Digital base business achievement management method and system

Abstract

The application provides a digital base business achievement management method and system, relates to the technical field of digital base business management, first collects a basic feature set of business achievement contained attributes, sources and process characteristics in a digital base, then mines the associated features of the basic feature set to form a business achievement association network, then constructs a multi-level management index containing horizontal attribute dimensions and vertical process node dimensions based on the business achievement association network, then generates a management strategy covering storage, update and sharing features in combination with index features and real-time state features of business achievements, and finally executes dynamic control on business achievements in the digital base according to the management strategy, so that the relationship between business achievements can be deeply mined, multi-dimensional fine management can be realized, dynamic adaptation to business changes can be realized, and management efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of digital infrastructure business management technology, and more specifically, to a method and system for managing the results of digital infrastructure business. Background Technology

[0002] With the deep integration of the energy revolution and the digital revolution, power grid companies are accelerating their digital transformation. Enterprises and organizations are widely building and applying digital infrastructure to support various business operations. In this process, massive amounts of multi-source and heterogeneous business outcome data are generated, including: laser point clouds, 3D terrain, and geological data in the survey phase; 3D GIM / BIM models, design drawings, and calculation sheets for power transmission, substation, and new energy projects in the design phase; and monitoring data in the operation and maintenance phase. These data have obvious spatial and geographical attributes and professional attributes, and are generated within strict business processes. From the aspects of organizational support, work standards, management processes, data classification, data grading, and compliance control, these business outcomes are of vital value to the company's decision-making, business optimization, and innovative development.

[0003] However, enterprises and organizations currently face significant challenges in managing business outcomes within a digital infrastructure: First, data is scattered across various professional systems, forming "information silos" and lacking unified governance; second, the spatial, business, and process dependencies between data have not been effectively explored and utilized; and finally, there is a lack of a comprehensive management strategy capable of simultaneously handling massive spatial data management, multi-professional collaboration, version traceability, and secure sharing. Therefore, there is an urgent need for new methods and systems that can achieve refined management of the entire lifecycle, all elements, and all connections of multi-professional business outcomes within the power grid on a unified digital infrastructure. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for managing the business results of a digital intelligence infrastructure, the method comprising:

[0005] Collect a set of basic features of business results in the digital intelligence foundation, wherein the set of basic features includes the attribute features, source features and process features of the business results;

[0006] The basic feature set is subjected to correlation feature mining to identify attribute correlation features and process transmission features between different business results, forming a business result correlation network;

[0007] A multi-level management index is constructed based on the business outcome association network. The multi-level management index includes a horizontal index divided by attribute dimensions and a vertical index divided by process nodes.

[0008] By combining the index characteristics of the multi-level management index with the real-time status characteristics of business results, a business result management strategy including storage characteristics, update characteristics, and sharing characteristics is generated.

[0009] Dynamic control is exercised over the business results in the digital infrastructure based on the aforementioned business results management strategy.

[0010] In another aspect, embodiments of the present invention also provide a digital intelligence foundation business results management system, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium and the processor are connected, the machine-readable storage medium is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-described method.

[0011] Based on the above, this embodiment of the invention comprehensively collects the basic feature set of business results in the digital intelligence foundation, covering multiple aspects such as attributes, sources, and processes. Then, it accurately identifies the attribute association features and process transmission features between different business results, constructs a business result association network, and builds a multi-level management index based on this business result association network, including horizontal attribute dimension indexes and vertical process node indexes. This enables refined management of business results from multiple dimensions, greatly improving the retrieval efficiency and accuracy of business results. The management strategy generated by combining the features of the multi-level management index and the real-time status features of business results can dynamically adapt to changes in business results, realize intelligent control of storage, updates, and sharing, and effectively improve the management efficiency of business results in the digital intelligence foundation. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the execution flow of the digital intelligence foundation business results management method provided in the embodiments of the present invention.

[0013] Figure 2 This is a schematic diagram of exemplary hardware and software components of the digital intelligence foundation business results management system provided in an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating the digital infrastructure business results management method provided in this embodiment of the invention. The following is a detailed description of the digital infrastructure business results management method.

[0015] Step S110: Collect the basic feature set of business results in the digital intelligence base. The basic feature set includes the attribute features, source features and process features of the business results.

[0016] In this embodiment, the business results of each department within the enterprise's digital intelligence foundation are taken as the management objects, and the digital intelligence foundation business result management method is implemented. The first step is to collect a set of basic characteristics of the business results. Business results can be project reports completed by each department, software modules developed, or phased project results generated. Attribute characteristics describe the inherent characteristics of the business results. For business results, attribute characteristics may include the category of the business results, such as electrical engineering, structural engineering, surveying, and planning; the format type of the business results, such as document format, table format, presentation format, video and image format; the creation time of the business results; and the fields involved in the business results, such as construction, surveying and design, development planning, and digital twins. Source characteristics point to the origin of the business results. For business results, source characteristics may include the department that created the business results, such as the digitalization department, energy planning department, surveying department, power grid department, technology research and development department, and finance department; the team that created the business results, such as the power grid department's transmission electrical team or the digital development core team; and the data source of the business results, such as internal databases, data provided by external partners, and publicly available market data. Process characteristics involve information related to the flow and processing of business results within the digital infrastructure. For project business results, process characteristics may include report approval process nodes, such as department manager approval, supervisor approval, general manager approval, etc.; report flow path, such as from the creation team to the department manager, and then to the supervisor; and report processing status, such as pending approval, under approval, approved, rejected, etc.

[0017] When collecting these features, it is necessary to connect to various business systems, such as project management systems, document management systems, and approval systems, through the data collection interface in the digital intelligence platform. Source features such as the creating department and team of the business deliverables are obtained from the project management system; attribute features such as format type and subject category of the business deliverables are obtained from the document management system; and process features such as approval process nodes and processing status of the business deliverables are obtained from the approval system. During the collection process, the raw data needs to be preliminarily processed to remove duplicate and invalid information. For example, for the same project report recorded repeatedly in different systems, the accurate time information needs to be confirmed and retained; unidentifiable garbled data or redundant data unrelated to the business deliverables are filtered and deleted. After processing, a basic feature set containing attribute features, source features, and process features is formed. The basic feature set of each business deliverable is stored in the feature database of the digital intelligence platform in a structured data format, facilitating subsequent operations such as related feature mining.

[0018] Step S120: Perform correlation feature mining on the basic feature set to identify attribute correlation features and process transmission features between different business results, and form a business result correlation network.

[0019] After obtaining the basic feature set, the next step is to mine related features of the basic feature set. This process aims to find the inherent connections between different business results.

[0020] Step S121: Extract the attribute features of each business outcome in the basic feature set, compare the category features and label features in the attribute features of different business outcomes, and divide the business outcome groups with common attributes.

[0021] In this embodiment, after extracting the attribute features of each business outcome from the basic feature set, the category features and tag features of the business outcomes are structurally analyzed and matched according to the predefined digital asset classification standards in the digital foundation (such as broad categories like survey data, power transmission results, substation results, and new energy results) and the tag system (such as fine-grained tags like data source, project stage, professional type, and format type). Category features correspond to the high-level classification of the business outcome in the digital foundation, while tag features reflect more specific business attributes.

[0022] By comparing the category and tag characteristics of different business deliverables, when multiple business deliverables have the same classification level and their tag characteristics overlap to a certain extent under preset rules, they are grouped into the same business deliverable group. For example, if multiple business deliverables belong to the "Power Transmission Deliverable Management" category and all have tags such as "3D Model Import," "Coordinate Management," and "Material Statistics," they are classified into the "Power Transmission 3D Design and Materials Management Business Deliverable Group." This grouping mechanism fully utilizes the standardized asset governance framework of the digital infrastructure to ensure that the division of business deliverable groups not only conforms to business logic but also supports subsequent correlation analysis, intelligent retrieval, and collaborative applications.

[0023] Step S122: For each business outcome in the business outcome group, analyze the co-occurrence feature combinations among attribute features, and identify attribute association features among different business outcomes based on the frequency of occurrence and association strength of co-occurrence feature combinations.

[0024] After dividing the business results into groups, we begin to analyze the co-occurrence feature combinations among the attribute features of each business result within each group.

[0025] Step S1221: Decompose the attribute features of each business result in the business result group into the smallest granularity attribute unit. Each attribute unit clearly represents an attribute item and its corresponding attribute value.

[0026] For each business deliverable in a deliverable group, its attribute characteristics are decomposed. For example, a design deliverable belonging to the substation design category, with tags for 3D model import and coordinate management, and a creation date of a specific date, can be decomposed into multiple attribute units, including the substation design category corresponding to the category characteristic, the 3D model import corresponding to the tag characteristic, the coordinate management corresponding to the tag characteristic, and the specific date corresponding to the creation time. Each attribute unit clearly contains attribute items and their attribute values.

[0027] Step S1222: Count the number of times any two attribute units co-occur in the same business outcome to obtain the frequency of occurrence of each co-occurrence feature combination.

[0028] After the attribute units are decomposed, the frequency of any two attribute units appearing together in the same business deliverable is counted. For example, in the substation 3D design business deliverable group, the number of times the attribute units "substation design category" and "3D model import" appear together in each business deliverable within the group is counted. If 8 out of 10 business deliverables in the group simultaneously possess both attribute units, then the frequency of this co-occurrence feature combination is 8. The frequency of occurrence of each pair of possible attribute units is then counted.

[0029] Step S1223: Calculate the association strength of each co-occurring feature combination. The association strength is obtained by dividing the number of times the two attribute units co-occur by the total number of times they appear in the group, and then multiplying the two ratios.

[0030] When calculating the association strength, the number of times two attribute units co-occur is used as the numerator. Each of these ratios is then divided by the total number of times each attribute unit appears in all business results within the group. The two ratios are then multiplied together, and the product is the association strength of the co-occurring feature combination. For example, if attribute unit A (substation design) appears 10 times in the group and attribute unit B (3D model import) appears 8 times, and their co-occurrence count is 6, then the association strength is 6 / 10 multiplied by 6 / 8.

[0031] Step S1224: Set the frequency threshold and association strength threshold for co-occurrence feature combinations, and filter out co-occurrence feature combinations that simultaneously meet the two threshold conditions to form effective feature combinations.

[0032] Based on actual business management needs and historical data experience, lower limits are set for occurrence frequency and association strength. For example, the occurrence frequency threshold is set to 5, and the association strength threshold is set to 0.3. All co-occurring feature combinations in the business outcome group are screened, and only combinations that simultaneously meet the frequency and strength thresholds are retained as valid feature combinations. For example, the combination of the category feature "substation design" and the tag feature "3D model import" with an occurrence frequency of 8 and an association strength of 0.4 is retained as a valid feature combination.

[0033] Step S1225: Identify the attribute association features between different business results based on the effective feature combination. If two business results contain different attribute units in the same effective feature combination, it is determined that there is an attribute association feature between them.

[0034] Based on the selected valid feature combinations, determine whether different business outcomes possess attribute association features. For example, business outcome X has the attribute unit category feature "substation design" and business outcome Y has the attribute unit label feature "coordinate management". If these two attribute units belong to the same valid feature combination, then it is determined that there is an attribute association feature between business outcome X and business outcome Y. Step S1226: Sort the attribute association features according to their association strength. The attribute association features with higher association strength are given more significant representation in the business outcome association network.

[0035] The identified attribute association features are sorted according to their corresponding association strength. Attribute association features with high association strength can be represented more prominently when constructing the business outcome association network, for example, by using thicker connecting lines or more vivid colors in the network to reflect their importance. For example, if the association strength of the attribute association feature between business outcome X and business outcome Y is 0.6, and the association strength of the attribute association feature between business outcome M and business outcome N is 0.4, then the connection between X and Y will be more significant in the association network than the connection between M and N.

[0036] Step S1227: When a new business outcome is added to the business outcome group, the frequency of occurrence and association strength of co-occurrence feature combinations within the group are recalculated, and the attribute association features are dynamically updated.

[0037] When a new business outcome is added to a business outcome group, the system needs to reprocess the co-occurrence relationships of all attribute features within that group. Specifically, this includes: parsing the attribute features of the new outcome into basic attribute units, recalculating the co-occurrence frequency of each group of attribute unit combinations, calculating their association strength accordingly, and re-selecting effective feature combinations to dynamically update attribute association features. For example, if a new outcome belonging to the "Substation Design" category and tagged with "3D Model" is added to the corresponding business group, the co-occurrence frequency of the attribute combination "Substation Design - 3D Model" will increase, and its association strength may change, thus triggering an update of the attribute association features.

[0038] Step S1228: Periodically evaluate the attribute association features and adjust the screening threshold of effective feature combinations according to the changes in the attribute features of business results.

[0039] Regularly evaluate attribute association features and observe changes in the attribute features of business outcomes. If significant changes are found in the attribute features of business outcomes, such as the emergence of new label features or a sharp decrease in the frequency of certain existing label features, then the screening threshold for effective feature combinations needs to be adjusted accordingly. For example, when a large number of new label features appear, it may be necessary to lower the frequency threshold to include more effective feature combinations related to the new labels; when the association strength of certain attribute units generally decreases, it may be necessary to lower the association strength threshold to ensure that there are enough effective feature combinations to identify attribute association features.

[0040] Step S123: Analyze the process features in the basic feature set, track the input-output relationship of business results in each design process stage of the digital foundation, determine the process transmission direction and dependence between different business results, and form process transmission features.

[0041] This study analyzes the process characteristics within the basic feature set, focusing on tracing the input-output dependencies of business deliverables in various design processes of power engineering. For example, in the substation engineering design process, business deliverable A, the geotechnical investigation report, serves as the input basis for the basic design model of business deliverable B. This means that the generation of business deliverable B depends on the engineering geological parameters provided by business deliverable A. In this case, there is a clear process transmission relationship from business deliverable A to business deliverable B, with the transmission direction being A to B. Simultaneously, the dependence value is determined based on the degree of dependence of business deliverable B on business deliverable A. If all structural designs of business deliverable B are based on the geological data of business deliverable A, the dependence is high; if only some auxiliary designs reference business deliverable A, the dependence is low. By systematically analyzing the input-output chains between business deliverables in various professional design processes, the transmission direction and dependence degree between different deliverables are clarified, ultimately forming quantifiable and traceable process transmission characteristics.

[0042] Step S124: Integrate the attribute association features and process transmission features, use nodes to represent business results, and use weighted directed edges to represent association features and strengths, and construct a business result association network.

[0043] The identified attribute association features and process transmission features are fused together to construct a business outcome association network. In this network, each business outcome is represented by a node, which may contain basic information such as a unique identifier for the business outcome. Attribute association features and process transmission features are represented by directed edges. The direction of the directed edges is determined by the direction of process transmission or the directional relationship of attribute associations, while the weight of the edges is determined by the association strength or dependency degree; a larger weight value indicates a tighter association. For example, if business outcome A and business outcome B have an attribute association feature with an association strength of 0.7, then a directed edge is drawn from A to B (or the direction is determined based on the actual association direction), and the edge weight is 0.7. If business outcome C and business outcome D have a process transmission feature with a dependency degree of 0.8, then a directed edge is drawn from C to D, and the edge weight is 0.8. Through this method, a complete business outcome association network is constructed.

[0044] Step S125: When a new business outcome is added or the characteristics of an existing business outcome change, re-analyze the co-occurrence feature combination and process transmission relationship, and update the node attributes and edge weights in the business outcome association network.

[0045] When a new business outcome is added to the digital intelligence foundation, it needs to be added as a new node to the business outcome association network. The co-occurrence feature combinations and process transmission relationships between this new outcome and existing business outcomes in the network need to be re-analyzed to determine new attribute association features and process transmission features. Corresponding directed edges and weights are then added to the network accordingly. If the characteristics of an existing business outcome change, such as a change in the label features of the attribute features or a change in the dependency relationships in the process features, then the co-occurrence feature combinations and process transmission relationships between this business outcome and other business outcomes need to be re-analyzed. The corresponding node attributes and the weights of the directed edges connected to other nodes need to be updated to ensure that the business outcome association network accurately reflects the associations between business outcomes.

[0046] Step S126: Through network topology analysis, identify the core business outcome nodes and key association paths in the business outcome association network, and strengthen the feature representation of core nodes and key paths.

[0047] A network topology analysis is performed on the constructed business outcome association network to calculate topological metrics such as degree and betweenness number for each node. Degree represents the number of edges connecting a node to other nodes; a higher degree indicates more connections the node has with other business outcomes. Betweenness number represents the frequency with which a node appears on the shortest path between other nodes; a higher betweenness number indicates a more important bridging role the node plays in the network. Based on these metrics, core business outcome nodes, i.e., nodes with high degree and betweenness number, are identified. Simultaneously, paths in the network are analyzed to identify those that carry the main associated traffic and are crucial to the flow of business outcomes; these are designated as critical association paths. For core business outcome nodes and critical association paths, their characteristics are enhanced using more prominent methods in the association network, such as displaying core nodes in a larger size and marking critical paths with more specific colors.

[0048] Step S127: Based on the degree of correlation between business results, group the business result association network so that business results within the same group have highly consistent attribute characteristics or close process dependencies.

[0049] Based on the degree of attribute association and process transmission between business deliverables, a grouping algorithm based on weights and connection density is used to partition the business deliverable association network. The degree of association is comprehensively measured by the weights of the edges between nodes and the connection density of nodes in the local network. For example, the ratio of the sum of the weights of all edges within a group to the total number of nodes in the group can be calculated. The higher this ratio, the stronger the association between business deliverables within the group. Through grouping, the entire network is divided into multiple logical groups. The business deliverables contained within each group exhibit highly consistent attribute characteristics or strong process dependencies. For example, in the power digital foundation, business deliverables related to substation engineering design, such as electrical main wiring diagrams, equipment layout schemes, and foundation selection calculations, form one logical group, while business deliverables related to transmission line design, such as tower positioning diagrams, conductor and ground wire mechanical calculations, and insulation coordination schemes, form another logical group. Business deliverables within the same group have high consistency in design standards, data interfaces, and engineering applications.

[0050] Step S130: Construct a multi-level management index based on the business outcome association network. The multi-level management index includes a horizontal index divided by attribute dimensions and a vertical index divided by process nodes.

[0051] After completing the construction of the business outcome association network, a multi-level management index is established based on this network to achieve efficient organization and retrieval of business outcomes.

[0052] Step S131: Analyze the attribute features of each node in the business outcome association network, cluster the nodes according to the category features, and generate multiple attribute dimension clusters.

[0053] By analyzing the attributes of each node in the business outcome association network, their category features are extracted, and the nodes are clustered accordingly to form multi-attribute dimension clusters. For example, all business outcome nodes belonging to power transmission outcome management are clustered into a power transmission outcome cluster, and all nodes belonging to geotechnical investigation data management are clustered into a geotechnical investigation cluster.

[0054] Step S132: Generate a corresponding attribute index table for each attribute dimension cluster. This table records the unique identifiers and core attribute characteristics of all business results within the cluster, thereby constructing a horizontal index.

[0055] Generate an attribute index table for each attribute dimension cluster for constructing the horizontal index.

[0056] Step S1321: Extract core attribute items from the attribute features of the attribute dimension cluster. These core attribute items are key attributes that can significantly distinguish different business results within the cluster.

[0057] Core attribute items are selected from attribute features with high discriminative power within the attribute dimension cluster. For example, in the power transmission results cluster, core attribute items may include voltage level, line type, tower model, etc.; in the geotechnical investigation cluster, core attribute items may include geological age, soil and rock type, borehole depth, etc.

[0058] Step S1322: Assign an index weight to each core attribute item. The weight value is determined based on the importance of the attribute item in distinguishing business results.

[0059] Each core attribute is assigned a corresponding index weight based on its importance in distinguishing business outcomes; the higher the importance, the greater the weight. For example, in the power transmission outcome cluster, voltage level may have a higher weight, while the design phase may have a relatively lower weight.

[0060] Step S1323: Collect the unique identifiers of all business results within the cluster of this attribute dimension and the attribute values ​​of their corresponding core attribute items, and organize them in a structured manner as index data.

[0061] The unique identifiers and core attribute values ​​of each business outcome within the cluster are collected and organized in a fixed format. For example, the unique identifier of a power transmission outcome is TL2023001, its voltage level is 500kV, its line type is an overhead line, and its tower model is JG1. This information is stored in a structured manner.

[0062] Step S1324: Sort the index data according to the core attribute items, so that records with the same attribute value are arranged adjacently, and generate an attribute index table accordingly.

[0063] The index data is sorted by one or more core attribute items, so that records with the same attribute values ​​are grouped together. For example, first sort by voltage level, and then sort by line type within the same voltage level, thus forming an ordered attribute index table.

[0064] Step S1325: Add storage pointers to the attribute index table, with each pointer pointing to the actual storage address of the corresponding business result in the digital base.

[0065] Add a storage pointer to each record in the attribute index table. This pointer can locate the specific storage path of the business results in the digital foundation, such as pointing to the file address in the distributed file system or the record location in the database.

[0066] Step S1326: Compress the attribute index table, use encoding to replace the complete attribute value description, and set up a high-frequency access cache in the horizontal index to store the attribute index table of the attribute dimension cluster whose query frequency exceeds the set threshold in the cache.

[0067] The attribute index table is compressed, and encoding is used instead of lengthy original attribute value descriptions. Simultaneously, based on query history data to determine access frequency, frequently accessed attribute index tables are cached in a high-speed storage area to improve query response performance.

[0068] Step S1327: When business results are added, changed, or removed in the attribute dimension cluster, update the records and storage pointers in the attribute index table in real time.

[0069] When a new business outcome is added to the attribute dimension cluster, the attribute values ​​of its core attributes need to be extracted, corresponding index data needs to be generated, and the data needs to be inserted into the attribute index table according to the ordered arrangement rules. A storage pointer pointing to its actual storage location also needs to be added. If the core attribute of an existing business outcome in the cluster changes, such as the analysis period of business outcome "SC002" changing from "monthly" to "quarterly," the record of that business outcome needs to be found in the attribute index table, its core attribute values ​​updated, and its position in the index table adjusted according to the new attribute values. When a business outcome is removed from the cluster, its record and its corresponding storage pointer need to be deleted from the attribute index table. Through these real-time updates, it is ensured that the attribute index table accurately reflects the latest status of business outcomes in the attribute dimension cluster.

[0070] Step S133: Establish association pointers between clusters with different attribute dimensions in the horizontal index, and realize the indexing and association of cross-cluster business results based on attribute association characteristics.

[0071] In the horizontal index, attribute associations between clusters of different attribute dimensions are identified, and association pointers are established accordingly. These association pointers are used to record the correspondence between business results in different clusters due to shared common attributes. For example, the 3D design results of a certain line in the power transmission results management cluster and the measurement control point data in the survey data management cluster under the same project have an attribute association because they have the same project number and coordinate benchmark. In this case, an association pointer is set in the line 3D design result record to point to the corresponding measurement control point data, and a reverse pointer is set in the measurement control point data record to point to the line design result. Through such association pointers, the system can realize the indexing and association of business results across attribute dimension clusters, thereby enabling users to easily discover and obtain related business result information in other clusters when querying results in a certain cluster.

[0072] Step S134: Based on the process transmission characteristics reflected in the business outcome association network, divide the business outcome into stages according to the business process nodes to generate multiple process node groups.

[0073] Based on the process transmission relationships reflected in the business outcome association network, key execution nodes in the business process are identified, and business outcomes are divided into stages based on these nodes, grouping business outcomes corresponding to the same node into the same process node group. For example, in the collaborative design process of power grid engineering, business outcomes in the preliminary design stage, such as electrical main wiring diagrams and site layout diagrams, can be divided into the preliminary design node group; business outcomes in the construction drawing design stage, such as foundation construction drawings and tower assembly drawings, are divided into the construction drawing design node group. This division method organizes business outcomes according to their respective business process stages, supporting orderly management and efficient indexing of business outcomes along the process dimension.

[0074] Step S135: Generate a corresponding process index sequence for each process node group. The process index sequence records unique identifiers and node position characteristics according to the transmission order of business results in the process, and constructs a vertical index.

[0075] Generate a process index sequence for each process node group to build a vertical index.

[0076] Step S1351: Determine the transmission order of business results in the process node group, the transmission order being determined based on the input-output relationship of business results in the process.

[0077] Analyze the input-output relationships of each business outcome in the process node group to determine their transmission order. If business outcome A is the input of business outcome B, meaning the generation of business outcome B depends on business outcome A, then the transmission order is A before B. If business outcome B is the input of business outcome C, then the transmission order is B before C, and so on, forming a chain-like transmission order. For example, in the design process node group, the business outcome "preliminary design scheme" is the input of "detailed design scheme," and "detailed design scheme" is the input of "design review report." Therefore, their transmission order is: preliminary design scheme → detailed design scheme → design review report.

[0078] Step S1352: Extract the node position features of each business outcome in the process node group. The node position features include the sequential position of the business outcome in the process and its relative positional relationship with other business outcomes.

[0079] For each business outcome in the process node group, its node position features are extracted. The sequential position can be represented by a sequence number; for example, in a process node group with the transmission order A→B→C→D, A's sequential position is 1, B's is 2, C's is 3, and D's is 4. The relative positional relationship describes the positional association between this business outcome and other business outcomes, such as A before B, B after A and before C, C after B and before D, etc. Through this information, the position of the business outcome within the process node group is comprehensively characterized.

[0080] Step S1353: Record the unique identifiers and node location characteristics of the business results in sequence according to the transmission order to form the initial structure of the process index sequence.

[0081] According to the determined transmission order, the unique identifiers and corresponding node position characteristics of each business result in the process node group are recorded sequentially to form the initial structure of the process index sequence. For example, if the transmission order of the design process node group is preliminary design scheme (unique identifier "SJ001") → detailed design scheme (unique identifier "SJ002") → design review report (unique identifier "SJ003"), and their node position characteristics are (position 1, relative position before SJ002), (position 2, relative position after SJ001 and before SJ003), and (position 3, relative position after SJ002), then the initial structure of the process index sequence is [SJ001, (1, before SJ002); SJ002, (2, after SJ001 and before SJ003); SJ003, (3, after SJ002)].

[0082] Step S1354: Add an association pointer to the process index sequence. The association pointer points to the corresponding record of the business result in the horizontal index, thereby realizing the association between the horizontal and vertical indexes.

[0083] A related pointer is added to each record in the process index sequence. This pointer points to the corresponding record in the attribute index table of the horizontal index for that business outcome. For example, in the process index sequence of the design process node group, the related pointer added for "SJ001" points to its record in the attribute index table of the product development attribute dimension cluster. This method establishes the link between the vertical and horizontal indexes. When a user queries a business outcome through the vertical index, they can quickly find the detailed attribute information of that business outcome in the horizontal index through the related pointer, and vice versa.

[0084] Step S1355: The process index sequence is segmented, and the continuous process node group is divided into multiple index segments, each index segment containing a preset number of business result records.

[0085] Based on the quantity of business results and management needs, the process index sequence is segmented. The number of business result records contained in each index segment is preset, such as 10 records per index segment. For example, a process index sequence containing 35 business result records can be divided into 4 index segments: the first 3 segments each contain 10 records, and the 4th segment contains 5 records. This segmentation makes the structure of the process index sequence clearer, facilitating management and querying. During a query, the corresponding index segment can be located first, and then the specific business result record can be found within that segment, improving query efficiency.

[0086] Step S1356: Generate a segment index for each index segment, wherein the segment index records the unique identifier range and starting position of the business results within the index segment.

[0087] A segment index is generated for each index segment. The segment index records the minimum and maximum values ​​of the unique identifiers of the business results within that index segment, forming a unique identifier range. It also records the starting position of the index segment in the process index sequence, such as the starting record number. For example, if an index segment contains unique identifiers for business results from SJ001 to SJ010, and its starting position in the process index sequence is record number 1, then its segment index would be (Unique identifier range: SJ001-SJ010, Starting position: 1). Generating segment indexes helps to quickly locate the index segment containing the target business results, further improving query speed.

[0088] Step S1357: When the order of business results transmission in the process node group changes or the node position characteristics are updated, regenerate the process index sequence.

[0089] If the transmission order of business results in the process node group changes, such as adding a new business result between two existing business results, or reversing the order of existing business results, then the transmission order needs to be redefined. The unique identifiers and node position characteristics of the business results must be re-recorded according to the new transmission order, the associated pointers updated, and the segmentation process and segment index regenerated to form a new process index sequence. When the node position characteristics of a business result are updated, such as a change in sequence number or relative position relationship, the corresponding node position characteristic information also needs to be updated in the process index sequence to ensure that the process index sequence accurately reflects the latest position of the business results in the process node group.

[0090] Step S1358: Associate and store the process index sequence with the process characteristics of the business results.

[0091] The generated process index sequence is stored together with the process characteristics of the business outcome, and a relationship is established between them. For example, in the storage system, the process index sequence file and the corresponding process characteristic data file are placed in the same directory, or they are bound together in the database through a correlation field. In this way, when it is necessary to query the process characteristics of a business outcome, the corresponding business outcome can be quickly found through the process index sequence, and its process characteristics can be obtained; conversely, when the process characteristics of a business outcome are known, its position in the process index sequence can also be found through the correlation, which facilitates management and analysis from a process perspective.

[0092] Step S136: Establish a connection index between adjacent process node groups in the vertical index, and associate the business results in adjacent groups in sequence according to the process transmission characteristics.

[0093] In the vertical index, the process transmission relationship between adjacent process node groups is identified, and a connection index is established accordingly. For example, in the power grid design process, the preliminary design node group and the construction drawing design node group are adjacent stages. The electrical main wiring diagram in the preliminary design node group is the design input for the foundation construction drawing in the construction drawing design node group, exhibiting clear process transmission characteristics. In this case, the sequential transmission relationship between the electrical main wiring diagram and the foundation construction drawing is recorded in the connection index, identifying the connection logic between node groups. Through the connection index, the business results in adjacent process node groups are associated according to the business process sequence, forming a coherent vertical index structure, supporting users to trace the evolution and transmission of results along the business process.

[0094] Step S137: Integrate the horizontal index and the vertical index to generate a unified index access entry, supporting index queries initiated by attribute dimension or process node.

[0095] The existing horizontal and vertical indexes are integrated to form a unified index access portal, such as an index service platform or a global query interface. This portal can respond to user query requests based on attribute dimensions or process nodes. For example, a user can query "3D model of 500kV overhead line" by attribute dimension, and the system will call the horizontal index to locate the result; or they can query "all results in the construction drawing design stage" by process node, and the system will call the vertical index to return the corresponding content. At the same time, this portal supports compound condition queries, such as requesting "all results related to geotechnical investigation in the preliminary design stage". The system can retrieve the horizontal and vertical indexes in parallel and return the results after merging them. By integrating the two types of indexes and providing a unified access portal, the retrieval efficiency and ease of use of business results are significantly improved. Step S138: When the business result association network is updated, the attribute index table of the horizontal index and the process index sequence of the vertical index are adjusted synchronously to maintain the consistency between the index and the business result association status.

[0096] When the business outcome relationship network is updated due to the addition of new business outcomes or changes in business outcome characteristics, both horizontal and vertical indexes need to be adjusted synchronously. For horizontal indexes, if changes in attribute relationship characteristics alter the composition of attribute dimension clusters (e.g., some business outcomes are moved from one cluster to another), the corresponding attribute index tables need to be updated, including adding or deleting records, adjusting storage pointers and relationship pointers, etc. For vertical indexes, if changes in process propagation characteristics alter the division of process node groups or the propagation order of business outcomes, the affected process index sequence needs to be regenerated, and the connecting indexes updated, etc. Through these synchronous adjustments, it is ensured that both horizontal and vertical indexes accurately reflect the latest state of the business outcome relationship network, maintaining consistency between the indexes and the business outcome relationship status, and guaranteeing the accuracy of query results.

[0097] Step S140: Combine the index features of the multi-level management index with the real-time status features of the business results to generate a business results management strategy that includes storage features, update features and sharing features.

[0098] After constructing a multi-level management index, a business outcome management strategy is generated by combining its index characteristics with the real-time status characteristics of business outcomes.

[0099] Step S141: Analyze the index features of the multi-level management index, and extract the storage features of the attribute dimensions in the horizontal index and the storage features of the process nodes in the vertical index.

[0100] The index characteristics of the multi-level management index are analyzed. Storage characteristics of attribute dimensions are extracted from the horizontal index. These characteristics include the storage location distribution of attribute dimension clusters, the storage density of business results within each cluster, and the preferred storage media type for different attribute dimensions. For example, business results of attribute dimension clusters are mostly stored on specific hard disk partitions with high storage density, and solid-state drives are preferred. Storage characteristics of process nodes are extracted from the vertical index, including the storage path planning of process node groups, the storage cycle for different process nodes, and the storage association methods for business results between process nodes. For instance, the business results of the requirements analysis process node group are stored under the path " / process / requirement_analysis / ", have a long storage cycle, and are associated with the storage location of the business results of the design process node group in a related folder.

[0101] Step S142: Obtain the real-time status characteristics of the business results, which include the access frequency, data volume, and activity level of the business results.

[0102] The monitoring module within the digital intelligence platform acquires real-time status characteristics of business outcomes. Access frequency refers to the number of times a business outcome is accessed by users per unit of time; for example, a business outcome might be accessed 5 times in a day. Data volume refers to the storage space occupied by the business outcome; for example, a report might be 5MB in size. Activity level comprehensively measures the frequency of operations performed on the business outcome, including the frequency of viewing, editing, and sharing. Business outcomes with high activity levels are typically the result of more recent operations. These real-time status characteristics are continuously collected and updated by the monitoring module to ensure that the latest status of the business outcomes is reflected.

[0103] Step S143: Based on the storage characteristics of the attribute dimension, the storage characteristics of the process node, and the data volume in the real-time status characteristics, generate the storage characteristics of the business results. The storage characteristics include the storage medium type, storage path structure, and backup cycle.

[0104] By combining the storage characteristics of attribute dimensions, the storage characteristics of process nodes, and the data volume in real-time status characteristics, the storage characteristics of business results are generated.

[0105] Step S1431: Divide the data scale into levels based on the data volume in the real-time status characteristics, with different levels corresponding to different storage media types.

[0106] Based on the size of the data in the business deliverables, they are categorized into different scale levels. For example, data smaller than 10MB is considered small data, 10MB to 100MB is medium data, and larger than 100MB is large data. Different scale levels are suited to different storage media: small data can be stored on ordinary hard drives, medium data is suitable for solid-state drives, and large data is suitable for distributed storage systems. This tiered storage strategy optimizes storage resource allocation and cost control while ensuring access efficiency. For example, a small survey data report containing only text and a few charts can be stored on an ordinary hard drive; while a large design deliverable containing a large amount of 3D models and point cloud data needs to be stored in a distributed storage system to meet its high capacity and high concurrency access requirements.

[0107] Step S1432: Combine the storage characteristics of the attribute dimensions in the horizontal index to match the corresponding storage media for each data scale level.

[0108] The storage characteristics of attribute dimensions in the horizontal index reflect the commonalities in the storage of business deliverables across different attribute categories. For example, report-type business deliverables are mostly document-type data, while surveying-type business deliverables may contain a large amount of geographic information in different formats, GPS points, design drawings, etc. When matching storage media to different data scales, adjustments should be made based on these attribute dimensions. For documents in small datasets, in addition to ordinary hard drives, dedicated storage areas based on document management systems can be considered to facilitate retrieval by attribute classification. For technical R&D business deliverables in medium-sized datasets, if they contain many design drawings, the high-speed read / write characteristics of solid-state drives are better suited to the frequent viewing and modification of drawings. For technical R&D deliverables in large datasets, distributed storage systems need to be configured with dedicated code repository partitions and drawing storage partitions to match their attribute dimension storage characteristics.

[0109] Step S1433: Based on the storage characteristics of process nodes in the vertical index, construct a hierarchical storage path structure according to the process node and attribute dimension to which the business result belongs, and store the business results of the same process node and attribute dimension in the same path branch.

[0110] The storage characteristics of process nodes in the vertical index reflect the dependencies and connections between business deliverables within the process. Based on this, when constructing the storage path, process nodes are used as the first-level directory, and attribute dimensions as the second-level directory, forming a clear hierarchical structure. For example, for a business deliverable with the process node "Preliminary Design" and the attribute dimension "Electrical Main Wiring," its storage path is "Preliminary Design / Electrical Main Wiring / ". All deliverables matching this node and dimension are centrally stored under this path, such as "Preliminary Design / Electrical Main Wiring / Main Wiring Scheme Diagram.dwg" and "Preliminary Design / Electrical Main Wiring / Equipment Selection List.xlsx". This storage organization method not only aligns with the natural stage division of the business process but also facilitates users in quickly locating and accessing the required deliverables based on business attributes.

[0111] Step S1434: Based on the attribute association characteristics and process transmission characteristics of business results, assess the association importance of business results.

[0112] Attribute association characteristics reflect the degree of connection between business deliverables and other business deliverables with the same attribute, while process transmission characteristics reflect their role in the business process. When assessing the importance of association, both characteristics are considered: if a business deliverable has attribute associations with multiple core business deliverables and serves as prerequisite data for multiple subsequent business deliverables in the process, its association importance is high. For example, a technical R&D document defining "core technical parameters" has attribute associations with multiple product design schemes and serves as the prerequisite for all production process documents; its association importance assessment result would be high.

[0113] Step S1435: Dynamically adjust the backup cycle based on the access frequency of business results.

[0114] Business deliverables with high access frequency, rapid data updates, and a high probability of change require shorter backup cycles. Business deliverables with low access frequency but high relevance, although updated slowly, also require shorter backup cycles due to their importance. Specifically, set a base backup cycle, such as 7 days. For business deliverables with access frequency exceeding the set threshold, shorten the backup cycle to half of the base cycle. For business deliverables with relevance exceeding the set threshold, regardless of access frequency, the backup cycle should not exceed the base cycle. For business deliverables with low access frequency and low relevance, the backup cycle can be extended to twice the base cycle. For example, a frequently accessed data item might have a backup cycle of 3 days; a core technical document with high relevance but low access frequency should have a backup cycle of 7 days; and a historical data report with low access frequency and low relevance could have a backup cycle of 14 days.

[0115] Step S1436: Specify the read and write permission configuration of the storage medium in the storage characteristics, and users with different permission levels correspond to different operation ranges.

[0116] Based on users' roles and responsibilities within the digital infrastructure, read / write permission levels for storage media are set. For example, administrator users have the highest privileges, allowing read, write, delete, and modify operations on all storage media; department manager users can read and write to storage media containing their department's business results, but do not have delete permissions; ordinary employee users can only read the content of storage media containing business results relevant to their specific role, without modification or deletion permissions. Permission configuration is linked to user identity information, and permission verification is performed through the digital infrastructure's identity authentication system to ensure that users can only operate on storage media within their authorized scope.

[0117] Step S1437: Define the naming rules for the storage path. These rules must include the unique identifier of the business outcome, attribute dimensions, and process node information.

[0118] The naming of storage paths should clearly reflect the core metadata of business deliverables to facilitate identification and management. The naming convention adopts the structure "Unique Identifier - Attribute Dimension - Process Node.Extension". For example, if a business deliverable's unique identifier is "TL2024001", its attribute dimension is "Tower Structure Design", and its process node is "Construction Drawing Design", its storage file name should be "TL2024001-Tower Structure Design-Construction Drawing Design.dwg". This naming method allows users to directly obtain the key attributes of the deliverable through the file name, and also facilitates the system's automated classification and organization of files according to rules.

[0119] Step S1438: Set up a capacity monitoring mechanism for the storage medium. When the remaining capacity of the storage medium is lower than a set threshold, trigger a medium expansion or data migration operation.

[0120] Each storage medium is equipped with a capacity monitoring module to monitor remaining storage space in real time. A capacity threshold is set; if the remaining capacity falls below 20% of the total capacity, the monitoring mechanism issues an alert. For regular hard drives and solid-state drives, a media expansion operation is triggered, automatically requesting additional storage capacity from the resource management system of the intelligent data platform. For distributed storage systems, a data migration operation is initiated, migrating some data to storage nodes with lower loads or adding new storage nodes to expand capacity. During the migration process, the consistency of data access interfaces is maintained to ensure that access to business results is not affected. After the migration is completed, the storage path pointer is updated to point to the new storage location.

[0121] Step S144: Determine the update priority of business results based on the association features in the multi-level management index and the activity level in the real-time status features.

[0122] The association characteristics in the multi-level management index include the strength of attribute association and the degree of process dependency. The activity level in the real-time status characteristics reflects the frequency of accessing, referencing, and modifying business results. These factors are considered to determine update priority: business results with high attribute association strength and core dependencies in the process, if also highly active, have the highest update priority; business results with low attribute association strength and non-core dependencies in the process, but high activity, have the next highest update priority; business results with high attribute association strength but low activity have a lower update priority; and business results with both low attribute association strength and low activity have the lowest update priority. For example, a market demand report strongly associated with multiple product design solutions and serving as a core input, if frequently referenced and modified recently, has the highest update priority; a technical specification with only weak associations with a few documents but frequently viewed has the next highest update priority.

[0123] Step S145: Generate update features of business results based on update priority. The update features include update trigger conditions, update cycle and verification method of update content.

[0124] Step S1451: Determine the update cycle of business results based on update priority.

[0125] For business deliverables with the highest update priority, set the shortest update cycle, such as once a day; for business deliverables with the next highest priority, set the update cycle to once a week; for business deliverables with even lower priority, set the update cycle to once a month; and for business deliverables with the lowest priority, set the update cycle to once a quarter. For example, core market demand reports have the highest update priority and are updated daily; general technical documents have a lower update priority and are updated monthly.

[0126] Step S1452: Set update trigger conditions, which include updates to related business results, reaching a preset time, or changes in the characteristics of the business results themselves.

[0127] When a related deliverable of a particular business outcome is updated, a synchronous update of that business outcome will be triggered. For example, if the design drawings for a tower structure are changed, the bill of quantities based on those drawings needs to be updated accordingly. When the system reaches a preset update time node, it will automatically perform the update operation, such as automatically updating the survey data statistical report at the end of each quarter. When the characteristics of the business outcome itself change significantly, such as when design parameters are adjusted due to the implementation of new standards, an update will also be triggered. For example, if the "Geotechnical Investigation Report.pdf" on which "Foundation Construction Drawings.dwg" depends is updated, or reaches the regular update time at the end of each month, or if its own key parameters are modified due to changes in design standards, the update process for that business outcome will be triggered.

[0128] Step S1453: When the update triggering condition is met, the update process of the business results is started. The update process includes the extraction, processing and storage of the update content.

[0129] Once the update trigger conditions are met, the update content is first extracted. For updates triggered by related business results, the changed portions of those results are extracted, such as changes in market share data. For updates triggered by a preset time, new data within that period is extracted, such as new sales data within a week. For updates triggered by changes in their own characteristics, the changed characteristics are extracted. The extracted update content is then processed, including format standardization and data validation. For example, new sales data is converted to a standardized table format, and data integrity is checked. Finally, the processed update content is stored in the original business result's storage location, overwriting the old version while retaining the update record.

[0130] Step S1454: Determine the verification method for the updated content. The verification method includes consistency check with related business results, integrity check of the updated content, and feature comparison before and after the update.

[0131] Consistency checks with related business deliverables refer to verifying whether the updated business deliverables match the data of related business deliverables. For example, after an update to a cost budget report, it must be consistent with the input data in the corresponding project progress report. Completeness checks of the updated content ensure that the updated business deliverables contain all necessary information and that no key data is missing. For example, an updated technical solution must include complete content such as design parameters, implementation steps, and expected results. Feature comparison before and after the update analyzes whether the changes in the core features of the updated business deliverables are reasonable and whether the logic is consistent.

[0132] Step S1455: Set up a verification node for each update step. The verification node verifies the phased results during the update process to ensure the accuracy of the update process.

[0133] After the update content extraction is complete, set the first verification node to check whether the extracted update content accurately corresponds to the triggering conditions. For example, if the update is triggered by an update of a related business outcome, it is necessary to verify whether the extracted changed parts do indeed come from that related outcome. After the update content is processed, set the second verification node to check whether the format of the processed content conforms to the standard and whether the data is valid. After the update content is stored, set the third verification node to check whether the storage location is correct, whether the old version is overwritten, and whether records are retained. If any problem is found at each verification node, the update process should be terminated immediately, and an error message should be returned. The update should be restarted after correction.

[0134] Step S1456: Record key information during the update process, including update time, update content and operation subject, to form an update log.

[0135] The update log must record all key information for each update in detail. Update time should be recorded with second-level precision, e.g., "2024-05-20 14:30:22"; update content should specifically describe the changed items and values, such as "adjusting the wire safety factor from 2.5 to 2.8"; the operating entity should record the unique identifier of the user or system performing the update operation, such as "design_user_001". Update logs are stored in association with the unique identifier of the corresponding business outcome to ensure that all historical update operations are traceable and auditable.

[0136] Step S1457: When an exception occurs during the update process, it is handled according to the recovery mechanism in the update features. The recovery mechanism includes rolling back to the state before the update or re-executing the update process.

[0137] If data corruption or verification node failure occurs during the update process, a recovery mechanism will be activated. If the exception occurs before the updated content is stored, the update process will be re-executed, starting from the content retrieval stage. If the exception occurs after storage, a rollback operation will be triggered, deleting the updated content and restoring the version state before the update. The exception and its handling result will be recorded in the update log, such as "Update failed at 2024-05-20 14:35:10, rolled back to version 2024-05-19, reason: data verification failed".

[0138] Step S1458: Based on the correlation characteristics between business results, when a certain business result is updated, an update check of its related results is automatically triggered to determine whether a synchronous update needs to be performed.

[0139] Once a business outcome is updated, the system automatically locates its associated nodes based on the business outcome's association network. For subsequent business outcomes with attribute associations exceeding a preset threshold or process propagation dependencies, an update check is triggered. The check primarily includes whether the core data of the associated outcomes is inconsistent with the current update. For example, if tower model parameters change, load calculation sheets relying on that model need to be checked and determined whether they need to be updated synchronously. If an update is confirmed, the corresponding update processes are initiated sequentially according to update priority.

[0140] Step S146: Based on the attribute association and process transmission characteristics in the multi-level management index, identify other results and related user groups that are associated with the target business results.

[0141] Horizontal indexing retrieves other business deliverables with attribute associations to the target deliverable, prioritizing them according to the strength of the association. Vertical indexing tracks preceding and subsequent deliverables that have a transmission relationship with it in the business process. Simultaneously, based on the source attributes and historical operation records of the business deliverable, it identifies associated user groups, including the set of users who have created, accessed, or modified the deliverable and its associated deliverables. For example, if the target deliverable is a "tower structure design report," horizontal indexing can identify associated "foundation treatment schemes" and "material mechanical property tables," while vertical indexing can locate its preceding "survey data analysis report" and subsequent "construction drawing design documents." Its associated user groups may include structural designers, survey engineers, and the construction drawing review team.

[0142] Step S147: Assign corresponding sharing permission levels to different associated user groups based on real-time access frequency.

[0143] Assess the degree of user dependence based on the historical access frequency of target business deliverables for each user group. Grant higher sharing permissions to groups with high access frequency to meet their frequent usage needs; restrict the scope of permissions for groups with low access frequency. For example, structural designers who frequently access "tower structure design reports" can be granted editing and download permissions; surveying engineers who need to refer to the report periodically can be granted only viewing permissions; users in other departments who access the report less frequently can be allowed only to view metadata or summary information.

[0144] Step S148: Generate the sharing characteristics of the business results based on the sharing permission level, including the sharing scope, sharing method and access control rules.

[0145] Step S1481: Determine the sharing scope based on the sharing permission level. Users with high permission levels can access the complete content of the business results and all historical versions, while users with low permission levels can only access the summary or partial metadata information of the results.

[0146] The scope of sharing expands as the access level increases. For example, structural designers with high access levels can access the complete design document, all historical versions, and associated calculation data of the "Tower Structure Design Report"; survey engineers with medium access levels can access the current complete document but cannot view historical versions; and construction personnel with low access levels can only access the summary of key parameters and the conclusions section of the report.

[0147] Step S1482: Determine the sharing method based on the sharing permission level and the work scenario of the user group, including online viewing, downloading, editing collaboration, and link sharing.

[0148] Users with high access levels can share reports in multiple ways, such as online collaborative editing, where market analysts can use the collaborative editing function of the digital platform to jointly modify reports; product managers with medium access levels can view and download reports online, but cannot edit them; and R&D personnel with low access levels can only view summaries online and cannot download them. Furthermore, different access points are provided for each sharing method, such as online viewing through the document center entry point of the digital platform, and collaborative editing through a dedicated collaborative platform entry point.

[0149] Step S1483: Formulate access control rules to control access to business results through user authentication and permission verification mechanisms.

[0150] Access control rules are integrated with the unified identity authentication system of the digital platform. Users must authenticate themselves before accessing business deliverables, and the system automatically verifies their user group and corresponding permission level. For example, when a user accesses the "Tower Structure Design Report," if they are authenticated as a structural designer with high privileges, they are allowed to edit and download the complete document; if they are authenticated as a construction worker with low privileges, they can only view summary information. The system records all access operations, including access time, user ID, and operation type, for security auditing and behavior tracking.

[0151] Step S149: Associate and integrate storage features, update features, and shared features according to the unique identifier of the business outcome to form an independent management strategy unit for each business outcome.

[0152] Each business outcome is identified by its unique identifier, and its storage characteristics (such as storage medium, path, backup strategy), update characteristics (such as update cycle, triggering conditions), and sharing characteristics (such as sharing scope, access rules) are associated and integrated to form a complete management strategy unit. For example, the business outcome corresponding to the unique identifier "TL2024001" has the following management strategy unit content: stored on a high-speed storage device under the path "Preliminary Design / Electrical Main Wiring / ", with incremental backups every 12 hours; automatic weekly updates, triggered by real-time updates when associated geotechnical data changes; the design team has editing permissions, the surveying team can view the full text, and other departments can only view metadata.

[0153] Step S1410: Aggregate all business outcome management strategy units to generate a business outcome management strategy covering all aspects of the digital foundation.

[0154] All business outcome management strategy units are categorized and summarized according to attribute dimensions (such as survey data, power transmission results, and substation results) and process nodes (such as preliminary design and construction drawing design) to form system-level business outcome management strategies. The summarized strategies are centrally stored in the management strategy library and kept in real-time synchronized with the business outcome association network to ensure the consistency and executability of the strategies throughout the entire digital infrastructure.

[0155] Step S150: Perform dynamic control over the business results in the digital foundation according to the business results management strategy.

[0156] After generating the business outcome management strategy, the business outcomes in the digital intelligence platform are dynamically controlled according to the strategy to ensure that the storage, updating and sharing of business outcomes meet management requirements.

[0157] For example, step S151: According to the sharing characteristics in the business results management strategy, identify the business results and related objects that need to be coordinated in terms of information, and the related objects include related business results and user groups.

[0158] Based on the sharing scope and relationships in the sharing characteristics, the system identifies business deliverables that require information collaboration. For example, the sharing characteristics of the "Tower Structure Design Report" clearly indicate that it has a collaboration requirement with the "Foundation Treatment Plan" and "Load Calculation Sheet," and the associated user groups are the structural design team and the civil engineering team. The system locates these associated deliverables and user groups by searching the business deliverable association network and marks them as information collaboration objects.

[0159] Step S152: Synchronize the latest information of business results to the access nodes of related objects. The synchronization method is determined based on the sharing method in the sharing characteristics.

[0160] For business deliverables requiring information collaboration, when updates or content changes occur, the latest information is synchronized to the access nodes of related objects based on the sharing method. If the sharing method is online viewing, a link to the latest information of the business deliverable is inserted into the details page of the related business deliverable, and related objects can view the updated content by clicking the link. If the sharing method is data push, the latest information of the business deliverable can be automatically packaged and pushed according to the preset receiving address of the related objects, such as the storage path of the related business deliverable or the message center of the related user.

[0161] During the synchronization process, the synchronized information needs to be verified to ensure that the latest synchronized information is consistent with the actual content of the business results, and to avoid related objects receiving incorrect information due to transmission errors. For example, when the core data of the business results changes, the information synchronized to the access nodes of related objects should also accurately reflect this change, without data deviation.

[0162] Step S153: Based on the sharing permission level in the sharing characteristics, configure corresponding access, modification and deletion permissions for different user groups, and associate the permission configuration with the identity information of the user group.

[0163] Based on the sharing permission levels set in the sharing features, and combined with the user group's identity information, corresponding operation permissions are configured for different user groups. Identity information includes the user's department, job level, and area of ​​responsibility.

[0164] For users with the highest access levels, such as the department heads who create business results, configure access, modification, and deletion permissions to enable them to fully manage business results. For users with medium access levels, such as employees in related business departments, configure access and modification permissions to allow them to view and participate in improving business results, but do not grant them deletion permissions. For users with low access levels, such as ordinary employees, configure only access permissions so that they can only view business results and cannot perform modification or deletion operations.

[0165] Permission configuration information is associated with the identity information of each user group and stored in the permission management module. When a user logs into the system, their identity information can be automatically identified, the corresponding permission configuration can be invoked, and their operation scope can be restricted.

[0166] Step S154: When the composition of the user group or business needs change, the permission configuration is automatically updated according to the permission adjustment rules in the shared features.

[0167] Changes in the user base may include the addition of new users, user departures, and changes in user roles; changes in business needs may involve the expansion of the use cases for business results and adjustments to the confidentiality level.

[0168] When these changes occur, permission configurations can be automatically updated based on the permission adjustment rules in the shared features. For example, if a new user joins a user group, the corresponding shared permission level can be matched according to the user's identity information, and the corresponding operation permissions can be automatically configured for them; if the confidentiality level of business results is increased, and the permission adjustment rules stipulate that the access permissions of ordinary user groups should be reduced, then the access permissions of ordinary user groups to the business results can be automatically canceled, and only the access permissions of specific high-privilege user groups can be retained.

[0169] After permissions are updated, a permission change log can be generated to record the permission information before and after the change, the time of the change, and the reason for the change, which is convenient for subsequent tracing and auditing.

[0170] Step S155: When business results are updated, a new version identifier is generated based on the update characteristics, and the content, time and operation subject information of the version change are recorded.

[0171] Updates to business deliverables may include content modifications, data additions, and adjustments to conclusions. When these updates occur, a new version identifier can be generated according to the rules in the update characteristics. The version identifier usually includes the basic information of the original identifier and the version number. For example, a business deliverable with the original identifier "SC001" will have the version identifier "SC001-V1" after the first update and "SC001-V2" after the second update.

[0172] Simultaneously, it can record detailed information about version changes, such as modified chapters, supplementary data sources, and adjusted conclusions; record the time of version changes, accurate to the hour, minute, and second; and record the identity information of the operator, such as the operator's username and department. This information is stored in association with the version identifier, forming a complete version record.

[0173] Step S156: Retain historical versions of business results. The retention period of the historical versions shall be executed in accordance with the provisions in the storage characteristics. Expired versions shall be processed in accordance with the storage policy.

[0174] Based on the retention period for historical versions specified in the storage characteristics, historical versions of business deliverables can be retained. For example, if the storage characteristics stipulate that historical versions must be retained for one year, then the system will retain all versions of business deliverables from the past year, while historical versions older than one year will be processed according to the storage policy.

[0175] Storage strategies may include archiving or deletion. Expired versions with significant historical value can be moved to an archive storage area, where the read / write speed of the storage media is relatively slow, but the storage cost is lower. Expired versions without special value can be deleted according to a pre-defined deletion process, freeing up storage space. Before processing expired versions, a prompt can be given to ensure that operators confirm before performing the corresponding operation.

[0176] Step S157: Track the impact of version changes on related business results through multi-level management indexes. When the version of a business result is updated, trigger information collaboration operations for related business results.

[0177] By utilizing horizontal and vertical indexes in a multi-level management index, the potential impact of version changes to current business deliverables on related business deliverables can be tracked. For example, a horizontal index can be used to query other business deliverables with attribute-related characteristics to the current business deliverable, while a vertical index can be used to query related business deliverables with process propagation characteristics.

[0178] When it is confirmed that a version update may affect these related business outcomes, an information collaboration operation can be triggered to send a collaborative notification to the management nodes of the related business outcomes. The notification includes key information about the version change and an explanation of its potential impact. After receiving the notification, the management nodes of the related business outcomes can decide whether to make corresponding updates or adjustments based on the actual situation to ensure the consistency and collaboration of business outcomes within the digital infrastructure.

[0179] Figure 2 The illustration shows exemplary hardware and software components of a digital intelligence-based business results management system 100 that can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used on the digital intelligence-based business results management system 100 and to perform the functions in this application.

[0180] For example, the digital infrastructure business results management system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the digital infrastructure business results management system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The digital infrastructure business results management system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0181] In addition, embodiments of the present invention also provide a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned digital intelligence base business results management method is implemented.

[0182] Furthermore, this invention can also be applied to the informatization and digitalization of power grids;

[0183] For core business results of the power grid, characteristics are collected from three aspects: attributes, sources, and processes.

[0184] Dispatch instructions: type (e.g., emergency power outage, load adjustment), voltage level (110kV / 220kV), affected area (e.g., East China, North China), data format (XML / JSON);

[0185] Equipment Operation and Maintenance Report: Equipment type (transmission line / transformer / circuit breaker), operation and maintenance level (daily inspection / fault repair), status evaluation (normal / abnormal);

[0186] Load forecasting model: forecasting period (day / week / month), accuracy (90% / 95%), algorithm type (LSTM / ARIMA).

[0187] Source characteristics:

[0188] Production departments (dispatch center, substation maintenance team, big data center);

[0189] Data sources (real-time monitoring data from SCADA system, manual inspection data entry, and third-party meteorological data);

[0190] Create teams (scheduling algorithm group, equipment operation and maintenance group).

[0191] Process characteristics: Dispatch instructions: approval nodes (dispatch chief review → chief engineer approval), flow path (dispatch center → substation → operation and maintenance team), processing status (pending execution / executed / rejected);

[0192] Fault handling plan: triggering conditions (equipment trip signal / current over-limit), execution steps (fault isolation → emergency repair → power restoration), completion time limit (within 4 hours).

[0193] The above features are structured and stored in the feature database through the interfaces of the power grid digital intelligence base (such as IEC61850 protocol to connect to SCADA system, OA system to connect to approval process).

[0194] Business results are clustered by "voltage level + region". For example, the "220kV East China Region" group includes dispatch instructions, transformer operation and maintenance reports and load forecasting models for that region.

[0195] Analysis of co-occurrence characteristics: The frequency of co-occurrence of "transformer abnormality" and "load adjustment command" in the "220kV East China Region" group is statistically analyzed (e.g., 12 times in 30 days). The correlation strength is calculated ((12 / 15) × (12 / 18) = 0.53, where 15 is the total number of times "transformer abnormality" occurs and 18 is the total number of times "load adjustment command" occurs). The results are selected as valid feature combinations, and it is determined that there is an attribute correlation between the two.

[0196] Identify process transmission characteristics

[0197] Analysis of process characteristics: Dispatch instruction (upstream) → Substation execution record (midstream) → Operation and maintenance acceptance report (downstream), forming a transmission chain of "instruction generation → execution → acceptance". The degree of dependency is calculated according to the "reference rate of the execution record to the instruction" (e.g., if the reference rate of an instruction by the execution record is 90%, the degree of dependency is 0.9).

[0198] Building an interconnected network

[0199] Nodes represent business results (such as "220kV East China Transformer Anomaly Report" and "East China Load Adjustment Instruction").

[0200] Weighted directed edges represent associations: attribute associations (e.g., "Transformer Anomaly Report" → "Load Adjustment Instruction", weight 0.53), and process propagation (e.g., "Load Adjustment Instruction" → "Substation Execution Record", weight 0.9).

[0201] Core node identification: The scheduling command with the highest degree value (e.g., connecting 8 nodes) based on associated device reports, execution records, etc. is identified as the core node; the critical path is "scheduling command → execution record → acceptance report".

[0202] Taking the construction of a 500kV substation as an application scenario, and based on the digital intelligence foundation business outcome management method and system, we can realize the full life cycle management of core business outcomes such as engineering design drawings, equipment commissioning reports, and construction logs.

[0203] Basic feature set collection

[0204] To address the unique business outcome types of power transmission and transformation projects, three basic characteristics are collected:

[0205] Attributes and characteristics: Design drawings (voltage level 500kV, equipment type GIS combined electrical appliance, preliminary design stage), commissioning report (test type withstand voltage test, data accuracy level 0.5, conclusion qualified), construction log (GIS installation on the work surface, safety level A, weather clear).

[0206] Source characteristics: Design drawings (generated by the design institute of the department, the civil engineering team, and the data source is the survey database); commissioning report (generated by the operation and maintenance center of the department, the high-voltage test team, and the data source is the intelligent testing terminal).

[0207] Process characteristics: Design drawings (approval node: project manager review → chief engineer signing, circulation path: design institute → owner's project department → construction unit, processing status: archived); construction log (record node: daily pre-shift meeting → work acceptance, circulation path: construction team → supervision unit, processing status: pending review).

[0208] By connecting to the Engineering Management System (PMS) and Document Management System (EDMS) through the digital base interface, the equipment test data is parsed using the IEC61850 protocol to form a structured feature database.

[0209] Building a network linking business outcomes;

[0210] Attribute association feature mining;

[0211] Business deliverables are clustered according to "voltage level + professional type," forming business deliverable groups such as "500kV substation primary" and "500kV civil engineering." Within the "500kV substation primary" group:

[0212] Disassemble attribute units: (Equipment type, GIS), (Test type, Pressure resistance), (Working area, GIS installation);

[0213] Co-occurrence frequency: "GIS equipment" and "pressure withstand test" appeared together 24 times in 30 results.

[0214] Calculate the correlation strength: (24 / 28) × (24 / 32) = 0.64 (28 is the total number of times the GIS equipment appeared, and 32 is the total number of times the pressure test occurred).

[0215] Filtering valid combinations: Set a frequency threshold of 20 and an intensity threshold of 0.5 to confirm that the two are valid associations.

[0216] Analysis of process transmission characteristics;

[0217] Track the dependencies of business outcomes in the engineering construction process:

[0218] The design drawings (upstream) → construction plan (midstream) → acceptance report (downstream) form a transmission chain;

[0219] The construction plan referenced the design drawings 92% of the time, with a dependence rating of 0.92.

[0220] 87% of the data in the acceptance report came from the construction log, with a conductivity of 0.87;

[0221] Network construction;

[0222] Nodes represent business outcomes, and weighted directed edges represent relationships:

[0223] "GIS Design Drawings" → "GIS Installation Plan" (Attribute Association, Weight 0.64);

[0224] "GIS Installation Plan" → "Installation Acceptance Report" (process flow, weight 0.92);

[0225] Identifying core nodes: The construction plan, due to its connection between the design and acceptance stages, has a betweenness centrality of 0.81 and is therefore identified as a core node;

[0226] Community division: forming three closely related communities: "equipment installation", "electrical testing" and "civil construction".

[0227] Management strategy generation;

[0228] Storage characteristics;

[0229] Based on the data volume (design drawings 50MB → medium-sized data), solid-state drive storage is selected;

[0230] Build path by "process node / attribute dimension": / construction / construction_prep / primary device / ;

[0231] For construction plans with high correlation and importance, set up daily incremental backups;

[0232] Update features;

[0233] Core node construction plan (update priority level 1): Updated daily, triggered by changes in associated drawings;

[0234] Verification methods: consistency verification with design drawings, and integrity check of signatures and seals;

[0235] Shared features;

[0236] Construction team (high-level access): Can edit construction logs and view associated drawings;

[0237] Supervision unit (medium access): can approve acceptance reports and download test data;

[0238] Access control: Verify identity through digital certificates and record operation logs;

[0239] Dynamic control and execution;

[0240] When GIS equipment parameters change:

[0241] Trigger a design drawing version update (V2.1) and automatically generate a version log;

[0242] The system uses vertical indexing to locate and associate construction plans, and pushes update notifications.

[0243] Horizontal indexes synchronously update attribute association pointers to ensure cross-cluster retrieval accuracy.

[0244] Send an access upgrade notification to the construction team, allowing them to view revision history;

[0245] Through this embodiment, the response time for retrieving business results of power transmission and transformation projects was reduced by 68%, the version update synchronization rate reached 100%, and the efficiency of cross-departmental collaboration was improved by 53%, fully verifying the applicability of the digital infrastructure management method in the field of power engineering.

[0246] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for managing the business results of a digital intelligence infrastructure, characterized in that, The method includes: Collect a set of basic features of business results in the digital intelligence foundation, wherein the set of basic features includes the attribute features, source features and process features of the business results; The basic feature set is subjected to correlation feature mining to identify attribute correlation features and process transmission features between different business results, forming a business result correlation network; A multi-level management index is constructed based on the business outcome association network. The multi-level management index includes a horizontal index divided by attribute dimensions and a vertical index divided by process nodes. By combining the index characteristics of the multi-level management index with the real-time status characteristics of business results, a business result management strategy including storage characteristics, update characteristics, and sharing characteristics is generated. Dynamic control is exercised over the business results in the digital infrastructure based on the aforementioned business results management strategy. The step of performing correlation feature mining on the basic feature set to identify attribute correlation features and process transmission features between different business outcomes, forming a business outcome correlation network, includes: Extract the attribute features of each business outcome from the basic feature set, compare the category features and tag features in the attribute features of different business outcomes, and divide the business outcomes into groups with common attributes. For each business outcome in the business outcome group, analyze the co-occurrence feature combinations among attribute features, and identify attribute association features among different business outcomes based on the frequency of occurrence and association strength of co-occurrence feature combinations. Analyze the process features in the basic feature set, track the input-output relationship of business results in the process links, determine the process transmission direction and degree of dependence between different business results, and form process transmission features; The attribute association features and process transmission features are integrated, with nodes representing business results and weighted directed edges representing association features and strengths, to construct a business result association network. When new business outcomes are added or the characteristics of existing business outcomes change, the co-occurrence feature combinations and process transmission relationships are re-analyzed, and the node attributes and edge weights in the business outcome association network are updated. Through network topology analysis, the core business outcome nodes and key association paths in the business outcome association network are identified, and the feature representation of core nodes and key paths is strengthened. Based on the degree of correlation between business outcomes, the business outcome correlation network is divided into communities, and business outcomes within the same community have closer attribute correlations or process dependencies. The process of combining the index characteristics of the multi-level management index with the real-time status characteristics of business results to generate a business result management strategy that includes storage characteristics, update characteristics, and sharing characteristics includes: The index features of the multi-level management index are analyzed, and the storage features of the attribute dimension in the horizontal index and the storage features of the process node in the vertical index are extracted. Obtain real-time status characteristics of business results, including access frequency, data volume, and activity level of business results; Based on the storage characteristics of attribute dimensions, the storage characteristics of process nodes, and the data volume in real-time status characteristics, storage characteristics of business results are generated. The storage characteristics include storage medium type, storage path structure, and backup cycle. Based on the association characteristics in the multi-level management index and the activity level in the real-time status characteristics, the update priority of business results is determined; Update features are generated based on update priority to produce business results. The update features include update triggering conditions, update cycle and verification methods for update content. Analyze the attribute association characteristics and process transmission characteristics in the multi-level management index to identify other business results and related user groups that are associated with the target business results; Based on the access frequency in real-time status characteristics, set corresponding sharing permission levels for different associated user groups; The sharing characteristics of business results generated based on the sharing permission level include the sharing scope, sharing method and access control rules. Storage characteristics, update characteristics, and sharing characteristics are associated and integrated according to the unique identifier of the business outcome to form a management strategy unit for each business outcome; The management strategy unit summarizes all business results and generates a business result management strategy that covers all business results of the digital intelligence foundation.

2. The method for managing the business results of the digital intelligence infrastructure according to claim 1, characterized in that, For each business outcome in the business outcome group, the co-occurrence feature combinations among attribute features are analyzed. Based on the frequency and correlation strength of the co-occurrence feature combinations, attribute correlation features between different business outcomes are identified, including: The attribute features of each business result in the business result group are decomposed into independent attribute units, and each attribute unit contains an attribute item and a corresponding attribute value. The frequency of co-occurrence feature combinations is obtained by counting the number of times any two attribute units appear simultaneously in the attribute features of the same business outcome. The association strength of co-occurrence feature combinations is calculated, and the association strength is determined by the product of the ratio of the number of times the two attribute units co-occur to the total number of times each of them occurs. Set a frequency threshold and an association strength threshold for co-occurrence feature combinations, and filter out co-occurrence feature combinations that simultaneously meet the two threshold conditions to form effective feature combinations; Based on effective feature combinations, attribute association features between different business results are identified. When two business results contain different attribute units in the same effective feature combination, it is determined that there is an attribute association feature between them. The attribute association features are sorted according to their association strength. The attribute association features with higher association strength are given more significant representation in the business outcome association network. When a new business outcome is added to the business outcome group, the frequency of occurrence and association strength of co-occurrence feature combinations are recalculated, and the attribute association features are dynamically updated. Periodically evaluate the attribute association features and adjust the screening threshold for effective feature combinations based on changes in the attribute features of business results.

3. The method for managing the business results of the digital intelligence infrastructure according to claim 1, characterized in that, The construction of a multi-level management index based on the business outcome association network includes: The node attributes of the business outcome association network are analyzed, and the nodes are clustered according to the category features in the attribute features to form clusters with multiple attribute dimensions. For each attribute dimension cluster, a corresponding attribute index table is generated. The attribute index table records the unique identifier and core attribute characteristics of all business results in the cluster, and a horizontal index is constructed. In the horizontal index, establish the association pointers between attribute dimension clusters, and use the attribute association features to perform cross-cluster index association of related business results in different clusters; Based on the process transmission characteristics in the business outcome association network, the business outcome nodes are divided into stages according to the execution nodes of the business process, forming multiple process node groups; A corresponding process index sequence is generated for each process node group. The process index sequence records unique identifiers and node position characteristics according to the transmission order of business results in the process, and constructs a vertical index. A connection index between process node groups is established in the vertical index, and the business results in adjacent node groups are sequentially associated through process transmission features; The horizontal and vertical indexes are integrated to generate a unified index access point, which supports index queries triggered by attribute dimensions or process nodes. When the business outcome association network is updated, the attribute index table of the horizontal index and the process index sequence of the vertical index are adjusted synchronously to maintain the consistency between the index and the business outcome association status.

4. The method for managing the business results of the digital intelligence infrastructure according to claim 3, characterized in that, The step involves generating a corresponding attribute index table for each attribute dimension cluster. This attribute index table records the unique identifiers and core attribute characteristics of all business results within the cluster, constructing a horizontal index, including: Extract core attribute items from the attribute features of the attribute dimension cluster. The core attribute items are the key attribute contents that significantly distinguish the business results within the attribute dimension cluster. Each of the core attribute items is assigned an index weight, which is determined based on the importance of the attribute item in distinguishing business results. Collect the unique identifiers of all business results within the attribute dimension cluster and the attribute values ​​of the corresponding core attribute items, and organize them in a structured manner to form index data; The index data is arranged in an orderly manner according to the core attribute items, so that business result records with the same attribute value are distributed adjacently, and an attribute index table is generated. Add storage pointers to the attribute index table, with each storage pointer pointing to the actual storage location of the corresponding business result in the digital intelligence base; The attribute index table is compressed, and the complete description of the core attribute value is replaced by an encoding method. A high-frequency access cache is set in the horizontal index, and the attribute index table of the attribute dimension cluster whose query frequency is greater than the set frequency threshold is stored in the high-frequency access cache. When business results are added, changed, or removed in the attribute dimension cluster, the records and storage pointers in the attribute index table are updated in real time.

5. The method for managing the business results of the digital intelligence infrastructure according to claim 3, characterized in that, The process index sequence is generated for each process node group. The process index sequence records unique identifiers and node position characteristics according to the transmission order of business results in the process, constructing a vertical index, including: The transmission order of business results in the process node group is determined, and the transmission order is determined based on the input-output relationship of the business results in the process; Extract the node position features of each business outcome in the process node group. The node position features include the sequential position of the business outcome in the process and its relative positional relationship with other business outcomes. The unique identifiers and node location characteristics of the business results are recorded sequentially according to the transmission order to form the initial structure of the process index sequence; Add an associated pointer to the process index sequence. The associated pointer points to the corresponding record of the business result in the horizontal index, thereby realizing the association between the horizontal and vertical indexes. The process index sequence is segmented, dividing a continuous group of process nodes into multiple index segments, each index segment containing a preset number of business result records. A segment index is generated for each index segment, and the segment index records the unique identifier range and starting position of the business results within the index segment; When the order of business results transmission in the process node group changes or the node position characteristics are updated, the process index sequence is regenerated. The process index sequence is associated with the process characteristics of the business outcome and stored accordingly.

6. The method for managing the business results of the digital intelligence infrastructure according to claim 1, characterized in that, The data volume in the attribute-based storage features, process node storage features, and real-time status features generates the storage features of the business results. These storage features include storage medium type, storage path structure, and backup cycle, including: Data size levels are classified based on the volume of data in real-time status characteristics, and different data size levels correspond to suitable storage media types. By combining the storage characteristics of the attribute dimensions in the horizontal index, the corresponding storage media are matched for each data size level; Based on the storage characteristics of process nodes in the vertical index, a hierarchical structure of storage paths is constructed according to the process node and attribute dimension to which the business results belong. Business results of the same process node and attribute dimension are stored together in the same path branch. Assess the importance of business outcomes based on their attribute association characteristics and process transmission characteristics. The backup cycle is dynamically adjusted based on the access frequency of business results. The read and write permissions of the storage medium are clearly defined in the storage characteristics, and users with different permission levels are assigned different operation ranges; Generate naming rules for storage paths, which include unique identifiers of business results, attribute dimensions, and process node information; Configure a capacity monitoring mechanism for the storage medium. When the remaining capacity of the storage medium is lower than a set threshold, trigger a medium expansion or data migration operation.

7. The method for managing the business results of the digital intelligence foundation according to claim 1, characterized in that, The update features for generating business results based on update priority include: The update cycle for business results is determined based on update priority; Set update trigger conditions, which include updates to related business results, the arrival of a preset time, and changes in the characteristics of business results; When the update triggering condition is met, the update process of the business results is started, which includes the extraction, processing and storage of the update content; The verification method for the updated content is determined, and the verification method includes consistency check with related business results, integrity check of the updated content, and feature comparison before and after the update. A verification node is set up for each update step, and the verification node verifies the phased results during the update process; Record key information during the update process, including update time, update content, and the subject of the operation, to form an update log; When an error occurs during the update process, it will be handled according to the recovery mechanism in the update features. The recovery mechanism includes rolling back to the state before the update or re-executing the update process. Based on the correlation characteristics of business results, when a business result is updated, an update check of related business results is triggered to determine whether synchronous updates are required.

8. A digital infrastructure business results management system, characterized in that, The digital intelligence foundation business results management system includes a processor and a memory, the memory and the processor are connected, the memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the digital intelligence foundation business results management method according to any one of claims 1-7.

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