Existing building retrofit cost estimation method fusing three-dimensional point cloud and cost index

Abstract

The application discloses a method for evaluating the reconstruction cost of existing buildings by fusing three-dimensional point clouds and cost indicators, and relates to the field of intelligent engineering cost analysis. The method comprises the following steps: obtaining point cloud evidence data, archive data, reconstruction scheme and cost indicator data of the existing building, and generating a plurality of reconstruction action bodies; dividing the interface micro area of the reconstruction action body and the building surface represented by the point cloud into a plurality of interfaces; generating a hidden structure candidate based on the archive data; determining the observation support degree and the reliability of each candidate according to the corresponding scanning ray path; combining the selected candidate and the reconstruction action body to form a hidden structure scenario, and determining the disposal procedure and the execution sequence under each scenario; calculating and merging the procedure cost based on the cost indicator data, and generating the cost distribution and the quantile budget by combining the candidate reliability and the scenario cost. Thus, the objectivity, completeness and budget reliability of the existing building reconstruction cost evaluation under the uncertain condition of hidden structure are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent engineering cost analysis technology, and in particular to a method for evaluating the cost of renovating existing buildings by integrating three-dimensional point clouds and cost indicators. Background Technology

[0002] As urban construction gradually shifts from large-scale incremental development to stock renewal, existing building renovation projects are increasingly adopting 3D laser scanning, building information modeling (BIM), and digital cost databases to assist in on-site investigations, quantity calculations, and investment estimations. Typically, point cloud data of the building's current condition is obtained through scanning, and this point cloud is then registered, segmented, and reconstructed to create 3D representations of building components such as walls, floors, and ceilings. Geometric quantities such as length, area, volume, and number of components are then extracted from these 3D representations, and the renovation costs are determined by combining these with bill of quantities, market prices, or historical cost indicators.

[0003] However, the renovation of existing buildings typically involves various operations such as partial demolition, opening, equipment replacement, facade restoration, and preservation of existing facilities. Renovation costs are not only related to the geometric quantities of visible components but also influenced by the existing conditions within the components, at component junctions, and in obscured areas. Due to differences in the construction date, usage history, and maintenance records of existing buildings, the drawings may not perfectly match the actual site conditions. The structural layers, material properties, and facility distribution in some areas are difficult to determine directly from surface information.

[0004] Currently, in point cloud modeling, for areas not covered or occluded by the scan, existing drawings, common construction methods, or information from adjacent components are typically referenced for model completion and parametric representation. While this facilitates the formation of a continuous and complete building model, it struggles to fully capture the uncertainties of the current situation reflected by insufficient observation. Subsequent cost calculations generally rely on matching the bill of quantities based on the defined components and geometric quantities in the model. However, the correspondence between point cloud observation information, existing building conditions, and renovation costs remains insufficient, making it difficult to comprehensively reflect the potential cost differences arising from different renovation areas and varying site conditions. Summary of the Invention

[0005] This application provides a method, system, storage medium, computer program product, and electronic device for assessing the renovation costs of existing buildings by integrating three-dimensional point clouds and cost indicators, in order to at least solve the problem that the renovation costs of existing buildings in the prior art cannot fully reflect the uncertainties on site.

[0006] In a first aspect, embodiments of this application provide a method for cost assessment of existing building renovation that integrates three-dimensional point cloud and cost indicators. The method includes: acquiring point cloud evidence data, existing building archive data, renovation plan, and cost indicator data of the existing building, and generating multiple renovation action objects based on the renovation plan; wherein, the point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point, and the ray path pointing from the corresponding scanning origin to the corresponding measurement point; the renovation action object is a spatial object representing the action type, construction influence range, and construction constraints of the renovation construction; dividing the boundary between each renovation action object and the existing building surface represented by the point cloud evidence data into multiple interface micro-regions, and generating multiple hidden structure candidates for each interface micro-region based on the existing building archive data; for each interface micro-region, adjusting the boundary based on the ray path corresponding to the interface micro-region. The observational support level of each of the hidden structure candidates corresponding to the micro-region is used to determine the credibility of each hidden structure candidate. One hidden structure candidate is selected from the multiple hidden structure candidates corresponding to each interface micro-region. The selected hidden structure candidate is combined with the corresponding modification action to form a hidden structure scenario corresponding to each interface micro-region. The multiple handling procedures to be performed under each hidden structure scenario and their execution order are determined. Based on the cost index data, the costs of the multiple handling procedures under each hidden structure scenario are calculated and combined according to the execution order to obtain the cost of each hidden structure scenario. A modification cost assessment result is generated based on the credibility of the hidden structure candidate corresponding to each hidden structure scenario and the cost of the hidden structure scenario. The modification cost assessment result includes cost distribution and quantile budget.

[0007] Secondly, embodiments of this application provide a cost assessment system for the renovation of existing buildings that integrates three-dimensional point clouds and cost indicators. The system includes: a renovation action body generation unit, used to acquire point cloud evidence data, existing building archive data, renovation plans, and cost indicator data of the existing building, and generate multiple renovation action bodies according to the renovation plans; wherein, the point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point, and the ray path from the corresponding scanning origin to the corresponding measurement point, and the renovation action body is a spatial object representing the action type, construction influence range, and construction constraints of the renovation construction; a candidate generation unit, used to divide the boundary between each renovation action body and the existing building surface represented by the point cloud evidence data into multiple interface micro-regions, and generate multiple hidden structure candidates for each interface micro-region according to the existing building archive data; and a candidate credibility determination unit, used to determine the credibility of each interface micro-region based on the ray path corresponding to the interface micro-region. The path determines the credibility of each hidden structure candidate based on the observation support level of the path for each hidden structure candidate corresponding to the interface micro-region; the scenario and procedure determination unit is used to select one hidden structure candidate from the multiple hidden structure candidates corresponding to each interface micro-region, combine the selected hidden structure candidate with the corresponding modification action body to form a hidden structure scenario corresponding to each interface micro-region, and determine the multiple handling procedures to be performed under each hidden structure scenario and their execution order; the modification cost evaluation unit is used to calculate and merge the costs of multiple handling procedures under each hidden structure scenario based on the cost index data and according to the execution order to obtain the cost of each hidden structure scenario, and generate a modification cost evaluation result based on the credibility of the hidden structure candidate corresponding to each hidden structure scenario and the cost of the hidden structure scenario; wherein, the modification cost evaluation result includes cost distribution and quantile budget.

[0008] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the existing building renovation cost assessment method integrating three-dimensional point clouds and cost indicators according to any embodiment of the present application.

[0009] Fourthly, embodiments of this application provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for evaluating the cost of existing building renovation by fusing three-dimensional point clouds and cost indicators according to any embodiment of this application.

[0010] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the existing building renovation cost assessment method that integrates three-dimensional point clouds and cost indicators according to any embodiment of this application.

[0011] The method and system for assessing the renovation costs of existing buildings by integrating three-dimensional point clouds and cost indicators provided in this application can achieve at least the following technical effects: By spatializing the renovation plan into renovation action bodies with action types, construction impact ranges, and construction constraints, and dividing the interface micro-regions based on the boundary between the renovation action body and the existing building surface, the analytical granularity of cost assessment can be further reduced from the overall component to the local construction interface where the specific renovation action actually takes place. Based on this, corresponding hidden structure candidates are generated for different interface micro-regions using existing building archives. The observational support level of each candidate is evaluated using the ray path from the point cloud scan origin to the measurement point, allowing scan coverage, occlusion relationships, and observable conditions to jointly contribute to determining the credibility of hidden structures. Thus, while preserving the differences in the state of hidden areas, different hidden structure judgments can have a degree of credibility corresponding to actual point cloud evidence, improving the matching between the current representation of the existing building and the specific renovation parts.

[0012] By combining hidden structure candidates in the interface micro-region with corresponding modification actions to form hidden structure scenarios, and further analyzing the necessary procedures and their sequential relationships under each scenario, the differences in hidden structures can be transformed into calculable differences in the construction process. The related procedures such as demolition, protection, relocation, repair, and restoration caused by different concealment conditions can form a continuous cost transmission chain according to construction logic. This allows cost calculation to reflect not only changes in the quantity of individual projects but also the linked costs arising from the addition or subtraction of procedures, pre- and post-constraints, and supporting measures. Based on this, correlating the cost of each scenario with the credibility of the corresponding hidden structure candidates can form a cost distribution and quantile budget with a probabilistic hierarchy, providing a basis for investment estimation and budget allocation under different risk levels.

[0013] This technical solution establishes a continuous mapping mechanism: point cloud observation evidence constrains the credibility of concealed structures; concealed structure scenarios drive the evolution of treatment procedures; and treatment procedures form cost distribution. This allows both observable and unobservable information from point cloud data to participate in cost assessment through a unified technical path, and orderly transmits the uncertainties of existing building concealed conditions to the final budget result, thereby improving the authenticity, interpretability, and risk adaptability of renovation cost assessment. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart is shown as an example of a method for assessing the cost of renovating existing buildings by fusing 3D point clouds and cost indicators according to an embodiment of this application. Figure 2 A flowchart illustrating an example of determining the degree of support for covert construction candidate observations in a method according to an embodiment of this application is shown. Figure 3 This paper shows an example operation flowchart of generating a modification cost assessment result according to the method of the embodiments of this application; Figure 4 A schematic diagram of the system operation mechanism of an example of a method for assessing the cost of renovating existing buildings by fusing 3D point clouds and cost indicators according to an embodiment of this application is shown. Figure 5 A schematic diagram of the comparative experimental simulation results of the absolute percentage error distribution and the contribution of overpriced samples for different evaluation methods is shown. Figure 6 A schematic diagram of comparative experimental simulation results is shown, illustrating the budget interval calibration results of different evaluation methods and an example of weighted interval scoring under different point cloud occlusion rates. Figure 7 A schematic diagram of comparative experimental simulation results is shown, illustrating the relationship between the overall exploration budget and normalized tail cost under different exploration strategies, as well as an example of recommended exploration locations. Figure 8 A structural block diagram of an example of an existing building renovation cost assessment system that integrates three-dimensional point clouds and cost indicators according to an embodiment of this application is shown. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be noted that current Scan-to-BIM (Scan to Building Information Modeling) reconstruction technology typically includes processes such as geometric modeling, object classification, and object relationship determination. It identifies building objects such as walls and floors through spatial or planar segmentation, converting the identification results into BIM (Building Information Modeling) or IFC (Industry Foundation Classes) objects. This type of technology primarily uses component recognition rate, model position deviation, and the degree of modeling automation as evaluation criteria. However, point clouds have limited ability to express semantic information such as material properties, functional uses, and internal structures, and data may be missing in occluded areas.

[0018] For historical buildings and irregular building components, HBIM (Historic Building Information Modeling) parametric modeling technology uses textured meshes, freeform surfaces, or editable parametric objects to express complex forms such as wall tilt, surface undulations, and component deformation. Some Scan-to-BIM technologies also analyze the propagation of point cloud measurement errors to component dimensions, boundary positions, and model regularization results through geodesic stochastic models or statistical error propagation methods. While these technologies can improve the accuracy of representing complex surfaces and evaluate the geometric uncertainty of the model, their focus remains on observable forms and geometric accuracy, with limited processing of non-geometric information such as building interior materials, facilities, and structural conditions.

[0019] In the field of construction monitoring, point cloud-BIM fusion technology can register and compare on-site point clouds with the planned model, and determine the completion status of building objects based on the spatial relationship between the point cloud and the model surface. 4D BIM (Four-Dimensional Building Information Modeling) technology can also associate BIM objects with construction schedule tasks for progress verification and period risk monitoring. These technologies primarily address the existence, completion, and conformity of building objects to the planned status, and their applicability to existing building renovation costs is relatively limited.

[0020] In engineering cost estimation, 5D BIM (Five-Dimensional Building Information Modeling) and BIM-database integrated cost estimation techniques typically obtain the length, area, volume, and quantity of components through QTO (Quantity Take-Off) and establish a link with bill of quantities, cost databases, or enterprise cost items. Some early cost estimation techniques also used low LOD (Level of Detail) models and historical project cost data to generate cost estimates. These techniques can improve the efficiency of quantity calculation and cost matching, but their applicability largely depends on the completeness of the model information and the comparability between the project being evaluated and historical samples.

[0021] Furthermore, statistical studies on existing building renovation projects indicate that insufficient prior knowledge of site conditions can lead to engineering changes and increased costs. However, such studies typically analyze existing change records and settlement data, offering limited support for detailed cost assessments before renovation implementation. Therefore, when existing building site information is incomplete, there are many hidden areas, or historical data differs from the actual condition, calculating costs based on reconstructed BIM objects, geometric quantities, and historical cost indicators is insufficient to fully meet the actual needs of cost assessment for existing building renovations.

[0022] It should be understood that the above description of the relevant technologies is intended only to help the public better understand the inventive spirit and motivation of this application, and is not intended to limit this application. Furthermore, the technical solutions described in the above-mentioned relevant technologies are not prior art, and may also be undisclosed technical solutions, such as those under research or in the laboratory stage.

[0023] The technical solutions in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of users' personal information, comply with relevant laws and regulations and do not violate public order and good morals.

[0024] Figure 1 A flowchart illustrating an example of a method for assessing the cost of renovating existing buildings by fusing 3D point clouds and cost indicators according to an embodiment of this application is shown.

[0025] The method for assessing the renovation costs of existing buildings by integrating 3D point cloud data and cost indicators provided in this application can be executed by electronic devices, servers, or cost assessment platforms. These electronic devices, servers, or cost assessment platforms can execute the method provided in this application through software, hardware, or a combination of both by running programs or instructions stored in their memory, and perform correlation processing on the point cloud evidence data of existing buildings, existing building archive data, renovation plans, and cost indicator data.

[0026] In some embodiments, the electronic device, server, or cost assessment platform can communicate with 3D scanning equipment, building archive database, renovation plan management system, and cost index database to obtain relevant data and output renovation cost assessment results. The embodiments of this application can be applied to renovation scenarios such as partial demolition, opening and penetration, surface covering, component retention, equipment relocation, or load adjustment of existing buildings; specific application scenarios do not constitute a limitation on the scope of protection of this application.

[0027] like Figure 1 As shown, in step S110, point cloud evidence data of existing buildings, existing building archive data, renovation plan and cost index data are acquired, and multiple renovation action objects are generated according to the renovation plan; wherein, the point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point and the ray path from the corresponding scanning origin to the corresponding measurement point, and the renovation action object is a spatial object that characterizes the action type, construction influence range and construction constraints of the renovation construction.

[0028] In practice, ground-based laser scanning equipment, mobile scanning equipment, or other scanning equipment capable of outputting three-dimensional spatial measurement results can be used to scan the renovation area and adjacent areas of the existing building. When acquiring the three-dimensional coordinates of each measurement point, the scanning equipment can also retain the scanning station position or movement trajectory position that generated the corresponding measurement point, and determine the scanning station position or movement trajectory position as the corresponding scanning origin. Based on the spatial positional relationship between the scanning origin and the corresponding measurement point, a ray path pointing from the scanning origin to the measurement point can be obtained. Each measurement point, the corresponding scanning origin, and the ray path are associated and saved according to the same spatial coordinate reference, so that the point cloud evidence data can represent both the spatial position of the visible surface of the existing building and the actual observation direction and observation range of the corresponding measurement results.

[0029] The existing building archive data can be obtained from as-built drawings, previous renovation drawings, maintenance records, equipment data, inspection records, and property management data, and the corresponding data sources and data creation times can be retained. The renovation plan can include the spatial outline of the renovation object, the scope of construction operations, adjacent preservation areas, and the building functional requirements that need to be met during construction. The cost index data can include pricing information related to labor, materials, machinery, inspection, transportation, restoration, and construction measures. When generating the renovation action, the renovation intent in the renovation plan is converted into a spatial object that can correspond to the spatial location of the existing building, and the construction impact range covers the renovation object and the space occupied by construction operations, adjacent protection, and partial restoration. The construction constraints are used to characterize the construction time, site disturbance, and building function preservation conditions that need to be met when implementing the renovation action, so that the direct object of this cost assessment can be defined by the location of the renovation action and the scope of construction impact.

[0030] In step S120, the boundary between each of the modified action bodies and the existing building surface represented by the point cloud evidence data is divided into multiple interface micro-regions, and multiple hidden structure candidates are generated for each interface micro-region according to the existing building archive data.

[0031] The existing building surface can be obtained from measurement points in point cloud evidence data through registration, surface partitioning, or local surface fitting. It represents walls, floors, ceilings, finishes, equipment casings, or other building surfaces observed by the scanning equipment. The existing building surface is maintained as a spatial representation of the scanned visible surface; the structural thickness, material layers, and internal occupancy of the scanned invisible areas are characterized by subsequently generated hidden structure candidates. Projecting or mapping each modification action onto the existing building surface identifies the location of the modification action on the existing building surface and forms the boundary between the modification action and the existing building surface. Combining the surface morphology, material properties, spatial use, scanning accessibility, and construction conditions within the boundary area, parts with different observation conditions or construction impacts can be divided into different interface micro-regions. Each interface micro-region is associated with a corresponding spatial location and modification action, enabling the subsequently determined hidden structure candidates, treatment procedures, and costs to correspond to the specific modification action location.

[0032] For each of the aforementioned interface micro-regions, multiple hidden structure candidates can be generated based on the building's construction details, purpose, structural components, and other existing building archive data. These candidates can explain the concealed state behind the interface micro-region. Different hidden structure candidates can represent different material layers, structural thicknesses, internal cavities, pipeline occupancy, or required building functions. When generating multiple hidden structure candidates, archival records, common construction practices, and on-site spatial conditions are used as the basis for candidate generation, and multiple possible states that are compatible with existing data and the site location are retained. Thus, invisible areas in the point cloud can be represented by multiple comparable hidden structure candidates, preserving the interpretative scope of current evidence for hidden structures.

[0033] In step S130, for each interface micro-region, the confidence level of each hidden structure candidate is determined based on the observation support level of the ray path corresponding to the interface micro-region for each hidden structure candidate corresponding to the interface micro-region.

[0034] The ray path corresponding to the interface micro-region can be determined based on the projection position of the ray path on the existing building surface, the spatial range traversed by the ray path, and the position of the corresponding measurement point. For each candidate concealed structure, the degree of conformity between the ray path and its measurement point and the candidate concealed structure can be judged based on the surface position, thickness range, material layer, or internal occupancy state characterized by the candidate concealed structure. When the ray path actually reaches the visible surface required by the candidate, the measurement point position matches the candidate surface, or the observed local structural relationship is consistent with the candidate, the corresponding observation provides positive support for the candidate concealed structure; when the ray path has the corresponding observation conditions and the observation results differ from the candidate structure, the corresponding observation reduces the degree of support for the candidate concealed structure.

[0035] When determining the level of support for observations, a distinction can be made between a lack of evidence (where the scanning rays are obstructed and do not reach the interface micro-region) and a disproving state (where the scanning rays have reached the interface and the returned results differ from the candidate structures). For the lack of evidence state, the corresponding hidden structure candidate remains, and its credibility is determined based on other currently available evidence. For the disproving state, the credibility of the corresponding hidden structure candidate can be adjusted based on the difference between the observation results and the candidate structures. The credibility of each hidden structure candidate is associated with its corresponding interface micro-region, characterizing the relative credibility of each candidate in interpreting the hidden state of the corresponding interface micro-region under the current point cloud evidence data and existing building archive data.

[0036] In step S140, a hidden structure candidate is selected from the plurality of hidden structure candidates corresponding to each interface micro-region. The hidden structure candidate selected in each interface micro-region is combined with the corresponding modification action body to form a hidden structure scenario corresponding to each interface micro-region. The plurality of processing procedures to be performed under each hidden structure scenario and their execution order are determined.

[0037] When generating concealed structure scenarios, for a given interface micro-region, one candidate is selected from multiple concealed structure candidates corresponding to that micro-region. The concealment state represented by the selected candidate is then combined with the modification action acting on that interface micro-region. The concealed structure scenario includes both the potential concealed structures within the interface micro-region and the construction impact of the current modification on those concealed structures. The concealed structure scenarios corresponding to each interface micro-region selected in the same instance are treated as a single cost assessment scenario. Multiple cost assessment scenarios are generated by changing the candidate selection results in different interface micro-regions, allowing the construction content and costs corresponding to different concealment states to be assessed separately.

[0038] For each concealed structure scenario, based on the action type of the modification action, the scope of construction influence, and construction constraints, as well as the structural materials, internal occupancy status, and required building functions represented by the selected concealed structure candidate, multiple treatment procedures are determined for the corresponding modification construction. These treatment procedures can cover on-site preparation before the modification, construction operations during the modification process, and restoration and inspection after completion. For treatment procedures with preconditions, the execution order can be determined based on the completion result of the previous treatment procedure and the triggering conditions of the subsequent treatment procedure. Since the combination of concealed structure candidates and modification action objects varies, the corresponding treatment procedures, construction scope, and execution order can also change, thereby converting the concealed state in the interface micro-area into measurable and priced construction work.

[0039] In step S150, based on the cost index data, the costs of multiple treatment procedures under each concealed structure scenario are calculated and merged according to the execution sequence to obtain the cost of each concealed structure scenario. Based on the credibility of the concealed structure candidate corresponding to each concealed structure scenario and the cost of the concealed structure scenario, a renovation cost assessment result is generated; wherein, the renovation cost assessment result includes cost distribution and quantile budget.

[0040] For each concealed structure scenario, pricing information can be obtained from the cost index data based on the labor, materials, machinery, testing, transportation, restoration, and construction measures required for each treatment procedure under that scenario. The measurement object, implementation conditions, and costs of each treatment procedure are then determined according to the execution sequence. For treatment procedures under the same concealed structure scenario that can be implemented jointly or share construction resources, the corresponding costs can be combined when the joint implementation conditions are met to obtain the cost of that concealed structure scenario. For the same cost assessment scenario consisting of concealed structure scenarios with multiple interface micro-regions, construction resources with joint implementation relationships can be further combined; mutually exclusive cost assessment scenarios formed by different candidate selection results are priced separately.

[0041] When determining the overall renovation cost, the costs incurred by the visible surface engineering volume represented by point cloud evidence data and the costs of treatment procedures triggered by each hidden structure scenario can be determined separately and then aggregated within the same cost assessment framework. For construction resources that need to reach a predetermined usage scale or have fixed entry requirements, the costs can be determined based on the activation conditions and pricing requirements in the cost index data; for construction resources that can be used by multiple treatment procedures, the pricing can be combined when the corresponding treatment procedures have common implementation conditions. Thus, the determined costs can correspond to the actual requirements for resource activation, procedure connection, interface restoration, and functional maintenance during construction.

[0042] Based on the credibility of the hidden structure candidates corresponding to each hidden structure scenario constituting the same cost assessment situation, the relative credibility of that cost assessment situation can be determined, and the costs of different cost assessment situations and their relative credibility are collectively formed into a cost distribution. This cost distribution is used to characterize multiple possible cost outcomes and their relative credibility under the current point cloud evidence data and existing building archive data. A corresponding quantile budget is determined in the cost distribution according to a preset confidence level, so that the output can simultaneously characterize the general cost level and the higher cost range caused by the uncertainty of hidden structures.

[0043] In this embodiment, the interface micro-region between the modification action and the existing building surface serves as a common spatial index for point cloud evidence, hidden structure candidates, and treatment procedures. Hidden areas with insufficient point cloud observation are retained as hidden structure candidates with varying degrees of confidence. Each candidate is then combined with the modification action to form corresponding treatment procedures and costs. By combining the relative confidence levels of different cost assessment scenarios with the costs to form a cost distribution and quantile budget, the modification cost assessment results can simultaneously correspond to the location of the modification action, the possible state of the hidden structure, and the actual construction work required.

[0044] Regarding the implementation details of generating modification action bodies, dividing interface micro-regions, and generating hidden structure candidates in the methods of this application embodiments, in some examples of this application embodiments, the outline of the modification object and construction requirements are extracted from the modification scheme to determine the action type of each modification object; wherein, the action type includes demolition, penetration, covering, retention, or load transfer.

[0045] The outline of the object to be modified can be extracted from modification drawings, construction organization documents, or a list of objects to be modified. When the modification boundaries are marked in the modification drawings, the corresponding wireframes, areas, or spatial enclosed ranges can be read. When the list of objects to be modified only records the object names and locations, the list records can be spatially matched with the corresponding objects on the existing building surface or in the archive drawings to obtain the outline of the object to be modified. When the outline of the object to be modified and the point cloud evidence data use different spatial coordinate references, coordinate transformation can be performed based on the building axis, floor elevation, or spatial control points to ensure that the outline of the object to be modified corresponds to the existing building surface represented by the point cloud evidence data.

[0046] The action type can be determined based on the changes in the state of the object before and after the renovation. Removing existing components is classified as demolition; creating through holes or channels in existing components is classified as penetration; adding a structural layer to the existing surface is classified as covering; maintaining the existing components and their architectural functions is classified as retention; and changing the load transfer path between existing components is classified as load transfer. Through these correspondences, the design expression in the renovation plan can be converted into action information for determining the construction impact.

[0047] Subsequently, the scope of construction impact is determined based on the outline of the object to be modified, the margin for construction operations, and the protection range of adjacent surfaces to be preserved. Construction constraints are determined based on construction time restrictions, vibration restrictions, business interruption restrictions, and functional continuity requirements. Each of the modification action bodies is then generated from the action type, the scope of construction impact, and the construction constraints.

[0048] The construction operation margin refers to the space reserved outside the outline of the object to be modified for tool entry, personnel operation, component transfer, or temporary support. The adjacent preserved surface protection range can be determined based on whether the surrounding walls, floors, ceilings, equipment surfaces, and decorative surfaces need to be used or kept intact. In implementation, the outline of the object to be modified can be spatially expanded according to the operation distance required for the corresponding construction activity, and then the expanded space range can be merged with the adjacent preserved surface protection range to obtain the actual space range occupied or potentially affected by the renovation construction.

[0049] Construction time restrictions, vibration restrictions, and business closure restrictions can be obtained from construction requirements, building operation plans, or site management regulations. Functional continuity requirements can be determined based on fire protection, waterproofing, structural load-bearing capacity, electromechanical operation, or other building functions that need to be maintained during the renovation. Each renovation action is recorded with its corresponding action type, construction impact range, and construction constraints, and is associated with the corresponding renovation object, enabling the renovation action to participate in spatial intersection processing and characterizing the applicable construction conditions at the corresponding location.

[0050] Next, the boundaries are divided according to the material edge, the position of surface normal change, the position of ray path reachability change, the spatial use boundary, the building function continuity boundary, and the construction measure sharing boundary to obtain multiple interface micro-regions, and associated with the corresponding modification action body, spatial location, construction reachable range, and construction grid identifier.

[0051] In implementation, the aforementioned boundaries can be mapped to the interface area between the modified object and the existing building surface, and segmented along the boundary falling within the interface area. Material edges can be determined based on point cloud color, reflection intensity, or material zoning data recorded in the archives; the location of surface normal changes can be determined based on the orientation differences between adjacent local surfaces; the location of ray path reachability changes can be determined based on the actual reach of scanned rays in adjacent areas; spatial usage boundaries and building function continuity boundaries can be obtained from existing building archive data or on-site functional zoning data. A connected region enclosed by a combination of identical boundaries and with consistent internal boundary attributes can be defined as an interface micro-region. For linearly extending boundaries, continuous segments between adjacent segmentation locations can be defined as interface micro-regions.

[0052] The construction segmentation markers can correspond to original construction joints, floor segments, wall segments, ceiling segments, or other construction units that reflect structural continuity. The shared boundaries of construction measures are used to distinguish the space that can be shared by enclosures, scaffolding, temporary supports, negative pressure isolation areas, or specialized equipment. The accessible construction range can be determined based on site access, operating clearance, and adjacent reserved areas. Each interface micro-zone corresponds to a corresponding modification action through its spatial location, and the construction segmentation markers preserve the structural connection with adjacent interface micro-zones, allowing boundary areas with different observation conditions or construction treatment requirements to participate in subsequent processing.

[0053] Subsequently, using the construction year, building use, structural location, structural thickness range, and building function to be maintained from the existing building archive data as search criteria, multiple known structural candidates matching each interface micro-region are obtained from a preset hidden structural candidate library, and unknown other candidates are added to form a corresponding hidden structural candidate set; wherein, each hidden structural candidate includes candidate thickness, structural layer sequence, material characterization range, pipeline occupancy status, hazardous material identification, and building function to be maintained, and the unknown other candidates are used to characterize the hidden state that exceeds the interpretation range of each known structural candidate.

[0054] The pre-defined candidate database of concealed structures organizes typical structural records according to construction year, building use, and structural location, and saves corresponding structural thickness, material layers, internal pipelines, hazardous materials, and building functional attributes for each typical structural record. During candidate retrieval, the candidate range can be determined based on the construction year and building use of the building where the interface micro-region is located. Then, based on the structural location, structural thickness range, and the building function to be preserved, typical structural records matching the current interface micro-region are selected. During the retrieval phase, multiple known structural candidates that meet the criteria are retained, allowing each candidate to participate in the confidence determination after obtaining observational support.

[0055] The micro-area index of the interface can be denoted as , make the interface micro area The corresponding set of candidate hidden structures is denoted as .in, Used to identify different interface micro-regions Used to represent micro-areas of the interface The set of all corresponding hidden construction candidates. Each known candidate construct is associated with its candidate database record and search criteria to trace the archival basis of the corresponding candidate. Other unknown candidates are treated as a set. The candidate elements are used to accommodate hidden states that are outside the scope of the existing known candidate constructions. The prior confidence of the unknown other candidates and the specific methods for correcting them based on observational evidence can be further explained in the implementation of the confidence determination of hidden construction candidates.

[0056] Furthermore, for each interface micro-region, the observation support level of each of the hidden structure candidates corresponding to the interface micro-region is determined based on the ray path corresponding to the interface micro-region.

[0057] For the interface micro-area The ray path projected onto the existing building surface can be used to obtain the ray path that passes through the micro-region of the interface. For the set... For each hidden structure candidate, the arrival status of the corresponding ray path, the location of the measurement point, and the local surface observation results can be compared with the visible surface, candidate thickness, or structural layer represented by the candidate to determine the support level of the corresponding observation for the candidate. When the ray path is blocked and does not reach the corresponding position, this situation is treated as a lack of observational evidence; when the ray path has reached the corresponding position and the returned result differs from the candidate structure, the difference is used as the basis for reducing the observational support level of the candidate. The observational support level of each hidden structure candidate is stored together with the interface micro-region index and candidate identifier to distinguish the interpretability of different candidates in the same interface micro-region for the current point cloud evidence.

[0058] Figure 2 A flowchart illustrating an example of determining the degree of support for a hidden construction candidate observation in a method according to an embodiment of this application is shown.

[0059] like Figure 2 As shown, in step S210, the incident angle, measurement distance, and measurement variance of each ray path, as well as the reflection intensity and color of each measurement point, are obtained; for each of the hidden structure candidates corresponding to each interface micro-region, the visible side surface range is determined, and the ray path whose projection on the existing building surface passes through the interface micro-region is determined as the effective ray path.

[0060] For the interface micro-area From the candidate set of hidden structures corresponding to the micro-region of this interface Candidates for concealed structures were selected. ,in, This is a candidate index for covert construction. Based on the covert construction candidates... Based on the candidate thickness, structural layers, and spatial location of the interface micro-regions, the surface position of the candidate facing the scanning origin is determined, and then transformed to the scanning coordinate system used for the point cloud evidence data to obtain the visible side surface range. When considering point cloud measurement errors and local surface fitting errors, It can include a preset position tolerance range around the candidate surface.

[0061] When multiple scanning origins exist, the visible portion of the candidate structure can be determined separately for each scanning origin, and the corresponding scanning origin can be associated with the visible side surface range record. For the first... Determine if the first and return points fall within the ray path. At that time, the visible portion corresponding to the scanning origin of the ray path is used, thereby preserving the differences in observation direction at different scanning stations.

[0062] Each of the aforementioned ray paths is projected onto the existing building surface, and the projection of the ray path passes through the interface micro-region. At that time, the corresponding ray path is determined as the effective ray path of the interface micro-region, and the effective ray path set is composed of all effective ray paths. The incident angle can be determined based on the angle between the effective ray path direction and the local surface normal at the projection position. The measurement distance is the path length from the scanning origin to the corresponding measurement point. The measurement variance can be output by the scanning equipment or determined based on repeated measurement results at the same scanning station.

[0063] In step S220, the ray weight is determined based on the incident angle, measurement distance, and measurement variance of each effective ray path, such that the ray weight decreases as the incident angle, measurement distance, or measurement variance increases.

[0064] In one example, the angle of incidence is limited to to Within the range, the ray weights are determined according to the following formula: Equation (1) In the formula, For valid ray path index; For the first Ray weights of each effective ray path; , and The first The incident angle, measurement distance, and measurement variance of each effective ray path; A positive distance attenuation scale; The measurement variance stability constant is positive, and its unit is... same; and These are the cosine function and the natural exponential function, respectively.

[0065] Distance attenuation scale The measurement variance stability constant can be set based on the effective range of the scanning equipment or the calibration data obtained within the current building space. The settings can be based on the minimum measurement variance that the scanning equipment can resolve and the numerical stability requirements. After determining the ray weights according to the above relationships, ray paths corresponding to large incident angles, long measurement distances, or large measurement variances have a smaller contribution to the calculation of ray visibility coverage.

[0066] In step S230, for each effective ray path, the measurement point located on the effective ray path and having the smallest distance from the corresponding scanning origin is determined as the first return point; the ray visibility coverage is determined according to the proportion of the sum of the ray weights of the effective ray paths falling within the visible side surface range of the first return point to the sum of the ray weights of all effective ray paths; when the number of effective ray paths is zero, the ray visibility coverage is determined to be zero.

[0067] When the same ray path corresponds to multiple return measurement points, the distance from each return measurement point to the corresponding scanning origin is determined, and the measurement point with the smallest distance is determined as the first return point of the ray path. First return point Falling into the visible side surface range corresponding to the origin of the ray path scan At that time, the corresponding ray path forms a candidate for concealed structure. The visible surface supports the return point; when the first return point is located outside the visible side surface range, the corresponding ray path is still counted as a total of all valid ray paths.

[0068] The visible coverage of rays can be determined by the following formula: Equation (2) In the formula, For the interface micro area Corresponding hidden structure candidates The visible coverage of rays; This is a conditional indicator function; the value is set to [value] when the condition within the parentheses is true. The value is taken when the condition in parentheses is not met. ; A positive stability constant with the same unit relation to the ray weights, used to limit the sum of effective ray path weights from approaching... The numerical fluctuation over time. It can be set to a value less than the sum of the weights of effective ray paths under normal scanning conditions, so that it mainly acts on the boundaries of small weights or zero weights. Effective ray path set When the number of ray paths in the data is zero, directly... Determined as .

[0069] The visible coverage of the rays represents the weighted coverage of the candidate visible side surface by the effective ray paths. When the number of rays is the same, ray paths with incident directions close to the surface normal, shorter measurement distances, and smaller measurement variances have a larger weight, reducing the support offset caused by equalizing low-quality measurement points with more reliable measurement points.

[0070] In step S240, the thickness deviation is determined based on the difference between the distance between the front and back surface point pairs and the candidate thickness, and the order deviation is determined based on the relative order difference between the exposed edge layer sequence and the construction layer sequence; a preset thickness deficiency amount is used when the number of front and back surface point pairs is zero, and a preset layer deficiency amount is used when the number of observed layers in the exposed edge layer sequence is zero.

[0071] Point matching of front and back surfaces can be obtained from the interface micro-area Measurements are obtained from points on both sides. For measurement points that correspond in spatial position and have opposite surface normal directions, projection can be performed along the local surface normal, and two measurement points whose projection position deviation meets the preset pairing tolerance can be formed into a front and back surface point pair. Let the interface micro-region... The number of points on the inner front and back surfaces is , No. The distance between each pair of points on the front and back surfaces is Candidate for concealed structure The candidate thickness is .exist The thickness deviation can be determined using the following formula:

[0072] Equation (3) In the formula, For the interface micro area Corresponding hidden structure candidates The non-negative thickness deviation This is the index for the front and back surface point pairs. When the number of front and back surface point pairs is zero, it will be... The preset thickness of missing evidence is set. The preset pairing tolerance can be set according to the point cloud spatial resolution and surface fitting error, and the preset thickness of missing evidence can be set according to historical exposure data of similar structures, scanning error range, or engineering conservatism.

[0073] In cases where exposed edges are observable within the interface micro-region, identifiable material layers are arranged in a unified direction from the outer surface layer towards the internal structure, forming a sequence of exposed edge layers. This sequence of exposed edge layers is then compared with candidate concealed structures. The hierarchical sequence of the construction is matched in an ordered manner, and the matching cost is accumulated based on the hierarchical misalignment, relative order reversal, and candidate level missingness among comparable material layers to obtain the non-negative order default quantity. The matching cost of reversing the relative order can be higher than the matching cost of a single missing level. The specific matching cost can be determined based on historically revealed samples or pre-defined matching rules. When the number of observed levels in the exposed edge hierarchical sequence is zero, Set the number of missing certificates at the preset level.

[0074] In step S250, the layer edge closure is determined based on the thickness deviation and the sequential default, such that the layer edge closure decreases as the thickness deviation or the sequential default increases, and the visible radiation coverage and the layer edge closure are used together as the observation support level.

[0075] The edge closure can be determined by the following formula: Equation (4) In the formula, For the interface micro area Corresponding hidden structure candidates Layer edge closure; A positive thickness deviation scale, its unit is the same as... same; A positive sequential default scale, whose unit of value is the same as... same; and These are the positive thickness deviation stability constant and the order default stability constant, respectively. The unit of value and same, The unit of value and same. The thickness deviation distribution can be set based on similar exposed structures or the allowable deviation on site. The two stability constants can be set according to the historical distribution of the ordered matching cost, and respectively according to the recording accuracy and numerical stability requirements of the corresponding input.

[0076] The value is greater than and less than or equal to Thickness deviation Or the amount of sequential default As the value increases, the magnitude of the negative value in the exponential function increases, and the layer edge closure decreases accordingly. (Radiation visibility coverage) Characterizes the support of the ray path and first return point for the candidate visible side surface, and the layer edge closure. Characterizing the consistency between candidate thickness and tectonic hierarchy sequence and in-situ layer boundary evidence. These two factors serve as two components of observational support, along with the interface micro-region index. and concealed structure candidates The associated data is saved for use in the process of determining the credibility of hidden construction candidates.

[0077] Regarding the implementation details of determining the credibility of concealed structure candidates, forming concealed structure scenarios and combined scenarios in the methods of this application embodiments, in some examples of this application embodiments, on-site inspection records are obtained, and the consistency of material evidence is determined based on at least one of the reflection intensity and the color, as well as the degree of consistency between the material inspection results in the on-site inspection records and the material characterization range.

[0078] The on-site inspection records may include material sampling results, endoscopic inspection results, non-destructive testing results, or material identification results from revealed locations. When using on-site inspection records, priority should be given to records that correspond to the current interface micro-area through spatial location, component identification, or construction grid identification. For reflection intensity and color, comparisons can be made within a range of similar incident angles and measurement distances. The comparison range can be set according to the calibration results of the scanning equipment to reduce the impact of differences in scanning conditions on material matching results.

[0079] Useful data from reflection intensity, color, and material testing results are compared with candidate concealed structures. The material characterization range is matched, and the matching result is converted to [0, 1] to obtain the material evidence consistency. The higher the degree to which the matching results fall within the material characterization range, the better. The larger the value, the greater the consistency of the material evidence. For other unknown candidates, the degree to which the current material testing results exceed the characterization range of each known structural candidate material can be determined, thus giving material evidence outside the interpretation range of each known structural candidate a corresponding candidate.

[0080] Subsequently, the prior credibility is determined based on the source reliability of the existing building archive data, and the continuity of the breach is determined based on adjacent exposed structures and exposed structures within the same construction section.

[0081] As-built drawings, change records, maintenance records, and on-site verification records can each be assigned a source reliability level. Based on the correspondence between these records and interface micro-regions in terms of spatial location, structural component, and formation time, candidate concealed structures are identified. Determine the non-negative prior confidence level Archive records with high source reliability and a clear correspondence to the current interface micro-region grant high prior confidence to the corresponding candidates. When multiple archive records describe different construction states, candidates corresponding to each record can be retained, and prior confidence can be set separately according to the source reliability of each record. For unknown other candidates, a prior confidence level strictly greater than [value missing] is set. The prior credibility.

[0082] Candidates for concealed structures The continuity of the violation is determined by comparing it with adjacent exposed structures and exposed structures within the same construction section, based on differences in candidate thickness, structural layer, material type, or pipeline occupancy. . The value range is [0, 1]; the weaker the continuity between the candidate structure and the revealed structure, the better. The larger the number of revealed structures that can be mapped to the current interface micro-region, the greater the number of such structures. In such cases, a pre-set quantity of missing certificates or breaches of contract can be adopted based on similar construction segmentation samples or the degree of conservatism of the project.

[0083] Furthermore, based on the prior confidence level, positive corrections are made according to the observation support level and the consistency of the material evidence, negative corrections are made according to the continuous breach amount, and the confidence level of each of the hidden structure candidates is obtained according to the interface micro-region normalization correction result; wherein, a positive prior confidence level is set for the unknown other candidates.

[0084] For example, the amount of correction for candidate evidence can be determined first using the following formula. : Equation (5) The credibility of the hidden structure candidates is then determined according to the following formula: Equation (6) In the formula, For the interface micro area Corresponding hidden structure candidates The amount of candidate evidence correction; , , and These are the non-negative adjustment coefficients corresponding to visible radiation coverage, layer edge closure, consistency of material evidence, and continuity of breach, respectively. Candidate for concealed structure Credibility; For candidate set The candidate index for concealed structures is provided. Each adjustment coefficient can be calibrated based on interface micro-region samples with actual exposure results, or it can be set according to the reliability of different evidence sources.

[0085] visible radiation coverage Layer edge closure Or consistency of material evidence When increased, the amount of candidate evidence revised. Correspondingly increased; Continuous default volume When it increases, The corresponding decrease. The denominator corresponds to the candidate set of the same interface micro-region. Normalization is performed internally so that the sum of the confidence scores of all hidden structure candidates within the micro-region of the interface is . Since other candidates are unknown and have positive prior confidence, a positive normalized confidence can be obtained, allowing hidden states that are beyond the scope of explanation of each known constructed candidate to continue to participate in scenario generation.

[0086] Then, based on the construction grid identifier, the interface micro-regions that are continuously constructed within the same original construction grid are grouped into micro-region groups. Multiple shared construction states and positive normalized state probabilities are set for each micro-region group. A compatibility coefficient positively correlated with the compatibility degree is determined based on the compatibility degree between the hidden construction candidate and the shared construction state.

[0087] The microregion group index can be denoted as , will the The set of interface microregions within a microregion group is denoted as And denote the shared construction state set of this micro-region group as The same original construction grid can correspond to the same batch of materials, the same construction method, or the state of continuous pipeline occupation. For interface micro-regions that do not form a structural continuity relationship with other interface micro-regions, the interface micro-region can be regarded as a micro-region group containing only one member, so that all interface micro-regions belong to the corresponding micro-region group.

[0088] For each shared construction state Set positive state probability And ensure that the state probabilities in the same shared set of constructed states satisfy The state probability can be determined based on exposed construction grid samples from buildings of the same era and purpose; if the number of samples is insufficient, a positive initial state probability can be set first, and then adjusted according to the on-site exposure results.

[0089] Based on the candidate of concealed structure With shared construction state The compatibility factor is determined by the degree of conformity in terms of candidate thickness, structural layer, material type, or pipeline occupancy. The compatibility coefficient ranges from (0, 1). The closer the concealed structure candidate matches the shared structure state, the larger the compatibility coefficient. The graded or continuous values ​​of the compatibility coefficient can be determined based on the exposed construction grid samples or according to preset structural compatibility rules.

[0090] Furthermore, a shared construction state is selected for each of the micro-region groups, and a hidden construction candidate with positive confidence is selected from each of the interface micro-regions within the micro-region group, which is then combined with the corresponding modification action to form a hidden construction scenario.

[0091] For microgroups From the shared set of constructed states Select a shared construction state from the set, and in that state, select from the set. Candidate set corresponding to each micro-region of the interface A candidate concealed structure with a positive confidence level is selected. The selection result retains the candidate's confidence level and the compatibility coefficient between the candidate and the selected shared structure state. The selected candidate concealed structure is combined with the modification action acting on the corresponding interface micro-region to form the concealed structure scenario corresponding to that interface micro-region.

[0092] Multiple hidden structural scenarios within the same micro-region group correspond to the same shared structural state. Each interface micro-region still participates in the selection based on the candidate confidence level corresponding to its own observational evidence. The shared structural state is used to constrain common structural attributes within the original construction grid, while the candidate confidence level is used to preserve the local observational differences among the interface micro-regions.

[0093] All the hidden construction scenarios formed in the same selection are combined into a single scenario. The selection result is changed to form multiple combined scenarios. The scenario confidence is determined to be positively correlated with the state probability of the selected shared construction state, the confidence of the hidden construction candidate, and the compatibility coefficient.

[0094] In the same selection process, the shared structural states selected by each microregion group and the hidden structural candidates selected by each interface microregion together define a combined scenario. Changing the shared construction state of any micro-region group or the candidate selection result of any interface micro-region can form another combined scenario. When the number of candidates allows for traversal, the selection results can be traversed to form multiple combined scenarios; when the number of candidates is large, uniform sampling can be performed on all candidate combinations, and each sampling result can be used as a combined scenario instance, and participate in the subsequent cost assessment according to the corresponding scenario credibility; the sampling generation process no longer repeatedly weights the combined scenarios according to the state probability or candidate credibility.

[0095] The credibility of a combined scenario can be determined using the following formula: Equation (7) In the formula, For combined scenario indexes; To combine scenarios The credibility of the scenario; Number of microregions; To combine scenarios Regarding the first The shared structural state selected by each micro-region group; To combine scenarios For UI micro-areas The selected candidate indexes for concealed constructs. Since the combined scenario only selects concealed construct candidates with positive confidence, and both the state probability and compatibility coefficient are positive, therefore... It is a positive value. The shared structural state and local observational evidence are retained as common constraints on the same combined scenario. When all other terms remain constant, a decrease in any of the following—state probability, candidate confidence, or compatibility coefficient—reduces the scenario confidence of the corresponding combined scenario.

[0096] Regarding the implementation details of determining the disposal procedures, execution sequence, and cost of concealed structure scenarios in the methods of this application embodiments, in some examples of this application embodiments, the concealed structure candidates, modification action bodies, and building functions to be maintained corresponding to the concealed structure scenario are input into a preset disposal rule library to generate multiple disposal procedures including protection or isolation, verification and detection, selective demolition, temporary stabilization, relocation or repair, classified transportation, interface restoration, and functional re-verification.

[0097] The preset disposal rule library can store corresponding process triggering rules based on the structural level, pipeline occupancy status, hazardous material identification, and building function to be maintained among the candidates for concealed structures, as well as the action type, construction impact range, and construction constraints of the modification action. Each process triggering rule records at least the applicable concealed structure attributes, modification action attributes, the disposal process to be generated, and its triggering conditions. The process triggering rules can be preset according to building construction specifications, material disposal requirements, construction methods, and acceptance requirements.

[0098] For example, when a candidate concealed structure has a hazardous material label, protection or isolation procedures and verification and testing procedures can be generated before selective demolition; when a candidate concealed structure represents pipelines that need to maintain building function, relocation or repair procedures can be generated, and the implementation scope of the corresponding protection or isolation procedures can be determined according to functional continuity requirements. When multiple rules generate disposal procedures with the same content, a disposal procedure node can be retained, and the triggering conditions of each rule can be associated with this node, so that the disposal procedure is activated when any corresponding triggering condition is met.

[0099] Subsequently, based on the triggering conditions, completion conditions, and acceptance prerequisites of the aforementioned processing steps, a directed graph is established with the processing steps as nodes, pointing from the preceding processing steps to the subsequent processing steps. The directed connection relationship is used as the execution sequence relationship. The combination scenarios corresponding to the directed graph of processing steps containing directed loops are determined as invalid combination scenarios, and the remaining combination scenarios are determined as valid combination scenarios. The directed graph of processing steps in the valid combination scenarios is then topologically sorted.

[0100] When constructing the directed graph of the disposal process, disposal processes that can trigger another disposal process, provide completion conditions for another disposal process, or constitute preconditions for acceptance of another disposal process are designated as pre-disposal processes. These pre-disposal processes are then linked to their corresponding subsequent disposal processes. A directed path represents the execution order that the corresponding disposal processes must follow. When there is no direct or indirect pre-disposal relationship between multiple disposal processes, they can be executed in parallel if construction constraints permit.

[0101] Depth-first search, in-degree reduction, or other directed graph cycle detection methods can be used to identify directed cycles. When a directed graph of processing steps contains directed cycles, the corresponding processing steps constitute prerequisites for each other, making it difficult to form an execution sequence that satisfies all prerequisites. Therefore, the corresponding combination scenario is identified as an invalid combination scenario. For combination scenarios identified as directed acyclic graphs through cycle detection, topological sorting is performed according to node in-degree and directed connectivity to obtain at least one processing step sequence that conforms to the aforementioned execution order relationship.

[0102] Subsequently, the process resource type, metering base, shared domain, operation duration, amount of damage restored, and acceptance conditions are associated with the treatment process. The shared domain is determined based on the construction measure sharing boundary, operation window, and construction constraints. Treatment processes with the same shared domain and process resource type in the same concealed structure scenario are merged into resource activation units, and the total resource amount is obtained by accumulating the metering base.

[0103] The process resource type is used to distinguish the work teams, equipment, testing services, or construction measures required to implement the treatment process. The measurement base can be area, length, volume, quantity, shifts, or duration. The operation duration is determined based on the workload, resource input, and operation efficiency of the corresponding treatment process; the recovery damage amount is used to record the openings, finishes, base layers, or other affected parts that need to be repaired after the treatment process is implemented.

[0104] The shared domain represents the construction area where resources for the same process can be jointly activated and used continuously. The construction measure shared boundary is used to define the spatial scope of the shared domain, the operation time window is used to determine whether multiple treatment processes can be implemented continuously during the corresponding construction period, and the construction constraints are used to distinguish treatment processes that need to activate resources separately due to shutdown requirements, vibration restrictions, or functional continuity requirements.

[0105] No. Within the shared domain, the first The total amount of resources for a given process can be determined using the following formula: Equation (8) In the formula, For shared domain indexing; Index for process resource types; For the interface micro area Candidates for concealed structures The set of handling procedures for corresponding concealed structural scenarios; For the processing procedure index; For the first The types of process resources for each processing step; For the first The shared domain to which each processing step belongs; For the first The non-negative measurement base for each processing step; For the first Within the shared domain, the first The total amount of resources for a given process. Two conditional indication functions jointly define the measurement range, ensuring that the measurement base for processes with the same resource type and shared domain is included in the same resource activation unit.

[0106] Then, the one-time activation fee, resource unit price, and minimum activation quantity are obtained from the cost index data; when the total resource quantity is greater than zero, the resource activation fee is determined based on the one-time activation fee, the larger of the total resource quantity and the minimum activation quantity, and the resource unit price.

[0107] The one-time activation fee represents a fixed cost charged when the corresponding work group, equipment, testing service, or construction measure begins to be used. The minimum activation quantity represents the minimum measurement base corresponding to the one-time pricing of the corresponding process resource activation. When the total resource amount is zero, the resource activation fee for the corresponding resource activation unit is zero; when the total resource amount is greater than zero, a one-time activation fee is charged, and the larger of the actual total resource amount and the minimum activation quantity is used as the pricing resource amount.

[0108] No. Within the shared domain, the first The resource activation cost for a certain type of process resource can be determined according to the following formula: Equation (9) In the formula, For the first Within the shared domain, the first Resource activation costs for similar process resources; It is a one-time activation fee; This refers to the unit price of the resource. This is the minimum number of uses, and , and All are non-negative numbers; This is the function with the maximum value. When... Greater than zero and less than At that time, the resource usage portion is in accordance with Pricing; when Reaching or exceeding At that time, the resource usage portion is in accordance with Pricing. This allows disparate disposal procedures within the same resource activation unit to share a single activation pricing, ensuring that resource usage fees meet the minimum entry or minimum service requirements for the corresponding resource.

[0109] Subsequently, the time period cost and recovery cost are determined based on the operation duration and the amount of damage restored. The cost of the concealed structure scenario is determined by combining the resource activation cost and the additional cost of acceptance closure. In the cost assessment stage, the expected state of functional re-inspection is determined based on the acceptance conditions in the preset disposal rule base. When the expected state is a state to be rectified, a rectification process and a second functional re-inspection process are added, and topology sorting, resource metering, and cost determination are performed again.

[0110] Time-based costs can be determined based on the duration of each treatment procedure and the corresponding personnel, equipment, or downtime impact. Restoration costs can be determined based on the area of ​​openings, the area of ​​surface removal, the area of ​​base layer damage, or other restoration damage and their unit price. Acceptance loop surcharges are included in the costs of functional re-inspection and rectification loops, in addition to resource activation costs, time-based costs, and restoration costs.

[0111] The shared domains involved in the concealed construction scenario are grouped into a set. The corresponding process resource types are grouped into sets. The cost of the concealed construction scenario can be determined according to the following formula:

[0112] Equation (10) In the formula, For the interface micro area Candidates for concealed structures The cost of the corresponding concealed construction scenario; This refers to the set of process resource types involved in the corresponding concealed construction scenario; For the first The duration of each processing step; For the first The amount of damage recovered in each treatment process; The time-period cost corresponding to a unit operation duration; The recovery cost per unit of damage recovered; Additional costs for the non-negative acceptance closed loop of the corresponding concealed construction scenario.

[0113] When the expected state of functional re-testing is "passed," the cost of the concealed structure scenario is determined according to the current set of handling procedures. When the expected state of functional re-testing is "pending rectification," rectification procedures and re-functional re-testing procedures are generated based on the acceptance conditions corresponding to the items to be rectified, and the newly added procedures are added to the corresponding directed graph of handling procedures. The set of handling procedures is then updated. Afterwards, a new cycle check and topology sort are performed, and the shared domain and total resources are redefined based on the resource type, metering base, operation window, and construction constraints of the newly added process. Resource activation fees and additional costs associated with the closed-loop acceptance process This leads to the updated cost of the concealed construction scenario. This allows the disposal procedures to be included in the pricing according to their executable sequence, and incorporates the resource activation threshold, the sharing relationship of construction measures, and the rectification costs triggered by functional re-inspection into the same hidden structure scenario cost.

[0114] Figure 3 A flowchart illustrating an example of the operation of generating a modification cost assessment result according to the method of an embodiment of this application is shown.

[0115] like Figure 3 As shown, in step S310, the scenario credibility of each effective combination scenario is normalized to obtain the scenario weight.

[0116] Let the set of all valid combination scenarios be denoted as . For effective combination scenarios The scenario weights can be determined using the following formula:

[0117] Equation (11) In the formula, To effectively combine scenarios Context weights; This is an index of valid combination scenarios. The scenario credibility of each valid combination scenario is positive; therefore, in the set... When not empty, the denominator is greater than zero, and the sum of the scenario weights of each valid combination scenario is... .

[0118] gather When empty, the system outputs a flag indicating no valid combination scenario and returns to the directed graph check stage of the handling process. Based on the preconditions for forming a directed cycle, the system checks the handling rules or combination scenario generation conditions to determine valid combination scenarios again. The cost distribution calculation only includes valid combination scenarios.

[0119] In step S320, for each of the effective combination scenarios, the resource activation units that meet the sharing conditions are merged across interface micro-areas to obtain the total amount of combined resources, and the duplicate sharing cost is determined based on the non-negative cost difference between independent pricing and combined pricing; the total cost of the combination scenario is determined based on the visible engineering quantity cost, the cost of each of the hidden construction scenarios and the duplicate sharing cost.

[0120] Specifically, for each effective combination scenario, resource activation units with the same type of cross-interface micro-region shared domain and process resource, overlapping operation windows, and compatible construction constraints are merged to obtain the total combined resource amount; the non-negative difference between the sum of resource activation costs independently priced for each of the concealed structure scenarios and the resource activation cost priced by combining the total combined resources is determined as the duplicate sharing cost; the visible engineering quantity cost is determined based on the visible surface engineering quantity represented by the point cloud evidence data and the cost index data, and the duplicate sharing cost is deducted from the sum of the visible engineering quantity cost and the cost of each of the concealed structure scenarios to obtain the total cost of the combination scenario.

[0121] For effective combination scenarios Within the same shared domain, resource activation units of each interface micro-area are aggregated according to process resource type, and then merged into groups based on the condition that the operation windows have common overlapping intervals and construction constraints are compatible. The merged group index is denoted as... , will the Within the shared domain, the first The first type of process resource corresponding to the The set of interface micro-regions contained in each merged group is denoted as Each resource activation unit is assigned to only one merge group, and different effective combination scenarios are grouped separately.

[0122] No. The total combined resources of each merged group are determined according to the following formula: Equation (12) The resource activation cost, calculated based on the total combined resources, is determined according to the following formula: Equation (13) The cost of repeated sharing is determined according to the following formula: Equation (14) In the formula, For merged group indexes; about Valid Combinations of Summation Traversal All merge groups corresponding to the relevant shared domain and process resource type. To effectively combine scenarios The cost of repeated sharing; For the interface micro area The corresponding concealed construction scenario is activated independently. Within the shared domain, the first Resource activation costs when using resources for similar processes. Regarding... Valid Combinations of Summation Traversal All shared domains in the system, regarding The summation iterates through all process resource types within the corresponding shared domain. When the independent pricing cost is higher than the combined pricing cost, the difference is included in the duplicate sharing cost; when the combined pricing cost reaches or exceeds the independent pricing cost, the corresponding duplicate sharing cost is set to zero.

[0123] Based on the visible surface area, length, volume, or number of components represented by point cloud evidence data, corresponding pricing information is obtained from cost index data to obtain the visible engineering quantity cost. Therefore, the pricing items included in the project cost and the handling procedures costs included in each concealed structure scenario cost are recorded separately, with only one item included for the same pricing item. The total cost of the combined scenario can be determined according to the following formula:

[0124] Equation (15) In the formula, To effectively combine scenarios The total cost of the combined scenarios; The number of interface micro-regions. The cost of each concealed construction scenario is determined separately for each local scenario. The cost of repeated sharing caused by merging resources across interface micro-regions is then deducted from the sum of their costs, so that the one-time activation fee and minimum activation amount are included in the total cost of the combined scenario according to the actual sharing scope.

[0125] In step S330, the effective combined scenarios are arranged in ascending order of total cost of the combined scenarios and the scenario weights are accumulated to form a cost distribution; the expected cost and median budget are determined based on the cost distribution, and the total cost of the combined scenarios when the accumulated scenario weights reach a preset target confidence level is determined as the target quantile budget.

[0126] Rank the effective combination scenarios from lowest to highest total cost. For any cost value... The cumulative cost distribution function can be determined by the following formula:

[0127] Equation (16) In the formula, Values ​​are assigned to costs; Let be the cumulative cost distribution function, indicating that the total cost of the combined scenarios is no higher than 100%. The cumulative scenario weights of effective combined scenarios. When multiple effective combined scenarios have the same total cost, the corresponding scenario weights are accumulated to the same cost position.

[0128] Calculate each valid combination scenario one by one. Then sum them up to obtain the expected cost. For satisfying confidence level The quantile budget can be determined using the following formula:

[0129] Equation (17) In the formula, Confidence level The corresponding quantile budget; This is the infimum operation. When set to 0.5, the median budget is obtained. ;Will When set to a preset target confidence level, the target quantile budget is obtained. This preset target confidence level can be set according to the project stage and cost risk control requirements. For example, [the following text is incomplete and requires further context: "will..."] When set to 0.8, the target quantile budget is denoted as... .

[0130] In step S340, a risk reserve is determined based on the non-negative difference between the target quantile budget and the expected cost, and a tail cost is determined based on the effective combined scenarios whose total cost of combined scenarios reaches or exceeds the target quantile budget and their corresponding scenario weights.

[0131] Target quantile budget With expected cost The larger of the difference and zero is recorded as the risk reserve. When the target quantile budget is higher than the expected cost, the difference is recorded in the risk reserve; when the target quantile budget is equal to or lower than the expected cost, the risk reserve is zero.

[0132] The total cost of the combined scenarios reaches or exceeds the target quantile budget. Effective combination of scenarios constitutes a tail scenario set Tail costs can be determined using the following formula:

[0133] Equation (18) In the formula, For tail costs; This is the set of tail scenarios. The target quantile budget corresponds to at least one valid combinatorial scenario, therefore the set... Not empty, denominator is positive. The scenario weights of the tail scenarios are in the set. Internal renormalization is used to determine the weighted average cost for high-cost scenarios above the target quantile.

[0134] In step S350, the effective combined scenarios whose total cost of combined scenarios is closest to the median budget and the target quantile budget are compared to determine the candidate budget jump sources. The target quantile budget is then re-determined by removing the candidate budget jump sources one by one, thereby obtaining the incremental contribution of each candidate budget jump source and its corresponding interface micro-region.

[0135] Specifically, the effective combined scenarios whose total cost is closest to the median budget and the target quantile budget are determined as the median scenario and the target scenario, respectively. The additional handling procedures, one-time activation fees, and acceptance and rectification procedures added by the target scenario compared to the median scenario are determined as candidate budget jump sources. Each candidate budget jump source is removed one by one, and the target quantile budget is re-priced and determined. The difference between the target quantile budget before and after removal is determined as the incremental contribution and associated with the corresponding interface micro-area.

[0136] For example, the absolute difference between the total cost of each effective combination scenario and the median budget and the target quantile budget is calculated, and the effective combination scenario with the smallest absolute difference is selected. When multiple effective combination scenarios have the same smallest absolute difference, the effective combination scenario with the larger scenario weight can be selected as the corresponding scenario.

[0137] The directed graph of handling procedures, resource activation cost records, and acceptance and rectification records are compared between the median scenario and the target scenario. Handling procedures, one-time activation costs, or acceptance and rectification procedures that only appear in the target scenario are identified as candidate budget jump sources, and the corresponding interface micro-areas are determined based on the hidden construction scenarios that trigger the corresponding projects.

[0138] For each candidate budget jump source, a counterfactual pricing result is generated. When the candidate budget jump source is a disposal procedure or an acceptance / rectification procedure, the corresponding procedure is removed from the directed graph of disposal procedures containing that source, and subsequent procedures triggered solely by that procedure and having lost other triggering conditions are also removed. Directed connections are re-established based on the triggering conditions of the remaining procedures, followed by loop detection, topology sorting, resource metering, and cost determination. When the candidate budget jump source is a one-time activation fee, the corresponding fee is canceled in the resource activation unit containing that fee, and the total cost of the combined scenario is re-determined.

[0139] Each time a candidate budget jump source is removed, the other candidate budget jump sources remain unchanged. The cost distribution is reconstructed based on the re-priced total cost of the combined scenario and the original scenario weights, and the target quantile budget is determined again. The difference between the target quantile budget before and after removal is used as the incremental contribution of the corresponding candidate budget jump source and is associated with and saved in relation to the micro-area of ​​the interface that triggered that source. This ensures that changes in the target quantile budget correspond to specific interface locations and action items.

[0140] In step S360, multiple candidate exploration locations are generated based on the modification cost assessment results, and the exploration value of each candidate exploration location is determined.

[0141] Before generating candidate exploration locations, cost distribution, expected cost, median budget, target quantile budget, risk reserve, tail cost, sources of candidate budget jumps, and their incremental contributions are written into the modification cost assessment results. The sources of candidate budget jumps, incremental contributions, and their associated interface micro-regions are used to generate candidate exploration locations, while the cost distribution and tail cost are used to determine the exploration value of the corresponding candidate exploration locations.

[0142] Regarding the implementation details of generating candidate exploration locations, determining exploration value, and updating the renovation cost assessment results based on actual exploration results in the method of this application embodiment, in some examples of this application embodiment, candidate exploration locations and a set of possible exploration results are determined based on the candidate budget jump source, the incremental contribution, and the construction reachability; exploration results are determined based on the hidden structure candidates corresponding to the candidate exploration locations according to the effective combination scenarios, and the scenario weights that produce the same exploration result are summarized to obtain the probability of occurrence.

[0143] For an interface micro-region associated with a candidate budget jump source, a spatial location within the construction reachable range is determined where the corresponding concealed structure can be observed. This spatial location can be a location where inspection holes, endoscopic devices, material sampling devices, or partial exposure operations can be performed. When an interface micro-region has multiple reachable locations, one or more candidate exploration locations are generated based on the incremental contribution of the corresponding candidate budget jump source, the spatial relationship between each location and the concealed structure to be confirmed, and the exploration operation range.

[0144] The concealed structural states that can be distinguished by candidate exploration locations constitute the corresponding set of possible exploration results. When candidate exploration locations are used to confirm structural layers, possible exploration results can be classified according to the actual exposed structural layers; when used for material sampling or observation of internal pipelines, possible exploration results can be classified according to material identification results or pipeline occupancy status. Exploration states that exceed the interpretation range of each known concealed structural candidate are retained as unknown other results in the set of possible exploration results.

[0145] Let the candidate exploration location index be denoted as Candidate exploration locations The corresponding set of possible exploration results is denoted as and will effectively combine scenarios In candidate exploration locations The resulting exploration results are recorded as Based on the candidate concealed structures corresponding to the candidate exploration locations and the exploration representations that the candidate can generate, each effective combination scenario is mapped to a set. One possible outcome from the investigation. Possible outcome from the investigation. The probability of occurrence can be determined by the following formula:

[0146] Equation (19) In the formula, Index for possible exploration results; In order to explore candidate locations Obtain possible exploration results The probability of occurrence. Valid combination scenarios that produce the same exploration result have the same exploration result mapping; summing the corresponding scenario weights yields the probability of that exploration result occurring under the current cost distribution. When the set When covering all possible exploration results that can be generated by all valid combination scenarios, the sum of the probabilities of each possible exploration result is 1.

[0147] Subsequently, the exploration results are used as new observational evidence for the corresponding interface micro-region, and at least one of the candidate set of hidden structures and candidate evidence indicators is updated. The candidate evidence indicators include the degree of observational support, the consistency of material evidence, and the continuity violation amount. For the newly added hidden structure candidates, the positive prior confidence and the candidate evidence indicators are determined based on the newly added observational evidence and the existing building archive data, and the compatibility coefficient is determined based on the degree of compatibility with the shared structural state.

[0148] When determining the exploration value, for the set For each possible exploration result, compare that result with the candidate exploration location. The corresponding interface micro-regions and construction grid markers are associated to form a new set of observational evidence. When the exploration results reveal candidate thicknesses, front and back surface distances, or exposed layers, the layer edge closure of the corresponding hidden structure candidates is updated based on the revealed results. The observational support level is re-established; when new visible surface or ray path evidence is formed during the exploration process, the ray visibility coverage is further updated. When characterizing material type based on investigation results, update the consistency of material evidence. When the investigation results characterize the structural continuity relationship between adjacent locations or within the same construction section, update the continuity violation quantity. .

[0149] When updating the candidate set of concealed structures, candidates that can explain newly added observational evidence are retained, while candidates excluded by clear exploration results are removed from the current candidate set. If the existing candidate set does not yet record the actual exposed structural state, that structural state is added as a new concealed structure candidate. For new candidates, a positive prior confidence level is determined based on the reliability of the source of the new observational evidence and existing building archive data, and corresponding candidate evidence indicators are determined according to the structural thickness, structural layers, material type, pipeline occupancy status, and continuity relationship obtained from exploration. A compatibility coefficient is determined based on the degree of conformity between the new candidate and the shared structural state in terms of the above structural attributes. .

[0150] Then, based on the update results, the credibility of the hidden construction candidates is updated, the combined scenarios and processing procedures are regenerated, and the scenario credibility, scenario weight, repeated sharing cost, total cost of combined scenarios, cost distribution, median budget, target quantile budget and updated tail cost are redefined.

[0151] Targeting candidate exploration locations Each corresponding possible exploration result Candidate update results are established separately. Based on the updated observational support, consistency of material evidence, continuity violation, and prior confidence, the confidence of each hidden structure candidate within the corresponding interface microregion is re-determined. When the candidate set changes, the shared structural state and the hidden structural candidates corresponding to each interface microregion are reselected based on the updated candidate set and compatibility coefficient, forming hidden structural scenarios and combined scenarios.

[0152] When the candidates for concealed structures change, the updated candidates, modification actions, and building functions to be maintained are input into the preset handling rule library. The handling procedures are then regenerated, and a cycle check and topological sorting are performed on the directed graph of the handling procedures. Subsequently, the costs of concealed structure scenarios are re-determined sequentially. Contextual credibility Context weighting Duplicate sharing fees Total cost of combined scenarios and cost cumulative distribution function This leads to a re-determination of the median budget, target quantile budget, and tail cost. Candidate exploration locations are then considered. Obtain possible exploration results The tail cost determined at that time is recorded as the updated tail cost. Each possible exploration result corresponds to a set of candidate scenarios, combined scenarios, and cost update results.

[0153] Subsequently, the expected shrinkage of tail costs is determined based on the difference between the probability-weighted expected tail costs and the updated tail costs, and the comprehensive exploration cost is determined based on the direct exploration costs and the costs of repair, shutdown, and on-site disturbance.

[0154] For candidate exploration locations The updated tail cost corresponding to each possible exploration result. According to the probability of occurrence Weighted summation yields the expected tail cost after exploring this location. (Tail cost before exploration) The difference between the expected tail cost and the expected tail cost is the expected tail cost reduction. If the investigation results exclude high-cost concealed construction scenarios or reduce their scenario weight, the updated expected tail cost will decrease; a negative difference indicates that new observational evidence is expected to increase the weight of high-cost scenarios or introduce new high-cost treatment scenarios.

[0155] The comprehensive cost of exploration includes the direct costs of exploration, as well as the costs corresponding to the amount of repair work, downtime, and site disturbance caused by the exploration. Direct costs of exploration can be obtained from cost indicators of testing, sampling, or partial exposure projects; the amount of repair work is determined based on the openings, finish removal, or base layer damage caused by the exploration; downtime and site disturbance are determined based on the operating window and operational constraints of the candidate exploration location. The amount of repair work, downtime, and site disturbance are converted to cost scale using appropriate conversion factors.

[0156] Then, the initial exploration value is determined based on the relative relationship between the expected shrinkage of tail costs and the comprehensive exploration cost, and the initial exploration value is reduced according to the degree of overlap with the construction grid coverage of the selected exploration location to obtain the exploration value.

[0157] Candidate exploration locations The exploration value can be determined according to the following formula: Equation (20) In the formula, Candidate exploration locations Its exploratory value; , , and These are the non-negative direct costs of exploration, the amount of repair work, the time of business closure, and the amount of on-site disturbance; , and These are positive coefficients that convert the amount of repair work, downtime, and on-site disturbance to a cost scale. It is a positive cost stability constant, and its unit is the same as the overall exploration cost. Candidate exploration locations The overlap ratio between the selected exploration location and the construction grid covered by the selected exploration location ranges from 0 to 1.

[0158] coefficient The coefficient can be set according to the unit price of repair for the corresponding part. The coefficient can be set according to the building operation loss standard. It can be set according to the requirements for site protection and disturbance control. When selecting the exploration location for the first time, Set to 0; when multiple exploration locations have been selected, the overlap ratio is determined based on the union of the construction grids covered by the selected exploration locations. The fractional part in the previous formula represents the initial exploration value, and the factor... The initial value of exploration is attenuated based on the degree of overlap between construction grids. Therefore, if exploration evidence has already been obtained within the same construction grid, the ranking value for setting up exploration positions within that grid will be correspondingly reduced.

[0159] Furthermore, when the number of candidate exploration locations is zero, exploration is stopped and the transformation cost assessment result is output; when the number of candidate exploration locations is greater than zero, the maximum exploration value is determined; when the maximum exploration value is not greater than a preset value threshold or the difference between the target quantile budget and the median budget is not greater than a preset budget bandwidth, exploration is stopped and the transformation cost assessment result is output; when the maximum exploration value is greater than the preset value threshold and the difference between the target quantile budget and the median budget is greater than the preset budget bandwidth, the candidate exploration location with the highest exploration value is selected within the exploration budget and operational constraints for exploration, the actual exploration result is written to the corresponding interface micro-area, and the transformation cost assessment result and exploration value are updated.

[0160] When the number of candidate exploration locations is zero, the current renovation cost assessment result is directly output according to the corresponding conditional branch. When the number of candidate exploration locations is greater than zero, the maximum value is determined from the corresponding exploration values. The preset value threshold can be set according to the minimum expected reduction in tail costs required per unit of exploration cost, and the preset budget bandwidth can be set according to the budget fluctuation range allowed by the current project investment decision.

[0161] When the conditions for continuing exploration are met, candidate exploration locations that meet the remaining exploration budget, permitted downtime, and on-site operational requirements are grouped into a current feasible candidate set, from which the candidate exploration location with the highest exploration value is selected. If the current feasible candidate set is empty, the current modification cost assessment result is output. When multiple candidate exploration locations with the same exploration value exist, priority can be given to selecting the location with the lower overall exploration cost or the larger incremental contribution from the candidate budget jump source.

[0162] After the exploration is completed, the actual exploration results, along with the exploration time, spatial location, and corresponding interface micro-region identifier, are written into the corresponding interface micro-region as new observation evidence. Potential exploration results are used for calculating the exploration value before implementation, while actual exploration results are used to update the evidence record of the corresponding interface micro-region. The transformation cost assessment results are updated in the following order: candidate set update, candidate confidence update, combined scenario reconstruction, treatment procedure update, and cost distribution recalculation. The overlap ratio of the remaining candidate exploration locations is then re-determined based on the new set of selected exploration locations. and exploration value Repeat the process of selecting candidate locations, conducting actual exploration, and updating evaluation results until the conditions for stopping exploration are met.

[0163] This allows the limited exploration budget to be prioritized for locations where the source of budget jumps can be identified and where there is a high expected reduction in tail costs, while reducing the duplication of exploration information within the same construction grid.

[0164] Figure 4 A schematic diagram of the system operation mechanism of an example of a method for assessing the cost of renovating existing buildings by fusing three-dimensional point clouds and cost indicators according to an embodiment of this application is shown.

[0165] like Figure 4 As shown, the multi-source point cloud evidence data retains the scanning origin and ray path corresponding to the measurement points, and spatial evidence is extracted under occlusion constraints. The modification action body represents the action type, construction impact range, and construction constraints corresponding to demolition, penetration, covering, retention, and load transfer. The intersection of the modification action body with the existing building surface represented by the multi-source point cloud evidence data is calculated to determine the corresponding boundaries and divide them into multiple interface micro-regions. Observational evidence such as visible surfaces, front and back surface pairings, and exposed layer edges are extracted from these interface micro-regions.

[0166] Multiple hidden structure candidates are retrieved by combining the aforementioned observational evidence and existing building archive data. The credibility of each candidate is determined and updated, and combined scenarios are generated based on the shared structural status within the construction grid. Hazardous material identification and pipeline occupancy status are included in the subsequent process generation along with the corresponding hidden structure candidates. Remedial procedures are generated based on each combined scenario. A directed graph representing the sequential relationship of each remedial procedure is established, and the implementation of the corresponding procedures is constrained by shutdown restrictions and vibration restrictions.

[0167] The disposal rule base provides rules for generating disposal procedures and their prerequisite relationships, while cost index data provides pricing parameters such as one-time activation fees, resource unit prices, and minimum activation quantities. The disposal procedure costs corresponding to each combined scenario are summarized, and a cost distribution is formed based on the total cost of the combined scenarios and the corresponding scenario weights. (See figure...) , and This is used to indicate the low, median, and high quantile cost positions. Candidate exploration positions are determined based on the expected contraction of tail costs, and targeted exploration is carried out at the corresponding positions according to the exploration value.

[0168] After the actual exploration is completed, the results are written back to the corresponding interface micro-regions and hidden structure candidates, updating the observational evidence, candidate set, and candidate confidence. Then, the combined scenario and treatment procedures are regenerated, and the combined scenario cost, cost distribution, and exploration value of the remaining candidate exploration locations are re-determined. In this way, the modification cost assessment results can be updated in a closed loop as the hidden structure evidence obtained from the actual exploration is updated.

[0169] In existing building renovation projects, the true structural state of concealed mezzanines typically needs to be confirmed through verification testing, partial exposure, or demolition work. Publicly available historical settlement reports mainly record the final work volume and settlement amount, but are often insufficient to reconstruct the scanning origin, ray paths, occlusion relationships, surface inspection evidence, and quantile budget changes under different concealed structural scenarios during the cost assessment phase. To supplement the above-mentioned process verification data, this application employs a synthetic simulation playback framework based on digital twin inverse mapping. This framework combines the real building space topology with a parameterized concealed structural field, controlling ray path occlusion rate, prior construction year, probability of hazardous materials, spatial conflicts of electromechanical pipelines, and structural continuity relationships within construction compartments. This is used to repeatedly verify the response of observational evidence, generate combined scenarios, form cost distributions, and update the closed-loop exploration process. The simulation results are mainly used to verify the response patterns of the method under different boundary conditions. When applied to specific projects, anonymized on-site exposure records and settlement data can also be used to localize the credibility of candidate priors, the probability of shared structural states, and related pricing parameters. For each replay project, the pre-set complete hidden structure state in the digital twin, the actual triggered handling procedures, resource usage, and the cost parameters generated in this instance are combined and calculated to obtain the simulation baseline cost used to evaluate the assessment results of each method.

[0170] The simulation platform constructs 12 typical existing building prototype scenarios, covering construction years from 1980 to 2015. These include a comprehensive office building, a tertiary-level Class A hospital outpatient area and clean area, an experimental school, and a multi-story light industrial plant. Five types of renovation action scenarios are set up: demolition of fire-resistant partition walls, partial opening of high-density suspended ceilings, large-diameter ducts penetrating shear walls, transfer of micro-vibration loads from machine room foundations, and reconstruction of wet and dry partitions in restrooms. Each simulation playback sets 18 to 35 interface micro-areas. Point cloud evidence data is generated using lidar ray tracking, retaining the scanning origin, ray path, incident angle, measurement distance, and measurement variance for each measurement point; the ranging noise is measured using a standard deviation that meets the following requirements. The Gaussian distribution is measured in mm. The reflection intensity is determined based on the material's reflection characteristics, the approximate Lambertian angle response, and the measurement distance. The color data is generated by adding sensor perturbation to the material surface color. The amplitude added to the incident angle measurement value does not exceed the angle measurement range. Random disturbances. By arranging dynamic furniture, temporary material stacks, and existing fixed ceilings, the ray path obstruction rate is set to low level 12% to 20%, medium level 28% to 42%, and high level 50% to 68%, respectively. Invisible areas are generated according to the construction year, building use, and construction grid, including cross-layouts of strong and weak current cables, missing fireproof sealing or damaged rock wool interlayers, rusted base keel, finishing materials containing asbestos or other hazardous components, and temporary reinforcement components not shown. The hidden structures of each interface micro-area within the same construction grid are constrained by a shared structural state.

[0171] The cost index data is based on the comprehensive cost index of a municipality over the past three years, with the benchmark index set at 100. The unit price of materials is distributed using a log-normal distribution with the benchmark unit price as the median and a coefficient of variation of 6%. The efficiency of manual and mechanical operations is adjusted within 10% above and below the benchmark efficiency to accommodate on-site environmental disturbances. The disposal rule library includes a minimum activation fee of 5,000 yuan per instance for special dustproof negative pressure isolation zones, a minimum activation quantity of 0.5 shifts for non-destructive testing teams, a nighttime construction efficiency reduction coefficient of 1.45, and nighttime business hours closure constraints. The execution sequence of testing, protection or isolation, selective demolition, temporary stabilization, relocation or repair, classified transportation, interface restoration, and functional retesting is represented and verified through a directed graph of disposal procedures.

[0172] The comparison methods include: M1 is the manual bill of quantities method, where cost estimators measure based on two-dimensional drawings and on-site sampling results, and set an experience risk reserve of 8% to 12% of the total bill of quantities; M2 is the Scan-BIM-QTO (Scan-to-BIM Quantity Takeoff) method, which converts the point cloud into defined surface components and performs linear pricing after completing the occluded areas according to standard construction; M3 is the Geom-UQ (Geometric Uncertainty Quantification) method, which, based on M2, randomly propagates the component thickness and boundary position to form a cost range corresponding to geometric tolerances; M4 is the ICOC (Integrated Costing of Occluded Construction) method provided in the embodiments of this application, which integrates ray visibility coverage, layer edge closure, material evidence consistency, occluded construction candidate credibility, shared construction status, directed graph of disposal procedures, and resource activation costs to form a combined scenario cost distribution and implement targeted exploration and updates. All methods use the same basic cost index data and resource unit price to control the impact of differences in pricing data on the comparison results.

[0173] Evaluation indicators include (Mean Absolute Percentage Error) (Median Absolute Percentage Error), 90th percentile of the absolute percentage error, (Interval Coverage Probability, 80% cost range coverage for central government) (Mean Interval Width, the average width of the central 80% cost interval normalized by the simulation baseline cost) and (Weighted Interval Score). The central 80% cost range is comprised of... Sure, This is used to calculate the proportion of replay projects whose simulation baseline cost falls within the central 80% cost range. This is used to characterize the average ratio of the width of the central 80% cost range to the simulation baseline cost. This is used to comprehensively evaluate the deviation of each preset quantile budget, the width of the cost range, and the penalty when the simulation baseline cost exceeds the corresponding cost range. For loop probing, tail costs are also calculated. The ratio of the expected contraction to the overall exploration cost is used as the tail cost reduction efficiency per unit of overall exploration cost, which is then used to evaluate the extent to which targeted exploration reduces high-cost combination scenarios and budget tail risks.

[0174] Figure 5 A schematic diagram of the comparative experimental simulation results shows an example of the absolute percentage error distribution and the contribution of overpriced samples for different evaluation methods.

[0175] Figure 5 The horizontal axis of the curves in the subplot represents the absolute percentage error, and the vertical axis represents the empirical cumulative probability. Each curve and its surrounding shaded area represent the ECDF (Empirical Cumulative Distribution Function) curve and its 95% confidence band for the corresponding evaluation method. The mean absolute percentage errors for M1 to M4 are 18.7%, 14.9%, 13.6%, and 7.2%, respectively; the median absolute percentage errors are 15.9%, 12.1%, 10.8%, and 5.9%, respectively; and the 90th percentiles of the absolute percentage errors are 35.8%, 30.4%, 27.5%, and 14.8%, respectively.

[0176] Figure 5 The stacked bar chart below the figure shows the number of replay samples where the simulation baseline cost is more than 5% higher than the corresponding cost point estimate. For the evaluation method of the output cost distribution, the corresponding expected cost is used as the cost point estimate. Each replay sample is uniquely classified into one of the following categories based on the source contributing most to the cost underestimation difference: geometric variance, conventional pipeline relocation, cross-fire zone handling, and hazardous material special handling. The segment values ​​within each bar represent the number of replay samples classified into the corresponding category. The labels "Geometric Variance," "Conventional Pipeline Relocation," "Cross-Zone Fire Barrier," and "Special Hazard Handling" in the figure correspond to the four categories mentioned above. Geometric variance characterizes component thickness, boundary position, and their quantity conversion deviations; conventional pipeline relocation characterizes the migration, rerouting, and restoration handling resulting from spatial conflicts between power lines, water supply and drainage pipelines, or ventilation pipelines; cross-fire zone handling characterizes the isolation, sealing, and restoration handling triggered by cross-fire zone modifications; and hazardous material special handling characterizes the verification testing, isolation protection, and special handling caused by hazardous materials. The number of replay samples for the four categories corresponding to M1 are 16, 19, 21 and 32 respectively, totaling 88; the number of replay samples for the four categories corresponding to M2 are 9, 14, 24 and 35 respectively, totaling 82; the number of replay samples for the four categories corresponding to M3 are 25, 17, 14 and 18 respectively, totaling 74; and the number of replay samples for the four categories corresponding to M4 are 6, 7, 8 and 17 respectively, totaling 38.

[0177] Under the same basic cost index data and resource unit price conditions, the empirical cumulative distribution function curve of M4 reaches the same empirical cumulative probability earlier. Its mean absolute percentage error, median absolute percentage error, and 90th percentile of absolute percentage error are all lower than other assessment methods, and the number of replay samples with cost underestimation exceeding 5% is also relatively small. The comparison results correspond to the processing process of the method in the embodiments of this application, which comprehensively utilizes ray visibility coverage, layer edge closure, material evidence consistency, and concealed structure candidate credibility, and forms a cost distribution based on the directed graph of disposal procedures, resource activation conditions, and combined scenario weights. The statistical results of various replay samples further reflect that the costs of pipeline relocation, cross-fire zone disposal, and special disposal of hazardous materials triggered by discrete concealed structures can be included in the cost of the corresponding combined scenarios.

[0178] Figure 6 The diagram shows a comparative simulation result of the budget interval calibration results of different evaluation methods and an example of weighted interval scoring under different point cloud occlusion rates.

[0179] like Figure 6 As shown in subplot (a), the horizontal axis represents the nominal confidence level. The vertical axis represents the actual coverage rate, and the dashed diagonal line represents the ideal calibration relationship when the actual coverage rate equals the nominal confidence level. The shaded area around the curve corresponding to each assessment method represents the estimated fluctuation range of the corresponding actual coverage rate. Specifically, when the nominal confidence levels are 0.50, 0.60, 0.70, 0.80, 0.90, and 0.95, the actual coverage rates of M1 are 0.49, 0.55, 0.61, 0.69, 0.75, and 0.81, respectively; the actual coverage rates of M2 are 0.47, 0.50, 0.55, 0.583, 0.61, and 0.64, respectively; the actual coverage rates of M3 are 0.51, 0.57, 0.62, 0.667, 0.73, and 0.78, respectively; and the actual coverage rates of M4 are 0.52, 0.61, 0.71, 0.817, 0.89, and 0.93, respectively. All methods were run on the same 120 project-level replay samples. For methods such as M1 and M2, which only output cost point estimates, the upper and lower quantiles of the relative error corresponding to each nominal confidence level were determined using calibration samples that did not participate in the evaluation of the 120 replay projects, and budget intervals were constructed around the corresponding cost point estimates accordingly. For M3 and M4, the corresponding quantile budgets were determined based on the geometric uncertainty propagation results and the cost distribution of the combined scenarios, respectively.

[0180] At a nominal confidence level of 80%, the actual coverage rates of M2 and M3 are 58.3% and 66.7%, respectively, both lower than the nominal confidence level of 80%. The actual coverage rate of M4 is 81.7%, which is 1.7 percentage points lower than the nominal confidence level of 80%. Within the nominal confidence level range of 70% to 90%, the difference between the actual coverage rate and the corresponding nominal confidence level of M4 is smaller than that of other evaluation methods. The annotation in the figure regarding the relatively wide interval of M3 indicates that M3 uses a relatively wide budget interval when propagating geometric uncertainty, and therefore its prediction results need to be evaluated in conjunction with the interval width. In the high occlusion rate stratification, the actual coverage rate of M2 at a nominal confidence level of 80% is 43.8%, lower than the corresponding 58.3% of M2 in all replay samples, indicating that as the lack of point cloud observation evidence increases, the calibration bias of the budget interval of M2 increases accordingly.

[0181] like Figure 6 As shown in subfigure (b), the heatmap stratifies the playback samples according to low occlusion rate, medium occlusion rate, and high occlusion rate. Each column corresponds to M1, M2, M3, and M4, respectively. The values ​​and color depths within the matrix squares represent the corresponding stratification. Darker colors indicate larger weighted interval scores. Smaller weighted interval scores indicate a smaller overall result resulting from the combined effects of quantile budget deviation, budget interval width, and penalties for exceeding the budget interval in the simulation baseline cost. In the low occlusion rate stratification, the weighted interval scores for M1 to M4 are 0.084, 0.109, 0.097, and 0.061, respectively; in the medium occlusion rate stratification, they are 0.102, 0.141, 0.115, and 0.076, respectively; and in the high occlusion rate stratification, they are 0.129, 0.186, 0.124, and 0.092, respectively. As the point cloud occlusion rate increases, the weighted interval scores for all evaluation methods increase, with M4 showing lower weighted interval scores than the other evaluation methods in all three occlusion rate stratifications.

[0182] The above comparison results correspond to the processing procedure in the embodiments of this application, which identifies visible and obscured areas based on ray paths and updates the credibility of hidden structure candidates using ray visibility coverage, layer edge closure, material evidence consistency, and continuity default. Other unknown candidates and other hidden structure candidates with positive credibility participate in the generation of combined scenarios. A quantile budget is determined based on the handling procedures, resource activation conditions, total cost of the combined scenario, and scenario weights corresponding to each combined scenario, allowing the budget range to vary with the uncertainty of point cloud observation evidence and hidden structures.

[0183] Figure 7 A comparative simulation diagram illustrating the relationship between the overall exploration budget and normalized tail cost under different exploration strategies, and an example of recommended exploration locations, is shown. Figure 7 As shown, the horizontal axis represents the proportion of the overall exploration budget to the actual total value of the project, and the vertical axis represents the tail costs. The ratio to the actual total value of the project. In this playback example, the actual total value of the project referred to in the figure is represented by the total cost of the project simulation baseline, which is used as a common normalized baseline for the three exploration strategies. The vertical axis in the figure is marked in the form of CVaR (Conditional Value at Risk) at a confidence level of 0.8, which is used to represent the tail cost evaluation value corresponding to the above target confidence level. The gray dot curve, the blue square curve, and the red dot curve represent the random exploration strategy, the maximum point cloud geometric gap exploration strategy, and the tail value-oriented exploration strategy, respectively. The arrows on the curves indicate the iterative change direction of the exploration comprehensive budget and normalized tail cost after increasing the exploration position according to the corresponding strategy.

[0184] As the overall exploration budget increases, the normalized tail costs for all three exploration strategies gradually decrease. When the overall exploration budget accounts for approximately 0.3% of the total project simulation baseline cost, the normalized tail costs for the random exploration strategy, the maximum point cloud geometric gap exploration strategy, and the tail value-oriented exploration strategy are approximately 0.199, 0.174, and 0.130, respectively. The tail value-oriented exploration strategy exhibits a larger tail cost contraction within the lower exploration budget range. After approximately 0.3%, the slope of the curve gradually decreases, and further flattens after approximately 0.6%, indicating that the marginal tail cost contraction resulting from further increasing the exploration location decreases accordingly. In this replay example, when forming the actual recommended exploration plan, the upper limit of the available overall exploration budget is set to 0.3% of the total project simulation baseline cost, and the corresponding recommended budget operating points are marked with stars; the curves after 0.3% in the figure are used to show the tail cost change trend under the extended exploration budget condition. The specific conditions for stopping exploration in the project are still determined based on the maximum exploration value, the preset value threshold, the difference between the target quantile budget and the median budget, the preset budget bandwidth, the exploration budget, and operational constraints.

[0185] Reference Figure 7 The dashed line in the diagram connects the recommended budget working point to a partial schematic of the outpatient building simulation model, illustrating the spatial relationship between the dominant conflict interface, high-value exploration locations, and recommended exploration points. The dominant conflict interface is the micro-region associated with the hidden structure that triggers the candidate budget jump; the high-value exploration location is a candidate exploration location that can distinguish the corresponding high-cost combination scenario and is within the construction reach; the recommended exploration point is a candidate exploration location preferentially selected under the exploration budget and operational constraints corresponding to the recommended budget working point.

[0186] The tail value-oriented exploration strategy determines the initial exploration value of each candidate exploration location based on the expected tail cost reduction corresponding to each possible exploration result, as well as the direct exploration cost, repair work volume, downtime, and site disturbance. This initial value is then attenuated according to the degree of overlap with the selected exploration location's construction grid coverage, resulting in the final exploration value. Based on this exploration value, candidate exploration locations that are relevant to the source of candidate budget jumps and have higher exploration values ​​are prioritized. Compared to the random exploration strategy and the maximum point cloud geometric gap exploration strategy, the tail value-oriented exploration strategy, as illustrated, has a lower normalized tail cost under the same comprehensive exploration budget. Its exploration locations are concentrated in interface micro-regions that may change high-cost combination scenarios and the target quantile budget.

[0187] Ablation experiments showed that ray path evidence, layer closure evidence, shared structural state of micro-regions, and treatment procedures and resource utilization pricing all independently influenced the assessment results. When ray paths were excluded and observational evidence was determined solely based on point density, An increase of 2.1 percentage points. A decrease of 5.8 percentage points. The value increased by 0.045; when removing evidence of layer closure, the three indicators changed by 1.6 percentage points, -3.3 percentage points, and 0.031, respectively; when removing shared structural state constraints of micro-groups, the three indicators changed by 1.9 percentage points, -6.7 percentage points, and 0.052, respectively. When using a comprehensive unit price to replace directed graphs of disposal procedures, resource activation units, and closed-loop acceptance pricing, An increase of 5.4 percentage points. A decrease of 14.2 percentage points. An increase of 0.128 indicates a relatively large change in the indicator. The above results demonstrate that point cloud geometric evidence can constrain candidates for concealed structures, while the execution sequence of handling procedures, minimum activation amount, shared resource pricing, and acceptance / rectification loops further determine the nonlinear cost variations caused by discrete concealed structures.

[0188] The applicability of the method in this application is mainly limited by the completeness of prior data, the scale of the combined scenarios, and the type of observational evidence. For early buildings lacking archival records, with significant differences in construction practices, and insufficient disclosed samples, the confidence distribution of hidden structure candidates may tend to be dispersed, and the confidence of unknown other candidates will correspondingly increase; in some replay samples, In such cases, it is advisable to supplement observational evidence through verification testing, partial exposure, or non-destructive testing. As the number of interface micro-regions, hidden structural candidates, and shared structural states increases, the computation time and storage space required for combined scenario generation, treatment procedure deduction, and cost distribution recalculation will also increase, necessitating a combination of candidate screening and scenario sampling to control the computational scale. For deep physical damage completely enclosed within the structure and lacking identifiable evidence in surface geometry, reflection intensity, color, or on-site inspection records, the method in this application's embodiments cannot determine its actual state solely based on point cloud evidence; corresponding specialized inspection results need to be used as additional observational evidence. When applied to specific engineering projects, anonymized on-site exposure records and settlement data should also be used to localize and calibrate the candidate prior credibility, shared structural state probability, and cost index parameters.

[0189] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of combined actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Secondly, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application. In the above embodiments, the descriptions of each embodiment have their own emphasis; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0190] Figure 8 A structural block diagram of an example of an existing building renovation cost assessment system that integrates three-dimensional point clouds and cost indicators according to an embodiment of this application is shown.

[0191] like Figure 8 As shown, the existing building renovation cost assessment system 800, which integrates three-dimensional point cloud and cost indicators, includes a renovation action body generation unit 810, a construction candidate generation unit 820, a candidate credibility determination unit 830, a scenario and process determination unit 840, and a renovation cost assessment unit 850.

[0192] The renovation action body generation unit 810 is used to acquire point cloud evidence data of existing buildings, existing building archive data, renovation plans and cost index data, and generate multiple renovation action bodies according to the renovation plans; wherein, the point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point, and the ray path from the corresponding scanning origin to the corresponding measurement point, and the renovation action body is a spatial object that characterizes the action type, construction influence range and construction constraints of the renovation construction.

[0193] The candidate generation unit 820 is used to divide the boundary between each of the modified action bodies and the existing building surface represented by the point cloud evidence data into multiple interface micro-regions, and generate multiple hidden construction candidates for each interface micro-region according to the existing building archive data.

[0194] The candidate confidence determination unit 830 is used to determine the confidence of each of the hidden structure candidates for each interface micro-region based on the degree of observational support of the ray path corresponding to the interface micro-region for each of the interface micro-regions.

[0195] The scenario and process determination unit 840 is used to select one hidden structure candidate from the multiple hidden structure candidates corresponding to each interface micro-region, combine the hidden structure candidate selected in each interface micro-region with the corresponding modification action body to form a hidden structure scenario corresponding to each interface micro-region, and determine the multiple processing procedures to be performed under each hidden structure scenario and their execution order.

[0196] The renovation cost assessment unit 850 is used to calculate and merge the costs of multiple treatment procedures under each of the concealed structure scenarios based on the cost index data and according to the execution sequence, to obtain the cost of each concealed structure scenario, and to generate a renovation cost assessment result based on the credibility of the concealed structure candidate corresponding to each concealed structure scenario and the cost of the concealed structure scenario; wherein, the renovation cost assessment result includes cost distribution and quantile budget.

[0197] In some embodiments, this application provides a non-volatile computer-readable storage medium storing one or more programs including execution instructions. The execution instructions can be read and executed by electronic devices (including but not limited to computers, servers, or network devices) to perform the steps of any of the above-described methods for evaluating the cost of existing building renovation that integrates three-dimensional point clouds and cost indicators.

[0198] In some embodiments, this application also provides a computer program product, the computer program product including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of any of the above-described methods for evaluating the cost of existing building renovation by integrating three-dimensional point clouds and cost indicators.

[0199] In some embodiments, this application also provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of an existing building renovation cost assessment method that integrates three-dimensional point clouds and cost indicators.

[0200] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0201] The electronic devices in this application can exist in various forms, including but not limited to: mobile communication devices, ultra-mobile personal computer devices, portable entertainment devices, or other airborne electronic devices with data interaction functions.

[0202] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the cost of renovating existing buildings by integrating three-dimensional point clouds and cost indicators, characterized in that, The method includes: The system acquires point cloud evidence data of existing buildings, existing building archive data, renovation plans and cost index data, and generates multiple renovation action objects based on the renovation plans. The point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point, and the ray path from the corresponding scanning origin to the corresponding measurement point. The renovation action objects are spatial objects that characterize the action type, construction impact range and construction constraints of the renovation construction. The boundary between each of the aforementioned modification actions and the existing building surface represented by the point cloud evidence data is divided into multiple interface micro-regions, and multiple hidden structure candidates are generated for each of the aforementioned interface micro-regions based on the existing building archive data. For each interface micro-region, the confidence level of each hidden structure candidate is determined based on the observation support level of each hidden structure candidate corresponding to the interface micro-region by the ray path corresponding to the interface micro-region. One hidden structure candidate is selected from the multiple hidden structure candidates corresponding to each interface micro-region. The hidden structure candidate selected in each interface micro-region is combined with the corresponding modification action body to form a hidden structure scenario corresponding to each interface micro-region. The multiple processing steps to be executed under each hidden structure scenario and their execution order are determined. Based on the cost index data, the costs of multiple treatment procedures under each concealed structure scenario are calculated and combined according to the execution sequence to obtain the cost of each concealed structure scenario. Based on the credibility of the concealed structure candidate corresponding to each concealed structure scenario and the cost of the concealed structure scenario, a renovation cost assessment result is generated; wherein, the renovation cost assessment result includes cost distribution and quantile budget.

2. The method according to claim 1, characterized in that, The generation of multiple modification action bodies according to the modification scheme includes: Extract the outline of the object to be modified and the construction requirements from the modification plan, and determine the action type of each object to be modified; wherein, the action type includes demolition, penetration, covering, retention or load transfer; The scope of construction impact is determined based on the outline of the object to be modified, the margin for construction operations, and the protection range of adjacent surfaces to be preserved. Construction constraints are determined based on the restrictions on construction time, vibration, business interruption, and functional continuity requirements. Each of the modification action bodies is generated from the action type, the scope of construction impact, and the construction constraints. The process involves dividing the boundary between each of the modified action objects and the existing building surface represented by the point cloud evidence data into multiple interface micro-regions, and generating multiple hidden structure candidates for each interface micro-region based on the existing building archive data, including: The boundaries are divided according to the material edge, the position of surface normal change, the position of ray path reachability change, the spatial use boundary, the building function continuity boundary and the construction measure sharing boundary to obtain multiple interface micro-regions, and associated with the corresponding modification action body, spatial location, construction reachable range and construction grid identifier; Using the construction year, building use, structural location, structural thickness range, and building function to be maintained from the existing building archive data as search criteria, multiple known structural candidates matching each interface micro-region are obtained from a preset hidden structural candidate library, and unknown other candidates are added to form a corresponding hidden structural candidate set; wherein, each hidden structural candidate includes candidate thickness, structural layer sequence, material characterization range, pipeline occupancy status, hazardous material identification, and building function to be maintained, and the unknown other candidates are used to characterize the hidden state that exceeds the interpretation range of each known structural candidate; For each interface micro-region, the observation support level of each hidden structure candidate corresponding to the interface micro-region is determined based on the ray path corresponding to the interface micro-region.

3. The method according to claim 2, characterized in that, For each interface micro-region, determining the observational support level of each of the hidden structure candidates corresponding to the interface micro-region based on the ray path corresponding to the interface micro-region includes: The incident angle, measurement distance, and measurement variance of each ray path, as well as the reflection intensity and color of each measurement point, are obtained; for each of the hidden structure candidates corresponding to each interface micro-region, the visible side surface range is determined, and the ray path whose projection on the existing building surface passes through the interface micro-region is determined as the effective ray path; The ray weights are determined based on the incident angle, measurement distance, and measurement variance of each effective ray path, such that the ray weights decrease as the incident angle, measurement distance, or measurement variance increases. For each effective ray path, the measurement point located on the effective ray path and having the smallest distance from the corresponding scanning origin is determined as the first return point; the ray visibility coverage is determined according to the proportion of the sum of the ray weights of the effective ray paths falling within the visible side surface range of the first return point to the sum of the ray weights of all effective ray paths; when the number of effective ray paths is zero, the ray visibility coverage is determined to be zero; The thickness deviation is determined based on the difference between the distance between the front and back surface point pairs and the candidate thickness; the order deviation is determined based on the relative order difference between the exposed edge layer sequence and the construction layer sequence; a preset thickness deficiency is used when the number of front and back surface point pairs is zero; and a preset layer deficiency is used when the number of observed layers in the exposed edge layer sequence is zero. The layer edge closure is determined based on the thickness deviation and the sequential default, such that the layer edge closure decreases as the thickness deviation or the sequential default increases, and the visible radiation coverage and the layer edge closure are used together as the observation support level.

4. The method according to claim 3, characterized in that, For each interface micro-region, the confidence level of each hidden structure candidate is determined based on the observational support level of the ray path corresponding to the interface micro-region for each hidden structure candidate corresponding to the interface micro-region, including: Obtain on-site inspection records, and determine the material evidence consistency degree based on at least one of the reflection intensity and the color, as well as the consistency degree between the material inspection results in the on-site inspection records and the material characterization range; The prior credibility is determined based on the reliability of the source of the existing building archive data, and the continuous breach amount is determined based on adjacent exposed structures and exposed structures within the same construction section. Based on the prior confidence level, positive corrections are made according to the observation support level and the consistency of the material evidence, and negative corrections are made according to the continuous breach amount. The confidence level of each of the hidden structure candidates is obtained according to the interface micro-region normalization correction result; wherein, a positive prior confidence level is set for the unknown other candidates. The step of selecting one concealed structure candidate from a plurality of concealed structure candidates corresponding to each interface micro-region, and combining the selected concealed structure candidate with the corresponding modification action body to form a concealed structure scenario corresponding to each interface micro-region includes: According to the construction grid identifier, the interface micro-regions that are continuously constructed within the same original construction grid are grouped into micro-region groups. Multiple shared construction states and positive normalized state probabilities are set for each micro-region group. A compatibility coefficient positively correlated with the compatibility degree is determined based on the compatibility degree between the hidden construction candidate and the shared construction state. For each of the micro-region groups, a shared construction state is selected, and a hidden construction candidate with positive confidence is selected from each of the interface micro-regions in the micro-region group, which is then combined with the corresponding modification action body to form a hidden construction scenario. All the hidden construction scenarios formed in the same selection are combined into a single scenario. The selection result is changed to form multiple combined scenarios. The scenario confidence is determined to be positively correlated with the state probability of the selected shared construction state, the confidence of the hidden construction candidate, and the compatibility coefficient.

5. The method according to claim 4, characterized in that, The determination of the multiple processing steps to be performed and their execution sequence in each of the concealed construction scenarios includes: The candidate concealed structures, the modification action objects, and the building functions that need to be maintained corresponding to the concealed structure scenario are input into the preset disposal rule library to generate multiple disposal procedures, including protection or isolation, verification and testing, selective demolition, temporary stabilization, relocation or repair, classified transportation, interface restoration, and functional re-verification. Based on the triggering conditions, completion conditions, and acceptance prerequisites of the aforementioned processing steps, a directed graph is established with the processing steps as nodes, pointing from the preceding processing steps to the subsequent processing steps. The directed connection relationship is used as the execution sequence relationship. The combination scenarios corresponding to the directed graph of processing steps containing directed loops are determined as invalid combination scenarios, and the remaining combination scenarios are determined as valid combination scenarios. The directed graph of processing steps in the valid combination scenarios is then topologically sorted. The process resource type, metering base, shared domain, operation duration, damage recovery amount and acceptance conditions are associated with the treatment process. The shared domain is determined according to the construction measure shared boundary, operation window and construction constraints. Treatment processes with the same shared domain and process resource type in the same concealed structure scenario are merged into resource activation units, and the total resource amount is obtained by accumulating the metering base. Based on the cost index data, the costs of multiple treatment procedures under each concealed structure scenario are calculated and combined according to the execution sequence to obtain the cost of each concealed structure scenario, including: The one-time activation fee, resource unit price, and minimum activation quantity are obtained from the cost index data; when the total resource quantity is greater than zero, the resource activation fee is determined based on the one-time activation fee, the larger of the total resource quantity and the minimum activation quantity, and the resource unit price. The time period cost and recovery cost are determined based on the operation duration and the amount of damage to be restored. The cost of the concealed structure scenario is determined by combining the resource activation cost and the additional cost of the acceptance closure loop. When the functional re-inspection result is in a state of pending rectification, a rectification process and a second functional re-inspection process are added, and the topology sorting, resource metering and cost determination are performed again.

6. The method according to claim 5, characterized in that, The step of generating a modification cost assessment result based on the credibility of the concealed structure candidate corresponding to each concealed structure scenario and the cost of the concealed structure scenario includes: The scenario credibility of each effective combination scenario is normalized to obtain the scenario weight; For each effective combination scenario, resource activation units with the same type of shared domain and process resource, overlapping operation windows, and compatible construction constraints are merged across interface micro-regions to obtain the total combined resource amount; the non-negative difference between the sum of resource activation costs independently priced for each of the hidden structure scenarios and the resource activation cost priced by combining the total combined resources is determined as the duplicate sharing cost; the visible engineering quantity cost is determined based on the visible surface engineering quantity represented by the point cloud evidence data and the cost index data, and the duplicate sharing cost is deducted from the sum of the visible engineering quantity cost and the cost of each of the hidden structure scenarios to obtain the total cost of the combination scenario; The effective combined scenarios are sorted in ascending order of total cost and the scenario weights are accumulated to form a cost distribution. The expected cost and median budget are determined based on the cost distribution. The total cost of the combined scenarios when the accumulated scenario weights reach a preset target confidence level is determined as the target quantile budget. The risk reserve is determined based on the non-negative difference between the target quantile budget and the expected cost, and the tail cost is determined based on the effective combined scenarios whose total cost of combined scenarios reaches or exceeds the target quantile budget and their corresponding scenario weights. The effective combined scenarios whose total cost is closest to the median budget and the target quantile budget are respectively identified as the median scenario and the target scenario. The additional handling procedures, one-time activation fees, and acceptance and rectification procedures added by the target scenario compared to the median scenario are identified as candidate budget jump sources. Each candidate budget jump source is removed one by one, and the target quantile budget is re-priced and determined. The difference between the target quantile budget before and after removal is identified as the incremental contribution and associated with the corresponding micro-area of ​​the interface. Based on the transformation cost assessment results, multiple candidate exploration locations are generated, and the exploration value of each candidate exploration location is determined.

7. The method according to claim 6, characterized in that, The step of generating multiple candidate exploration locations based on the modification cost assessment results and determining the exploration value of each candidate exploration location includes: Candidate exploration locations and a set of possible exploration results are determined based on the candidate budget jump sources, the incremental contributions, and the construction reachability; exploration results are determined based on the hidden structure candidates corresponding to the candidate exploration locations according to the effective combination scenarios; and the scenario weights that produce the same exploration result are summarized to obtain the probability of occurrence. The exploration results are used as new observation evidence for the corresponding interface micro-region. At least one of the candidate set of hidden structures and candidate evidence indicators is updated. The candidate evidence indicators include the degree of observational support, the consistency of material evidence, and the continuity violation amount. For the new hidden structure candidates, the positive prior confidence and the candidate evidence indicators are determined based on the new observation evidence and the existing building archive data. The compatibility coefficient is determined based on the degree of compatibility with the shared structural state. The credibility of the hidden construction candidate is updated based on the update results, the combined scenario and processing procedure are regenerated, and the scenario credibility, scenario weight, repeated sharing cost, total cost of combined scenario, cost distribution, median budget, target quantile budget and updated tail cost are redefined. The expected reduction in tail costs is determined based on the difference between the probability-weighted expected tail costs and the updated tail costs. The overall cost of exploration is determined based on the direct exploration costs and the costs of repair, downtime, and on-site disturbance. The initial exploration value is determined based on the relative relationship between the expected shrinkage of tail costs and the comprehensive exploration cost. The initial exploration value is then reduced according to the degree of overlap between the initial exploration value and the construction grid coverage of the selected exploration location to obtain the exploration value. When the number of candidate exploration locations is zero, exploration is stopped and the transformation cost assessment result is output. When the number of candidate exploration locations is greater than zero, the maximum exploration value is determined. When the maximum exploration value is not greater than a preset value threshold or the difference between the target quantile budget and the median budget is not greater than a preset budget bandwidth, exploration is stopped and the transformation cost assessment result is output. When the maximum exploration value is greater than the preset value threshold and the difference between the target quantile budget and the median budget is greater than the preset budget bandwidth, the candidate exploration location with the highest exploration value is selected within the exploration budget and operational constraints and exploration is carried out. The actual exploration result is written to the corresponding interface micro-area, and the transformation cost assessment result and exploration value are updated.

8. A cost assessment system for the renovation of existing buildings that integrates three-dimensional point clouds and cost indicators, characterized in that, The system includes: The renovation action body generation unit is used to acquire point cloud evidence data of existing buildings, existing building archive data, renovation plan and cost index data, and generate multiple renovation action bodies according to the renovation plan; wherein, the point cloud evidence data includes the three-dimensional coordinates of multiple measurement points, the scanning origin corresponding to each measurement point, and the ray path from the corresponding scanning origin to the corresponding measurement point; the renovation action body is a spatial object that represents the action type, construction impact range and construction constraints of the renovation construction. A candidate generation unit is constructed to divide the boundary between each of the modified action objects and the existing building surface represented by the point cloud evidence data into multiple interface micro-regions, and to generate multiple hidden structure candidates for each interface micro-region according to the existing building archive data. The candidate confidence determination unit is used to determine the confidence of each of the hidden structure candidates for each interface micro-region based on the observation support degree of each of the hidden structure candidates corresponding to the interface micro-region by the ray path corresponding to the interface micro-region. The scenario and process determination unit is used to select one hidden structure candidate from the multiple hidden structure candidates corresponding to each interface micro-region, combine the hidden structure candidate selected in each interface micro-region with the corresponding modification action body to form a hidden structure scenario corresponding to each interface micro-region, and determine the multiple processing procedures to be executed under each hidden structure scenario and their execution order. The renovation cost assessment unit is used to calculate and merge the costs of multiple treatment procedures under each concealed structure scenario based on the cost index data and according to the execution sequence, to obtain the cost of each concealed structure scenario, and to generate a renovation cost assessment result based on the credibility of the concealed structure candidate corresponding to each concealed structure scenario and the cost of the concealed structure scenario; wherein, the renovation cost assessment result includes cost distribution and quantile budget.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor, characterized in that the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-7.

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