Historical building operation and maintenance management method and system based on digital twinning

Abstract

The invention relates to the technical field of operation and maintenance management, and discloses a historical building operation and maintenance management method and system based on digital twinning, and the method comprises the steps: building a digital substrate based on historical data and point cloud, dividing a monitoring region according to a bearing structure, value elements and a function space, and associating an identifier. And mapping the operation data to a corresponding component storage space according to the area to which the operation data belongs, and forming a dynamic operation and maintenance file and a monitoring data stream. And performing visual coding on the data stream by taking the acquisition time as a horizontal axis and the spatial topology as a longitudinal axis to generate a spatial-temporal distribution map. And determining an abnormal area according to fluctuation form difference and trend deviation degree of real-time and historical data in the map, and binding identifiers and time periods to generate an abnormal list. Updating the file by taking the list as an index, and matching the disease record with a historical maintenance record to generate an operation and maintenance management scheme; the efficiency of historical building operation and maintenance management based on digital twinning can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance, and particularly relates to a historical building operation and maintenance method and system based on digital twinning. BACKGROUND

[0002] The existing historical building operation and maintenance method has obvious deficiencies in data integration and utilization. Usually, historical drawings, point cloud scanning, repair records and sensor real-time monitoring data are stored in different systems or media, resulting in the holographic information of the building being fragmented. Due to the lack of a unified digital base and standardized spatial index, various operation data are difficult to establish an accurate correspondence with the specific components of the building, and the monitoring indicators only exist as isolated numerical values, and cannot form effective correlations on the spatial structure and time axis of the building. This data island phenomenon makes it difficult for management personnel to intuitively grasp the real-time state and historical evolution law of the building as a whole and key parts when facing a large amount of information, reducing the supporting effectiveness of data on operation and maintenance decisions.

[0003] In the aspect of abnormality identification and maintenance decision, the traditional method mainly relies on artificial regular inspection and alarm mechanism based on single threshold, and the depth of mining of monitoring data is insufficient, which cannot effectively distinguish the normal form and abnormal signs of data fluctuation, and is also difficult to capture the subtle trends of long-term evolution of building structure. When abnormal signs are found, the subsequent investigation, diagnosis and maintenance scheme are usually carried out separately, and there is a lack of a systematic framework that can intelligently associate abnormal areas, disease characteristics and historical maintenance experience. This leads to the need to re-research and re-analyze each maintenance, which cannot fully reuse the successful cases and knowledge accumulation in the past, resulting in low efficiency of operation and maintenance, and the timeliness and scientificity of problem solving are restricted. SUMMARY

[0004] The present application provides a historical building operation and maintenance method and system based on digital twinning, which mainly aims to solve the problem of low efficiency in historical building operation and maintenance based on digital twinning.

[0005] To achieve the above-mentioned purpose, the present application provides a historical building operation and maintenance method based on digital twinning, which comprises:

[0006] constructing a digital base of the historical building based on historical data and point cloud data of the historical building;

[0007] dividing monitoring areas in the digital base according to the load-bearing structure, value element distribution and functional space of the historical building, and associating data identifiers for the monitoring areas;

[0008] mapping the operation data of the historical building into the storage space of the corresponding component in the digital substrate based on the monitoring area to which the operation data respectively belongs, to obtain a dynamic operation archive and corresponding monitoring data stream of the historical building;

[0009] visually encoding the monitoring data stream with the collection time of the monitoring data stream as the horizontal axis and the spatial topology structure of the digital substrate as the vertical axis, to obtain a space-time distribution atlas of the historical building;

[0010] determining an abnormal area of the historical building according to the fluctuation pattern difference and trend evolution deviation of real-time data and historical archive data in the space-time distribution atlas, and binding the area identifier and abnormal time period of the abnormal area, to obtain an abnormal area list of the historical building;

[0011] updating the dynamic operation archive with the area identifier in the abnormal area list as an index, to obtain a current archive of the historical building, and matching the disease record set in the current archive with the historical maintenance record of the dynamic operation archive, to obtain an operation and maintenance management scheme of the historical building.

[0012] In a preferred embodiment, the digital substrate of the historical building is constructed based on historical data and point cloud data of the historical building, comprising:

[0013] digitally scanning the architectural drawings in the historical data to obtain a raster image of the historical building, and performing geometric correction on the raster image to obtain a reference base map of the historical building;

[0014] spatially registering the point cloud data with the reference base map to obtain an aligned point cloud set of the historical building;

[0015] connecting the architectural outer contour line and edge feature line in the aligned point cloud set according to the spatial adjacent relationship to obtain a closed contour line of the historical building, and combining the closed contour line according to the spatial position of the closed contour line in the historical building to obtain a wireframe structure of the historical building;

[0016] attaching the repair records and material information in the historical data as attribute fields to the corresponding component nodes in the wireframe structure to obtain a reference base map of the historical building.

[0017] In a preferred embodiment, the monitoring areas are divided in the digital substrate according to the load-bearing structure, value element distribution and functional space of the historical building, and data identifiers are associated with the monitoring areas, comprising:

[0018] The nodes of the continuous components in the digital base belonging to the same structural stress path of the historical building are grouped into a load-bearing structure area of the historical building;

[0019] The nodes of the components in the digital base that are spatially adjacent and have the same value level are merged into a value element area of the historical building;

[0020] According to the functional partition boundary line in the digital base, the nodes of the components located in the same functional partition are aggregated into a functional space area of the historical building;

[0021] The boundaries of the load-bearing structure area, the value element area and the functional space area are superimposed to obtain a comprehensive area framework of the historical building, and the overlapping parts of the comprehensive area framework are cut and divided to obtain a defined block of the historical building;

[0022] A feature area code is generated for the defined block, and the feature area code is written into a metadata field contained in a spatial block of the defined block to obtain a data identifier of the spatial block.

[0023] In a preferred embodiment, based on the monitoring area to which the operation data of the historical building respectively belongs, the operation data is mapped into the storage space of the corresponding component in the digital base to obtain a dynamic operation and maintenance archive of the historical building and a corresponding monitoring data stream, including:

[0024] An operation data packet of the historical building is obtained, and the operation data includes a collection timestamp, a collection point spatial coordinate and a monitoring index value;

[0025] The collection point spatial coordinate is matched with the spatial range of the monitoring area in the digital base to determine the reference monitoring area to which the operation data packet belongs;

[0026] The corresponding component node space in the digital base is located according to the data identifier of the reference monitoring area;

[0027] The monitoring index value is sequentially written into the monitoring data field of the component node space in the order of the timestamp to obtain an independent area monitoring sequence of the historical building;

[0028] The independent area monitoring sequences are merged to obtain a monitoring data stream of the historical building;

[0029] The monitoring data stream and the real-time data field of the corresponding component node are summarized to obtain a dynamic operation and maintenance archive of the historical building.

[0030] In a preferred embodiment, the visual encoding of the monitoring data stream with the collection time of the monitoring data stream as the horizontal axis and the spatial topology of the digital base as the vertical axis obtains the spatiotemporal distribution atlas of the historical building, including:

[0031] Determining the mapping rule between the index value and the visual feature parameter in the monitoring data stream, the mapping rule including: determining the color hue according to the value interval in the monitoring data stream, determining the color saturation according to the value change rate in the monitoring data stream, and determining the graph transparency according to the value accumulation in the monitoring data stream;

[0032] According to the area identifier of the monitoring area to which the monitoring data stream belongs, the corresponding component node in the digital base is retrieved, and the vertical axis coordinate value of the component node in the spatial topology of the digital base is read;

[0033] The index value is converted into the corresponding visual feature parameter value according to the mapping rule;

[0034] A two-dimensional coordinate system of the historical building is constructed with the collection time stamp of the monitoring data stream as the horizontal coordinate and the vertical axis coordinate value as the vertical axis positioning point, and a pixel point is drawn in the two-dimensional coordinate system point by point with the visual feature parameter value as the pixel attribute value;

[0035] The pixel points are arranged in a regular row-column structure in the order of horizontal coordinate time and vertical coordinate space, and a spatiotemporal two-dimensional grayscale matrix of the historical building is obtained;

[0036] The spatiotemporal two-dimensional grayscale matrix is spliced according to the spatial topology of the digital base, and a spatiotemporal distribution atlas of the historical building is obtained.

[0037] In a preferred embodiment, the abnormal area of the historical building is determined according to the fluctuation pattern difference and trend evolution deviation of the real-time data and historical archive data in the spatiotemporal distribution atlas, and the area identifier and abnormal period of the abnormal area are bound to obtain an abnormal area list of the historical building, including:

[0038] The real-time data and historical archive data of the spatiotemporal distribution atlas are respectively subjected to fluctuation pattern decomposition, and the number of rising sections, the number of falling sections, the number of wave crests, and the number of wave troughs of the real-time data and the historical archive data are obtained, generating a real-time fluctuation feature vector and a historical fluctuation feature vector of the historical building;

[0039] The Euclidean distance between the real-time fluctuation feature vector and the historical fluctuation feature vector is taken as the fluctuation pattern difference value of the historical building;

[0040] a difference between the linear regression slope of the real-time data and the linear regression slope of the historical archive data as a trend evolution deviation degree of the historical building;

[0041] fusing the fluctuation pattern difference value and the trend evolution deviation degree to obtain a comprehensive anomaly index of the monitoring area;

[0042] determining an anomaly determination reference value of the historical building according to a statistical distribution feature of the comprehensive anomaly index, and marking a monitoring area whose comprehensive anomaly index exceeds the anomaly determination reference value as an anomaly area of the historical building.

[0043] In a preferred embodiment, the calculation formula of the comprehensive anomaly index comprises:

[0044]

[0045] wherein, the comprehensive anomaly index, a fluctuation pattern term weight coefficient, a natural constant, a fluctuation pattern difference value, a fluctuation pattern difference mean value, a fluctuation pattern difference maximum value, a fluctuation pattern nonlinearity index, a trend evolution term weight coefficient, a trend evolution deviation degree, a trend evolution deviation degree standard deviation, a trend evolution deviation degree maximum value, a trend evolution nonlinearity index, a value coupling term weight coefficient, a comprehensive value coefficient, a comprehensive value coefficient maximum value.

[0046] In a preferred embodiment, the dynamic operation and maintenance archive is updated with the area identifier in the anomaly area list as an index to obtain a current archive of the historical building, and a disease record set in the current archive is matched with a historical maintenance record of the dynamic operation and maintenance archive to obtain an operation and maintenance management scheme of the historical building, comprising:

[0047] locating to a corresponding monitoring area in the dynamic operation and maintenance archive according to the area identifier in the anomaly area list, and locking a component node storage space contained by the monitoring area;

[0048] A special exploration data field is newly created in the component node storage space, and start and end identifiers of the abnormal time period in the abnormal area list are written into a metadata area of the special exploration data field;

[0049] A special exploration data packet for the component node in the abnormal time period is acquired, and the special exploration data packet contains a physical and chemical analysis result of a material sample and a high-definition image file path;

[0050] Feature data in the special exploration data packet are written into corresponding subfields of the special exploration data field respectively, data update of the component node is completed, and a current archive of the historical building is obtained;

[0051] A disease type label and a disease degree grade of a disease record set in the current archive are matched with historical maintenance records in the dynamic operation and maintenance archive, historical maintenance measures obtained through matching are associated and summarized with corresponding component node information, and an operation and maintenance management scheme of the historical building is obtained.

[0052] In a preferred embodiment, the matching of the disease type label and the disease degree grade of the disease record set in the current archive with the historical maintenance records in the dynamic operation and maintenance archive, and the association and summarization of the historical maintenance measures obtained through matching with the corresponding component node information to obtain the operation and maintenance management scheme of the historical building, comprises:

[0053] A historical maintenance record with the same disease type label is searched from a historical maintenance record library of the dynamic operation and maintenance archive, and a preliminary matching record set of the historical building is obtained;

[0054] A historical maintenance record with the smallest difference from the disease degree grade is filtered from the preliminary matching record set, and an optimal matching record of the historical building is obtained;

[0055] Maintenance measure text and maintenance effect evaluation data in the optimal matching record are associated with corresponding component node information in the disease record set, and an operation and maintenance management scheme of the historical building is obtained.

[0056] In order to solve the above problems, the application further provides a historical building operation and maintenance management system based on digital twinning, the system comprising:

[0057] A digital base module constructs a digital base of the historical building based on historical data and point cloud data of the historical building;

[0058] A monitoring area module divides monitoring areas in the digital base according to a load-bearing structure, a value element distribution and a functional space of the historical building, and associates data identifiers with the monitoring areas;

[0059] An operation and maintenance archive module maps the operation data of the historical building to the storage space of the corresponding component in the digital substrate based on the monitoring area to which the operation data belongs, to obtain a dynamic operation and maintenance archive of the historical building and corresponding monitoring data flow;

[0060] A space-time distribution map module visually encodes the monitoring data flow with the collection time of the monitoring data flow as the horizontal axis and the spatial topology structure of the digital substrate as the vertical axis, to obtain a space-time distribution map of the historical building;

[0061] An abnormal area module determines an abnormal area of the historical building according to the fluctuation pattern difference and trend evolution deviation of real-time data and historical archive data in the space-time distribution map, and binds the area identifier and the abnormal time period of the abnormal area to obtain an abnormal area list of the historical building;

[0062] An operation and maintenance management scheme module updates the dynamic operation and maintenance archive with the area identifier in the abnormal area list as the index to obtain a current archive of the historical building, and matches the disease record set in the current archive with the historical maintenance record of the dynamic operation and maintenance archive to obtain an operation and maintenance management scheme of the historical building.

[0063] Compared with the prior art, the present application has the following beneficial effects:

[0064] 1. The historical building operation and maintenance management method based on digital twinning realizes fine organization and spatial indexing of historical building data by constructing a digital substrate that integrates geometric structure and historical information, and dividing the monitoring area according to load-bearing structure, value elements and functional space. The method accurately maps real-time operation data to the storage space of the corresponding component node according to the spatial position, forms a dynamically updated operation and maintenance archive, and further generates a space-time distribution map through visual coding technology, so that the evolution law of the monitoring index in the time and space dimensions is intuitively presented, greatly improving the management efficiency and readability of massive heterogeneous data, and providing a highly structured and visualized data basis for subsequent analysis.

[0065] 2. The method can accurately identify abnormal areas and their occurrence time periods by quantitatively analyzing the fluctuation pattern difference and trend evolution deviation of real-time data and historical archive data in the space-time distribution map, and bind abnormal information with area identifiers to form an abnormal area list. On this basis, dynamically updating the archive with the abnormal area as the index and matching the historical maintenance record can quickly generate an operation and maintenance management scheme for the current disease type and degree, realize the whole-process data-driven from abnormal early warning, on-site investigation to scheme formulation, significantly improve the accuracy of abnormal response and the scientificity of maintenance decision, and ensure the effectiveness and pertinence of historical building protection measures. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 A flowchart of a historical building operation and maintenance management method based on digital twinning provided by an embodiment of the present application is shown in the figure.

[0067] Figure 2 A function module diagram of a historical building operation and maintenance management system based on digital twinning provided by an embodiment of the present application is shown in the figure.

[0068] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application.

[0070] The embodiments of the present application provide a historical building operation and maintenance management method based on digital twinning. The execution subject of the historical building operation and maintenance management method based on digital twinning includes but is not limited to at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the historical building operation and maintenance management method based on digital twinning can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0071] Referring to Figure 1 The figure shows a flowchart of a historical building operation and maintenance management method based on digital twinning provided by an embodiment of the present application. In this embodiment, the historical building operation and maintenance management method based on digital twinning includes:

[0072] In the embodiments of the present application, when the historical data and point cloud data based on the historical building are used to construct the digital basement of the historical building, they are specifically used for:

[0073] The architectural drawings in the historical data are digitally scanned to obtain a raster image of the historical building, and the raster image is geometrically corrected to obtain a reference base map of the historical building;

[0074] The point cloud data and the reference base map are spatially registered to obtain an aligned point cloud set of the historical building;

[0075] The building outer contour line and edge feature line in the aligned point cloud set are connected according to the spatial adjacent relationship, to obtain a closed contour line of the historical building, and the closed contour line is combined according to the spatial position of the closed contour line in the historical building, to obtain a wireframe structure of the historical building.

[0076] The repair record and material information in the historical data are taken as attribute fields, and are attached to the corresponding component node in the wireframe structure, to obtain a reference base map of the historical building.

[0077] Specifically, the paper architectural drawings of the historical building are scanned, and a corresponding scanning resolution is set to ensure that the lines and characters on the drawings are clear and distinguishable, and the scanned drawings are saved as lossless compressed format raster images. At least four feature points are selected on the raster images, which are uniformly distributed on the drawings, such as corner points of walls and corner points of door and window openings, and the real-world three-dimensional coordinates of these feature points are measured on site, or the coordinate values thereof are obtained from existing accurate surveying data.

[0078] Specifically, the point cloud data contains dense three-dimensional coordinate points of the surface of the historical building. The same-named points corresponding to the control points on the reference base map are manually or automatically identified in the point cloud, such as the prominent corners of the building facade and the eave end points. An iterative closest point algorithm is used to constantly calculate the closest point correspondence between the point cloud and the reference base map, to solve the optimal rotation matrix and translation vector, so that the same-named points in the point cloud and the corresponding points on the reference base map accurately coincide in spatial position.

[0079] Specifically, the point sets belonging to the building outer contour and edge features are separated from the aligned point cloud set by a point cloud segmentation algorithm, such as wall boundary points, roof ridge line points, and door and window opening edge points. The contour points are sorted, and the nearest adjacent points are sequentially connected according to the principle of spatial Euclidean distance, to form a series of continuous polylines. For the contour of a closed region, such as a wall section contour, the first and last points of the polylines are connected to generate a closed contour line.

[0080] Specifically, the repair records in the historical archives are sorted, including detailed text information such as the time, reason, construction technology, and construction unit of each repair, as well as description information of the material types, material sources, and material properties of each part of the building. For each component node in the wireframe structure, such as each wall, each column, and each floor slab, an independent attribute storage area is opened in the data structure.

[0081] Further, taking these feature points as control points, the mapping relationship between image pixel coordinates and real world coordinates is established through a polynomial transformation model, the whole grid image is resampled, geometric distortion caused by paper deformation and scanning angle deviation is eliminated, and each detail in the image is accurately corresponded to the actual space position, and finally a reference base map after geometric correction is obtained, which is used as a unified reference for alignment of all subsequent spatial data.

[0082] Further, after multiple iterations of optimization until the registration error is less than the preset allowable range, the transformed point cloud coordinate system is converted into the coordinate system of the reference base map, at this time the point cloud data and the reference base map are consistent in spatial position, and an aligned point cloud set is obtained.

[0083] Further, according to the actual positions of these closed contour lines in three-dimensional space, such as different floor elevations and different facade partitions, the contour lines belonging to the same structural member are combined, such as combining the inner and outer two closed contour lines of the same wall into a wireframe of the wall, and combining the bottom and side contour lines of the same beam into a wireframe of the beam, and through such combination operations, a wireframe structural model of the whole building is gradually constructed, which accurately expresses the geometric shapes and spatial relationships of the building members.

[0084] Further, according to the positions and numbers of the members in the building, the corresponding repair records and material information are added to the attribute fields of the members in the form of key-value pairs, such as adding a repair history field to a wall node, which contains the text content of the repair records, and adding a main material field, which is the specific material name. After completing the attribute addition of all members, the wireframe structure is upgraded from a simple geometric model to a digital model that integrates rich historical information, i.e. the final reference base map.

[0085] In summary, the paper historical drawings are converted into digitized images with accurate spatial reference, eliminating the geometric distortion introduced by the original drawings due to aging, paper deformation or scanning process, providing a unified and accurate coordinate reference for the alignment and fusion of all subsequent spatial data, ensuring the reliability of the digital base in spatial accuracy.

[0086] In summary, the point cloud data obtained by high-precision three-dimensional scanning is accurately unified with the reference base map after geometric correction in spatial position, so that the fine three-dimensional geometric information carried by the point cloud can be strictly corresponded to the two-dimensional contour and historical information in the drawings, laying a data foundation for accurately extracting the structural contour and feature line of the building from the point cloud, and realizing the effective fusion of multi-source heterogeneous data.

[0087] In general, the discrete point cloud data is converted into a wireframe structure model composed of continuous closed contour lines with clear topological relationship, which not only accurately expresses the geometric shape and spatial position relationship of each component of the building, but also provides a structured carrier for subsequent bearing attribute information, so that the building information can be organized and managed according to the components.

[0088] In general, the non-geometric information originally dispersed in the historical archives is associated with the specific component nodes in the wireframe structure one by one, so that the digital model not only has geometric shape, but also carries rich historical and attribute information, thereby completing the sublimation from a pure geometric model to an information model, and finally forming a reference base map which provides a data basis containing complete historical background for subsequent monitoring, analysis and management based on digital twinning.

[0089] In the embodiment of the present application, when the monitoring area is divided in the digital base according to the load-bearing structure, value element distribution and functional space of the historical building, and the data identifier is associated with the monitoring area, it is specifically used for:

[0090] The continuous component nodes belonging to the same structural stress path in the digital base are collected into the load-bearing structure area of the historical building;

[0091] The component nodes which are spatially adjacent and have the same value level in the digital base are merged into the value element area of the historical building;

[0092] According to the functional partition boundary line in the digital base, the component nodes located in the same functional partition are aggregated into the functional space area of the historical building;

[0093] The boundaries of the load-bearing structure area, the value element area and the functional space area are superimposed to obtain a comprehensive area framework of the historical building, and the overlapping parts of the comprehensive area framework are cut and divided to obtain a defined block of the historical building;

[0094] A feature area code is generated for the defined block, and the feature area code is written into the metadata field contained in the spatial block of the defined block to obtain a data identifier of the spatial block.

[0095] Specifically, in the digital base, first, all component nodes are traversed, and the structural function borne by each node is identified, such as beams, columns, load-bearing walls, etc. According to the principle of building structural mechanics, the main path of load transmission from the roof, floor to the foundation is determined, which is composed of a series of interconnected component nodes.

[0096] Specifically, in the digital substrate, all component nodes are traversed, and the pre-stored value evaluation attribute value of each node is read, which is determined according to the value evaluation standard of the historical building, for example, components with extremely high historical value are marked as first-class value, and components with higher historical value are marked as second-class value.

[0097] Specifically, in the digital substrate, according to the functional zoning design drawing or the use status survey result of the historical building, the clear functional zoning boundary lines are pre-drawn in the spatial coordinate system of the digital substrate, and these boundary lines are closed polylines, which divide the interior space of the building into different functional areas, such as exhibition area, office area, storage area, traffic area, etc.

[0098] Specifically, during superimposition, the original boundaries of all areas are retained, and these boundaries are merged in three-dimensional space to form a comprehensive area framework diagram covering the entire historical building, which is composed of a plurality of complex boundary lines. In this comprehensive area framework diagram, the originally independently divided area boundaries intersect with each other, resulting in a plurality of overlapping space blocks.

[0099] Specifically, the generation rule of the code adopts a hierarchical combination manner, first, the building floor number or the partition number of the defined block in the overall space of the building is determined as the first part of the code according to the position of the block in the overall space of the building, then the structure type code of the block is determined as the second part of the code according to the main load-bearing structure characteristics contained in the block, then the value level code of the block is determined as the third part of the code according to the main value element level contained in the block, and finally the function type code of the block is determined as the fourth part of the code according to the function space type to which the block belongs, and the four parts are combined by a connecting symbol to form a complete feature area code.

[0100] Further, by analyzing the connection relationship between the nodes, the nodes located on the same load transfer path and adjacent to each other are connected in series to form a continuous node set. For example, starting from the main beam node of the roof, connecting to the column node supporting it, and then connecting to the cross beam node below, until the foundation node, these nodes together constitute a complete structure stress path. All continuous node sets belonging to different stress paths are marked respectively, and each such set is an independent load-bearing structure area.

[0101] Further, by using a spatial neighborhood search method, for each node with the same value level, the nodes directly adjacent in space position around the node are found, and these adjacent nodes may be connected by shared faces, shared edges or very short distances. All nodes that are adjacent in space and have the same value level are gradually merged to form a continuous value element area, for example, an area formed by merging a plurality of adjacent and first-class value fine wood carving component nodes, or an area formed by merging a plurality of adjacent and first-class value doors and windows nodes.

[0102] Further, all component nodes in the digital base are traversed, and it is determined one by one whether the spatial coordinate point where each node is located is located inside a functional partition boundary line. A spatial judgment method of point and polygon relationship is adopted, a ray is emitted in an arbitrary direction from the component node coordinate point, the number of intersection points of the ray and the functional partition boundary line is calculated, if the number of intersection points is odd, the node is located inside the boundary line, and it is determined that it belongs to the functional partition. All component nodes located inside the same functional partition boundary line are aggregated together to form a functional space region, for example, all wall bodies, floors, showcases and other component nodes located inside the exhibition area boundary line jointly constitute the exhibition functional space region.

[0103] Further, these overlapping blocks are cut and divided, that is, the boundary lines are broken at the intersection points of all boundary lines, and then the surface domains are regenerated according to the broken line segments, so that each new surface domain generated finally has a unique boundary combination and no longer overlaps with other surface domains. These new surface domains obtained after cutting and having independent and non-overlapping spatial ranges are the defined blocks of the historical building, and each defined block corresponds to a spatially uniquely determined part.

[0104] Further, in the data structure of the digital base, each defined block corresponds to a spatial block, the metadata storage area of the spatial block is found, and the generated feature region code is written into the metadata storage area as a piece of metadata. The feature region code becomes the data identifier of the spatial block, which is used to uniquely identify and index the spatial block in subsequent operation and maintenance.

[0105] In summary, the key structural system bearing the overall load of the building is extracted from the complex building model, the dispersed beams, columns, walls and other components are reorganized according to the mechanical transmission path to form a load-bearing region with clear physical meaning, so that the subsequent monitoring and maintenance can focus on the parts that have a decisive influence on the safety of the building, and a targeted spatial unit is provided for structural safety evaluation.

[0106] In summary, the components with the same protection value level scattered in the building are spatially clustered to form a continuous value element region, so that the high-value components are no longer considered in isolation, but as a whole for unified management and protection, and it is convenient to subsequently develop differentiated monitoring frequency and protection strategies for regions with different value levels to ensure the overall preservation of historical value.

[0107] Overall, the building model is combined with the actual use function, the components are re-grouped according to different function requirements such as exhibition, office, storage, etc., so that the subsequent operation data can correspond to the specific use scene, and an organizational framework is provided for evaluating the environmental adaptability, use intensity of different function spaces and formulating function-based operation and maintenance measures.

[0108] Overall, the three sets of division systems based on mechanics, value and use function are spatially integrated, the boundary conflicts between different division standards are eliminated through boundary superposition and cutting division, and the minimum management unit, i.e. the defined block, which is unique and non-overlapping in space, is generated, each block has structure attribute, value attribute and function attribute at the same time, which lays a spatial foundation for subsequent multi-dimensional and refined data management.

[0109] Overall, a globally unique identity is given to each spatially unique defined block, the code comprehensively reflects the structure, value and function characteristics of the block, realizes the accurate correspondence between the space unit and the data record, and makes all subsequent monitoring data, maintenance records and management information able to be quickly indexed and associated through the identifier, providing a key data retrieval means for building a traceable and queryable dynamic operation and maintenance archive.

[0110] In the embodiment of the application, when the operation data of the historical building is respectively mapped to the storage space of the corresponding component in the digital substrate based on the monitoring area to which the operation data belongs, the dynamic operation and maintenance archive of the historical building and the corresponding monitoring data stream are obtained, and the operation data is specifically used for:

[0111] Obtaining the operation data packet of the historical building, the operation data including a collection timestamp, a collection point spatial coordinate and a monitoring index value;

[0112] Matching the collection point spatial coordinate with the spatial range of the monitoring area in the digital substrate to determine the reference monitoring area to which the operation data packet belongs;

[0113] Locating the corresponding component node space in the digital substrate according to the data identifier of the reference monitoring area;

[0114] Writing the monitoring index value into the monitoring data field of the component node space in sequence according to the timestamp order to obtain an independent area monitoring sequence of the historical building;

[0115] Merging the independent area monitoring sequence to obtain a monitoring data stream of the historical building;

[0116] Summarizing the monitoring data stream and the real-time data field of the corresponding component node to obtain a dynamic operation and maintenance archive of the historical building.

[0117] Specifically, raw monitoring data is received from sensor networks, IoT data collectors or manual inspection terminals deployed in each monitoring area of the historical building in real time or at a fixed time. The data is encapsulated into standard format operation data packets according to a predefined data structure.

[0118] Specifically, each monitoring area in the digital substrate stores the spatial range enclosed by the boundary polygon of the monitoring area in the data structure. For each collection point coordinate, a ray method is used to test the inclusion of the point in the polygon.

[0119] Specifically, according to the data identifier of the reference monitoring area matched from the operation data packet, an accurate search is performed in the data structure of the digital substrate. The digital substrate uses a tree-like or graph-like data organization method, and the data identifier of each monitoring area is used as the primary key to establish a mapping relationship with the corresponding component node storage space.

[0120] Specifically, all operation data packets belonging to the same reference monitoring area are sorted in order of their collection time stamps. After sorting, the ordered data packets are iterated in turn to extract the monitoring indicator values. For each monitoring indicator value, a new data record is created in the monitoring data field of the component node space according to its collection time stamp, and the record contains two parts: the time stamp and the indicator value.

[0121] Specifically, the independent area monitoring sequences of all monitoring areas in the digital substrate are obtained respectively. These sequences are distributed in different component node spaces. In order to form a global monitoring data view, all independent area monitoring sequences are globally merged and sorted according to the collection time stamps to build a unified time sequence covering all monitoring points of the historical building.

[0122] Specifically, the historical building monitoring data stream generated in the previous step is integrated with the real-time data fields of all component nodes in the digital substrate. The real-time data field is a data area reserved in each component node space for storing the latest monitoring state, such as the current latest monitoring value, data update time, warning state, etc. By iterating through each component node in the digital substrate, the content of the real-time data field is updated according to the latest record of the corresponding node in the monitoring data stream.

[0123] Further, each operation data packet contains three core elements: a collection time stamp, which accurately records the time when the data is generated or collected, in the format of a standardized time string including year, month, day, hour, minute and second; a collection point space coordinate, which represents the position of the monitoring point in the actual space with three-dimensional coordinate values, and the coordinate is based on the same spatial reference system as the digital substrate; and a monitoring indicator value, which is the specific value of the physical quantity measured by the sensor, such as vibration frequency, inclination angle, crack width, temperature and humidity value, etc.

[0124] Furthermore, draw a ray from this point in any direction and calculate the number of intersections between the ray and the polygonal boundary of the monitoring area. If the number of intersections is odd, the point is located inside the polygon, and the coordinates of the acquisition point are determined to fall within this monitoring area. Traverse all monitoring areas until a unique monitoring area containing the coordinates of the acquisition point is found. This area is then identified as the baseline monitoring area to which this running data packet belongs, and the unique identifier of this baseline monitoring area is recorded and associated with this data packet.

[0125] Furthermore, by constructing a hash index and directly searching using the data identifier as the key, the storage space address within the digital matrix that stores all component data within the monitoring area can be instantly located. This component node space is a memory region or database record set specifically used to store the geometry, attributes, and monitoring data of each component node belonging to this monitoring area. Once the location is successful, a channel for writing data to this component node space is opened.

[0126] Furthermore, the monitoring data fields adopt a time-series database storage method, with new records being appended to the monitoring sequence corresponding to each component node. As data is continuously written, each component node in the monitoring area forms a time-ordered sequence of monitoring values, which together constitute an independent regional monitoring sequence for that monitoring area.

[0127] Furthermore, during the merging process, the original regional identifiers and component node information of each monitoring data point are retained to ensure data traceability. The long sequence obtained after merging and sorting is the monitoring data stream that reflects the changes of various parts of the historical building over time as a whole. This data stream provides a unified data source for subsequent analysis and visualization.

[0128] Furthermore, the static attribute data of all component nodes, historical maintenance records, real-time monitoring data, and the complete monitoring data stream are organized into a unified archive database to form a dynamic operation and maintenance archive for historical buildings. This archive uses component nodes as the basic unit and fully records comprehensive data from the building's historical information to its current real-time status, supporting queries and analyses by time, region, component, and other dimensions.

[0129] In summary, it standardizes the unified format of raw data collected by various sensors and monitoring equipment, and encapsulates the three core elements of time, space and numerical values ​​in a structured manner. This provides a standardized data carrier for monitoring data from different sources and of different types, and provides a complete and resolvable data source for all subsequent time- and space-based data processing and analysis, eliminating integration barriers caused by differences in data formats.

[0130] Overall, the real-time monitoring data of the physical world is accurately matched with the virtual space position in the digital twin model, and through the spatial inclusion relationship judgment, the discrete collection point data is attributed to the pre-divided monitoring area, ensuring that each data packet can be correctly classified to the actual spatial position, providing a spatial index basis for subsequent data storage, retrieval and analysis by area.

[0131] Overall, the association path from the monitoring area to the specific component storage unit is established, and using the data identifier as the retrieval key, the physical memory or database location responsible for storing the area component data in the digital base can be quickly and accurately located, realizing accurate jumping from macro area division to micro component storage, and establishing an efficient access channel for subsequent fine-grained data writing.

[0132] Overall, the continuous monitoring data is organized according to the component nodes, and a strictly ordered monitoring value sequence is formed in the storage space of each component node, which records the state evolution process of the component on the time axis, providing a structured data basis for subsequent analysis of the long-term change law and fluctuation characteristics of individual components and time series analysis.

[0133] Overall, the independent time series dispersed in each component node is globally merged to form a unified time series data stream covering all monitoring points of the historical building, which breaks the spatial isolation between components and presents the global trend of monitoring indicators over time from the perspective of the whole building, providing a complete data view for subsequent global spatio-temporal analysis and anomaly detection.

[0134] Overall, the deep fusion of static historical information and dynamic real-time monitoring data is completed, and the global monitoring data stream and the latest state data of each component node are integrated into a unified file structure to form a comprehensive information library with historical background and real-time state. This dynamic operation file can be updated automatically with new data, providing comprehensive, accurate and timely data support for subsequent anomaly diagnosis, maintenance decision and value assessment.

[0135] In the embodiment of the application, when the monitoring data stream is visually encoded with the collection time of the monitoring data stream as the horizontal axis and the spatial topology structure of the digital base as the vertical axis to obtain the spatio-temporal distribution map of the historical building, it is specifically used for:

[0136] determine a mapping rule between the index value and the visual feature parameter in the monitoring data stream, the mapping rule comprising: determining a color hue according to a value interval in the monitoring data stream, determining a color saturation according to a value change rate in the monitoring data stream, and determining a graph transparency according to a value accumulation in the monitoring data stream;

[0137] retrieve a corresponding component node in the digital substrate according to a region identifier of a monitoring region to which the monitoring data stream belongs, and read a longitudinal axis coordinate value of the component node in a spatial topology of the digital substrate;

[0138] convert the index value into a corresponding visual feature parameter value according to the mapping rule;

[0139] construct a two-dimensional coordinate system of the historical building with the collection timestamp of the monitoring data stream as the horizontal coordinate and the longitudinal axis coordinate value as the longitudinal axis positioning point, and draw a pixel point in the two-dimensional coordinate system point by point with the visual feature parameter value as the pixel attribute value;

[0140] arrange the pixel points into a regular row-column structure in the order of horizontal coordinate time sequence and longitudinal coordinate space sequence to obtain a time-space two-dimensional grayscale matrix of the historical building;

[0141] splice the time-space two-dimensional grayscale matrix according to the spatial topology of the digital substrate to obtain a time-space distribution map of the historical building.

[0142] Specifically, first, statistical analysis is performed on all index values in the monitoring data stream to determine the entire value change range of each monitoring index, and the range is divided into multiple continuous value intervals, for example, the vibration amplitude is divided into low, medium and high frequency intervals from minimum to maximum, or more finely. A specific color hue is assigned to each value interval, for example, the low frequency interval corresponds to blue, the medium frequency interval corresponds to green, and the high frequency interval corresponds to red, so that the size of the monitoring index value can be directly represented by the color hue.

[0143] Specifically, in order to determine the longitudinal axis position of each pixel point in the time-space map, the spatial height information corresponding to each monitoring data stream needs to be obtained. According to the region identifier of the monitoring region to which the monitoring data stream belongs, all component nodes contained in the monitoring region are directly located in the data structure of the digital substrate by a hash retrieval method.

[0144] Specifically, for each monitoring record in the monitoring data stream, i.e., the index value corresponding to each collection time, the mapping rule determined in the first step is applied to convert the visual feature parameter. First, determine which pre-divided value interval the index value falls into, and determine the color hue of the record according to the corresponding relationship between the interval and the color hue.

[0145] Specifically, a two-dimensional coordinate system is constructed for drawing the space-time atlas, taking the time stamp as the horizontal axis and the vertical axis coordinate value of the component node read from the digital base as the vertical axis. In this two-dimensional plane, for each monitoring record in the monitoring data stream, the horizontal coordinate position is determined according to the time stamp, and the vertical coordinate position is determined according to the vertical axis coordinate value of the component node to which it belongs, thereby determining a unique pixel point drawing point.

[0146] Specifically, in the horizontal axis direction, all pixel points are arranged in equal intervals from left to right according to the order of the time stamps, ensuring the continuity in time. In the vertical axis direction, all pixel points are arranged from low to high according to the size of the vertical axis coordinate value read from the digital base, ensuring the order in space.

[0147] Specifically, the spatial topology of the digital base not only contains the height information, but also contains the plan layout in the horizontal direction, such as the spatial relationship of different rooms and different facades. According to these horizontal spatial relationships, the space-time two-dimensional gray matrix corresponding to different horizontal positions is spliced, for example, the gray matrices of the east facade, south facade, west facade and north facade are combined according to their actual orientation, or the gray matrices of the first floor, second floor and third floor are stacked in order from top to bottom.

[0148] Further, for the value of each time point in the monitoring data stream, the rate of change of the value relative to the value of the previous time point is calculated, the rate is determined by comparing the difference between the values of the adjacent two time points, and the high and low of the change rate is mapped to the strength of the color saturation, the higher the change rate, the higher the color saturation, the brighter the color, and vice versa. The lower the saturation, the darker the color. Finally, for each monitoring point in the monitoring data stream, the cumulative amount of the value from the start of monitoring to the current time is calculated, such as the cumulative deformation or cumulative vibration energy, and the size of the cumulative amount is mapped to the strength of the graph transparency, the larger the cumulative amount, the lower the transparency, the more opaque the graph, and vice versa. The higher the transparency, the more transparent the graph, and the three mapping rules together determine the complete conversion basis from the index value to the visual feature parameter.

[0149] Further, the spatial topology of the digital base presets a vertical axis coordinate value for each component node, which can be the center point height of the component node or the height of the floor where the component node is located relative to the building datum, representing the position of the component in the vertical direction of the building. The vertical axis coordinate value is read from the located component node, which will be used as the vertical axis positioning point for drawing the pixel points of the monitoring data stream in the two-dimensional coordinate system, ensuring that the monitoring data at different heights can be arranged according to their actual spatial height in the atlas.

[0150] Further, the difference between the index value and the value of the adjacent previous time is calculated, and the color saturation is determined according to the difference, the larger the difference, the higher the saturation. Then, all the index values of the monitoring point from the monitoring starting time to the current time are accumulated to obtain the current time cumulative amount, and the graph transparency is determined according to the cumulative amount, the larger the cumulative amount, the lower the transparency. After the three steps of conversion, the index value of each monitoring time corresponds to a complete set of visual feature parameter values, including the determined hue, saturation and transparency, which will be used for subsequent pixel point drawing.

[0151] Further, the visual feature parameter values obtained by mapping and converting the monitoring record, i.e. hue, saturation and transparency, are combined into the color attribute value of the pixel point. In the two-dimensional coordinate system, each pixel point is drawn point by point according to the determined horizontal and vertical coordinate positions, and each pixel point represents the visual expression of the monitoring index value at a specific time and a specific height position.

[0152] Further, after such arrangement, the originally discrete pixel points are organized into a two-dimensional matrix with fixed number of rows and columns, and each element in the matrix corresponds to the color value of a pixel point. This matrix is a space-time two-dimensional gray matrix, which completely records the distribution of the monitoring index in the entire time period and the entire spatial height.

[0153] Further, after such splicing operation, a space-time distribution map comprehensively reflecting the overall space-time change characteristics of the historical building is finally generated, which intuitively displays the evolution law of the monitoring index in the time and space dimensions in the form of a two-dimensional image.

[0154] In summary, a quantitative conversion relationship between the physical values of the monitoring index and the image visual elements is established. The color hue is determined by the numerical interval, so that the observer can quickly judge the numerical size range by the color type. The color saturation is determined by the numerical change rate, so that the change intensity can be intuitively reflected by the color brightness. The graph transparency is determined by the numerical cumulative amount, so that the long-term cumulative effect can be perceived by the image permeability. This mapping rule provides a complete coding basis for subsequent conversion of abstract multi-dimensional monitoring data into intuitive two-dimensional images.

[0155] In summary, the accurate association between the monitoring data stream and the spatial height position is realized, the specific component node is quickly located through the region identifier, and the pre-defined vertical axis coordinate value is extracted therefrom. The vertical axis coordinate value represents the actual height of the component in the vertical direction of the building, which provides a spatial positioning reference for accurately arranging the monitoring data at different height positions in the two-dimensional coordinate system in the subsequent process, and ensures that the space-time map can truly reflect the distribution characteristics of the monitoring index with the change of height.

[0156] Overall, the indicator value of each time point in the monitoring data stream is converted into visual feature parameter values composed of hue, saturation and transparency according to a predetermined mapping rule, and this conversion process enables the physical monitoring data that cannot be directly observed to obtain color properties that can be processed and rendered by a computer graphics system, providing directly usable pixel color values for subsequent pixel point drawing in a two-dimensional coordinate system, and completing the digital mapping from the physical world to the visual world.

[0157] Overall, the time and space information is fused with the visual features of the monitoring indicators, and in the constructed two-dimensional plane, the horizontal coordinate of each pixel point corresponds to the time of data collection, the vertical coordinate corresponds to the height position of the component, and the color of the pixel carries the monitoring indicator value feature at the time and position, which condenses multi-dimensional heterogeneous data into a single image element and realizes the visualization mapping of monitoring information in the two-dimensional space of time and space.

[0158] Overall, the discrete pixel points are regularly arranged according to strict time sequence and spatial sequence to form a regular matrix structure with fixed number of rows and columns, the rows of the matrix correspond to different spatial height positions, the columns of the matrix correspond to different collection time points, and each element value in the matrix is the color value of the corresponding pixel, which provides standardized data input for subsequent computer image processing, pattern recognition and quantitative analysis.

[0159] Overall, a plurality of two-dimensional gray-scale matrices representing different building facades or different floors are combined and spliced according to the real spatial position relationship recorded in the digital base, and finally a comprehensive image is generated, which can comprehensively reflect the change rule of the monitoring indicators of the entire historical building in the two dimensions of time and space, and this space-time distribution map enables managers to observe the spatial distribution position of the abnormal area, the time range of the abnormal occurrence and the evolution trend of the abnormality with time and space, providing an intuitive and efficient decision support tool for quickly locating problems, analyzing causes and formulating maintenance strategies.

[0160] In the embodiment of the application, when the abnormal area of the historical building is determined according to the fluctuation pattern difference and trend evolution deviation of the real-time data and the historical archive data in the space-time distribution map, and the area identifier and the abnormal time period of the abnormal area are bound to obtain an abnormal area list of the historical building, it is specifically used for:

[0161] The real-time data and the historical archive data of the space-time distribution map are respectively subjected to fluctuation pattern decomposition to obtain the number of rising sections, the number of falling sections, the number of wave crests and the number of wave troughs of the real-time data and the historical archive data, and generate real-time fluctuation feature vectors and historical fluctuation feature vectors of the historical building.

[0162] The Euclidean distance between the real-time fluctuation feature vector and the historical fluctuation feature vector is taken as the fluctuation pattern difference value of the historical building;

[0163] The difference between the linear regression slope of the real-time data and the linear regression slope of the historical archive data is taken as the trend evolution deviation of the historical building;

[0164] The fluctuation pattern difference value and the trend evolution deviation are fused to obtain a comprehensive anomaly index of the monitoring area;

[0165] The statistical distribution characteristics of the comprehensive anomaly index are used to determine an anomaly judgment reference value of the historical building, and a monitoring area whose comprehensive anomaly index exceeds the anomaly judgment reference value is marked as an anomaly area of the historical building.

[0166] Specifically, real-time time series data of each monitoring area and historical archive time series data corresponding to the historical period are extracted from the spatiotemporal distribution map, and fluctuation pattern decomposition is performed on the two sequences respectively. During decomposition, first, each data point on the entire time series is traversed, the numerical values of the current point and the adjacent points before and after it are compared, and the number of wave peaks and wave troughs is identified by detecting local extreme points: if the numerical value of a point is greater than that of its adjacent two points before and after it, the point is marked as a wave peak; if the numerical value of a point is less than that of its adjacent two points before and after it, the point is marked as a wave trough.

[0167] Specifically, the real-time fluctuation feature vector obtained in the previous step and the historical fluctuation feature vector are subjected to difference measurement, and the Euclidean distance is taken as the fluctuation pattern difference value. Specifically, the two vectors are regarded as two points in a four-dimensional space, and first, the difference in each dimension between the two vectors is calculated, i.e., the number of real-time rising sections minus the number of historical rising sections, the number of real-time falling sections minus the number of historical falling sections, the number of real-time wave peaks minus the number of historical wave peaks, and the number of real-time wave troughs minus the number of historical wave troughs, to obtain four difference values.

[0168] Specifically, linear regression analysis is performed on the real-time time series data and the historical archive time series data of the same monitoring area to obtain the trend evolution slope of each. The purpose of linear regression is to find a straight line that best represents the overall trend of the data, i.e., the linear relationship between time and index value. First, the average value of all time points and the average value of all index values in the real-time data and the historical archive data are calculated respectively.

[0169] Specifically, the calculated fluctuation pattern difference value and the trend evolution deviation degree are fused to obtain a comprehensive abnormality index of each monitoring area. The fusion adopts a weighted summation manner, and first, a weight coefficient is respectively assigned to the fluctuation pattern difference value and the trend evolution deviation degree. The two weight coefficients are pre-set according to factors such as the structural characteristics of the historical building and the importance of the monitoring index.

[0170] Specifically, the comprehensive abnormality indexes of all monitoring areas at the current time are collected to form a data set. The statistical distribution characteristics of the data set are calculated, for example, the average value and the standard deviation of all comprehensive abnormality indexes are calculated, and then the average value plus twice the standard deviation is taken as the abnormality judgment reference value; or the percentile method is adopted, the data is sorted from small to large, and the corresponding percentile is taken as the abnormality judgment reference value.

[0171] Further, while marking the wave peaks and wave troughs, the total number of all marked wave peaks and wave troughs in the entire sequence is counted. Next, the time sequence is divided into continuous rising sections and falling sections according to the monotonic change of the values: starting from the beginning of the sequence, the values of adjacent points are compared, if the value of the latter point is greater than that of the former point, it enters the rising section, and continues until a decrease occurs, and each continuous rising area is counted as a rising section; similarly, a continuous falling area is counted as a falling section, and after traversing the entire sequence, the total number of rising sections and falling sections is obtained. The four statistical quantities, i.e. the number of rising sections, the number of falling sections, the number of wave peaks and the number of wave troughs, are combined in order to form a four-dimensional vector, which is the fluctuation feature vector of the time sequence. Real-time data corresponds to generate real-time fluctuation feature vectors, and historical archive data corresponds to generate historical fluctuation feature vectors.

[0172] Further, each difference value is squared to obtain four square values, and then the four square values are added to obtain a total sum. Finally, the square root of the total sum is calculated, and the obtained value is the fluctuation pattern difference value, which quantitatively reflects the overall deviation degree between the real-time fluctuation pattern of the monitoring area and the historical typical fluctuation pattern.

[0173] Further, for each time point of the real-time data, the difference between the time point and the time average value, and the difference between the value of the index at the time point and the average value of the index are calculated, the two differences are multiplied and accumulated to obtain the numerator; at the same time, the difference between each time point and the time average value is squared and accumulated to obtain the denominator; the numerator is divided by the denominator to obtain the linear regression slope of the real-time data. The linear regression slope of the historical archive data is calculated in the same way. Finally, the linear regression slope of the real-time data is subtracted from the linear regression slope of the historical archive data, and the obtained difference is the trend evolution deviation degree, which reflects the deviation direction and amplitude of the change trend of the current monitoring area relative to the historical long-term trend.

[0174] Further, the fluctuation pattern difference value of each monitoring area is then multiplied by its weight to obtain a weighted fluctuation difference value; the trend evolution deviation of each monitoring area is multiplied by its weight to obtain a weighted trend deviation value; and finally, the two weighted values are added to obtain a comprehensive anomaly index of the monitoring area. The index is a dimensionless value that integrates the information of local fluctuation anomaly and overall trend anomaly, and the larger the value, the higher the possibility of abnormality in the area.

[0175] Further, after determining the reference value, each monitoring area is traversed, and its comprehensive anomaly index is compared with the reference value. If the comprehensive anomaly index of the area is greater than the reference value, the area is marked as an abnormal area of the historical building, and the area identifier of the abnormal area is recorded for subsequent detailed investigation and operation management. All marked abnormal areas and their identifiers collectively constitute an abnormal area list.

[0176] In summary, complex time series monitoring data is converted into a quantified vector composed of four key pattern features. By extracting local extreme points and monotonic change intervals of data sequences on the time axis, the internal structure of data fluctuations can be accurately described, making it possible to mathematically compare fluctuation patterns of different time periods and different monitoring areas, and providing stable and quantifiable feature inputs for anomaly detection.

[0177] In summary, the Euclidean distance, a mature measurement method, is used to quantitatively compare the fluctuation patterns of current monitoring data and historical normal data. By calculating the straight-line distance of two four-dimensional vectors in space, the similarity in pattern is converted into a single numerical indicator. The greater the distance, the more significant the difference between the current fluctuation pattern and the historical typical pattern, thereby providing an objective basis for determining whether there is a local abnormal fluctuation.

[0178] In summary, by comparing the slope difference between the overall trend of current data and the long-term trend of historical data, the cumulative changes of monitoring indicators on a long time scale can be effectively captured, such as slow evolution processes such as structural continuous deformation and gradual degradation of material performance. This deviation index and the pattern difference value reflecting local fluctuation complement each other and jointly constitute a comprehensive description of abnormal states.

[0179] In summary, the pattern difference reflecting short-term local fluctuation and the evolution deviation reflecting long-term overall trend are comprehensively considered, and a single index that can fully represent the abnormal state of the monitoring area is generated through weighted fusion. This index contains both sudden abnormal information and gradual degradation information, avoiding the possible misjudgment or omission of a single index, and improving the accuracy and robustness of anomaly detection.

[0180] Overall, the method based on the statistical characteristics of data itself dynamically determines the abnormal threshold, avoids the subjectivity and randomness of manually setting the threshold, and can adaptively identify the regions that significantly deviate from the normal range by analyzing the distribution of the comprehensive anomaly index of all monitoring regions, and record them in the abnormal region list together with the corresponding region identifier, thereby providing accurate target positioning for subsequent targeted exploration and maintenance.

[0181] In the embodiment of the present application, the calculation formula of the comprehensive anomaly index is specifically used for:

[0182]

[0183] wherein, is the comprehensive anomaly index, is the fluctuation form item weight coefficient, is a natural constant, is the fluctuation form difference value, is the fluctuation form difference mean value, is the fluctuation form difference maximum value, is the fluctuation form nonlinear index, is the trend evolution item weight coefficient, is the trend evolution deviation degree, is the trend evolution deviation degree standard deviation, is the trend evolution deviation degree maximum value, is the trend evolution nonlinear index, is the value coupling item weight coefficient, is the comprehensive value coefficient, is the comprehensive value coefficient maximum value.

[0184] Specifically, the source of the fluctuation pattern difference value is the Euclidean distance calculation of the real-time fluctuation feature vector and the historical fluctuation feature vector. When calculating, the first difference value is obtained by subtracting the number of rising segments of the historical data from the number of rising segments of the real-time data, the second difference value is obtained by subtracting the number of falling segments of the historical data from the number of falling segments of the real-time data, the third difference value is obtained by subtracting the number of wave crests of the historical data from the number of wave crests of the real-time data, and the fourth difference value is obtained by subtracting the number of wave troughs of the historical data from the number of wave troughs of the real-time data. After the four difference values are squared and added, the square root of the sum is calculated to obtain the fluctuation pattern difference value. The source of the fluctuation pattern difference average value is the arithmetic average calculation of the fluctuation pattern difference values of all monitoring areas. The fluctuation pattern difference values of all monitoring areas are added one by one to obtain a total sum, and the total sum is divided by the total number of monitoring areas to obtain the fluctuation pattern difference average value. The source of the fluctuation pattern difference maximum value is the traversal comparison of the fluctuation pattern difference values of all monitoring areas, and the one with the largest value is selected as the fluctuation pattern difference maximum value. The source of the fluctuation pattern nonlinear index is a fixed value preset according to the structural characteristics of the historical building and the monitoring index type, which is used to adjust the nonlinear influence degree of the fluctuation pattern difference value in the comprehensive anomaly index. The source of the trend evolution deviation degree is the linear regression of the overall trend of the real-time data to obtain the real-time linear regression slope, and the linear regression of the overall trend of the historical archive data to obtain the historical linear regression slope. The difference between the real-time linear regression slope and the historical linear regression slope is the trend evolution deviation degree. The source of the trend evolution deviation degree standard deviation is the standard deviation calculation of the trend evolution deviation degrees of all monitoring areas. First, the average value of the trend evolution deviation degrees of all monitoring areas is calculated, then the difference between the trend evolution deviation degree of each monitoring area and the average value is calculated, each difference value is squared and added to obtain a square sum, the square sum is divided by the total number of monitoring areas, and the result is square rooted to obtain the trend evolution deviation degree standard deviation. The source of the trend evolution deviation degree maximum value is the traversal comparison of the trend evolution deviation degrees of all monitoring areas, and the one with the largest value is selected as the trend evolution deviation degree maximum value. The source of the trend evolution nonlinear index is a fixed value preset according to the structural characteristics of the historical building and the monitoring index type, which is used to adjust the nonlinear influence degree of the trend evolution deviation degree in the comprehensive anomaly index. The source of the comprehensive value coefficient is the value obtained by comprehensively evaluating the historical value, cultural value, artistic value of the components included in the monitoring area and their importance in the building structure. This value is obtained by weighting the average of the scores given by multiple experts according to the unified value evaluation standard. The source of the maximum value of the comprehensive value coefficient is the traversal comparison of the comprehensive value coefficients of all monitoring areas, and the one with the largest value is selected as the maximum value of the comprehensive value coefficient.The source of the weight coefficient of the fluctuation pattern item is a fixed value preset according to the influence degree of the fluctuation pattern on the safety of the building, which is used to adjust the proportion of the fluctuation pattern difference item in the comprehensive anomaly index. The source of the weight coefficient of the trend evolution item is a fixed value preset according to the influence degree of the trend evolution on the safety of the building, which is used to adjust the proportion of the trend evolution deviation item in the comprehensive anomaly index. The source of the weight coefficient η of the value coupling item is a fixed value preset according to the priority of the value element in the operation and maintenance management, which is used to adjust the proportion of the value coupling item in the comprehensive anomaly index. The source of the natural constant is a fixed constant in mathematics, which is used to construct the exponential decay function.

[0185] Further, the formula is meaningful in constructing a comprehensive evaluation index that can comprehensively reflect the abnormal state of the monitoring area of the historical building. The first item in the formula is normalized by comparing the fluctuation pattern difference value with the fluctuation pattern difference mean value, combined with the fluctuation pattern difference maximum value, and then introducing a natural constant to construct an exponential function, so that when the fluctuation pattern difference value is less than the fluctuation pattern difference mean value, the output value is compressed, and when the fluctuation pattern difference value is greater than the fluctuation pattern difference mean value, the output value is significantly amplified. The fluctuation pattern nonlinear index further strengthens this amplification effect, thereby achieving sensitive capture of local fluctuation anomalies. The second item in the formula is normalized by comparing the trend evolution deviation degree with the trend evolution deviation degree standard deviation, combined with the trend evolution deviation degree maximum value, and then introducing a natural logarithm function to construct a growth function, so that when the trend evolution deviation degree is small, the output value grows faster with the growth, and when the trend evolution deviation degree is large, the growth speed of the output value gradually slows down. The trend evolution nonlinear index further adjusts the shape of the growth curve, thereby achieving reasonable measurement of long-term trend deviation. The third item in the formula ternary couples the comprehensive value coefficient with the fluctuation pattern difference value and the trend evolution deviation degree, and divides by the product of the maximum value of the comprehensive value coefficient, the maximum value of the fluctuation pattern difference, and the maximum value of the trend evolution deviation degree for global normalization, so that the higher the value of the monitoring area, the greater the weight of the fluctuation anomaly and the trend anomaly in the comprehensive anomaly index, reflecting the greater influence of the abnormality of the high-value area on the overall evaluation. After multiplying the three items by their respective weight coefficients, the comprehensive anomaly index can reflect the information of local fluctuation anomaly, long-term trend deviation, and value element coupling.

[0186] In general, the formula trend shows that the first output value gradually increases from zero, and when the fluctuation pattern difference value exceeds the fluctuation pattern difference mean value, the growth rate significantly accelerates, and finally tends to the limit value determined by the fluctuation pattern term weight coefficient and the fluctuation pattern nonlinear index. The second output value increases from zero at a faster rate, and as the trend evolution deviation continues to increase, the growth rate gradually slows down, and finally tends to the limit value determined by the trend evolution term weight coefficient and the trend evolution nonlinear index. The third output value is directly proportional to the product of the comprehensive value coefficient, the fluctuation pattern difference value and the trend evolution deviation T, and when the comprehensive value coefficient, the fluctuation pattern difference value and the trend evolution deviation all increase, the output value of this item presents a cubic level growth trend, but when any one of the parameters is zero, the output value of this item is zero. The overall trend of the comprehensive anomaly index is to increase with the increase of the fluctuation pattern difference value, the trend evolution deviation and the comprehensive value coefficient, and when the fluctuation pattern difference value and the trend evolution deviation respectively exceed the fluctuation pattern difference mean value and the trend evolution deviation standard deviation in numerical value, the growth rate of the comprehensive anomaly index will significantly improve, and when the comprehensive value coefficient approaches the maximum value of the comprehensive value coefficient, the contribution proportion of the third item to the comprehensive anomaly index will significantly increase, making the comprehensive anomaly index of the high value area more sensitive to fluctuations and trend changes.

[0187] In the embodiment of the present application, when the area identifier in the abnormal area list is used to update the dynamic operation and maintenance archive to obtain the current archive of the historical building, and the disease record set in the current archive is matched with the historical maintenance record of the dynamic operation and maintenance archive to obtain the operation and maintenance management scheme of the historical building, it is specifically used for:

[0188] According to the area identifier in the abnormal area list, the corresponding monitoring area in the dynamic operation and maintenance archive is located, and the component node storage space contained in the monitoring area is locked;

[0189] A special investigation data field is newly created in the component node storage space, and the start and end identifiers of the abnormal time period in the abnormal area list are written into the metadata area of the special investigation data field;

[0190] A special investigation data packet for the component node in the abnormal time period is obtained, and the special investigation data packet contains material sample physicochemical analysis results and high-definition image file paths;

[0191] The feature data in the special investigation data packet are written into the corresponding subfield of the special investigation data field respectively, the data update of the component node is completed, and the current archive of the historical building is obtained;

[0192] The disease type label and disease severity level of the disease record set in the current archive are matched with the historical maintenance records in the dynamic operation and maintenance archive, and the matching historical maintenance measures are associated and summarized with the corresponding component node information to obtain the operation and maintenance management scheme of the historical building.

[0193] Specifically, after receiving the abnormal area list, according to each area identifier recorded in the list, the storage location of the monitoring area corresponding to the identifier in the database is found by accurately searching in the index structure of the dynamic operation and maintenance archive through hash mapping. The dynamic operation and maintenance archive adopts a component-oriented storage model, and each monitoring area is associated with a group of specific component node storage spaces, which collectively store the geometric data, attribute data and historical monitoring data of all components in the area.

[0194] Specifically, in the locked component node storage space, the data structure of each component node is expanded, and a field specially used for storing special investigation data is newly created, named special investigation data field. The field is designed as a composite data structure containing multiple subfields to accommodate different types of investigation results.

[0195] Specifically, according to the area identifier and abnormal time period start and end identifier in the abnormal area list, a professional or automated detection device is organized to conduct a special investigation on the state of the component nodes in the specified abnormal area within the abnormal time period. During the investigation process, material samples are collected from each component node and sent to a laboratory for physicochemical analysis to obtain the physicochemical analysis results of the material samples.

[0196] Specifically, the special investigation data packet of each component node is opened, and the physicochemical analysis results of the material samples are extracted and written into the corresponding subfields of the newly created special investigation data field in the component node storage space according to the pre-defined subfield names.

[0197] Specifically, from the current archive obtained after updating, the disease record set recorded in the special investigation data field in the abnormal area component node storage space is extracted, which contains the disease type label of each component node. These disease type labels are used as keywords to accurately match and search in the historical maintenance record library of the dynamic operation and maintenance archive to find all historical maintenance records with the same disease type label, forming a preliminary matching record set.

[0198] Further, after locating the monitoring area, all component node storage spaces contained in the area are locked through the row-level locking mechanism of the database to prevent concurrent write conflicts during subsequent data updating and to ensure data consistency and integrity. At this time, the positioning and locking operation of the abnormal area component node storage space is completed.

[0199] Further, the abnormal period start and end identifiers corresponding to the region are extracted from the abnormal region list, which includes the precise time point of abnormal start and the precise time point of abnormal end. These two time points are written in the metadata area of the special investigation data field in the standardized time format string as attribute information of the field, which is used to mark the specific time period targeted by this special investigation.

[0200] Further, high-definition digital cameras or industrial endoscopes are used to capture the surface state and disease details of the component nodes, obtain high-definition image files, and store these files in a designated file server according to the component node number and shooting time, generating high-definition image file paths. These physicochemical analysis results and image file paths are packaged according to the component nodes to form a special investigation data package for each component node.

[0201] Further, the high-definition image file path is written into the image path sub-field of the special investigation data field. During the writing process, ensure that the data format strictly matches the field definition, such as storing physicochemical analysis results in text or numerical form, and storing image paths in string form. After completing the data writing of all component nodes in the abnormal area, release the previously locked storage space, and at this time, the dynamic operation archive is updated to include the latest special investigation data, and the entire archive is updated to the latest state containing these new data, i.e., the current archive of the historical building is obtained.

[0202] Further, the most similar historical maintenance record to the current disease degree level is selected from the preliminary matching record set, and the semantic similarity of the disease degree level is compared. The historical maintenance measure text and maintenance effect evaluation data in the selected optimal matching record are associated with the corresponding component node information in the current disease record set, and all associated results are summarized to form an operation and maintenance management scheme for the current disease condition.

[0203] In summary, the region identifier output by the abnormality detection stage is used as an entry to accurately navigate to the physical location of all component data stored in the dynamic operation archive for that region, and the locking mechanism ensures that concurrent access conflicts do not occur during subsequent data writing, providing precise target positioning and a safe operating environment for the smooth storage of special investigation data, ensuring the atomicity and consistency of the data update process.

[0204] In summary, the existing data structure is dynamically expanded to pre-designate a dedicated storage area for the upcoming special investigation results, and the start and end times of the abnormal period are added as metadata to the field, so that the subsequent investigation data can be clearly associated with the time window it targets, providing necessary context information for distinguishing investigation records in different periods and conducting time-dimension traceability analysis.

[0205] In summary, the results of the field investigation are structured and packaged, the physical and chemical analysis of the material samples provides quantitative evidence of the cause of the disease and the state of the material, and the high-definition video file path records the intuitive evidence of the apparent characteristics of the disease. Together, they constitute a complete component health status assessment basis, providing real and verifiable data support for subsequent disease diagnosis and maintenance decisions.

[0206] In summary, the disease evidence obtained from the field investigation is permanently associated with the corresponding component in the digital twin model, enabling centralized management of scattered physical and chemical analysis reports and image files by component node. The business closed loop from abnormal early warning to on-site verification to data archiving is completed. The updated current archives contain the latest disease information and become the authoritative data source reflecting the current real situation of the building.

[0207] In summary, the knowledge value of the historical maintenance experience library is activated. Through the matching of disease type and degree level, successful handling cases for similar problems are accurately retrieved from the vast historical records. The specific maintenance measures in these cases are associated with the current disease component, so that historical experience can be reused in new disease scenarios. The generated operation and maintenance management scheme provides managers with practical and targeted maintenance decision reference.

[0208] In the embodiment of the application, when the disease type label and the disease degree level of the disease record set in the current archive are matched with the historical maintenance records in the dynamic operation and maintenance archive, and the historical maintenance measures obtained by matching are associated with the corresponding component node information to obtain the operation and maintenance management scheme of the historical building, it is specifically used for:

[0209] Retrieving historical maintenance records with the same disease type label from the historical maintenance record library of the dynamic operation and maintenance archive to obtain a preliminary matching record set of the historical building;

[0210] Selecting the historical maintenance records with the smallest difference from the disease degree level from the preliminary matching record set to obtain the optimal matching record of the historical building;

[0211] Associating the maintenance measure text and maintenance effect evaluation data in the optimal matching record with the corresponding component node information in the disease record set to obtain the operation and maintenance management scheme of the historical building.

[0212] Specifically, in the historical maintenance record database of the dynamic operation archive, each disease type label extracted from the disease record set in the current archive is taken as a retrieval keyword to perform an exact match retrieval on the historical maintenance record database. The historical maintenance record database is stored in a relational database, and each record contains a disease type label field, the value of which is the standardized disease name extracted from the historical maintenance archive. During retrieval, an equality query command in the structured query language is executed to filter out all records in the database whose disease type label field values are exactly the same as the current retrieval keyword.

[0213] Specifically, from the obtained preliminary matching record set, the historical maintenance record with the smallest difference from the current disease severity level is filtered out as the optimal matching record. First, the disease severity level needs to be quantitatively converted, or mapped to a continuous numerical value according to a more detailed level division. Each disease severity level in the current disease record set is subjected to the same quantitative conversion.

[0214] Specifically, the obtained optimal matching record is extracted in its entirety from the historical maintenance record database, including the maintenance measure text and the maintenance effect evaluation data. At the same time, the corresponding component node information currently requiring maintenance is obtained from the disease record set, including the unique identifier of the component node, the spatial position of the node in the digital foundation, the material properties of the node, etc.

[0215] Further, these filtered records contain various maintenance measures taken for this type of disease in the past, maintenance time, maintenance personnel, maintenance effect evaluation, and other detailed information. All records that meet the conditions are collected together to obtain the preliminary matching record set of the historical building, which reflects all the handling experience in history for the same type of disease.

[0216] Further, the quantitative value of the disease severity level of each historical maintenance record in the preliminary matching record set is compared with the quantitative value of the current disease severity level, and the absolute value of the difference between the two is calculated, which represents the difference in level. All calculated absolute values of the difference are sorted to find the minimum value corresponding to the historical maintenance record. If there are multiple records with the same minimum difference, the maintenance time of these records is further compared, and the one with the most recent maintenance time is selected as the final optimal matching record, ensuring that the selected historical experience is matched in disease type and most closely in severity, and is also timely.

[0217] Further, the maintenance measure text and maintenance effect evaluation data are associated with the current component node information, for example, in the data structure of the operation and maintenance scheme, a scheme entry is established for each component node, and the entry contains component node information and corresponding historical optimal matching record content. The scheme entries of all component nodes are summarized to form a complete operation and maintenance scheme, which provides each abnormal component with a suggested maintenance measure based on historical successful experience and for the current disease type and degree.

[0218] In summary, the knowledge reuse value of the historical maintenance experience library is activated, and through accurate matching of the disease type label, all treatment records for the same disease are quickly screened from the accumulated maintenance archives over the years, and the originally dispersed and isolated historical data are gathered into an experience set directly related to the current problem, providing a rich reference case library for subsequent scheme development, avoiding the blindness of starting from scratch to develop maintenance measures, and significantly improving the decision-making efficiency.

[0219] In summary, accurate orientation of experience matching is achieved, and by further comparing the disease degree level on the basis of the same disease type, the treatment case closest to the current disease severity in history is selected, which ensures the applicability and pertinence of the selected historical experience, avoids the risk of ineffective maintenance measures or excessive maintenance due to too large difference in disease degree, and enables the historical experience to serve the current problem in the most appropriate way.

[0220] In summary, the knowledge transformation from historical experience to current decision is completed, and the verified maintenance measures and quantifiable effect evaluation are bound to the specific disease component, generating a customized operation and maintenance scheme that contains both historical successful practices and accurate targeting of the current disease location. This scheme provides clear operation instructions and expected effect reference for the on-site maintenance team, realizing the effective inheritance and precise application of historical building maintenance experience.

[0221] Compared with the prior art, the present application has the following beneficial effects:

[0222] 1. The historical building operation and maintenance management method based on digital twinning realizes fine organization and spatial indexing of historical building data by constructing a digital substrate integrating geometric structure and historical information, and dividing the monitoring area in multiple dimensions according to load-bearing structures, value elements and functional spaces. The method accurately maps real-time operation data to the storage space of the corresponding component node according to the spatial position, forms a dynamically updated operation and maintenance archive, and further generates a spatio-temporal distribution map through visual coding technology, so that the evolution law of the monitoring index in the time and space dimensions can be intuitively presented, greatly improving the management efficiency and readability of massive heterogeneous data, and providing a highly structured and visualized data basis for subsequent analysis.

[0223] 2. The method can accurately identify abnormal areas and their occurrence time periods by quantitatively analyzing the fluctuation pattern differences and trend evolution deviation degrees of real-time data and historical archive data in the spatiotemporal distribution map, and bind the abnormal information with the area identifier to form an abnormal area list. On this basis, the archive is dynamically updated and matched with the historical maintenance records with the abnormal area as the index, so as to quickly generate an operation and maintenance management scheme for the current disease type and degree, realize the whole-process data-driven from abnormal early warning, on-site investigation to scheme formulation, significantly improve the accuracy of abnormal response and the scientificity of maintenance decision, and ensure the effectiveness and pertinence of the protection measures for historical buildings.

[0224] As Figure 2 shown, it is a functional module diagram of a historical building operation and maintenance management system based on digital twinning provided by an embodiment of the present application.

[0225] The historical building operation and maintenance management system based on digital twinning 100 can be installed in an electronic device. According to the implemented functions, the historical building operation and maintenance management system based on digital twinning 100 can include a digital base module 101, a monitoring area module 102, an operation and maintenance archive module 103, a spatiotemporal distribution map module 104, an abnormal area module 105, and an operation and maintenance management scheme module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0226] In this embodiment, the functions of each module / unit are as follows:

[0227] The digital base module is based on historical data and point cloud data of the historical building to construct a digital base of the historical building;

[0228] The monitoring area module divides monitoring areas in the digital base according to the load-bearing structure, value element distribution, and functional space of the historical building, and associates data identifiers with the monitoring areas;

[0229] The operation and maintenance archive module maps the operation data of the historical building to the storage space of the corresponding components in the digital base based on the monitoring area to which the operation data belongs, to obtain the dynamic operation and maintenance archive of the historical building and the corresponding monitoring data stream;

[0230] The spatiotemporal distribution map module visually encodes the monitoring data stream with the collection time of the monitoring data stream as the horizontal axis and the spatial topology structure of the digital base as the vertical axis to obtain the spatiotemporal distribution map of the historical building;

[0231] An abnormal area module determines an abnormal area of the historical building according to a difference in fluctuation patterns and a trend evolution deviation between real-time data and historical archive data in the space-time distribution atlas, and binds a region identifier and an abnormal time period of the abnormal area to obtain an abnormal area list of the historical building;

[0232] An operation and maintenance management scheme module updates the dynamic operation and maintenance archive with the region identifier in the abnormal area list as an index to obtain a current archive of the historical building, and matches a disease record set in the current archive with historical maintenance records of the dynamic operation and maintenance archive to obtain an operation and maintenance management scheme of the historical building.

[0233] In several embodiments provided by the present application, it should be understood that the disclosed method and system can be implemented in other manners. For example, the above-described system embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there can be another division manner in actual implementation.

[0234] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0235] In addition, each function module in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

[0236] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0237] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system for simulating, extending and expanding human intelligence by using digital computers or computer-controlled machines, perceiving environment, acquiring knowledge and using knowledge to obtain optimal results.

[0238] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for the operation and maintenance management of historical buildings based on digital twins, characterized in that, The method includes: A digital base for the historical building is constructed based on historical data and point cloud data of the historical building. Based on the load-bearing structure, distribution of value elements, and functional space of the historical building, monitoring areas are divided in the digital base, and data identifiers are associated with the monitoring areas. Based on the monitoring areas to which the operation data of the historical buildings belong, the operation data is mapped to the storage space of the corresponding components in the digital base to obtain the dynamic operation and maintenance files of the historical buildings and the corresponding monitoring data streams. Using the acquisition time of the monitoring data stream as the horizontal axis and the spatial topology of the digital substrate as the vertical axis, the monitoring data stream is visually encoded to obtain the spatiotemporal distribution map of the historical buildings; Based on the difference in fluctuation patterns and trend deviation between the real-time data and historical archive data in the spatiotemporal distribution map, the abnormal areas of the historical buildings are determined, and the area identifiers and abnormal time periods of the abnormal areas are bound together to obtain a list of abnormal areas of the historical buildings. The dynamic maintenance file is updated using the area identifier in the abnormal area list as an index to obtain the current file of the historical building. The set of disease records in the current file is then matched with the historical maintenance records of the dynamic maintenance file to obtain the maintenance management plan for the historical building.

2. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 1, characterized in that, The construction of the digital base for the historical building based on historical data and point cloud data includes: The architectural drawings in the historical data are digitally scanned to obtain raster images of the historical buildings, and the raster images are geometrically corrected to obtain the base map of the historical buildings. Spatially register the point cloud data with the base map to obtain the alignment point set of the historical building; The building's outer contour line and edge feature line, which are concentrated at the alignment points, are connected according to their spatial adjacency to obtain the closed contour line of the historical building. The closed contour lines are then combined according to their spatial position within the historical building to obtain the wireframe structure of the historical building. The repair records and material information in the historical data are used as attribute fields and attached to the corresponding component nodes in the wireframe structure to obtain the base map of the historical building.

3. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 1, characterized in that, The monitoring area is divided in the digital base according to the load-bearing structure, value element distribution, and functional space of the historical building, and a data identifier is associated with the monitoring area, including: The continuous component nodes belonging to the same structural force path in the digital base are grouped into the load-bearing structural area of ​​the historical building; The component nodes that are spatially adjacent and have the same value level in the digital base are merged into the value element area of ​​the historical building. Based on the functional zoning boundary lines in the digital base, component nodes located within the same functional zoning are aggregated into the functional space areas of the historical building; The boundaries of the load-bearing structure area, the value element area, and the functional space area are superimposed to obtain the comprehensive regional framework of the historical building. The overlapping part of the comprehensive regional framework is then cut and divided to obtain the defining blocks of the historical building. A feature region code is generated for the defined block, and the feature region code is written into the metadata field contained in the spatial block within the defined block to obtain the data identifier of the spatial block.

4. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 1, characterized in that, The monitoring areas to which the operational data of the historical buildings belong are respectively mapped to the storage space of the corresponding components in the digital substrate, thereby obtaining the dynamic operation and maintenance files of the historical buildings and the corresponding monitoring data streams, including: Obtain the operational data package of the historical building, wherein the operational data includes the collection timestamp, spatial coordinates of the collection point, and monitoring index values; The spatial coordinates of the acquisition points are matched with the spatial range of the monitoring area in the digital base to determine the reference monitoring area to which the running data packet belongs; The corresponding component node space in the digital substrate is located based on the data identifier of the benchmark monitoring area; The monitoring index values ​​are written sequentially into the monitoring data field of the component node space according to the timestamp order to obtain the independent area monitoring sequence of the historical building; By merging the monitoring sequences of the independent areas, the monitoring data stream of the historical building is obtained; The monitoring data stream and the real-time data fields of the corresponding component nodes are summarized to obtain the dynamic operation and maintenance file of the historical building.

5. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 1, characterized in that, The process of visually encoding the monitoring data stream, with the acquisition time as the horizontal axis and the spatial topology of the digital substrate as the vertical axis, to obtain the spatiotemporal distribution map of the historical buildings includes: Determine the mapping rules between indicator values ​​and visual feature parameters in the monitoring data stream. The mapping rules include: determining color hue based on the value range in the monitoring data stream, determining color saturation based on the rate of change of values ​​in the monitoring data stream, and determining graphic transparency based on the cumulative value in the monitoring data stream. Based on the area identifier of the monitoring area to which the monitoring data stream belongs, the corresponding component node in the digital base is retrieved, and the vertical axis coordinate value of the component node in the spatial topology of the digital base is read. The index values ​​are converted into corresponding visual feature parameter values ​​according to the mapping rules; A two-dimensional coordinate system for the historical building is constructed using the collection timestamp of the monitoring data stream as the horizontal axis and the vertical axis coordinate value as the vertical axis positioning point. Pixels are drawn point by point in the two-dimensional coordinate system using the visual feature parameter value as the pixel attribute value. The pixels are arranged into a regular row and column structure according to the time order of the horizontal axis and the spatial order of the vertical axis to obtain the spatiotemporal two-dimensional grayscale matrix of the historical building. The spatiotemporal two-dimensional grayscale matrix is ​​spliced ​​according to the spatial topology of the digital substrate to obtain the spatiotemporal distribution map of the historical building.

6. The historical building operation and maintenance management method based on digital twin as described in claim 1, characterized in that, The method involves determining the abnormal areas of the historical buildings based on the differences in fluctuation patterns and trend deviations between real-time data and historical archive data in the spatiotemporal distribution map, and binding the area identifiers and abnormal time periods of the abnormal areas to obtain a list of abnormal areas of the historical buildings, including: The real-time data and historical archive data of the spatiotemporal distribution map are decomposed into fluctuation patterns to obtain the number of rising segments, falling segments, peaks and troughs in the real-time data and the historical archive data, and to generate the real-time fluctuation feature vector and the historical fluctuation feature vector of the historical building. The Euclidean distance between the real-time fluctuation feature vector and the historical fluctuation feature vector is used as the fluctuation pattern difference value of the historical building. The difference between the linear regression slope of the real-time data and the linear regression slope of the historical archive data is used as the deviation of the trend evolution of the historical building. The comprehensive anomaly index of the monitored area is obtained by integrating the fluctuation pattern difference value with the trend evolution deviation. The statistical distribution characteristics of the comprehensive anomaly index are used to determine the anomaly judgment benchmark value of the historical building, and the monitoring area where the comprehensive anomaly index exceeds the anomaly judgment benchmark value is marked as the anomaly area of ​​the historical building.

7. The historical building operation and maintenance management method based on digital twin as described in claim 6, characterized in that, The formula for calculating the comprehensive anomaly index includes: in, The comprehensive anomaly index, The weighting coefficient for the volatility pattern term. It is a natural constant. This represents the difference in fluctuation patterns. The mean of the fluctuation pattern difference. This represents the maximum difference in fluctuation patterns. It is a non-linear index with fluctuating patterns. The weighting coefficients for the trend evolution term. The deviation from the trend evolution, The standard deviation of the trend evolution. This represents the maximum deviation of the trend evolution. It is a non-linear index for trend evolution. For the weighting coefficients of the value coupling term, The comprehensive value coefficient, This represents the maximum value of the comprehensive value coefficient.

8. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 1, characterized in that, The process involves updating the dynamic maintenance file using the region identifier in the abnormal region list as an index to obtain the current file of the historical building, and matching the set of disease records in the current file with the historical maintenance records of the dynamic maintenance file to obtain the maintenance management plan for the historical building, including: Based on the region identifier in the abnormal region list, locate the corresponding monitoring region in the dynamic operation and maintenance file, and lock the component node storage space contained in the monitoring region; Create a special exploration data field in the component node storage space, and write the start and end identifiers of the abnormal time periods in the abnormal area list into the metadata area of ​​the special exploration data field; Obtain a special survey data package for the component nodes during the abnormal period, the special survey data package including the physicochemical analysis results of material samples and the path of high-definition image files; The feature data in the special survey data package is written into the corresponding subfields of the special survey data field to complete the data update of the component node and obtain the current archive of the historical building. The disease type labels and disease severity levels in the existing disease record set are matched with the historical maintenance records in the dynamic operation and maintenance archive, and the matched historical maintenance measures are associated with the corresponding component node information to obtain the operation and maintenance management plan for the historical building.

9. The method for operation and maintenance management of historical buildings based on digital twins as described in claim 8, characterized in that, The process involves matching the disease type tags and disease severity levels in the current disease record set with the historical maintenance records in the dynamic operation and maintenance archive, and then associating and summarizing the matched historical maintenance measures with the corresponding component node information to obtain the operation and maintenance management plan for the historical building, including: Retrieve historical maintenance records with the same disease type tag from the historical maintenance record database of the dynamic operation and maintenance archive to obtain a preliminary matching record set of the historical building; The optimal matching record for the historical building is obtained by selecting the historical maintenance record with the smallest difference from the severity level of the damage from the preliminary matching record set. The maintenance measures text and maintenance effect evaluation data in the optimal matching record are associated with the component node information corresponding to the disease record set to obtain the maintenance management plan for the historical building.

10. A historical building operation and maintenance management system based on digital twins, used to implement the historical building operation and maintenance management method based on digital twins as described in any one of claims 1-9, characterized in that, The system includes: The digital infrastructure module constructs a digital infrastructure for the historical building based on historical data and point cloud data of the historical building. The monitoring area module divides the monitoring area in the digital base according to the load-bearing structure, value element distribution and functional space of the historical building, and associates the monitoring area with data identifiers. The operation and maintenance file module maps the operation data of the historical building to the storage space of the corresponding component in the digital base based on the monitoring area to which the operation data belongs, so as to obtain the dynamic operation and maintenance file of the historical building and the corresponding monitoring data stream. The spatiotemporal distribution mapping module uses the acquisition time of the monitoring data stream as the horizontal axis and the spatial topology of the digital substrate as the vertical axis to visually encode the monitoring data stream, thereby obtaining the spatiotemporal distribution map of the historical building. The abnormal area module determines the abnormal areas of the historical buildings based on the differences in fluctuation patterns and trend deviations between the real-time data and historical archive data in the spatiotemporal distribution map, and binds the area identifier and abnormal time period of the abnormal areas to obtain a list of abnormal areas of the historical buildings. The operation and maintenance management solution module updates the dynamic operation and maintenance file using the area identifier in the abnormal area list as an index to obtain the current file of the historical building, and matches the set of disease records in the current file with the historical maintenance records of the dynamic operation and maintenance file to obtain the operation and maintenance management solution for the historical building.

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