A data asset sharing method and system
By screening and archiving project completion data, combined with multi-dimensional verification and hierarchical storage, the problem of accurate screening and storage of data assets has been solved, achieving efficient and accurate data asset management and sharing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA RAILWAY NO 10 ENG GRP CO LTD
- Filing Date
- 2025-07-09
- Publication Date
- 2026-06-02
AI Technical Summary
In existing technologies, data assets are difficult to accurately filter from large and complex enterprise databases. The storage pressure between different enterprise units is high, and the query and access are difficult, which restricts the digital development of enterprises.
By acquiring project completion data, data assets are screened and archived, search queries are created, and hierarchical storage and precise querying are performed based on asset type and storage conditions. Asset data is extracted using multi-dimensional verification methods, and modulotable search queries are configured to achieve accurate positioning and efficient retrieval.
It enables efficient filtering and accurate storage of asset data, reduces storage pressure, improves query accuracy and retrieval efficiency, ensures the orderly and collaborative storage of data assets, and supports data sharing and utilization among enterprises.
Smart Images
Figure CN120930913B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data asset management technology, and in particular to a data asset sharing method and system. Background Technology
[0002] With the rapid development of information technology, the engineering field is accelerating its transformation towards digitalization and intelligence. Traditional construction management models face pain points such as low efficiency, severe information silos, and insufficient resource collaboration. Especially in large and complex engineering projects, manual management is insufficient to meet the dynamic and refined needs. The development of technologies such as the Internet of Things (IoT), Building Information Modeling (BIM), big data, and artificial intelligence provides technical support for the intelligentization of the entire construction process. At the same time, from an industry perspective, enterprises also need to drive digital transformation and upgrading through technological innovation.
[0003] When enterprises undertake various projects, they generate a large amount of data resources at each stage of the project. However, the data assets in these data resources are difficult to accurately filter out from the large and complex enterprise databases. Moreover, due to the large amount of data assets, the storage pressure on each enterprise unit is relatively high, and the accurate query and access of data assets between different enterprise units is difficult, which brings many adverse effects to the digital development of enterprises. Summary of the Invention
[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a data asset sharing method and system to solve the problems in the prior art where data assets are difficult to accurately filter from large and complex enterprise databases, and where the storage pressure on each enterprise is large due to the large amount of data assets, and the difficulty in accurately querying and calling data assets between enterprises.
[0005] To achieve the above and other related objectives, this invention provides a data asset sharing method, comprising: acquiring completed item data during project execution; wherein the completed item data during project execution includes: first completed item data during project execution and second completed item data upon project completion; filtering the completed item data to obtain asset data; obtaining the archiving unit of the asset data based on the asset type and storage conditions of each unit; archiving the asset data according to the archiving unit; creating a search query based on the asset data's definition terms, asset type, and archiving unit during asset data archiving; performing asset data retrieval and analysis through the search query based on sharing request information to obtain analysis results; and retrieving the corresponding asset data based on the analysis results.
[0006] In one embodiment of the present invention, data asset screening is performed on completed item data to obtain asset data, including: performing a format query on the completed item data to obtain an initial data format; performing an architecture query on the completed item data to obtain an initial data architecture; determining whether the completed item data can form asset data based on the initial data format and the initial data architecture: if so, then based on the asset type corresponding to the completed item data, obtaining an initial data architecture combination, and re-integrating the completed item data to obtain asset data; if not, then the completed item data is filtered out.
[0007] In one embodiment of the present invention, determining whether completed item data can form asset data based on the initial data format and initial data architecture includes: sequentially extracting the baseline data architecture combination corresponding to each asset type and comparing it with the initial data architecture of the completed item data; when there is an initial data architecture combination that satisfies a first preset condition with the baseline data architecture combination in the initial data architecture, then comparing the initial data format combination corresponding to the initial data architecture combination with the baseline data format combination corresponding to the baseline data format combination, and when the initial data format combination and the baseline data format combination satisfy a second preset condition, it is indicated that the completed item data can form asset data; when there is no initial data architecture combination that satisfies the first preset condition with the baseline data architecture combination in the initial data architecture, it is indicated that the completed item data cannot form asset data.
[0008] In one embodiment of the present invention, the first preset condition satisfied between the initial data architecture combination and the benchmark data architecture combination in the initial data architecture includes: the initial data architecture combination and the benchmark data architecture combination satisfying the following conditions: a portion of the initial data architecture in the initial data architecture combination is the same as the main benchmark data architecture in the benchmark data architecture combination, and the comprehensive similarity score between another portion of the initial data architecture in the initial data architecture combination and the auxiliary benchmark data architecture in the benchmark data architecture combination reaches a set value; the second preset condition satisfied by the initial data format combination and the benchmark data format combination includes: the initial data format in the initial data format combination is the same as the benchmark data format in the benchmark data format combination, or the initial data format in the initial data format combination is the same as the benchmark data format in the benchmark data format combination after format conversion.
[0009] In one embodiment of the present invention, an initial data architecture combination is obtained based on the asset type corresponding to the completed item data, and the completed item data is re-integrated to obtain asset data. This includes: obtaining a first data item in the completed item data corresponding to a portion of the initial data architecture based on a portion of the initial data architecture in the initial data architecture combination; obtaining a second data item in the completed item data corresponding to a portion of the initial data architecture based on another portion of the initial data architecture in the initial data architecture combination; and re-combining the first data item and the second data item to obtain asset data. The benchmark data architecture for the asset type includes a primary benchmark data architecture identical to the portion of the initial data architecture, and an auxiliary benchmark data architecture whose similarity score with the other portion of the initial data architecture reaches a set value.
[0010] In one embodiment of the present invention, obtaining the archiving unit of asset data according to the asset type and storage conditions of each unit includes: obtaining a first asset type set of the current unit based on the current unit of the asset data, wherein the asset data includes first asset data and second asset data; obtaining a second asset type set of each superior unit based on each superior unit of the current unit; obtaining the archiving unit of the first asset data of the current unit corresponding to the first target asset type as the superior unit corresponding to the second asset type set based on the first asset data of the current unit; obtaining the archiving unit of the second asset data of the current unit corresponding to the second target asset type as the current unit corresponding to the first asset type set; wherein the first asset type set includes the second target asset type, and the second asset type set includes the first target asset type.
[0011] In one embodiment of the present invention, a search query is created based on the definition terms, asset type, and archiving unit of asset data, including: obtaining standard keywords based on the asset type of the asset data; extracting information from the asset data to obtain query keywords; comparing the deviation between the standard keywords and the query keywords to obtain deviation conversion parameters; and using the standard keywords and deviation conversion parameters as definition terms to create a search query based on the definition terms, asset type, and archiving unit of the asset data.
[0012] In one embodiment of the present invention, a deviation comparison is made between standard keywords and query keywords to obtain a deviation conversion parameter, including: comparing the semantic strength of standard keywords and query keywords to obtain a semantic strength deviation parameter; comparing the semantic relevance of standard keywords and query keywords to obtain a semantic relevance deviation parameter; and obtaining a deviation conversion parameter based on the semantic strength deviation parameter and the semantic relevance deviation parameter.
[0013] In one embodiment of the present invention, asset data is retrieved and analyzed using a search formula based on shared request information to obtain analysis results. This includes: obtaining the requested asset type based on the shared request information; generating a standard search formula based on the requested asset type, wherein the standard search formula includes standard keywords, asset type, and archiving unit; obtaining demand deviation conversion parameters based on the shared request information and the standard search formula; converting the standard keywords into query keywords based on the demand deviation conversion parameters, wherein the deviation conversion parameters corresponding to the query keywords are closest to the demand deviation conversion parameters; calculating the approximation index between the deviation conversion parameters and the demand deviation conversion parameters; updating the standard search formula based on the query keywords to obtain a query search formula; and using the query search formula and the approximation index as the analysis results.
[0014] To achieve the above and other related objectives, the present invention also provides a data asset sharing system, comprising: an acquisition unit for acquiring completed item data during project execution; wherein the completed item data during project execution includes: first completed item data during project execution and second completed item data upon project completion; a filtering unit for filtering the completed item data to obtain asset data; a query unit for obtaining the archiving unit of the asset data based on the asset type and storage conditions of each unit; an archiving unit for archiving the asset data based on the archiving unit; a creation unit for creating a search query based on the asset data's definition terms, asset type, and archiving unit during asset data archiving; an analysis unit for performing asset data retrieval and analysis based on the search query according to sharing request information to obtain analysis results; and a calling unit for calling the corresponding asset data based on the analysis results.
[0015] As described above, the data asset sharing method and system of the present invention have the following beneficial effects: By extracting asset data from all completed items in the process of undertaking various projects within an enterprise, and based on multi-dimensional verification methods such as data architecture and data structure, the efficiency and accuracy of asset data screening can be effectively guaranteed. Furthermore, when storing asset data, hierarchical storage of asset data can be implemented according to the control needs of upper and lower level units, effectively ensuring collaborative storage of asset data among different archiving units, reducing the data storage pressure on each unit while also achieving orderly asset data storage. When archiving asset data, a modulotable retrieval formula can be configured according to the actual situation of the asset data, thereby enabling precise location of relevant asset data based on sharing request information during data asset sharing, avoiding inaccurate query results due to the retrieval formula; moreover, modulating the retrieval through a configurable retrieval formula can effectively reduce the operational load during retrieval and improve the accuracy of asset data retrieval. Attached Figure Description
[0016] Figure 1 This is a flowchart illustrating the data asset sharing method provided in an embodiment of the present invention.
[0017] Figure 2 The diagram shown is a structural block diagram of a data asset sharing system provided in an embodiment of the present invention.
[0018] Figure 3 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.
[0019] Component designation explanation
[0020] Electronic device 1; Data asset sharing system 11; Memory 12; Processor 13; Acquisition unit 111; Filtering unit 112; Querying unit 113; Archiving unit 114; Creation unit 115; Analysis unit 116; Calling unit 117. Detailed Implementation
[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.
[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.
[0024] This invention provides a data asset sharing method. During the implementation of each enterprise project, by filtering the completed data at each project stage, the corresponding asset data can be accurately identified. Then, based on the storage conditions of each unit within the enterprise, newly generated asset data is archived and stored in a database suitable for different archiving units. This effectively ensures collaborative storage of asset data among different archiving units, achieving orderly asset data storage. Furthermore, during asset data archiving, a retrieval-based design for each asset data point allows resource requesters to adjust the retrieval method according to their own sharing needs, enabling accurate searching of the required asset data. This facilitates direct application for asset data utilization if it exists, and if not, further consideration can be given to building the corresponding asset data before project launch. This allows for accurate control of the enterprise's data assets, preventing existing data assets from going undiscovered and difficult to utilize due to insufficient understanding of the enterprise's data assets.
[0025] Figure 1 A flowchart illustrating a data asset sharing method in an exemplary embodiment of this application, applied to a data asset sharing system, is shown, including steps S10-S70. The following will be combined with... Figure 1 The technical solution of this application will be described in detail below.
[0026] First, execute step S10 to obtain the completed item data in progress of the project; wherein, the completed item data in progress of the project includes: the first completed item data during the project execution process and the second completed item data when the project is completed.
[0027] During the completion item data acquisition phase, the completion item data may include the first completion item data. Second completion item data Among them, the data for the first completed item. Data generated during the project's execution phase; second completion item. Data assets are generated upon project completion. For example, during the implementation of an engineering project, the project cycle can be divided into multiple execution phases. Data assets may be generated at each execution phase, and similarly, data assets will also be generated at the end of the project cycle. Therefore, data on completed items during the project can be collected through manual methods or data monitoring systems. and The data is sent to the data asset sharing system so that the relevant asset data can be filtered and obtained through the data asset sharing system, in order to prevent the loss of asset data due to neglect.
[0028] Next, step S20 is executed to filter the completed data into asset data and obtain asset data.
[0029] After obtaining the completed item data through the data asset sharing system, the data assets within the completed item data will be further searched to accurately locate the asset data contained in the completed item data for archiving and storage, thereby ensuring the accuracy and efficiency of data asset retrieval.
[0030] In step S20, the completed item data is filtered to obtain asset data, which may further include:
[0031] Perform a format query on the completed data to obtain the initial data format;
[0032] Perform a schema query on the completed data to obtain the initial data schema;
[0033] Based on the initial data format and initial data architecture, determine whether the completed item data can be combined into asset data:
[0034] If so, based on the asset type corresponding to the completed item data, obtain the initial data architecture combination, re-integrate the completed item data, and obtain the asset data;
[0035] If not, the completed data will be filtered out.
[0036] Completed data items typically contain at least one data format and at least one data schema. Therefore, when filtering completed data items for asset purposes, the data asset sharing system first searches for the data formats and schemas within the completed data items to identify all initial data formats and schemas present in the current completed data items. Further, the initial data formats and schemas are used to determine whether the completed data items can form asset data. If they can, the system further determines the initial data schema combinations that can form asset data based on the asset type corresponding to the completed data items. Then, based on these initial data schema combinations, the completed data items are extracted and re-integrated to filter out the asset data. Conversely, if completed data items cannot form asset data, it indicates that the current data is invalid for data asset sharing and can be directly filtered out.
[0037] In the process of performing a format query on completed item data to obtain the initial data format, a comprehensive scan and identification of the completed item data can be performed to search for data format attributes of various types of data. For example, this could include drawing format, table format, document format, etc. In the process of performing a structure query on completed item data to obtain the initial data structure, the completed item data can first be sorted according to the corresponding arrangement order. For example, a specific data item in the completed item data may include multiple data fragments. Each data fragment With different data architectures Corresponding preset segment The corresponding similarity difference when comparing It must be less than the set value and will be less than the set value Difference in similarity Corresponding data architecture As the corresponding data fragment The initial data architecture.
[0038] Specifically, when reading data fragments, the initial data structure is identified by increasing the data length according to the corresponding arrangement order of the completed items. Specifically, assuming the initial length of the data fragment is... However, regarding this data fragment During identification, due to data fragments Unable to form a data architecture, the processing will still rely on... Based on this, we continue to identify as the data fragments increase. At this point, the corresponding data architecture can be identified. When identifying data fragments, the data fragments will be used first. Starting from the end of the data length position, increase the data length sequentially. , where data length Corresponding to a data unit, such as the value 0.32, the entire "0.32" needs to be recognized to be considered a data length. Then, the data lengths will be sequentially... The data fragments obtained later With different data architectures Corresponding preset segment Compare the results; if a corresponding preset segment exists... Then it is used as a data fragment. The data architecture. And if data fragments... There is no corresponding preset segment. Then, it can be further divided into data fragments. The data length position after the end is then crossed sequentially. Data length Next, set a new starting point and increase the data length sequentially. To obtain data fragments With different data architectures Corresponding preset segment Compare them. And so on, each data fragment can be read. The corresponding data architecture.
[0039] Next, based on the initial data format and initial data architecture, it is determined whether the completed item data can be combined into asset data, which may further include:
[0040] The baseline data schema combination corresponding to each asset type is extracted sequentially and compared with the initial data schema of the completed item data:
[0041] When there is an initial data architecture combination and a baseline data architecture combination that satisfy the first preset condition, the initial data format combination corresponding to the initial data architecture combination is compared with the baseline data format combination corresponding to the baseline data format combination. When the initial data format combination and the baseline data format combination satisfy the second preset condition, it is indicated that the completed item data can form asset data; wherein, the baseline data format combination corresponds to the baseline data architecture combination.
[0042] If there is no initial data architecture combination that satisfies the first preset condition with the baseline data architecture combination, then the completed item data cannot be used to form asset data.
[0043] In determining whether completed data can form asset data through a data asset sharing system, asset types can be pre-defined according to enterprise needs, and the baseline data architecture combination and baseline data format combination corresponding to each asset type can also be pre-defined manually. Then, the initial data architecture combination of the completed data is compared with each baseline data format in the baseline data format combination to determine if it meets the first preset condition. If it does, the initial data architecture of the completed data meets the requirements. Next, the initial data format combination corresponding to the initial data architecture combination is compared with the baseline data format combination corresponding to the baseline data format combination. If the two meet the second preset condition, the completed data meets the conditions for forming asset data; therefore, the completed data can form asset data. If there is no initial data architecture combination that satisfies the first preset condition with the baseline data architecture combination, then the completed data cannot form asset data. At the same time, even if there is an initial data architecture combination that satisfies the first preset condition with the baseline data architecture combination, but the initial data format combination and the baseline data format combination do not satisfy the second preset condition, then the completed data has not met the conditions for forming asset data. Therefore, the completed data cannot form asset data.
[0044] Specifically, the first preset condition that exists between the initial data architecture combination and the baseline data architecture combination in the initial data architecture includes:
[0045] The initial data architecture contains a combination of initial data architectures that satisfies the following relationship with the baseline data architecture combination:
[0046] The overall similarity score between some initial data architectures in the initial data architecture combination and the main baseline data architecture in the baseline data architecture combination, and between other initial data architectures in the initial data architecture combination and the auxiliary baseline data architecture in the baseline data architecture combination, reaches the set value.
[0047] In other words, when determining whether the initial data architecture combination and the benchmark data architecture combination meet the first preset condition, it is necessary to use the data asset sharing system to determine whether some of the initial data architectures in the initial data architecture combination are the same as the main benchmark data architecture in the benchmark data architecture combination. In addition, it is also necessary to determine whether the comprehensive similarity score between other initial data architectures in the initial data architecture combination and the auxiliary benchmark data architecture in the benchmark data architecture combination reaches the set value. If some initial data architectures are the same as the main benchmark data architecture and the comprehensive similarity score between other initial data architectures and the auxiliary benchmark data architecture reaches the set value, it means that the corresponding initial data architecture combination and the benchmark data architecture combination meet the first preset condition.
[0048] Specifically, the completed item data includes multiple initial data schemas. The set of existing initial data architecture combinations can be represented as ,in, In the initial data architecture combination This includes the primary baseline data architecture in combination with the baseline data architecture. Corresponding initial data architecture and the auxiliary benchmark data architecture in combination with the benchmark data architecture The corresponding other part of the initial data architecture In the initial data architecture With the main baseline data architecture When making comparisons, due to the primary baseline data architecture As these are the main elements constituting asset data, it is therefore necessary to ensure that some initial data structure exists within the completed item data. With the main baseline data architecture The same applies. For example, data assets can include various drawing assets, equipment data assets, property data assets, etc., and the main elements of asset data can be some elements that are necessary and consistent to make up various drawing assets, equipment data assets, property data assets, etc. Of course, some other elements with relatively lower urgency are also needed, which correspond to the secondary baseline data architecture. In determining the other part of the initial data architecture With auxiliary benchmark data architecture When determining whether the overall similarity score between the data structures reaches the set value, first calculate the similarity score for each auxiliary baseline data architecture. With the corresponding auxiliary benchmark data architecture Difference in similarity between Then, based on the similarity difference... and rating factors The similarity difference Convert to similarity score This yields a set of similarity scores. Then, a comprehensive similarity score is calculated. Then, a comprehensive score is given based on the similarity. and setting value The relationship between the initial data architecture and the baseline data architecture is used to determine whether the initial data architecture combination and the baseline data architecture combination satisfy the first preset condition. Specifically, when Partial Initial Data Architecture With the main baseline data architecture If they remain consistent, it means that there exists an initial data architecture combination and a baseline data architecture combination that satisfy the first preset condition.
[0049] In addition, the second preset condition that the initial data format combination and the baseline data format combination satisfy includes:
[0050] The initial data format combination and the baseline data format combination satisfy:
[0051] The initial data format in the initial data format combination is the same as the reference data format in the reference data format combination, or the initial data format in the initial data format combination is the same as the reference data format in the reference data format combination after format conversion.
[0052] The initial data format in the initial data format combination corresponding to the completed item data may be inconsistent with the reference data format in the reference data format combination. Therefore, it is necessary to ensure that the initial data format in the initial data format combination can complete the corresponding conversion to the reference data format in order to prove that the initial data format combination and the reference data format combination meet the second preset condition, thereby effectively ensuring the correspondence of the initial data format combination.
[0053] When there is an initial data architecture combination and a baseline data architecture combination that satisfy the first preset condition, for the initial data format combination Including combinations with reference data formats The reference data format in Same initial data format And the other initial data format that is the same as the reference data format in the combination of the reference data format after format conversion. In the initial data format of another part. After format conversion, combined with part of the initial data format If the combination of the initial data format and the reference data format are identical, it means that the initial data format combination and the reference data format combination meet the second preset condition.
[0054] Based on the asset type corresponding to the completed item data, an initial data architecture combination is obtained. The completed item data is then reorganized to obtain asset data, which may further include:
[0055] Based on a portion of the initial data architecture in the initial data architecture combination, obtain the first data item in the completed data that corresponds to the portion of the initial data architecture;
[0056] Based on another part of the initial data architecture in the initial data architecture combination, obtain the second data item in the completed data that corresponds to the part of the initial data architecture;
[0057] The first and second data items are recombined to obtain asset data;
[0058] The benchmark data architecture for asset types includes a primary benchmark data architecture that is the same as some of the initial data architectures, and a secondary benchmark data architecture whose comprehensive similarity score with other initial data architectures reaches a set value.
[0059] Based on the asset type corresponding to the completed item data, the initial data architecture combination can be determined through the steps described above to determine whether the completed item data can form asset data. Then, based on a portion of the initial data architecture in the initial data architecture combination, information is extracted from corresponding regions of the completed item data to obtain the first data item corresponding to that portion of the initial data architecture. Similarly, information is extracted from corresponding regions of the completed item data based on another portion of the initial data architecture in the initial data architecture combination to obtain the second data item corresponding to that portion of the initial data architecture. The first and second data items can then be recombine to obtain the asset data. The asset data obtained through this method accurately and completely captures all the information that makes up the asset data from the completed item data, thus ensuring the integrity of the acquired asset data.
[0060] Specifically, assuming the completed item data is from It consists of data fragments, and data fragments This can form a data architecture. Asset data, on the other hand, consists of data fragments. Composed of, that is, data fragments The initial data architecture combination Therefore, in determining the initial data architecture combination of asset data... For data fragments in the completed item data Removed from and What is obtained later is the data fragment. .
[0061] Next, step S30 is executed to obtain the archiving unit of the asset data based on the asset type and storage conditions of each unit.
[0062] Due to hierarchical relationships among different units, their storage conditions may differ. Therefore, when storing asset data, the archiving unit can be further determined based on the asset type and the storage conditions of each unit. This ensures hierarchical storage management of asset data and achieves orderly storage of asset data across unit levels.
[0063] In step S30, based on the asset type and storage conditions of each unit, the archiving unit of the asset data is obtained, including:
[0064] Based on the current unit of the asset data, obtain the first set of asset types for the current unit, where the asset data includes the first asset data and the second asset data;
[0065] Based on the current unit's superior units, obtain the second asset type set of each superior unit;
[0066] Based on the first asset data of the current unit corresponding to the first target asset type, the archiving unit of the first asset data is the superior unit corresponding to the second asset type set;
[0067] Based on the second asset data of the current unit corresponding to the second target asset type, the archiving unit of the second asset data is obtained as the current unit corresponding to the first asset type set;
[0068] The first set of asset types includes the second target asset type, and the second set of asset types includes the first target asset type.
[0069] When archiving asset data, since the asset data is generated by the current unit, the archiving unit can include the current unit. However, the current unit is also subject to the jurisdiction of other superior units; therefore, some asset data also needs to be archived to superior units to achieve hierarchical management based on the needs of each level of units for asset data. Specifically, asset data can be divided into primary asset data for archiving to superior units and secondary asset data for archiving to the current unit. During archiving, the secondary asset type sets of each superior unit are first determined to identify the primary asset data that needs to be transferred to the superior units, and then the secondary asset data to be stored in the current unit is determined. For example, when the secondary asset type sets of each superior unit of the current unit are arranged in ascending order of hierarchy as follows... , , When, the asset type is It can then be archived to the highest-level superior unit, and the asset type is It can be archived to the intermediate-level superior unit, and the asset type is... Asset types can be archived up to the lowest level of the parent organization; the remaining asset types... Then it can be archived to the current unit. Of course, the second set of asset types of each superior unit in ascending order of hierarchy can also be other inclusion relationships.
[0070] Next, step S40 is executed to archive the asset data according to the archiving unit.
[0071] After determining the archiving unit of asset data through the data asset sharing system, the various asset data that can be formed from the completed item data are archived to the superior unit and the current unit at different levels in turn, so as to achieve orderly storage of asset data.
[0072] Next, step S50 is executed, where a search query is created based on the definition terms, asset type, and archiving unit of the asset data during the asset data archiving process.
[0073] When archiving and storing asset data, it is not only necessary to archive each asset data at different levels of superior units and current units, but also to create corresponding search queries based on the asset data so as to share the corresponding asset data with external parties, ensure the upstream and downstream interconnection of data resources, and realize the utilization of the developed asset data in other projects, thereby saving project development resources and costs.
[0074] In step S50, a search query is created based on the definition terms, asset type, and archiving unit of the asset data, which may further include:
[0075] Based on the asset type in the asset data, obtain standard keywords;
[0076] Extract information from asset data to obtain query keywords;
[0077] The deviation between the standard keywords and the query keywords is compared to obtain the deviation conversion parameters.
[0078] Standard keywords and deviation transformation parameters are used as definition terms to create search queries based on the definition terms, asset types, and archiving units of asset data.
[0079] During the creation of search queries, several standard keywords can be identified based on the asset types in the asset data. Then, information extraction is performed on the asset data to obtain query keywords. These query keywords are then compared with the standard keywords to determine their semantic differences. This semantic difference can then be calculated as a deviation transformation parameter. The standard keywords and deviation transformation parameter are then bound together as definition terms. When asset data is archived, a shared search query is created based on these definition terms, asset type, archiving unit, etc., to achieve accuracy and efficiency in asset data queries using standard keywords.
[0080] Specifically, comparing the deviations between standard keywords and query keywords to obtain deviation conversion parameters may further include:
[0081] The semantic strength of the standard keywords and the query keywords is compared to obtain the semantic strength deviation parameter.
[0082] The semantic relevance of standard keywords and query keywords is compared to obtain the semantic relevance deviation parameter.
[0083] Based on the semantic strength deviation parameter and the semantic relevance deviation parameter, the deviation transformation parameter is obtained.
[0084] When calculating the bias transformation parameters, the semantic strength bias parameters can be obtained from the semantic strength dimension. Obtain the semantic relevance deviation parameter from the semantic relevance dimension. Of course, other dimensions can also be included to obtain the corresponding deviation parameters for other dimensions. Finally, the deviation parameters of each dimension are used as deviation transformation parameters. This allows the deviation transformation parameters to be updated by adjusting the deviation parameters of the corresponding dimensions during retrieval, resulting in new deviation transformation parameters. This allows for quick retrieval of the closest asset data corresponding to the shared request information. This refers to the adjustment amount of the deviation parameter for each dimension D.
[0085] Next, step S60 is executed, and asset data is retrieved and analyzed using a search function based on the sharing request information to obtain the analysis results.
[0086] After the retrieval criteria for each asset data are defined through the data asset sharing system, the asset data can be retrieved and analyzed using the retrieval criteria based on the user's sharing request information. Then, the accurate asset data can be retrieved based on the analysis results.
[0087] In step S60, based on the sharing request information, asset data is retrieved and analyzed using a retrieval formula to obtain the analysis results, including:
[0088] Based on the shared request information, obtain the type of the requested asset;
[0089] Based on the requested asset type, a standard search expression is generated, which includes standard keywords, asset type, and archiving unit.
[0090] Based on the shared request information and standard search terms, obtain the demand deviation conversion parameters;
[0091] Based on the demand deviation conversion parameters, standard keywords are converted into query keywords, where the deviation conversion parameters corresponding to the query keywords are closest to the demand deviation conversion parameters.
[0092] Approximation indices are obtained by calculating the approximation degree of the deviation conversion parameters and the demand deviation conversion parameters.
[0093] Update the standard search expression based on the query keywords to obtain the query expression;
[0094] The query expression and approximation index are used as the analysis results.
[0095] After receiving a sharing request, the data asset sharing system extracts the requested asset type. Based on this type, a standard search expression is derived, including standard keywords, asset type, and archiving unit. This expression allows for direct retrieval of asset data. To ensure search accuracy, the system further identifies the corresponding request keywords based on the sharing request. These keywords are then compared with the standard keywords in the standard search expression across different dimensions to obtain demand deviation transformation parameters. Based on these parameters, the system finds the closest matching query keyword. Finally, the search results are output based on the query keyword corresponding to the deviation transformation parameter, combined with the asset type (corresponding to the requested asset type in the sharing request) and archiving unit. Furthermore, to inform users of the search result deviation, an approximation calculation is performed on the deviation transformation parameter and the demand deviation transformation parameter. This approximation index is also included as part of the analysis results in the search output.
[0096] In the process of finding the deviation transformation parameter corresponding to the query keyword that is closest to the demand deviation transformation parameter based on the demand deviation transformation parameter, for example, when the demand deviation transformation parameter under each dimension D is... The deviation transformation parameters corresponding to the query keywords under each dimension D are: The differences between the two are then obtained as follows: Based on the deviation weights corresponding to each dimension D It is possible to calculate the degree of closeness between the deviation transformation parameter corresponding to each query keyword and the demand deviation transformation parameter. Then, find the degree of similarity. Minimum of closeness The standard search expression is updated using the query keyword that is closest to the demand deviation conversion parameter, thus obtaining the search expression.
[0097] Step S70: Based on the analysis results, retrieve the corresponding asset data.
[0098] After obtaining the analysis results through the data asset sharing system, when the user determines that the approximate index in the analysis results meets expectations, they can directly send a call request to the archiving unit corresponding to the asset data to call the corresponding asset data, thereby ensuring the hierarchical allocation and management of asset data.
[0099] Please refer to 2. This invention also provides a data asset sharing system 11, comprising: an acquisition unit 111, used to acquire completed item data in progress of a project; wherein, the completed item data in progress of a project includes: first completed item data during project execution and second completed item data upon project completion; a filtering unit 112, used to filter the completed item data for data assets and acquire asset data; a query unit 113, used to acquire the archiving unit of the asset data according to the asset type and storage conditions of each unit; an archiving unit 114, used to archive the asset data according to the archiving unit; a creation unit 115, used to create a search expression according to the definition terms, asset type and archiving unit of the asset data when archiving the asset data; an analysis unit 116, used to perform retrieval and analysis of the asset data through the search expression according to the sharing request information and obtain analysis results; and a calling unit 117, used to call the corresponding asset data according to the analysis results.
[0100] It should be noted that the data asset sharing system 11 provided in the above embodiments and the data asset sharing method provided in the above embodiments can form the same concept. The specific ways in which each module and unit performs operations have been described in detail in the method embodiments, and will not be repeated here. In practical applications, the data asset sharing system 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.
[0101] Please see Figure 3 The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a data asset sharing program.
[0102] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as data asset sharing code, but also to temporarily store data that has been output or will be output.
[0103] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 through various interfaces and lines. It executes programs or modules (such as data asset sharing programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.
[0104] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the data asset sharing method described above.
[0105] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a data asset sharing system.
[0106] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module stored in the storage medium includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute some functions of the data asset sharing method described in the various embodiments of this application.
[0107] In summary, the data asset sharing method and system disclosed in this invention effectively ensures the efficiency and accuracy of asset data screening by extracting asset data from all completed data during the project undertaking process within an enterprise, based on multi-dimensional verification methods such as data architecture and data structure. Furthermore, when storing asset data, hierarchical storage can be implemented according to the control needs of upper and lower level units, effectively ensuring collaborative storage of asset data among different archiving units, reducing the data storage pressure on each unit while also achieving orderly asset data storage. During asset data archiving, adjustable retrieval formulas can be configured according to the actual situation of the asset data, enabling precise location of relevant asset data based on sharing request information during data asset sharing, avoiding inaccurate query results due to retrieval formula issues. Moreover, adjusting the retrieval through configurable retrieval formulas can effectively reduce the operational load during retrieval and improve the accuracy of asset data retrieval. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.
[0108] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.
Claims
1. A method for sharing data assets, characterized in that, include: Acquire the completion data of the project in progress; wherein, the completion data of the project in progress includes: the first completion data during the project execution process and the second completion data when the project is completed; Based on the baseline data architecture combination and baseline data format combination corresponding to each asset type, the data completion item data is subjected to data asset composition integrity detection and screening to obtain complete asset data; Based on the asset type and storage conditions of each unit, obtain the archiving unit of the asset data; The asset data is archived according to the archiving unit; When archiving the asset data, a search query is created based on the definition terms of the asset data, the asset type, and the archiving unit. The definition terms include standard keywords and deviation conversion parameters corresponding to each asset data. Based on the sharing request information, the asset data is retrieved and analyzed using the search query to obtain the analysis results; Based on the analysis results, retrieve the corresponding asset data; Specifically, based on the shared request information, the asset data is retrieved and analyzed using the search formula to obtain the analysis results, including: Based on the shared request information, the type of the requested asset is obtained; Based on the requested asset type, a standard search expression is generated, wherein the standard search expression includes standard keywords, the asset type, and the archiving unit; Based on the shared request information and the standard search formula, obtain the demand deviation conversion parameters; Based on the demand deviation conversion parameters, the standard keywords are converted into query keywords, wherein the deviation conversion parameters corresponding to the query keywords are closest to the demand deviation conversion parameters. The approximation index is obtained by performing an approximation calculation on the deviation conversion parameter and the demand deviation conversion parameter; Based on the query keywords, update the standard search expression to obtain the query search expression; The query expression and the approximate index are used as the analysis results; Based on the aforementioned demand deviation conversion parameters, the standard keywords are converted into query keywords, including: Based on the shared request information, the request keywords corresponding to the requested asset type are found; then, the request keywords and standard keywords in the standard search expression are compared across different dimensions to obtain the demand deviation transformation parameters under different dimensions; when the demand deviation transformation parameters under each dimension D are... The deviation transformation parameters corresponding to the query keywords under each dimension D are: The differences between the two are then obtained as follows: Based on the deviation weights corresponding to each dimension D The degree of closeness between the deviation transformation parameter corresponding to each query keyword and the demand deviation transformation parameter is calculated. Find the degree of closeness Minimum of closeness The corresponding query keywords that are closest to the demand deviation conversion parameters.
2. The data asset sharing method according to claim 1, characterized in that: Based on the baseline data architecture and baseline data format combinations corresponding to each asset type, the completed item data undergoes data asset composition integrity detection and filtering to obtain complete asset data, including: Perform a format query on the completed item data to obtain the initial data format; Perform a schema query on the completed item data to obtain the initial data schema; Based on the initial data format and the initial data architecture, determine whether the completed item data can form asset data: If so, based on the asset type corresponding to the completed item data, an initial data architecture combination is obtained, and the completed item data is re-integrated to obtain asset data; If not, then the completed item data will be filtered out.
3. The data asset sharing method according to claim 2, characterized in that: Based on the initial data format and the initial data architecture, determining whether the completed item data can form asset data includes: The baseline data architecture combination corresponding to each asset type is extracted sequentially and compared with the initial data architecture of the completed item data: When the initial data architecture combination and the benchmark data architecture combination satisfy the first preset condition, the initial data format combination corresponding to the initial data architecture combination is compared with the benchmark data format combination corresponding to the benchmark data format combination. When the initial data format combination and the benchmark data format combination satisfy the second preset condition, it is indicated that the completed item data can form asset data. If there is no initial data architecture combination that satisfies the first preset condition with the baseline data architecture combination in the initial data architecture, then it means that the completed item data cannot form asset data.
4. The data asset sharing method according to claim 3, characterized in that: The first preset conditions include: The similarity score between some initial data architectures in the initial data architecture combination and the main benchmark data architecture in the benchmark data architecture combination, and between other initial data architectures in the initial data architecture combination and the auxiliary benchmark data architecture in the benchmark data architecture combination, reaches a set value. The second preset condition includes: The initial data format in the initial data format combination is the same as the reference data format in the reference data format combination, or the initial data format in the initial data format combination is the same as the reference data format in the reference data format combination after format conversion.
5. The data asset sharing method according to claim 2, characterized in that: Based on the asset type corresponding to the completed item data, an initial data architecture combination is obtained, and the completed item data is re-integrated to obtain asset data, including: Based on a portion of the initial data architecture in the initial data architecture combination, obtain the first item data in the completed item data that corresponds to the portion of the initial data architecture; Based on another portion of the initial data architecture in the initial data architecture combination, obtain the second item data in the completed item data that corresponds to the portion of the initial data architecture; The first and second data items are recombined to obtain asset data; The benchmark data architecture for asset types includes a primary benchmark data architecture that is the same as the aforementioned initial data architecture, and a secondary benchmark data architecture whose comprehensive similarity score with the other initial data architecture reaches a set value.
6. The data asset sharing method according to claim 1, characterized in that: Based on the asset type and storage conditions of each unit, the archiving unit of the asset data is obtained, including: Based on the current unit of the asset data, obtain the first set of asset types for the current unit, wherein the asset data includes first asset data and second asset data; Based on the current unit's superior units, obtain the second asset type set of each superior unit; Based on the first asset data of the current unit corresponding to the first target asset type, the archiving unit of the first asset data is obtained as the superior unit corresponding to the second asset type set; Based on the second asset data of the current unit corresponding to the second target asset type, the archiving unit of the second asset data is obtained as the current unit corresponding to the first asset type set; The first set of asset types includes the second target asset type, and the second set of asset types includes the first target asset type.
7. The data asset sharing method according to claim 1, characterized in that: Based on the definition terms of the asset data, the asset type, and the archiving unit, create a search query, including: Based on the asset type in the asset data, obtain standard keywords; Information is extracted from the asset data to obtain query keywords; The standard keywords and the query keywords are compared to obtain the deviation conversion parameters. The standard keywords and the deviation conversion parameters are used as the definition terms to create a search query based on the definition terms of the asset data, the asset type, and the archiving unit.
8. The data asset sharing method according to claim 7, characterized in that: The standard keywords and the query keywords are compared to obtain deviation conversion parameters, including: The semantic strength of the standard keywords and the query keywords is compared to obtain the semantic strength deviation parameter. The semantic relevance of the standard keywords and the query keywords is compared to obtain the semantic relevance deviation parameter. Based on the semantic intensity deviation parameter and the semantic relevance deviation parameter, the deviation transformation parameter is obtained.
9. A data asset sharing system applying the data asset sharing method according to any one of claims 1-8, characterized in that, include: The acquisition unit is used to acquire completed item data during the project's execution; wherein, the completed item data during the project's execution includes: first completed item data during the project's execution and second completed item data upon project completion; The filtering unit is used to filter the completed item data for data assets and obtain asset data. The query unit is used to obtain the archiving unit of the asset data based on the asset type and storage conditions of each unit. An archiving unit is used to archive the asset data according to the archiving unit; A creation unit is used to create a search query based on the definition terms of the asset data, the asset type, and the archiving unit when the asset data is archived. The analysis unit is configured to retrieve and analyze the asset data according to the shared request information and the search query, and obtain the analysis results; and The calling unit is used to call the corresponding asset data based on the analysis results.