A civil engineering quantity storage method, device and medium for cement industry
By decomposing, standardizing, and modeling the civil engineering quantity data of the cement industry, multi-dimensional data integration was achieved, solving the problems of poor data scalability and insufficient parameter coupling, supporting full-process tracking and design optimization, and expanding the application scenarios of engineering quantity data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU DESIGN & RES INST OF BLDG MAT IND CO LTD
- Filing Date
- 2026-03-18
- Publication Date
- 2026-06-12
AI Technical Summary
In existing technologies, the quantity data of civil engineering works in the cement industry has failed to achieve multi-dimensional integration of spatial, temporal, technological, and structural design parameters, resulting in poor data scalability, lack of coupling between technological and structural parameters, and difficulty in effective application during the bidding and design stages.
By acquiring engineering quantities and design data, decomposing them into parameter-type data and further decomposing them according to spatial and temporal dimensions, and after standardization, reconstructing them into a data hierarchy with logical levels and parameter dependencies, and building a dynamic storage structure, the multi-dimensional integration and dynamic expansion of data can be achieved.
It supports dynamic tracking and design optimization throughout the entire process, realizing the dual coupling of civil engineering quantity data with process equipment and structural design parameters, improving the standardization and scalability of data, and providing basic data support for big data analysis of engineering quantities.
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Figure CN122195984A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building engineering information processing technology, and in particular to a method, equipment and medium for storing civil engineering quantities in the cement industry. Background Technology
[0002] The cement industry is a typical continuous production industry with "two grinding and one calcination" (raw material grinding—high-temperature calcination—clinker grinding) as its core process. Its construction projects typically include multiple workshops and auxiliary facilities. Civil engineering works account for approximately half of the total cost and are a crucial component of project investment control and management. Unlike general civil buildings, the primary function of cement industry buildings is to ensure the smooth operation of the production process. Their building scale, structural form, and spatial layout must consider the comprehensive influence of various factors such as production scale, equipment specifications, equipment layout, and site conditions. Therefore, the amount of civil engineering work for its structures is closely related to process-related parameters.
[0003] While engineering quantity data is inherently highly structured, it often appears in unstructured or semi-structured forms in the actual workflow of the construction industry. More importantly, the generation and application of civil engineering quantities are concentrated during the construction phase. The core purpose of its data storage is to meet the needs of on-site construction management activities. This data asset is primarily held by the construction and development companies; the design unit's main task is to provide design drawings, without needing to deliver the structural design parameters of those drawings.
[0004] In the cement industry, the production of building design drawings is influenced by cement process-related equipment or plans. However, this influence is only reflected as process data, implicitly contained in the design drawings, and fails to explicitly establish a direct coupling relationship between the building construction drawings and process equipment parameters.
[0005] In summary, due to the multi-party involvement in engineering projects, the three closely related sets of data—quantity data, structural design parameters, and process design parameters—are currently separated and held by different stakeholders. Therefore, current mainstream quantity data storage schemes only consider data specified in relevant engineering specifications, excluding structural design parameters and process equipment parameters. This makes it difficult to extend the application of quantity data beyond the bidding and design stages, rendering quantity data merely process data whose value ends with project completion. It also fails to unlock the value of historical quantity data.
[0006] To address this issue and maximize the value of historical engineering quantity data, patent document CN109492853A discloses a method, apparatus, storage medium, and computer equipment for simulating engineering quantity calculation. This method simulates the engineering quantity of the project under construction by matching historical indicators corresponding to similar attributes in historical projects, based on the engineering quantity list attributes of the project to be constructed, and combining this with a calculation benchmark. However, this method does not provide standardized constraints on the attribute values of the attribute information, which will lead to multiple similar but different semantic descriptions of the same attribute value as historical data increases, making it increasingly difficult to match similar historical attributes.
[0007] To address the aforementioned issues, patent document CN115659962B discloses a method and storage medium for standardizing and correcting engineering quantity lists. This method corrects the engineering quantity list to be processed based on historical and standard list data. By using historical engineering quantity data and relevant engineering quantity specifications, this method constructs a data dictionary of project feature names and a data dictionary of project feature values, enabling the standardization and correction of engineering data with inconsistent semantic descriptions. However, this method is still limited to data related to engineering quantity specifications and fails to consider the introduction of design parameters.
[0008] Furthermore, due to the limitations of application scenarios involving engineering quantities, systems that use database technology to store engineering quantities typically store information such as numerical values, units, locations, and project characteristics of engineering quantities through hierarchical structures such as unit project tables, sub-item tables, and bill of quantities tables. Therefore, the relationships between data entities are relatively simple, which can meet the needs of construction management activities. However, because issues such as design parameters, time dimensions, data standardization, and data scalability are not considered, this type of database structure generally suffers from the following shortcomings: 1. It only reflects the spatial hierarchy and lacks time-dimensional management, making it impossible to track the quantity of work during bidding, design, and settlement. 2. Failed to form an effective coupling with equipment process parameters; only basic information of the project and components is stored, and the structural design parameters are not linked with the engineering quantity data, making it difficult to achieve design optimization based on large engineering quantity data; 3. The input methods for parameter data and list items are inconsistent, resulting in redundancy and ambiguity, which affects subsequent statistics and analysis; 4. The field structure is fixed, making it difficult to dynamically expand new project parameters.
[0009] With the deepening of digitalization in the cement industry, the demand for multi-dimensional integration of engineering quantity data is becoming increasingly prominent, and structured storage of engineering quantity data is an indispensable part of its underlying technology. How to achieve multi-dimensional integration of spatial, temporal, process, and structural design parameters within the same database structure has become a core technical problem that urgently needs to be solved in the industry's digital transformation. Summary of the Invention
[0010] To overcome the problems of poor data scalability and lack of coupling between process and structural parameters in existing engineering quantity management systems, this invention proposes a method, equipment, and medium for storing civil engineering quantities in the cement industry. This method enables structured, standardized, and dynamic management of civil engineering quantity data in the cement industry. This invention can simultaneously manage the spatial hierarchy, time stage, process equipment parameters, and structural design parameters of engineering quantities, achieving multi-dimensional data fusion and dynamic expansion.
[0011] The technical solution adopted in this invention is as follows: A method for storing civil engineering quantities in the cement industry, comprising: Data acquisition: Acquire engineering quantity data and design data of historical projects. The engineering quantity data includes bill of quantities data, and the design data includes structural design data and process design data. Data decomposition: The design data is decomposed into parameter type data, which is further decomposed into project parameters, sub-item parameters, and partition parameters according to the spatial dimension, and stage parameters are added according to the time dimension; the bill of quantities data is decomposed according to the characteristics of the component materials and the structural parts to which they belong. Data standardization: Standardize the decomposed engineering quantity data and design data, including parameter field standardization and parameter data value standardization; the parameter field standardization is achieved by associating the dataset with the corresponding parameter field set to ensure that the key structure of similar datasets is consistent; the parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standard optional values for project parameters, sub-item parameters and partition parameters. Data modeling: The standardized engineering quantity data and design data are reconstructed into a whole with logical hierarchy and parameter dependencies, thus forming three data levels: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset, and the partition level also contains a process equipment dataset. The association between parameter type data and bill of quantities data is performed using the partition stage data as a link.
[0012] Furthermore, during the data decomposition, parameter-type data is decomposed spatially into project parameters, sub-item parameters, and partition parameters, and stage parameters are added along the time dimension, including: Based on the spatial composition hierarchy of cement plant buildings from largest to smallest, the parameter type data is divided into project parameters, sub-item parameters, and zone parameters; Considering the time dimension of the project quantity, a stage parameter is added to the parameter type data to characterize the attribute characteristics of the project quantity at different construction stages.
[0013] Furthermore, during the data decomposition, the bill of quantities data is categorized according to the characteristics of the component materials and their respective structural locations, including: Based on the material properties of cement industry components, the bill of quantities data is divided into steel structure data and non-steel structure data; Based on the structural location of the component, the non-steel structure data is divided into foundation engineering data and superstructure data.
[0014] Furthermore, during data standardization, parameter field standardization is achieved by associating datasets with corresponding parameter field sets, ensuring consistent key structures across similar datasets, including: Each dataset contains all the keys of its corresponding parameter field set, ensuring that the field entries and field names of the same dataset remain consistent across different projects; the data structures at the level of project parameters, sub-item parameters, and partition parameters are all standardized by associating parameter field sets with datasets.
[0015] Furthermore, during data standardization, parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standard optional values for project parameters, sub-item parameters, and partition parameters, including: Establish a standardized parameter dataset, predefining a complete and standardized set of optional values for each enumerable project parameter, sub-item parameter, and partition parameter; when assigning values to project data, sub-item data, and partition data, the parameter values are selected from the corresponding standardized parameter data. The selection method includes: first, matching the parameter name with the standardized parameter type, and then selecting the corresponding standardized parameter data from the matched standardized parameter type.
[0016] Furthermore, during data modeling, three data levels are formed: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset, and the partition level also contains a process equipment dataset, specifically including: Project-level association: One project corresponds to one project dataset and is associated with one or more sub-items; the project parameters in the project dataset come from the project parameter set, and some project data in the project dataset come from the standardized parameter dataset; Sub-item level association: One sub-item corresponds to one sub-item dataset and is associated with one or more partitions; the sub-item parameters in the sub-item dataset come from the sub-item parameter set, and some sub-item data in the sub-item dataset come from the standardized parameter dataset; Partition-level association: one partition corresponds to one partition dataset; the partition parameters in the partition dataset come from the partition parameter set, and some partition data in the partition dataset come from the standardized parameter dataset and the process equipment dataset.
[0017] Furthermore, during data modeling, the association between parameter type data and bill of quantities data is performed using partitioned stage data as a link, including: Joint association of partitions and phases: The unique identifiers within the partition set and the phase parameter set are combined to form a new unique identifier, which is then used to associate the basic engineering dataset, the superstructure dataset, and the steel structure dataset. Quantity association: The basic engineering dataset is simultaneously associated with the partition stage dataset, the basic engineering bill of quantities item set, and the bill of quantities item feature set; the superstructure dataset is simultaneously associated with the partition stage dataset, the superstructure bill of quantities item set, and the bill of quantities item feature set; the steel structure dataset is simultaneously associated with the partition stage dataset, the steel structure bill of quantities item set, and the bill of quantities item feature set.
[0018] Furthermore, the method for storing civil engineering quantities for the cement industry also includes: Dynamic storage structure configuration: Construct a core database structure, which includes a project table, a sub-item table, a partition table, a stage parameter table, a partition stage table, a foundation engineering data table, a superstructure data table, a steel structure data table, a parameter field table, parameter-related data tables, a list item field table, a list item characteristic table, a process equipment data table, a standardized parameter data table, and a standardized parameter type table; the parameter field table includes a project parameter field table, a sub-item parameter field table, and a partition parameter field table; the parameter-related data tables include a project data table, a sub-item data table, and a partition data table; and the list item field table includes a foundation engineering list item table, a superstructure list item table, and a steel structure list item table. Dynamic expansion mechanism configuration: Set the adaptive mapping between parameter set and dataset. When a new field is added to the parameter set, a new data item is automatically generated in the corresponding dataset. The initial value of the new data item is empty until new data is entered, at which point the corresponding value is filled in.
[0019] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for storing civil engineering quantities for the cement industry.
[0020] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for storing civil engineering quantities for the cement industry.
[0021] The beneficial effects of this invention are as follows: Based on a comprehensive consideration of structural design data and process design data, this invention organizes and formats unstructured or semi-structured engineering quantity data according to a predefined data model, enabling the storage of separate engineering quantity data, structural design data, and process design data in a structured form. This provides basic data support for the realization of engineering big data analysis, as detailed below.
[0022] 1. Supports dynamic tracking and design optimization throughout the entire process. This invention connects project, sub-item, zone, stage, and various quantity data tables into an organic whole by jointly identifying engineering quantity data in different zones and stages. It expands previously isolated quantity data points into a data network reflecting the entire process of a cement plant, from equipment selection and building design to construction. This solves the problem of integrating quantity data across time and space, extending the application of quantity data to the bidding and design stages. For example, it can extract the differences and relationships between the quantities of the raw material grinding sub-item and the mill zone during the bidding, design, and settlement stages from structured data. Furthermore, it can predict the actual construction quantities based on the design quantities, providing data support for the efficient organization of construction activities.
[0023] 2. It achieves dual coupling of civil engineering quantity data with process equipment and structural design parameters, and stores these three sets of data in a structured form, providing objective conditions for realizing big data analysis of engineering quantities based on process parameters and structural parameters, and greatly expanding the application scenarios of engineering quantities.
[0024] 3. Achieving the integration of data structuring and standardization. This invention manages core data fields as parameter data value field tables (standardized parameter data tables, process equipment data tables, and list item feature tables), which effectively improves the standardization of data, avoids data inconsistencies caused by human error, and provides a data foundation for big data analysis based on structured engineering quantities.
[0025] 4. Improved scalability and stability. This invention introduces a dynamic storage structure and a dynamic expansion mechanism, allowing new parameter types to be added dynamically as needed without modifying the core table structure. This enables the engineering quantity system to dynamically expand fields while maintaining database structure stability. Attached Figure Description
[0026] Figure 1 This is a flowchart of a method for storing civil engineering quantities in the cement industry according to Embodiment 1 of the present invention.
[0027] Figure 2 This is a schematic diagram of data decomposition in Embodiment 2 of the present invention.
[0028] Figure 3 This is a schematic diagram of the project parameters, project data, and project entity model of Embodiment 2 of the present invention.
[0029] Figure 4 This is a schematic diagram of the entity model of project parameters, project data, and standardized parameter data in Embodiment 2 of the present invention.
[0030] Figure 5 This is a schematic diagram of the conceptual data model of Embodiment 2 of the present invention. Detailed Implementation
[0031] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0032] Example 1 like Figure 1 As shown, this embodiment provides a method for storing civil engineering quantities in the cement industry, including: Data acquisition: Acquire engineering quantity data and design data of historical projects. The engineering quantity data includes bill of quantities data, and the design data includes structural design data and process design data. Data decomposition: The design data is decomposed into parameter type data, which is further decomposed into project parameters, sub-item parameters, and partition parameters according to the spatial dimension, and stage parameters are added according to the time dimension; the bill of quantities data is decomposed according to the characteristics of the component materials and the structural parts to which they belong. Data standardization: Standardize the decomposed engineering quantity data and design data, including parameter field standardization and parameter data value standardization; the parameter field standardization is achieved by associating the dataset with the corresponding parameter field set to ensure that the key structure of similar datasets is consistent; the parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standard optional values for project parameters, sub-item parameters and partition parameters. Data modeling: The standardized engineering quantity data and design data are reconstructed into a whole with logical hierarchy and parameter dependencies, thus forming three data levels: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset, and the partition level also contains a process equipment dataset. The association between parameter type data and bill of quantities data is performed using the partition stage data as a link.
[0033] It should be noted that this method can realize the structured sorting and storage of civil engineering quantity data in the cement industry, allowing scattered engineering quantity data and design data to form a whole with logical hierarchy and dependency, improving the correlation and logic between data, and laying a structured data foundation for the reuse, analysis and calculation of engineering quantity data in subsequent civil engineering projects in the cement industry.
[0034] Preferably, during data decomposition, the parameter type data is decomposed into project parameters, sub-item parameters, and partition parameters according to the spatial dimension, and stage parameters are added according to the time dimension. This includes: dividing the parameter type data into project parameters, sub-item parameters, and partition parameters according to the spatial composition hierarchy of the cement plant's buildings from largest to smallest; and adding stage parameters to the parameter type data to characterize the attribute characteristics of the project quantity at different construction stages, considering the time dimension characteristics of the project quantity.
[0035] Specifically, we first sort out the spatial composition hierarchy of the cement plant's buildings and structures, and then classify and split the parameter type data obtained from the design data according to the hierarchical order from large to small. The parameters of different dimensions are respectively classified into the categories of project parameters, sub-item parameters, and zoning parameters. Then, combined with the time dimension characteristics of the cement industry's civil engineering quantities during the construction period, we add stage parameters to the parameter type data system. Through these parameters, we can mark and distinguish the various attribute characteristics of the engineering quantities at different construction stages.
[0036] It should be noted that this step allows the parameter division of the design data to fit the spatial layout and construction cycle characteristics of cement industry civil engineering, realizing the dimensional and refined splitting of parameter type data, improving the pertinence and rationality of data decomposition, and making the structure of parameter type data match the actual cement industry engineering.
[0037] Preferably, during data decomposition, the bill of quantities data is classified according to the material characteristics of the components and their structural parts, including: dividing the bill of quantities data into steel structure data and non-steel structure data according to the material characteristics of cement industry components; and dividing the non-steel structure data into foundation engineering data and superstructure data according to the structural parts to which the components belong.
[0038] Specifically, the material properties of various civil engineering components in the cement industry are used as the core classification basis. All data in the bill of quantities are screened and classified one by one. Data related to steel structure components are collected as steel structure data, and data of other components are classified as non-steel structure data. Then, the non-steel structure data is further subdivided according to the actual foundation engineering part and superstructure part to which the component belongs, forming foundation engineering data and superstructure data respectively.
[0039] It should be noted that this step enables refined and targeted classification of bill of quantities data, which aligns with the structural and material characteristics of components in cement industry civil engineering projects. This ensures that the classification structure of the bill of quantities data is consistent with the classification logic of actual construction and design, facilitating subsequent standardized processing and correlation analysis of the data.
[0040] Preferably, during data standardization, parameter field standardization is achieved by associating datasets with corresponding parameter field sets, ensuring consistent key structures for similar datasets. This includes: each dataset contains all keys of its corresponding parameter field set, ensuring consistent field entries and field names across different projects for similar datasets; and standardization is achieved by associating parameter field sets with datasets for the data structures at the levels of project parameters, sub-item parameters, and partition parameters.
[0041] Specifically, corresponding parameter field sets are configured for parameters at each level of project, sub-item, and partition. Each dataset is designed to fully contain all keys of its corresponding parameter field set, thereby ensuring that the same type of dataset maintains the same field entries and field names in different cement industry civil engineering projects. At the same time, all data structures at the level where the project, sub-item, and partition parameters are located are uniformly associated with the dataset using the parameter field set, thus completing the standardization process at the field level.
[0042] It should be noted that this step eliminates the differences in field structure between similar datasets across different cement industry engineering projects, achieves the unification of parameter fields, improves the universality and comparability of data, and provides a field-level foundation for the integration and analysis of engineering quantity data across projects.
[0043] Preferably, during data standardization, parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standardized optional values for project parameters, sub-item parameters, and partition parameters. This includes: establishing a standardized parameter dataset, predefining a complete and standardized set of optional values for each enumerable project parameter, sub-item parameter, and partition parameter; when assigning values to project data, sub-item data, and partition data, the parameter values are selected from the corresponding standardized parameter data. The selection method includes: first, matching the parameter name with the standardized parameter type, and then selecting the corresponding standardized parameter data from the matched standardized parameter types.
[0044] Specifically, a standardized parameter dataset system adapted to cement industry civil engineering is established. For all enumerable parameters among project parameters, sub-item parameters, and zone parameters, a complete set of optional values conforming to cement industry standards is predefined. When assigning parameters to project data, sub-item data, and zone data for cement industry civil engineering projects, the parameter name is first matched with the standardized parameter type, and then the corresponding standardized parameter data is selected as the parameter value from the matched standardized parameter types. For example, if the parameter name is "seismic intensity," a data entry with the same name is defined in the parameter type table, and each data entry in the standardized parameter data table is marked with a parameter type identifier. In this way, the correspondence between parameter names and standardized parameter data can be achieved.
[0045] It should be noted that this step avoids arbitrariness in the parameter assignment process, achieves standardized and unified parameter data values at all levels, effectively reduces data redundancy and assignment errors, improves the accuracy and standardization of engineering quantity data and design data, and reduces the error correction cost of subsequent data processing.
[0046] Preferably, during data modeling, three data levels are formed: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset, and the partition level also contains a process equipment dataset, specifically including: Project-level association: One project corresponds to one project dataset and is associated with one or more sub-items; the project parameters in the project dataset come from the project parameter set, and some project data in the project dataset come from the standardized parameter dataset; Sub-item level association: One sub-item corresponds to one sub-item dataset and is associated with one or more partitions; the sub-item parameters in the sub-item dataset come from the sub-item parameter set, and some sub-item data in the sub-item dataset come from the standardized parameter dataset; Partition-level association: one partition corresponds to one partition dataset; the partition parameters in the partition dataset come from the partition parameter set, and some partition data in the partition dataset come from the standardized parameter dataset and the process equipment dataset.
[0047] Specifically, a unique project dataset is matched for each project. The project parameters of this dataset are retrieved from the project parameter set, and some project data is selected from the standardized parameter dataset. At the same time, a one-to-many relationship between projects and sub-items is established. A unique sub-item dataset is matched for each sub-item. The sub-item parameters of this dataset are retrieved from the sub-item parameter set, and some sub-item data is selected from the standardized parameter dataset. At the same time, a one-to-many relationship between sub-items and partitions is established. A unique partition dataset is matched for each partition. The partition parameters of this dataset are retrieved from the partition parameter set, and some partition data is selected from the standardized parameter dataset and the process equipment dataset.
[0048] It should be noted that this step constructs a data analysis model that fits the hierarchical division of civil engineering in the cement industry, realizing the orderly association and hierarchical management of data at each level, making the hierarchical relationship of data clear and traceable, and greatly improving the efficiency of data retrieval, access, and management.
[0049] Preferably, during data modeling, the association between parameter type data and bill of quantities data is performed using partition stage data as a link, including: Joint association of partitions and phases: The unique identifiers within the partition set and the phase parameter set are combined to form a new unique identifier, which is then used to associate the basic engineering dataset, the superstructure dataset, and the steel structure dataset. Quantity association: The basic engineering dataset is simultaneously associated with the partition stage dataset, the basic engineering bill of quantities item set, and the bill of quantities item feature set; the superstructure dataset is simultaneously associated with the partition stage dataset, the superstructure bill of quantities item set, and the bill of quantities item feature set; the steel structure dataset is simultaneously associated with the partition stage dataset, the steel structure bill of quantities item set, and the bill of quantities item feature set.
[0050] Specifically, firstly, unique identifiers are extracted from the partition set and the stage parameter set, and then combined to generate a new unique identifier. Based on this new identifier, associations are established with the foundation engineering dataset, the superstructure dataset, and the steel structure dataset, respectively. Then, associations are established with the foundation engineering, superstructure, and steel structure datasets, respectively, so that the foundation engineering dataset can simultaneously connect with the partition stage dataset, the foundation engineering list item set, and the list item feature set. The superstructure dataset and the steel structure dataset complete the association of multiple datasets according to the same logic.
[0051] It should be noted that this step establishes a precise correspondence between parameter type data and bill of quantities data through a unique identifier, realizing a deep and organic connection between the two types of data, improving the accuracy and efficiency of data association, and allowing different types of datasets to form an interconnected organic whole, which facilitates subsequent joint queries and data analysis.
[0052] Preferably, the method for storing civil engineering quantities further includes: Dynamic storage structure configuration: Construct a core database structure, which includes a project table, a sub-item table, a partition table, a stage parameter table, a partition stage table, a foundation engineering data table, a superstructure data table, a steel structure data table, a parameter field table, parameter-related data tables, a list item field table, a list item characteristic table, a process equipment data table, a standardized parameter data table, and a standardized parameter type table; the parameter field table includes a project parameter field table, a sub-item parameter field table, and a partition parameter field table; the parameter-related data tables include a project data table, a sub-item data table, and a partition data table; and the list item field table includes a foundation engineering list item table, a superstructure list item table, and a steel structure list item table. Dynamic expansion mechanism configuration: Set the adaptive mapping between parameter set and dataset. When a new field is added to the parameter set, a new data item is automatically generated in the corresponding dataset. The initial value of the new data item is empty until new data is entered, at which point the corresponding value is filled in.
[0053] Specifically, during dynamic storage structure configuration, a core database structure adapted to the storage of civil engineering quantities in the cement industry is built. Parameter field tables are subdivided into project parameter field tables, sub-item parameter field tables, and partition parameter field tables according to parameter hierarchy. Data tables related to parameters are subdivided into project data tables, sub-item data tables, and partition data tables. List item field tables are subdivided into basic engineering list item tables, superstructure list item tables, and steel structure list item tables according to list type. Various data tables related to process equipment and standardized parameters are also included. During dynamic expansion mechanism configuration, an adaptive mapping relationship between parameter sets and datasets is established. When a new field is added to any parameter set, the system will automatically identify it and generate a matching new data item in the corresponding dataset. The initial state of this new data item is empty. When new engineering data is entered, the corresponding value is then filled into the data item.
[0054] It should be noted that this step constructs a complete database structure that is highly adapted to the storage needs of civil engineering projects in the cement industry, meeting the storage requirements of multi-type and multi-level engineering data; at the same time, it realizes the automatic expansion of data fields, improves the flexibility and scenario adaptability of the storage method, and can quickly respond to the new parameters and data storage needs in civil engineering projects in the cement industry without the need for manual readjustment of the database structure.
[0055] Example 2 This embodiment provides a method for storing civil engineering quantities in the cement industry, including: 1. Data acquisition.
[0056] Obtain the engineering quantity data and design data of historical projects. The engineering quantity data includes bill of quantities data, and the design data includes structural design data and process design data.
[0057] Preferably, the bill of quantities includes data from three stages: the quantity of work in the tender, the quantity of work in the design, and the quantity of work in the final settlement.
[0058] 2. Data decomposition, as shown in the appendix. Figure 2 As shown.
[0059] a) Decompose the design data into parameter type data, and further decompose the parameter type data according to the time and space dimensions.
[0060] i. Based on the spatial composition hierarchy of the cement plant's buildings and structures, the parameter data are divided into project parameters, sub-item parameters, and zoning parameters, from largest to smallest. Project parameters are those relevant to the entire plant, including project type, project scale, project area, applicable standards, seismic intensity, seismic grouping, site category, basic wind pressure, basic snow pressure, and frost depth. Sub-item parameters are those related to specific sub-items, including system classification and whether pile foundations are used. Zoning parameters are those related to the buildings and structures within a given area, including main equipment, auxiliary equipment, building length, building width, building height, building diameter, quantity, pile end bearing capacity characteristic value, and foundation bearing capacity characteristic value. A sub-item refers to one or more functional rooms in the cement plant used to implement a specific process.
[0061] ii. Considering the time dimension of the project scope, add a stage parameter to the parameter type data. Stages refer to the bidding stage, design stage, and settlement stage of the project.
[0062] b) Decompose the bill of quantities data according to the characteristics of the component materials and the structural parts to which they belong.
[0063] i. Based on the characteristics of building materials in the cement industry, the bill of quantities data is divided into steel structure data and other material data.
[0064] ii. Based on the location of the component, divide the component data from other materials into foundation engineering data and superstructure data.
[0065] The bill of quantities for steel structure data can be categorized as follows: storage sheds, conveyor frames, frame structures, steel platforms, steel canopies, belt conveyor corridors, and embedded parts. The bill of quantities for foundation engineering data can be categorized as follows: pile foundations, isolated foundations, pile caps, strip foundations, raft foundations, pits, and underground corridors. The bill of quantities for superstructure data can be categorized as follows: columns, beams, slabs, walls, circular storage silos, and chimneys. This categorization is based on the characteristics of cement plant buildings and takes into account the contents of the bill of quantities items in the bill of quantities pricing model, resulting in standardized classification fields.
[0066] 3. Data standardization.
[0067] Data standardization includes two aspects: standardization of parameter fields and standardization of parameter data values.
[0068] a) Standardization of parameter fields In this embodiment, parameter field standardization is achieved by associating datasets with parameter field sets. Each dataset contains all the keys (unique identifiers in the database table) of its corresponding parameter field set, ensuring that similar datasets maintain a consistent key structure across different projects, i.e., consistent field entries and field names, thereby guaranteeing the standardization and uniformity of the parameter structure.
[0069] As attached Figure 3 As shown, the core mechanism of this standardized data model is: The project parameter (such as seismic intensity and seismic group) entity stores standardized parameter field definitions.
[0070] The relationship between project data entities and project parameter entities is many-to-one (n:1). This means that a standardized parameter can be shared and referenced by multiple project data entries, while a single project data entry contains only one project parameter.
[0071] The relationships between project data entities are many-to-one (n:1). This indicates that a project can contain multiple project data entries, ensuring data ownership.
[0072] The data structures of other levels (such as sub-items and partitions) are the same as those of the project level. They are all standardized by associating parameter field sets with datasets, so they are not shown again in the diagram.
[0073] b) Standardization of parameter data values The purpose of standardizing parameter data values is to ensure the consistency and standardization of parameter values, and to avoid multiple expressions of the same meaning. This embodiment achieves this goal by establishing a standardized parameter dataset (or dictionary table).
[0074] As attached Figure 4 As shown, the core of this mechanism is the standardized parameter data entity, which predefines a complete and standardized set of optional values (e.g., 6 degrees (0.05g), 7 degrees (0.1g), 7 degrees (0.15g) etc.) for each enumerable project parameter (e.g., earthquake intensity).
[0075] When assigning a value to each enumerable piece of project data (such as a specific engineering parameter), the parameter value must be selected from the corresponding standardized parameter data entity. That is, all enumerable structural design parameters are included in the standardized parameter data.
[0076] This design, by using dropdown selections instead of free input, fundamentally eliminates the problem of inconsistent value ranges. For example, data for an earthquake intensity of 7 degrees in all projects will be uniformly represented as 7 degrees (0.15g), without any variations such as 7 degrees, 7, or 0.15g.
[0077] This method not only ensures the standardization of data values, but also greatly facilitates subsequent data retrieval, statistics and analysis.
[0078] 4. Data modeling.
[0079] This step is crucial for connecting the decomposed datasets. Its core idea is to reconstruct discrete data units into a cohesive whole with logical hierarchy and parameter dependencies through explicit association rules. The conceptual data model is attached. Figure 5 As shown.
[0080] a) Project-level association: One project corresponds to one project dataset, which is associated with one or more sub-items; the project parameters in the project dataset come from the project parameter set, and some project data in the project dataset comes from the standardized parameter dataset.
[0081] b) Sub-item level association: A sub-item corresponds to a sub-item dataset and is associated with one or more partitions; the sub-item parameters in the sub-item dataset come from the sub-item parameter set, and some sub-item data in the sub-item dataset come from the standardized parameter dataset.
[0082] c) Partition-level association: One partition corresponds to one partition dataset; the partition parameters in the partition dataset come from the partition parameter set, and some partition data in the partition dataset come from the standardized parameter dataset and the process equipment dataset; d) Joint association of partitions and stages: The partition and stage parameter sets form a new unique identifier through their internal unique identifiers, and are associated with the basic engineering dataset, the superstructure dataset, and the steel structure dataset respectively through this identifier.
[0083] e) Project quantity correlation: i. The basic engineering dataset is simultaneously associated with the partition stage dataset, the basic engineering inventory item set, and the inventory item feature set.
[0084] ii. The upper structure dataset is simultaneously associated with the partition stage dataset, the upper structure list item set, and the list item feature set.
[0085] iii. The steel structure dataset is simultaneously associated with the partition stage dataset, the steel structure inventory item set, and the inventory item feature set.
[0086] 5. Dynamic storage structure design and expansion mechanism.
[0087] To implement the aforementioned association model at the physical level, this embodiment designs a storage structure that balances standardization and flexibility. The core database structure of the system includes: Project table, item table, area table, stage parameter table; Partition stage table (item_stage); Foundation data table (foundation_data), superstructure data table (structure_data), steel structure data table (steel_data); Parameter field table (project_parameter, item_parameter, area_parameter); Data tables related to the parameters (project_data, item_data, area_data); List of field items (foundation_field, structure_field, steel_field); List of Item Feature Table (feature_field); Process equipment data sheet; Standardized parameter data table (standard_data); Standardized parameter type table (standard_type).
[0088] The dynamic expansion mechanism is reflected in the adaptive mapping between the parameter set and the dataset: when a new field is added to the parameter set, the system automatically generates a new item in the corresponding dataset, but the value of this item is empty until the new data is entered. This mechanism enables the system to dynamically expand fields while maintaining the stability of the database structure.
[0089] Example 3 This embodiment is based on embodiment 1: This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for storing civil engineering quantities for the cement industry described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form.
[0090] Example 4 This embodiment is based on embodiment 1: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for storing civil engineering quantities for the cement industry described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form. The storage medium includes any entity or device capable of carrying computer program code, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0091] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.
[0092] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
Claims
1. A method for storing civil engineering quantities in the cement industry, characterized in that, include: Data acquisition: Acquire engineering quantity data and design data of historical projects. The engineering quantity data includes bill of quantities data, and the design data includes structural design data and process design data. Data decomposition: The design data is decomposed into parameter type data, which is further decomposed into project parameters, sub-item parameters, and partition parameters according to the spatial dimension, and stage parameters are added according to the time dimension; the bill of quantities data is decomposed according to the characteristics of the component materials and the structural parts to which they belong. Data standardization: Standardize the decomposed engineering quantity data and design data, including parameter field standardization and parameter data value standardization; the parameter field standardization is achieved by associating the dataset with the corresponding parameter field set to ensure that the key structure of similar datasets is consistent; the parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standard optional values for project parameters, sub-item parameters and partition parameters. Data modeling: The standardized engineering quantity data and design data are reconstructed into a whole with logical hierarchy and parameter dependencies, thus forming three data levels: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset, and the partition level also contains a process equipment dataset. Using the data from the partition stage as a link, the parameter type data and the bill of quantities data are associated.
2. The method for storing civil engineering quantities in the cement industry according to claim 1, characterized in that, During the data decomposition, parameter-type data is decomposed spatially into project parameters, sub-item parameters, and partition parameters, and stage parameters are added along the time dimension, including: Based on the spatial composition hierarchy of cement plant buildings from largest to smallest, the parameter type data is divided into project parameters, sub-item parameters, and zone parameters; Considering the time dimension of the project quantity, a stage parameter is added to the parameter type data to characterize the attribute characteristics of the project quantity at different construction stages.
3. The method for storing civil engineering quantities in the cement industry according to claim 1, characterized in that, During the data decomposition, the bill of quantities data is classified according to the characteristics of the component materials and the structural parts to which they belong, including: Based on the material properties of cement industry components, the bill of quantities data is divided into steel structure data and non-steel structure data; Based on the structural location of the component, the non-steel structure data is divided into foundation engineering data and superstructure data.
4. The method for storing civil engineering quantities in the cement industry according to claim 3, characterized in that, During data standardization, parameter field standardization is achieved by associating datasets with corresponding parameter field sets, ensuring consistent key structures across similar datasets, including: Each dataset contains all the keys of its corresponding parameter field set, ensuring that the field entries and field names of the same dataset remain consistent across different projects; the data structures at the level of project parameters, sub-item parameters, and partition parameters are all standardized by associating parameter field sets with datasets.
5. The method for storing civil engineering quantities in the cement industry according to claim 4, characterized in that, During data standardization, parameter data value standardization is achieved by establishing a standardized parameter dataset, which predefines a set of standard optional values for project parameters, sub-item parameters, and partition parameters, including: Establish a standardized parameter dataset, predefining a complete and standardized set of optional values for each enumerable project parameter, sub-item parameter, and partition parameter; when assigning values to project data, sub-item data, and partition data, the parameter values are selected from the corresponding standardized parameter data. The selection method specifically includes: first, matching the parameter name with the standardized parameter type, and then selecting the corresponding standardized parameter data from the matched standardized parameter types.
6. The method for storing civil engineering quantities in the cement industry according to claim 5, characterized in that, The data modeling process involves three data levels: project, sub-item, and partition. Each data level contains a parameter set and a standardized parameter dataset. The partition level also includes a process equipment dataset, specifically: Project-level association: One project corresponds to one project dataset and is associated with one or more sub-items; the project parameters in the project dataset come from the project parameter set, and some project data in the project dataset come from the standardized parameter dataset; Sub-item level association: One sub-item corresponds to one sub-item dataset and is associated with one or more partitions; the sub-item parameters in the sub-item dataset come from the sub-item parameter set, and some sub-item data in the sub-item dataset come from the standardized parameter dataset; Partition-level association: one partition corresponds to one partition dataset; the partition parameters in the partition dataset come from the partition parameter set, and some partition data in the partition dataset come from the standardized parameter dataset and the process equipment dataset.
7. The method for storing civil engineering quantities in the cement industry according to claim 6, characterized in that, During data modeling, the data from different partition stages is used as a link to associate parameter type data and bill of quantities data, including: Joint association of partitions and phases: The unique identifiers within the partition set and the phase parameter set are combined to form a new unique identifier, which is then used to associate the basic engineering dataset, the superstructure dataset, and the steel structure dataset. Quantity association: The basic engineering dataset is simultaneously associated with the partition stage dataset, the basic engineering bill of quantities item set, and the bill of quantities item feature set; the superstructure dataset is simultaneously associated with the partition stage dataset, the superstructure bill of quantities item set, and the bill of quantities item feature set; the steel structure dataset is simultaneously associated with the partition stage dataset, the steel structure bill of quantities item set, and the bill of quantities item feature set.
8. The method for storing civil engineering quantities in the cement industry according to claim 7, characterized in that, Also includes: Dynamic storage structure configuration: Construct a core database structure, which includes a project table, a sub-item table, a partition table, a stage parameter table, a partition stage table, a foundation engineering data table, a superstructure data table, a steel structure data table, a parameter field table, parameter-related data tables, a list item field table, a list item characteristic table, a process equipment data table, a standardized parameter data table, and a standardized parameter type table; the parameter field table includes a project parameter field table, a sub-item parameter field table, and a partition parameter field table; the parameter-related data tables include a project data table, a sub-item data table, and a partition data table; and the list item field table includes a foundation engineering list item table, a superstructure list item table, and a steel structure list item table. Dynamic expansion mechanism configuration: Set the adaptive mapping between parameter set and dataset. When a new field is added to the parameter set, a new data item is automatically generated in the corresponding dataset. The initial value of the new data item is empty until new data is entered, at which point the corresponding value is filled in.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method for storing civil engineering quantities for the cement industry as described in any one of claims 1-8.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for storing civil engineering quantities for the cement industry as described in any one of claims 1-8.