A method for applying digital models throughout the entire process of interior decoration projects

Abstract

The present invention discloses a full-process application method for a digital model of an interior decoration project, which relates to the technical field of building information models. The method comprises the following steps: deploying a decoration semantic mapping engine, standardizing multi-source model attributes into a unified attribute dictionary and generating a neutral semantic package; splitting the neutral semantic package into single component nodes, storing them in a temporal graph to implement object-level version management; generating incremental packages by changing monitoring streams and fingerprint comparisons; utilizing a conflict resolution pipeline to automatically merge decisions or generate a list to be confirmed; synchronizing updated fragments to a scheduling system and a logistics interface via a service bus; and finally, dynamically superimposing scan data and sensor readings based on component fingerprints at the on-site terminal, and writing back feedback to correct mapping rules. The method's technical features encompass semantic management, data synchronization, and on-site feedback. It improves semantic consistency and data accuracy, effectively suppresses information islands, and improves the management efficiency of the entire interior decoration project cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a full-process application method of a digital model for interior decoration engineering. Background Art

[0002] Throughout the entire design and operation process of interior decoration projects, project teams generally use building information models (BIMs) as the data hub, hoping to collaborate on conceptual design, detailed design, and on-site construction using diverse modeling software. However, when models are converted between various proprietary and open model formats, fine-grained semantics such as common finish layer names, material processes, and installation sequences are often lost or omitted, creating an awkward situation where "the geometry remains, but the meaning is incomplete." This gap is particularly sensitive during the decoration phase, where components such as finishes, soft furnishings, and electromechanical terminals are rapidly iterative and numerous. Once semantics are broken, rapid on-site changes cannot be promptly replicated back to the model, leading to inaccurate construction scheduling and material coordination. The industry is beginning to experiment with tracking model object changes using ontology mapping and graph databases, reducing version management granularity from "entire document" to "individual component" to mitigate semantic loss and version bifurcation. Meanwhile, the gradual implementation of point cloud scanning-driven digital twins and real-time on-site updates has further exacerbated the problem of model semantic inconsistency: if the scanned data cannot be semantically aligned with the original model, the model cannot accurately reflect the actual working conditions.

[0003] A search revealed a patent application with publication number CN111597170A, which discloses a method for losslessly constructing a spatial semantic database from BIM models. This method, which falls within the technical field of spatial geographic information data conversion methods, includes creating a new table, defining table structure fields, defining relationships between tables, and organizing BIM objects into table records according to the defined database structure; ultimately, building a lossless BIM spatial semantic database. This method can directly preserve the BIM's attribute structure and geometric data, manage BIM models as a spatial semantic database, and express semantic relationships between entities through the database's organizational structure. This method enables the lossless introduction of BIM models into the spatial information field, providing data support for visualization applications and intelligent analysis.

[0004] In combination with the above background and existing technology:

[0005] Because interior decoration models possess richer and more refined semantic fields than the main structure, the lack of object-level version management during repeated conversion and merging of multi-source models can lead to the phenomenon of the same component being "visible in shape but unrecognizable" both on the design side and on the site. This mismatch stems primarily from the loss of key attributes describing finish levels, packaging methods, and installation cadence during format conversion. Furthermore, the lack of a change tracking mechanism prevents attribute updates from being written back. Ultimately, progress reconciliation, material ordering, and IoT sensor binding all rely on manual verification or even remodeling, which wastes costs and undermines the real-time value of digital twins. Therefore, it is imperative to establish a unified mapping strategy and object-level version control mechanism for decoration-specific semantics. This ensures that the model accurately preserves component identity and attributes at all stages and can be instantly reused for on-site scanning, schedule scheduling, and operation and maintenance monitoring, fundamentally addressing the industry pain points of model semantic disconnection and version drift. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a full-process application method for digital models of interior decoration projects. By deploying a decoration semantic mapping engine, the multi-source model attributes are standardized into a unified attribute dictionary and a neutral semantic package is generated; the neutral semantic package is split into single component nodes and stored in a temporal graph to achieve object-level version management; incremental packages are generated through change monitoring streams and fingerprint comparisons; conflict resolution pipelines are used to automatically merge decisions or generate a list to be confirmed; updated fragments are synchronized to the scheduling system and logistics interface through a service bus; finally, the on-site terminal dynamically overlays scan data and sensor readings based on component fingerprints, and writes back feedback to correct the mapping rules. Its technical features cover semantic management, data synchronization, and on-site feedback. It improves semantic consistency and data accuracy, and improves the efficiency of full-cycle management of interior decoration projects; thereby solving the technical problems recorded in the background technology.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for applying a digital model of an interior decoration project throughout the entire process, comprising: deploying a decoration semantic mapping engine on a design collaboration server, mapping component attributes in a multi-format model to a unified attribute dictionary and generating a neutral semantic package through attribute extraction, standardization, and mapping rules;

[0010] The neutral semantic package is split into single component nodes, which are stored in a temporal graph in combination with the creator, source format and spatial location fingerprint to form a traceable object family.

[0011] When a new model or point cloud fragment is submitted by the design end or the on-site scanning end, the change monitoring flow is triggered, and an incremental package containing only the different attributes and geometry is generated based on the spatial positioning fingerprint comparison;

[0012] Based on the incremental package, the attribute consistency calibration index and component active change entropy index are calculated. The automatic merge confidence coefficient is generated through the semantic conflict prediction model to determine whether to automatically merge or generate a pending confirmation list.

[0013] Synchronize updated fragments to the scheduling system and logistics interface via the service bus, maintaining consistent component identification, so that schedules and material orders automatically reference the latest attributes and adjust in real time;

[0014] Based on the component fingerprint, the on-site terminal dynamically superimposes the new scan data and sensor readings onto the model view, and writes the feedback results back to the semantic mapping engine to correct the rules.

[0015] Furthermore, a decoration semantic mapping engine is deployed on the design collaboration server, and the basic attribute data of the finishing layer, soft furnishings and electromechanical terminal components are extracted from the multi-format model through a dedicated parser to generate an original attribute set; the original attribute set is converted into a standard attribute set based on a unified attribute dictionary and name mapping table.

[0016] Furthermore, semantic matching rules are used to process attributes in the standard attribute set that cannot be directly mapped, generating a configurable mapping rule set;

[0017] Assign a unique identifier and type information to each component, integrate the standard attribute set into a component semantic unit, and aggregate all component semantic units to generate a neutral semantic package; store the neutral semantic package in the database and record the timestamp.

[0018] Further, the neutral semantic package is parsed into a component list, a node is created for each component in a temporal graph, and a creator, source format, and spatial location fingerprint are attached;

[0019] Establish connections between nodes based on spatial or functional associations, use temporal characteristics to record historical versions of attribute changes, classify component nodes into object families by type or source format, and create family nodes to connect with component nodes.

[0020] Furthermore, an event monitoring mechanism is deployed on the design collaboration server to monitor data events submitted by the design end and the on-site scanning end in real time; when a new model file or point cloud fragment submission is detected, the change monitoring flow is triggered and the submission time and submitter identity are recorded, and the component attribute set, geometric data and spatial positioning fingerprint are extracted from the newly submitted model file or point cloud fragment.

[0021] Furthermore, the spatial positioning fingerprint is compared with the component fingerprint stored in the temporal graph to determine whether the newly submitted component is a brand new component or an updated component. For brand new components, its complete attribute set and geometric data are packaged to generate an incremental package;

[0022] For the updated component, the difference between its attribute set and geometric data and the latest data of the existing component in the temporal graph is calculated, and an incremental package containing only the difference is generated. The incremental package is stored in the database, and the submission time and submitter identity are recorded.

[0023] Furthermore, the difference attribute set and difference geometric data are extracted from the incremental package, and the existing attribute set and spatial positioning fingerprint of the component are obtained from the temporal graph storage;

[0024] The attribute consistency calibration index is calculated based on the ratio of the difference attribute set to the matching attributes in the existing attribute set to measure consistency. The component active change entropy index is calculated by analyzing the modification frequency of each attribute field in the component historical change record to evaluate the component's activity and potential conflict risks.

[0025] Furthermore, the attribute consistency rate calibration index and component active change entropy index are input into the semantic conflict prediction model, and the logistic function is used to generate the automatic merging confidence coefficient;

[0026] Based on the comparison result of the automatic merge confidence coefficient and the preset threshold, it is decided to execute the automatic merge of incremental packages and update the component data in the temporal graph storage, or to generate a pending confirmation list containing component identification, difference attribute set, difference geometric data, attribute consistency rate calibration index and component active change entropy index.

[0027] Furthermore, a service bus is deployed on the design collaboration server to configure a message queue, and the automatically merged component update data and the manually reviewed update data are merged into update fragments, encapsulated into a standard format, and pushed to the scheduling system and logistics interface through message routing rules.

[0028] Furthermore, the scheduling system locates the schedule item based on the component identifier and adjusts the schedule based on the latest attribute set in the update segment, including updating the installation status, estimated completion time, and task dependency order;

[0029] The logistics interface locates the material order based on the component identifier and adjusts the material order according to the latest attribute set in the update fragment, including updating the purchase details, delivery time and delivery address.

[0030] Furthermore, the on-site terminal regularly collects point cloud data and environmental data of the component through laser scanners and IoT sensors, and associates the collected point cloud data and environmental data with the unique identifier of the component based on the component fingerprint;

[0031] The on-site terminal uses the iterative closest point algorithm to calculate the geometric difference between the collected point cloud data and the latest geometric data in the model, and uses the Euclidean distance to calculate the environmental difference between the collected environmental data and the environmental attributes in the model.

[0032] Furthermore, the on-site terminal converts the point cloud data into a mesh model to replace the latest geometric data in the model, attaches the environmental data to the environmental properties of the components, and highlights the components with geometric differences or environmental difference warnings in the model view; the on-site terminal writes the calculated geometric differences and environmental differences back to the decoration semantic mapping engine to correct the mapping rules.

[0033] (3) Beneficial effects

[0034] The present invention provides a method for applying a digital model of an interior decoration project throughout the entire process, which has the following beneficial effects:

[0035] By deploying a decoration semantic mapping engine, the attributes of multi-source models are standardized into a unified attribute dictionary, generating a neutral semantic package to ensure semantic consistency of component attributes. This solves the common semantic loss problem in format conversion, avoids misunderstandings caused by data heterogeneity, and effectively prevents the formation of information islands.

[0036] By splitting the neutral semantic package into single-component nodes and storing them in a temporal graph, each component is given an independent identity and history tracking capabilities, achieving object-level version management. This refined management significantly improves the semantic consistency and management accuracy of the model in design and on-site collaboration, enabling designers and on-site personnel to collaborate efficiently based on the same data baseline, ensuring high credibility of data throughout the entire lifecycle.

[0037] By triggering the change monitoring flow and generating incremental packages based on fingerprint comparison, we can quickly respond to newly submitted data and accurately capture differential data, improving response speed and processing efficiency. The generation of incremental packages not only provides technical support for real-time updates, but also reduces redundant operations through accurate differential identification, ensuring efficient and agile data processing.

[0038] The incremental package-based conflict resolution pipeline calculates the attribute consistency calibration index and the component active change entropy index, and combines it with a semantic conflict prediction model to automate merge decisions or generate a pending confirmation list. This integration of intelligence and manual intervention improves conflict resolution efficiency while ensuring data accuracy and semantic stability. Intelligent prediction and calibration avoid the inefficiencies and errors of traditional manual processing, balancing automation efficiency with data quality.

[0039] Updated fragments are synchronized to the scheduling system and logistics interface via the service bus, maintaining consistent component identification. This allows schedules and material orders to automatically reference the latest attributes and adjust in real time. This real-time synchronization mechanism improves the accuracy of construction progress and material management, reducing construction delays and material supply errors caused by information lags. Seamlessly connecting model data with actual working conditions creates an efficient collaborative closed loop between design, construction, and logistics, enhancing the responsiveness of full-cycle management.

[0040] Through the on-site terminal, new scan data and sensor readings are dynamically superimposed on the model view based on the component fingerprint, and the feedback results are written back to correct the mapping rules, so as to achieve dynamic consistency between the model and the on-site working conditions, deeply integrate the on-site data with the digital model, and ensure the accuracy and practicality of the model during the construction and operation and maintenance stages; the mapping rules are continuously optimized through real-time feedback, which significantly improves the real-time value of the digital twin and reduces rework caused by model distortion. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the whole process application method of the interior decoration engineering digital model of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] See also Figure 1 The present invention provides a method for applying a digital model of an interior decoration project throughout the entire process, comprising:

[0044] Step 1: Deploy the decoration semantic mapping engine on the design collaboration server, extract the basic attribute data of the finishing layer, soft furnishings, and electromechanical terminal components from the multi-format model through a dedicated parser, and generate an original attribute set; convert the original attribute set into a standard attribute set based on the unified attribute dictionary and name mapping table, and perform unit conversion to ensure the consistency of attribute values; use semantic matching rules to process attributes that cannot be directly mapped, and generate a configurable mapping rule set; assign a unique identifier and type information to each component, integrate the standard attribute set into a component semantic unit, and aggregate all component semantic units to generate a neutral semantic package; store the neutral semantic package in the database, and record the timestamp to achieve version traceability.

[0045] The step 1 includes the following:

[0046] Step 101: Attribute extraction

[0047] The goal of attribute extraction is to obtain the basic attribute data of the surface layer, soft furnishings, and electromechanical end components from multi-format models. For each model format, a dedicated parser is designed and used to read the component attribute information.

[0048] Dedicated parsers are customized based on the specific characteristics of the model format. For example, for industry-based formats, the corresponding parsing library is used to read component attribute fields. For formats based on parametric modeling software, component parameter data is extracted through an application programming interface (API). This extraction includes attribute information such as component material, color, dimensions, and installation method. Once extracted, a raw attribute set is generated, which records each component's attributes as key-value pairs. For example, the material is recorded as wood, the color is recorded as brown, and the dimensions are recorded as specific values ​​for length, width, and height.

[0049] Dedicated parsers enable accurate and efficient extraction of component attribute data from diverse model formats, avoiding errors or omissions that can be introduced by manual manipulation. The customized design of dedicated parsers enhances the adaptability of the attribute extraction process to multiple model formats, ensuring comprehensive and reliable data acquisition.

[0050] Step 102: Attribute Standardization

[0051] The goal of attribute standardization is to transform the original attribute set into a standard attribute set in a unified attribute dictionary.

[0052] The unified attribute dictionary predefines standard attribute names and units. For example, material is represented as a string, color is represented as a string, dimension is expressed in millimeters, and installation method is represented as a string. Each attribute in the original attribute set is processed: if the attribute name does not match the standard name in the unified attribute dictionary, the name is converted according to a pre-established name mapping table. If the unit of the attribute value does not meet the requirements of the unified attribute dictionary, a unit conversion is performed, for example, adjusting the dimension from inches to millimeters. After processing, a standard attribute set is generated, for example, material is wood, color is brown, and dimensions are 500 mm long, 300 mm wide, and 20 mm high.

[0053] Attribute standardization ensures that component attributes in different model formats have unified names and unit expressions, which facilitates subsequent semantic mapping and data integration, making the attribute standardization process highly accurate and consistent, avoiding information misunderstanding or loss due to name or unit differences, and thus improving the credibility of data processing.

[0054] Step 103: Mapping rule definition

[0055] The goal of mapping rule definition is to establish accurate and repeatable transformation rules from the original attribute set to the standard attribute set;

[0056] For attributes that can be directly mapped, a name mapping table is used to complete the conversion. For attributes that cannot be directly mapped, semantic matching rules are used to match by analyzing the contextual description of the attribute value. For example, when the attribute key is the description field and the value is red paint, it is converted to a dual attribute mapping of red color and paint material. All mapping rules are organized into a configurable rule set, which can be adjusted and expanded as needed.

[0057] Mapping rule definitions provide clear guidance and flexible adjustments for attribute conversion, ensuring accuracy and consistency. The application of semantic matching rules enables the system to handle complex attribute descriptions, expand the scope of attribute mapping, and improve the efficiency and accuracy of converting raw data to standard data.

[0058] Step 104: Neutral semantic package generation

[0059] The goal of generating a neutral semantic package is to integrate the standard property sets of all components into a unified data structure. Each component is assigned a unique identifier, such as component 001; its type information is recorded, such as whether it belongs to the finishing layer, soft furnishings, or electromechanical terminal. The component's unique identifier, type, and standard property set are combined into a component semantic unit. All component semantic units are then aggregated to form a neutral semantic package and stored in a structured data format, such as a lightweight data exchange format based on key-value pairs, to record the component's identity, type, and property information.

[0060] The generation of neutral semantic packages enables unified encapsulation and structured expression of component attribute data, facilitating data sharing and processing across different systems. Neutral semantic packages are stored in the database of the design collaboration server, which can be either a relational or document-based database to meet efficient query requirements. Timestamps are added to neutral semantic packages during storage to ensure data version traceability.

[0061] During use, the data was mapped from the multi-format model's surface layer, soft furnishings, and electromechanical end-component attributes to a unified attribute dictionary through attribute extraction, attribute standardization, mapping rule definition, neutral semantic package generation, and data storage. This process then generated a structured neutral semantic package. Attribute extraction ensured the accuracy of data acquisition.

[0062] Step 2: Parse the neutral semantic package into a component list, create a node for each component in the temporal graph, and attach the creator, source format, and spatial location fingerprint; establish connections between nodes based on spatial or functional associations; use temporal characteristics to record historical versions of attribute changes; classify component nodes into object families by type or source format, and create family nodes to connect with component nodes.

[0063] The second step includes the following:

[0064] Step 201: Parsing the neutral semantic package

[0065] The goal of parsing neutral semantic packages is to extract the standardized attribute information of each component from the structured neutral semantic package.

[0066] Neutral semantic packages are stored in a lightweight data exchange format and contain the unique identifier, type, and standard property set of each component. The processing process uses a data parsing tool to read the contents of the neutral semantic package and identify and extract the unique identifier, type, and standard property set of each component according to a predefined structure. Once extracted, a component list is generated, in which each component is represented by its unique identifier, type, and standard property set.

[0067] By processing neutral semantic packages through data parsing tools, component information can be efficiently and accurately extracted from structured data, avoiding errors or omissions that may be caused by manual operations; data parsing tools work according to the specifications of lightweight data exchange formats, ensuring the comprehensiveness and consistency of the extraction process, and providing reliable input data for subsequent component node creation.

[0068] Step 202: Create component nodes

[0069] The goal of creating component nodes is to establish an independent node in the temporal graph for each component in the component list generated by parsing the neutral semantic package. A temporal graph is a graph database that supports the time dimension and can record the changes of nodes over time.

[0070] The processing is based on a component list. For each component in the list, a node is generated in the temporal graph. The node is identified by the component's unique identifier. The component type and standard attribute set are stored as node attributes, ensuring that node information is consistent with the data in the neutral semantic package. The use of a temporal graph enables the storage of component nodes with a time dimension, recording historical attribute changes.

[0071] Step 203: Add metadata

[0072] The goal of additional metadata is to add attribute information for each component node, including the creator, source format, and spatial positioning fingerprint. The creator records the identity of the user or tool that generated the component data; the source format records the original model format from which the component attributes were extracted, such as a specific 3D modeling file format; and the spatial positioning fingerprint generates a unique identifier by calculating the coordinates of the component's center point in 3D space. This is done by hashing the center point coordinates to generate a fixed-length string. The creator, source format, and spatial positioning fingerprint are stored as additional attributes in the corresponding component node.

[0073] Attaching the creator, source format, and spatial location fingerprint to component nodes provides rich contextual information, enhancing data traceability and management capabilities. Recording the creator and source format facilitates tracing the source and responsibility for component data generation. Spatial location fingerprints, generated through hash calculations, ensure spatial uniqueness and identifiability of components, supporting subsequent spatial correlation analysis.

[0074] Step 204: Establishing relationships between nodes

[0075] The goal of establishing relationships between nodes is to create connections between nodes in the temporal graph based on the spatial or functional associations between components. The processing process first identifies components that are located adjacently in three-dimensional space, such as components belonging to the same room, based on spatial positioning fingerprints. Then, connections are created for these component nodes in the temporal graph, and the type of connection is defined as spatial association. In addition, if there is a functional dependency between components, such as the installation order relationship between the finishing layer and the soft furnishing components, another type of connection is created, and the type is defined as functional association. All connections are stored in the temporal graph in the form of edges;

[0076] By establishing spatial and functional associations between component nodes, semantic richness is added to the data model; the creation of spatial associations facilitates the identification and processing of components with adjacent locations, improving the efficiency of spatial data analysis.

[0077] The goal of leveraging temporal properties is to record historical changes to component attributes and ensure data version management. The process stipulates that when component attributes are updated, the original node's attributes are not directly modified. Instead, a new version is generated for the component node. This new version stores the updated standard attribute set, the time the update occurred, and the identifier of the user or tool that performed the update. By querying the update time, any historical version of a component node can be retrieved.

[0078] Leveraging temporal properties to implement version control of component attributes ensures data integrity and historical traceability. Generating new versions avoids overwriting existing data, enabling the system to record every change to component attributes, facilitating audits and historical data backtracking, thereby improving the reliability and transparency of data management.

[0079] Step 205: Form a trackable object family

[0080] The goal of forming a traceable object family is to group component nodes into object families according to categories for easy management and tracking.

[0081] The process categorizes components based on their type or source format, for example, grouping all surface layer components into a single object family. For each category, a corresponding family node is created in the temporal graph. Attributes of the family node include the family name and creation time. Subsequently, edges connect the family nodes to the corresponding component nodes. Edges are defined as family affiliation, indicating that the component belongs to a specific object family.

[0082] Grouping component nodes into object families enables data classification management and tracking, improving data organization efficiency. The creation of family nodes and the establishment of family affiliation edges enable the system to quickly identify and process components of the same category, enhancing the convenience of data analysis and management while providing clear structural support for component-level tracking.

[0083] Step 3. Deploy an event monitoring mechanism on the design collaboration server to monitor data events submitted by the design end and the on-site scanning end in real time; when a new model file or point cloud fragment submission is detected, trigger the change monitoring flow and record the submission time and submitter identity; extract the component's attribute set, geometric data and spatial positioning fingerprint from the newly submitted model file or point cloud fragment; use the spatial positioning fingerprint to compare with the component fingerprint stored in the temporal graph to determine whether the newly submitted component is a new component or an updated component; for a new component, package its complete attribute set and geometric data to generate an incremental package; for an updated component, calculate the difference between its attribute set and geometric data and the latest data of the existing component in the temporal graph, and generate an incremental package containing only the difference; store the incremental package in the database, record the submission time and submitter identity to support version traceability.

[0084] The step three includes the following:

[0085] Step 301: Monitor data submission

[0086] The goal of data submission monitoring is to monitor data events submitted by the design and field scanning ends in real time to ensure that the system can respond promptly to newly submitted model files or point cloud fragments. This process deploys an event monitoring mechanism on the design collaboration server to continuously monitor new model files from the design end and point cloud fragments from the field scanning end. When data submission is detected by either end, the event monitoring mechanism triggers the change monitoring flow and records the time and identity of the submission. This recorded submission time and identity information serves as the data's timestamp and identity tag for subsequent version management and audit tracking.

[0087] Deploying an event monitoring mechanism enables real-time monitoring of data submission, ensuring that the system can quickly sense the arrival of new data and improve response efficiency.

[0088] The goal of extracting component information is to obtain the key information of the component from the newly submitted model file or point cloud fragment, including attribute set, geometric data and spatial positioning fingerprint. For the new model file from the design end, the data parsing method consistent with step one is used to decompose the content of the model file, extract the attribute set and geometric data of the component, and calculate the spatial positioning fingerprint based on the position of the component in three-dimensional space. For the point cloud fragment from the on-site scanning end, the point cloud segmentation algorithm and feature extraction technology are applied to segment the point cloud data into independent component units, identify the geometric shape and spatial position of each component, generate the corresponding geometric data and spatial positioning fingerprint, and derive the attribute set of the component according to pre-defined rules. After processing, the information of each component contains three parts: spatial positioning fingerprint, attribute set and geometric data.

[0089] By extracting component information and obtaining key features of components from data from different sources, the integrity and comparability of data content are ensured.

[0090] Step 302: Fingerprint comparison

[0091] The goal of fingerprint comparison is to use spatial positioning fingerprints to identify the correspondence between the newly submitted component and the existing components in the temporal graph, and to determine whether the newly submitted component is a brand new component or an updated version of an existing component;

[0092] The processing process first calculates the spatial positioning fingerprint for each newly submitted component. Then, the temporal graph storage constructed in step S2 is queried to see if the same spatial positioning fingerprint exists. If a matching spatial positioning fingerprint is found, the newly submitted component is confirmed to be an updated version of an existing component in the temporal graph, and the latest attribute set and geometric data of the existing component in the temporal graph are extracted for subsequent use. If no matching spatial positioning fingerprint is found, the newly submitted component is confirmed to be a brand new component. After processing is complete, the corresponding state of each newly submitted component (brand new component or updated component) is determined, and for updated components, the latest attribute set and geometric data of the existing version are also obtained.

[0093] When used, the spatial positioning fingerprint is used for comparison, which can quickly and accurately determine the identity and status of the component, avoiding the tedious matching process based on attribute or geometric data; this method improves the efficiency of the system in processing large amounts of component data, while ensuring the accuracy of component identification, providing a reliable basis for subsequent difference calculations.

[0094] Step 303: Generate incremental package

[0095] The goal of generating incremental packages is to generate incremental packages that only contain different attributes and geometries based on the results of fingerprint comparison, so as to reduce the resource consumption of data transmission and storage.

[0096] For newly submitted components that are confirmed to be completely new, their complete attribute sets and geometric data are directly packaged to form an incremental package. For newly submitted components that are confirmed to be updated, their attribute sets and geometric data are compared with the latest attribute sets and geometric data of existing components in the temporal graph, and the differences are calculated. Specifically, the attribute sets of the newly submitted component are compared item by item with the latest attribute sets of the existing component, and newly added, modified, or deleted attribute items are identified to form an attribute difference set. Simultaneously, the geometric data of the newly submitted component is compared with the latest geometric data of the existing component to identify changes in shape or position, forming a geometric difference set. Subsequently, the component identifier, attribute difference set, geometric difference set, submission time, and submitter identity information are integrated to form an incremental package. The structure of the incremental package is defined as a data packet containing the component identifier, attribute difference set, geometric difference set, submission time, and submitter identity information.

[0097] When used, the generated incremental package retains only the difference data, significantly reducing data transmission and storage space requirements, and improving system operation efficiency. The structured incremental package design facilitates subsequent model updates and conflict resolution, ensuring the accuracy and consistency of data updates.

[0098] Step 304: Store the incremental package

[0099] The goal of storing incremental packages is to persist them in a database, providing data support for subsequent conflict resolution and model updates. Each incremental package is written to the design collaboration server's database, along with the submission time and submitter's identity to support version traceability and auditing. After writing, the database generates an index for each incremental package, facilitating rapid retrieval and access.

[0100] When in use, the long-term availability and security of data are ensured by persistently storing incremental packages, the transparency of version management is enhanced by recording submission time and submitter identity information, and the generation of indexes improves the efficiency of data retrieval.

[0101] Step 4. Extract the difference attribute set and difference geometry data from the incremental package, and obtain the existing attribute set and spatial positioning fingerprint of the component from the temporal graph storage; calculate the attribute consistency rate calibration index based on the ratio of matching attributes in the difference attribute set and the existing attribute set to measure consistency; calculate the component active change entropy index by analyzing the modification frequency of each attribute field in the component historical change record to evaluate the activity and potential conflict risk of the component; input the attribute consistency rate calibration index and the component active change entropy index into the semantic conflict prediction model, and use the logistic function to generate the automatic merge confidence coefficient; based on the comparison result of the automatic merge confidence coefficient with the preset threshold, decide to execute the automatic merge of the incremental package and update the component data in the temporal graph storage, or generate a pending confirmation list containing component identification, difference attribute set, difference geometry data, attribute consistency rate calibration index and component active change entropy index.

[0102] The goal of step 4 is to generate automated merge decisions or a checklist based on the incremental package to ensure the stability and consistency of the model semantics.

[0103] The step 4 includes the following contents:

[0104] Step 401: Prepare input data

[0105] The purpose of input data preparation is to obtain and organize incremental packages and existing component data to provide a complete data foundation for subsequent analysis. The processing process first obtains the incremental package from step three. The incremental package contains the difference attribute set and difference geometry data of each component, where the difference attribute set refers to the part of the component attributes in the incremental package that has changed compared with the existing attributes, and the difference geometry data refers to the change data of the component geometric shape or position. Then, the existing attribute set and spatial positioning fingerprint of the corresponding component are extracted from the temporal graph storage of step S2. The existing attribute set is the complete attribute description of the component in the current version, and the spatial positioning fingerprint is the unique position identifier of the component in space. The final output is the difference attribute set, difference geometry data, existing attribute set and spatial positioning fingerprint of each component, which will serve as input for subsequent calculations.

[0106] By clearly distinguishing between incremental packages and existing component data, the integrity and accuracy of input data are ensured, providing a reliable basis for conflict analysis. A unified data preparation approach facilitates systematic processing and avoids analysis errors caused by missing data or inconsistent formats.

[0107] Step 402: Calculate the attribute consistency calibration index

[0108] The purpose of calculating the attribute consistency calibration index is to measure the degree of consistency between the differential attribute set in the incremental package and the existing attribute set, thereby quantifying the similarity between the two. The process first counts the total number of attributes in the existing attribute set, that is, the number of all attribute fields in the current version of the component. Then, the number of attributes in the differential attribute set that completely match the existing attribute set is counted, that is, the number of attribute fields that have not changed in the incremental package. Next, the consistency rate is calculated by dividing the number of matching attributes by the total number of existing attributes to obtain a ratio. The final output is the attribute consistency calibration index, which ranges from 0 to 1. The closer the value is to 1, the smaller the difference between the incremental package and the existing data. By comparing the ratio of matching attributes to total attributes, the degree of similarity between the incremental package and the existing data can be objectively reflected, providing a quantitative standard for conflict determination.

[0109] Step 403: Calculate the component active change entropy index

[0110] The purpose of calculating the component active change entropy index is to evaluate the change frequency and dispersion of the component in historical versions to reflect its activity and potential conflict risk.

[0111] The process first extracts the component's historical change records from the temporal graph storage, including attribute modifications during each component version update. Next, the number of modifications to each attribute field during the historical change is counted, representing the total number of times each field has been modified. Next, the proportion of each attribute field's modification count to the total number of modifications is calculated, where the total number of modifications is the sum of all attribute field modifications. Finally, using the information entropy calculation principle, the proportions of each attribute field are weighted and summarized to obtain the component's active change entropy index. A larger value indicates more dispersed and frequent component changes, and a higher potential conflict risk.

[0112] When used, the information entropy method is used to analyze the change frequency and distribution, which can comprehensively capture the historical activity of components and identify high-risk components; quantifying the change characteristics of components helps the system to be more cautious when handling frequently changing components, thereby reducing the possibility of incorrect merging.

[0113] Step 404: Build a semantic conflict prediction model

[0114] The purpose of constructing a semantic conflict prediction model is to combine the attribute consistency rate calibration index and the component active change entropy index to generate an automatic merge confidence coefficient to guide merge decisions. The process uses the computational principle of the logistic function to generate the confidence coefficient, which consists of three components: a constant term, the weighted influence of the attribute consistency rate calibration index, and the weighted influence of the component active change entropy index. Specifically, the constant term is initially set to 0, representing the base offset; the coefficient of the attribute consistency rate calibration index is then initially set to 5, indicating a significant positive influence on the confidence coefficient; and the coefficient of the component active change entropy index is initially set to 3, indicating a relatively small negative influence on the confidence coefficient. Through weighted aggregation and nonlinear mapping, the automatic merge confidence coefficient is output. Its value ranges from 0 to 1, with values ​​closer to 1 indicating that the incremental package is suitable for automatic merging.

[0115] When used, the logistic function can nonlinearly combine multiple quantitative indicators to comprehensively reflect the impact of attribute consistency and change activity on the merger decision, balance the effects of different factors, and improve the accuracy of the merger decision.

[0116] Step 405: Conflict determination and output

[0117] The purpose of conflict determination and output is to make a decision based on the automatic merge confidence coefficient to determine whether the incremental package should be automatically merged or a list to be confirmed should be generated. The processing process first sets a threshold, such as 0.75, as the judgment standard for automatic merging. If the automatic merge confidence coefficient is greater than or equal to the threshold, the difference attribute set and difference geometry data in the incremental package are directly merged into the temporal graph storage, and the attribute set and geometry data of the component are updated, and the component identifier of the successfully merged and the updated attribute set are output. If the automatic merge confidence coefficient is less than the threshold, a list to be confirmed is generated, which contains the component identifier, difference attribute set, difference geometry data, attribute consistency calibration index and component active change entropy index for manual review. The final output is the successfully merged component data or the list to be confirmed.

[0118] When in use, thresholds are set to distinguish between automated and manual processing, ensuring the reliability of high-confidence merging while retaining the review mechanism for low-confidence situations, which can ensure data accuracy and adapt to the needs of different conflict scenarios.

[0119] Step 5. Deploy a service bus on the design collaboration server to configure a message queue. Merge the automatically merged component update data obtained in step 4 with the manually reviewed update data into an update segment, encapsulate it in a standard format, and push it to the scheduling system and logistics interface through message routing rules. The scheduling system locates the schedule item based on the component identifier and adjusts the schedule based on the latest attribute set in the update segment, including updating the installation status, estimated completion time, and task dependency sequence. The logistics interface locates the material order based on the component identifier and adjusts the material order based on the latest attribute set in the update segment, including updating the purchase details, delivery time, and delivery address.

[0120] The step five includes the following:

[0121] Step 501: Update fragment acquisition

[0122] The purpose of acquiring update segments is to obtain the automatically merged component update data and the manually reviewed update data from step 4 and merge them into a complete update segment. The processing begins by acquiring the automatically merged component update data from step 4. This data contains the component's latest attribute set (e.g., installation status, material type, etc.), the latest geometric data (e.g., component shape, dimensions, etc.), and the component identifier (unique identifier). For components whose pending confirmation list was generated in step 4, after manual review is completed, the reviewed update data is acquired, also containing the latest attribute set, latest geometric data, and component identifier. Subsequently, the automatically merged update data and the reviewed update data are merged into a complete update segment, ensuring that all component identifiers are unique and that data fields are complete. During the merge, the component identifiers are first compared. After confirming the data source, the two data sets are integrated into a unified data set.

[0123] By merging automatically merged and manually reviewed data, we ensure that the updated fragment contains the latest information of all components, and the data is comprehensive and accurate. The unified data integration method avoids data omissions or duplications, improves the efficiency and reliability of data processing, and ensures the integrity of synchronized data.

[0124] Step 502: Service bus configuration

[0125] The purpose of service bus configuration is to deploy and configure the service bus as data exchange middleware, supporting messaging and protocol conversion between multiple systems. The process begins by deploying the service bus (enterprise service bus) and configuring its message queue. Complete update fragments are encapsulated in a standard format (e.g., JSON). Each component's data structure includes the component identifier, the latest property set, and the latest geometry. Next, message routing rules are defined to direct update fragments to the scheduling system and logistics interface based on the attribute type in the latest property set (e.g., installation status change, material specification change). Specifically, if the attribute type involves installation status, it is pushed to the scheduling system; if it involves material specification, it is pushed to the logistics interface, ensuring accurate data flow.

[0126] When used, the service bus acts as a middleware that can uniformly manage data exchange between multiple systems, support conversion between different protocols, and ensure the compatibility and stability of data transmission. Through message queues and routing rules, the service bus achieves efficient data distribution, reduces the complexity of communication between systems, and improves the accuracy and controllability of the synchronization process.

[0127] Step 503: Data synchronization

[0128] The purpose of data synchronization is to push complete update segments to the scheduling system and logistics interface via the service bus, ensuring the consistency of component identifiers. The service bus pushes the complete update segments to the scheduling system and logistics interface. After receiving the update segments, the scheduling system locates the corresponding schedule item based on the component identifier. Specifically, the steps involve comparing the component identifier with the task identifier in the schedule, confirming a match, and then updating the relevant data. After receiving the update segments, the logistics interface also locates the corresponding material order based on the component identifier. By comparing the component identifier with the identifier in the order record, confirming a match, and then updating the relevant data. During the synchronization process, the component identifier remains consistent as the unique identifier, avoiding data errors caused by identifier mismatches.

[0129] When in use, data is pushed through the service bus and matched based on component identification, ensuring the real-time transmission of data and the accuracy of data associations between systems; this synchronization method quickly responds to component updates, ensuring that the scheduling system and logistics interface use consistent and latest data, improving the coordination and timeliness of project management.

[0130] Step 504: Schedule adjustment

[0131] The purpose of schedule adjustment is to automatically adjust the schedule in the scheduling system based on the latest attribute set in the update segment. The processing process is performed by the scheduling system based on the latest attribute set in the update segment.

[0132] The specific adjustment logic includes the following: If the latest attribute set contains the installation status attribute and its value is "Installed," the status of the corresponding schedule item is updated to "Complete" and the update time is recorded. If the latest attribute set contains the installation time attribute, the estimated completion time of the schedule is recalculated and updated based on this time. If the latest attribute set contains the dependency attribute (for example, component A must be installed after component B is completed), the order of tasks in the schedule is reordered based on the dependency relationship. The adjusted schedule is saved and refreshed in real time to ensure that it reflects the latest construction status.

[0133] When in use, the schedule is automatically adjusted according to the latest set of attributes, which can quickly respond to changes in component status and ensure that the schedule is consistent with the actual construction situation. This automated adjustment method reduces the need for manual intervention, improves the efficiency and accuracy of schedule management, and reduces the risk of construction delays due to information asynchrony.

[0134] Step 505: Material order adjustment

[0135] The purpose of material order adjustment is to automatically adjust the material order in the logistics interface according to the latest attribute set in the update segment. The processing process is adjusted by the logistics interface according to the latest attribute set in the update segment.

[0136] The specific adjustment logic includes: if the latest attribute set includes a change in material type or specification, the material order's purchase details are updated, for example, replacing the original material model with the new one; if the latest attribute set includes a change in installation time, the material delivery time is rescheduled based on the new time, such as moving it forward or backward; if the latest attribute set includes a change in installation location, the material order's delivery address is updated, for example, to the new construction area. Adjusted material orders are updated in real time and notified to suppliers to ensure that material supply is consistent with on-site demand.

[0137] When in use, material orders are automatically adjusted according to the latest attribute set, which can respond to changes in component attributes in a timely manner, ensure the accuracy and adaptability of material supply, improve the flexibility and response speed of material management, and reduce the risk of construction interruption due to material mismatch or delivery delays.

[0138] Step 6. The on-site terminal regularly collects point cloud data and environmental data of the component through laser scanners and IoT sensors, and associates the collected point cloud data and environmental data with the unique identifier of the component based on the component fingerprint; the on-site terminal calculates the geometric difference between the collected point cloud data and the latest geometric data in the model using the iterative nearest point algorithm, and calculates the environmental difference between the collected environmental data and the environmental attributes in the model using the Euclidean distance; the on-site terminal converts the point cloud data into a mesh model to replace the latest geometric data in the model, attaches the environmental data to the environmental attributes of the component, and highlights the components with geometric differences or environmental difference warnings in the model view; the on-site terminal writes the calculated geometric differences and environmental differences back to the decoration semantic mapping engine to correct the mapping rules.

[0139] The step six includes the following contents:

[0140] Step 601: Field data collection

[0141] The purpose of field data collection is to regularly collect geometric and environmental data of components through field terminals and associate them with component fingerprints. The processing process is performed by field terminals equipped with laser scanners and IoT sensors.

[0142] The laser scanner generates point cloud data of the component. This data consists of a series of 3D coordinate points that reflect the component's actual geometry and spatial position. IoT sensors collect real-time data about the component's environment, such as temperature and humidity. Based on the component fingerprint (the spatial location of the component in the temporal graph) generated in the previous step, the on-site terminal associates the point cloud data and environmental data with the corresponding component's unique identifier, ensuring that the data is accurately matched to the specific component.

[0143] During use, laser scanners and IoT sensors are used to collect the geometric data and environmental data of components, which can accurately reflect the actual status of the components and the surrounding environment, provide real and reliable input data for subsequent processing, ensure the timeliness and accuracy of the data, enable the model to be updated based on the latest on-site information, and improve the credibility of the entire system.

[0144] Step 602: Data superposition

[0145] The purpose of data overlay is to compare the point cloud and environmental data collected on-site with the latest data in the model, calculating geometric and environmental differences. The process begins by comparing the point cloud data with the model geometry synchronized in the previous step (i.e., the latest geometric representation of the component). The geometric differences between the two are calculated using an iterative closest point algorithm.

[0146] The specific method is to find the best matching position of the two sets of data by repeatedly adjusting the spatial transformation (including rotation and translation) between the point cloud data and the model geometric data, and then calculate the average square value of the distance between all corresponding points, which is called the residual. The residual reflects the degree of geometric deviation between the two sets of data. If the residual exceeds the pre-set empirical value (the empirical value represents the acceptable range of geometric deviation), a geometric inconsistency warning is generated. At the same time, the environmental data is compared with the environmental attributes in the model (i.e., the design environmental parameters), and the environmental differences are quantified by calculating the straight-line distance (called Euclidean distance) between the environmental data and the design environmental parameters in multidimensional space. If the Euclidean distance exceeds the pre-set upper limit (the upper limit represents the acceptable range of environmental deviation), an environmental inconsistency warning is generated.

[0147] When in use, the iterative nearest point algorithm and Euclidean distance are used to calculate geometric differences and environmental differences, which can objectively and quantitatively evaluate the degree of deviation between field data and model data, provide a clear judgment basis for subsequent model updates, and clearly present inconsistencies between data, thereby improving the accuracy and automation level of model adjustment.

[0148] Step 603: Model view update

[0149] The purpose of updating the model view is to dynamically overlay the point cloud data and environmental data collected on-site onto the model view and highlight components based on difference warnings. The processing process is for the on-site terminal to convert the point cloud data into a mesh model, replace the latest geometric data in the model, and attach the environmental data as a real-time attribute to the unique identifier of the corresponding component to update the environmental attribute. If there is a geometric inconsistency warning or an environmental inconsistency warning, the corresponding component is highlighted in the model view and a warning message is annotated. The warning message includes the specific value of the residual or Euclidean distance. The highlighting is color-coded, and the updated model view is displayed in real time on the on-site terminal for construction personnel and supervisors to view and analyze.

[0150] When in use, by dynamically superimposing field data onto the model view and combining it with warning highlighting, the differences between the field conditions and the model can be intuitively reflected, facilitating timely identification of problems by field personnel, enhancing the visualization and real-time performance of the model view, and improving the efficiency of field management while reducing the possibility of construction errors due to data deviations.

[0151] Step 604: Feedback writeback

[0152] The purpose of feedback writeback is to feed back the differences between field data and model data to the decoration semantic mapping engine to correct the mapping rules. During this process, the field terminal writes back geometric inconsistency warnings, environmental inconsistency warnings, as well as geometric differences (expressed as spatial transformations and residuals) and environmental differences (expressed as Euclidean distances) to the decoration semantic mapping engine. The decoration semantic mapping engine adjusts the mapping rules based on the feedback. Specifically, if geometric inconsistency warnings occur frequently, the acceptable range of geometric deviations is increased; if environmental inconsistency warnings occur frequently, the acceptable range of environmental deviations is increased or the definition of environmental attributes is expanded. These revised mapping rules are then applied to subsequent attribute mapping processes to ensure that the model can adapt to changing field conditions.

[0153] When in use, by writing back the difference information and adjusting the mapping rules, a closed-loop correction is achieved between the model and the on-site working conditions, enabling the model to dynamically adapt to actual changes, improving the model's adaptability and accuracy, and providing higher reliability for decision-making support in the construction and operation and maintenance stages.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The whole process application method of digital model for interior decoration engineering is characterized by: include, Deploy a decoration semantic mapping engine on the design collaboration server to map component attributes in the multi-format model to a unified attribute dictionary and generate a neutral semantic package through attribute extraction, standardization and mapping rules; Split the neutral semantic package into single component nodes, store them in a temporal graph with the creator, source format and spatial location fingerprint, and form a traceable object family; When a new model or point cloud fragment is submitted by the design end or the on-site scanning end, the change monitoring flow is triggered, and an incremental package containing only the different attributes and geometry is generated based on the spatial positioning fingerprint comparison; Based on the incremental package, the attribute consistency calibration index and component active change entropy index are calculated. The automatic merge confidence coefficient is generated through the semantic conflict prediction model to determine whether to automatically merge or generate a pending confirmation list. Synchronize updated fragments to the scheduling system and logistics interface via the service bus, maintaining consistent component identification, so that schedules and material orders automatically reference the latest attributes and adjust in real time; The on-site terminal dynamically overlays new scan data and sensor readings onto the model view based on the component fingerprint, and writes the feedback results back to the semantic mapping engine to modify the mapping rules; Deploy a decoration semantic mapping engine on the design collaboration server, extract basic attribute data of the finishing layer, soft furnishings, and electromechanical end components from the multi-format model through a dedicated parser, and generate an original attribute set; convert the original attribute set into a standard attribute set based on a unified attribute dictionary and name mapping table; Using semantic matching rules to process attributes that cannot be directly mapped in the standard attribute set, a configurable mapping rule set is generated; a unique identifier and type information are assigned to each component, the standard attribute set is integrated into a component semantic unit, and all component semantic units are aggregated to generate a neutral semantic package; the neutral semantic package is stored in a database and a timestamp is recorded; Parsing the neutral semantic package into a component list, creating a node for each component in a temporal graph, and attaching a creator, a source format, and a spatial location fingerprint; Establish connections between nodes based on spatial or functional associations, use temporal features to record historical versions of attribute changes, classify component nodes into object families based on type or source format, and create family nodes to connect with component nodes; Extracting the difference attribute set and difference geometry data from the incremental package, obtaining the existing attribute set and spatial positioning fingerprint of the component from the temporal graph storage; calculating the attribute consistency calibration index based on the ratio of matching attributes in the difference attribute set and the existing attribute set, for measuring consistency; The component active change entropy index is calculated by analyzing the modification frequency of each attribute field in the component historical change record, which is used to evaluate the component's activity and potential conflict risk; The attribute consistency rate calibration index and component active change entropy index are input into the semantic conflict prediction model, and the logistic function is used to generate the automatic merging confidence coefficient. Based on the comparison result of the automatic merge confidence coefficient and the preset threshold, it is decided to execute the automatic merge of incremental packages and update the component data in the temporal graph storage, or to generate a pending confirmation list containing component identification, difference attribute set, difference geometric data, attribute consistency rate calibration index and component active change entropy index.

2. The method for applying a digital model of an interior decoration project throughout the entire process as claimed in claim 1, wherein: Deploy an event monitoring mechanism on the design collaboration server to monitor data events submitted by the design end and the on-site scanning end in real time; When a new model file or point cloud fragment submission is detected, the change monitoring flow is triggered and the submission time and submitter identity are recorded. The component attribute set, geometric data and spatial positioning fingerprint are extracted from the newly submitted model file or point cloud fragment.

3. The method for applying the digital model of interior decoration engineering throughout the entire process as claimed in claim 2, characterized in that: Compare the spatial positioning fingerprint with the component fingerprint stored in the temporal graph to determine whether the newly submitted component is a new component or an updated component. For new components, package their complete attribute set and geometric data to generate an incremental package. For the updated component, the difference between its attribute set and geometric data and the latest data of the existing component in the temporal graph is calculated, and an incremental package containing only the difference is generated. The incremental package is stored in the database, and the submission time and submitter identity are recorded.

4. The method for applying a digital model of an interior decoration project throughout the entire process as claimed in claim 3, wherein: A service bus is deployed on the design collaboration server to configure a message queue. The automatically merged component update data and the manually reviewed update data are merged into update fragments, which are encapsulated into a standard format and pushed to the scheduling system and logistics interface through message routing rules.

5. The method for applying the digital model of interior decoration engineering throughout the entire process as claimed in claim 4, characterized in that: The scheduling system locates the schedule item based on the component identifier and adjusts the schedule based on the latest attribute set in the update segment, including updating the installation status, estimated completion time and task dependency order; The logistics interface locates the material order based on the component identifier and adjusts the material order according to the latest attribute set in the update fragment, including updating the purchase details, delivery time and delivery address.

6. The method for applying the digital model of interior decoration engineering throughout the entire process as claimed in claim 5, characterized in that: The on-site terminal regularly collects the point cloud data and environmental data of the component through laser scanners and IoT sensors, and associates the collected point cloud data and environmental data with the unique identifier of the component based on the component fingerprint; The on-site terminal uses the iterative closest point algorithm to calculate the geometric difference between the collected point cloud data and the latest geometric data in the model, and uses the Euclidean distance to calculate the environmental difference between the collected environmental data and the environmental attributes in the model.

7. The method for applying a digital model of an interior decoration project throughout the entire process as claimed in claim 6, wherein: The field terminal converts point cloud data into a mesh model to replace the latest geometric data in the model, attaches environmental data to the environmental properties of the component, and highlights components with geometric differences or environmental difference warnings in the model view; The on-site terminal writes the calculated geometric differences and environmental differences back to the decoration semantic mapping engine to correct the mapping rules.

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