder and the group. The formula is:

[0144]

[0145] where \(SD hIndicates the preference similarity between the h-th stakeholder and the group.

[0146] S503-5: Calculate the relative similarity between each stakeholder and the group according to the preference similarity between each stakeholder and the group. The formula is:

[0147]

[0148] Among them, RSD h Indicates the relative similarity between the h-th stakeholder and the group; SD h and SD l respectively indicate the preference similarities between the h-th stakeholder and the l-th stakeholder and the group.

[0149] S504: Calculate the preference proximity between each stakeholder and the group based on the fuzzy decision matrix and complementary preference relationship of each stakeholder.

[0150] The preference proximity quantifies the distance between each stakeholder's preference and the group's preference, which reflects the similarity between the individual stakeholder's preference and the group's preference. When accumulating the collective stakeholder preference, the importance degree of the stakeholder must be considered first. The calculation steps are as follows:

[0151] S504-1: Calculate the importance degree of each stakeholder according to the relative preference similarity between each stakeholder and the group. The formula is:

[0152] w h = η·AID h +(1 - η)·RSD h , h = 1, 2, …, H

[0153] Among them, w h Indicates the importance degree of the h-th stakeholder, which is used to represent the importance degree of different stakeholders in the design decision-making; η represents the importance degree coefficient of the stakeholder; AID h Indicates the relevant importance degree of the h-th stakeholder, which can be determined by the stakeholder's subjective evaluation of their own design experience, authority, or spatial use priority relative to other stakeholders.

[0154] When the importance degree coefficient η of the stakeholder > 0.5, it means that when determining the importance degree of the stakeholder, the weight given to the characteristics of the stakeholder is higher than the relative similarity. In a homogeneous collaborative design scenario, the value of η applied is 0.

[0155] S503-4: Calculate the collective decision matrix according to the fuzzy decision matrix of each stakeholder. The formula is:

[0156]

[0157] Among them, is a triangular fuzzy number representing the preference attitude of the j-th design element in the i-th alternative in the collective decision-making matrix, is a triangular fuzzy number representing the preference attitude of the h-th stakeholder towards the j-th design element in the i-th alternative.

[0158] S504-3: Calculate the group preference relationship based on the complementary preference relationships of the stakeholders The formula is:

[0159]

[0160] Among them, represents the preference degree of the group for the i-th alternative relative to the k-th alternative, w H represents the importance degree of the H-th stakeholder, represents the preference degree of the H-th stakeholder for the i-th alternative relative to the k-th alternative.

[0161] S504-4: Calculate the preference proximity between each stakeholder and the group for each design element in each alternative. The formula is:

[0162]

[0163] Among them, represents the preference proximity between the h-th stakeholder and the group for the j-th design element in the i-th alternative.

[0164] S504-5: Calculate the preference proximity between each stakeholder and the group for each pair of alternatives. The formula is:

[0165]

[0166] Among them, represents the preference proximity between the h-th stakeholder and the group for the pair of alternatives (x i , x k ); represents the preference degree of the h-th stakeholder for the i-th alternative relative to the k-th alternative.

[0167] S504-6: Calculate the preference proximity between each stakeholder and the group for each alternative. The formula is:

[0168]

[0169] Among them, Indicates the preference proximity between the h-th stakeholder and the group for the i-th alternative; n represents the number of alternatives.

[0170] S504-7: Calculate the preference proximity between each stakeholder and the group, with the formula:

[0171]

[0172] where PD h Indicates the preference proximity between the h-th stakeholder and the group.

[0173] S505: Obtain the consensus degree of each current stakeholder based on the preference similarity and preference proximity between each stakeholder and the group.

[0174] After defining the preference similarity and preference proximity, the consensus degree among stakeholder preferences can be defined at four levels: design elements, alternative pairs, alternatives, and the preference relationship of stakeholders. The calculation steps are as follows:

[0175] S505-1: Calculate the consensus degree of each stakeholder for design elements, with the formula:

[0176]

[0177] where Indicates the consensus level of the h-th stakeholder for the j-th design element in the i-th alternative; and respectively indicate the preference similarity and preference proximity between the h-th stakeholder and the group for the j-th design element in the i-th alternative; ψ ∈ [0, 1] represents the weight parameter of the consensus level, used to control the standard weights of preference similarity and preference proximity.

[0178] S505-2: Calculate the consensus degree of each stakeholder for alternative pairs, with the formula:

[0179]

[0180] where Indicates the consensus level of the h-th stakeholder for the alternative pair (x i , x k ); and respectively indicate the preference similarity and preference proximity between the h-th stakeholder and the group for the alternative pair (x i , x k ).

[0181] S505-3: Calculate the consensus degree of each stakeholder for the alternative solutions. The formula is:

[0182]

[0183] where, represents the consensus level of the h-th stakeholder for the i-th alternative solution; and represent the preference similarity and preference proximity between the h-th stakeholder and the group for the i-th alternative solution, respectively.

[0184] S505-4: Calculate the consensus degree of each stakeholder. The formula is:

[0185] CL h = ψ·SD h +(1 - ψ)·PD h , h = 1, 2, …, H

[0186] where, CL h represents the consensus degree of the h-th stakeholder; ψ represents the weight parameter of the consensus level; SD h and PD h represent the preference similarity and preference proximity between the h-th stakeholder and the group, respectively.

[0187] S6: If the consensus degrees of all stakeholders are greater than or equal to the minimum satisfaction threshold, then execute S7; if there exists a stakeholder whose consensus degree is less than the minimum satisfaction threshold, then mark this stakeholder as an "inconsistent stakeholder" and execute S8.

[0188] To reach a consensus solution, the consensus degree CL of each stakeholder should exceed the minimum satisfaction threshold γ. It should be noted that in most cases, if more than half of the stakeholders reach a consensus, the design decision can be considered acceptable. Therefore, the minimum satisfaction threshold can be defined within the range of γ ∈ [0.5, 1).

[0189] S7: Calculate the group preference value of each alternative solution according to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative solution, the preset weights of the design elements, and the importance degree of the stakeholders, and sort all alternative solutions. Select the alternative solution with the highest group preference value as the target design solution, and terminate this collaborative decision-making.

[0190] The calculation method of the group preference value of each alternative solution is to multiply the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative solution by their corresponding preset weights of the design elements and then sum them up to obtain the preference attitudes of different stakeholders towards each alternative solution Multiply by the importance degree of the corresponding stakeholders and then sum them up to obtain the group preference value g of each alternative n . Rank the alternatives according to the group preference values of the alternatives, select the alternative with the highest group preference value as the target design solution, and terminate this collaborative decision-making.

[0191] When outputting the target design solution, several items before ranking can also be selected as feasible alternatives for further discussion or modification by the stakeholders, and the visual presentation of each design element in the VR system and its corresponding preference information are saved as the basis for subsequent implementation or further discussion.

[0192] If there are stakeholders with different backgrounds or different usage frequencies, different weights can be applied during the scheme scoring process; if the group is relatively homogeneous, the weight differences can be ignored. Finally, select the optimal design solution in the ranking list.

[0193] S8: To ensure the efficiency of group decision-making, whether the number of consensus updates reaches the preset maximum number of iterations, or whether the change in the consensus degree of "inconsistent stakeholders" is less than the tolerance value, such as 0.01, in two consecutive iterations;

[0194] If not, for the "inconsistent stakeholders" marked, obtain the design elements that do not meet the consensus, and calculate the recommended preference values of the design elements that do not meet the consensus; according to the difference between the preference attitude of the "inconsistent stakeholders" towards the design elements that do not meet the consensus and the recommended preference values, use the VR system to provide the stakeholders with preference adjustment suggestions; after the "inconsistent stakeholders" adjust their preference attitudes towards the design elements that do not meet the consensus, return to execute S4 to update the consensus degrees of all stakeholders;

[0195] If so, execute S7.

[0196] "Inconsistent stakeholders" may need to change their opinions / decisions to reach a consensus design solution. In order to help keep close to the original design preferences of the stakeholders and minimize the adjustment requirements, and to achieve a higher level of consensus in the final design decision, the present invention needs to adopt a minimum adjustment optimization feedback mechanism, determine the feedback mechanism parameters, and generate detailed suggestions for the given stakeholders accordingly. The specific steps are as follows:

[0197] S801: For the "inconsistent stakeholders" marked, obtain the design elements that do not meet the consensus, including:

[0198] The "inconsistent stakeholders" are defined as IND = {h|CL h <γ}, where CL hrepresents the consensus degree of the h-th stakeholder, and γ represents the preset threshold;

[0199] The alternative solutions that do not meet the consensus are defined as

[0200] The design elements that do not meet the consensus are defined as

[0201] S802: After identifying the inconsistent stakeholders, the automatic feedback mechanism calculates the recommended preference value to achieve a higher degree of consensus.

[0202] The formula for calculating the recommended preference value of the design elements that do not meet the consensus is:

[0203]

[0204] where represents the recommended preference value of the design elements that do not meet the consensus, δ ∈ [0, 1] represents the feedback mechanism parameter for controlling the acceptance degree of the recommendation; APS represents the design elements that do not meet the consensus; represents the triangular fuzzy number of the preference attitude of the j-th design element in the i-th alternative solution in the collective decision matrix, represents the triangular fuzzy number of the preference attitude of the h-th stakeholder towards the j-th design element in the i-th alternative solution.

[0205] The following suggestions are provided for all "inconsistent stakeholders": "Do you consider changing your preference degree for the design element j in alternative solution x i to make it closer to the value."

[0206] When δ = 1, the original preference is completely replaced by the group preference. When δ = 0, it means the original preference remains unchanged. Therefore, the larger the feedback mechanism parameter δ, the greater the adjustment recommended for the stakeholders. In this type of group decision-making problem, selecting the boundary parameter (δ min ) is an important issue.

[0207] To determine this feedback mechanism parameter, the present invention adopts a minimum adjustment optimization model to minimize the change between the adjusted preference decision and the original preference decision while ensuring that both the adjusted preference relationship and the unchanged preference relationship can meet the minimum consensus requirement.

[0208] The minimum adjustment optimization model is expressed as:

[0209]

[0210] Among them, APS represents the design elements that do not meet the consensus. and respectively represent the triangular fuzzy number of the original preference attitude and the triangular fuzzy number of the adjusted preference attitude of the h-th stakeholder towards the j-th design element in the i-th alternative; CL h' represents the adjusted consensus degree of the h-th stakeholder, and CL s represents the consensus degree of other stakeholders.

[0211] S803: According to the difference between the preference attitude of the "inconsistent stakeholder" towards the design elements that do not meet the consensus and the recommended preference value, use the VR system to provide the stakeholder with preference adjustment suggestions.

[0212] Preferably, the recommended preference value should be calculated as a set of fuzzy numbers. In order to maintain the principle that the recommendation must be easy to understand and apply, the recommended preference value should be presented in the same way as the stakeholder preference input, that is, in the form of linguistic variables.

[0213] Therefore, it is necessary to calculate the difference diff between the original preference attitude and the adjusted preference attitude. The formula is:

[0214]

[0215] Among them, (g l' , g m' , g u' ) is the triangular fuzzy number of the original preference attitude, and (r l' , r m' , r u' ) is the triangular fuzzy number of the adjusted preference attitude.

[0216] If the recommended preference value is lower than the original preference, the system will recommend that the "inconsistent stakeholder" reduce the preference for the given design element option; otherwise, the system will recommend that the "inconsistent stakeholder" increase the preference for the given design element option. After the "inconsistent stakeholder" accepts the recommendation and implements the alternative adjustment, the system will reallocate the fuzzy decision matrix according to the adjusted preference attitude.

[0217] The automatic feedback mechanism of this step can be either through simple numerical and text prompts or through the VR system to dynamically display the changes in the design scheme.

[0218] Example 2

[0219] As shown in Table 1, the alternative kitchen design provided in this embodiment is defined by four design elements: layout (3 options), design style (3 options), cabinet color style (4 options), and floor material (2 options), resulting in 72 different design solutions. To verify the feasibility of the present invention and evaluate the perception of using the proposed system for the design collaboration process, three postgraduate students were invited as stakeholders (d 1 ,d 2 ,d 3 ) to participate in the collaborative design task.

[0220] Table 1. Design Element Options

[0221]

[0222] In this embodiment, stakeholders were required to use linguistic variables to indicate their preference levels for various design element options, namely "like", "acceptable", and "dislike", and then these linguistic variables were converted into triangular fuzzy numbers in the system, corresponding to (0.5, 0.75, 1), (0.25, 0.5, 0.75), and (0, 0.25, 0.5) respectively. For simplicity, a 3-level granularity fuzzy term set was used to represent different satisfaction levels for various design element options.

[0223] In this embodiment, stakeholders d 2 and d 3 have more design experience than stakeholder d 1 . Assuming d 2 as the main user of this space, will spend more time in this design space than other stakeholders; therefore, for illustrative purposes, corresponding weights were objectively assigned to these three stakeholders, namely AID 1 = 0.15, AID 2 = 0.5, AID 3 = 0.35. The importance of design elements in influencing the selection of design alternatives was equally distributed among the three stakeholders, and for the four design elements, their weights were [0.3, 0.3, 0.2, 0.2].

[0224] For the consensus-based decision-making model, this embodiment assumed the following parameters to control the process: the weight parameter ψ of the consensus level = 0.5, the importance degree coefficient η of stakeholders = 0.5, and the minimum satisfaction threshold γ = 0.9. In the following section, the perspective of stakeholder d 1 will be used to describe how stakeholders collaborate on the kitchen design under the guidance of the VR-based collaborative design system; the processes of other participants are similar.

[0225] Stakeholder d1 Log in to the VR system. After viewing the basic room information and design guidelines, indicate preferences for various design element options, as shown in Figure 2 . The stakeholder d 1 's fuzzy decision matrix is shown in Table 2 below. It can be expressed in the form of a fuzzy multi-criteria decision-making model (MCDM) and converted into a complementary preference relationship among 72 design alternatives.

[0226] Table 2. Initial stakeholder preference attitudes towards various design element options

[0227]

[0228] As shown in Figure 3 , based on the preference relationship, calculate the consensus level for each stakeholder and draw the following conclusions: CL 1 = 0.88, CL 2 = 0.93, CL 3 = 0.92. This degree of consensus is returned to the stakeholders in the VR interface so that they can easily capture the current consensus state, as shown in Figure 3 (a). It can be seen that stakeholder d 1 does not reach the minimum satisfaction threshold, i.e., γ = 0.9. Therefore, the system's automatic feedback mechanism designates stakeholder d 1 as an "inconsistent stakeholder" and begins the step of generating suggestions. Before creating specific suggestions for stakeholder d 1 , use the minimum adjustment optimization model to obtain the feedback mechanism parameters. It is found that when δ = 0.87, the consensus degree of stakeholder d 1 will reach the minimum satisfaction threshold while minimizing the preference adjustment required by stakeholder d 1 . It should be noted that during the entire consensus state analysis phase, all stakeholders are present in the virtual meeting room. Once feedback information is generated, as shown in Figure 3 (b), and suggestions are made regarding the preferences for the presented specific design element options (e.g., "Are you willing to reduce your preference for [gallery kitchen] to a near-acceptable level?"), the stakeholders exchange opinions on the specific design element options, access design knowledge and information, as shown in Figure 3 (c), and re-evaluate the relevant design information in the database, as shown in Figure 3 (d), and allow the stakeholders to change the design options so that they can re-evaluate them in an inconsistent design environment, as shown in Figure 4 , where Figure 4 (a) is a schematic diagram of changing the layout of design element D1 from a long corridor type to a U type, Figure 4In (b), it is a schematic diagram of changing the floor of design element D4 from ceramic tile to wood.

[0229] After the review feedback, stakeholder d 1 Finally followed the suggestions of D1 and D2, while rejecting the suggestions of D3 and D4. Therefore, in the new co - design round, the stakeholders updated the fuzzy decision matrix for the four design elements, as shown in Table 3. The calculation module returned the following current consensus level values, each of which meets the minimum satisfaction threshold CL 1 = 0.96, CL 2 = 0.96, CL 3 = 0.95. Therefore, it can be concluded that the system successfully executed the selection and aggregation process.

[0230] Table 3. Stakeholder preference attitudes for the second round of each design element option

[0231]

[0232] Finally, alternatives [No.71], [No.47] and [No.72] were considered the top three collaborative consensus design solutions considering all stakeholder preferences, as Figure 5 shown, where Figure 5 in (a) is a schematic diagram of alternative 71, Figure 5 in (b) is a schematic diagram of alternative 47, Figure 5 in (c) is a schematic diagram of alternative 72. According to the subsequent interviews, all three decision - makers were satisfied with these generated design solutions.

[0233] Embodiment 3

[0234] Based on the building design collaboration method based on virtual reality and consensus model described in Embodiment 1, this embodiment provides a building design collaboration system based on virtual reality and consensus model, including:

[0235] An alternative acquisition module, configured to acquire design elements of a building environment and alternatives constituted by them;

[0236] A visualization module, configured to build an initial scene of the building environment in a virtual reality environment and import each alternative into the VR system in a visual manner;

[0237] A preference attitude acquisition module, configured to enable each stakeholder to express preference attitudes for different design elements in each alternative in the form of linguistic variables;

[0238] A fuzzy decision matrix construction module, configured to construct a fuzzy decision matrix for each stakeholder according to the triangular fuzzy numbers of the preference attitudes of each stakeholder for each design element in each alternative;

[0239] A consensus degree calculation module, which is used to calculate the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each alternative according to the fuzzy decision-making matrices of each stakeholder, and then calculate the complementary preference relationships of each stakeholder by using the membership function of the preference relationship; based on the fuzzy decision-making matrices and complementary preference relationships of each stakeholder, calculate the preference similarity and preference proximity between each stakeholder and the group, and obtain the consensus degree of each stakeholder.

[0240] A target design scheme output module, which is used to, if the consensus degrees of all stakeholders are greater than or equal to the minimum satisfaction threshold, calculate the group preference value of each alternative according to the triangular fuzzy numbers of the preference attitudes of each stakeholder towards each design element in each alternative, the preset weights of the design elements, and the importance degree of the stakeholders, and sort all alternatives, and select the alternative with the highest group preference value as the target design scheme to terminate the current collaborative decision-making.

[0241] In summary, for the building design collaboration method based on virtual reality and consensus model described in the present invention, the consensus model enhanced by virtual reality is used to solve the problem of group decision-making in building environment design collaboration. A fuzzy decision-making matrix is constructed according to the preference attitudes of each stakeholder, and the complementary preference relationships of each stakeholder are calculated by using the membership function of the preference relationship. Furthermore, the preference similarity and preference proximity between each stakeholder and the group are calculated to obtain the current consensus degree of each stakeholder. When the consensus degrees of all stakeholders are greater than or equal to the minimum satisfaction threshold, the target design scheme is output according to the group preference value of the alternative. When the consensus degree of a stakeholder is less than the minimum satisfaction threshold, the automatic feedback mechanism is used to calculate the recommended preference value of the design element that does not meet the consensus for feedback, and guide the stakeholder with insufficient consensus degree to make modifications until all stakeholders reach a consensus. The present invention uses virtual reality technology to create a multi-sensory three-dimensional environment, enhance the user experience, help stakeholders visually examine the design scheme from all angles, can eliminate potential errors or conflicts, greatly reduce the communication cost caused by imagination deviation, and improve the satisfaction of the end user. And the collaborative decision-making method adopted by the present invention can promote effective cooperation and negotiation, promote the development of an acceptable building environment design, and improve the efficiency and quality of the collaborative design result.

[0242] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0243] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a plurality of blocks.

[0244] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a plurality of blocks.

[0245] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a plurality of blocks.

[0246] Obviously, the above embodiments are only examples for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A collaborative architectural design method based on virtual reality and consensus model, characterized in that: include: S1: Acquire design elements of the built environment and alternatives for their composition; S2: Build the initial scene of the building environment in the virtual reality environment and import each alternative plan into the VR system in a visual way; S3: Each stakeholder expresses his / her preference attitude towards different design elements in each alternative plan in the form of linguistic variables, and obtains the triangular fuzzy number of each stakeholder's preference attitude towards each design element in each alternative plan; S4: Construct the fuzzy decision matrix of each stakeholder based on the triangular fuzzy numbers of each stakeholder’s preference attitude towards each design element in each alternative plan; S5: Calculate the triangular fuzzy number of each stakeholder's preference attitude towards each alternative plan based on the fuzzy decision matrix of each stakeholder, and then use the preference relationship membership function to calculate the complementary preference relationship of each stakeholder; Based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, the preference similarity and preference proximity between each stakeholder and the group are calculated to obtain the consensus of each stakeholder. S6: If the consensus of all stakeholders is greater than or equal to the minimum satisfaction threshold, the group preference value of each alternative is calculated based on the triangular fuzzy number of each stakeholder's preference attitude towards each design element in each alternative, the preset weight of the design element and the importance of the stakeholder, and all alternatives are sorted. The alternative with the highest group preference value is selected as the target design solution, and this collaborative decision-making is terminated.

2. The architectural design collaboration method based on virtual reality and consensus model according to claim 1 is characterized in that: After obtaining consensus from all stakeholders, it also includes: If there is a stakeholder whose consensus is less than the minimum satisfaction threshold, the stakeholder is marked as an "inconsistent stakeholder"; Determine whether the number of consensus updates reaches the preset maximum number of iterations, or whether the change in the consensus of "inconsistent stakeholders" in two consecutive iterations is less than the tolerance value; If not, then for the stakeholders marked as "inconsistent", the design elements that do not meet the consensus are obtained, and the recommended preference values ​​of the design elements that do not meet the consensus are calculated; according to the difference between the preference attitude of the "inconsistent stakeholders" towards the design elements that do not meet the consensus and the recommended preference values, Use the VR system to provide the stakeholder with preference adjustment suggestions; after the "inconsistent stakeholders" adjust their preference attitudes towards the design elements that do not meet the consensus, return to execute S4 to update the consensus of all stakeholders; If so, the alternative with the highest group preference value is selected as the target design solution, and this collaborative decision is terminated.

3. The architectural design collaboration method based on virtual reality and consensus model according to claim 1 is characterized in that: In S4, the fuzzy decision matrix of each stakeholder is constructed, including: Among them, g h represents the fuzzy decision matrix of the hth stakeholder, n represents the number of alternatives, and m represents the number of design elements; The triangular fuzzy number representing the preference attitude of the h-th stakeholder towards the m-th design element in the n-th alternative, l', m' and u' represent the lower limit, middle value and upper limit of the triangular fuzzy number respectively; The triangular fuzzy number representing the preference attitude of the hth stakeholder towards the mth design element in the first alternative; A triangular fuzzy number that represents the preference attitude of the hth stakeholder toward the first design element in the nth alternative.

4. The architectural design collaboration method based on virtual reality and consensus model according to claim 3 is characterized in that: In S5, the triangular fuzzy number of each stakeholder's preference attitude towards each alternative is calculated based on the fuzzy decision matrix of each stakeholder, and then the complementary preference relationship of each stakeholder is calculated using the preference relationship membership function, including: According to the fuzzy decision matrix of each stakeholder, the triangular fuzzy number of each stakeholder's preference attitude towards each alternative is calculated; among them, the triangular fuzzy number of the hth stakeholder's preference attitude towards the i-th alternative is The triangular fuzzy number of the hth stakeholder's preference attitude towards the kth alternative is ξ j is the preset weight of the jth design element; the preference relationship membership function is then used to calculate the complementary preference relationship of each stakeholder Among them, P h represents the complementary preference relationship of the hth stakeholder; represents the preference of the hth stakeholder for the ith alternative relative to the kth alternative; μ P represents the preference relationship membership function, and the formula is expressed as:

5. The architectural design collaboration method based on virtual reality and consensus model according to claim 4 is characterized in that: In S5, based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, the preference similarity between each stakeholder and the group is calculated, including: The similarity of preferences between two different stakeholders for each design element in each alternative is calculated based on the fuzzy decision matrix of each stakeholder. The formula is: in, represents the preference similarity between the hth stakeholder and the lth stakeholder for the jth design element in the ith alternative, d(·) represents the distance function, and The triangular fuzzy numbers representing the preference attitudes of the h-th stakeholder and the l-th stakeholder towards the j-th design element in the ith alternative respectively; n represents the number of alternatives, m represents the number of design elements; H represents the number of stakeholders; The similarity of preferences between stakeholders and groups for each design element in each alternative is calculated using the formula: in, represents the preference similarity between the hth stakeholder and the group for the jth design element in the ith alternative; The preference similarity between two different stakeholders for each pair of alternatives is calculated based on the complementary preference relationship of each stakeholder. The formula is: in, represents the difference between the hth stakeholder and the lth stakeholder on the alternative pair (x i ,x k )’s preference similarity, x i and x k They represent the i-th alternative and the k-th alternative respectively, k = 1, 2, …, n and k ≠ i; and They represent the preference of the hth stakeholder and the lth stakeholder for the ith alternative over the kth alternative respectively; The similarity of preferences between stakeholders and groups for each pair of alternatives is calculated using the formula: in, represents the relationship between the hth stakeholder and the group regarding the alternative plan (x i ,x k )’s preference similarity; The similarity of preferences between stakeholders and groups for alternatives is calculated using the formula: in, represents the preference similarity between the hth stakeholder and the group for the i-th alternative; The similarity of preferences between stakeholders and groups is calculated as follows: Among them, SD h represents the preference similarity between the hth stakeholder and the group.

6. The architectural design collaboration method based on virtual reality and consensus model according to claim 5 is characterized in that: In S5, based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, the preference proximity between each stakeholder and the group is calculated, including: The relative similarity between each stakeholder and the group is calculated based on the preference similarity between each stakeholder and the group. The formula is: Among them, RSD h represents the relative similarity between the hth stakeholder and the group; SD h and SD l They represent the preference similarity between the hth stakeholder and the lth stakeholder and the group respectively; the importance of each stakeholder is calculated according to the relative preference similarity between each stakeholder and the group, and the formula is: w h =η·AID h +(1-n)·RSD h ,h=1,2,…,H Among them, w h represents the importance of the hth stakeholder, η represents the importance coefficient of the stakeholder, and AID h represents the relative importance of the h-th stakeholder; The collective decision matrix is ​​calculated based on the fuzzy decision matrix of each stakeholder, and the formula is: in, The triangular fuzzy number representing the preference attitude towards the jth design element in the i-th alternative in the collective decision matrix, The triangular fuzzy number representing the preference attitude of the hth stakeholder towards the jth design element in the ith alternative; Calculate group preference relations based on the complementary preference relations of each stakeholder The formula is: in, represents the group's preference for the ith alternative relative to the kth alternative, w H represents the importance of the Hth stakeholder, represents the preference of the Hth stakeholder for the ith alternative relative to the kth alternative; The preference proximity between stakeholders and groups for each design element in each alternative is calculated using the formula: in, represents the preference proximity between the hth stakeholder and the group for the jth design element in the ith alternative; The preference proximity between each stakeholder and group for each pair of alternatives is calculated using the formula: in, represents the relationship between the hth stakeholder and the group regarding the alternative plan (x i ,x k )’s preference proximity; It represents the preference of the h-th stakeholder for the ith alternative over the k-th alternative; The preference proximity between stakeholders and groups for alternative options is calculated using the formula: in, represents the preference proximity between the hth stakeholder and the group for the i-th alternative; n represents the number of alternatives; The preference proximity between each stakeholder and the group is calculated using the formula: Among them, PD h represents the preference proximity between the hth stakeholder and the group.

7. The architectural design collaboration method based on virtual reality and consensus model according to claim 6 is characterized in that: According to the preference similarity and preference proximity between each stakeholder and the group, the consensus degree of the current stakeholders is obtained, and the formula is: CL h =ψ·SD h +(1-ψ)·PD h ,h=1,2,…,H Among them, CL h represents the consensus of the hth stakeholder; ψ represents the weight parameter of the consensus level; SD h and PD h They represent the preference similarity and preference proximity between the h-th stakeholder and the group, respectively.

8. The architectural design collaboration method based on virtual reality and consensus model according to claim 2 is characterized in that: For stakeholders marked as "inconsistent", obtain the design elements that do not meet consensus, including: Calculate the consensus of each stakeholder on the design elements. The formula is: in, represents the consensus level of the h-th stakeholder on the j-th design element in the ith alternative; and denote the preference similarity and preference proximity between the hth stakeholder and the group for the jth design element in the ith alternative respectively; ψ denotes the weight parameter of the consensus level; n denotes the number of alternatives, m denotes the number of design elements; H denotes the number of stakeholders; Calculate the consensus of stakeholders on the alternatives using the formula: in, represents the consensus level of the h-th stakeholder on the i-th alternative; and They represent the preference similarity and preference proximity between the h-th stakeholder and the group for the i-th alternative respectively; "Inconsistent stakeholders" are defined as IND = {h|CL h <γ}, where CL h represents the consensus of the hth stakeholder, and γ represents the minimum satisfaction threshold; The alternatives that do not satisfy the consensus are defined as Design elements that do not meet consensus are defined as 9. The architectural design collaboration method based on virtual reality and consensus model according to claim 8 is characterized in that: The recommended preference value of the design elements that do not meet the consensus is calculated as follows: in, represents the suggested preference value of the design element that does not meet the consensus, δ represents the feedback mechanism parameter; APS represents the design element that does not meet the consensus; The triangular fuzzy number representing the preference attitude towards the jth design element in the i-th alternative in the collective decision matrix, A triangular fuzzy number representing the preference attitude of the h-th stakeholder toward the j-th design element in the ith alternative.

10. The architectural design collaboration method based on virtual reality and consensus model according to claim 9 is characterized in that: The feedback mechanism parameter δ is calculated using the minimum adjustment optimization model; The minimum adjustment optimization model is expressed as: Among them, APS represents the design elements that do not meet the consensus. and are the original and adjusted triangular fuzzy numbers of the h-th stakeholder’s preference attitude toward the j-th design element in the ith alternative; CL h' represents the adjusted consensus of the hth stakeholder, CL s Indicates the degree of consensus among other stakeholders.

11. An architectural design collaboration system based on virtual reality and consensus model, characterized in that: include: An alternative scheme acquisition module is used to acquire design elements of the building environment and alternative schemes of their composition; The visualization module is used to build the initial scene of the building environment in the virtual reality environment and import various alternative plans into the VR system in a visual way; The preference attitude acquisition module is used by stakeholders to express their preference attitudes towards different design elements in each alternative plan in the form of language variables; A fuzzy decision matrix construction module is used to construct the fuzzy decision matrix of each stakeholder based on the triangular fuzzy numbers of each stakeholder's preference attitude towards each design element in each alternative plan; The consensus calculation module is used to calculate the triangular fuzzy number of each stakeholder's preference attitude towards each alternative plan based on the fuzzy decision matrix of each stakeholder, and then use the preference relationship membership function to calculate the complementary preference relationship of each stakeholder; Based on the fuzzy decision matrix and complementary preference relationship of each stakeholder, the preference similarity and preference proximity between each stakeholder and the group are calculated to obtain the consensus of each stakeholder. The target design scheme output module is used to calculate the group preference value of each alternative scheme based on the triangular fuzzy number of each stakeholder's preference attitude towards each design element in each alternative scheme, the preset weight of the design element and the importance of the stakeholder if the consensus of all stakeholders is greater than or equal to the minimum satisfaction threshold, and sort all alternative schemes, select the alternative scheme with the highest group preference value as the target design scheme, and terminate this collaborative decision-making.

Images