A quantum annealing algorithm-based optimization design method for existing building regeneration

Abstract

This invention discloses a method for optimizing the regeneration of existing buildings based on the quantum annealing algorithm, comprising: S1, establishing a digital model of the existing building; S2, establishing a discrete state model of multi-level regeneration units of the building; S3, establishing a multi-index evaluation model for building regeneration; S4, establishing a comprehensive cost function for building regeneration; S5, establishing a coupling relationship model of building regeneration units; S6, establishing a quantum annealing optimization model for building regeneration; and S7, solving the quantum annealing optimization model for building regeneration using the quantum annealing algorithm to output the globally optimal building regeneration scheme. This method constructs an energy evaluation model for building regeneration based on various factors by establishing discrete regeneration state models of each building unit, and uses the quantum annealing algorithm to perform a global search of the building regeneration state space, achieving collaborative optimization of the regeneration strategy of existing buildings to generate optimized design schemes for existing building regeneration, thereby improving the overall performance and intelligent optimization efficiency of building renewal design.

Description

Technical Field

[0001] This invention relates to the field of building optimization technology, and specifically discloses a method for regenerative optimization design of existing buildings based on quantum annealing algorithm. Background Technology

[0002] Compared to the traditional demolition and reconstruction model, the regeneration of existing buildings can effectively reduce carbon emissions throughout the building's life cycle, improve the utilization efficiency of existing building resources, and reduce the generation of construction waste, thus having significant environmental and economic value.

[0003] However, the regeneration design process for existing buildings typically requires simultaneous consideration of multiple complex factors, including: structural safety, retention rate of original components, renovation costs, building energy consumption, lighting and ventilation performance, material reuse rate, functional adaptability, life-cycle carbon emissions, and spatial renewal needs. Significant conflicts often exist between these different objectives. For example, increasing the retention rate of the original structure can reduce demolition carbon emissions but may limit spatial reorganization and functional renewal; large-scale renovation of the building envelope can improve energy efficiency but increases material consumption and construction costs. Therefore, the regeneration design of existing buildings is essentially a complex combinatorial optimization problem characterized by multiple objectives, strong constraints, and a large number of discrete variables.

[0004] Current building regeneration design primarily relies on designers' experience, manual scheme comparison, and traditional intelligent optimization methods such as genetic algorithms and particle swarm optimization. As the number of building components, spaces, and equipment systems increases, the number of possible combinations of building regeneration states grows exponentially, forming a typical large-scale discrete combinatorial optimization problem. Traditional optimization methods are prone to decreased search efficiency, increased computation time, and getting trapped in local optima in high-dimensional state spaces, making it difficult to achieve efficient global optimization of complex building regeneration schemes. Quantum annealing is a global search method suitable for large-scale discrete combinatorial optimization problems and has strong potential in searching complex state spaces. However, there is currently a lack of a method to transform the existing building regeneration design problem into a quantum annealing solvable model and to perform collaborative optimization in conjunction with the needs of low-carbon building renewal.

[0005] Therefore, it is necessary to propose an optimization design method for existing building regeneration that can transform the problem of existing building regeneration into a quantum annealing solvable discrete optimization model and perform collaborative global optimization at the building component layer, space layer, and equipment system layer. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the aforementioned issues, this invention provides a multi-level regeneration unit optimization design method for existing buildings based on the quantum annealing algorithm. By establishing a discrete regeneration state model of three-level units—building components, space, and equipment systems—a building regeneration energy evaluation model integrating factors such as carbon emissions, economic costs, structural safety, building performance, and material reuse rate is constructed. Furthermore, the quantum annealing algorithm is used to perform a global search of the building regeneration state space, enabling collaborative optimization of regeneration strategies such as preservation, demolition, reinforcement, replacement, functional reorganization, and system updates. This generates optimized regeneration design schemes for existing buildings, improving the overall performance and intelligent optimization efficiency of building renewal design.

[0008] (II) Technical Solution

[0009] To address the aforementioned technical problems, this invention proposes a regeneration optimization design method for existing buildings based on the quantum annealing algorithm. This method, used for the regeneration optimization of existing buildings, includes:

[0010] S1. Establish a digital model of the existing building: acquire the building information model, 3D point cloud model, building drawings or building component database of the existing building, and perform digital analysis on the existing building;

[0011] S2. Establish a discrete state model of a multi-level building regeneration unit: The existing building is analyzed into component units, space units and equipment system units and uniformly defined as building regeneration units. Each building regeneration unit is assigned a discrete regeneration state to construct a discrete state model of the building regeneration unit.

[0012] S3. Establish a multi-index evaluation model for building regeneration: Establish multi-index evaluation parameters for each building regeneration unit and its discrete state model, including: carbon emission index, economic cost index, building performance index, structural safety index, and reuse index, and evaluate each building regeneration unit and its discrete state model respectively.

[0013] S4. Establish a comprehensive cost function for building regeneration: Based on the multi-index evaluation model for building regeneration, establish a comprehensive cost function for building regeneration for each building regeneration unit;

[0014] S5. Establish a coupling relationship model for building regeneration units: Based on the relationships between the component units, spatial units, and equipment system units of the existing building, establish a coupling relationship model for building regeneration units;

[0015] S6. Establish a quantum annealing optimization model for building regeneration: Based on the comprehensive cost function of building regeneration in step S4 and the coupling relationship model of building regeneration units in step S5, establish a quantum annealing optimization model for building regeneration.

[0016] S7. Quantum Annealing Optimization Solution: The quantum annealing algorithm is used to solve the quantum annealing optimization model of building regeneration to output the globally optimal or near-optimal building regeneration solution.

[0017] Preferably, in step S2, different building regeneration units have different candidate regeneration states, and establishing a set of candidate regeneration states includes:

[0018] For the Each regeneration unit establishes a state set: ;

[0019] State encoding is performed using discrete binary variables: ;

[0020] in: Indicates the first The regeneration unit selects the first... Regeneration state, This indicates that the state was not selected; and the constraints are satisfied: That is, each regeneration unit is only allowed to select one regeneration state.

[0021] Preferably, the carbon emission index in step S3 is defined as follows: , indicating the first The regeneration unit adopts the first The carbon emission value corresponding to the regeneration state; the economic cost indicator is defined as follows: , indicating the first The regeneration unit adopts the first The economic cost value corresponding to the regeneration state; the building performance index is defined as follows: , indicating the first The regeneration unit adopts the first The building performance benefit value corresponding to the regeneration state; the structural safety index is defined as follows: , indicating the first The regeneration unit adopts the first The structural risk value corresponding to the regeneration state; the reuse index is defined as follows: , indicating the first The regeneration unit adopts the first The reuse revenue value corresponding to each regeneration state.

[0022] Preferably, in step S4, a comprehensive cost function for building regeneration is established. ;

[0023] ;

[0024] in: These are the weighting parameters for carbon emission indicators, economic cost indicators, building performance indicators, structural safety indicators, and reuse indicators, respectively. The sum of these is 1; carbon emission indicators, economic cost indicators, and structural safety indicators are minimization indicators, while building performance indicators and reuse indicators are benefit indicators. In the actual implementation of the project, the benefit indicators are converted into normalized costs that can be minimized before being included in the comprehensive score.

[0025] Preferably, the building regeneration unit coupling relationship model in step S5 is set according to the following multiple relationships: structural dependency relationship between the component units; functional linkage relationship between the spatial units; operational coordination relationship between the equipment system units; spatial organization relationship between the component units and the spatial units; load coupling relationship between the spatial units and the equipment system units; and constraint conflict relationship between the component units, spatial units, and equipment system units.

[0026] Preferably, a coupling relationship matrix of the building regeneration units is established based on the interrelationships between the building regeneration units:

[0027]

[0028] Where: matrix elements Used to describe the The first building regeneration unit was selected. The regeneration state and the first The first building regeneration unit was selected. The coupling influence relationship between regeneration states is defined as follows: when two regeneration state combinations have structural constraint conflicts, functional conflicts, or implementation conflicts, they are considered hard conflicts, and a high penalty value is introduced to negate them in the optimization process; when two state combinations have synergistic optimization effects, they are considered synergistic, and a negative reward value is introduced to reduce energy and increase the probability of being selected in the optimization process; when two state combinations increase the system's operating load or structural risk, they are considered soft conflicts, and an additional energy cost is introduced to increase energy and reduce the probability of being selected in the optimization process.

[0029] Preferably, in step S6, the building regeneration combinatorial optimization problem is transformed into an energy function solvable by quantum annealing:

[0030] ;

[0031] Wherein: the first term is the cost of the first-order term of the regeneration unit state; the second term is the coupling term of the regeneration unit state; the third term is the penalty term for the uniqueness constraint of the One-Hot state, so as to ensure that each building regeneration unit selects only one of the four candidate states. The penalty coefficient is introduced;

[0032] The solvable energy function of quantum annealing is expanded and rearranged into the standard quadratic unconstrained binary optimization (QUBO) form to establish a quantum annealing optimization model for building regeneration.

[0033] ;

[0034] in: Optimization matrix for building regeneration; This is a vector of building state variables; T represents transpose, that is, transpose columns into rows.

[0035] Preferably, step S7 uses the quantum annealing algorithm to solve the quantum annealing optimization model for building regeneration:

[0036] First, multiple initial building regeneration schemes are randomly generated, and multiple virtual quantum replicas are created: ;in: The number of virtual quantum layers; each layer corresponds to a set of building regeneration state combinations;

[0037] Establish the simulated quantum annealing energy function:

[0038]

[0039] in: Indicates the first The QUBO energy function for building regeneration corresponding to each virtual quantum layer. For quantum coupling strength, This represents the number of iteration steps.

[0040] The building regeneration state is initialized using the simulated quantum annealing energy function. The energy of the initial building regeneration scheme is calculated. Some building regeneration state variables are randomly flipped and the state is updated according to the quantum annealing acceptance probability. Then, the intensity of quantum perturbation is gradually reduced, and a low-energy building regeneration state combination is searched. Finally, the globally optimal or near-optimal existing building regeneration scheme is output.

[0041] (III) Beneficial Effects

[0042] Compared with existing technologies, the existing building regeneration optimization design method based on quantum annealing algorithm of the present invention has the following advantages:

[0043] 1. The existing building regeneration optimization design method establishes a discrete state model of existing building regeneration through step S2. It transforms the regeneration behaviors such as retention, reinforcement, replacement, demolition, reuse and functional transformation of building components, spaces or systems into discrete state variables, which are used to construct the building regeneration state space. This realizes the unified discretization expression of the building regeneration state space and improves the mathematical modeling ability and overall optimization ability of complex building renewal problems.

[0044] 2. This invention establishes a unified energy evaluation model that integrates factors such as building carbon emissions, economic costs, structural safety, building performance, and material reuse rate, and realizes multi-objective collaborative optimization among component layer, space layer, and equipment system layer.

[0045] 3. This method transforms the existing building regeneration optimization problem into a high-dimensional discrete combinatorial optimization problem solvable by quantum annealing through step S6, realizing a global search of the building regeneration state space. Compared with traditional genetic algorithms and particle swarm algorithms, it is less likely to get trapped in local optima, thus improving the global optimization capability of complex building regeneration optimization problems.

[0046] 4. This method uses the quantum annealing algorithm to address the ultra-large-scale discrete combinatorial problem formed by the combination of a large number of component states, spatial states, and system states in the process of existing building regeneration. It can complete large-scale state search and optimization iteration in a short time. Compared with traditional multi-objective optimization methods, it has higher solution efficiency, can reduce the combinatorial explosion effect of complex building regeneration problems, and improve the solvability of complex building regeneration solutions.

[0047] In summary, this invention provides a quantum annealing algorithm-based optimization design method for existing building regeneration. This method transforms various complex factors in different existing building regeneration design processes into a building regeneration energy evaluation model, evaluating and optimizing building optimization schemes under different regeneration states. It also facilitates collaborative optimization design of multiple factors, solving the problem of traditional optimization design's over-reliance on empiricism by digitizing and modeling the optimization process of each factor. Furthermore, for ultra-large-scale discrete combination problems formed by numerous component states, spatial states, and system states in the existing building regeneration process, this method can complete large-scale state search and optimization iteration in a short time, effectively reducing the combinatorial explosion effect of complex building regeneration problems and improving the solvability of complex building regeneration schemes. Simultaneously, this method does not rely on real quantum computing hardware; it can be implemented in engineering applications by running a simulated quantum annealing algorithm on a regular computer, while also being compatible with future quantum annealing hardware platforms, demonstrating strong engineering feasibility and technical scalability. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 The flowchart shows the existing building regeneration optimization design method based on quantum annealing algorithm of the present invention.

[0050] Figure 2This is a schematic diagram of the coupling relationship matrix of the regeneration unit in an embodiment of the existing building regeneration optimization design method based on quantum annealing algorithm of the present invention;

[0051] Figure 3 This is a schematic diagram of the optimization result index in an embodiment of the existing building regeneration optimization design method based on quantum annealing algorithm of the present invention;

[0052] Figure 4 This is a comparison table of optimization results of various algorithms in the embodiments of the quantum annealing algorithm-based optimization design method for regeneration of existing buildings of the present invention;

[0053] Figure 5 The graphs show the convergence of various algorithms in the embodiments of the quantum annealing-based existing building regeneration optimization design method of the present invention. Detailed Implementation

[0054] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] The following is in conjunction with the appendix Figure 1-5 The present invention provides a further explanation of the existing building regeneration optimization design method based on quantum annealing algorithm.

[0056] Please refer to this carefully. Figure 1 This invention discloses a regeneration optimization design method for existing buildings based on the quantum annealing algorithm. This method is used for the regeneration optimization of existing buildings and includes:

[0057] S1. Establish digital models of existing buildings: acquire building information models, 3D point cloud models, building drawings or building component databases of existing buildings, and perform digital analysis on existing buildings;

[0058] S2. Establish a discrete state model of a multi-level building regeneration unit: The existing building is analyzed into component units, space units and equipment system units and uniformly defined as building regeneration units. Each building regeneration unit is assigned a discrete regeneration state to construct a discrete state model of the building regeneration unit.

[0059] In step S2, different building regeneration units have different candidate regeneration states. These candidate states vary depending on the structure and equipment of the existing buildings, but all include the following states: component units mainly correspond to retention, reinforcement, replacement, demolition, and reuse; space units mainly correspond to function preservation, function conversion, space merging, and space reorganization; equipment system units mainly correspond to retention, partial renewal, overall replacement, and intelligent upgrade. Establishing the candidate regeneration state set includes: for the... Each regeneration unit establishes a state set: State encoding is performed using discrete binary variables. ;

[0060] in: Indicates the first The regeneration unit selects the first... Regeneration state, This indicates that the state was not selected; and the constraints are satisfied: That is, each regeneration unit is only allowed to select one regeneration state.

[0061] S3. Establish a multi-index evaluation model for building regeneration: Establish multi-index evaluation parameters for each building regeneration unit and its discrete state model, including: carbon emission index, economic cost index, building performance index, structural safety index, and reuse index, and evaluate each building regeneration unit and its discrete state model respectively.

[0062] In step S3, the carbon emission index is defined as , indicating the first The regeneration unit adopts the first The carbon emission value corresponding to each recycling state includes carbon emissions from demolition, material production, material transportation, renovation construction, and energy conservation and carbon reduction benefits during building operation; the economic cost indicator is defined as... , indicating the first The regeneration unit adopts the first The economic cost value corresponding to each regeneration state includes material costs, construction costs, and life-cycle operation and maintenance costs; building performance indicators are defined as follows: , indicating the first The regeneration unit adopts the first The building performance benefits corresponding to each regeneration state include building energy consumption, lighting performance, ventilation performance, and thermal comfort performance; structural safety indicators are defined as follows: , indicating the first The regeneration unit adopts the first The structural risk value corresponding to each type of regeneration state includes structural safety indicators such as component safety margin, structural stability, and component aging risk; the reuse index is defined as... , indicating the first The regeneration unit adopts the first The reuse benefit value corresponding to each type of regeneration state includes the component retention rate, material reuse rate, and building life cycle continuity.

[0063] S4. Establish the comprehensive cost function for building regeneration: Based on the multi-index evaluation model for building regeneration, establish the comprehensive cost function for building regeneration for each building regeneration unit;

[0064] In step S4, the comprehensive cost function for building regeneration is established. ;

[0065] ;

[0066] in: These are the weighting parameters for carbon emission indicators, economic cost indicators, building performance indicators, structural safety indicators, and reuse indicators, respectively. The sum of these is 1; carbon emission indicators, economic cost indicators, and structural safety indicators are minimization indicators, while building performance indicators and reuse indicators are benefit indicators. In the actual implementation of the project, the benefit indicators are converted into normalized costs that can be minimized before being included in the comprehensive score.

[0067] S5. Establish a coupling relationship model for building regeneration units: Establish a coupling relationship model for building regeneration units based on the relationships between component units, spatial units, and equipment system units of existing buildings;

[0068] In step S5, the building regeneration unit coupling relationship model is set based on the following relationships: structural dependency between component units; functional linkage between spatial units; operational coordination between equipment system units; spatial organization between component units and spatial units; load coupling between spatial units and equipment system units; and constraint conflict between component units, spatial units, and equipment system units.

[0069] Based on the above relationships between building regeneration units, establish a coupling relationship matrix for the building regeneration units:

[0070] ;

[0071] Where: matrix elements Used to describe the The first building regeneration unit was selected. The regeneration state and the first The first building regeneration unit was selected. The coupling influence relationship between regeneration states is defined as follows: when two regeneration state combinations have structural constraint conflicts, functional conflicts, or implementation conflicts, they are considered hard conflicts, and a high penalty value is introduced to negate them in the optimization process; when two state combinations have synergistic optimization effects, they are considered synergistic, and a negative reward value is introduced to reduce energy and increase the probability of being selected in the optimization process; when two state combinations increase the system's operating load or structural risk, they are considered soft conflicts, and an additional energy cost is introduced to increase energy and reduce the probability of being selected in the optimization process.

[0072] In a specific embodiment, when the external wall insulation unit and the window and door renovation unit are implemented simultaneously, their combined energy-saving effect is significantly higher than the simple sum of the effects of implementing the two measures individually, thus indicating a synergistic optimization relationship between them. Conversely, when the simultaneous implementation of the roof greening unit and the roof photovoltaic unit leads to a significant increase in roof load, or when the simultaneous implementation of the fresh air system upgrade and the large-area glass curtain wall renovation leads to an increase in building operating energy consumption, this is considered an increase in system operating load or structural risk. In this invention, the above relationships can be quantitatively characterized by constructing synergistic benefit terms and risk penalty terms among the regeneration units.

[0073] S6. Establish a quantum annealing optimization model for building regeneration: Based on the comprehensive cost function of building regeneration in step S4 and the coupling relationship model of building regeneration units in step S5, establish a quantum annealing optimization model for building regeneration.

[0074] In step S6, the building regeneration combinatorial optimization problem is transformed into an energy function solvable by quantum annealing:

[0075] ;

[0076] Wherein: the first term is the cost of the first-order term of the regeneration unit state; the second term is the coupling term of the regeneration unit state; the third term is the penalty term for the uniqueness constraint of the One-Hot state, so as to ensure that each building regeneration unit selects only one term. The penalty coefficient is introduced;

[0077] The solvable energy function of quantum annealing is expanded and rearranged into the standard quadratic unconstrained binary optimization (QUBO) form to establish a quantum annealing optimization model for building regeneration.

[0078] ;

[0079] in: Optimization matrix for building regeneration; is the building state variable vector; T represents the matrix transpose operation, which converts the building state variable vector x from a column vector to a row vector.

[0080] S7. Quantum Annealing Optimization Solution: The quantum annealing algorithm is used to solve the quantum annealing optimization model of building regeneration to output the globally optimal or near-optimal building regeneration solution.

[0081] Step S7 uses the quantum annealing algorithm to solve the quantum annealing optimization model for building regeneration:

[0082] First, multiple initial building regeneration schemes are randomly generated, and multiple virtual quantum replicas are created: ;in: The number of virtual quantum layers; each layer corresponds to a set of building regeneration state combinations;

[0083] Establish the simulated quantum annealing energy function:

[0084]

[0085] in: Indicates the first The QUBO energy function for building regeneration corresponding to each virtual quantum layer. For quantum coupling strength, This represents the number of iteration steps.

[0086] The building regeneration state is initialized by simulating the quantum annealing energy function. The energy of the initial building regeneration scheme is calculated. Some building regeneration state variables are randomly flipped and the state is updated according to the quantum annealing acceptance probability. Then, the intensity of quantum perturbation is gradually reduced, and a low-energy building regeneration state combination is searched. Finally, the globally optimal or near-optimal existing building regeneration scheme is output. The output includes one or more of the following:

[0087] (1) Component retention plan, component reinforcement plan, component replacement plan, component removal plan; (2) Building space function preservation plan, building space function conversion plan, building space reorganization plan; (3) Building equipment system retention plan, building equipment system update plan, building equipment system replacement plan; (4) Building performance improvement results, life cycle carbon emission results, comprehensive optimization score results, etc.

[0088] In this embodiment, the existing building regeneration optimization design method establishes a discrete state model of existing building regeneration through step S2. The regeneration behaviors such as retention, reinforcement, replacement, demolition, reuse and functional transformation of building components, spaces or systems are transformed into discrete state variables and used to construct the building regeneration state space. This realizes a unified discretized expression of the building regeneration state space and improves the mathematical modeling ability and overall optimization ability of complex building renewal problems.

[0089] Steps S3 and S4 establish a unified energy mapping model for building regeneration based on the discrete state model of the existing building. This model maps carbon emissions, economic costs, structural safety, building performance, and material reuse rate into energy functions in the quantum annealing optimization process. After mapping the comprehensive cost function of building regeneration, step S5 constructs a coupling relationship model between building regeneration units based on the relationships between the component units, spatial units, and equipment system units of the existing building. This model describes the structural dependencies, spatial linkages, and functional constraints between regeneration units and transforms them into state coupling terms to participate in the optimization solution. This further establishes a unified energy evaluation model that integrates factors such as building carbon emissions, economic costs, structural safety, building performance, and material reuse rate, achieving multi-objective collaborative optimization between the component layer, spatial layer, and equipment system layer.

[0090] Furthermore, step S6 transforms the existing building regeneration optimization problem into a high-dimensional discrete combinatorial optimization problem solvable by quantum annealing, enabling a global search of the building regeneration state space. Finally, step S7 employs the quantum annealing algorithm to iteratively optimize the building regeneration state combinations. Through state flipping, energy updates, and quantum perturbation, low-energy building regeneration state combinations are searched, outputting the existing building regeneration optimization design results. This allows the building performance evaluation module to be invoked during the building regeneration optimization process to evaluate the building's energy consumption, lighting, ventilation, or thermal comfort performance under different regeneration states, and to participate in the calculation of the building regeneration energy function. Compared to traditional genetic algorithms and particle swarm optimization algorithms, it is less prone to getting trapped in local optima, improving the global optimization capability for complex building regeneration optimization problems.

[0091] The proposed multi-level regeneration unit optimization design method for existing buildings enables construction personnel to transform various complex factors in the regeneration design process into a building regeneration energy evaluation model. This model allows for the evaluation and optimization of building optimization schemes under different regeneration states, while also facilitating collaborative optimization design of multiple factors. It addresses the problem of traditional optimization design relying excessively on empiricism by digitizing and modeling the optimization process of each factor. Furthermore, for ultra-large-scale discrete combination problems formed by numerous component states, spatial states, and system states during the regeneration process of existing buildings, this method can complete large-scale state search and optimization iteration in a short time. Compared with traditional multi-objective optimization methods, it has higher solution efficiency, reduces the combinatorial explosion effect of complex building regeneration problems, and improves the solvability of complex building regeneration schemes. Simultaneously, this method does not rely on real quantum computing hardware and can be implemented in engineering applications by running simulated quantum annealing algorithms on ordinary computers. It is also compatible with future quantum annealing hardware platforms, demonstrating strong engineering feasibility and technical scalability.

[0092] In one specific embodiment, the project object is a six-story existing office building in a certain region, with a building area of ​​approximately 12,000 m², a reinforced concrete frame structure, built in 2003, and an analysis period of 20 years. The digital model includes component information, spatial information, and equipment system information. Basic data includes material carbon emission factors, cost parameters, grid emission factors, electricity prices, benchmark energy consumption intensity, and current status risks of components.

[0093] S1. Obtain the Building Information Model (BIM), 3D point cloud model, architectural drawings, or building component database of the existing building, and perform digital analysis on the existing building.

[0094] Extract the following information: component type, component size, material properties, component connection relationships, building structural topology, service life, material carbon emission factors, building functional space information, building envelope thermal parameters, building equipment system parameters; renovation cost parameters, and building performance parameters. Establish an existing building component-space-system relational database.

[0095] S2. In this embodiment, the building regeneration optimization unit is divided into 64 units, including 48 component units, 10 space units and 6 equipment system units.

[0096] The 48 component units specifically include: 12 reinforced concrete column clusters, 12 reinforced concrete beam and slab areas, 8 exterior facade and window areas, 8 interior partition wall areas, 2 roof waterproofing and insulation areas, 2 staircase and core tube areas, and 4 interior decoration units.

[0097] The 10 spatial units specifically include: the first floor lobby and shared service space, the second floor east open office area, the second floor west open office area, the third floor flexible office area, the fourth floor meeting and training area, the fifth floor administrative office area, the sixth floor archives and storage area, vertical circulation space, service and equipment rooms, and roof terrace and public activity space.

[0098] The six equipment system units specifically include: air conditioning and ventilation system, lighting system, elevator system, water supply and drainage system, building control and sensing system, and rooftop photovoltaic system.

[0099] Each regeneration unit is configured with four candidate regeneration states, resulting in a total of 256 binary state variables. However, each regeneration unit can only select one of the four candidate states. Component-related states include: retention monitoring, reinforcement, low-carbon replacement, partial opening, reuse and renovation, roof insulation repair, roof photovoltaic adaptation, and modular decoration. Space-related states include: functional retention, conversion to flexible office space, open merging, and low-carbon functional reorganization. Equipment-related states include: retention, partial update, high-efficiency replacement, and intelligent upgrade; photovoltaic systems also include: no installation, small-scale pilot, complete roof photovoltaic, and photovoltaic and energy storage reservation.

[0100] S3. In the implementation project, multi-index evaluation parameters are established for different regeneration units and different regeneration states.

[0101] S4. Establish a comprehensive cost function based on different indicators. The example project normalizes the five types of indicators and sets default weights: 0.35 For 0.20, For 0.20, 0.15 The value is 0.10. Carbon emissions, costs, and risks are minimized, while performance and reuse are considered as benefits. In actual project implementation, benefit indicators are converted into minimized normalized costs before being included in the comprehensive scoring, resulting in the comprehensive cost function for building regeneration. ; .

[0102] S5. Establish the coupling relationship matrix of the building regeneration unit: In this embodiment, when two state combinations do not satisfy the constraints, it is considered a hard conflict, and a high penalty value is introduced. The maximum penalty value is 20;

[0103] When two state combinations have a synergistic optimization effect, they are considered synergistic and negative rewards are introduced in the example. The negative reward range is (-1, -0.1).

[0104] When a combination of two states increases the system's operating load or structural risk, it is considered a soft conflict, introducing additional energy costs. (Example:) The additional energy cost ranges from 0.1 to 4.

[0105] like Figure 2 The B.256x256 state variable coupling matrix is ​​shown in the figure. Figure 2The horizontal and vertical axes are both binary state variables, arranged in the original order of the regeneration units; every four consecutive variables correspond to one regeneration unit. The example project constructed 439 state coupling rules, including hard conflict, soft conflict, and synergy. The coupling relationships cover the synergy between components and space, space and equipment, external envelope and air conditioning, structural columns and beams, roof and photovoltaics, and equipment systems. For example, load-bearing columns and large spaces are soft conflicts; retaining the roof and a complete photovoltaic system are hard conflicts; upgrading the external envelope and high-efficiency air conditioning or smart air conditioning have synergistic effects; low-carbon space reorganization and smart lighting and smart control have synergistic effects.

[0106] S6. In this embodiment, the actual model constructed is a QUBO model. QUBO includes the cost of a first-order state term, a second-order coupling term between states, and a One-Hot uniqueness constraint penalty term. In this embodiment, the 64 regeneration units correspond to 256 binary variables, and the One-Hot penalty coefficient λ is set to 8.0 by default.

[0107] S7. Finally, the computation was performed using simulated quantum annealing on a standard computer. Default settings included 16 virtual replicas, 6000 iterations, a temperature decreasing from 2.5 to 0.03, and a quantum coupling strength decreasing from 1.8 to 0.02. The solution process included generating an initial scheme, calculating the QUBO energy, randomly selecting regenerating units and switching their states, calculating energy changes and the impact of quantum coupling between replicas, updating the scheme according to the annealing acceptance probability, gradually reducing the temperature and quantum perturbation, and outputting the scheme with the lowest energy. The project also included greedy algorithms, simulated annealing, genetic algorithms, and small-scale exact enumeration as controls.

[0108] See Figure 3 The example project outputs an optimized unit status list, a summary of comprehensive indicators, algorithm comparison results, and a report. The optimization results show that, compared to the baseline, the optimized scheme achieves approximately 12,808.8 tCO2e of net carbon reduction over a 20-year lifecycle, approximately 5.313 million yuan in net lifecycle cost reduction, approximately 1.267 million kWh of annual energy savings, a structural risk reduction of approximately 79.0%, an improvement in performance index of approximately 18.6, and an improvement in reuse index of approximately 2.31%.

[0109] And, as Figure 4 , 5As shown, this embodiment compares the building regeneration optimization process using greedy algorithms, genetic algorithms, traditional simulated annealing algorithms, and simulated quantum annealing algorithms. The results show that while the greedy algorithm solves quickly, it is prone to getting trapped in local optima; the genetic algorithm decreases rapidly in the early stages of optimization but tends to plateau in the later stages; the traditional simulated annealing algorithm can further reduce system energy, but the optimization process suffers from local fluctuations and periodic stagnation. In contrast, the simulated quantum annealing algorithm exhibits more continuous and stable low-energy state search characteristics during the optimization process, continuously approximating better combinations of building regeneration states in complex discrete state spaces. In more detailed regeneration optimization designs, as the number of regeneration units increases to over 100, the scale of the building regeneration state space further expands, and the global search advantage of the simulated quantum annealing algorithm in high-dimensional discrete combinatorial optimization problems becomes even more pronounced.

[0110] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components; and they can also refer to a "transmission connection," that is, a power connection through various suitable methods such as belt drive, gear drive, or sprocket drive. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

Claims

1. A regeneration optimization design method for existing buildings based on quantum annealing algorithm, wherein the regeneration optimization design method for existing buildings based on quantum annealing algorithm is used for the regeneration optimization of existing buildings, characterized in that, The existing building regeneration optimization design method based on quantum annealing algorithm includes: S1. Establish a digital model of the existing building: acquire the building information model, 3D point cloud model, building drawings or building component database of the existing building, and perform digital analysis on the existing building; S2. Establish a discrete state model of a multi-level building regeneration unit: The existing building is analyzed into component units, space units and equipment system units and uniformly defined as building regeneration units. Each building regeneration unit is assigned a discrete regeneration state to construct a discrete state model of the building regeneration unit. S3. Establish a multi-index evaluation model for building regeneration: Establish multi-index evaluation parameters for each building regeneration unit and its discrete state model, including: carbon emission index, economic cost index, building performance index, structural safety index, and reuse index, and evaluate each building regeneration unit and its discrete state model respectively. S4. Establish a comprehensive cost function for building regeneration: Based on the multi-index evaluation model for building regeneration, establish a comprehensive cost function for building regeneration for each building regeneration unit; S5. Establish a coupling relationship model for building regeneration units: Based on the relationships between the component units, spatial units, and equipment system units of the existing building, establish a coupling relationship model for building regeneration units; S6. Establish a quantum annealing optimization model for building regeneration: Based on the comprehensive cost function of building regeneration in step S4 and the coupling relationship model of building regeneration units in step S5, establish a quantum annealing optimization model for building regeneration. S7. Quantum Annealing Optimization Solution: The quantum annealing algorithm is used to solve the quantum annealing optimization model of building regeneration to output the globally optimal or near-optimal building regeneration solution.

2. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 1, characterized in that, In step S2, different building regeneration units have different candidate regeneration states. Establishing a set of candidate regeneration states includes: For the Each regeneration unit establishes a state set: ; State encoding is performed using discrete binary variables: ; in: Indicates the first The regeneration unit selects the first... Regeneration state, This indicates that the state was not selected; and the constraints are satisfied: That is, each regeneration unit is only allowed to select one regeneration state.

3. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 2, characterized in that, The carbon emission index in step S3 is defined as follows: , indicating the first The regeneration unit adopts the first The carbon emission value corresponding to the regeneration state; the economic cost indicator is defined as follows: , indicating the first The regeneration unit adopts the first The economic cost value corresponding to each regeneration state; Building performance indicators are defined as follows: , indicating the first The regeneration unit adopts the first The building performance benefit value corresponding to each type of regeneration state; Structural safety index is defined as , indicating the first The regeneration unit adopts the first The structural risk value corresponding to the regeneration state; the reuse index is defined as follows: , indicating the first The regeneration unit adopts the first The reuse revenue value corresponding to each regeneration state.

4. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 3, characterized in that, In step S4, the comprehensive cost function for building regeneration is established. ; ； in: These are the weighting parameters for carbon emission indicators, economic cost indicators, building performance indicators, structural safety indicators, and reuse indicators, respectively. The sum of these is 1; carbon emission indicators, economic cost indicators, and structural safety indicators are minimization indicators, while building performance indicators and reuse indicators are benefit indicators. In the actual implementation of the project, the benefit indicators are converted into normalized costs that can be minimized before being included in the comprehensive score.

5. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 4, characterized in that, The building regeneration unit coupling relationship model described in step S5 is set based on the following relationships: structural dependency between component units; functional linkage between spatial units; operational coordination between equipment system units; and spatial organization between component units and spatial units. The load coupling relationship between the spatial unit and the equipment system unit; the constraint conflict relationship between the component unit, the spatial unit and the equipment system unit.

6. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 5, characterized in that, Establish a coupling relationship matrix for the building regeneration units based on the interrelationships between them: Where: matrix elements Used to describe the The first building regeneration unit was selected. The regeneration state and the first The first building regeneration unit was selected. The coupling influence relationship between regeneration states is defined as follows: when two regeneration state combinations have structural constraint conflicts, functional conflicts, or implementation conflicts, they are considered hard conflicts, and a high penalty value is introduced to negate them in the optimization process; when two state combinations have synergistic optimization effects, they are considered synergistic, and a negative reward value is introduced to reduce energy and increase the probability of being selected in the optimization process; when two state combinations increase the system's operating load or structural risk, they are considered soft conflicts, and an additional energy cost is introduced to increase energy and reduce the probability of being selected in the optimization process.

7. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 6, characterized in that, In step S6, the building regeneration combinatorial optimization problem is transformed into an energy function solvable by quantum annealing: ； Wherein: the first term is the cost of the first-order term of the regeneration unit state; the second term is the coupling term of the regeneration unit state; the third term is the penalty term for the uniqueness constraint of the One-Hot state, so as to ensure that each building regeneration unit selects only one of the candidate regeneration states. The penalty coefficient is introduced; The solvable energy function of quantum annealing is expanded and rearranged into the standard quadratic unconstrained binary optimization (QUBO) form to establish a quantum annealing optimization model for building regeneration. ； in: Optimization matrix for building regeneration; This is a vector of building state variables; T represents transpose, that is, transpose columns into rows.

8. The method for regenerative optimization design of existing buildings based on quantum annealing algorithm according to claim 7, characterized in that, Step S7 uses the quantum annealing algorithm to solve the quantum annealing optimization model for building regeneration: First, multiple initial building regeneration schemes are randomly generated, and multiple virtual quantum replicas are created: ;in: The number of virtual quantum layers; each layer corresponds to a set of building regeneration state combinations; Establish the simulated quantum annealing energy function: in: Indicates the first The QUBO energy function for building regeneration corresponding to each virtual quantum layer. For quantum coupling strength, This represents the number of iteration steps. The building regeneration state is initialized using the simulated quantum annealing energy function. The energy of the initial building regeneration scheme is calculated. Some building regeneration state variables are randomly flipped and the state is updated according to the quantum annealing acceptance probability. Then, the intensity of quantum perturbation is gradually reduced, and a low-energy building regeneration state combination is searched. Finally, the globally optimal or near-optimal existing building regeneration scheme is output.

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