Building digital operation and maintenance method and system combined with BIM

Abstract

The application provides a kind of building facility digital operation and maintenance method and system combined with BIM, it is related to building information model technical field, first, the BIM model data set of target building facility is acquired, including geometric feature data, attribute feature data and facility correlation data, and pre-processing operation is carried out, subsequently, the BIM model data set after pre-processing is carried out state feature analysis processing, and the operating state feature set of building facility is obtained, then, according to operating state feature set and preset operation and maintenance rule set, strategy matching processing is carried out, and facility operation and maintenance optimization strategy set is generated, finally, based on facility operation and maintenance optimization strategy set, the BIM model data set is updated, and the updated BIM model data set is generated and fed back to building facility operation and maintenance system to trigger periodic updating process, whereby the digital, intelligent operation and maintenance of building facility is realized, and the efficiency and accuracy of operation and maintenance management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a digital operation and maintenance method and system for building facilities combined with BIM. Background Art

[0002] In the construction industry, the continued expansion and increasing complexity of buildings present numerous challenges in the operation and maintenance of building facilities. Traditional building facility operation and maintenance methods rely primarily on manual inspections, paper records, and empirical judgment. This approach is not only inefficient but also makes it difficult to fully and accurately understand the operational status of building facilities. Furthermore, the fragmented and non-uniform format of building facility information makes it difficult to effectively integrate and utilize operation and maintenance data, further increasing the difficulty and cost of operation and maintenance management.

[0003] In recent years, with the rapid development of Building Information Modeling (BIM) technology, its application in the operation and maintenance of building facilities has gradually attracted attention. BIM technology can provide three-dimensional digital models of building facilities, integrating multi-dimensional information such as geometric characteristics, attribute characteristics, and facility relationships, providing a new perspective and method for building facility operation and maintenance management. However, how to deeply integrate BIM technology with building facility operation and maintenance management to achieve digital and intelligent operation and maintenance of building facilities remains a pressing issue. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a digital operation and maintenance method for building facilities combined with BIM, the method comprising:

[0005] Acquire a BIM model data set of a target building facility, wherein the BIM model data set includes geometric feature data, attribute feature data, and facility association relationship data;

[0006] Performing a preprocessing operation on the BIM model data set to generate a preprocessed BIM model data set;

[0007] Performing state feature analysis on the preprocessed BIM model data set to obtain an operating state feature set of the building facility;

[0008] Performing strategy matching processing based on the operating status feature set and the preset operation and maintenance rule set to generate a facility operation and maintenance optimization strategy set;

[0009] An update operation is performed on the BIM model data set based on the facility operation and maintenance optimization strategy set to generate an updated BIM model data set, and the updated BIM model data set is fed back to the building facility operation and maintenance system to trigger a periodic update process.

[0010] On the other hand, an embodiment of the present invention also provides a digital operation and maintenance system for building facilities combined with BIM, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0011] Based on the above aspects, the embodiment of the present invention achieves comprehensive integration and efficient utilization of multi-dimensional information of building facilities by acquiring and pre-processing the BIM model data set of the target building facilities. By performing state feature analysis on the pre-processed BIM model data set, the operating state features of the building facilities can be accurately extracted. Furthermore, by performing strategy matching processing on the operating state feature set and the preset operation and maintenance rule set, a facility operation and maintenance optimization strategy set is generated, thereby realizing intelligent formulation and dynamic adjustment of the operation and maintenance strategy. Finally, based on the facility operation and maintenance optimization strategy set, the BIM model data set is updated, and the updated BIM model data set is fed back to the building facility operation and maintenance system, triggering a periodic update process, ensuring the real-time and accuracy of the operation and maintenance data, significantly improving the efficiency, accuracy and intelligence level of building facility operation and maintenance management, and reducing operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the execution flow of the digital operation and maintenance method of building facilities combined with BIM provided by an embodiment of the present invention.

[0013] Figure 2 Schematic diagram of exemplary hardware and software components of a digital operation and maintenance system for building facilities combined with BIM provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a digital operation and maintenance method for building facilities combined with BIM provided by an embodiment of the present invention. The digital operation and maintenance method for building facilities combined with BIM is introduced in detail below.

[0015] Step S110: Acquire a BIM model data set of a target building facility, wherein the BIM model data set includes geometric feature data, attribute feature data, and facility association relationship data.

[0016] In this embodiment, for the target building facilities, their BIM model data set is the basic data source for the entire digital operation and maintenance. Taking a comprehensive office building as an example, during the early design and construction process of the project, a detailed BIM model of the building was created using professional BIM software. The BIM model contains rich data information, among which the geometric feature data describes the spatial form and size of each component of the building. For example, for the walls of the building, the geometric feature data will record its length, height, thickness and specific position coordinates in three-dimensional space; for the columns, its cross-sectional shape (such as round, square, etc.), diameter or side length, and height and other geometric parameters will be clearly defined. These geometric feature data are stored in a structured manner to form a multi-dimensional data set, and the geometric information of each component can be uniquely identified by a specific identifier.

[0017] Attribute data contains information about the physical, chemical, and functional properties of each building component. For example, attribute data for a wall might include its material (e.g., concrete, brick, etc.), thermal insulation performance, and fire rating. For elevator equipment within a building, attribute data might include information such as its rated load, operating speed, and manufacturer. This attribute data also exists as a data set and is linked to the corresponding geometric data through component identification, accurately describing the comprehensive characteristics of each component.

[0018] Facility relationship data reflects the interconnections and interactions between various facilities within a building. In electrical systems, facility relationship data records the wiring connections between various electrical devices (such as distribution boxes, lamps, and sockets), clarifying the flow of current and the control logic between devices. In water supply and drainage systems, it describes the connections and flow paths between pipes and various water-using devices (such as faucets, toilets, and pumps). Facility relationship data is stored in a graph structure, with each facility as a node and the relationships between facilities as edges. This method clearly displays the topology of the entire building's facility system.

[0019] There are many ways to obtain these BIM model data sets. One common way is to extract them directly from the project's BIM database, which is usually maintained and updated by the design team and the construction team during the building design and construction phases, and contains BIM data for the entire life cycle of the project. In addition, it is also possible to obtain the latest BIM model data in real time by interfacing with relevant BIM software. During the acquisition process, it is necessary to ensure the integrity and accuracy of the data. For possible data transmission errors or losses, corresponding verification and error correction mechanisms must be implemented, such as using technical means such as data encryption and checksums to ensure that the acquired BIM model data set is reliable.

[0020] Step S120: performing a preprocessing operation on the BIM model data set to generate a preprocessed BIM model data set.

[0021] After obtaining the BIM model data set, it is necessary to perform preprocessing operations on it to improve data quality because the data may be incomplete, inconsistent, or redundant. The preprocessing operation specifically includes the following sub-steps:

[0022] Step S121: Performing geometric integrity verification on the geometric feature data to detect missing component geometric information in the geometric feature data, and based on a preset component template library and in accordance with the design specifications of the target building facility, screening templates that match the missing component type, filling in the missing component geometric information, and verifying the spatial compatibility of the filled geometric information with adjacent components.

[0023] When processing geometric feature data, the first thing to do is to perform a geometric integrity check. For the geometric feature data of a comprehensive office building, there may be cases where the geometric information of some components is missing due to data entry errors or omissions in the model creation process. For example, in a certain area on a certain floor, the thickness information of some walls is not recorded, or the height data of some columns is missing. In order to detect these missing component geometric information, a method of traversing the geometric feature data set can be used to check whether the geometric information of each component contains all the necessary parameters. Let the geometric feature data set be G, where the geometric information of each component is represented as Gi (i is the component identifier). For a standard wall component, its necessary geometric parameters may include length L, height H and thickness T. If the thickness T of a wall component Gi is found to be missing during the inspection process, it will be marked as a component with missing geometric information.

[0024] Once the missing component geometry information is detected, it is necessary to fill in the information based on the preset component template library. The component template library is a pre-established database that contains standard geometry templates for various types of components. For example, for wall templates, there will be wall geometry templates of different materials and different uses, and each template contains complete geometric parameter information. According to the design specifications of the target building facilities, screen the templates that match the missing component type. Assuming that the wall component with missing geometry information is an ordinary brick wall used to separate office areas, then screen out templates that match this type of wall from the component template library. Let the template library be M, and the screened matching template be Mj, and fill the thickness information Tj in the template Mj into the wall component Gi with missing geometry information.

[0025] After completing the information filling, it is necessary to verify the spatial compatibility of the filled geometry with adjacent components. For example, for a wall filled with thickness information, it is necessary to check whether its new geometry will cause spatial conflicts with adjacent components such as columns, doors, and windows. This verification can be performed by calculating the spatial distance and relative positional relationship between the wall and adjacent components. Let the adjacent component be Gk, and the minimum distance Dik between the wall Gi and the adjacent component Gk in three-dimensional space is calculated. If Dik is less than a preset safety distance threshold Ds, it is considered a spatial compatibility issue and the filled geometry needs to be adjusted until the spatial compatibility requirements are met.

[0026] Step S122: performing attribute consistency verification on the attribute feature data, detecting attribute fields in the attribute feature data that do not match the geometric feature data, and correcting the mismatched attribute fields according to the component type in the geometric feature data.

[0027] The consistency check of attribute feature data is an important step to ensure that the attribute information and geometric feature data match each other. In the attribute feature data of a comprehensive office building, there may be some attribute fields that are inconsistent with the corresponding geometric feature data. For example, the geometric feature data of a wall shows that it is made of concrete, but the wall insulation performance index recorded in the attribute feature data is applicable to insulation brick walls. In order to detect these mismatched attribute fields, the attribute feature data set A can be associated and matched with the geometric feature data set G. For each component, according to the component type in its geometric feature data, check whether the corresponding attribute field in the attribute feature data meets the characteristics of this type of component. Let the geometric feature data of the component be Gi and the attribute feature data be Ai. For a wall component, if Gi indicates that its material is concrete, and the thermal insulation performance index in Ai does not match the common thermal insulation performance range of concrete walls, then the thermal insulation performance index is marked as an unmatched attribute field.

[0028] When mismatched attribute fields are detected, they need to be corrected based on the component type in the geometric feature data. For example, for a mismatched wall insulation performance indicator, appropriate insulation performance indicators are obtained from relevant building codes or empirical data, based on the standard insulation performance range for concrete walls. These values ​​are then replaced with the original mismatched attribute field values. Let the corrected attribute feature data be Ai'. This ensures consistency between the attribute feature data and the geometric feature data, improving data accuracy and usability.

[0029] Step S123: performing redundant association verification processing on the facility association relationship data, detecting repeated association paths in the facility association relationship data, and deduplicating the repeated association paths based on a preset topology rule.

[0030] Redundant association checking of facility association relationship data is intended to eliminate duplicate information in the data and simplify the representation of association relationships. In the facility association relationship data of a comprehensive office building, some duplicate association paths may exist due to repeated data entry or system errors. For example, in an electrical system, the connection relationship between two distribution boxes may be recorded multiple times. To detect these duplicate association paths, the facility association relationship data set R can be traversed and compared. Let each association path in the facility association relationship data set R be denoted as Rj (j is the identifier of the association path). For any two association paths Rj and Rk, if they connect to the same facility nodes and have the same connection method and direction, then these two association paths are considered duplicates.

[0031] Once duplicate association paths are detected, they must be deduplicated based on pre-set topological rules. These pre-set topological rules can be defined based on the specific characteristics of the facility system. For example, in an electrical system, only the oldest recorded association path can be retained. Let R' be the deduplicated facility association relationship data set. Duplicate association paths are removed from R, and only those that conform to the topological rules are retained, resulting in simplified and accurate facility association relationship data.

[0032] Step S124: merging the filled geometric feature data, the corrected attribute feature data, and the deduplicated facility association relationship data to generate a pre-processed BIM model data set.

[0033] After completing the separate processing of the geometric feature data, attribute feature data, and facility association relationship data, they need to be merged into a new data set. The filled geometric feature data G', the corrected attribute feature data A', and the deduplicated facility association relationship data R' are merged, and these data are re-associated through component identification. For example, for each component, its filled geometric information, corrected attribute information, and deduplicated association relationship information are integrated together to form a complete component data record. Let the preprocessed BIM model data set be P. Through this merging operation, a preprocessed BIM model data set containing accurate, complete, and consistent data is obtained.

[0034] Step S130: performing state feature analysis on the pre-processed BIM model data set to obtain an operating state feature set of the building facility.

[0035] The pre-processed BIM model data set contains rich information. By analyzing and processing its status characteristics, we can gain a deeper understanding of the operating status of building facilities. The specific steps include the following:

[0036] Step S131: extracting geometric deformation parameters of the geometric feature data from the preprocessed BIM model data set, and performing trend analysis on the geometric deformation parameters to generate geometric deformation trend features.

[0037] From the preprocessed BIM model data set P, the geometric deformation parameters of the geometric feature data are first extracted. For the geometric feature data of a comprehensive office building, as time passes and under the influence of various external factors, the building's components may deform to a certain extent. For example, due to foundation settlement, the building's columns may tilt, and cracks may appear in the walls. To extract the geometric deformation parameters, the geometric feature data at different time points can be obtained by regularly performing three-dimensional laser scanning on the building or using sensor monitoring. Let the geometric feature data at the initial moment be G0 and the geometric feature data at the current moment be Gt. For each component, the change in its geometric parameters at different time points is calculated, such as the length change ΔL, the angle change Δθ, etc. These changes are the geometric deformation parameters.

[0038] Trend analysis is performed on the extracted geometric deformation parameters to generate a geometric deformation trend signature. Time series analysis can be used to arrange the geometric deformation parameters in chronological order and observe their changing trends. For example, by plotting the change in the column's tilt angle Δθ over time, we can analyze whether it exhibits a gradual increase or cyclical trend. Let Tg be the geometric deformation trend signature. Through trend analysis of the geometric deformation parameters, we can obtain a geometric deformation trend signature that reflects the geometric deformation of the building facility.

[0039] Step S132: extracting attribute change parameters of the attribute feature data from the preprocessed BIM model data set, and performing anomaly detection processing on the attribute change parameters to generate attribute abnormal fluctuation features.

[0040] In addition to geometric deformation parameters, attribute change parameters of the attribute feature data must be extracted from the preprocessed BIM model data set P. During the operation of a complex office building, the attributes of individual components may change. For example, attributes such as the operating speed and energy consumption of elevator equipment may change with age and equipment status; and attributes such as the cooling efficiency and air volume of air conditioning systems are also affected by environmental factors and equipment operating conditions. To extract attribute change parameters, attribute feature data can be collected regularly, and the changes in attribute values ​​at different time points can be calculated. Let the attribute feature data at the initial moment be A0 and the attribute feature data at the current moment be At. For each component's attribute, calculate its attribute value change, such as the speed change Δv and the energy consumption change ΔE. These changes are the attribute change parameters.

[0041] The extracted attribute change parameters are processed for anomaly detection to generate an abnormal attribute fluctuation signature. Statistical analysis can be used to establish a normal distribution model for the attribute change parameters, and the normal fluctuation range of the attribute change parameters can be determined based on historical data. For example, for the elevator's operating speed change Δv, analyzing extensive historical data reveals a normal fluctuation range of [-Δvmin, Δvmax]. When the current attribute change parameter exceeds this normal fluctuation range, the attribute is considered to have experienced an abnormal fluctuation. Let the abnormal attribute fluctuation signature be Ta. By performing anomaly detection on the attribute change parameters, an abnormal attribute fluctuation signature is obtained that can reflect abnormalities in the building facility's attributes, providing important clues for promptly identifying equipment failures and performance degradation.

[0042] Step S133: According to the component type corresponding to the geometric deformation trend feature, the attribute fields of the same component type in the attribute abnormal fluctuation feature are matched.

[0043] After obtaining the geometric deformation trend feature Tg and the attribute abnormal fluctuation feature Ta, they need to be associated and matched. According to the component type corresponding to the geometric deformation trend feature, the attribute field of the same component type is searched in the attribute abnormal fluctuation feature. For example, for the geometric deformation trend feature of the column, the attribute field related to the column is searched in the attribute abnormal fluctuation feature, such as the change in the column's bearing capacity and stress. Suppose a component type in the geometric deformation trend feature is Ci, and the attribute field set corresponding to the same component type Ci in the attribute abnormal fluctuation feature is Ai. Through this matching operation, the geometric deformation situation is associated with the attribute change situation, providing more comprehensive information for the comprehensive evaluation of the operating status of the building facilities.

[0044] Step S134: normalizing the matched geometric deformation trend characteristics and attribute abnormal fluctuation characteristics, and performing weighted calculation according to a preset weight coefficient allocation rule to generate a comprehensive status score of the component type.

[0045] To comprehensively assess the operational status of building facilities, it's necessary to normalize the matched geometric deformation trend characteristics and attribute abnormal fluctuation characteristics. Because the dimensions and value ranges of these characteristics may differ, direct calculations can lead to inaccurate results. A normalization method can be used to unify the value ranges of these characteristics to the [0, 1] interval. Let the standardized geometric deformation trend characteristic be Tg', and the standardized attribute abnormal fluctuation characteristic be Ta'.

[0046] Perform weighted calculation according to the preset weight coefficient distribution rule to generate the comprehensive status score of the component type. The preset weight coefficient distribution rule can be set according to the importance of different component types and the degree of influence on the overall operation of the building facilities. For example, for the load-bearing columns of a building, the weight of the geometric deformation trend feature may be set relatively high because the geometric deformation of the columns has a greater impact on the structural safety of the building; while for some non-critical equipment, the weight of the attribute abnormal fluctuation feature may be relatively high. Let the weight of the geometric deformation trend feature be wg, the weight of the attribute abnormal fluctuation feature be wa, and wg + wa = 1. The comprehensive status score S of the component type can be calculated by the following formula: S = wg * Tg' + wa * Ta'. Through this weighted calculation, a comprehensive status score that can comprehensively reflect the operation status of the component type is obtained.

[0047] Step S135: Compare the comprehensive status score with the preset status threshold to determine the status label of the component type, and merge the status labels of all component types into the operation status feature set.

[0048] Compare the calculated comprehensive status score of the component type with the preset status threshold to determine the status label of the component type. The preset status threshold can be set according to the design standards and safety requirements of the building facilities. For example, set the normal status threshold S1, the warning status threshold S2, and the dangerous status threshold S3 (S1 < S2 < S3). When the comprehensive status score S is less than S1, mark the status label of the component type as "normal"; when S is greater than or equal to S1 and less than S2, mark it as "warning"; when S is greater than or equal to S2, mark it as "dangerous". Let the status label of the component type be St. By comparing the comprehensive status scores of all component types, determine the status label of each component type.

[0049] Merge the status labels of all component types into the operation status feature set. Let the operation status feature set be Sr. Integrate the status labels of each component type according to the component identifier to form an operation status feature set that contains the status information of all component types. This operation status feature set can intuitively reflect the operation status of each component of the building facilities.

[0050] Step S140: Perform policy matching processing according to the operation status feature set and the preset operation and maintenance rule set to generate the facility operation and maintenance optimization policy set.

[0051] After obtaining the operation status feature set of the building facilities, it is necessary to perform policy matching processing according to the preset operation and maintenance rule set to generate the corresponding facility operation and maintenance optimization policy set. Specifically, it includes the following sub-steps:

[0052] Step S141: extracting the component type with the status label of abnormal state from the operating status feature set, and obtaining the geometric deformation trend feature and attribute abnormal fluctuation feature corresponding to the component type.

[0053] From the operational status feature set Sr, we first extract component types with abnormal status labels (i.e., "warning" or "dangerous"). For a complex office building, some columns might have a "dangerous" status label, while some elevators might have a "warning" status label. Let Ca be the set of component types with abnormal status labels. Component types with abnormal status labels are then filtered out from the operational status feature set.

[0054] For abnormal component types, the corresponding geometric deformation trend characteristics and abnormal property fluctuation characteristics are obtained. Taking a column with a status label of "dangerous" as an example, the corresponding geometric deformation trend characteristics Tgc and abnormal property fluctuation characteristics Tac are extracted from the previously calculated geometric deformation trend characteristics Tg and abnormal property fluctuation characteristics Ta. In this way, detailed status information of abnormal component types is obtained.

[0055] Step S142: matching a first optimization strategy template from the preset operation and maintenance rule set according to the type of the geometric deformation trend feature; matching a second optimization strategy template from the preset operation and maintenance rule set according to the abnormal type of the abnormal attribute fluctuation feature.

[0056] The preset operation and maintenance rule set is a pre-established rule library containing optimization strategy templates for different types of geometric deformation and attribute anomalies. Based on the geometric deformation trend characteristics of the abnormal component type, a corresponding first optimization strategy template is matched from the preset operation and maintenance rule set. For example, for column tilt deformation, an optimization strategy template for column tilt, such as reinforcement solutions and support structure adjustments, is searched from the operation and maintenance rule set. Let the first optimization strategy template set be F. Based on the type of geometric deformation trend characteristics, a matching first optimization strategy template Fm is selected from F.

[0057] Similarly, based on the anomaly type of the abnormal attribute fluctuation characteristics, a second optimization strategy template is matched from the preset operation and maintenance rule set. For an abnormal elevator speed, an optimization strategy template specific to the abnormal elevator speed is searched from the operation and maintenance rule set, such as a maintenance plan or control system parameter adjustment. Let the set of second optimization strategy templates be S. Based on the anomaly type of the abnormal attribute fluctuation characteristics, a matching second optimization strategy template Sm is selected from S.

[0058] Step S143: Prioritizing the first optimization strategy template and the second optimization strategy template to generate an optimization strategy sequence for the component type.

[0059] After obtaining the matching first optimization strategy template Fm and second optimization strategy template Sm, they need to be prioritized to generate a component type optimization strategy sequence. Prioritization requires comprehensive consideration of multiple factors, such as the strategy's urgency in resolving the problem, the difficulty and cost of implementing the strategy, and so on.

[0060] Considering the degree of urgency, if the geometric deformation trend characteristics of a certain abnormal component type indicate that it may pose a serious threat to the safety of building facilities in a short period of time, then the corresponding first optimization strategy template may have a higher priority. For example, if the tilt deformation of a column is close to the critical value and may cause structural instability at any time, then the first optimization strategy template, such as the reinforcement solution for the tilted column, should be prioritized. For abnormal attribute fluctuation characteristics, if only some non-critical attributes of the equipment are abnormal and have little impact on the normal operation of the equipment, then the corresponding second optimization strategy template may have a relatively low priority.

[0061] The difficulty and cost of implementing a policy are also important considerations. If a particular optimization policy template requires significant manpower, material resources, and time, and is costly, while another policy template is relatively simple, easy to implement, and inexpensive, the simpler and less expensive policy template may be given higher priority.

[0062] Let each strategy in the first optimization strategy template Fm be Fmi (i is the strategy identifier), and let each strategy in the second optimization strategy template Sm be Smj (j is the strategy identifier). A priority assessment model is established, using factors such as urgency, implementation difficulty, and cost as input parameters to assign a score to each strategy. Assume that the weight for urgency is w1, the weight for implementation difficulty is w2, and the weight for cost is w3, with w1 + w2 + w3 = 1. For strategy Fmi, its urgency score is E1i, its implementation difficulty score is D1i, and its cost score is C1i. Therefore, the comprehensive score of strategy Fmi is S1i = w1*E1i + w2*D1i + w3*C1i. Similarly, for strategy Smj, its comprehensive score is S2j = w1*E2j + w2*D2j + w3*C2j.

[0063] All strategies are sorted based on their comprehensive scores, with strategies with higher scores placed first, forming the component type optimization strategy sequence Op. During the sorting process, it is important to ensure that the relative order within strategies of the same type (the first optimization strategy template or the second optimization strategy template) is reasonable, and that the sorting between strategies of different types complies with priority rules.

[0064] Step S144: performing conflict detection processing on the policy execution conditions in the optimization policy sequence and the facility association data, removing optimization policies that conflict with the facility association data, and generating a final optimization policy for the component type.

[0065] After obtaining the component type optimization strategy sequence Op, conflict detection is performed based on the strategy execution conditions in the optimization strategy sequence and the facility association data. Facility association data describes the interconnections and interactions between various devices and components in the building facilities. The execution of certain optimization strategies may conflict with these associations.

[0066] For example, in an electrical system, if one of the optimization strategies involves overhauling a distribution box connected to multiple critical devices, the maintenance process could affect the normal operation of these devices. In this case, the facility association data needs to be used to determine whether the execution of this strategy will conflict with the operation of other devices.

[0067] Let Opi be the strategy in the optimization strategy sequence Op (where i is the strategy identifier). Each strategy has its execution condition Ci. Let R' be the set of facility association data. For each strategy Opi, its execution condition Ci is compared with R'. If executing strategy Opi disrupts the connection between certain devices in the facility association data or affects the normal operation of other devices, then the strategy is considered to conflict with the facility association data.

[0068] Conflicting strategies are removed from the optimization strategy sequence Op. After conflict detection, the final set of optimized strategies for the component type, Of, is obtained. When removing conflicting strategies, it is important to ensure that the remaining strategies can best resolve component type anomalies without affecting facility associations.

[0069] Step S145: Merge the final optimization strategies of all component types to generate a facility operation and maintenance optimization strategy set.

[0070] After obtaining the final optimization strategy set Of for each component type, the final optimization strategies for all component types are combined to generate a set of facility operation and maintenance optimization strategies. Let the set of all component types be C, and the final optimization strategy set for each component type be Ofc (where c is the component type identifier).

[0071] By traversing all component types, the strategies in the final optimization strategy set Ofc for each component type are sequentially added to a new set, forming the facility operation and maintenance optimization strategy set Os. During the merging process, care should be taken to avoid duplicate additions of strategies. For identical strategies, only one copy is retained. Furthermore, it is important to ensure that the strategies in the facility operation and maintenance optimization strategy set Os cover all component types experiencing abnormal conditions.

[0072] Step S150: performing an update operation on the BIM model data set based on the facility operation and maintenance optimization strategy set to generate an updated BIM model data set, and feeding the updated BIM model data set back to the building facility operation and maintenance system to trigger a periodic update process.

[0073] After obtaining the facility operation and maintenance optimization strategy set Os, it is necessary to update the BIM model data set based on this set to reflect the actual status of the building facilities after the operation and maintenance strategy is implemented. This includes the following sub-steps:

[0074] Step S151: extracting the component type and strategy parameters corresponding to the final optimization strategy from the facility operation and maintenance optimization strategy set.

[0075] In the facility operation and maintenance optimization strategy set Os, each final optimization strategy corresponds to a specific component type and strategy parameters. By traversing the facility operation and maintenance optimization strategy set Os, the component type and strategy parameters corresponding to each final optimization strategy are extracted. Let the final optimization strategy be Osi (i is the strategy identifier), its corresponding component type be Csi, and its strategy parameter be Psi.

[0076] For example, for a final optimization strategy for column reinforcement, the corresponding component type is "column", and the strategy parameters may include information such as the type of reinforcement material, the reinforcement method, and the reinforcement location.

[0077] Step S152: performing geometric adjustment processing on the geometric feature data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated geometric feature data.

[0078] According to the extracted strategy parameters Psi, the geometric feature data of the corresponding component type Csi in the BIM model data set is geometrically adjusted. Different strategy parameters require different geometric adjustment methods.

[0079] If the strategy parameter involves column reinforcement, such as using external steel reinforcement, then the column's geometric data within the BIM model dataset must be adjusted accordingly. This may include increasing the column's cross-sectional dimensions or changing its shape. Let's denote the original geometric data set G, and the geometric data corresponding to component type Csi as Gsi. Based on the strategy parameter Psi, Gsi is adjusted to obtain the updated geometric data Gsi'.

[0080] During the adjustment process, the rationality and accuracy of the geometric adjustment must be ensured to avoid geometric conflicts or unreasonable geometric shapes. At the same time, the spatial compatibility of the updated geometric feature data with adjacent components must be guaranteed. By checking the spatial distance and relative position relationship between the updated geometric data and adjacent components, it is ensured that the normal operation of other components will not be affected.

[0081] Step S153: performing attribute revision processing on the attribute feature data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated attribute feature data.

[0082] In addition to geometric feature data, attribute feature data for corresponding component types in the BIM model data set must also be revised based on strategy parameters. Strategy parameters may affect certain component attribute values. For example, after a column is reinforced, its load-bearing capacity, stress distribution, and other attributes will change.

[0083] Let A be the original attribute feature data set, and Asi be the attribute feature data corresponding to component type Csi. Based on the strategy parameter Psi, Asi is revised to obtain the updated attribute feature data Asi'. For example, if the bearing capacity of a column increases after reinforcement, the column's bearing capacity attribute value in the attribute feature data needs to be adjusted accordingly.

[0084] During the attribute revision process, it is necessary to ensure that the revised attribute feature data complies with building regulations and actual conditions. At the same time, the consistency of attribute feature data and geometric feature data must be guaranteed. By associating geometric feature data with attribute feature data, the logical relationship between the two must be correct.

[0085] Step S154: performing path update processing on the facility association relationship data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated facility association relationship data.

[0086] Strategy parameters may also affect the facility relationships between components. Therefore, it is necessary to update the path of the facility relationship data of the corresponding component type in the BIM model data set based on the strategy parameters. For example, if a piece of equipment is upgraded, its connection relationship with other equipment may change.

[0087] Let R' be the original facility association data set, and Rsi be the facility association data corresponding to component type Csi. Based on the policy parameter Psi, Rsi is path-updated to obtain the updated facility association data Rsi'. During the path update process, it is important to ensure that the updated facility association data conforms to the actual connectivity and does not disrupt the topology of the entire facility system.

[0088] Step S155: merging the updated geometric feature data, the updated attribute feature data and the updated facility association relationship data to generate an updated BIM model data set.

[0089] After updating the geometric feature data, attribute feature data, and facility association data, you need to merge them into a new dataset. Merge the updated geometric feature data G' (containing the updated geometric feature data for all component types), the updated attribute feature data A' (containing the updated attribute feature data for all component types), and the updated facility association data R'' (containing the updated facility association data for all component types). Reassociate these data using component identifiers.

[0090] Let the updated BIM model data set be P'. Through this merging operation, an updated BIM model data set containing the latest, accurate and consistent data is obtained, which can reflect the actual status of the building facilities after the implementation of the operation and maintenance strategy.

[0091] Step S156: performing a difference comparison process on the updated BIM model data set and the historical version data, generating a version update log, and associating the version update log with the building facility operation and maintenance system.

[0092] The updated BIM model data set P' is compared with the historical version data to record data changes. The historical version data can be a set of BIM model data from a previous point in time. This comparison can reveal which components' geometric features, attribute characteristics, or facility relationships have changed.

[0093] Let the historical version data set be Ph. By traversing the data of each component in P' and Ph, their geometric characteristics, attribute characteristics, and facility associations are compared. If a component's data differs, the specific content of the difference is recorded, such as the change in geometric dimensions or attribute value.

[0094] Based on the results of the difference comparison, a version update log L is generated. The version update log L records in detail the changes in the updated BIM model data set relative to the historical version data set, including which components have changed, the specific content of the changes, and the time of the changes.

[0095] By linking the version update log L to the building facility operation and maintenance system, operation and maintenance personnel can check data updates at any time. At the same time, the building facility operation and maintenance system can trigger a periodic update process based on the version update log to ensure that the BIM model data set is always kept up to date.

[0096] In a possible implementation, the method may further include:

[0097] Step S210: performing lightweight processing on the BIM model data set to generate a lightweight BIM model data set.

[0098] After obtaining the BIM model data set of the target building facility, it is necessary to perform lightweight processing on it in order to improve the processing efficiency and real-time performance of the data. This includes the following sub-steps:

[0099] Step S211: simplifying the complex geometric structure in the geometric feature data to retain key geometric contour features.

[0100] The geometric feature data of the BIM model data set may contain some complex geometric structures, which increase the difficulty of data processing and storage cost. Therefore, it is necessary to simplify them while retaining the key geometric contour features.

[0101] For example, decorative components on building facades may have complex shapes and details, but such detailed information is not necessary for real-time operation and maintenance analysis. Geometric simplification algorithms can be used to process these complex geometric structures, such as approximating irregular curved surfaces to flat surfaces and removing small protrusions and depressions.

[0102] Let the original geometric feature data set be G. After simplifying the complex geometric structure, we obtain the simplified geometric feature data set G1. During the simplification process, it is necessary to ensure that key geometric features, such as the overall shape of the building and the approximate shapes of the main components, are retained to accurately reflect the basic geometric form of the building facility.

[0103] Step S212: compressing the redundant attribute fields in the attribute feature data, and retaining the core attribute fields related to operation and maintenance.

[0104] There may be some redundant attribute fields in the attribute feature data, which have no practical significance for real-time operation and maintenance analysis. Therefore, they need to be compressed to retain only the core attribute fields related to operation and maintenance.

[0105] For a device in a building facility, its attribute characteristic data may contain a lot of detailed technical parameters and production information. However, during real-time operation and maintenance, we only need to focus on attribute fields related to the device's operating status and performance, such as the device's operating temperature, power consumption, etc.

[0106] Let the original attribute feature data set be A. Through filtering and compression, redundant attribute fields are removed, and the core attribute fields related to operation and maintenance are retained to obtain the compressed attribute feature data set A1. During the compression process, it is necessary to ensure that the retained core attribute fields can meet the needs of real-time operation and maintenance analysis.

[0107] Step S213: Filter the non-critical associated paths in the facility association relationship data and retain the main associated paths related to operation and maintenance decisions.

[0108] There may be some non-critical association paths in the facility association relationship data. These paths have little impact on operation and maintenance decisions. Therefore, they need to be filtered and only the main association paths related to operation and maintenance decisions are retained.

[0109] In the facility relationship data of an electrical system, there may be some minor line connection relationships that have little impact on the operation of the main equipment. Non-critical relationship paths can be filtered based on their importance and impact on operation and maintenance decisions.

[0110] Let the original facility association relationship data set be R'. Through filtering, non-critical association paths are removed, and the backbone association paths relevant to operation and maintenance decisions are retained, resulting in the filtered facility association relationship data set R1. During the filtering process, it is necessary to ensure that the retained backbone association paths accurately reflect the connection relationships between the main equipment and systems in the building facilities.

[0111] Step S214: merging the simplified geometric feature data, the compressed attribute feature data, and the filtered facility association relationship data into a lightweight BIM model data set, and loading the lightweight BIM model data set into a real-time operation and maintenance analysis module for real-time status monitoring processing.

[0112] The simplified geometric feature data set G1, the compressed attribute feature data set A1 and the filtered facility association relationship data set R1 are merged, and these data are re-associated through component identification to form a lightweight BIM model data set P1.

[0113] The lightweight BIM model data set P1 is loaded into the real-time operation and maintenance analysis module, which monitors and processes the real-time status of building facilities. By acquiring real-time operating data of building facilities and comparing and analyzing it with the lightweight BIM model data set, abnormal conditions and potential problems of building facilities can be promptly identified.

[0114] Step S220: Loading the lightweight BIM model data set into the real-time operation and maintenance analysis module for real-time status monitoring processing.

[0115] After loading the lightweight BIM model data set P1 into the real-time operation and maintenance analysis module, the module will perform real-time status monitoring of building facilities. This includes the following sub-steps:

[0116] Step S221: periodically extracting real-time geometric parameters of the geometric feature data from the lightweight BIM model data set.

[0117] The real-time operation and maintenance analysis module periodically extracts real-time geometric parameters of geometric feature data from the lightweight BIM model data set P1. Let the extraction period be T. Within each period T, the real-time geometric parameters of each component, such as length, angle, and position, are extracted from the geometric feature data set G1 within the lightweight BIM model data set P1.

[0118] For columns in building facilities, their real-time geometric parameters such as height and tilt angle are extracted. By periodically extracting real-time geometric parameters, changes in the column geometry can be detected in a timely manner.

[0119] Step S222: periodically extracting real-time attribute parameters of the attribute feature data from the lightweight BIM model data set.

[0120] In addition to geometric parameters, the real-time operation and maintenance analysis module also periodically extracts real-time attribute parameters from the lightweight BIM model data set P1. Within each cycle T, the module extracts real-time attribute parameters of each component, such as the operating temperature and power consumption of the equipment, from the attribute feature data set A1 within the lightweight BIM model data set P1.

[0121] For elevators in building facilities, extract their real-time operating speed, load, and other attribute parameters. By periodically extracting real-time attribute parameters, you can promptly monitor the operating status of the elevator equipment and detect any abnormalities.

[0122] Step S223: performing difference calculation processing on the real-time geometric parameters and the historical geometric parameters to generate geometric variation features.

[0123] The extracted real-time geometric parameters are compared with historical geometric parameters to generate geometric variation features. The historical geometric parameters can be the geometric parameters extracted at a previous point in time or the initial geometric parameters recorded in the lightweight BIM model data set P1.

[0124] Assume that the real-time geometric parameter is Gt and the historical geometric parameter is Gh. For the geometric parameters of each component, calculate their difference values, such as length change ΔL=Gt_L-Gh_L (Gt_L is the real-time length parameter, Gh_L is the historical length parameter), angle change Δθ=Gt_θ-Gh_θ (Gt_θ is the real-time angle parameter, Gh_θ is the historical angle parameter), etc.

[0125] The geometric variation parameters of all components are integrated together to form a geometric variation feature set Gv. The geometric variation feature set Gv can reflect the changes in the geometric form of each component of the building facility at different time points.

[0126] Step S224: performing difference calculation processing on the real-time attribute parameters and the historical attribute parameters to generate attribute change characteristics.

[0127] Similarly, the difference between the extracted real-time attribute parameters and the historical attribute parameters is calculated to generate attribute change features. The historical attribute parameters can be attribute parameters extracted at a previous time point or the initial attribute parameters recorded in the lightweight BIM model data set P1.

[0128] Assume that the real-time attribute parameter is At and the historical attribute parameter is Ah. For the attribute parameters of each component, calculate their difference values, such as temperature change ΔT=At_T-Ah_T (At_T is the real-time temperature parameter, Ah_T is the historical temperature parameter), power change ΔP=At_P-Ah_P (At_P is the real-time power parameter, Ah_P is the historical power parameter), etc.

[0129] The attribute change parameters of all components are integrated together to form an attribute change feature set Av. The attribute change feature set Av can reflect the changes in the attributes of each component of the building facility at different time points.

[0130] Step S225: performing spatial distribution analysis on the geometric variation characteristics to determine the spatial aggregation area of ​​the geometric deformation.

[0131] Spatial distribution analysis is performed on the geometric variation feature set Gv to identify the spatial clusters of geometric deformation. Spatial distribution analysis can help identify areas within a building where components have experienced concentrated geometric deformation, which is important for assessing the safety status of the building structure and identifying key monitoring areas.

[0132] To perform spatial distribution analysis, we first need to associate the geometric variation parameters of each component in the geometric variation feature set Gv with its location information in the building space. The building space can be divided into multiple small regional units, each with a unique spatial identifier. Assume that the building space is divided into n regional units, denoted as Z1, Z2, …, Zn. For each component in the geometric variation feature set Gv, its regional unit is determined based on its spatial position.

[0133] Next, the geometric variation parameters of the components within each area unit are statistically analyzed. For each area unit Zi, the sum or average of the geometric variation parameters of all components within that area is calculated. For example, for length variation, the sum ΔLi of the length variations of all components within the area unit Zi is calculated; for angular variation, the average Δθi of the angular variations of all components within the area unit Zi is calculated.

[0134] By comparing the statistics of different regional units, we can identify spatial clusters of geometric deformation. If the statistics of a regional unit are significantly greater than those of other regional units, it indicates that the components in that area have undergone relatively concentrated geometric deformation, and this region is marked as a spatial cluster of geometric deformation. Let the set marked as spatial clusters of geometric deformation be Zg, and the elements in this set are the identified spatial clusters.

[0135] Step S226: performing a time series fluctuation analysis on the attribute variation characteristics to determine a time series fluctuation pattern of attribute anomalies.

[0136] We perform time series fluctuation analysis on the attribute variation feature set Av to identify the time series fluctuation pattern of attribute anomalies. The time series fluctuation pattern of attributes can reflect the changing patterns of the equipment's operating status and help to promptly detect potential equipment failures and anomalies.

[0137] First, the attribute change parameters of each component in the attribute change feature set Av are arranged in chronological order to form time series data. Let the attribute change parameters of a component be At. After arranging them in chronological order, we get the time series {At1, At2, …, Atm}, where m is the number of time points.

[0138] Next, we use time series analysis methods to analyze these time series data. Common time series analysis methods include the moving average method and the autoregressive integrated moving average (ARIMA) model. Taking the moving average method as an example, for the time series {At1, At2, …, Atm}, we calculate its moving average. Assuming the size of the moving window is k, the moving average Mt at time point t can be obtained by averaging the attribute change parameters at time point t and the k-1 time points before it.

[0139] By analyzing the changing trends and fluctuations of the moving average, we can identify the time series fluctuation patterns of abnormal attributes. If the moving average shows a sudden and significant rise or fall, or the fluctuation amplitude increases significantly, it indicates that the attribute may be experiencing abnormal fluctuations. Based on the characteristics of these abnormal fluctuations, abnormal time series fluctuation patterns of attributes can be classified into different types, such as periodic fluctuations and sudden fluctuations. Let the set of abnormal time series fluctuation patterns of attributes be Af, and the elements in this set are the different types of identified time series fluctuation patterns.

[0140] Step S227: Map the spatial aggregation area to the time dimension, analyze the correlation between the spatial distribution of the geometric deformation and the temporal fluctuation of the attribute anomaly in the time and space dimensions, and generate the risk level of the real-time status monitoring result according to the preset time and space influence coefficient.

[0141] By mapping the spatial clusters of geometric deformations, Zg, to the time dimension, we analyze the correlation between the spatial distribution of geometric deformations and the temporal fluctuations of attribute anomalies in both time and space. This helps us comprehensively consider the structural safety and equipment operating status of building facilities, and more accurately assess the real-time risks of building facilities.

[0142] First, determine the geometric deformation of each spatial cluster Zgi at different time points. Based on the previously calculated geometric change statistics of the regional units, such as the sum of length changes ΔLi and the average angle changes Δθi, we can analyze the temporal trend of the geometric deformation of the spatial cluster Zgi.

[0143] At the same time, the set of abnormal attribute time series fluctuation patterns Af is associated with the spatial clustering region Zg. For each spatial clustering region Zgi, the abnormal attribute time series fluctuation patterns of the components associated with it are searched. For example, if a spatial clustering region contains multiple electrical devices, the abnormal attribute time series fluctuation patterns of these electrical devices are searched.

[0144] By analyzing the correlation between the spatial distribution of geometric deformation and the temporal fluctuations of attribute anomalies in the spatiotemporal dimension, we can identify potential risk factors. For example, if the geometric deformation of a certain spatially concentrated area continues to increase while the attributes of the equipment in that area experience abnormal fluctuations, this indicates that the area may be at high risk.

[0145] The risk level of real-time condition monitoring results is generated based on preset spatiotemporal impact coefficients. These coefficients are determined based on the characteristics of the building and historical data and are used to measure the combined impact of geometric deformation and attribute anomalies in the spatiotemporal dimension. Let the geometric deformation impact coefficient be wg, and the attribute anomaly impact coefficient be wa, where wg + wa = 1. For each spatial cluster Zgi, a weighted calculation is performed based on its geometric deformation and attribute anomalies, combined with the spatiotemporal impact coefficients, to determine the risk score Ri for that region.

[0146] According to the size of the risk score Ri, the risk level of the real-time status monitoring results is divided into different levels, such as low risk, medium risk, high risk, etc. Let the risk level set of the real-time status monitoring results be Rr, and the elements in this set are the different risk levels determined according to the risk score.

[0147] Step S228: When the risk level exceeds a preset threshold, the process of generating the facility operation and maintenance optimization strategy set is triggered.

[0148] Each risk level in the risk level set Rr of real-time condition monitoring results corresponds to a risk score. When the risk score of a spatially clustered area exceeds a preset threshold, it indicates that the area is at high risk and requires timely action. This triggers the generation of a set of facility operation and maintenance optimization strategies.

[0149] The preset threshold is determined based on the safety standards and operation and maintenance requirements of the building facility. When the risk score exceeds the preset threshold, the system automatically initiates the process of generating a set of facility operation and maintenance optimization strategies. Starting from step S140, the system re-matches strategies based on the real-time status monitoring results and generates a new set of facility operation and maintenance optimization strategies to address high-risk situations in the building facility.

[0150] In a possible implementation, the method may further include:

[0151] Step S310: Determine the emergency execution level of the facility operation and maintenance optimization strategy set according to the risk level of the real-time status monitoring result.

[0152] After triggering the generation process for the facility operation and maintenance optimization strategy set, the emergency execution level of the facility operation and maintenance optimization strategy set needs to be determined based on the risk level set Rr of the real-time status monitoring results. The emergency execution level can help operation and maintenance personnel rationally allocate resources and prioritize high-risk situations.

[0153] For each risk level in the risk level set Rr of the real-time status monitoring results, the optimization strategies in the facility operation and maintenance optimization strategy set are divided into different emergency execution levels according to the corresponding risk score. For example, for the optimization strategy corresponding to the high risk level, its emergency execution level is set to high; for the optimization strategy corresponding to the medium risk level, its emergency execution level is set to medium; and for the optimization strategy corresponding to the low risk level, its emergency execution level is set to low.

[0154] Let Os be the set of facility operation and maintenance optimization strategies, and Ej be the set of emergency execution levels determined by the risk level set Rr. The elements in Ej correspond one-to-one to the optimization strategies in Os, representing the degree of emergency execution for each optimization strategy.

[0155] Step S320: Sorting the optimization strategies in the facility operation and maintenance optimization strategy set according to the emergency execution level to generate a priority execution sequence.

[0156] The optimization strategies in the facility operation and maintenance optimization strategy set Os are sorted according to the set of emergency execution levels Ej to generate a priority execution sequence. The priority execution sequence ensures that operation and maintenance personnel execute optimization strategies in order of urgency, thereby improving operation and maintenance efficiency.

[0157] A sorting algorithm is used to sort the optimization strategies in the facility operation and maintenance optimization strategy set Os. The sorting is based on the emergency execution level in the emergency execution level set Ej. The optimization strategies with higher emergency execution levels are ranked first, and the optimization strategies with lower emergency execution levels are ranked last.

[0158] Let Os' be the set of sorted facility operation and maintenance optimization strategies. This set is the generated priority execution sequence. The optimization strategies in the priority execution sequence are arranged from high to low urgency. Operations and maintenance personnel can execute the optimization strategies sequentially according to this priority execution sequence, prioritizing high-risk situations.

[0159] Step S330: Matching corresponding operation and maintenance execution equipment according to the optimization strategy type in the priority execution sequence.

[0160] After generating the priority execution sequence Os', it is necessary to match the corresponding operation and maintenance execution device according to the optimization strategy type in the sequence. Different types of optimization strategies require different operation and maintenance execution devices to implement.

[0161] For each optimization strategy in the priority execution sequence Os', analyze its strategy type. For example, for an optimization strategy for equipment maintenance, it is necessary to match equipment with corresponding maintenance capabilities, such as maintenance tools and testing instruments. For an optimization strategy for equipment replacement, it is necessary to match the corresponding new equipment.

[0162] Let De be the set of O&M execution devices. Based on the optimization strategy type in the priority execution sequence Os', select the appropriate O&M execution device from De. For each optimization strategy, establish an association between it and the corresponding O&M execution device. Let Rd be the set of associations between optimization strategies and O&M execution devices. This association record the O&M execution device information corresponding to each optimization strategy.

[0163] Step S340: Dynamically adjust the policy execution order in the priority execution sequence according to the current load status of the operation and maintenance execution device and the preset load threshold, wherein security policies cannot be downgraded and adjusted, and the adjusted order must meet the time window constraint of policy execution.

[0164] The policy execution order in the priority execution sequence Os' is dynamically adjusted based on the current load status and preset load threshold of each operation and maintenance execution device in the operation and maintenance execution device set De. The current load status of the operation and maintenance execution device reflects the number of tasks and workload currently being executed by the device, while the preset load threshold is determined based on the device's performance and tolerance.

[0165] For each device Dei in the O&M execution device set De, obtain its current load status Ldi. Compare the current load status Ldi with the preset load threshold Lti. If the current load status of a device exceeds the preset load threshold, it means that the device is already in a high load state and may not be able to execute the new optimization strategy in time.

[0166] When adjusting the execution order of policies within the priority execution sequence Os', certain rules must be followed. Security policies cannot be downgraded or adjusted because they are crucial for ensuring building safety and must be executed first. Furthermore, the adjusted order must satisfy the policy execution time window constraints. Each optimization policy has a specified execution time range, and the adjusted order must ensure that each optimization policy can execute within the specified time frame.

[0167] By dynamically adjusting the policy execution order, we obtain the adjusted priority execution sequence Os''. This sequence not only takes into account the load status of the operation and maintenance execution equipment, but also ensures the priority execution of security policies and the time window constraints of policy execution.

[0168] Step S350: Generate a resource allocation plan according to the adjusted policy execution order, and send the resource allocation plan to the operation and maintenance execution device.

[0169] Generate a resource allocation plan based on the adjusted priority execution sequence Os''. The resource allocation plan includes allocating corresponding operation and maintenance execution equipment, manpower, and materials for each optimization strategy.

[0170] For each optimization strategy in the adjusted priority execution sequence Os'', a detailed resource allocation plan is developed based on the corresponding O&M execution equipment information and the required manpower, materials, and other resources. For example, for an optimization strategy for equipment maintenance, the required resources, such as maintenance tools, testing equipment, and the number of maintenance personnel, are determined.

[0171] Let the resource allocation plan be Ra, which records the resource information and allocation status of each optimization strategy in detail. The resource allocation plan Ra is distributed to each operation and maintenance execution device in the operation and maintenance execution device set De, and the operation and maintenance personnel can execute the corresponding optimization strategy according to the resource allocation plan.

[0172] In a possible implementation, the method may further include:

[0173] Step S410: receiving the policy execution progress data fed back by the operation and maintenance execution device.

[0174] After the resource allocation plan Ra is delivered to the O&M execution device, the system receives feedback on the policy execution progress data from the O&M execution device. This data reflects the execution status of each optimization policy, including whether execution has started, the execution progress percentage, and whether any problems have been encountered.

[0175] The O&M execution device periodically sends policy execution progress data to the system. The system receives this feedback data by establishing a communication connection with the O&M execution device. Let the policy execution progress data set be Sp. The elements in this data set record the execution progress information of each optimization policy.

[0176] Step S420: performing local correction processing on the updated BIM model data set according to the strategy execution progress data to generate a corrected BIM model data set.

[0177] The updated BIM model data set P' is partially corrected based on the strategy execution progress data set Sp. The strategy execution progress data reflects the actual state changes of the building facility after the implementation of the operation and maintenance strategy. By correcting the updated BIM model data set, it can more accurately reflect the current state of the building facility.

[0178] For each optimization strategy's execution progress information in the strategy execution progress dataset Sp, analyze its impact on related components in the BIM model dataset P'. For example, if an optimization strategy involves repairing a piece of equipment, and the performance of the equipment improves after the repair, then the attribute characteristic data of the equipment in the BIM model dataset P', such as operating temperature and power consumption, needs to be updated.

[0179] Based on the analysis results, the geometric feature data, attribute feature data, and facility association data of the relevant components in the updated BIM model data set P' are partially corrected. Let the corrected BIM model data set be P''. This corrected BIM model data set is obtained by performing partial corrections based on the updated BIM model data set P' and the policy execution progress data.

[0180] Step S430: performing version consistency verification on the revised BIM model data set to ensure that the revised BIM model data set is consistent with the actual status of the building facility.

[0181] Perform version consistency check on the revised BIM model data set P'' to ensure that it is consistent with the actual status of the building facilities. The version consistency check process includes the following sub-steps:

[0182] Step S431: extracting geometric verification parameters and attribute verification parameters of key components from the corrected BIM model data set.

[0183] Extract the geometric and attribute verification parameters of key components from the revised BIM model data set P''. Key components are those that have a significant impact on the safety and normal operation of building facilities, such as columns, load-bearing walls, and important equipment.

[0184] For each key component, extract its geometric verification parameters, such as length, angle, position, etc.; extract its attribute verification parameters, such as load capacity, operating temperature, power consumption, etc. Let the geometric verification parameter set of the key component be Gc, and the attribute verification parameter set be Ac.

[0185] Step S432: Obtaining the actually measured geometric parameters and actually measured attribute parameters of the key component in the building facility.

[0186] The actual measured geometric parameters and actual measured property parameters of key components in building facilities are obtained through on-site measurement and other methods. The geometric parameters of key components can be measured using measuring instruments such as total stations and laser rangefinders; the property parameters of key components can be monitored in real time using sensors and other equipment.

[0187] Assume that the actual measured geometric parameter set of the key components is Gm, and the actual measured attribute parameter set is Am.

[0188] Step S433: converting the deviation value between the geometric verification parameter and the actual measured geometric parameter into a standard multiple relative to a preset geometric error threshold to generate a geometric deviation index.

[0189] Compare the geometric verification parameter set Gc with the actual measured geometric parameter set Gm to calculate the geometric parameter deviation for each key component. For a geometric parameter of a key component, such as length, the deviation is ΔL = Gc_L - Gm_L (Gc_L is the length of the geometric verification parameter, Gm_L is the length of the actual measured geometric parameter).

[0190] Convert the geometric parameter deviation value to a standard multiple of the preset geometric error threshold. The preset geometric error threshold is determined based on architectural design standards and construction specifications and is used to measure the allowable error range of geometric parameters. Assuming the preset geometric error threshold is ΔLt, the geometric deviation index is Ic = ΔL / ΔLt.

[0191] The geometric deviation indicators of all key components are integrated together to form a geometric deviation indicator set Ic_set.

[0192] Step S434: converting the deviation value between the attribute verification parameter and the actual measured attribute parameter into a percentage relative to a preset attribute error threshold to generate an attribute deviation index.

[0193] Similarly, the attribute verification parameter set Ac is compared with the actual measured attribute parameter set Am to calculate the attribute parameter deviation for each key component. For a key component attribute parameter, such as operating temperature, the deviation is ΔT = Ac_T - Am_T (where Ac_T is the operating temperature in the attribute verification parameter set and Am_T is the operating temperature in the actual measured attribute parameter set).

[0194] Convert the attribute parameter deviation value into a percentage relative to a preset attribute error threshold. The preset attribute error threshold is determined based on the device's performance standards and operational requirements and is used to measure the allowable error range of the attribute parameter. Assuming the preset attribute error threshold is ΔTt, the attribute deviation index is Ia = (ΔT / ΔTt) × 100%.

[0195] The attribute deviation indicators of all key components are integrated together to form the attribute deviation indicator set Ia_set.

[0196] Step S435: performing weighted calculation according to preset influence weights corresponding to the geometric deviation index and the attribute deviation index to determine the version consistency score.

[0197] The version consistency score is determined by performing a weighted calculation based on the preset impact weights corresponding to the geometric deviation indicator set Ic_set and the attribute deviation indicator set Ia_set. The preset impact weights are determined based on the importance of the geometric and attribute parameters of key components to the building facilities. Let the impact weight of the geometric deviation indicator be wc, and the impact weight of the attribute deviation indicator be wa, and wc + wa = 1.

[0198] For each key component, a weighted calculation is performed based on its geometric deviation index and attribute deviation index, combined with preset influence weights, to obtain the version consistency score Si of the key component. The version consistency scores of all key components are aggregated, and the average or other statistical value is calculated to obtain the version consistency score S of the revised BIM model data set P''.

[0199] Step S436: When the version consistency score is lower than the preset threshold, the versions are determined to be inconsistent and a rollback operation is triggered; when the version consistency score is higher than or equal to the preset threshold, the versions are determined to be consistent and the revised BIM model data set is marked as a valid version.

[0200] The version consistency score S is compared with a preset threshold. The preset threshold is determined based on the quality requirements and operation and maintenance standards of the building facility and is used to determine whether the revised BIM model data set is consistent with the actual status of the building facility.

[0201] When the version consistency score S falls below the preset threshold, it indicates that the revised BIM model data set P'' deviates significantly from the actual state of the building facility, resulting in a version inconsistency. At this point, a rollback operation is triggered, restoring the BIM model data set to the previous version, the updated BIM model data set P'. The generation process for the facility operation and maintenance optimization strategy set is then retriggered to re-formulate and implement the operation and maintenance strategy.

[0202] When the version consistency score S is greater than or equal to the preset threshold, the revised BIM model data set P'' is consistent with the actual state of the building facility, and the versions are determined to be consistent. The revised BIM model data set P'' is marked as the valid version and can be used as the basis for subsequent operation and maintenance management.

[0203] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a BIM-integrated building facility digital operation and maintenance system 100 that can implement the concepts of the present application, as provided in some embodiments of the present application. For example, a processor 120 can be used in the BIM-integrated building facility digital operation and maintenance system 100 to perform the functions described in the present application.

[0204] The BIM-integrated building facility digital operation and maintenance system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the BIM-integrated building facility digital operation and maintenance method of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0205] For example, the BIM-integrated digital operation and maintenance system 100 for building facilities may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. For example, the BIM-integrated digital operation and maintenance system 100 for building facilities may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of the present application may be implemented based on these program instructions. The BIM-integrated digital operation and maintenance system 100 also includes an I / O interface 150 between the computer and other input and output devices.

[0206] For ease of explanation, only one processor is described in the digital operation and maintenance system 100 for building facilities combined with BIM. However, it should be noted that the digital operation and maintenance system 100 for building facilities combined with BIM in this application may also include multiple processors, so the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the digital operation and maintenance system 100 for building facilities combined with BIM executes step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A and the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0207] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the digital operation and maintenance method of building facilities combined with BIM as described above is implemented.

[0208] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A digital operation and maintenance method for building facilities combined with BIM, characterized in that: The method comprises: Acquire a BIM model data set of a target building facility, wherein the BIM model data set includes geometric feature data, attribute feature data, and facility association relationship data; Performing a preprocessing operation on the BIM model data set to generate a preprocessed BIM model data set; Performing state feature analysis on the preprocessed BIM model data set to obtain an operating state feature set of the building facility; Performing strategy matching processing based on the operating status feature set and the preset operation and maintenance rule set to generate a facility operation and maintenance optimization strategy set; performing an updating operation on the BIM model data set based on the facility operation and maintenance optimization strategy set to generate an updated BIM model data set, and feeding the updated BIM model data set back to the building facility operation and maintenance system to trigger a periodic update process; The performing of state feature analysis on the pre-processed BIM model data set to obtain an operating state feature set of the building facility includes: Extracting geometric deformation parameters of the geometric feature data from the preprocessed BIM model data set, and performing trend analysis on the geometric deformation parameters to generate geometric deformation trend features; Extracting attribute change parameters of the attribute feature data from the preprocessed BIM model data set, and performing anomaly detection processing on the attribute change parameters to generate attribute abnormal fluctuation features; According to the component type corresponding to the geometric deformation trend feature, matching the attribute field of the same component type in the attribute abnormal fluctuation feature; The matched geometric deformation trend characteristics and attribute abnormal fluctuation characteristics are standardized, and weighted calculation is performed according to a preset weight coefficient allocation rule to generate a comprehensive status score of the component type; Determine the status label of the component type according to the comparison of the comprehensive status score with a preset status threshold, and merge the status labels of all component types into an operating status feature set; The performing strategy matching processing based on the operation status feature set and the preset operation and maintenance rule set to generate a facility operation and maintenance optimization strategy set includes: Extracting the component type with the state label of abnormal state from the operating state feature set, and obtaining the geometric deformation trend feature and the attribute abnormal fluctuation feature corresponding to the component type; Matching a first optimization strategy template from the preset operation and maintenance rule set according to the type of the geometric deformation trend feature; Matching a second optimization strategy template from the preset operation and maintenance rule set according to the abnormal type of the abnormal attribute fluctuation feature; Prioritizing the first optimization strategy template and the second optimization strategy template to generate an optimization strategy sequence for the component type; Performing conflict detection processing on the policy execution conditions in the optimization policy sequence and the facility association relationship data, removing the optimization policies that conflict with the facility association relationship data, and generating a final optimization policy for the component type; Merging the final optimization strategies of all component types to generate a set of facility operation and maintenance optimization strategies; The updating operation on the BIM model data set based on the facility operation and maintenance optimization strategy set to generate an updated BIM model data set includes: Extracting the component type and strategy parameters corresponding to the final optimization strategy from the facility operation and maintenance optimization strategy set; Performing geometric adjustment processing on the geometric feature data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated geometric feature data; Performing attribute revision processing on the attribute feature data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated attribute feature data; Performing path update processing on the facility association relationship data of the corresponding component type in the BIM model data set according to the strategy parameters to generate updated facility association relationship data; Merging the updated geometric feature data, the updated attribute feature data, and the updated facility association relationship data to generate an updated BIM model data set; The updated BIM model data set is compared with the historical version data to generate a version update log, and the version update log is associated with the building facility operation and maintenance system.

2. The digital operation and maintenance method for building facilities combined with BIM according to claim 1 is characterized in that: The preprocessing operation on the BIM model data set to generate a preprocessed BIM model data set includes: Performing a geometric integrity check on the geometric feature data to detect missing component geometric information in the geometric feature data, and selecting templates matching the missing component types based on a preset component template library and the design specifications of the target building facility, filling in the missing component geometric information, and verifying the spatial compatibility of the filled geometric information with adjacent components; Performing attribute consistency verification on the attribute feature data, detecting attribute fields in the attribute feature data that do not match the geometric feature data, and correcting the mismatched attribute fields according to the component type in the geometric feature data; Performing redundant association check processing on the facility association relationship data, detecting repeated association paths in the facility association relationship data, and removing duplicates from the repeated association paths based on preset topological rules; The filled geometric feature data, the corrected attribute feature data and the deduplicated facility association relationship data are merged to generate a pre-processed BIM model data set.

3. The digital operation and maintenance method for building facilities combined with BIM according to claim 1 is characterized in that: After obtaining the BIM model data set of the target building facility, the method further includes: Lightweight processing is performed on the BIM model data set to generate a lightweight BIM model data set, specifically including: Simplifying the complex geometric structure in the geometric feature data to retain key geometric contour features; Compressing redundant attribute fields in the attribute feature data and retaining core attribute fields related to operation and maintenance; Filtering non-critical association paths in the facility association relationship data to retain the main association paths related to operation and maintenance decisions; The simplified geometric feature data, compressed attribute feature data and filtered facility association relationship data are merged into a lightweight BIM model data set, and the lightweight BIM model data set is loaded into a real-time operation and maintenance analysis module for real-time status monitoring processing.

4. The digital operation and maintenance method for building facilities combined with BIM according to claim 3 is characterized in that: The step of loading the lightweight BIM model data set into the real-time operation and maintenance analysis module for real-time status monitoring includes: Periodically extracting real-time geometric parameters of the geometric feature data from the lightweight BIM model data set; Periodically extracting real-time attribute parameters of the attribute feature data from the lightweight BIM model data set; Performing difference calculation on the real-time geometric parameters and the historical geometric parameters to generate geometric variation features; Performing difference calculation on the real-time attribute parameters and the historical attribute parameters to generate attribute change characteristics; Performing spatial distribution analysis on the geometric variation characteristics to determine the spatial aggregation area of ​​the geometric deformation; Performing a time series fluctuation analysis on the attribute change characteristics to determine an abnormal time series fluctuation pattern of the attribute; Mapping the spatial aggregation area to the time dimension, analyzing the correlation between the spatial distribution of the geometric deformation and the temporal fluctuation of the attribute anomaly in the time and space dimensions, and generating a risk level of the real-time status monitoring result according to a preset time and space impact coefficient; When the risk level exceeds a preset threshold, the generation process of the facility operation and maintenance optimization strategy set is triggered.

5. The digital operation and maintenance method for building facilities combined with BIM according to claim 4 is characterized in that: After triggering the generation process of the facility operation and maintenance optimization strategy set, the method further includes: Determining an emergency execution level of the facility operation and maintenance optimization strategy set according to the risk level of the real-time status monitoring result; Sorting the optimization strategies in the facility operation and maintenance optimization strategy set according to the emergency execution level to generate a priority execution sequence; Matching corresponding operation and maintenance execution devices according to the optimization strategy type in the priority execution sequence; Dynamically adjust the policy execution order in the priority execution sequence based on the current load status of the operation and maintenance execution device and the preset load threshold, wherein security policies cannot be downgraded and adjusted, and the adjusted order must meet the time window constraint of policy execution; A resource allocation plan is generated according to the adjusted policy execution order, and the resource allocation plan is sent to the operation and maintenance execution device.

6. The digital operation and maintenance method for building facilities combined with BIM according to claim 5 is characterized in that: After generating a resource allocation plan according to the adjusted policy execution order and sending the resource allocation plan to the operation and maintenance execution device, the method further includes: Receiving policy execution progress data fed back by the operation and maintenance execution device; Performing local correction processing on the updated BIM model data set according to the strategy execution progress data to generate a corrected BIM model data set; Performing version consistency verification on the revised BIM model data set to ensure that the revised BIM model data set is consistent with the actual state of the building facility; When version inconsistency is detected, roll back to the previous version of the BIM model data set and re-trigger the generation process of the facility operation and maintenance optimization strategy set; The performing of version consistency verification on the revised BIM model data set includes: Extracting geometric verification parameters and attribute verification parameters of key components from the revised BIM model data set; Obtaining actual measured geometric parameters and actual measured attribute parameters of the key component in the building facility; Converting the deviation between the geometric verification parameter and the actual measured geometric parameter into a standard multiple relative to a preset geometric error threshold to generate a geometric deviation index; Converting the deviation between the attribute verification parameter and the actual measured attribute parameter into a percentage relative to a preset attribute error threshold to generate an attribute deviation index; Perform weighted calculation based on preset impact weights corresponding to the geometric deviation index and the attribute deviation index to determine the version consistency score; When the version consistency score is lower than a preset threshold, the versions are determined to be inconsistent and a rollback operation is triggered; When the version consistency score is higher than or equal to a preset threshold, the versions are determined to be consistent, and the revised BIM model data set is marked as a valid version.

7. A digital operation and maintenance system for building facilities combined with BIM, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the digital operation and maintenance method of building facilities combined with BIM as described in any one of claims 1 to 6 above.

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