Fabricated building design and construction integrated decision optimization method, system and equipment

Abstract

The invention discloses a fabricated building design and construction integrated decision optimization method, system and equipment, and particularly relates to the technical field of building design, and the technical key points are as follows: obtaining user historical behavior data, environment feature influence data and user interest deviation data extracted from user demand information; based on a pre-constructed weight distribution matrix, performing multi-source data fusion on the user historical behavior data, the environment feature influence data and the user interest deviation data to obtain a user comprehensive interest index; obtaining a historical weight vector and an exponential attenuation coefficient, and obtaining an optimized weight vector by using the user comprehensive interestingness index, the historical weight vector and the exponential attenuation coefficient; and on the basis of a pre-constructed target conflict matrix, constructing a multi-target optimization function by using the optimization weight vector in combination with the building construction data, and solving the multi-target optimization function by using an optimization algorithm to obtain a fabricated building design and construction integrated decision optimization scheme.

Description

Technical Field

[0001] The present invention relates to the field of architectural design technology, and in particular to a method, system and equipment for integrated decision-making optimization of prefabricated building design and construction. Background Art

[0002] At present, the construction industry has entered a new stage of development. Prefabricated buildings are a construction method that conforms to the concept of "carbon neutrality". Promoting the development of prefabricated buildings is of great significance to resource conservation and reducing construction pollution. At the same time, it helps to promote the deep integration of the construction industry with informatization and industrialization, and promote the resolution of the problem of overcapacity.

[0003] However, the prefabricated building industry chain is long and involves many participants, covering various stages such as development, design, production, transportation, and installation. As a result, the industry has a mixed quality of personnel, a complex set of standards and specifications, and an imperfect system. The traditional extensive management system is characterized by inefficiency, serious waste, frequent changes, and environmental damage, and its negative impact is becoming increasingly serious. The further development of prefabricated buildings is also subject to certain constraints.

[0004] A Chinese patent with publication number CN113569382A discloses a BIM-based integrated decision-making optimization method and system for prefabricated building design and construction. The method includes the following steps: completing structural design and splitting based on the BIM building model, obtaining basic information of each split component, and obtaining complete information of each split component based on the BIM component library; the decision maker inputs decision requirement information according to demand, and automatically imports objective related information, and saves the decision requirement information and objective related information to the information library; establishing an objective function library, a constraint function library and an algorithm library; automatically selecting objective functions and constraint functions from the objective function library and the constraint function library to form a mathematical model according to the type of decision requirement information input by the decision maker, automatically matching the most relevant optimization algorithm from the algorithm library, and solving the mathematical model using the optimization algorithm and all required known information to obtain multiple sets of optimized component procurement plans; the decision maker determines the final optimization plan based on the objective related information.

[0005] In the above technical solution, a mathematical model is constructed by selecting objective functions and constraints from the objective function library and the constraint function library based on user needs, and then the optimization algorithm is matched in the algorithm library based on the characteristics of the mathematical model. The procurement plan is optimized using the matched optimization algorithm, thereby solving the complex multi-objective optimization problem in prefabricated buildings. However, the above technical solution lacks a dynamic adjustment mechanism in the process of matching user needs with objective functions or constraints, and at the same time, the influence of the correlation between features on the matching of the optimization algorithm is not considered when matching the optimization algorithm.

[0006] Therefore, the present invention aims to provide an integrated decision-making optimization method, system and equipment for prefabricated building design and construction to solve the above-mentioned related problems. Summary of the Invention

[0007] The technical problem to be solved by the present invention is the problem related to the lack of a dynamic adjustment mechanism in the existing technology during the matching process of user needs with objective functions or constraints. The purpose is to provide an integrated decision-making optimization method, system and equipment for the design and construction of prefabricated buildings. By integrating user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information, a user comprehensive interest index is constructed, thereby realizing multi-dimensional dynamic perception of user needs; by introducing a time decay factor, the historical weight automatically decays over time, ensuring that the optimization weight always reflects the user's latest preference, while retaining a reasonable part of the historical weight to avoid weight oscillations caused by short-term behavioral fluctuations; through a pre-constructed target conflict matrix, the contradictory relationship between targets of different dimensions is quantified, and a conflict correction term is introduced in the multi-objective optimization function to automatically suppress the over-optimization of contradictory targets.

[0008] The present invention is achieved through the following technical solutions:

[0009] A method for integrated decision-making and optimization of prefabricated building design and construction, comprising:

[0010] Obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information; based on a pre-built weight distribution matrix, perform multi-source data fusion on user historical behavior data, environmental feature impact data, and user interest bias data to obtain a comprehensive user interest index;

[0011] Obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector;

[0012] Based on the pre-constructed objective conflict matrix, a multi-objective optimization function is constructed using the optimized weight vector combined with building construction data. The multi-objective optimization function is solved using the optimization algorithm to obtain an integrated decision-making optimization scheme for prefabricated building design and construction.

[0013] Furthermore, the user's historical behavior data, environmental feature impact data, and user interest bias data extracted from the user demand information are obtained, specifically:

[0014] Obtain user historical behavior data, environmental feature impact data, and user interest bias data in different dimensions, where different dimensions include cost dimension, construction period dimension, and quality dimension; user historical behavior data includes user historical browsing data and user historical search data; environmental feature impact data includes market fluctuation data and policy indicator data; user interest bias data includes user search text data.

[0015] Furthermore, based on the pre-built weight distribution matrix, multi-source data fusion is performed on the user's historical behavior data, environmental feature impact data, and user interest bias data to obtain the user's comprehensive interest index, which is specifically:

[0016] Based on the pre-built weight distribution matrix combined with the time decay factor, the dynamic weights of user historical behavior data, environmental feature impact data, and user interest bias data are calculated;

[0017] Extract environmental impact factors from environmental characteristic impact data, and analyze environmental characteristic impact data to obtain correction coefficients of different dimensions;

[0018] By utilizing the dynamic weights of user historical behavior data, environmental feature impact data, and user interest preference data, combined with environmental impact factors and correction coefficients of different dimensions, the user's comprehensive interest index is obtained.

[0019] Furthermore, by utilizing the dynamic weights of user historical behavior data, environmental feature impact data, and user interest bias data, combined with environmental impact factors and correction coefficients of different dimensions, we can obtain the user's comprehensive interest index, specifically:

[0020]

[0021] in, represents the comprehensive interest index of the user under dimension i; w s (t) represents the dynamic weight of data source s; represents the normalized value of data source s under dimension i; β represents the environmental impact factor; Represents the correction coefficient under dimension i.

[0022] Furthermore, the historical weight vector and exponential decay coefficient are obtained, and the optimized weight vector is obtained using the user comprehensive interest index, the historical weight vector and the exponential decay coefficient. The optimized weight vector is specifically:

[0023]

[0024] Among them, W i (t) represents the optimized weight vector in dimension i; γ Δtrepresents the exponential decay coefficient under the time interval Δt; j, m both represent iteration dimension variables; n represents the dimension set; represents the historical weight vector; Represents the comprehensive interest index of the user under dimension i.

[0025] Furthermore, based on the pre-constructed objective conflict matrix, a multi-objective optimization function is constructed using the optimized weight vector combined with the building construction data. Specifically, the multi-objective optimization function is:

[0026]

[0027] Among them, W i (t) represents the optimized weight vector in dimension i; C ij represents the correlation coefficient between latitude i and latitude j in the target conflict matrix; f i (x),f j (x) represents the building construction data at latitude i and latitude j respectively.

[0028] The present invention further provides a prefabricated building design and construction integrated decision-making optimization system, which is used in any of the prefabricated building design and construction integrated decision-making optimization methods described above, and the system includes:

[0029] The first module is used to obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information. Based on a pre-built weight distribution matrix, multi-source data fusion is performed on user historical behavior data, environmental feature impact data, and user interest bias data to obtain a comprehensive user interest index.

[0030] The second module is used to obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector;

[0031] The third module is used to construct a multi-objective optimization function based on the pre-built target conflict matrix, using the optimized weight vector combined with the building construction data, and solve the multi-objective optimization function using the optimization algorithm to obtain an integrated decision-making optimization plan for the design and construction of prefabricated buildings.

[0032] The present invention also provides a computer device, comprising a system memory and a processor, wherein the system memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any one of the above-described methods when executed by a processor.

[0034] The present invention also provides a computer program product comprising instructions, wherein when the instructions are executed by a computer device cluster, the computer device cluster is caused to execute any of the above methods.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0036] In the present invention, by integrating user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information to construct a user comprehensive interest index, multi-dimensional dynamic perception of user needs is achieved; by introducing a time decay factor, the historical weight automatically decays over time, ensuring that the optimization weight always reflects the user's latest preference, while retaining a reasonable part of the historical weight to avoid weight oscillations caused by short-term behavior fluctuations; through a pre-constructed target conflict matrix to quantify the contradictory relationship between targets of different dimensions, a conflict correction term is introduced into the multi-objective optimization function to automatically suppress over-optimization of contradictory targets, thereby solving the related problem of the lack of a dynamic adjustment mechanism in the matching process of user needs with objective functions or constraints in the existing technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0038] Figure 1 This is a schematic diagram of a method flow for an integrated decision-making optimization method for designing and constructing prefabricated buildings in this embodiment;

[0039] Figure 2 This is a schematic diagram of system modules of an integrated decision-making optimization system for designing and constructing prefabricated buildings in this embodiment;

[0040] Figure 3 This is a structural diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0041] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0042] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0043] The terms used in the descriptions of various examples in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0044] Example 1: See Figure 1 , Figure 1 A schematic diagram of a method flow for an integrated decision-making optimization method for designing and constructing prefabricated buildings is shown, wherein the method comprises:

[0045] S1: Obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information; based on the pre-built weight distribution matrix, perform multi-source data fusion on user historical behavior data, environmental feature impact data, and user interest bias data to obtain a comprehensive user interest index.

[0046] Specifically, in this embodiment, user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information are obtained, specifically: user historical behavior data, environmental feature impact data, and user interest bias data in different dimensions are obtained, wherein the different dimensions include cost dimension, construction period dimension, and quality dimension; user historical behavior data includes user historical browsing data and user historical search data; environmental feature impact data includes market fluctuation data and policy indicator data; user interest bias data includes user search text data;

[0047] Then, based on the pre-built weight distribution matrix, multi-source data fusion is performed on the user's historical behavior data, environmental feature impact data, and user interest bias data to obtain the user's comprehensive interest index. Specifically, based on the pre-built weight distribution matrix combined with the time decay factor, the dynamic weights of the user's historical behavior data, environmental feature impact data, and user interest bias data are calculated; the environmental impact factor is extracted from the environmental feature impact data, and the environmental feature impact data is analyzed to obtain correction coefficients of different dimensions; the dynamic weights of the user's historical behavior data, environmental feature impact data, and user interest bias data are combined with the environmental impact factor and the correction coefficients of different dimensions to obtain the user's comprehensive interest index, which is specifically:

[0048]

[0049] in, represents the comprehensive interest index of the user under dimension i; w s (t) represents the dynamic weight of data source s; represents the normalized value of data source s under dimension i; β represents the environmental impact factor; Represents the correction coefficient under dimension i.

[0050] S2: Obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector;

[0051] Specifically, in this embodiment, the optimized weight vector is:

[0052]

[0053] Among them, W i (t) represents the optimized weight vector in dimension i; γ Δt represents the exponential decay coefficient under the time interval Δt; j, m both represent iteration dimension variables; n represents the dimension set; represents the historical weight vector; Represents the comprehensive interest index of the user under dimension i.

[0054] It should be noted that, in this embodiment, the exponential decay coefficient is a time decay coefficient used to characterize the decay of the weight vector over time, where γ = 0.9, indicating that 90% of the historical weight is retained every day; for example, if Δt is 2, the corresponding exponential decay coefficient is 0.81.

[0055] S3: Based on the pre-constructed objective conflict matrix, a multi-objective optimization function is constructed using the optimized weight vector combined with building construction data. The multi-objective optimization function is solved using the optimization algorithm to obtain an integrated decision-making optimization solution for prefabricated building design and construction.

[0056] It should be noted that, in this embodiment, the pre-built target conflict matrix is ​​specifically:

[0057]

[0058] Among them, C 11 represents the correlation coefficient between cost and cost; C 12 and C 21 Both represent the correlation coefficient between cost and construction period; C 13 and C 31 Represents the correlation coefficient between cost and quality; C 22 represents the correlation coefficient between construction period and construction period; C 23 and C 32 Represents the correlation coefficient between construction period and quality; C 33 It represents the correlation coefficient between mass and quality.

[0059] Specifically, in this embodiment, the multi-objective optimization function is:

[0060]

[0061] Among them, W i (t) represents the optimized weight vector in dimension i; C ij represents the correlation coefficient between latitude i and latitude j in the target conflict matrix; f i (x),f j (x) represents the building construction data at latitude i and latitude j respectively.

[0062] Specifically, in this embodiment, by integrating user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information to construct a user comprehensive interest index, multi-dimensional dynamic perception of user needs is achieved; by introducing a time decay factor, the historical weight automatically decays over time, ensuring that the optimization weight always reflects the user's latest preference, while retaining a reasonable part of the historical weight to avoid weight oscillations caused by short-term behavioral fluctuations; through a pre-constructed target conflict matrix, the contradictory relationship between targets of different dimensions is quantified, and a conflict correction term is introduced into the multi-objective optimization function to automatically suppress over-optimization of contradictory targets, thereby solving the problem of the lack of a dynamic adjustment mechanism in the matching process of user needs with objective functions or constraints in existing technical solutions.

[0063] Example 2: See Figure 2 As shown, the present invention also provides an integrated decision-making optimization system for designing and constructing prefabricated buildings, which is used in any of the above-mentioned integrated decision-making optimization methods for designing and constructing prefabricated buildings, and the system includes:

[0064] The first module 100 is used to obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information; based on a pre-built weight distribution matrix, multi-source data fusion is performed on the user historical behavior data, environmental feature impact data, and user interest bias data to obtain a user comprehensive interest index;

[0065] The second module 200 is used to obtain a historical weight vector and an exponential decay coefficient, and obtain an optimized weight vector using the user's comprehensive interest index, the historical weight vector and the exponential decay coefficient;

[0066] The third module 300 is used to construct a multi-objective optimization function based on a pre-constructed target conflict matrix, using an optimized weight vector combined with building construction data, and solve the multi-objective optimization function using an optimization algorithm to obtain an integrated decision-making optimization solution for prefabricated building design and construction.

[0067] It should be noted that the modules in the system of Example 2 correspond to the steps in the method of Example 1. The steps in the method of Example 1 have been described in detail in Example 1. The contents of the modules in the system will not be described in detail in this Example 2.

[0068] Example 3: See Figure 3 As shown, this embodiment further provides a computer device, including a system memory 1005 and a processor 1001, wherein the system memory 1005 stores a computer program, and the processor 1001 implements the steps of any of the above methods when executing the computer program.

[0069] It should be noted that the processor 1001 is configured to execute the steps of the above method embodiments according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of the modules / units in the above system / device embodiments when executing the computer program.

[0070] Specifically, in this embodiment, the computer program may be divided into one or more modules / units, one or more modules / units being stored in the system memory 1005 and executed by the processor 1001 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0071] The terminal device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will appreciate that this does not limit the terminal device and may include more or fewer components than shown, or a combination of certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, and the like.

[0072] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0073] The system memory 1005 can be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The system memory 1005 can also be the storage device 1004 of the terminal device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device. Furthermore, the system memory 1005 can also include both the internal storage unit of the terminal device and the storage device 1004. The system memory 1005 is used to store computer programs and other programs and data required by the terminal device. The system memory 1005 can also be used to temporarily store data that has been output or is about to be output.

[0074] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] Embodiment 4: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0076] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art.

[0077] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.

[0078] Embodiment 5: This embodiment further provides a computer program product comprising instructions. When the instructions are executed by a computer device cluster, the computer device cluster executes the method described in embodiment 1.

[0079] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for integrated decision-making and optimization of prefabricated building design and construction, characterized in that: Methods include: Obtain user historical behavior data, environmental feature impact data, and user interest preference data extracted from user demand information; Based on the pre-built weight distribution matrix, multi-source data fusion is performed on user historical behavior data, environmental feature impact data, and user interest bias data to obtain the user's comprehensive interest index; Obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector; Based on the pre-constructed objective conflict matrix, a multi-objective optimization function is constructed using the optimized weight vector combined with building construction data. The multi-objective optimization function is solved using the optimization algorithm to obtain an integrated decision-making optimization scheme for prefabricated building design and construction.

2. The method for integrated decision-making and optimization of prefabricated building design and construction according to claim 1, characterized in that: Obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information, specifically: Obtain user historical behavior data, environmental feature impact data, and user interest bias data in different dimensions, where different dimensions include cost dimension, construction period dimension, and quality dimension; user historical behavior data includes user historical browsing data and user historical search data; environmental feature impact data includes market fluctuation data and policy indicator data; user interest bias data includes user search text data.

3. The method for integrated decision-making and optimization of prefabricated building design and construction according to claim 1, characterized in that: Based on the pre-built weight distribution matrix, multi-source data fusion is performed on user historical behavior data, environmental feature impact data, and user interest bias data to obtain the user's comprehensive interest index, which is specifically: Based on the pre-built weight distribution matrix combined with the time decay factor, the dynamic weights of user historical behavior data, environmental feature impact data, and user interest bias data are calculated; Extract environmental impact factors from environmental characteristic impact data, and analyze environmental characteristic impact data to obtain correction coefficients of different dimensions; By utilizing the dynamic weights of user historical behavior data, environmental feature impact data, and user interest preference data, combined with environmental impact factors and correction coefficients of different dimensions, the user's comprehensive interest index is obtained.

4. The method for integrated decision-making and optimization of prefabricated building design and construction according to claim 3 is characterized in that: By using the dynamic weights of user historical behavior data, environmental feature impact data, and user interest bias data, combined with environmental impact factors and correction coefficients of different dimensions, the user's comprehensive interest index is obtained, specifically: in, represents the comprehensive interest index of the user under dimension i; w s (t) represents the dynamic weight of data source s; represents the normalized value of data source s under dimension i; β represents the environmental impact factor; Represents the correction coefficient under dimension i.

5. The method for integrated decision-making and optimization of prefabricated building design and construction according to claim 1, characterized in that: Obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector, where the optimized weight vector is specifically: Among them, W i (t) represents the optimized weight vector in dimension i; γ Δt represents the exponential decay coefficient under the time interval Δt; j, m both represent iteration dimension variables; n represents the dimension set; represents the historical weight vector; Represents the comprehensive interest index of the user under dimension i.

6. The method for integrated decision-making and optimization of prefabricated building design and construction according to claim 1, characterized in that: Based on the pre-built objective conflict matrix, the multi-objective optimization function is constructed by using the optimized weight vector combined with the building construction data. The multi-objective optimization function is specifically as follows: Among them, W i (t) represents the optimized weight vector in dimension i; C ij represents the correlation coefficient between latitude i and latitude j in the target conflict matrix; f i (x),f j (x) represents the building construction data at latitude i and latitude j respectively.

7. An integrated decision-making and optimization system for prefabricated building design and construction, characterized in that: The system is used in the integrated decision-making optimization method for design and construction of prefabricated buildings as described in any one of claims 1 to 6, and the system includes: The first module is used to obtain user historical behavior data, environmental feature impact data, and user interest bias data extracted from user demand information. Based on a pre-built weight distribution matrix, multi-source data fusion is performed on user historical behavior data, environmental feature impact data, and user interest bias data to obtain a comprehensive user interest index. The second module is used to obtain the historical weight vector and exponential decay coefficient, and use the user's comprehensive interest index, historical weight vector and exponential decay coefficient to obtain the optimized weight vector; The third module is used to construct a multi-objective optimization function based on the pre-built target conflict matrix, using the optimized weight vector combined with the building construction data, and solve the multi-objective optimization function using the optimization algorithm to obtain an integrated decision-making optimization plan for the design and construction of prefabricated buildings.

8. A computer device comprising a system memory and a processor, wherein the system memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising instructions, characterized in that When the instructions are executed by a computer device cluster, the computer device cluster is caused to perform the method according to any one of claims 1 to 6.

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