A structured storage method and system of a smart water project BIM model

Abstract

The application discloses a kind of structured storage method and system of wisdom water conservancy engineering BIM model, it is related to BIM model storage technical field, the method includes: obtaining the basic data of water facilities in wisdom water conservancy engineering, and division is carried out, forms basic data element layer set and carries out element extraction to obtain target structured data unit, and target structured data unit is carried out data format specification processing to obtain target data storage block;Target data storage block is stored medium adaptation analysis, obtains target storage medium, and carries out structured storage.The application solves the technical problem that existing wisdom water conservancy BIM model is not layered and is not uniformly stored in block, is not stored carrier according to access heat, leading to model slow, storage resource allocation is not balanced, achieves to realize wisdom water conservancy BIM model layered and block structured hierarchical storage, improves the technical effect that model response speed and storage resource utilization balance are balanced.

Description

Technical Field

[0001] This invention relates to the field of BIM model storage technology, specifically to a structured storage method and system for BIM models of smart water conservancy projects. Background Technology

[0002] Currently, in the digital construction of smart water projects, the complete BIM model data of water facilities is generally stored in a packaged manner. This approach fails to perform layered classification and splitting of massive amounts of basic geometric, attribute, and topological information. All model data is stored uniformly on the same storage medium, making it impossible to generate lightweight data blocks based on the required accuracy for different business scenarios, or to allocate storage resources differently based on model access frequency. When performing operations such as browsing regional models, querying equipment parameters, and simulating pipeline networks, the entire large BIM file must be loaded, resulting in long data reading times, noticeable lag, and a lack of standardized correlation indexes and complete traceability links for multiple versions of model data. After facility information is updated, problems such as data chaos across multiple versions, incorrect topological associations, and lack of data consistency easily arise. This makes it difficult to meet the needs of large-scale water projects for multi-terminal, multi-scenario, and high-real-time BIM data access and full lifecycle operation and maintenance management.

[0003] The existing smart water management BIM models are not stored in a unified manner with different layers and blocks, and the storage carriers are not distinguished according to access frequency, which leads to technical problems such as slow model retrieval and uneven distribution of storage resources. Summary of the Invention

[0004] This application provides a structured storage method and system for BIM models of smart water projects, which addresses the technical problems of existing smart water BIM models not being stored in a unified hierarchical and block manner, and not distinguishing storage carriers according to access frequency, resulting in slow model retrieval and uneven distribution of storage resources.

[0005] In view of the above problems, this application provides a structured storage method and system for BIM models of smart water engineering projects.

[0006] The first aspect of this application provides a structured storage method for a BIM model of a smart water management project, the method comprising: The process involves acquiring basic data on water facilities in a smart water management project, dividing the basic data according to predetermined classification rules to form a basic data element layer set; extracting elements from the target layer in the basic data element layer set to obtain target structured data units, and performing data format standardization processing on the target structured data units to obtain target data storage blocks; introducing a predetermined adaptation strategy to perform storage medium adaptation analysis on the target data storage blocks to obtain target storage media, and then performing structured storage on the target data storage blocks through the target storage media; wherein, the predetermined adaptation strategy means that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in a first type of storage medium, and if the access frequency of the target data storage block is lower than the preset threshold, it is stored in a second type of storage medium.

[0007] A second aspect of this application provides a structured storage system for BIM models of smart water management projects, the system comprising: The system comprises the following modules: a basic layer set formation module, used to acquire basic data of water facilities in smart water management projects, and divide the basic data according to predetermined classification rules to form a basic data element layer set; a target data storage block acquisition module, used to extract elements from the target layer in the basic data element layer set to obtain target structured data units, and to perform data format standardization processing on the target structured data units to obtain target data storage blocks; a structured storage module, used to introduce a predetermined adaptation strategy to perform storage medium adaptation analysis on the target data storage blocks to obtain target storage media, and to perform structured storage on the target data storage blocks through the target storage media; and a predetermined adaptation strategy determination module, wherein the predetermined adaptation strategy means that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in a first type of storage medium, and if the access frequency of the target data storage block is lower than the preset threshold, it is stored in a second type of storage medium.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Basic data of water facilities in smart water management projects is acquired and divided into a basic data element layer set. Target structured data units are extracted from the target layer of the basic data element layer set, and these target structured data units are then processed to standardize their data format, resulting in target data storage blocks. Storage media adaptation analysis is performed on the target data storage blocks to determine the target storage media, which are then stored in a structured manner. The predetermined adaptation strategy means that if the access frequency of a target data storage block is higher than a preset threshold, it is stored in a first-type storage medium; if the access frequency is lower than the preset threshold, it is stored in a second-type storage medium. This achieves the technical effect of hierarchical and structured storage of the smart water management BIM model, improving model retrieval response speed and the balance of storage resource utilization. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic diagram of a structured storage method for a BIM model of a smart water management project provided in this application embodiment; Figure 2 This is a schematic diagram of a structured storage system for a smart water management engineering BIM model provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached figures: 10 for forming a base layer set, 20 for acquiring target data storage blocks, 30 for structured storage, and 40 for determining a predetermined adaptation strategy. Detailed Implementation

[0012] This application provides a structured storage method and system for BIM models of smart water projects, which addresses the technical problems of existing smart water BIM models not being stored in a unified hierarchical and block manner, and not differentiating storage carriers according to access frequency, resulting in slow model retrieval and uneven distribution of storage resources.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] Example 1 like Figure 1 As shown, this application provides a structured storage method for BIM models of smart water conservancy projects, the method comprising: Step S100: Obtain basic data of water facilities in smart water management projects, and divide the basic data according to predetermined classification rules to form a basic data element layer set.

[0015] Specifically, the system connects to multiple business ports related to the design, construction, and operation and maintenance of smart water projects, and collects complete basic data of water facilities in batches. The data includes three-dimensional geometric data, facility attribute parameter data, and topological connection relationship data including pipeline connections, upstream and downstream logic, equipment control, and spatial adjacency. Subsequently, all basic data is classified and categorized hierarchically using predetermined classification rules. These rules can be broken down and sorted according to three dimensions: water facility function type, project section or spatial region, and data dimension type. The original data from different sources, types, and spatial ranges are broken down, sorted, and uniformly packaged to construct a set of basic data elements that are well-defined, well-classified, and fully correlated. This provides a standardized raw data base for subsequent extraction of target structured data units and data standardization processing.

[0016] Step S200: Extract elements from the target layer in the basic data element layer set to obtain target structured data units, and perform data format standardization processing on the target structured data units to obtain target data storage blocks.

[0017] Specifically, the target layer data required for business processing is selected from the basic data element layer set. Independent target structured data units are separated through element extraction methods such as field filtering, topological association matching, and geometric information stripping. Then, data format standardization processing is carried out on the structured data units. First, the corresponding data precision level is matched according to the semantic category of the unit itself. The target level index is calculated by weighting the three level parameters of design precision level, construction precision level, and operation and maintenance precision level. The corresponding target data storage strategy is matched according to the obtained target level index. The preset geometric simplification operation and attribute field reduction operation within the strategy are executed simultaneously to unify and standardize the data size, redundant fields, and model refinement. Finally, the standardized encapsulation is completed to generate target data storage blocks that can be directly partitioned and stored.

[0018] Step S300: Introduce a predetermined adaptation strategy to perform storage medium adaptation analysis on the target data storage block, obtain the target storage medium, and perform structured storage on the target data storage block through the target storage medium.

[0019] Specifically, historical access frequency statistics for each target data storage block are retrieved, and a storage medium adaptation analysis is conducted using a predetermined adaptation strategy. The access frequency of a single data block is compared with a preset access threshold. If the access frequency of the target data storage block is higher than the preset threshold, the first type of storage medium with faster read and write speed is assigned as the target storage medium. If the access frequency is lower than the preset threshold, the second type of storage medium with lower storage cost is assigned as the target storage medium. After determining the suitable target storage medium, the standardized target data storage blocks are written into the corresponding storage medium in an orderly manner according to the hierarchical structure. This completes the partitioned structured storage of multi-precision and multi-type water affairs BIM block data, realizes the differentiated media deployment of hot and cold data, and balances data reading efficiency and overall storage cost.

[0020] Step S400: Wherein, the predetermined adaptation strategy means that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in the first type of storage medium, and if the access frequency of the target data storage block is lower than the preset threshold, it is stored in the second type of storage medium.

[0021] Specifically, the system continuously monitors the historical access frequency of each target data storage block in real time. This access frequency is then compared with a pre-configured access frequency threshold. If the statistical access frequency of a target data storage block exceeds the threshold, the block is identified as high-frequency hot data and is allocated to a high-speed, first-class storage medium. If the statistical access frequency of a target data storage block is less than the threshold, the block is identified as low-frequency cold data and is allocated to a large-capacity, low-cost second-class storage medium. This differentiated approach to storage media based on hot and cold data balances the response speed of BIM model data reading with the overall storage deployment cost.

[0022] In one possible implementation, step S100 further includes: Step S110: The basic data includes at least three-dimensional geometric data, attribute parameter data, and topological connection relationship data, wherein the topological connection relationship data includes pipeline connection relationships, upstream and downstream logical relationships, equipment control relationships, and spatial adjacency relationships between facilities.

[0023] Specifically, the complete composition of basic data for smart water management projects is defined. This basic data is divided into three core categories: The first category is 3D geometric data, which refers to 3D model information that accurately depicts the external dimensions, physical contours, and spatial forms of water management facilities. This includes the 3D coordinates, contour patches, and physical boundaries of all water management equipment such as pipes, valves, pumps, and sedimentation tanks, serving as the carrier for building BIM visualization models. The second category is attribute parameter data, which refers to business attribute information bound to various water management facility entities, covering static and dynamic business parameters such as equipment model, material specifications, rated operating parameters, construction period, operation and maintenance years, cost, and maintenance records. The third category is topology data. Connection relationship data is used to characterize the logical relationships between various facilities within the water system. This data is further divided into four categories of relationship information: pipeline connection relationship records the physical docking points and connection paths between different pipe sections, fittings, and equipment; upstream and downstream logical relationship characterizes the sequential flow of water, the water transmission hierarchy of the pipeline network, and the logic of water catchment zones; equipment control relationship records the linkage control correspondence between controlled equipment such as pumps, valves, and instruments and control cabinets and automatic control systems; and spatial location adjacency relationship describes the spatial distribution relationship information of various water facilities within the engineering site, such as spatial adjacency, overlap, and spacing. These three types of data work together to fully cover all the underlying original information required for the modeling, analysis, and operation and maintenance of the smart water system BIM model.

[0024] In one possible implementation, step S100 further includes: Step S120: The predetermined classification rules include classification according to the functional type of water facilities, the engineering section or spatial area to which they belong, and the data dimension type.

[0025] Specifically, the predetermined classification rules used for the basic data segmentation are fully defined. These rules include three independent and combinable classification dimensions: The first category is classification by the functional type of water facilities. The functional type of water facilities refers to the categories based on the business role that equipment and pipeline components play in the water system, including water intake facilities, water transmission pipelines, booster pump stations, water treatment structures, water distribution networks, valves and instruments, drainage facilities, etc., thereby collecting basic data of equipment with similar functions; The second category is classification by the engineering section or spatial area to which they belong. The engineering section refers to the segmented construction unit divided into the construction phase of the project, such as the first phase pipeline section, the water purification plant civil engineering section, etc. The first category is spatial regions, which refer to the physical spatial range divided by geographic coordinates and site grids. Data can be collected according to spatial boundaries such as factory areas, districts, and street pipe networks, enabling independent management of data by region and section. The second category is classified by data dimension type. Data dimension type is a category divided according to the information attributes of the basic data itself, namely, the three-dimensional geometric data, attribute parameter data, and topological connection relationship data mentioned above. Through this dimension, the three types of information, namely model shape, business parameters, and correlation logic, can be classified separately. The three classification dimensions can be used individually or combined in combination to complete the orderly splitting of massive water affairs raw basic data and form a set of basic data element layers with clear hierarchy and convenient retrieval.

[0026] In one possible implementation, step S200 further includes: Step S210: Determine the target data precision level based on the semantic category of the target structured data unit.

[0027] Step S220: Perform a weighted analysis on the design accuracy level, construction accuracy level, and operation and maintenance accuracy level in the target data accuracy level to obtain the target level index.

[0028] Step S230: Match the target data storage strategy corresponding to the target level index.

[0029] The target data storage strategy includes a predetermined geometric simplification strategy and a predetermined attribute field reduction strategy.

[0030] Specifically, a one-to-one mapping table between semantic categories and data precision levels is pre-built. The table is pre-entered with semantic tags corresponding to each water affairs BIM structured data unit. The semantic category refers to the business purpose identifier corresponding to the information carried by the target structured data unit, including semantics for water plant structure modeling, pipeline construction and metering, equipment operation and maintenance monitoring, and pipeline topology analysis. The semantic tag fields built into the target structured data unit are read, and the semantic tags are matched and searched against the mapping table one by one. After a successful match, the corresponding precision identifier bound in the mapping table is retrieved to determine the target data precision level of the target structured data unit. The precision level is divided into three categories: design precision level, construction precision level, and operation and maintenance precision level. Different semantic categories correspond to different levels of model storage standards. For example, design planning semantic units are matched with design precision level, on-site construction and metering semantic units are matched with construction precision level, and equipment inspection and maintenance semantic units are matched with operation and maintenance precision level. This realizes automatic matching of the corresponding precision level according to the data business purpose without manual configuration.

[0031] Pre-configure design accuracy weighting coefficients Construction accuracy weighting coefficient Maintenance accuracy weighting coefficient The weight values ​​are preset based on the usage scenarios of the water affairs BIM model and meet the following requirements. First, assign quantitative scores to the design accuracy level, construction accuracy level, and operation and maintenance accuracy level respectively. , , The higher the score, the higher the model's precision; the higher the precision level, the greater the quantization score. A weighted summation formula is then used. Complete the weighted calculation, where This is the target level index. During the calculation process, the precision scores of the current structured data unit that do not correspond to the business scenario are automatically removed, and their scores are set to 0 before participating in the calculation. Finally, the target level index in the form of a unique quantitative value is output, which intuitively represents the comprehensive storage precision required for the target structured data unit and provides a quantitative basis for subsequent matching and adaptation of data storage strategies.

[0032] A mapping database is pre-constructed to correspond one-to-one with target level index intervals and storage strategies. Multiple continuous index intervals are pre-divided, and each interval is bound to an independent target data storage strategy. Each strategy includes two main branches: a preset geometric simplification strategy and a preset attribute field reduction strategy. The output quantified target level index is read and compared with all index intervals in the mapping database to locate the target interval to which the index belongs. The complete storage strategy bound to that interval is then retrieved. The preset geometric simplification strategy specifies the specific thresholds for thinning 3D patches, removing component details, and simplifying surfaces of the BIM model at the corresponding precision. The preset attribute field reduction strategy defines the list of redundant business parameters that can be removed and the list of core attribute fields to be retained. After matching is completed, the two sets of rules are issued and executed. In this way, the structured data units are lightweighted and regularized according to the comprehensive precision requirements to generate standardized target data storage blocks.

[0033] The target data storage strategy consists of two main components: a predetermined geometric simplification strategy and a predetermined attribute field reduction strategy. Different geometric simplification coefficients and specific attribute field retention rules are configured for the three accuracy levels: design accuracy level, construction accuracy level, and operation and maintenance accuracy level. The predetermined geometric simplification strategy is a lightweight processing specification for water affairs BIM 3D geometric models. The geometric simplification coefficient is a core parameter for quantitatively controlling the model's precision; a larger coefficient value indicates that more details of model faces, surfaces, and components are retained, resulting in higher geometric data completeness. Different accuracy levels are matched with independent coefficients. The design accuracy level corresponds to the minimum simplification coefficient, maximizing the retention of complete geometric features of the model. The construction accuracy level uses a medium simplification coefficient to moderately simplify minor shapes, and the operation and maintenance accuracy level uses the maximum simplification coefficient. The data is significantly reduced by removing non-critical geometric details; the pre-defined attribute field reduction strategy is a standardized rule for retaining and removing business parameters for control facilities. The attribute field retention strategy refers to the pre-defined whitelist of core attributes and blacklist of redundant attributes under each accuracy level. The design accuracy level retains all design fields such as design drawings, dimensional parameters, and planning indicators. The construction accuracy level retains key on-site construction fields such as construction technology, installation parameters, and material specifications, and removes redundant information such as planning drafts. The operation and maintenance accuracy level only retains fields necessary for operation and maintenance monitoring such as equipment number, rated operating conditions, maintenance cycle, and fault threshold, and removes temporary auxiliary data in the design and construction stages. The two strategies are executed in coordination according to the corresponding accuracy level matching parameters, reducing the overall storage volume of the BIM model while matching the precision requirements of business use.

[0034] Taking a newly built water purification plant's smart water management BIM project as an example, the plant area includes three typical water management facilities: sedimentation tanks, water transmission mains, and booster pump stations. These three facilities correspond to three business scenarios: planning and design, on-site construction, and equipment operation and maintenance, respectively. The semantic categories are clearly distinguished, and precision-weighted calculations and lightweight block processing are performed respectively. The calculation results of each parameter and the matching storage strategy are detailed in Table 1 below: Table 1. Data on the Matching of Precision Weighting and Storage Strategy for Structured Units of Water Plant Structures

[0035] The target level index in the table follows a weighted formula. Calculate, and the weights satisfy The following explanation is based on the actual business scenarios of this water plant: The horizontal flow sedimentation tank is used for plant planning and structural simulation. It has the highest design weight and an index of 7.8 to match the high-precision storage strategy. The model has complete geometric details and all design parameters are retained. The daily planning and simulation call frequency of this block exceeds the threshold, and it is determined to be hot data stored in the first type of high-speed storage medium.

[0036] The DN1200 water transmission trunk line service includes construction layout and project quantity settlement. Construction has the highest weight, with an index of 6.9. A moderate simplification scheme is adopted, which removes minor model features that have no construction value and retains only key fields for metering and installation. It is frequently accessed during the construction phase and automatically migrated to the second type of storage after completion as cold data.

[0037] Centrifugal water supply pump sets are only used for mobile terminal inspection and monitoring. They have the highest maintenance weight and an index of 4.3. They are subjected to significant geometric thinning, which removes complex internal surfaces and retains only the parameters required for maintenance monitoring. The average daily number of visits is lower than the preset threshold, and they are stored in low-cost Class II storage media for a long time.

[0038] For the three types of structured data units in the table, independent storage blocks of three precision levels (design, construction, and operation and maintenance) are generated simultaneously and written into the precision level mapping table to record the association relationship of the multi-precision blocks. After the blocks are generated, the consistency of geometry, attributes, and topology is automatically checked to correct abnormal data such as reversed upstream and downstream pipelines and blank equipment attributes. After the verification is correct, the hash index construction process is started.

[0039] In one possible implementation, step S200 further includes: Step S240: Establish the target hash index structure for the target data storage block.

[0040] Step S250: When the address of the target data storage block changes or its content is updated, the target hash index structure is updated synchronously to form an index update log.

[0041] The target hash index structure includes a first-level index and a second-level index. The first-level index uses the spatial grid code of the water facility as the key, and the second-level index uses the type code of the water facility as the key.

[0042] Specifically, the process begins by extracting two unique identifier fields: the spatial grid code and the facility type code, corresponding to the target data storage blocks. An index system is then built based on a two-level hierarchical index logic. The first-level index uses the spatial grid code of the water facility as the hash key, while the second-level index uses the facility type code. First, a hash operation is performed on the spatial grid code to generate first-level hash index entries, unifying all target data storage blocks within the same spatial grid into the corresponding first-level index node. Then, within each first-level index node, a hash operation is performed on the facility type code to generate second-level hash index entries, classifying data storage blocks of the same type of water facility within the same grid into the corresponding second-level index node. Each index entry synchronously records the storage medium address, block size, precision level, and version number association information of the corresponding target data storage block. After binding the two-level hash mapping relationship, a complete target hash index structure is generated. Relying on the hierarchical hash index, the corresponding BIM data blocks can be quickly located by spatial range and facility type, significantly improving the efficiency of model retrieval and query.

[0043] The system monitors in real time for storage medium address change commands and data content modification commands for each target data storage block. Once a change event is detected, the system first locates the corresponding first-level spatial grid index node and second-level facility type index node in the two-level target hash index structure. It then clears the original storage address and old data version identifier recorded in the old index entry and fills in the updated storage address, latest version number, and update timestamp to complete the synchronous correction of the hash index structure. Throughout the entire process of performing any add, delete, or modify operation on the hash index, the system automatically captures the complete metadata of this change behavior. The metadata includes the change trigger time, operator identifier, unique number of the target data storage block that was changed, change type (address change / content update), original index fields before the change, updated index fields after the change, corresponding spatial grid code and facility type code, and operation result status. All of the above metadata is serialized and written to an independent log data table in chronological order to generate a standardized index update log and persist it. Subsequently, the entire index change process can be completely traced through this log to achieve traceable management of BIM block data index operations.

[0044] The target hash index structure adopts a two-level hierarchical hash index architecture, consisting of a first-level index and a second-level index nested together. Each level of index is configured with an independent hash key to achieve hierarchical retrieval. The first-level index selects the spatial grid code corresponding to the water facility as the hash key. The spatial grid code is a unique identifier assigned to each partition after the engineering site has been pre-divided into geographical grids. The system performs hash mapping based on this code, uniformly aggregating the target data storage blocks corresponding to all water facilities within the same geographical grid to the corresponding first-level index node, achieving coarse retrieval by spatial range. The second-level index is nested within each first-level index node, using the water facility's own type code as the hash key. The type code is a unique identification code pre-compiled based on the functional types of facilities such as water intake, water transmission, water treatment, and water distribution. A secondary hash mapping is performed based on this code, aggregating and classifying the data storage blocks of facilities with the same functional type within the same spatial grid. This ultimately forms a two-level hash index system of coarse spatial grid positioning and fine facility type selection. When retrieving the BIM model, the target area can be quickly located first through the spatial grid, and then the required data blocks can be accurately matched by facility type, effectively shortening the time required for BIM data retrieval and positioning.

[0045] In one possible implementation, step S200 further includes: Step S260: Perform data consistency verification on the target data storage block. If the verification finds missing data or incorrect association, perform data completion or association correction on the target data storage block according to the predetermined verification rules.

[0046] Specifically, the system retrieves a pre-defined water BIM data consistency verification rule library and performs automated verification on the target data storage blocks from three dimensions: geometric integrity, attribute parameter integrity, and topological association matching degree. First, it reads the 3D geometric entities, attribute fields, and pipeline topology association information within each block and compares them item by item with the original benchmark data in the basic data element layer set. During the verification process, two types of anomalies are automatically identified: first, data loss, including missing geometric patches, blank key equipment attribute fields, and missing topology connection records; second, association errors, covering anomalies such as reversed upstream and downstream pipeline logic, disordered facility spatial adjacency relationships, and mismatched equipment control objects. Once the above anomalies are detected, The system invokes predefined verification rules to perform repair operations. For data missing issues, it fills in missing geometric fragments, attribute parameters, and topological records based on the complete original information retained in the original basic data element layer set to complete the data. For association error issues, it re-matches the logical relationships of pipeline connections, upstream and downstream, equipment control, and spatial adjacency between facilities, and corrects erroneous association mapping relationships. After all repairs are completed, a consistency review is performed again. Once no anomalies are confirmed, the data storage blocks are sent to the subsequent hash index construction process to ensure that the geometric, attribute, and topological information of each block is complete and the association logic is consistent, avoiding BIM model loading distortion and business analysis errors caused by data incompleteness and disordered associations.

[0047] In one possible implementation, step S200 further includes: For the same structured data unit, multiple data storage blocks at different precision levels are generated and saved simultaneously, and a precision level mapping table is established. The precision level mapping table records the association between the data storage blocks of the same structured data unit at different precision levels.

[0048] Specifically, for a single target structured data unit, a set of target data storage strategies corresponding to the design accuracy level, construction accuracy level, and operation and maintenance accuracy level are matched respectively. Differentiated geometric simplification processing and attribute field reduction processing are performed sequentially to generate independent target data storage blocks corresponding to the three accuracy specifications. Multiple data storage blocks of different accuracy levels are synchronously and persistently cached to the corresponding storage media. A dedicated accuracy level mapping table is created simultaneously, using the unique identifier of the original structured data unit as the primary key. The table records each accuracy level corresponding to the original unit, the unique number of the data storage block under that accuracy, the storage media address, the block file size, the version number, the hash index association code, and other association information, clearly establishing a one-to-one binding relationship between the same original structured unit and multiple accuracy blocks. When the business side initiates a model retrieval request, it can quickly match the data storage block of the corresponding accuracy according to the usage scenario requirements by reading the mapping table. At the same time, relying on the mapping table, it can horizontally retrieve blocks of other accuracy specifications of the same unit for comparison and switching, realizing unified association management of multi-accuracy BIM model data without repeatedly parsing the original basic data, which greatly improves the efficiency of multi-accuracy model switching and loading.

[0049] In one possible implementation, step S300 further includes: Step S310: When it is necessary to call or display the BIM model, read the storage address identifier of the target data storage block from the target storage medium according to the spatial range information or facility type information carried in the call or display instruction.

[0050] Step S320: Retrieve the corresponding data storage block according to the storage address identifier, load and assemble the data to form the target BIM model data.

[0051] During the data loading and assembly process, version consistency checks are performed on the corresponding data storage blocks retrieved based on version management information. When version inconsistency is detected, the latest data storage block corresponding to the latest version is locked, and version difference prompt information is output to the user.

[0052] Specifically, after receiving the BIM model retrieval or visualization command from the front end, the system first parses the search conditions carried in the command payload. These search conditions include two key parameters: spatial range information and facility type information. The spatial range information is the spatial grid code corresponding to the user-selected area, and the facility type information is the type code corresponding to the required water facility. Then, the parsed spatial grid code is used as the first-level hash index key, and the facility type code is used as the second-level hash index key. The system traverses the aforementioned constructed two-layer target hash index structure, matches the index nodes layer by layer, locates all target data storage blocks corresponding to the matching conditions, and extracts the pre-recorded storage address identifiers in the index entries. These identifiers contain addressing information such as the storage medium type, physical storage path, and unique block number, thus completing the address location of the BIM data blocks to be loaded and providing a precise reading basis for subsequent data loading and assembly.

[0053] The system parses the storage medium type, physical storage path, and unique data block number recorded in the storage address identifier. It then initiates read requests to the corresponding first or second type of storage medium based on the address information, batch-retrieving all target data storage blocks that match the conditions. After reading, it systematically reassembles the discrete blocks according to the precision level mapping table, the spatial grid association relationship recorded by the two-level hash index, and the facility topology connection relationship. First, it completes the splicing of data blocks within the same grid according to spatial regions. Then, it supplements the pipeline, control, and spatial adjacency logic between facilities based on topology association information. Simultaneously, it integrates the three-dimensional geometric model, attribute parameters, and topology association data of each block, merging the fragmented standardized storage blocks into an integrated target BIM model data with complete shape, business parameters, and system association logic, directly supporting front-end visualization rendering, engineering query, simulation analysis, and other business call requirements.

[0054] In the execution process of loading and assembling target data storage blocks, the system extracts the version management information of each block to be assembled. This information includes the version number, update timestamp, and update operation identifier. This information is used to perform version consistency comparison and verification on all data storage blocks retrieved under the same water facility unit. The system traverses all version numbers belonging to the same block and performs horizontal matching. If multiple different version numbers are found, it is determined to be a version inconsistency problem. The system automatically selects the block with the largest version number and the latest update timestamp as the benchmark, locks the latest data storage block corresponding to the latest version as the assembly benchmark data, and discards the other older version blocks. At the same time, the system automatically extracts the differences between the old and new versions, such as geometric model changes, attribute parameter changes, and topological relationship adjustments. The facility number, old and new version numbers, change details, and update time are integrated into standardized prompt information and pushed to the operation front end to inform the user that there are multiple version conflicts and specific differences in the currently loaded data. This ensures the uniformity and accuracy of the BIM model assembly results while providing a visual reminder of version changes.

[0055] In one possible implementation, step S320 further includes: Step S321: When receiving updated data from water facilities, determine the corresponding first structured data unit based on the data type of the updated data, and compare the updated data with the first structured data unit to determine the data change type and amount.

[0056] Step S322: If the determination result is new data, the new data is stored as the second structured data unit and the corresponding second version management information is generated.

[0057] Step S323: If the judgment result is changed data, the changed third structured data unit is generated as the new version and stored, and a version association relationship is established with the first structured data unit before the change to form a version traceability chain.

[0058] Specifically, upon receiving updated water facility data uploaded from the water plant's automatic control system, operation and maintenance terminals, and design modification ports, the system first reads the data type identifier bound to the header of the updated data to distinguish between three categories: 3D geometric data, attribute parameter data, and topological connection relationship data. Combining this with the facility's unique code carried in the updated data to retrieve the accuracy level mapping table, the system identifies the first structured data unit to which the updated content belongs. In the comparison phase, each of the three data categories is independently verified item by item. For attribute parameter data, each field key-value pair in the updated data is matched one by one with the original attribute key-value pair of the first structured data unit. If a field has been added, deleted, or the numerical difference is non-zero, the system will verify the data. If the change is determined to be an attribute change, the number of modified fields and parameter differences are counted as the change amount. For 3D geometric data, the coordinate differences between the updated model vertices, faces, and boundary coordinates and the original geometric information are extracted and calculated. The number of vertices that have shifted and the number of added / deleted faces are counted as the change amount, and the change type is determined to be geometric shape modification. For topology connection relationship data, each pipe connection, upstream and downstream logic, equipment control, and spatial adjacency record is compared. The number of added, deleted, and modified topology entries is counted as the change amount, and the change type is determined to be topology association change. The change type and quantified change amount corresponding to this update are fully output.

[0059] After comparing and determining that the updated data is newly added data, the complete information on newly added water facilities is integrated and packaged into an independent second structured data unit. Simultaneously, a preset multi-precision data processing flow is invoked. Based on the geometric simplification coefficients and attribute field retention strategies corresponding to the design precision level, construction precision level, and operation and maintenance precision level, lightweight processing is performed on the second structured data unit to generate multiple target data storage blocks corresponding to different precision levels. Each block is stored in a matching first or second type of storage medium according to a hot / cold data adaptation strategy. At the same time, exclusive second version management information is automatically generated. This version management information includes a unique version number, the facility code corresponding to the newly added data, the data addition timestamp, the operation source identifier, the storage address of each precision block, the associated hash index code, and the record of changes in the newly added data. This version management information is written into the precision level mapping table to complete the association and binding. The two-layer target hash index structure is updated simultaneously, and this index change is recorded in the index update log, achieving complete storage of the newly added water facility structured units, multi-precision blocks, and version information.

[0060] When the updated data is determined to be changed data after comparison, the original complete basic information of the first structured data unit is extracted. Combined with the received updated data, the corresponding content of 3D geometry, attribute parameters, and topological connection relationships is overwritten and replaced. The updated third structured data unit is then generated. The geometric simplification coefficients and attribute field retention strategies corresponding to each accuracy level are called to process and generate multi-precision target data storage blocks. According to the access frequency determination rules, the data is stored in the corresponding first or second type of storage medium, and the hash index structure is updated and written to the index update log. Subsequently, the unique identifier of the original first structured data unit is used as the associated primary key. An associated entry is added to the accuracy level mapping table to record the new version number, update timestamp, mapping and binding relationship between the old and new units, and the type and amount of data change corresponding to the third structured data unit. The old version of the first structured data unit and the new version of the third structured data unit are linked and bound together to form a version traceability chain that can be traced back and forth. When retrieving data later, the version traceability chain can be used to view the content of each update in the forward direction and to trace back to the original complete data before the change, so as to realize the full life cycle version traceability management of water affairs BIM structured units.

[0061] Example 2 Based on the same inventive concept as the structured storage method for a smart water engineering BIM model in the foregoing embodiments, such as Figure 2 As shown, this application provides a structured storage system for BIM models of smart water conservancy projects. The system and method embodiments in this application are based on the same inventive concept. The system includes: The element set formation module 10 is used to acquire basic data of water facilities in smart water projects, and divide the basic data according to a predetermined classification rule to form a basic data element layer set.

[0062] The target data storage block acquisition module 20 is used to extract elements from the target layer in the basic data element layer set to obtain target structured data units, and to perform data format standardization processing on the target structured data units to obtain target data storage blocks.

[0063] The structured storage module 30 is used to introduce a predetermined adaptation strategy to perform storage medium adaptation analysis on the target data storage blocks, obtain the target storage medium, and perform structured storage on the target data storage blocks through the target storage medium.

[0064] The predetermined adaptation strategy determination module 40 states that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in a first type of storage medium; if the access frequency of the target data storage block is lower than the preset threshold, it is stored in a second type of storage medium.

[0065] Furthermore, the system is also used to implement the following functions: The basic data includes at least three-dimensional geometric data, attribute parameter data, and topological connection relationship data, wherein the topological connection relationship data includes pipeline connection relationships, upstream and downstream logical relationships, equipment control relationships, and spatial adjacency relationships between facilities.

[0066] Furthermore, the system is also used to implement the following functions: The predetermined classification rules include classification according to the functional type of water facilities, the engineering section or spatial area to which they belong, and the data dimension type.

[0067] Furthermore, the system is also used to implement the following functions: The target data accuracy level is determined based on the semantic category of the target structured data unit; a weighted analysis is performed on the design accuracy level, construction accuracy level, and operation and maintenance accuracy level in the target data accuracy level to obtain the target level index; the target data storage strategy corresponding to the target level index is matched; wherein, the target data storage strategy includes a predetermined geometric simplification strategy and a predetermined attribute field reduction strategy.

[0068] Furthermore, the system is also used to implement the following functions: Establish a target hash index structure for the target data storage block; when the address of the target data storage block changes or its content is updated, the target hash index structure is updated synchronously to form an index update log; wherein, the target hash index structure includes a first-level index and a second-level index, the first-level index using the spatial grid code of the water facility as the key, and the second-level index using the type code of the water facility as the key.

[0069] Furthermore, the system is also used to implement the following functions: The target structured data unit is processed to obtain a target data storage block by standardizing its data format. Then, the method further includes: performing a data consistency check on the target data storage block. If the check finds missing data or incorrect association, the target data storage block is supplemented or the association is corrected according to a predetermined check rule.

[0070] Furthermore, the system is also used to implement the following functions: For the same structured data unit, multiple data storage blocks at different precision levels are generated and saved simultaneously, and a precision level mapping table is established. The precision level mapping table records the association between the data storage blocks of the same structured data unit at different precision levels.

[0071] Furthermore, the system is also used to implement the following functions: When a BIM model needs to be invoked or displayed, the storage address identifier of the target data storage block is read from the target storage medium based on the spatial range information or facility type information carried in the invoke or display instruction; the corresponding data storage block is retrieved according to the storage address identifier for data loading and assembly to form the target BIM model data; during the data loading and assembly process, a version consistency check is performed on the retrieved corresponding data storage block based on version management information; when a version inconsistency is detected, the latest data storage block corresponding to the latest version is locked, and a version difference prompt message is output to the user.

[0072] Furthermore, the system is also used to implement the following functions: When updated data from water facilities is received, a corresponding first structured data unit is determined based on the data type of the updated data. The updated data is compared with the first structured data unit to determine the type and amount of data change. If the determination result is new data, the new data is stored as a second structured data unit and corresponding second version management information is generated. If the determination result is changed data, a changed third structured data unit is generated and stored as a new version. A version association relationship is established with the first structured data unit before the change to form a version traceability chain.

[0073] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0075] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A structured storage method for BIM models of smart water conservancy projects, characterized in that, include: Acquire basic data of water facilities in smart water management projects, and divide the basic data according to predetermined classification rules to form a set of basic data element layers; The target layer in the basic data element layer set is subjected to element extraction to obtain target structured data units, and the target structured data units are subjected to data format standardization processing to obtain target data storage blocks. A predetermined adaptation strategy is introduced to perform storage medium adaptation analysis on the target data storage block to obtain the target storage medium, and the target data storage block is structured and stored through the target storage medium. The predetermined adaptation strategy means that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in a first type of storage medium; if the access frequency of the target data storage block is lower than the preset threshold, it is stored in a second type of storage medium.

2. The structured storage method for a BIM model of a smart water conservancy project according to claim 1, characterized in that, The basic data includes at least three-dimensional geometric data, attribute parameter data, and topological connection relationship data, wherein the topological connection relationship data includes pipeline connection relationships, upstream and downstream logical relationships, equipment control relationships, and spatial adjacency relationships between facilities.

3. The structured storage method for a smart water conservancy engineering BIM model according to claim 2, characterized in that, The predetermined classification rules include classification according to the functional type of water facilities, the engineering section or spatial area to which they belong, and the data dimension type.

4. The structured storage method for a smart water conservancy engineering BIM model according to claim 3, characterized in that, The target structured data unit is processed to standardize its data format to obtain target data storage blocks, including: The target data precision level is determined based on the semantic category of the target structured data unit; A weighted analysis is performed on the design accuracy level, construction accuracy level, and operation and maintenance accuracy level in the target data accuracy level to obtain the target level index; Match the target data storage strategy corresponding to the target level index; The target data storage strategy includes a predetermined geometric simplification strategy and a predetermined attribute field reduction strategy.

5. The structured storage method for a BIM model of a smart water conservancy project according to claim 4, characterized in that, The target structured data unit is processed to standardize its data format to obtain target data storage blocks, and then the process further includes: Establish the target hash index structure for the target data storage blocks; When the address of the target data storage block changes or its content is updated, the target hash index structure is updated synchronously to form an index update log; The target hash index structure includes a first-level index and a second-level index. The first-level index uses the spatial grid code of the water facility as the key, and the second-level index uses the type code of the water facility as the key.

6. The structured storage method for a smart water conservancy engineering BIM model according to claim 4, characterized in that, The target structured data unit is processed to standardize its data format to obtain target data storage blocks, and then the process further includes: The target data storage block is subjected to data consistency verification. If the verification finds missing data or incorrect association, the target data storage block is supplemented or the association is corrected according to the predetermined verification rules.

7. The structured storage method for a smart water conservancy engineering BIM model according to claim 4, characterized in that, Also includes: For the same structured data unit, multiple data storage blocks at different precision levels are generated and saved simultaneously, and a precision level mapping table is established. The precision level mapping table records the association between the data storage blocks of the same structured data unit at different precision levels.

8. The structured storage method for a BIM model of a smart water conservancy project according to claim 1, characterized in that, The target data is stored in structured blocks using the target storage medium, and then the process further includes: When it is necessary to call or display a BIM model, the storage address identifier of the target data storage block is read from the target storage medium according to the spatial range information or facility type information carried in the call or display instruction. According to the storage address identifier, the corresponding data storage block is retrieved for data loading and assembly to form the target BIM model data; During the data loading and assembly process, version consistency checks are performed on the corresponding data storage blocks retrieved based on version management information. When version inconsistency is detected, the latest data storage block corresponding to the latest version is locked, and version difference prompt information is output to the user.

9. The structured storage method for a smart water conservancy engineering BIM model according to claim 8, characterized in that, Also includes: When updated data of water facilities is received, the corresponding first structured data unit is determined according to the data type of the updated data, and the updated data is compared with the first structured data unit to determine the data change type and amount. If the determination result is new data, the new data will be stored as the second structured data unit and the corresponding second version management information will be generated. If the judgment result is changed data, the changed third structured data unit is generated as the new version and stored, and a version association relationship is established with the first structured data unit before the change to form a version traceability chain.

10. A structured storage system for BIM models of smart water conservancy projects, characterized in that, The system is used to implement the structured storage method for a smart water conservancy engineering BIM model according to any one of claims 1-9, the system comprising: The element layer set forming module is used to acquire basic data of water facilities in smart water management projects, and divide the basic data according to a predetermined classification rule to form a basic data element layer set; The target data storage block acquisition module is used to extract elements from the target layer in the basic data element layer set to obtain target structured data units, and to perform data format standardization processing on the target structured data units to obtain target data storage blocks. The structured storage module is used to introduce a predetermined adaptation strategy to perform storage medium adaptation analysis on the target data storage blocks, obtain the target storage medium, and perform structured storage on the target data storage blocks through the target storage medium; The pre-defined adaptation strategy determination module specifies that if the access frequency of the target data storage block is higher than a preset threshold, it is stored in a first type of storage medium; if the access frequency of the target data storage block is lower than the preset threshold, it is stored in a second type of storage medium.

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