Building construction data processing method, system, equipment, medium and program product

Abstract

The invention relates to the technical field of data processing, and discloses a building construction data processing method, system and device, a medium and a program product. The method comprises the following steps: extracting attribute information of a first business entity from a data production tool; integrating the attribute information of the first service entity into a data feature carrier corresponding to the first service entity to generate a first data feature carrier; mapping entity identification information included in the first data feature carrier into extended identification information, and updating the first data feature carrier based on the extended identification information to generate a second data feature carrier; and storing the second data feature carrier to the cloud platform according to the extended identification information, so that the data consumption tool obtains the second data feature carrier from the cloud platform. By implementing the technical scheme of the invention, data compatibility and efficient exchange between different data production tools and data consumption tools can be realized, and accurate and smooth circulation and use of data in each link are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method, a system, a device, a medium and a program product for processing construction data. Background Art

[0002] In the field of Architecture, Engineering, and Construction (AEC), different stages of project advancement often rely on different tool software, and multiple tools are often involved in the same stage, such as project planning software, civil engineering quantity calculation software, etc. However, due to significant differences in the data formats, structures, and interfaces of various software, data exchange is extremely difficult. Especially when working across stages, data transfer and integration usually can only be completed by manual summarization. Due to inconsistent data formats and standards, manual entry not only easily causes fatigue for staff due to long-term repeated operations, resulting in data misfilling and missing filling, but also easily leads to incorrect understanding of data meanings when interpreting data with different standards. Coupled with the huge amount of data generated in each stage, it is difficult to ensure the integrity and consistency of data during manual integration, ultimately resulting in data errors, omissions, and misinterpretation of meanings, seriously hindering the improvement of work efficiency. Summary of the Invention

[0003] In view of this, the present invention provides a method, a system, a device, a medium and a program product for processing construction data, so as to solve the problems of difficult cross-stage data transfer and integration caused by differences in data formats, structures, and interfaces of tool software, and easy errors and low efficiency in manual processing.

[0004] In a first aspect, the present invention provides a method for processing construction data, including: extracting attribute information of a first business entity from a data production tool, where the attribute information refers to data for describing the characteristics of the first business entity in the data production tool; integrating the attribute information of the first business entity into a data feature carrier corresponding to the first business entity to generate a first data feature carrier, where the first data feature carrier refers to a data set generated after integrating the attribute information into the data feature carrier; mapping entity identification information included in the first data feature carrier to extended identification information, and updating the first data feature carrier based on the extended identification information to generate a second data feature carrier; storing the second data feature carrier in a cloud platform according to the extended identification information, so that a data consumption tool can obtain the second data feature carrier from the cloud platform.

[0005] The construction data processing method provided by the embodiments of the present invention extracts attribute information from data production tools and integrates it to generate a first data feature carrier, realizing the preliminary standardized sorting of data and making the scattered data have a clear set. Secondly, mapping the entity recognition information to extended recognition information and updating the data feature carrier enhances the recognition dimension and accuracy of the data. Finally, storing it in the cloud platform according to the extended recognition information facilitates the acquisition by data consumption tools, breaks the data exchange barrier, effectively solves the data exchange problem caused by the data format differences between different software, improves the data processing efficiency and circulation, and ensures the smooth flow of data in each stage of the project.

[0006] In an alternative embodiment, integrating the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate the first data feature carrier includes: obtaining the entity recognition information of the first business entity, the tool identification information of the data production tool, and the purpose information recorded by the data feature carrier; based on the preset data structure of the data feature carrier, embedding the entity recognition information, the tool identification information, and the purpose information into the header area of the data feature carrier to generate the first data feature carrier.

[0007] The construction data processing method provided by the embodiments of the present invention can make the data feature carrier have a clear identity identification and usage description at the beginning of construction by obtaining the entity recognition information, the tool identification information, and the purpose information and embedding them into the header area of the data feature carrier according to the preset data structure. This not only helps to quickly locate and identify the business entity and source tool to which the data belongs, but also clarifies the data recording purpose, provides key clues for subsequent data processing, analysis, and management, significantly improves the data processing efficiency and accuracy, enhances the traceability and management convenience of the data, and ensures the standardization and orderliness of the construction data during the process of circulation and use.

[0008] In an alternative embodiment, mapping the entity recognition information included in the first data feature carrier to extended recognition information includes: obtaining a set of data feature carriers generated by the data production tool, where the set of data feature carriers includes the data feature carriers corresponding to each first business entity; searching for the extended recognition information corresponding to the entity recognition information in the first data feature carrier from the set of data feature carriers based on the preset matching rule; if it is determined that there is extended recognition information corresponding to the entity recognition information in the set of data feature carriers, then mapping the entity recognition information included in the first data feature carrier to extended recognition information based on the preset data mapping rule.

[0009] The construction data processing method provided by the embodiments of the present invention obtains a set of data feature carriers, covering the data feature carriers corresponding to each first business entity, providing a rich data basis for comprehensive search and mapping. Secondly, based on preset matching rules, extended identification information is searched, making the search process follow rules and ensuring accuracy and systematicness. Furthermore, if the corresponding information is found, mapping is performed according to the preset data mapping rules, ensuring the standardization and consistency of mapping. Therefore, this method effectively utilizes existing data resources, enhances the relevance and expandability of data, and improves the accuracy and efficiency of data processing.

[0010] In an alternative embodiment, if it is determined that the set of data feature carriers does not include extended identification information corresponding to the entity identification information, extended identification information corresponding to the entity identification information is generated.

[0011] The construction data processing method provided by the embodiments of the present invention, when the set of data feature carriers lacks extended identification information corresponding to the entity identification information, actively generates the corresponding information, ensuring the integrity and coherence of the data processing process. This avoids data processing interruption or errors caused by information absence, providing comprehensive data support for subsequent data integration, analysis, and storage and use on the cloud platform.

[0012] In an alternative embodiment, if the data production tools include a first production tool and a second production tool, and there is a many-to-many relationship among multiple second business entities corresponding to the first production tool, and there is also a many-to-many relationship among multiple third business entities corresponding to the second production tool, then the many-to-many relationship between the multiple second business entities and the multiple third business entities is decomposed into a one-to-one relationship; the one-to-one relationship is stored in a preset relationship record component.

[0013] The construction data processing method provided by the embodiments of the present invention, when facing the complex many-to-many business entity relationship in the first production tool and the second production tool, decomposes it into a one-to-one relationship, greatly simplifying the data structure and making the originally intricate data associations clear and intuitive. This not only effectively reduces the difficulty of data processing, improves the data processing efficiency, but also significantly improves the accuracy of data query and analysis, avoiding understanding deviations and operation mistakes caused by complex relationships. At the same time, storing the one-to-one relationship in a preset relationship record component provides convenience for data management, facilitating subsequent maintenance, update, and traceability of data relationships, and enhancing the stability and expandability of the entire construction data management system.

[0014] In an alternative embodiment, when a data consumption tool obtains data from multiple data production tools, if the to-be-executed data generated by the multiple data production tools is inconsistent, the target execution data corresponding to the data consumption tool in the to-be-executed data is determined based on a preset business rule or an interactive operation.

[0015] The building construction data processing method provided by the embodiments of the present invention, when the data consumption tool obtains data from multiple data production tools, in the face of the complex situation of inconsistent to-be-executed data, determines the target execution data according to the preset business rule or interactive operation, can ensure that the data consumption tool obtains data that meets its own needs, effectively avoids incorrect use caused by data chaos, and greatly improves the accuracy of data use. At the same time, the target execution data is quickly screened out through the preset rule and interactive operation, saving the data screening time and improving the data consumption efficiency.

[0016] In an alternative embodiment, if it is determined that the to-be-executed data generated by the data production tool does not meet the preset requirements, the to-be-executed data is adjusted based on the preset requirements.

[0017] The building construction data processing method provided by the embodiments of the present invention, when the to-be-executed data generated by the data production tool does not meet the preset requirements, adjusts it in a timely manner based on the preset requirements, can control the data quality from the source, avoid incorrect results in subsequent data processing due to data deviation, and ensure the accuracy and reliability of the entire building construction data processing process.

[0018] In a second aspect, the present invention provides a building construction data processing system, including: a data production tool, configured to extract the attribute information of a first business entity from the data production tool, where the attribute information refers to the data used to describe the characteristics of the first business entity in the data production tool, integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate a first data feature carrier, and the first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier; the data production tool is further configured to map the entity identification information included in the first data feature carrier to extended identification information, and update the first data feature carrier based on the extended identification information to generate a second data feature carrier; a cloud platform, communicatively connected to the data production tool, configured to receive the second data feature carrier uploaded by the data production tool and store the second data feature carrier generated by the data production tool according to the extended identification information; and a data consumption tool, communicatively connected to the cloud platform, configured to obtain the second data feature carrier from the cloud platform.

[0019] In the construction data processing system provided by the embodiments of the present invention, the data production tool can complete a series of complex operations from extracting attribute information, integrating and generating the first data feature carrier to updating the mapping recognition information to the second data feature carrier, ensuring the coherence and accuracy of data processing. The cloud platform, as the storage and transfer hub of data, is communicatively connected to the data production tool to efficiently receive and orderly store the second data feature carrier, providing guarantee for the secure storage and management of data. The data consumption tool can conveniently obtain the required data by communicating with the cloud platform, building a complete data production-storage-consumption link, breaking the data exchange barrier, greatly improving the data circulation efficiency, and enabling all links of the construction project to be efficiently promoted based on accurate and timely data.

[0020] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the construction data processing method according to the first aspect or any corresponding embodiment thereof.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the construction data processing method according to the first aspect or any corresponding embodiment thereof.

[0022] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the construction data processing method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is a flowchart of the construction data processing method according to the embodiments of the present invention;

[0025] Figure 2 is a flowchart of another construction data processing method according to the embodiments of the present invention;

[0026] Figure 3 is a flowchart of yet another construction data processing method according to the embodiments of the present invention;

[0027] Figure 4 It is a schematic diagram of the calculation process of the Id alignment algorithm according to an embodiment of the present invention;

[0028] Figure 5 It is a schematic flowchart of another building construction data processing method according to an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the second data feature carrier Schema according to an embodiment of the present invention;

[0030] Figure 7 It is a structural block diagram of a building construction data processing system according to an embodiment of the present invention;

[0031] Figure 8 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] In the AEC field, at different stages of project advancement and within the same stage, it is necessary to work collaboratively with a variety of tool software. For example, project planning software is used for preliminary project concept and framework construction, digital building design software focuses on the building design link, and cloud pricing software is responsible for project cost accounting, etc. In the Engineering, Procurement, Construction, EPC mode, to achieve the integration of the entire project process, the data generated by the tool software at each stage must be pulled through across stages.

[0034] However, the data formats, structures, and meanings of different software vary greatly, resulting in extremely difficult data exchange. There are mainly the following thorny problems: First, the issue of entity identity identification. Each software has an independent identification system, making it difficult for upstream and downstream software to accurately identify the same entity. For example, a certain building component in the upstream design software cannot be accurately corresponded in the downstream construction management software. Second, the issue of entity deepening. Different software faces different business sub - domains, and the creation, modification, and usage methods of the same entity vary. When deepening across stages, not only does the authoritative party need to intervene and coordinate, but the existing simple conflict - resolution methods are difficult to meet complex business requirements. Third, the issue of entity relationship mapping. Currently, the point - to - point method is mostly used, which is difficult to expand. Once out of the software environment, the mapping relationship is easily lost. Just like the entity mapping between design software and construction software, it is difficult to trace after leaving their respective software. Fourth, the issue of entity data exchange and reuse. Due to the differences in software descriptions of entities and the limitation that deserialization requires loading all data into memory, it is extremely difficult for downstream software to reuse upstream data.

[0035] In view of this, the present invention proposes a technical solution based on the data base of the Entity Component System (ECS) (including the ECS - side base and the ECS - cloud base) to solve the data - exchange problem in the AEC field. First, extract the attribute information of the first business entity from the data - production tool, and integrate these attributes into a data - feature carrier that conforms to the ECS specification to form the first data - feature carrier, thereby ensuring the relevance and integrity of the data. Then, use the ECS - side base to map the entity identification information in the first data - feature carrier into extended identification information, breaking through the data - identification barriers between different tools. Finally, store the updated second data - feature carrier in the cloud platform (ECS - cloud base) according to the extended identification information. Utilize the homologous characteristics of the end - cloud storage and computing engine to achieve efficient data storage and processing, facilitating data - consumption tools to obtain data. This not only enhances the compatibility and circulation of data but also improves the processing efficiency, ensuring the accurate and timely transfer of data in all stages of the project, providing strong support for the efficient progress of construction projects.

[0036] According to an embodiment of the present invention, an embodiment of a method for processing construction data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer - executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] In this embodiment, a method for processing construction data is provided, which can be used in computer devices such as desktop computers and laptop computers. Figure 1is a flowchart of a building construction data processing method according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0038] Step S101: Extract the attribute information of the first business entity from the data production tool. The attribute information refers to the data in the data production tool that describes the characteristics of the first business entity.

[0039] The data production tool refers to various software or systems that generate data related to business entities during the building construction process, and can also be called the producer. For example, during the building design stage, the software used to create a building model and generate data of building components such as walls, doors, windows, beams, and columns is a data production tool; in terms of construction progress management, the project management software used to generate data such as construction tasks, construction periods, and resource allocations is also a data production tool.

[0040] The first business entity refers to a specific object with independent meaning and characteristics in a building construction project. Taking a residential building project as an example, a room can be used as the first business entity, and its characteristics include area, height, function (such as bedroom, living room, etc.); a steel beam is another business entity, and its characteristics include length, cross-sectional dimensions, material, etc.

[0041] The attribute information is data used to accurately describe various characteristics of the first business entity. For example, the attribute information of a room includes area, height, function, etc.; the attribute information of a steel beam includes length, cross-sectional dimensions (such as width, height, thickness, etc.), material type (such as Q345B steel), etc. Specifically, in the design software, these attribute information can be obtained through the built-in data export function or application programming interface (API). For example, when using Revit software, developers can write programs through RevitAPI and use specific functions and query statements to retrieve attribute data such as the area and height of a room from the database; in the project management software, through database query statements (such as the SELECT and WHERE clauses in SQL statements), according to conditions such as the unique identifier or name of the steel beam, its length, material, and other attribute information can be obtained.

[0042] Step S102: Integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate a first data feature carrier. The first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier.

[0043] A data feature carrier is a structured or semi-structured medium used to organize and store data related to a first business entity, and it can be the data structure of a file in a specific format. Under the ECS architecture, the design of the data feature carrier needs to conform to the specifications of the ECS data base (including the ECS terminal base and the ECS cloud base) to achieve unified management and interaction of data. Its main purpose is to provide a unified, manageable, and transmissible framework for data, ensuring the integrity and relevance of data.

[0044] The first data feature carrier is a specific data set formed after integrating the attribute information of the first business entity into the data feature carrier according to specific rules, and is also called the first feature component. Specifically, according to the type of the data feature carrier and the predetermined integration rules, the attribute information of the first business entity can be integrated into the corresponding carrier to generate the first data feature carrier. This process can also be understood as integrating the attribute information extracted from different data production tools to form a first feature component that conforms to the ECS specifications, laying a foundation for subsequent data processing and exchange.

[0045] Step S103: Map the entity identification information included in the first data feature carrier to extended identification information, and update the first data feature carrier based on the extended identification information to generate a second data feature carrier.

[0046] Entity identification information is the information used by a data production tool to identify the first business entity within its own project scope and is unique within this scope. For example, in architectural design software, each building component may have a unique component identity within the software system, and this component identity is the entity identification information. In a construction progress management system, a construction task also has a specific task code, which serves as the entity identification information to distinguish different task entities.

[0047] Extended identification information (eeid) refers to the unique identifier within the scope of the ECS data base project, which is used to unify the identification of the same first business entity by different data production tools. Under the ECS architecture, the extended identification information is the key to realizing unified data management and interaction. By mapping the entity identification information of different tools to the extended identification information, data barriers can be broken, and the interconnection and interoperability of data from different tools at the ECS data base level can be achieved.

[0048] The second data feature carrier is a new data set formed on the basis of the first data feature carrier by mapping entity recognition information into extended recognition information and updating it, and is also called the second feature component. The second data feature carrier inherits the business entity attribute information in the first data feature carrier and enhances the identifiability and relevance of data in different environments through the updated recognition information. Specifically, the ECS-side base can automatically and transparently determine extended recognition information for data production tools through a preset algorithm to ensure the consistency of the identification of the same business entity by different tools.

[0049] For example, a mapping rule or transformation function (preset algorithm) is established in advance to map the entity recognition information in the first data feature carrier into extended recognition information, and the first data feature carrier is updated based on this information to obtain the second data feature carrier. Mapping the entity recognition information into extended recognition information is equivalent to assigning a "identity identifier" that is common within the scope of the data base project to the same business entity of different data production tools. For example, the entity recognition information "1" of the wall component in data production tool A and the entity recognition information "5" of the same wall component in tool B both correspond to the same extended recognition information (such as a specific GUID format string) after mapping. In this way, at the ECS data base level, it can be determined that they represent the same business entity in the project, facilitating subsequent data interaction, integration, and management between different tool software.

[0050] Step S104, store the second data feature carrier in the cloud platform according to the extended recognition information, so that the data consumption tool can obtain the second data feature carrier from the cloud platform.

[0051] The cloud platform refers to the ECS cloud base, which provides powerful computing, storage capabilities, as well as data management and processing functions. Under the ECS architecture, the cloud platform realizes end-cloud integrated data storage and management. Through the characteristics of end-cloud storage, the cloud platform can reasonably allocate data between the local and the cloud, thereby improving the flexibility and efficiency of data storage. Specifically, according to the extended recognition information, the cloud platform creates a unique index or identifier for the second data feature carrier. This identifier can be regarded as the "address" of the data in the cloud platform, facilitating subsequent searching and accessing. Then, the cloud platform transfers the second data feature carrier to the cloud platform through an application programming interface (API) or a dedicated data upload tool. After receiving the data, the cloud platform stores it in the corresponding storage medium according to the storage policy, such as a distributed file system or a database. At the same time, the cloud platform will associate the extended recognition information with the data storage location and establish an index table for quickly locating the data.

[0052] A data consumption tool refers to software or a system that needs to use construction data to complete specific business functions, also known as the consumer side. These tools include project management software, cost accounting software, construction progress monitoring software, etc. For example, cost accounting software can obtain data such as the purchase price of building materials and labor costs from the cloud platform to accurately calculate the total project cost; construction progress monitoring software can obtain the actual progress data of construction tasks and compare it with the planned progress to promptly detect deviations and take measures. Specifically, when a data consumption tool needs to obtain a second data feature carrier, it will send a request to the cloud platform, and the request contains extended identification information. After receiving the request, the cloud platform looks up the corresponding storage location in the index table according to the extended identification information. Once the storage location is found, the cloud platform reads the data from the corresponding storage medium and transmits the data to the data consumption tool through the network. After receiving the data, the data consumption tool will perform verification and parsing to ensure the accuracy and availability of the data. Then, it can use this data for subsequent business processing. Therefore, thanks to the ECS cloud base, data consumption tools can obtain and use data more efficiently, breaking down the data exchange barriers between different software.

[0053] The construction data processing method provided by the embodiment of the present invention extracts attribute information from the data production tool and integrates it to generate a first data feature carrier, realizing the preliminary standardized sorting of data and making the scattered data have a clear collection. Secondly, mapping the entity identification information to extended identification information and updating the data feature carrier enhances the identification dimension and accuracy of the data. Finally, storing it in the cloud platform according to the extended identification information facilitates the data consumption tool to obtain, breaks down the data exchange barriers, effectively solves the data exchange problem caused by the data format differences between different software, improves the data processing efficiency and liquidity, and ensures the smooth flow of data in each stage of the project.

[0054] In this embodiment, a construction data processing method is provided, which can be used in computer devices such as desktop computers and laptop computers. Figure 2 It is a flowchart of the construction data processing method according to the embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0055] Step S201, extract the attribute information of the first business entity from the data production tool. The attribute information refers to the data used to describe the characteristics of the first business entity in the data production tool. For details, please refer to Figure 1 step S101 of the embodiment shown here, which will not be elaborated here.

[0056] Step S202: Integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate a first data feature carrier. The first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier.

[0057] Specifically, the above step S202 includes:

[0058] Step S2021: Obtain the entity recognition information of the first business entity, the tool identification information of the data production tool, and the purpose information recorded in the data feature carrier.

[0059] The entity recognition information corresponds to the "id" field in the identification group at the head of the first data feature carrier, also known as the product entity ID (peid). It is ensured to be unique within the project scope of the data production tool and does not need to be globally unique. The field type is string, aiming to give the id system of the data production tool freedom of implementation. For example, in tool software (data production tool) A, a specific beam can be assigned the entity recognition information "Beam - 001", and this beam can be accurately found in the tool software A through this information.

[0060] The tool identification information corresponds to the "product_code" field in the commit and content_meta groups at the head of the first data feature carrier. It is the code of the data production tool that generates this record and is used to uniquely identify the data production tool, as shown in Table 1. Different data production tools have different product_codes, which enables clear distinction of the data sources during data processing. For example, the product_code of tool software A can be "ToolA_2025", and the product_code of tool software B can be "ToolB_V3". Then, different tool software will not generate the same record because of different product_codes. That is to say, within their respective fields, or even within the same stage, they can independently model and store the same first business entity for exchange. The same tool software can also generate records for the same first business entity but for different purposes due to different consumption scenarios. Eventually, if there are multiple records for the same first business entity, these multiple records can coexist in a first data feature carrier.

[0061] The purpose information corresponds to the "content_purpose" field in the commit and content_meta groups at the head of the first data feature carrier. It describes the purpose or intention of this record. In construction projects, the purposes of recording data in different stages and business scenarios may vary, as shown in Table 1. For example, when recording data in the design stage for building structure analysis, the content_purpose can be "StructuralAnalysis"; when recording data in the construction stage for progress management, the content_purpose can be "ProgressManagement".

[0062] Table 1

[0063] Field Name product_code content_purpose Field Type string string Necessity Yes No

[0064] In step S2022, based on the preset data structure of the data feature carrier, the entity recognition information, tool identification information, and purpose information are embedded into the head area of the data feature carrier to generate the first data feature carrier.

[0065] Physically, the data feature carrier is manifested as a file, and logically, it supports data expressions between two-dimensional relational tables and objects. Its columns are divided into two parts: the head and the content. The head columns are fixed and used to record the metadata of the content; the content columns are defined by the producer or after negotiation, and different components have different content columns. The head is further divided into four groups. The identification group contains important fields such as "id" and "eeid" for entity identification; the commit and content_meta groups contain fields such as "product_code" and "content_purpose" for recording tool software information and data recording purposes. Specifically, the obtained entity recognition information (id), tool identification information (product_code), and purpose information (content_purpose) are embedded into the head area according to the preset data structure of the data feature carrier, forming the first data feature carrier. For example, the entity recognition information of the first business entity is filled into the "id" field of the identification group; the tool identification information of the data production tool is filled into the "product_code" field of the commit and content_meta groups; the purpose information recorded by the data feature carrier is filled into the "content_purpose" field.

[0066] The construction data processing method provided by the embodiments of the present invention generates a first data feature carrier by obtaining entity recognition information, tool identification information, and purpose information and embedding them in the head area of the data feature carrier according to a preset data structure. This enables the data feature carrier to have a clear identity identifier and usage description at the beginning of its construction. This not only helps to quickly locate and identify the business entity and source tool to which the data belongs, but also clarifies the purpose of data recording, providing key clues for subsequent data processing, analysis, and management, significantly improving the efficiency and accuracy of data processing, enhancing the traceability and management convenience of data, and ensuring the standardization and orderliness of construction data during its circulation and use.

[0067] Step S203: Map the entity recognition information included in the first data feature carrier to extended recognition information, and update the first data feature carrier based on the extended recognition information to generate a second data feature carrier.

[0068] Specifically, the above step S203 includes:

[0069] Step S2031: Obtain a set of data feature carriers generated by a data production tool, where the set of data feature carriers includes data feature carriers corresponding to each first business entity.

[0070] In the construction data processing scenario, the data production tool will generate multiple data feature carriers, and each data feature carrier corresponds to one or more first business entities. Specifically, obtaining the set of data feature carriers means collecting all the data feature carriers generated by the data production tool and containing information about each first business entity. This set provides a comprehensive data basis for subsequent searching for extended recognition information.

[0071] Step S2032: Search for extended recognition information corresponding to the entity recognition information in the first data feature carrier from the set of data feature carriers based on a preset matching rule.

[0072] The preset matching rule is related to the "id" field in the identification group of the characteristic component header. Specifically, when searching, the entity recognition information (i.e., id) in the first data feature carrier will be used as a basis to perform matching in the identification group of the header of each data feature carrier in the set of data feature carriers.

[0073] The extended recognition information (eeid) is the identifier of the entity in the ECS data base and is unique within the project scope of the ECS data base, as shown in Table 2. The ECS data base includes a terminal base and a cloud base (cloud platform) part, and also provides an ECS Hub panel that can be embedded in desktop or Web-based tool software, such as Figure 3As shown in the figure. In the tool software of the same project, the ECSHub panel provides the functions of sending and receiving data to be exchanged for users. The ECSHub panel provides a function entry for the tool software, and the underlying layer uses the ECS end base. When sending, the ECS end base converts the data to be exchanged in the tool software into a second data feature carrier for storage and incrementally pushes it to the cloud base. When receiving, the ECS end base pulls the second data feature carrier from the ECS cloud base and converts it into an object-oriented in-domain entity for the tool software to use. The ECS end base also provides rich and efficient query capabilities for the data in the second data feature carrier to meet the deeper customization needs of the tool software. The eeid is used to connect different ids when describing the same first business entity in multiple tool softwares. That is to say, the tool software can use its own way to express the entity id. As long as the eeids in the records are the same, it means that the same first business entity in the same project is described.

[0074] Table 2

[0075]

[0076] Step S2033, if it is determined that there is an extended identification information corresponding to the entity identification information in the data feature carrier set, then map the entity identification information included in the first data feature carrier to the extended identification information based on a preset data mapping rule.

[0077] The preset data mapping rule stipulates how to accurately convert the entity identification information in the first data feature carrier into the extended identification information. Specifically, according to the preset data mapping rule, map the entity identification information id in the first data feature carrier to the extended identification information eeid.

[0078] Step S2034, if it is determined that the data feature carrier set does not include the extended identification information corresponding to the entity identification information, then generate the extended identification information corresponding to the entity identification information.

[0079] If, after searching in the data feature carrier set, no extended identification information matching the entity identification information in the first data feature carrier is found, then according to the preset Id connection algorithm, use the Globally Unique Identifier (GUID) generation algorithm to generate a new extended identification information for the entity identification information. The GUID is a 128-bit identifier composed of numbers and letters, with extremely high uniqueness. The generated new extended identification information will establish a mapping relationship with the entity identification information and be updated to the "mapping of entity id and eeid" (such as Output1).

[0080] Among them, the calculation process of the Id connection algorithm is as Figure 4As shown below:

[0081] (1) Mandatory: Input1 of the tool software: set of entity IDs, Input3 obtained from the terminal base: set of production data feature carriers;

[0082] (2) Optional: Input2 of the tool software: mapping of consumer entity ID and producer entity ID, Input4 obtained from the terminal base: set of consumer data feature carriers;

[0083] (3) From Input3: set of production data feature carriers of the tool software, for each data feature carrier, for each ID in Input1: set of entity IDs, search for records according to the query condition that the ID is equal to the ID in the identification grouping at the head of the data feature carrier. If found, record the found eeid in Output1: mapping of entity ID and eeid;

[0084] (4) If the tool software provides Input2: mapping of consumer entity ID and producer entity ID, obtain Input4: set of consumer data feature carriers from the terminal base. In Input4: set of consumer data feature carriers, entities that need to be consumed by the current tool software are generated. Search for the eeid corresponding to the upstream producer entity ID according to the conditions that product_code, ID are equal to product_code in the commit grouping and ID in the identification grouping at the head of the data feature carrier. Record the found eeid and product_code together in Output1: mapping of entity ID and eeid.

[0085] (5) During the process of generating data feature carriers in the terminal base, for each data feature carrier to be generated / updated, determine its eeid for each ID in Input1: set of entity IDs. The method is to search for the eeid by ID in Output1: mapping of entity ID and eeid: if found, use the found eeid; if not found, generate an eeid for the ID using the GUID generation algorithm and update the new ID-to-eeid mapping to Output1: mapping of entity ID and eeid; record the ID and eeid together with the record in the identification field at the head of the data feature carrier.

[0086] In addition, when modifying the data feature carrier of the ECS-side base, the specified method is to add a new record line, and existing record lines cannot be modified. For modifications to the same entity from the same tool software and for the same purpose, multiple historical records are formed. The tool software can generate an entity ID using its own ID system. If the entity consumes an upstream entity for further refinement, when generating the data feature carrier, the tool software needs to inform the ECS data base of the upstream product code and entity ID. The ECS-side base will automatically search for the corresponding eeid of the consumed entity, and then fill the generated entity ID and this eeid into the header of the new record. In this way, for the same business entity within the project scope, the same eeid is used. Thus, among different data feature carriers, as long as the eeids of multiple records are the same, it means they describe the same business entity; in the same data feature carrier, when the eeids of multiple records are the same but the product_codes are different, it means they are descriptions of the same business entity by different tool software; in the same data feature carrier, when the eeids, product_codes are the same but the content_purposes are different, it means they are descriptions of different purposes of the same business entity by the same tool software; in the same data feature carrier, when the eeids, product_codes, and content_purposes of multiple records are the same, it means they are all historical descriptions of the same purpose of the same first business entity by the tool software.

[0087] Step S2035, update the first data feature carrier based on the extended identification information to generate a second data feature carrier.

[0088] Embed the mapped extended identification information (eeid) into the header area according to the preset data structure of the data feature carrier to update the first data feature carrier, thus forming a second data feature carrier. That is, fill the extended identification information of the first business entity into the "eeid" field of the identification group.

[0089] Step S204, store the second data feature carrier in the cloud platform according to the extended identification information, so that the data consumption tool can obtain the second data feature carrier from the cloud platform. For details, please refer to Figure 1 Step S104 of the illustrated embodiment, which will not be elaborated here.

[0090] The construction data processing method provided by the embodiment of the present invention obtains a set of data feature carriers, covering the data feature carriers corresponding to each first business entity, providing a rich data basis for comprehensive search and mapping. Secondly, based on the preset matching rules, extended identification information is searched, making the search process follow rules and ensuring accuracy and systematicness. Furthermore, if the corresponding information is found, mapping is performed according to the preset data mapping rules, ensuring the standardization and consistency of the mapping. Therefore, this method effectively utilizes existing data resources, enhances the relevance and expandability of data, and improves the accuracy and efficiency of data processing. When there is no extended identification information corresponding to the entity identification information in the set of data feature carriers, the corresponding information is actively generated, ensuring the integrity and coherence of the data processing process, which avoids data processing interruption or errors caused by information loss, and provides comprehensive data support for subsequent data integration, analysis, and storage and use on the cloud platform.

[0091] In this embodiment, a construction data processing method is provided, which can be used in computer devices such as desktop computers and laptop computers. Figure 5 It is a flowchart of the construction data processing method according to the embodiment of the present invention, as Figure 5 shown, and this process includes the following steps:

[0092] Step S301: Extract the attribute information of the first business entity from the data production tool. The attribute information refers to the data used to describe the characteristics of the first business entity in the data production tool. For details, please refer to Figure 2 step S201 of the embodiment shown herein, which will not be elaborated here.

[0093] Step S302: Integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate a first data feature carrier. The first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier. For details, please refer to Figure 2 step S202 of the embodiment shown herein, which will not be elaborated here.

[0094] Step S303: Map the entity identification information included in the first data feature carrier to extended identification information, and update the first data feature carrier based on the extended identification information to generate a second data feature carrier. For details, please refer to Figure 2 step S203 of the embodiment shown herein, which will not be elaborated here.

[0095] Step S304: Store the second data feature carrier on the cloud platform according to the extended identification information, so that the data consumption tool can obtain the second data feature carrier from the cloud platform. For details, please refer to Figure 2 step S204 of the embodiment shown herein, which will not be elaborated here.

[0096] Step S305. When the data production tool includes a first production tool and a second production tool, and there is a many-to-many relationship among multiple second business entities corresponding to the first production tool, and there is also a many-to-many relationship among multiple third business entities corresponding to the second production tool, then decompose the many-to-many relationship between the multiple second business entities and the multiple third business entities into a one-to-one relationship; store the one-to-one relationship in a preset relationship record component.

[0097] If entity mapping between cross-tool software needs to be implemented, for example, entity mapping between the first production tool and the second production tool. The first production tool corresponds to multiple second business entities, and there is a many-to-many relationship among these second business entities; at the same time, the second production tool corresponds to multiple third business entities, and there is also a many-to-many relationship among these third business entities. Here, the many-to-many relationship means that each of the multiple second business entities may be associated with multiple third business entities, and vice versa. Based on the ECS data base, a relationship record component is defined, which is specifically used to store the M:N mapping relationship between cross-tool software, not limited to 1:1 mapping, and M and N can be any positive integers. The specific approach is as follows:

[0098] ① When generating, the production-side tool software (data production tool):

[0099] 1) Decompose the relationship into M 1:N relationships. Specifically, during the process, keep the relationship type and object of the original relationship unchanged, and change the subject object from a set of size M to M single values.

[0100] 2) Decompose the M 1:N relationships into N 1:1 relationships. Specifically, during the process, keep the relationship type and subject object of the 1:N relationships generated in 1) unchanged, and change the object from a set of size N to N single values.

[0101] 3) Each resulting 1:1 relationship generated in 1) and 2) is stored as a row record in the FC_OUTP_RELATION relationship record component preset in the ECS data base.

[0102] ② When consuming, the consuming-side tool software (data consumption tool) queries or assembles as needed.

[0103] In addition, the predefined relationship record component FC_OUTP_RELATION is shown in Table 3 below:

[0104] Table 3

[0105]

[0106] Among them, the relationship record component stores the subject, object, and relationship type of the relationship, and contains the headers that all the second data feature carriers have, including the aforementioned id, eeid, product_code, content_purpose, etc. The relationship record component also contains all the modification histories of the same relationship, and uses the technologies of the same origin of the end-cloud integrated data format and the same origin of the computing engine, which can provide structured query capabilities and occupy less storage space.

[0107] For example, for the "room - device" relationship, in the FC_OUTP_RELATION component, the "related_entity_eeid" field records the eeid corresponding to the device, and the "relation_type" field records the relationship type, such as "installed in". At the same time, the record information is improved by using fields such as "id", "eeid", "product_code", "content_purpose" in the component header, and the "product_code" is recorded as the code of the architectural design software. For the "construction task - construction worker" relationship, the eeid corresponding to the construction worker, the relationship type such as "participate in", and the "product_code" of the construction progress management software are also recorded in the component. In this way, the complex many-to-many relationships between business entities in different production tools are stored in the preset component in the form of one-to-one relationships, which is convenient for subsequent data query, management, and cross-tool software data interaction and integration, ensuring the smooth flow and efficient utilization of data in each stage of the project.

[0108] The building construction data processing method provided by the embodiments of the present invention, when facing the complex many-to-many business entity relationships in the first production tool and the second production tool, disassembles them into one-to-one relationships, greatly simplifies the data structure, makes the originally intricate data associations clear and intuitive, which not only effectively reduces the difficulty of data processing, improves the data processing efficiency, but also significantly improves the accuracy of data query and analysis, avoiding understanding deviations and operation errors caused by complex relationships. At the same time, storing the one-to-one relationship in the preset relationship record component provides convenience for data management, facilitating subsequent maintenance, update, and traceability of data relationships, and enhancing the stability and scalability of the entire building construction data management system.

[0109] Step S306, when the data consumption tool obtains data from multiple data production tools, if the to-be-executed data generated by the multiple data production tools is inconsistent, then determine the target execution data corresponding to the data consumption tool in the to-be-executed data based on the preset business rules or interaction operations.

[0110] The ECS data base provides the ability to register and publish the Schema of the second data feature carrier online. The Schema stipulates the format and value range of the name, producer, consumer, description, fields, etc. of the second data feature carrier, as shown in Figure 6 the figure. The most important elements in the Schema are the various fields of the content, the nesting or repetition levels of the fields, and the names and value types of the fields. These Schemata can be used internally within a tool software or for exchange. When used for exchange, the Schemata agreed upon by multiple tool-side software are defined and published on the ECS data base. Since the tool software differentiates between production and consumption roles for a feature component, an upstream and downstream relationship of production and consumption around the second data feature carrier is formed during an exchange. For example, in the cross-phase digitalization of the entire process of a construction project, floors are required. Then, there needs to be a pre-defined and published Schema for the floor feature component in this project. Each producer (providing floor data) tool software can deepen the floor feature component, and the consumer (only using, not creating, not modifying floor data) uses the floor feature component.

[0111] In the scenario of construction data processing, data consumption tools often need to obtain data from multiple data production tools. Due to differences in business logics, data collection methods, etc. among different data production tools, the to-be-executed data generated by multiple data production tools may be inconsistent, that is, for the same field of the same entity in the same data feature carrier, different data production tools may have different values. For example, in a construction project, for the field of floor height, different data production tools may give different values. Specifically, data is filtered according to pre-defined business logics and algorithms, which is similar to the consumer encoding rules in the program. When it is found that there are multiple records with different values for a certain field of the same entity, a certain value is selected as the final result according to the established rules. For example, it is stipulated to select the value provided by a tool with a high data source credibility, or select the latest value according to the data update time, etc. Or, let the user intervene to select appropriate data. When the data conflict situation is relatively complex and difficult to handle through preset rules, the user can select the target to-be-executed data that meets the requirements of the data consumption tool from multiple data with different values according to their professional knowledge and actual needs. If at the same time the data consumption tool is also a producer, it can generate a new record with its own product_code in the feature component and record the determined target to-be-executed data.

[0112] Step S307, if it is determined that the to-be-executed data generated by the data production tool does not meet the preset requirements, then adjust the to-be-executed data based on the preset requirements.

[0113] The to-be-executed data generated by the data production tool may not meet the preset requirements of the data consumption tool due to reasons such as semantic inconsistency, unit inconsistency, or precision inconsistency. In this case, the data needs to be adjusted to meet the usage requirements. For example, in a construction project, the data consumption tool expects the length data unit to be meters, but the data production tool provides feet; or the data consumption tool requires the data precision to be retained to two decimal places, while the data provided by the production tool has only one decimal place, etc. Specifically, when different tool software generates data, it will fill in the fields it cares about, and the fields that are not cared about or are optional can be left blank. Similarly, when consuming data, the data consumption tool can only focus on the fields that do not meet the preset requirements for adjustment. When it is found that the value given by the upstream does not meet the expectation, the field is modified. For example, if the downstream requires the value to be in the metric unit, when it is found that a certain field given by the upstream is in the imperial unit, the imperial unit is converted to the metric unit by itself.

[0114] In addition, if the data consumption tool is also a producer role at the same time, after adjusting the data, new records can be added for this. Use its own product_code and another content_purpose to record the adjusted data, so that both the original data is retained and new data that meets the preset requirements is available for use.

[0115] The building construction data processing method provided by the embodiments of the present invention, when the data consumption tool obtains data from multiple data production tools, in the face of the complex situation of inconsistent to-be-executed data, determines the target execution data according to the preset business rules or interaction operations, can ensure that the data consumption tool obtains data that meets its own needs, effectively avoids incorrect use caused by data chaos, and greatly improves the accuracy of data use. At the same time, the target execution data is quickly screened out through the preset rules and interaction operations, saving the data screening time and improving the data consumption efficiency. When the to-be-executed data generated by the data production tool does not meet the preset requirements, it is adjusted in a timely manner based on the preset requirements, which can control the data quality from the source, avoid incorrect results in subsequent data processing due to data deviation, and ensure the accuracy and reliability of the entire building construction data processing process.

[0116] In this embodiment, a building construction data processing system is provided, as Figure 7 shown, the system includes: a data production tool 1, a cloud platform 2, and a data consumption tool 3.

[0117] The data production tool 1 is used to extract the attribute information of the first business entity from the data production tool 1. The attribute information refers to the data used to describe the characteristics of the first business entity in the data production tool 1. Integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity to generate a first data feature carrier. The first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier. The data production tool 1 is also used to map the entity identification information included in the first data feature carrier to extended identification information, and update the first data feature carrier based on the extended identification information to generate a second data feature carrier.

[0118] In the construction scenario, the first business entity is a specific object with independent meaning and characteristics, such as rooms, steel beams in a residential building project, etc. The attribute information is the data that accurately describes the characteristics of these first business entities. For example, the area, height, and function of a room, the length, cross-sectional size, and material of a steel beam, etc. The data production tool 1 can be Autodesk Revit software in the architectural design stage, or Primavera P6 software in construction progress management, etc. Through the built-in data export function and application programming interface (API) of the software, these attribute information can be extracted from the software database. Taking Revit software as an example, developers use Revit API to write programs, and by means of specific function calls and query statements, they can retrieve the attribute data such as the area and height of the room entity.

[0119] The data feature carrier is a structured or semi-structured medium used to organize and store data related to the first business entity, such as files in a specific format, database tables, or custom data structures. It provides a unified framework for data that is convenient for management and transmission, ensuring the integrity and relevance of the data. The data production tool 1 integrates the extracted attribute information of the first business entity into the corresponding data feature carrier according to the type of the data feature carrier and the pre-designed integration rules, thereby generating the first data feature carrier, also known as the first characteristic component.

[0120] The entity identification information is the key data that uniquely identifies the first business entity in the data production tool or a specific data environment, such as the unique number of building components in architectural design software, or the specific code of construction tasks in the construction progress management system. The data production tool 1 pre-establishes mapping rules or conversion functions to map the entity identification information in the first data feature carrier to extended identification information. The extended identification information can enhance the identifiability and relevance of the data in different environments. Then, the first data feature carrier is updated based on the extended identification information to obtain the second data feature carrier, that is, the second characteristic component.

[0121] A cloud platform 2, communicatively connected to the data production tool 1, is configured to receive the second data feature carrier uploaded by the data production tool 1 and store the second data feature carrier generated by the data production tool according to the extended identification information.

[0122] The cloud platform 2 creates a unique index or identifier for the second data feature carrier according to the extended identification information. This identifier is like the "address" of the data in the cloud platform, facilitating subsequent searching and accessing. The data production tool 1 uses the API provided by the cloud platform 2 or a dedicated data upload tool to transmit the second data feature carrier to the cloud platform 2. After receiving the data, the cloud platform 2 stores the data in the corresponding storage medium according to its storage policy, such as a distributed file system or a database. At the same time, the cloud platform 2 associates the extended identification information with the storage location and establishes an index table. In this way, when data needs to be searched, the storage location of the data can be quickly located according to the extended identification information.

[0123] A data consumption tool 3, communicatively connected to the cloud platform 2, is configured to obtain the second data feature carrier from the cloud platform 2.

[0124] When the data consumption tool 3 needs to obtain the second data feature carrier, it sends a request to the cloud platform 2. The request contains the extended identification information. The cloud platform 2 searches for the corresponding data storage location in the index table based on this information. After finding the storage location, the cloud platform 2 reads the data from the corresponding storage medium and transmits the data to the data consumption tool 3 through the network. After receiving the data, the data consumption tool 3 will perform verification and parsing to ensure the accuracy and availability of the data, and then these data can be used for subsequent business processes, such as a cost accounting software to calculate the total project cost, a construction progress monitoring software to compare the actual and planned progress, etc.

[0125] In the building construction data processing system provided by the embodiments of the present invention, the data production tool can complete a series of complex operations from extracting and integrating attribute information to generating the first data feature carrier, and then updating the mapping identification information to the second data feature carrier, ensuring the coherence and accuracy of data processing. The cloud platform, as the storage and transfer hub of data, is communicatively connected to the data production tool, realizing the efficient reception and orderly storage of the second data feature carrier, providing guarantee for the secure storage and management of data. The data consumption tool communicates with the cloud platform to conveniently obtain the required data, building a complete data production-storage-consumption link, breaking the data exchange barrier, greatly improving the data circulation efficiency, and enabling all links of the building construction project to be efficiently promoted based on accurate and timely data.

[0126] Embodiments of the present invention also provide a computer device having the above Figure 7 shown building construction data processing system.

[0127] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 8 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 8 In

[0128] FIG. 9, a single processor 10 is taken as an example.

[0129] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0129] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0130] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0132] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected through a bus or other means. For example, the connection through the bus is taken as an example. Figure 8 For example, the connection through the bus is taken as an example.

[0133] The input device 30 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor), etc. The above display device includes, but is not limited to, a liquid crystal display, a light emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0134] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading through a network and originally stored in a remote storage medium or a non-transitory machine-readable storage medium and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiment is implemented.

[0135] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be called or provided. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0136] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for processing construction data, characterized in that, The method includes: Extracting the attribute information of the first business entity from a data production tool, where the attribute information refers to the data in the data production tool for describing the characteristics of the first business entity; Integrating the attribute information of the first business entity into a data feature carrier corresponding to the first business entity to generate a first data feature carrier, where the first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier; Mapping the entity identification information included in the first data feature carrier to extended identification information, and updating the first data feature carrier based on the extended identification information to generate a second data feature carrier; Storing the second data feature carrier in a cloud platform according to the extended identification information, so that a data consumption tool can obtain the second data feature carrier from the cloud platform.

2. The method according to claim 1, wherein The integrating the attribute information of the first business entity into a data feature carrier corresponding to the first business entity to generate a first data feature carrier includes: Obtaining the entity identification information of the first business entity, the tool identification information of the data production tool, and the purpose information recorded in the data feature carrier; Based on the preset data structure of the data feature carrier, embedding the entity identification information, the tool identification information, and the purpose information into the header area of the data feature carrier to generate the first data feature carrier.

3. The method according to claim 1 or 2, characterized in that, Mapping the entity identification information included in the first data feature carrier to extended identification information includes: Obtaining a set of data feature carriers generated by the data production tool, where the set of data feature carriers includes the data feature carriers corresponding to each first business entity; Searching for the extended identification information corresponding to the entity identification information in the first data feature carrier from the set of data feature carriers based on a preset matching rule; If it is determined that there is extended identification information corresponding to the entity identification information in the set of data feature carriers, mapping the entity identification information included in the first data feature carrier to extended identification information based on a preset data mapping rule.

4. The method according to claim 3, wherein The method further includes: If it is determined that the set of data feature carriers does not include the extended identification information corresponding to the entity identification information, generating the extended identification information corresponding to the entity identification information.

5. The method according to claim 1, wherein The method further includes: If the data production tool includes a first production tool and a second production tool, and there is a many-to-many relationship among multiple second business entities corresponding to the first production tool, and there is also a many-to-many relationship among multiple third business entities corresponding to the second production tool, then decomposing the many-to-many relationship between the multiple second business entities and the multiple third business entities into a one-to-one relationship; Storing the one-to-one relationship in a preset relationship record component.

6. The method according to claim 1, wherein The method further includes: When the data consumption tool obtains data from multiple data production tools, if the to-be-executed data generated by the multiple data production tools is inconsistent, the target execution data corresponding to the data consumption tool in the to-be-executed data is determined based on a preset business rule or an interaction operation.

7. The method according to claim 6, characterized in that, The method further includes: If it is determined that the to-be-executed data generated by the data production tool does not meet the preset requirements, the to-be-executed data is adjusted based on the preset requirements.

8. A building construction data processing system, characterized in that, The system includes: A data production tool, configured to extract the attribute information of the first business entity from the data production tool, where the attribute information refers to the data used to describe the characteristics of the first business entity in the data production tool, integrate the attribute information of the first business entity into the data feature carrier corresponding to the first business entity, and generate a first data feature carrier, where the first data feature carrier refers to the data set generated after integrating the attribute information into the data feature carrier; the data production tool is further configured to map the entity recognition information included in the first data feature carrier to extended recognition information, and update the first data feature carrier based on the extended recognition information to generate a second data feature carrier; A cloud platform, communicatively connected to the data production tool, configured to receive the second data feature carrier uploaded by the data production tool and store the second data feature carrier generated by the data production tool according to the extended recognition information; A data consumption tool, communicatively connected to the cloud platform, configured to obtain the second data feature carrier from the cloud platform.

9. A computer device, characterized in that, Including: A memory and a processor, communicatively connected to each other between the memory and the processor, where computer instructions are stored in the memory, and the processor executes the computer instructions to execute the building construction data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the building construction data processing method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, Including computer instructions, and the computer instructions are used to cause a computer to execute the building construction data processing method according to any one of claims 1 to 7.

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