Calculation method for additionally arranging photovoltaic reinforcement on roof of existing building

Abstract

The invention relates to the technical field of building structure reinforcement, and discloses a calculation method for increasing photovoltaic reinforcement on the roof of an existing building, which comprises the following steps of: constructing a load distribution model containing a vertical load layer (self weight of a photovoltaic module and accumulated snow load) and a horizontal load layer (wind load and earthquake load), and cooperatively transmitting the load through a node support system and an anchoring connecting piece; generating a roof reinforcement analysis model containing a supporting node model and a connecting component model; generating a reinforcement assembly sequence according to the photovoltaic system installation position distribution information and the reinforcement analysis model; generating a load distribution scheme based on the component sequence; and the distribution scheme and the reinforcement analysis model are combined, and the supporting node arrangement density and the connecting component material specification are determined through multi-objective optimization. According to the method, multi-load and structure aging are comprehensively considered, multi-target optimization is achieved, the safety and economical efficiency of the reinforcement scheme are improved, and the method is suitable for photovoltaic reinforcement calculation of the existing building roof.

Description

Technical Field

[0001] The present invention relates to the technical field of building structure reinforcement, and in particular to a calculation method for adding photovoltaic reinforcement to the roof of an existing building. Background Art

[0002] With the growing global demand for renewable energy, photovoltaic energy, as a clean and sustainable form of energy, is increasingly being used in the construction sector. Existing building rooftops, with their large usable area, are a prime location for photovoltaic system installation. However, the additional loads of photovoltaic systems are often not considered during the design and construction of existing buildings. Simply adding photovoltaic modules can lead to insufficient roof structural bearing capacity, posing a safety hazard. Therefore, scientific and rational reinforcement calculations for existing building rooftops to ensure structural safety and stability after the installation of photovoltaic systems have become a pressing technical challenge.

[0003] From the perspective of existing technologies, traditional building structure reinforcement methods have many limitations when applied to the scenario of adding photovoltaic systems. On the one hand, traditional methods often only analyze a single load type (such as vertical load), ignoring the combined effects of horizontal loads such as wind loads and seismic loads on the roof structure, resulting in an incomplete reinforcement design and an inability to accurately reflect the actual stress conditions. On the other hand, existing technologies are relatively simple in analyzing load transfer paths, and fail to fully consider the synergy between the support node system and the anchor connectors, which can easily lead to unclear load transfer and affect the reinforcement effect. In addition, traditional methods lack systematic evaluation indicators and optimization methods when determining the sequence of reinforcement components and load distribution schemes, making it difficult to achieve comprehensive optimization of multiple objectives such as node redundancy, stress diffusion uniformity, and material utilization, which may lead to excessively high costs or insufficient reliability of reinforcement schemes.

[0004] The addition of photovoltaic systems to existing rooftops presents the challenge of aging materials in the roof's structural beams and slabs. Over time, the performance of building materials gradually degrades, and their bearing capacity and durability may no longer meet the load requirements of the new photovoltaic system. However, existing reinforcement calculations often fail to fully consider the impact of material aging on structural performance, resulting in deviations between reinforcement designs and actual conditions, increasing structural safety risks.

[0005] Furthermore, the installation requirements of photovoltaic systems are diverse, with different installation locations and load levels placing varying demands on reinforcement solutions. Traditional methods struggle to flexibly adjust reinforcement strategies to specific installation requirements, lacking specificity and adaptability. For example, when determining the density of support nodes and the material specifications of connecting components, traditional methods typically rely on empirical formulas or fixed standards. These methods fail to achieve dynamic optimization based on actual load distribution and structural performance. Consequently, reinforcement solutions can be either overly conservative, resulting in a waste of resources, or overly simplified, posing safety risks. Summary of the Invention

[0006] The purpose of the present invention is to provide a calculation method for adding photovoltaic reinforcement to the roof of an existing building to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a calculation method for adding photovoltaic reinforcement to the roof of an existing building, the method comprising: A load distribution model for the photovoltaic reinforcement system is constructed based on the bearing capacity parameters of the existing building roof; wherein the existing building roof is a reinforced structure that requires the addition of photovoltaic modules, and the load distribution model includes a vertical load layer and a horizontal load layer, wherein the vertical load layer is composed of the deadweight of the photovoltaic modules and the snow load, and the horizontal load layer is composed of wind load and seismic load. Load is transferred between the vertical load layer and the horizontal load layer via a node support system, and the vertical load layer and the roof structure beams are force-bound via anchor connectors. The node support system and the anchor connectors work together to form an overall load transfer path; generating a roof reinforcement analysis model based on the load distribution model; In response to installation requirements of the photovoltaic system, generating a reinforcement component sequence based on location distribution information of the installation requirements and the roof reinforcement analysis model; A load distribution scheme for the photovoltaic system is generated according to the reinforcement component sequence; a structural reinforcement strategy for the photovoltaic system is generated based on the load distribution scheme and the roof reinforcement analysis model, and the structural reinforcement strategy is used to determine the arrangement density of support nodes and the material specifications of connection components.

[0008] Preferably, the roof reinforcement analysis model includes a support node model and a connection member model, wherein the support node model is composed of a plurality of support nodes and their corresponding bearing capacity thresholds, and the connection member model is composed of a plurality of connection members and their tensile and compressive strength parameters.

[0009] Preferably, generating a roof reinforcement analysis model based on the load distribution model includes: Determining the vertical bearing capacity limit of each support node in the vertical load layer and the horizontal shear strength limit of each connecting member in the horizontal load layer based on the load distribution model; Obtaining load transfer path distribution information of the support node and stress concentration area distribution information of the connection member according to the vertical bearing capacity limit and the horizontal shear strength limit; Obtaining dynamic response parameters of each supporting node and connecting member based on the load transfer path distribution information and the stress concentration area distribution information; Obtaining the existing bearing capacity parameters and material aging coefficient of the roof structure beams and slabs; A roof reinforcement analysis model is generated according to the dynamic response parameters and the existing bearing capacity parameters.

[0010] Preferably, in response to the installation requirements of the photovoltaic system, generating a reinforcement component sequence based on the location distribution information of the installation requirements and the roof reinforcement analysis model includes: In response to the installation requirements of the photovoltaic system, a plurality of candidate support nodes are screened from the support node model based on the position distribution information, the candidate support nodes being used to bear the concentrated load of the photovoltaic assembly; generating a plurality of candidate reinforcement schemes based on the candidate support nodes; Performing a bearing capacity matching evaluation on each candidate reinforcement scheme to obtain a reinforcement evaluation result, wherein the reinforcement evaluation result includes a graded score of multiple evaluation indicators, including a node redundancy index, a stress diffusion uniformity index, a material utilization index, and a deformation coordination index; Determine the weight coefficient of each evaluation index according to the load level of the installation requirement; Comprehensively scoring the reinforcement evaluation results of each candidate reinforcement scheme based on the weight coefficient to obtain a priority ranking of each candidate reinforcement scheme; A target reinforcement component sequence is screened out from the candidate reinforcement schemes according to the priority ranking.

[0011] Preferably, generating a load distribution scheme for the photovoltaic system according to the reinforcement component sequence includes: Obtaining weight distribution data of the photovoltaic module and wind pressure distribution data of the installation area; Determining a superposition action interval of a static load and a dynamic load of the photovoltaic system based on the weight distribution data and the wind pressure distribution data; Dividing the load distribution priorities of the support nodes according to the superposition action interval; Based on the priority, a load transfer relationship model between each support node and the connection member is constructed, and the specific form is:

[0012] in, Indicates the The load transfer relationship of each photovoltaic module, Represents the support node set The nodes, Represents a collection of connected components The components; determining node layout parameters of the reinforcement component sequence according to the load transfer relationship model; A load distribution plan for the photovoltaic system is generated based on the node layout parameters.

[0013] Preferably, generating the structural reinforcement strategy of the photovoltaic system based on the load distribution scheme and the roof reinforcement analysis model includes: Obtaining the maximum stress value of the support node and the fatigue life prediction value of the connection component according to the load distribution scheme; generating reinforcement constraints based on the maximum stress value and the fatigue life prediction value, wherein the reinforcement constraints include spacing constraints of support nodes, cross-sectional size constraints of connecting members, pull-out force constraints of anchors, and material strength matching constraints; Constructing a multi-objective optimization model based on the reinforcement constraint conditions, wherein the multi-objective optimization model includes a first optimization sub-model and a second optimization sub-model; The first optimization sub-model is used to minimize the total number of supporting nodes while meeting the bearing capacity threshold requirements of each node; The second optimization sub-model is used to maximize the stress diffusion range of the connection component while controlling the upper limit of material cost; The multi-objective optimization model is iteratively solved, and the structural reinforcement strategy is generated based on the solution results.

[0014] Preferably, the iterative solution method includes: Using a genetic algorithm to perform multi-generation population evolution on the multi-objective optimization model to generate an initial solution set; The correlation degree of each objective function value of the initial solution set is sorted by grey correlation analysis method; Based on the sorting results, the Pareto optimal solution set is screened, and the final structural reinforcement strategy is determined in combination with the decision maker's preferences.

[0015] Preferably, the method for determining the arrangement density of the support nodes includes: Obtaining local stiffness degradation coefficients and load concentration area distribution data of the roof structure beams and slabs; dividing the roof structure into weak areas and strengthened areas based on the local stiffness degradation coefficient; Calculate the minimum spacing threshold of support nodes in each area based on the load concentration area distribution data; Adjust the density coefficient of the supporting nodes based on the stiffness compensation requirements of the weak areas, and optimize the node arrangement spacing in combination with the existing bearing capacity redundancy of the strengthened areas; A final arrangement solution of the supporting nodes is generated according to the density coefficient and the minimum spacing threshold.

[0016] Preferably, the multi-objective optimization model further includes a third optimization sub-model, which is used to balance material costs and construction period constraints, including: Obtaining market price fluctuation data of materials of the connecting components and construction process time parameters; Constructing a dynamic material cost forecast curve based on the market price fluctuation data; Generate construction period segmentation constraints according to the construction process time-consuming parameters; The third optimization sub-model is used to simultaneously optimize the material procurement batches and the resource allocation plan for the construction phase to generate a cost-construction period balance strategy.

[0017] Preferably, the present invention also includes a photovoltaic reinforcement calculation device for adding a roof of an existing building, the device including: a memory, a processor, and a reinforcement calculation program stored in the memory and executable on the processor, the reinforcement calculation program being configured to implement the photovoltaic reinforcement calculation method for adding a roof of an existing building as described in any one of the above items.

[0018] Compared with the prior art, the present invention has the following beneficial effects: By constructing a comprehensive load distribution model, combining vertical load layers (PV module deadweight, snow load) with horizontal load layers (wind load, seismic load), and forming an overall load transfer path through the synergistic effect of the node support system and anchor connectors, the shortcomings of single load analysis in traditional methods are resolved, and the actual stress state of the roof structure after the PV system is installed can be more realistically reflected, significantly improving the comprehensiveness and accuracy of load analysis.

[0019] When generating the roof reinforcement analysis model, not only the vertical bearing capacity limits of the support nodes and the horizontal shear strength limits of the connecting components were taken into account, but also dynamic response parameters and the existing bearing capacity parameters and material aging coefficients of the roof structure beams and slabs were introduced. This enables the model to fully reflect the actual performance and aging status of the structure, avoids reinforcement design deviations caused by ignoring material aging, and improves the reliability and durability of the reinforcement scheme.

[0020] In the process of generating a reinforcement component sequence in response to PV system installation requirements, the team screened candidate support nodes, generated candidate reinforcement solutions, and conducted comprehensive scoring and prioritization based on multi-dimensional evaluation indicators such as node redundancy, stress diffusion uniformity, material utilization, and deformation coordination, achieving multi-objective optimization of the reinforcement solutions. This systematic evaluation method flexibly adjusts the weights of evaluation indicators based on different installation requirements and load levels, ensuring that the selected reinforcement component sequence achieves an optimal balance between safety, economy, and adaptability, overcoming the shortcomings of traditional methods that lack specificity and optimization capabilities.

[0021] During the load distribution plan generation process, the weight distribution data of the photovoltaic modules and the wind pressure distribution data were analyzed to determine the superposition range of static and dynamic loads. A load transfer relationship model was constructed to clarify the load transfer relationship between each support node and the connecting components. This refined analysis method based on the actual load distribution can reasonably divide the load distribution priority and optimize the node layout parameters, making the load transfer more clear and uniform, effectively reducing stress concentration and improving the overall stability of the structure.

[0022] The generation of structural reinforcement strategies is based on a multi-objective optimization model, using iterative genetic algorithms and the combination of grey correlation analysis to screen the Pareto optimal solution set. This approach can simultaneously minimize the number of support nodes, maximize the stress diffusion range, and balance material costs with the construction period. The first optimization sub-model reduces the number of nodes while meeting bearing capacity requirements, lowering construction costs and complexity. The second optimization sub-model improves structural safety by expanding the stress diffusion range. The third optimization sub-model combines material price fluctuations and construction process time to generate a cost-period balance strategy, further improving the economic and feasibility of the reinforcement solution. This multi-objective collaborative optimization method breaks through the limitations of traditional single-objective optimization and achieves comprehensive optimization of safety, cost, and construction period.

[0023] The method for determining support node density fully considers the local stiffness degradation coefficient of the roof structure's beams and slabs and the distribution data of concentrated load areas. By dividing the roof structure into weak and strengthened areas, the support node density coefficient and spacing threshold are adjusted in a targeted manner. Increasing node density in weak areas compensates for stiffness, while optimizing spacing in strengthened areas by utilizing existing bearing capacity redundancy. This ensures structural safety while avoiding blind node placement and resource waste, achieving a scientific and rational node arrangement. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a working principle diagram of the calculation method for adding photovoltaic reinforcement to the roof of an existing building according to the present invention; Figure 2 Schematic diagram of the roof reinforcement analysis model; Figure 3 A flow chart for generating a load distribution scheme for a PV system based on the sequence of reinforced components; Figure 4 Generate a flow chart for structural reinforcement strategies based on load distribution schemes and analytical models; Figure 5 Flowchart of the support node arrangement density determination method. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1-Figure 5 The present invention relates to a calculation method for adding photovoltaic reinforcement to the roof of an existing building, and the specific implementation steps are as follows: First, for the roof reinforcement structure of an existing building where photovoltaic panels are to be added, the load-bearing parameters of the existing building roof are collected, including the material strength and geometric dimensions of the roof structure beams and slabs, as well as the existing load-bearing history data. Based on these parameters, a load distribution model is constructed, which is divided into vertical and horizontal load layers.

[0027] The vertical load layer includes the weight of the photovoltaic modules and the snow load. The weight of the photovoltaic modules is determined by the actual module model, size and installation quantity. For example, if a certain model of photovoltaic module weighs 20kg and 100 modules are installed on the roof, the weight of the module is 2000kg. The snow load needs to be calculated based on local meteorological data, the snow distribution coefficient and snow thickness specified in the building code. For example, the basic snow pressure in a certain area is , combined with the roof slope correction factor, determine the snow load value.

[0028] The horizontal load layer includes wind load and seismic load. The calculation of wind load needs to consider the local basic wind pressure, wind pressure height variation coefficient, wind load body shape coefficient and gust coefficient, etc. For example, when the basic wind pressure is For areas with a height of 10m, the standard value of wind load is calculated for the roof of a building with a height of 10m; the seismic load is calculated using the mode decomposition response spectrum method according to the seismic design code based on the seismic fortification intensity, design basic earthquake acceleration, site category and other parameters of the area where the building is located.

[0029] The vertical and horizontal load-bearing layers are loaded through a node support system. This node support system can be implemented as steel supports or concrete piers, and its placement must align with the load-bearing nodes of the roof's structural beams and slabs. Anchor connectors (such as chemical anchors and expansion bolts) secure the vertical load-bearing layers to the roof's structural beams and slabs, ensuring effective load transfer to the roof's main structure. The node support system and anchor connectors work together to form an integrated load transfer path from the PV panels to the roof structure.

[0030] After constructing the load distribution model, a roof reinforcement analysis model is generated based on it. This process requires comprehensive consideration of the impact of load distribution on the roof structure and analysis of the stress state of support nodes and connecting components to provide a basis for subsequent reinforcement design.

[0031] Respond to PV system installation requirements and obtain location distribution information, such as the specific placement and spacing of PV panels on the roof. Combined with the roof reinforcement analysis model, determine the support nodes and connection components that need to be added or strengthened, generate a reinforcement component sequence, and clearly define the type, specifications, and installation location of each reinforcement component.

[0032] According to the sequence of reinforced components, taking into account factors such as the weight distribution of photovoltaic modules and the wind pressure distribution in the installation area, the loads of each supporting node and connecting component are reasonably distributed to ensure a clear load transfer path and balanced force.

[0033] Based on the load distribution scheme and roof reinforcement analysis model, structural reinforcement strategies such as the layout density of support nodes and material specifications of connection components are determined to meet the safety and stability requirements of the roof structure after the photovoltaic system is installed.

[0034] The present invention will be further described below in conjunction with Examples 1 to 4: Example 1: When generating a roof reinforcement analysis model based on a load distribution model, it is first necessary to clarify the vertical bearing capacity limit of each support node in the vertical load layer and the horizontal shear strength limit of each connecting member in the horizontal load layer. The vertical load layer is composed of the deadweight of the photovoltaic modules and the snow load. These loads will be transmitted to the supporting nodes through the node support system. Therefore, it is necessary to calculate the maximum vertical load that each supporting node can withstand based on the weight of the photovoltaic modules, the possible snow thickness, and the structural characteristics of the supporting nodes, that is, the vertical bearing capacity limit. The horizontal load layer includes wind loads and seismic loads. These loads will generate horizontal shear forces on the connecting members. It is necessary to combine the local wind speed, seismic intensity, and the material and structural form of the connecting members to determine the maximum shear force that each connecting member can resist in the horizontal direction, that is, the horizontal shear strength limit.

[0035] After obtaining the vertical bearing capacity limit and the horizontal shear strength limit, the load transfer path distribution information of the support nodes and the stress concentration area distribution information of the connection components are further obtained. The acquisition of load transfer path distribution information requires analyzing how the load is transferred from the photovoltaic module through the node support system between the various support nodes under the action of vertical load, which nodes are the main load-bearing nodes, and which nodes play an auxiliary transfer role, so as to form a clear load transfer route map. For connection components, under the action of horizontal loads, due to the different shapes, connection methods and contact conditions of the components with other structures, stress concentration will occur in certain areas. By analyzing the horizontal shear strength limit, the location and range of these stress concentration areas are determined, and the stress concentration area distribution information is formed.

[0036] Based on the aforementioned load transfer path distribution information and stress concentration area distribution information, the dynamic response parameters of each support node and connecting member are obtained. The acquisition of dynamic response parameters requires considering load variations, such as the intermittent nature of wind loads and the sudden nature of seismic loads. The dynamic response parameters are analyzed to determine how the displacement, velocity, and acceleration of the support nodes change over time under these dynamic loads, as well as the stress changes and deformation degree of the connecting members at different times. These parameters reflect the real-time stress state and movement of the support nodes and connecting members under dynamic loads.

[0037] At the same time, it is necessary to obtain the existing bearing capacity parameters and material aging coefficient of the roof structure beams and slabs. Existing bearing capacity parameters refer to the bearing capacity indicators of the roof structure beams and slabs during construction, including compressive strength, tensile strength, flexural strength, etc., which can be obtained by consulting the original building design data and structural inspection reports. The material aging coefficient needs to consider the impact of the service life of the roof structure beams and slabs and the environment (such as temperature, humidity, corrosive substances, etc.) on material properties. Through testing and analysis of material samples, the degree of material performance degradation due to aging is determined, thereby obtaining the material aging coefficient.

[0038] Finally, a roof reinforcement analysis model is generated based on the dynamic response parameters and existing bearing capacity parameters. During the generation process, the dynamic response parameters must be compared and analyzed with the existing bearing capacity parameters to determine whether the existing bearing capacity of the roof structure beams and slabs meets the requirements under the current dynamic load. The existing bearing capacity parameters are corrected based on the material aging coefficient to obtain the actual bearing capacity after accounting for aging. The dynamic response parameters of the support nodes are matched with the corrected existing bearing capacity parameters to determine the bearing capacity threshold of the support nodes. The dynamic response parameters of the connecting components are combined with the corrected existing bearing capacity parameters to determine the tensile and compressive strength parameters of the connecting components. The resulting roof reinforcement analysis model includes a support node model and a connecting component model. The support node model consists of multiple support nodes and their corresponding bearing capacity thresholds, while the connecting component model consists of multiple connecting components and their tensile and compressive strength parameters. This model comprehensively reflects the stress conditions and bearing capacity of the roof structure after the addition of photovoltaic panels.

[0039] Example 2: To meet the installation requirements of a PV system, the specific installation locations of the PV modules on the roof must be determined. This location information constitutes the location distribution information of the installation requirements. Based on this location distribution information, multiple candidate support nodes are screened from the support node model. The support node model contains multiple support nodes and their corresponding load-bearing capacity thresholds. The screening process considers whether the location of the support node matches the installation location of the PV module, ensuring that the candidate support node covers the load area of ​​the PV module and that its load-bearing capacity threshold has the potential to support the concentrated load of the PV module.

[0040] Based on the selected candidate support nodes, multiple candidate reinforcement schemes are generated. Each candidate reinforcement scheme includes a different combination of candidate support nodes and the corresponding connection component configurations. Different combinations result in different load transfer paths, which in turn affect the stress state of the entire reinforcement system. For example, some schemes may adopt a denser distribution of support nodes, while others may reduce the number of support nodes by optimizing the configuration of connection components.

[0041] The bearing capacity matching of each candidate reinforcement scheme is evaluated to obtain the reinforcement evaluation results. The evaluation process involves multiple evaluation indicators, and each indicator has a corresponding grading score. The node redundancy indicator is used to measure whether the support node still has additional bearing capacity in addition to bearing the expected load. The grading score is determined according to the size of the redundancy. The larger the redundancy, the higher the score. The stress diffusion uniformity indicator focuses on the distribution of stress in the support nodes and connecting components under the action of load. If the stress distribution is relatively dispersed and there is no obvious concentration phenomenon, the grading score of this indicator is higher. The material utilization index determines the grading score by calculating the ratio of the actual efficiency of the material used to the theoretical maximum efficiency. The higher the ratio, the more fully the material is used. The deformation coordination index examines the consistency of the support node and the connecting component when subjected to force and deformation. If the deformation amplitude of each part is small and the degree of coordination is high, the grading score of this indicator is correspondingly higher.

[0042] Determine the weighting coefficients for each evaluation indicator based on the load level required for installation. When the load level is low, material utilization may be given a higher weight to prioritize cost control. When the load level is high, the weights for node redundancy and stress diffusion uniformity are increased to ensure structural safety. The weighting coefficients should comprehensively consider the impact of the load level on structural safety and economic costs to form a reasonable weighting scheme.

[0043] The reinforcement assessment results for each candidate reinforcement solution are comprehensively scored based on the weight coefficients. The comprehensive score is calculated by multiplying the graded score for each evaluation indicator by the corresponding weight coefficient and then adding up all the products to obtain the total score for each candidate reinforcement solution. The candidate reinforcement solutions are prioritized based on the total score, with the higher the total score, the higher the priority.

[0044] The target reinforcement component sequence is selected from the candidate reinforcement solutions based on priority. The highest-priority solution is selected as the basis. If some details of this solution do not meet the actual installation conditions, such as the location of the support node conflicting with existing roof facilities, the next-highest-priority solution is considered in turn until a reinforcement component sequence that fully meets the requirements is selected. This sequence includes the specific support node and connection component model, quantity, and layout.

[0045] Example 3: This embodiment is used to describe generating a load distribution scheme for a photovoltaic system based on a reinforced component sequence. The specific implementation process is as follows: 1. Basic data acquisition and analysis requires two types of key data: PV module weight distribution data: clarify the weight of the module itself and the distribution characteristics after installation. For example, a certain model of PV module has a single weight of 25kg and is arranged in a matrix (10 modules horizontally and 8 modules vertically). The total weight is Components are connected to support nodes via brackets, and their weight is transferred to the support nodes below. Typically, each component is supported by four support nodes, with a single node bearing approximately 6.25 kg (uniformly distributed). Note that if the component is tilted or installed in a special way, the weight distribution may be eccentric, requiring mechanical calculations to correct the force applied to each node.

[0046] Installation area wind pressure distribution data: Based on local meteorological parameters and building codes (such as the "Building Structure Load Code" GB50009), determine the wind load size in different areas. For example, in the basic wind pressure In areas where the wind pressure on the windward side of the roof is × body shape coefficient 1.3 × height variation coefficient 1.2 = The standard value of leeward wind pressure is (The negative sign indicates suction.) Wind pressure distribution exhibits significant regional differences. The windward edge experiences the highest pressure, followed by the central region. Suction predominates in the leeward region, necessitating separate load analysis for support nodes and connecting components in different regions.

[0047] 2. Determination of the superposition range of static load and dynamic load The static load includes the weight of the photovoltaic modules and the snow load (calculated according to the local maximum snow thickness, for example, 0.3m thick snow corresponds to a load of Dynamic loads include wind loads and earthquake loads (calculated based on seismic fortification intensity 7, with a maximum earthquake influence coefficient of 0.08). Through load combination analysis, the superposition action range under the most unfavorable working condition is determined: Winter working conditions: component deadweight + snow load + wind load (considering both headwind and tailwind conditions).

[0048] Summer operating conditions: Module deadweight + wind load (maximum typhoon conditions) + seismic load (occasional combination). Taking winter headwind conditions as an example, a support node must simultaneously bear the vertical deadweight of the module (6.25 kg), snow load (a uniformly distributed load acting on the module surface, converted by the bracket into a concentrated force at the node; for example, per square meter of snow load is converted into 0.8 kN per node), and horizontal wind load (a horizontal force of approximately 1.5 kN transmitted to the node through the bracket). These loads must be vector-superimposed to calculate the node's composite load value and direction of action.

[0049] 3. Prioritization of load distribution at support nodes Based on the load distribution in the superposition area, the roof is divided into areas with different risk levels, and the load distribution priority of the supporting nodes is determined: High-priority areas: including the edge of the windward side, the corners where PV panels are arranged, etc. These areas are subject to large wind loads and snow loads, and have a small number of nodes (such as corner nodes that only connect brackets in two directions). Load-bearing capacity needs to be allocated first.

[0050] Medium priority area: Component support nodes located in the middle of the roof, where load distribution is relatively uniform, have the second highest priority.

[0051] Low priority area: Nodes at the leeward edge or non-major stress-bearing areas, which mainly bear the deadweight of components and have smaller loads.

[0052] Priority division needs to be combined with structural mechanics analysis. For example, by simulating the stress distribution of nodes in different areas through finite element software, nodes with stress exceeding 60% of the material design strength can be classified as high priority, nodes with stress between 30% and 60% can be classified as medium priority, and nodes with stress below 30% can be classified as low priority.

[0053] 4. Load transfer relationship model construction and parameter analysis Based on the priority division, a load transfer relationship model between the support node and the connection member is constructed, and its mathematical expression is:

[0054] Description of the meaning of the characters in the formula: :Indicates the The load transfer relationship set of a photovoltaic module includes all related support nodes and connection components of the module. : Support node set The nodes, each node corresponds to a unique spatial coordinate and carrying capacity threshold. : Connection component collection The components, including support beams, columns, diagonal braces, etc., each component has specific tensile, compressive and shear strength parameters. :Carrying the A set of supporting nodes for a photovoltaic module, usually containing 2-4 nodes (determined by the module size and bracket design). :Connect A photovoltaic module is a collection of components that support nodes. The number of components is related to the number of nodes and the connection method (for example, each node is connected by 2 beams and 1 diagonal brace).

[0055] The model clarifies the transfer path of component loads through set theory. For example, the fifth photovoltaic module ( ) loads passing through the nodes 、 (belong ) and components 、 、 (belong ) is transferred to the roof structure, and the force values ​​of each node and component can be calculated using the static equilibrium equation.

[0056] 5. Determine node layout parameters and generate load distribution scheme According to the load transfer relationship model, the following node layout parameters are determined: Node spacing: The node spacing in high-priority areas shall not exceed 1.5m (such as the first row on the windward side), the spacing in medium-priority areas shall be 2-2.5m, and the spacing in low-priority areas may be relaxed to 3m to balance carrying capacity and economy.

[0057] Number of nodes: For single-block components, 4 support nodes are configured in high-priority areas, and 2-3 nodes are configured in medium and low-priority areas.

[0058] Node location: The nodes need to be arranged at the key stress-bearing positions of the roof structure beams and slabs (such as the mid-span of the beams, the top of the columns, etc.) to avoid direct action on the weak areas of the floor slabs (such as the mid-span where there is no beam).

[0059] Based on the above parameters, a load distribution plan for the photovoltaic system is generated, including: Load distribution table for each component: List the supporting node number corresponding to each photovoltaic component, the vertical load and horizontal load value borne by each node. For example, the node of component k=5 The node bears a vertical load of 1.2kN and a horizontal load of 0.8kN. It can bear vertical load of 1.0kN and horizontal load of 0.7kN.

[0060] Connecting member force list: clearly define the force type (tension, compression, shear) and magnitude of each connecting member, such as the member Withstand a tensile force of 1.5kN, the component Withstands shear force of 0.6kN.

[0061] Load transfer path diagram: A diagram showing the complete load transfer path from the PV panels to the supporting nodes, connecting components and then to the roof structure, with the force directions of key nodes and components marked.

[0062] Through these steps, the load distribution scheme ensures that it meets structural safety requirements while optimizing the layout of nodes and components, reducing material consumption and construction costs. This scheme can serve as the basis for subsequent structural reinforcement design and provide a basis for determining the density of support nodes and the specifications of connection components.

[0063] Example 4: Based on the load distribution scheme, the maximum stress value of the support node and the fatigue life prediction value of the connected component are first obtained. The maximum stress value of the support node is calculated by analyzing the load borne by each node in the load distribution scheme and combining it with the structural characteristics of the node itself. This value reflects the peak stress value that the node may reach under the action of load. The fatigue life prediction value of the connected component is determined by comprehensively considering the frequency and amplitude of the alternating loads to which the component is subjected, as well as the fatigue properties of the material. It is used to determine the expected time when the component will experience fatigue failure due to repeated stress during long-term use.

[0064] Based on the above maximum stress values ​​and fatigue life prediction values, reinforcement constraints are generated. The spacing constraints of the supporting nodes need to take into account the bearing range of the nodes. If the maximum stress value of the node is large, it means that the force is concentrated. At this time, the node spacing needs to be appropriately reduced to avoid excessive burden on a single node; if the stress value is small, the spacing can be appropriately increased to reduce the number of nodes. The cross-sectional size constraints of the connecting components need to be adapted to the stress they bear. In areas with greater stress, the cross-sectional size of the components needs to be increased accordingly to ensure sufficient strength. The pull-out force constraint of the anchor needs to be determined based on the tensile force transmitted to the anchor by the vertical load layer to ensure that the anchor will not be pulled out when bearing these tensile forces. The material strength matching constraint requires that the material strength of the connecting component matches the stress level it bears to avoid waste caused by excessive material strength or safety hazards caused by insufficient strength.

[0065] A multi-objective optimization model was constructed based on reinforcement constraints, consisting of three optimization sub-models. The first optimization sub-model aims to minimize the total number of supporting nodes while meeting the bearing capacity threshold requirements of each node. This operation requires first determining the bearing capacity threshold of each node. Based on the load distribution, the node layout is adjusted to ensure that the load borne by each node does not exceed the threshold. Nodes in less stressed areas are then merged to minimize the total number of nodes.

[0066] The goal of the second optimization sub-model is to maximize the stress diffusion range of the connection components while controlling the upper limit of material cost. A larger stress diffusion range indicates a more uniform stress distribution, which reduces the risk of localized stress concentration. During implementation, while maintaining the material cost upper limit, stress diffusion is promoted by optimizing the shape, size, and layout of the connection components, such as by adopting a gradient component cross-section design.

[0067] The third optimization sub-model is used to balance material costs and construction period constraints. First, market price fluctuation data for connecting components is collected. This data can be the changes in material prices over a period of time. Based on this data, a dynamic material cost prediction curve is constructed, which can reflect the possible trends of material prices at different time points. At the same time, the time-consuming parameters of the construction process are obtained, including the time required for each construction step. The construction phases are divided according to these parameters, and construction period segmentation constraints are generated, such as a certain stage must be completed within a specified time. Through the third optimization sub-model, material price fluctuations and construction period requirements are comprehensively considered, and material procurement batches are reasonably arranged. More materials are purchased when prices are low, and the purchase amount is reduced when prices are high. At the same time, resource allocation during the construction phase is optimized, such as increasing manpower and equipment investment in key processes to ensure that the construction period can be effectively guaranteed while controlling costs, thereby generating a cost-construction period balance strategy.

[0068] When iteratively solving a multi-objective optimization model, a genetic algorithm is first used to generate an initial set of solutions through multi-generational population evolution. Genetic algorithms mimic the biological evolution process, performing operations such as selection, crossover, and mutation on multiple possible solutions (i.e., populations). Over multiple generations, a series of initial solutions with different characteristics are obtained.

[0069] Afterwards, the grey correlation analysis method is used to sort the correlation of the objective function values ​​of the initial solution set. Grey correlation analysis can measure the closeness between different solutions and the ideal solution. The higher the correlation, the closer the solution is to the ideal state.

[0070] Based on the sorting results, a Pareto-optimal solution set was identified. Each solution in the Pareto-optimal solution set possesses the characteristic that no objective can be improved without degrading the others. Finally, based on the specific circumstances of the actual project, such as the size of the construction site, available construction equipment, and material availability, a final structural reinforcement strategy was determined from the Pareto-optimal solution set. This strategy specifies the specific layout of support nodes, the material selection, and the size specifications of the connecting components.

[0071] When determining the arrangement density of support nodes, it is first necessary to obtain the local stiffness degradation coefficient and load concentration area distribution data of the roof structure beams and slabs. The local stiffness degradation coefficient can be obtained by inspecting the roof structure. For example, for a concrete roof that has been used for many years, after experiencing long-term wind and rain erosion and temperature changes, the concrete in certain areas may crack and carbonize. The local stiffness degradation coefficient of these areas will be relatively large; while the local stiffness degradation coefficient of areas with better maintenance and less frequent use will be smaller. The distribution data of the load concentration area needs to be determined in conjunction with the installation plan of the photovoltaic modules. For example, in the area in the middle of the roof where a large number of photovoltaic modules are planned to be installed, the load will be relatively concentrated, while the load in the edge area will be more dispersed.

[0072] The roof structure is divided into weak and strengthened areas based on the local stiffness degradation coefficient. For example, if testing reveals that the local stiffness degradation coefficient of the concrete beam and slab in the northeast corner of the roof is 0.7 (assuming an initial stiffness coefficient of 1.0), while the degradation coefficient in the southwest corner is 0.9, the northeast corner can be classified as a weak area, while the southwest corner is a strengthened area. The weak area means that its inherent load-bearing capacity has been significantly reduced and needs to be strengthened by means such as increasing the number of support nodes. The strengthened area, however, has less stiffness degradation and better retained its inherent load-bearing capacity, so the number of support nodes can be appropriately reduced.

[0073] The minimum spacing threshold for support nodes in each area is calculated based on the distribution data for concentrated load areas. For example, if the load per square meter in the middle of a roof is twice that of the edge areas, the threshold for the middle area will be lower than that for the edge areas when calculating the minimum spacing threshold. This is because the more concentrated the load, the more support nodes are required per unit area, and the smaller the spacing between nodes needs to be to ensure that the load borne by each node is within a reasonable range.

[0074] Adjust the density coefficient of the supporting nodes based on the stiffness compensation requirements of the weak areas. For the weak area in the northeast corner mentioned above, assuming that its initial density coefficient is 1.0, in order to compensate for the insufficient stiffness, the density coefficient can be adjusted to 1.2, that is, the number of supporting nodes in this area is increased by 20%. At the same time, the node arrangement spacing is optimized in combination with the existing bearing capacity redundancy of the strengthened area. For the southwest corner as a strengthened area, if its existing bearing capacity redundancy is high, the originally calculated minimum spacing threshold is 1.5 meters, and the spacing can be optimized to 1.8 meters. This reduces the number of nodes and reduces costs while ensuring the bearing capacity.

[0075] The final layout plan for the support nodes is generated based on the adjusted density coefficient and minimum spacing threshold. For example, after adjusting the density coefficient, the support nodes in the weak area of ​​the northeast corner are arranged at a spacing of 0.8 meters by 0.8 meters, combined with its minimum spacing threshold of 0.8 meters. In the load-concentrated area in the middle of the roof, the minimum spacing threshold is 1.0 meters, the density coefficient remains at 1.0, and the support nodes are arranged at a spacing of 1.0 meters by 1.0 meters. The spacing in the strengthened area of ​​the southwest corner is optimized to 1.8 meters, and the support nodes are arranged at a spacing of 1.8 meters by 1.8 meters. This layout plan takes into account both the stiffness conditions of different areas and the differences in load distribution, allowing the support nodes to evenly and effectively bear the loads brought by the photovoltaic system, forming a coordinated force system with the roof structure beams and slabs. When generating the final plan, it is also necessary to consider the actual structure of the roof, such as avoiding obstacles such as vents and exhaust pipes, to ensure that the layout of the support nodes meets both calculation requirements and practical construction feasibility.

[0076] Example 5: In one specific embodiment of the present invention, a calculation method for adding photovoltaic reinforcement to existing building roofs is described in detail. This method addresses the existing technical challenges of inadequately calculating reinforcement for existing building roofs to ensure the structural safety and stability of the installed photovoltaic system. This embodiment comprehensively considers load distribution, structural aging, load-bearing capacity assessment, and cost and construction schedule optimization.

[0077] First, for existing building roof reinforcement structures where photovoltaic panels are to be added, the bearing capacity parameters of the roof structure beams and slabs are collected, including material strength, geometric dimensions, and historical load data. Based on these parameters, a load distribution model is constructed, divided into vertical and horizontal load layers. The vertical load layer consists of the deadweight of the photovoltaic modules and snow loads. For example, the load values ​​corresponding to the deadweight of the modules and snow depth are calculated based on the actual photovoltaic module model and local meteorological data. The horizontal load layer consists of wind loads and seismic loads. Wind pressure and seismic forces are determined by combining local wind speed, seismic fortification intensity, and other parameters. The vertical and horizontal load layers transmit loads through a node support system and are connected to the roof structure beams and slabs through anchor connectors, forming an integrated transmission path. When generating a roof reinforcement analysis model based on this model, the vertical bearing capacity limits of each support node in the vertical load layer and the horizontal shear strength limits of each connecting member in the horizontal load layer must be determined. Subsequently, the load transfer path distribution information for the support nodes and the stress concentration area distribution information for the connecting components are obtained, and dynamic response parameters are analyzed, such as the displacement changes of the support nodes under wind loads and the stress fluctuations of the connecting components under dynamic loads. Simultaneously, the existing bearing capacity parameters and material aging coefficients of the roof structure beams and slabs are obtained. For example, by measuring the carbonization degree of the concrete beams and slabs or the corrosion rate of the steel, the aging coefficient is calculated (e.g., a coefficient of 0.8 for a service life exceeding 20 years). Based on the dynamic response parameters and the corrected existing bearing capacity parameters (e.g., multiplying the original compressive strength by the aging coefficient), the bearing capacity threshold of the support nodes and the tensile and compressive strength parameters of the connecting components are determined, thereby generating a roof reinforcement analysis model that includes the support node model and the connecting component model.

[0078] When responding to PV system installation requirements, candidate support nodes are screened from the support node model based on installation location distribution information (e.g., the PV module layout coordinates on the roof) to ensure they cover the load area. Multiple candidate reinforcement schemes are generated, each containing a different combination of support nodes and connection component configurations. Each scheme is evaluated for load capacity compatibility, including node redundancy (e.g., calculating the redundancy ratio between the support node's bearing capacity threshold and the actual load), stress diffusion uniformity (analyzing whether stress distribution is uniform), material utilization (evaluating the ratio of the material's actual efficiency to its theoretical maximum efficiency), and deformation compatibility (checking the consistency of deformation between support nodes and connection components). Weighting factors for each metric are determined based on the required load level. For example, for high load levels, the node redundancy weight is set to 0.4, and the material utilization weight is set to 0.2; for low load levels, the weighting factors are reversed. Based on the weighting factors, the candidate schemes are comprehensively scored (e.g., a weighted average score) and prioritized to select a target reinforcement component sequence. The sequence specifies the support node model, connection component specifications, and layout.

[0079] When generating a load distribution plan based on the sequence of reinforced components, obtain the weight distribution data of the photovoltaic components and the wind pressure distribution data of the installation area. Based on the weight and wind pressure data, determine the range of superposition of static and dynamic loads, such as the combined deadweight, snow and wind loads in winter working conditions. Divide the priority of load distribution of support nodes. The node spacing in high-priority areas (such as the windward edge) shall not exceed 1.5 meters, the spacing in medium-priority areas shall be 2-2.5 meters, and the spacing in low-priority areas can be relaxed to 3 meters. Construct a load transfer relationship model to clarify the transfer path between each support node and the connecting components. For example, the load of a photovoltaic component is transferred through specified nodes and components. Generate a load distribution plan based on the node layout parameters, including a load distribution table for each component, a list of connecting component forces, and a transfer path diagram.

[0080] When generating a structural reinforcement strategy based on the load distribution scheme and roof reinforcement analysis model, the maximum stress values ​​at the support nodes and the fatigue life predictions of the connected components are obtained. Reinforcement constraints are generated, including support node spacing constraints (e.g., reducing spacing when maximum stress values ​​are high), connection component cross-sectional dimensions constraints, anchor pullout force constraints, and material strength matching constraints. A multi-objective optimization model is constructed. The first optimization sub-model minimizes the total number of support nodes to ensure that the load at each node does not exceed the bearing capacity threshold. The second optimization sub-model maximizes the stress diffusion range of the connected components to control the upper limit of material costs. The third optimization sub-model balances material costs with construction period. Market price fluctuation data for connection component materials (e.g., historical steel price trends) is obtained to construct a dynamic material cost prediction curve. Construction period constraints are generated based on construction process time parameters (e.g., anchor installation requires 2 hours per point). Material procurement batches (e.g., bulk purchases when prices are low) and resource allocation during construction phases are optimized to generate a cost-period balance strategy. A genetic algorithm is used to perform multi-generation population evolution to generate an initial solution set. This solution set is then sorted using gray correlation analysis to select the Pareto optimal solution set, and the final strategy is determined based on decision preferences.

[0081] When determining the density of support node arrangements, obtain the local stiffness degradation coefficient of the roof structure beams and slabs and the distribution data of load concentration areas. Based on the stiffness degradation coefficient, divide the weak areas (such as a degradation coefficient of 0.7) and the strengthened areas (such as a degradation coefficient of 0.9), and calculate the minimum spacing threshold of the support nodes in each area (such as a weak area threshold of 0.8 meters). Adjust the density coefficient based on the stiffness compensation requirements of the weak areas (such as from 1.0 to 1.2), and optimize the node spacing (such as from 1.5 meters to 1.8 meters) in combination with the bearing capacity redundancy of the strengthened areas to generate the final layout plan. Through this embodiment, technical personnel can clearly implement load parameter correction, bearing capacity assessment and procurement optimization to ensure that the reinforcement plan achieves the optimal balance in safety, economy and construction period, and effectively improve the stability of the roof structure.

[0082] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0083] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A calculation method for adding photovoltaic reinforcement to the roof of an existing building, characterized in that: The method comprises: A load distribution model for the photovoltaic reinforcement system is constructed based on the bearing capacity parameters of the existing building roof; wherein the existing building roof is a reinforced structure that requires the addition of photovoltaic modules, and the load distribution model includes a vertical load layer and a horizontal load layer, wherein the vertical load layer is composed of the deadweight of the photovoltaic modules and the snow load, and the horizontal load layer is composed of wind load and seismic load. Load is transferred between the vertical load layer and the horizontal load layer via a node support system, and the vertical load layer and the roof structure beams are force-bound via anchor connectors. The node support system and the anchor connectors work together to form an overall load transfer path; generating a roof reinforcement analysis model based on the load distribution model; In response to installation requirements of the photovoltaic system, generating a reinforcement component sequence based on location distribution information of the installation requirements and the roof reinforcement analysis model; A load distribution scheme for the photovoltaic system is generated according to the reinforcement component sequence; a structural reinforcement strategy for the photovoltaic system is generated based on the load distribution scheme and the roof reinforcement analysis model, and the structural reinforcement strategy is used to determine the arrangement density of support nodes and the material specifications of connection components.

2. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 1 is characterized in that: The roof reinforcement analysis model includes a support node model and a connection component model. The support node model consists of multiple support nodes and their corresponding bearing capacity thresholds, and the connection component model consists of multiple connection components and their tensile and compressive strength parameters.

3. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 1 is characterized in that: Generating a roof reinforcement analysis model based on the load distribution model includes: Determining the vertical bearing capacity limit of each support node in the vertical load layer and the horizontal shear strength limit of each connecting member in the horizontal load layer based on the load distribution model; Obtaining load transfer path distribution information of the support node and stress concentration area distribution information of the connection member according to the vertical bearing capacity limit and the horizontal shear strength limit; Obtaining dynamic response parameters of each supporting node and connecting member based on the load transfer path distribution information and the stress concentration area distribution information; Obtaining the existing bearing capacity parameters and material aging coefficient of the roof structure beams and slabs; A roof reinforcement analysis model is generated according to the dynamic response parameters and the existing bearing capacity parameters.

4. The calculation method for adding photovoltaic reinforcement to the roof of an existing building as claimed in claim 2 is characterized in that: The step of generating a reinforcement component sequence in response to the installation requirements of the photovoltaic system and based on the location distribution information of the installation requirements and the roof reinforcement analysis model includes: In response to the installation requirements of the photovoltaic system, a plurality of candidate support nodes are screened from the support node model based on the position distribution information, the candidate support nodes being used to bear the concentrated load of the photovoltaic assembly; generating a plurality of candidate reinforcement schemes based on the candidate support nodes; Performing a bearing capacity matching evaluation on each candidate reinforcement scheme to obtain a reinforcement evaluation result, wherein the reinforcement evaluation result includes a graded score of multiple evaluation indicators, including a node redundancy index, a stress diffusion uniformity index, a material utilization index, and a deformation coordination index; Determine the weight coefficient of each evaluation index according to the load level of the installation requirement; Comprehensively scoring the reinforcement evaluation results of each candidate reinforcement scheme based on the weight coefficient to obtain a priority ranking of each candidate reinforcement scheme; A target reinforcement component sequence is screened out from the candidate reinforcement schemes according to the priority ranking.

5. The calculation method for adding photovoltaic reinforcement to the roof of an existing building as claimed in claim 4 is characterized in that: Generating a load distribution scheme for the photovoltaic system according to the reinforcement component sequence includes: Obtaining weight distribution data of the photovoltaic module and wind pressure distribution data of the installation area; Determining a superposition action interval of a static load and a dynamic load of the photovoltaic system based on the weight distribution data and the wind pressure distribution data; Dividing the load distribution priorities of the support nodes according to the superposition action interval; Based on the priority, a load transfer relationship model between each support node and the connection member is constructed, and the specific form is: in, Indicates the The load transfer relationship of each photovoltaic module, Represents the support node set The nodes, Represents a collection of connected components The components; determining node layout parameters of the reinforcement component sequence according to the load transfer relationship model; A load distribution plan for the photovoltaic system is generated based on the node layout parameters.

6. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 5, characterized in that: Generating a structural reinforcement strategy for the photovoltaic system based on the load distribution scheme and the roof reinforcement analysis model includes: Obtaining the maximum stress value of the support node and the fatigue life prediction value of the connection component according to the load distribution scheme; generating reinforcement constraints based on the maximum stress value and the fatigue life prediction value, wherein the reinforcement constraints include spacing constraints of support nodes, cross-sectional size constraints of connecting members, pull-out force constraints of anchors, and material strength matching constraints; Constructing a multi-objective optimization model based on the reinforcement constraint conditions, wherein the multi-objective optimization model includes a first optimization sub-model and a second optimization sub-model; The first optimization sub-model is used to minimize the total number of supporting nodes while meeting the bearing capacity threshold requirements of each node; The second optimization sub-model is used to maximize the stress diffusion range of the connection component while controlling the upper limit of material cost; The multi-objective optimization model is iteratively solved, and the structural reinforcement strategy is generated based on the solution results.

7. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 6, characterized in that: The iterative solution method includes: Using a genetic algorithm to perform multi-generation population evolution on the multi-objective optimization model to generate an initial solution set; The correlation degree of each objective function value of the initial solution set is sorted by grey correlation analysis method; Based on the sorting results, the Pareto optimal solution set is screened, and the final structural reinforcement strategy is determined in combination with the decision maker's preferences.

8. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 1, characterized in that: The method for determining the arrangement density of the support nodes includes: Obtaining local stiffness degradation coefficients and load concentration area distribution data of the roof structure beams and slabs; dividing the roof structure into weak areas and strengthened areas based on the local stiffness degradation coefficient; Calculate the minimum spacing threshold of support nodes in each area based on the load concentration area distribution data; Adjust the density coefficient of the supporting nodes based on the stiffness compensation requirements of the weak areas, and optimize the node arrangement spacing in combination with the existing bearing capacity redundancy of the strengthened areas; A final arrangement solution of the supporting nodes is generated according to the density coefficient and the minimum spacing threshold.

9. The calculation method for adding photovoltaic reinforcement to the roof of an existing building according to claim 6, characterized in that: The multi-objective optimization model further includes a third optimization sub-model, which is used to balance material costs and construction period constraints, including: Obtaining market price fluctuation data of materials of the connecting components and construction process time parameters; Constructing a dynamic material cost forecast curve based on the market price fluctuation data; Generate construction period segmentation constraints according to the construction process time-consuming parameters; The third optimization sub-model is used to simultaneously optimize the material procurement batches and the resource allocation plan for the construction phase to generate a cost-construction period balance strategy.

10. A photovoltaic reinforcement computing device is added to the roof of an existing building, characterized in that: The device includes: a memory, a processor, and a reinforcement calculation program stored in the memory and executable on the processor, wherein the reinforcement calculation program is configured to implement the calculation method for adding photovoltaic reinforcement to the roof of an existing building as described in any one of claims 1 to 9.

Images