Digital intelligent base service achievement management method and system

Abstract

The invention provides a digital intelligent base service achievement management method and system, and relates to the technical field of digital intelligent base service management.The method comprises the steps that firstly, a basic feature set containing attributes, sources and process features of service achievements in a digital intelligent base is collected, then associated features of the basic feature set are mined, and a service achievement associated network is formed; then, a multi-level management index containing a transverse attribute dimension and a longitudinal flow node dimension is constructed based on the service achievement association network, and a management strategy covering storage, updating and sharing features is generated in combination with index features and service achievement real-time state features; and finally, performing dynamic management and control on the business achievements in the digital intelligent base according to the management strategy, thereby deeply mining the relationship between the business achievements, realizing multi-dimensional fine management, dynamically adapting to business changes, and improving the management efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital infrastructure business management, in particular to a digital infrastructure business achievement management method and system. BACKGROUND

[0002] With the deep integration of energy revolution and digital revolution, power grid enterprises are accelerating the digital transformation, and enterprises and organizations widely build and apply digital infrastructure to support the development of various businesses. In this process, a large amount of multi-source heterogeneous business achievement data is generated, including laser point cloud, three-dimensional terrain, and geological data in the survey stage; three-dimensional GIM / BIM models, design drawings, and calculation books in the power transmission, power transformation, and new energy engineering design stage; and monitoring data in the operation and maintenance stage. These data have obvious spatial and professional attributes and are generated in strict business processes. Business achievements have crucial value for decision-making, business optimization, and innovation development of enterprises from aspects of organization guarantee, work specification, management process, data classification, data classification, and compliance control.

[0003] However, the current management of business achievements in digital infrastructure faces great challenges: first, data is scattered in various professional systems, forming an "information island" and lacking unified governance; second, the spatial correlation, business correlation, and process dependency between data are not effectively mined and utilized; and finally, there is a lack of a comprehensive management strategy that can simultaneously manage massive spatial data, multi-specialty collaboration, version tracking, and secure sharing. Therefore, there is an urgent need for new methods and systems that can achieve fine-grained management of power grid multi-specialty business achievements throughout their life cycle, all elements, and all correlations on a unified digital infrastructure. SUMMARY

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present application, the embodiments of the present application provide a digital infrastructure business achievement management method, which comprises: Collecting a basic feature set of business achievements in the digital infrastructure, the basic feature set including attribute features, source features, and process features of the business achievements; Performing correlation feature mining on the basic feature set to identify attribute correlation features and process conduction features between different business achievements, and forming a business achievement correlation network; Building a multi-level management index based on the business achievement correlation network, the multi-level management index including a horizontal index divided by attribute dimensions and a vertical index divided by process nodes; Generating a business achievement management strategy including storage features, update features, and sharing features in combination with index features of the multi-level management index and real-time state features of the business achievements; According to the business result management strategy, dynamic control is performed on the business results in the digital infrastructure.

[0005] In another aspect, the embodiment of the present application also provides a digital infrastructure business result management system, comprising a processor, a machine readable storage medium, the machine readable storage medium is connected with the processor, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine readable storage medium to realize the above-mentioned method.

[0006] Based on the above aspects, the embodiment of the present application collects the basic feature set of the business results in the digital infrastructure, covers various features such as attributes, sources and processes, then accurately identifies the attribute association features and process transmission features between different business results, constructs a business result association network, and based on the multi-level management index constructed by the business result association network, contains horizontal attribute dimension index and vertical process node index, realizes fine management of business results from multiple dimensions, greatly improves the retrieval efficiency and accuracy of business results, and the management strategy generated by combining the multi-level management index features and the real-time state features of the business results can dynamically adapt to the changes of the business results, realize intelligent control of storage, update and sharing, and effectively improve the management efficiency of the business results in the digital infrastructure. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 is the execution flow diagram of the digital infrastructure business result management method provided by the embodiment of the present application.

[0008] Figure 2 is the schematic diagram of exemplary hardware and software components of the digital infrastructure business result management system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application will be specifically described below in conjunction with the drawings of the specification, Figure 1 is the flow diagram of the digital infrastructure business result management method provided by the embodiment of the present application, and the digital infrastructure business result management method will be described in detail below.

[0010] Step S110: Collect the basic feature set of the business results in the digital infrastructure, and the basic feature set contains attribute features, source features and process features of the business results.

[0011] In this embodiment, the business results of each department in the enterprise digital foundation are taken as the management object, and the digital foundation business result management method is started. First, the basic feature set of the business result is collected. The business result can be a project report completed by each department, a software module developed, a project phase result generated, etc. The attribute feature is the information describing the characteristics of the business result itself. For the business result, the attribute feature may include the category of the business result, such as electrical specialty, structural specialty, measurement specialty, planning specialty, etc.; the format type of the business result, such as document format, table format, presentation format, video picture format, etc.; the creation time of the business result; the field involved in the business result, such as construction and construction, survey and design, development planning, digital twin, etc. The source feature points to the source of the business result. For the business result, the source feature may include the department that creates the business result, such as the digital department, the energy planning department, the measurement department, the power grid department, the technology research and development department, the finance department, etc.; the team that creates the business result, such as the power grid transmission and electrical team, the digital development core team, etc.; and the data source of the business result, such as internal database, data provided by external cooperation institutions, market open data, etc. The process feature involves the relevant information of the business result in the digital foundation. For the project business result, the process feature may include the approval process node of the report, such as department manager approval, division leader approval, general manager approval, etc.; the transfer path of the report, such as from the creation team to the department manager, and then to the division leader, etc.; the processing state of the report, such as pending approval, in approval, passed, rejected, etc.

[0012] In collecting these features, the data collection interface in the digital foundation needs to be connected to various business systems, such as project management system, document management system, approval system, etc. The source features of the business result, such as the creation department and team, are obtained from the project management system; the attribute features of the business result, such as the format type and topic category, are obtained from the document management system; the process features of the business result, such as the approval process node and processing state, are obtained from the approval system. During the collection process, the collected raw data needs to be preliminarily processed to remove duplicate information and invalid information. For example, for the creation time of the same project report recorded repeatedly in different systems, the accurate time information needs to be confirmed and retained; for the garbled data that cannot be identified or the redundant data irrelevant to the business result, filtering and deletion are performed. After processing, the basic feature set containing attribute features, source features and process features is formed, and each business result basic feature set is stored in the feature database of the digital foundation in a structured data form, which is convenient for subsequent associated feature mining and other operations.

[0013] Step S120: performing associated feature mining on the basic feature set to identify the attribute associated features and process conduction features between different business results, and forming a business result associated network.

[0014] After obtaining the basic feature set, the process of correlation feature mining on the basic feature set is entered, which aims to find the internal relationship between different business results.

[0015] Step S121: Extract the attribute features of each business result in the basic feature set, compare the category features and label features in the attribute features of different business results, and divide the business result groups with common attributes.

[0016] In this embodiment, after extracting the attribute features of each business result from the basic feature set, the category features and label features of the business results are structurally analyzed and matched according to the pre-defined digital asset classification standard (such as survey data, power transmission results, power transformation results, and new energy results) and the label system (such as data source, engineering stage, professional type, and format type) in the digital foundation. The category features correspond to the high-level classification of the business results in the digital foundation, and the label features represent more specific business attributes.

[0017] By comparing the category features and label features of different business results, when multiple business results have the same classification level and the label features reach a certain degree of coincidence under the preset rules, they are merged into the same business result group. For example, if multiple business results belong to the "power transmission result management" category and all have the labels "three-dimensional model import", "coordinate management", and "material statistics", they are divided into the "power transmission three-dimensional design and material management business result group". This grouping mechanism fully utilizes the standardized asset management framework of the digital foundation to ensure that the division of the business result group not only conforms to the business logic but also supports subsequent correlation analysis, intelligent retrieval, and collaborative application.

[0018] Step S122: For each business result in the business result group, analyze the co-occurrence feature combination between the attribute features, and identify the attribute correlation features between different business results based on the occurrence frequency and correlation strength of the co-occurrence feature combination.

[0019] After dividing the business result group, the co-occurrence feature combination between the attribute features of each business result in the group is analyzed.

[0020] Step S1221: Decompose the attribute features of each business result in the business result group into the smallest granularity attribute units, and each attribute unit explicitly represents an attribute item and its corresponding attribute value.

[0021] For each business result in the business result group, the attribute features thereof are decomposed. For example, a design result belonging to the transformer design class and having the three-dimensional model import and coordinate management tags and created on a specific date can be decomposed into multiple attribute units, including the transformer design class corresponding to the class feature, the three-dimensional model import corresponding to the tag feature, the coordinate management corresponding to the tag feature, and the specific date corresponding to the creation time. Each attribute unit clearly contains an attribute item and its attribute value.

[0022] Step S1222: The number of times that any two attribute units co-occur in the same business result is counted to obtain the occurrence frequency of each co-occurring feature combination.

[0023] After the decomposition of the attribute units, the frequency of co-occurrence of any two attribute units in the same business result is counted. For example, in the transformer three-dimensional design business result group, the number of times that the class feature transformer design class and the tag feature three-dimensional model import co-occur in each business result in the group is counted. If 8 out of 10 business results in the group have both attribute units, the occurrence frequency of the co-occurring feature combination is 8. The occurrence frequencies of all possible two-by-two combinations of attribute units are counted.

[0024] Step S1223: The association strength of each co-occurring feature combination is calculated, which is obtained by dividing the number of times that the two attribute units co-occur by the total number of times that each attribute unit occurs in the group, and then multiplying the two obtained ratios.

[0025] When calculating the association strength, the number of times that the two attribute units co-occur is taken as the numerator, and each is divided by the total number of times that it occurs in all business results in the group. The product of the two obtained ratios is the association strength of the co-occurring feature combination. For example, the total number of times that attribute unit A, the class feature transformer design class, occurs in the group is 10, the total number of times that attribute unit B, the tag feature three-dimensional model import, occurs is 8, and the number of times that they co-occur is 6. Therefore, the association strength is six-tenths multiplied by six-eighths.

[0026] Step S1224: The occurrence frequency threshold and the association strength threshold of the co-occurring feature combination are set, and the co-occurring feature combinations that simultaneously satisfy the two threshold conditions are screened to form effective feature combinations.

[0027] According to actual business management needs and historical data experience, the lower limit of the occurrence frequency and the lower limit of the association strength are set. 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 result group are screened, and only combinations that simultaneously meet the frequency and strength thresholds are retained as effective feature combinations. For example, if the combination of the class feature transformer design class and the tag feature three-dimensional model import has an occurrence frequency of 8 and an association strength of 0.4, it is retained as an effective feature combination.

[0028] Step S1225: Identify attribute association features between different business achievements according to the effective feature combinations. If two business achievements respectively contain different attribute units in the same effective feature combination, it is determined that there is an attribute association feature between them.

[0029] Based on the screened effective feature combinations, it is determined whether different business achievements have attribute association features. For example, business achievement X has attribute unit category feature variable design class, and business achievement Y has attribute unit label feature coordinate management. If the two attribute units belong to the same effective feature combination, it is determined that there is an attribute association feature between business achievement X and business achievement Y. Step S1226: Sort the attribute association features according to the association strength. The attribute association features with higher association strength are given more prominent representation in the business achievement association network.

[0030] The identified attribute association features are sorted according to their corresponding association strength. The attribute association features with high association strength can be represented in a more prominent way in the subsequent construction of the business achievement association network, such as thicker and brighter connection lines in the network, to reflect their importance in the association network. For example, the attribute association feature between business achievement X and business achievement Y has an association strength of 0.6, and the attribute association feature between business achievement M and business achievement N has an association strength of 0.4. In the association network, the connection between X and Y will be more prominent than the connection between M and N.

[0031] Step S1227: When a new business achievement joins the business achievement group, recalculate the occurrence frequency and association strength of the co-occurrence feature combination in the group, and dynamically update the attribute association features.

[0032] When a new business achievement joins the business achievement group, the system needs to reprocess the co-occurrence relationship of all attribute features in the group. Specifically, it includes: analyzing the attribute features of the new achievement into basic attribute units, re-counting the co-occurrence frequency of each group attribute unit combination, calculating its association strength accordingly, and re-screening the effective feature combinations to dynamically update the attribute association features. For example, if a new achievement belonging to the "variable design" category and with the "three-dimensional model" label joins the corresponding business group, the co-occurrence frequency of the attribute combination "variable design-three-dimensional model" increases, and its association strength may change, thereby triggering the update of the attribute association features.

[0033] Step S1228: Periodically evaluate the attribute association features, and adjust the screening threshold of the effective feature combinations according to the changes of the business achievement attribute features.

[0034] Periodically evaluate the attribute association features to observe the changes in the business outcome attribute features. If it is found that the attribute features of the business outcomes have changed significantly, such as the appearance of new label features or the significant decline in the frequency of some original label features, the filtering threshold of the effective feature combination needs to be adjusted accordingly. For example, when new label features appear in large quantities, the frequency threshold may need to be lowered to include more effective feature combinations related to the new label; when the association strength of some attribute units generally decreases, the association strength threshold may need to be lowered to ensure that there are enough effective feature combinations to identify the attribute association features.

[0035] Step S123: Analyze the process features in the basic feature set, track the input-output relationship of the business outcomes in each design process link of the digital foundation, determine the process transmission direction and dependency degree between different business outcomes, and form process transmission features.

[0036] Analyzing the process features in the basic feature set focuses on tracking the input-output dependency relationship of the business outcomes in each design process of power engineering. For example, in the design process of a substation engineering, the business outcome A, the geotechnical investigation report, is the input basis for the business outcome B, the basic design model, that is, the generation of the business outcome B depends on the engineering geological parameters provided by the business outcome A, at this time there is a clear process transmission relationship between the business outcome A and the business outcome B, and the transmission direction is A pointing to B. At the same time, the dependency degree value is determined according to the dependency degree of the business outcome B on the business outcome A, if all the structural design of the business outcome B is based on the geological data of the business outcome A, the dependency degree is high, if only part of the auxiliary design refers to the business outcome A, the dependency degree is lower. By analyzing the input-output chain between business outcomes in each professional design process, the transmission direction and dependency degree between different outcomes are determined, and finally the quantifiable and traceable process transmission features are formed.

[0037] Step S124: Fuse the attribute association features and the process transmission features, represent the business outcomes as nodes, and represent the association features and strengths as weighted directed edges to construct a business outcome association network.

[0038] The identified attribute association features and process conduction features are fused to construct a business achievement association network. In the network, each business achievement is represented by a node, which can contain basic information such as the unique identifier of the business achievement. The attribute association features and process conduction features are represented by directed edges, where the direction of the directed edge is determined according to the process conduction direction or the pointing relationship of the attribute association, and the weight of the edge is determined by the association strength or the dependence degree, with a larger weight value indicating a closer association. For example, business achievement A has an attribute association feature with business achievement B, with an association strength of 0.7, so a directed edge from A to B (or according to the actual association direction) is connected with a weight of 0.7; business achievement C has a process conduction feature with business achievement D, with a dependence degree of 0.8, so a directed edge from C to D is connected with a weight of 0.8. In this way, a complete business achievement association network is constructed.

[0039] Step S125: When a new business achievement joins or the features of an existing business achievement change, the co-occurrence feature combination and process conduction relationship are re-analyzed, and the node attributes and edge weights in the business achievement association network are updated.

[0040] When a new business achievement joins the digital foundation, it needs to be added as a new node to the business achievement association network, and the co-occurrence feature combination and process conduction relationship between it and the existing business achievements in the network are re-analyzed to determine the new attribute association features and process conduction features, and then the corresponding directed edges and weights are added to the network. If the features of an existing business achievement change, such as the label feature in the attribute feature changing or the dependence relationship in the process feature changing, then the co-occurrence feature combination and process conduction relationship between the business achievement and other business achievements are re-analyzed, and the corresponding node attributes and the weights of the directed edges connected to other nodes are updated to ensure that the business achievement association network accurately reflects the association between business achievements.

[0041] Step S126: Through network topology analysis, the core business achievement nodes and key association paths in the business achievement association network are identified, and the feature representation of the core nodes and key paths is strengthened.

[0042] The constructed business achievement association network is subjected to network topology analysis, and the degrees and betweenness of each node are calculated. The degree represents the number of edges connected to other nodes, and the greater the degree, the more associated the node is with other business achievements. The betweenness represents the frequency of the node appearing on the shortest path between other nodes, and the greater the betweenness, the more important the node is as a bridge in the network. According to these indicators, core business achievement nodes, i.e., nodes with high degrees and betweenness, are identified. At the same time, the paths in the network are analyzed to find those that carry the main associated traffic and are crucial to the flow of business achievements, and are determined as key association paths. For the core business achievement nodes and the key association paths, their features are highlighted in the association network in a more eye-catching way, such as displaying the core nodes in a larger size and marking the key paths with a more special color.

[0043] Step S127: According to the closeness of the association between business achievements, the business achievement association network is grouped and divided, so that the business achievements in the same group have highly consistent attribute characteristics or close process dependency relationships.

[0044] According to the closeness of the attribute association and process transmission between business achievements, a grouping algorithm based on weight and connection density is used to divide the business achievement association network. The closeness of the association is comprehensively measured by the weight of the edges between nodes and the connection density of the nodes in the local network. For example, the ratio of the sum of the weights of all edges in the group to the total number of nodes in the group can be calculated. The higher the ratio, the closer the association between the business achievements in the group. Through grouping and division, the entire network is divided into multiple logical groups, and each group contains business achievements that exhibit highly consistent attribute characteristics or strong process dependency relationships. For example, in the digital foundation of electric power, the electrical main wiring diagram, equipment arrangement scheme, and foundation selection calculation related to substation engineering design form a logical group, and the tower positioning diagram, ground wire mechanics calculation, and insulation coordination scheme related to power transmission line design form another logical group. The business achievements in the same group have high consistency in design standards, data interfaces, and engineering applications.

[0045] Step S130: Based on the business achievement association network, a multi-level management index is constructed, which includes a horizontal index divided by attribute dimensions and a vertical index divided by process nodes.

[0046] After the construction of the business achievement association network is completed, a multi-level management index is established based on the network to achieve efficient organization and retrieval of business achievements.

[0047] Step S131: The attribute characteristics of each node in the business achievement association network are analyzed, the nodes are clustered according to the category characteristics, and multiple attribute dimension clusters are generated.

[0048] By analyzing the attributes of each node in the business achievement association network, the category features are extracted, and the nodes are clustered accordingly to form multiple attribute dimension clusters. For example, all business achievement nodes belonging to power transmission achievement management are clustered into a power transmission achievement cluster, and all nodes belonging to geotechnical investigation data management are clustered into a geotechnical investigation cluster.

[0049] Step S132: For each attribute dimension cluster, a corresponding attribute index table is generated, which records the unique identifiers and core attribute features of all business achievements in the cluster, thereby constructing a horizontal index.

[0050] For each attribute dimension cluster, an attribute index table is generated for the construction of a horizontal index.

[0051] 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 achievements within the cluster.

[0052] The core attribute items are selected from the attribute features with high discriminability in the attribute dimension cluster. For example, in the power transmission achievement cluster, the core attribute items can include voltage level, line type, tower model, etc.; in the geotechnical investigation cluster, the core attribute items can include geological age, geotechnical type, exploration hole depth, etc.

[0053] Step S1322: Assign an index weight to each core attribute item, and the weight value is determined according to the importance of the attribute item in distinguishing business achievements.

[0054] According to the importance of each core attribute item in distinguishing business achievements, a corresponding index weight is assigned, and the higher the importance, the greater the weight. For example, in the power transmission achievement cluster, the voltage level may have a higher weight, while the design stage weight is relatively low.

[0055] Step S1323: Collect the unique identifiers of all business achievements in the attribute dimension cluster and their corresponding core attribute item values, and organize them in a structured manner as index data.

[0056] Collect the unique identifiers of all business achievements in the cluster and their core attribute values, and arrange them in a fixed format. For example, the unique identifier of a certain power transmission achievement is TL2023001, its voltage level is 500kV, its line type is overhead line, and its tower model is JG1. These information is stored in a structured manner.

[0057] Step S1324: Sort the index data by core attribute items so that records with the same attribute value are arranged adjacent to each other, and generate an attribute index table accordingly.

[0058] The index data is sorted by one or more core attribute items, so that records with the same attribute value are arranged in a cluster. For example, first sort by voltage level, and then sort by line type under the same voltage level, so as to form an ordered attribute index table.

[0059] Step S1325: Add a storage pointer in the attribute index table, and each pointer points to the actual storage address of the corresponding business result in the digital base.

[0060] A storage pointer is added in each record of the attribute index table, which can locate the specific storage path of the business result in the digital base, such as the file address in the distributed file system or the record position in the database.

[0061] Step S1326: Data compression is performed on the attribute index table, encoding is used instead of complete attribute value description, and a high-frequency access cache area is set up in the transverse index, and the attribute index table of the attribute dimension cluster whose query frequency exceeds the set threshold is stored in the cache area.

[0062] The attribute index table is compressed, and encoding is used instead of long attribute value original text description. At the same time, according to the query history record statistics access frequency, the high-frequency access attribute index table is cached in the high-speed storage area to improve the query response performance.

[0063] Step S1327: When the business result in the attribute dimension cluster is added, changed or removed, the record in the attribute index table and the storage pointer are updated in real time.

[0064] When a new business result is added to the attribute dimension cluster, the attribute value of the core attribute item needs to be extracted for the business result, the corresponding index data is generated, and the attribute index table is inserted according to the ordered arrangement rule, and a storage pointer pointing to the actual storage position is added. If the core attribute item of an existing business result in the cluster is changed, such as the analysis period of the business result "SC002" from "monthly" to "quarterly", the record of the business result in the attribute index table needs to be found, the content corresponding to the core attribute value is updated, and the arrangement position of the business result in the index table is adjusted according to the new attribute value. When the business result is removed from the cluster, the record of the business result and the corresponding storage pointer in the attribute index table need to be deleted. Through the above real-time update, it is ensured that the attribute index table can accurately reflect the latest situation of the business result in the attribute dimension cluster.

[0065] Step S133: Establish an association pointer between different attribute dimension clusters in the transverse index, and realize the index and association of cross-cluster business results according to the attribute association characteristics.

[0066] In the transverse index, the attribute association features existing between different attribute dimension clusters are identified, and association pointers are established accordingly. These association pointers are used to record the corresponding relationship between business achievements in different clusters due to the sharing of common attributes. For example, a certain line three-dimensional design achievement in the power transmission achievement management cluster has attribute association with the survey control point data in the survey data management cluster under the same project due to the same project number and coordinate reference. At this time, an association pointer is set in the line three-dimensional design achievement record to point to the corresponding survey control point data, and a reverse pointer is set in the survey control point data record to point to the line design achievement. Through such association pointers, the system can realize the index and association of business achievements across attribute dimension clusters, thereby supporting users to conveniently find and obtain business achievement information having association relationship in other clusters when querying achievements in a cluster.

[0067] Step S134: According to the process conduction features reflected in the business achievement association network, the business achievements are divided into stages according to business process nodes, and a plurality of process node groups are generated.

[0068] According to the process conduction relationship embodied by the business achievement association network, the key execution nodes in the business process are identified, and the business achievements are divided into stages based on these nodes, and the business achievements corresponding to the same node are merged into the same process node group. For example, in the power grid engineering collaborative design process, the business achievements in the preliminary design stage, such as the electrical main wiring diagram and the plane layout diagram, can be divided into a preliminary design node group; the business achievements in the construction drawing design stage, such as the foundation construction drawing and the tower assembly drawing, are divided into a construction drawing design node group. Through this division method, the business achievements are organized according to their belonging business process stage, supporting the ordered management and efficient index of business achievements along the process dimension.

[0069] Step S135: A corresponding process index sequence is generated for each process node group, and the process index sequence records the unique identifier and node position features in the conduction order of the business achievements in the process, to build a longitudinal index.

[0070] A process index sequence is generated for each process node group to build a longitudinal index.

[0071] Step S1351: The conduction order of the business achievements in the process node group is determined, and the conduction order is determined based on the input-output relationship of the business achievements in the process.

[0072] The input and output relationship of each business result in the process is analyzed, and the transmission order is determined. If business result A is the input of business result B, that is, the generation of business result B depends on business result A, then the transmission order is A before B; if business result B is the input of business result C, then the transmission order is B before C, and so on, to form a chain transmission order. For example, in the design process node group, the business result "preliminary design scheme" is the input of "detailed design scheme", and "detailed design scheme" is the input of "design review report", so the transmission order is preliminary design scheme→detailed design scheme→design review report.

[0073] Step S1352: Extract the node position characteristics of each business result in the process node group, which includes the position and relative position relationship of the business result in the process.

[0074] For each business result in the process node group, its node position characteristics are extracted. The position can be represented by a serial number, such as in the process node group with the transmission order A→B→C→D, the position of A is 1, B is 2, C is 3, and D is 4. The relative position relationship describes the position relationship between the business result and other business results, such as A before B, B after A and before C, C after B and before D, etc. Through these information, the position of the business result in the process node group is fully characterized.

[0075] Step S1353: Record the unique identifier and node position characteristics of the business result in sequence according to the transmission order, to form the initial structure of the process index sequence.

[0076] According to the determined transmission order, the unique identifier and corresponding node position characteristics of each business result in the process node group are recorded in sequence to form the initial structure of the process index sequence. For example, 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, before SJ002), (position 2, after SJ001 and before SJ003), and (position 3, after SJ002), respectively. 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)].

[0077] Step S1354: Adding an association pointer in the process index sequence, the association pointer points to the corresponding record of the business result in the horizontal index, realizing the association of horizontal and vertical indexes.

[0078] Adding an association pointer in each record of the process index sequence, the association pointer points to the corresponding record of the attribute index table of the business result in the horizontal index. For example, the association pointer added for "SJ001" in the process index sequence of the designed process node group points to its record in the attribute index table of the product development attribute dimension cluster. In this way, the association of the vertical index and the horizontal index is realized. When a user queries a business result through the vertical index, the detailed attribute information of the business result in the horizontal index can be quickly found through the association pointer, and vice versa.

[0079] Step S1355: Segmenting the process index sequence to divide the continuous process node groups into multiple index segments, each index segment containing a preset number of business result records.

[0080] The process index sequence is segmented according to the number of business results and management requirements. A preset number of business result records are contained in each index segment, 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 index segments each containing 10 records, and the 4th index segment containing 5 records. Through segmentation, the structure of the process index sequence is more clear, facilitating management and query of the index sequence. When querying, the corresponding index segment can be located first, and then the specific business result record can be found in the segment, improving the query efficiency.

[0081] Step S1356: Generating a segment index for each index segment, the segment index records the unique identification range and starting position of the business results in the index segment.

[0082] A segment index is generated for each index segment, which records the minimum and maximum values of the unique identification of the business results in the index segment, forming a unique identification range, and records the starting position of the index segment in the process index sequence, such as the starting record number. For example, a certain index segment contains business result unique identifications SJ001 to SJ010, and the starting position in the process index sequence is the first record, so its segment index is (unique identification range: SJ001-SJ010, starting position: 1). The generation of the segment index helps to quickly locate the index segment containing the target business result, further improving the query speed.

[0083] Step S1357: When the transmission order of the business results in the process node group changes or the node position feature is updated, the process index sequence is regenerated.

[0084] If the conduction order of the business results in the process node group changes, such as a new business result is inserted between two business results, or the order of the original business results is exchanged, the conduction order needs to be determined again, and the unique identifier and node position characteristics of the business results are recorded again according to the new conduction order, the associated pointers are updated, the segmentation processing and segment index are generated again, so as to form a new process index sequence. When the node position characteristics of the business results are updated, such as the sequence number or the relative position relationship changes, the corresponding node position characteristic information in the process index sequence also needs to be updated to ensure that the process index sequence can accurately reflect the latest position of the business results in the process node group.

[0085] Step S1358: The process index sequence is stored in association with the process characteristics of the business results.

[0086] The generated process index sequence and the process characteristics of the business results are stored together and an association relationship is established. 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 in the database, the two are bound through the association field. In this way, when the process characteristics of the business results need to be queried, the corresponding business results can be quickly found through the process index sequence, and then the process characteristics are obtained; conversely, when the process characteristics of the business results are known, the position of the business results in the process index sequence can also be found through the association relationship, which is convenient for management and analysis from the process perspective.

[0087] Step S136: Establish a connection index between adjacent process node groups in the longitudinal index, and associate the business results in the adjacent groups in order according to the process conduction characteristics.

[0088] In the longitudinal index, the process conduction relationship between adjacent process node groups is identified, and the 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 of the basic construction drawing in the construction drawing design node group, and there is a clear process conduction characteristic. At this time, the order conduction relationship between the electrical main wiring diagram and the basic construction drawing is recorded in the connection index, and the connection logic between the node groups is identified. Through the connection index, the business results in the adjacent process node groups are associated in the order of the business process, forming a coherent longitudinal index structure, which supports the user to trace the evolution and transmission of the results along the business process.

[0089] Step S137: The transverse index and the longitudinal index are integrated to generate a unified index access portal, which supports index query according to attribute dimensions or process nodes.

[0090] The constructed horizontal index and the longitudinal index are integrated to form a unified index access portal, such as an index service platform or a global query interface. The portal can respond to user query requests based on attribute dimensions or process nodes. For example, a user can query "500kV overhead line three-dimensional model" by attribute dimensions, and the system calls the horizontal index to locate the result; or the user can query "all results of the construction drawing design stage" by process nodes, and the system calls the longitudinal index to return the corresponding content. At the same time, the portal supports composite condition queries, such as requesting "all results related to geotechnical investigation in the preliminary design stage", and the system can search the horizontal and longitudinal indexes in parallel and return the results after fusion. By integrating the two types of indexes and providing a unified access portal, the retrieval efficiency and usability of business results are significantly improved.

[0091] When the business result association network is updated due to the addition of new business results, changes in business result characteristics, etc., the horizontal index and the longitudinal index need to be adjusted synchronously. For the horizontal index, if the attribute association characteristics change, causing the composition of the attribute dimension cluster to change, such as some business results being transferred from one cluster to another, the corresponding attribute index table needs to be updated, including adding or deleting records, adjusting storage pointers and association pointers, etc. For the longitudinal index, if the process conduction characteristics change, causing the division of the process node group or the change of the conduction order of the business results, the affected process index sequence needs to be regenerated, and the connection index needs to be updated. Through the above synchronous adjustment, it is ensured that the horizontal index and the longitudinal index can accurately reflect the latest state of the business result association network, maintain the consistency of the index and the business result association state, and ensure the accuracy of the query results.

[0092] Step S140: Based on the index characteristics of the multi-level management index and the real-time state characteristics of the business results, a business result management strategy is generated, which includes storage characteristics, update characteristics and sharing characteristics.

[0093] After the multi-level management index is constructed, the index characteristics of the multi-level management index and the real-time state characteristics of the business results are combined to generate a business result management strategy.

[0094] Step S141: The index characteristics of the multi-level management index are analyzed, and the storage characteristics of the attribute dimensions in the horizontal index and the storage characteristics of the process nodes in the longitudinal index are extracted.

[0095] The index features of the multi-level management index are analyzed, and the storage features of the attribute dimension are extracted from the transverse index, including the storage location distribution of the attribute dimension cluster, the storage density of the business results in each cluster, and the storage medium type preference corresponding to different attribute dimensions. For example, the business results of the attribute dimension cluster are mostly stored in a specific hard disk partition, the storage density is high, and the preference is to use solid state disk storage. The storage features of the process nodes are extracted from the longitudinal index, including the storage path planning of the process node group, the storage period corresponding to different process nodes, and the storage association mode of the business results between process nodes. For example, the business results of the requirement analysis process node group are stored in the " / process / requirement_analysis / " path, and the storage period is long, and there is an associated folder with the storage location of the business results of the design process node group.

[0096] Step S142: Obtain the real-time state features of the business results, which include the access frequency, data volume, and activity level of the business results.

[0097] Through the monitoring module in the digital foundation, the real-time state features of the business results are obtained in real time. The access frequency refers to the number of times the business results are accessed by users within a unit time, such as a business result being accessed 5 times within a day. The data volume refers to the size of the storage space occupied by the business results, such as the size of a report being 5MB. The activity level is a comprehensive measure of the frequency of operations on the business results, including the frequency of viewing, editing, and sharing operations. Business results with high activity levels are usually operated more frequently in the near future. These real-time state features are continuously collected and updated by the monitoring module to ensure that the latest state of the business results can be reflected.

[0098] Step S143: Based on the storage features of the attribute dimensions, the storage features of the process nodes, and the data volume in the real-time state features, generate the storage features of the business results, which include the storage medium type, the storage path structure, and the backup period.

[0099] The storage features of the business results are generated in combination with the storage features of the attribute dimensions, the storage features of the process nodes, and the data volume in the real-time state features.

[0100] Step S1431: According to the data volume in the real-time state features, divide the data size level, and different levels correspond to different storage medium types.

[0101] According to the data size of the business achievements, the data size is divided into different size levels. For example, data size less than 10MB is small data, data size between 10MB and 100MB is medium data, and data size greater than 100MB is large data. Different size levels are adapted to different storage media: small data can use ordinary hard disk storage, medium data is adapted to solid state disk storage, and large data is adapted to distributed storage system. Through such a hierarchical storage strategy, the access efficiency can be guaranteed while optimizing storage resource allocation and cost control. For example, a small survey data report containing only text and a small amount of charts can be stored in an ordinary hard disk; and a large design achievement containing a large amount of three-dimensional models and point cloud data needs to be stored in a distributed storage system to meet its large capacity and high concurrent access requirements.

[0102] Step S1432: According to the storage characteristics of the attribute dimensions in the transverse index, the corresponding storage media is matched for each data size level.

[0103] The storage characteristics of the attribute dimensions in the transverse index reflect the storage commonality of different attribute category business achievements. For example, report type business achievements are mostly document type data, and survey type business achievements may contain a large amount of geographic information data in different formats, GPS points, design drawings, etc. When matching the storage media for each data size level, these storage characteristics of the attribute dimensions are adjusted. For documents in small data, in addition to ordinary hard disks, special storage areas based on document management systems can also be considered to facilitate attribute classification retrieval; for technical research and development type business achievements in medium data, if they contain more design drawings, the high-speed read-write characteristics of solid state disks can better adapt to the frequent viewing and modification operations of the drawings; for technical research and development achievements in large data, the distributed storage system needs to be configured with special code repository partitions and drawing storage partitions to match the storage characteristics of the attribute dimensions.

[0104] Step S1433: According to the storage characteristics of the process nodes in the longitudinal index, a hierarchical storage path structure is constructed according to the process nodes and attribute dimensions to which the business achievements belong, and business achievements with the same process node and attribute dimension are stored in the same path branch.

[0105] The storage feature of the process node in the longitudinal index reflects the front and back dependence and connection relationship of the business results in the process. Based on this, when building the storage path, the process node is taken as the first-level directory, and the attribute dimension is taken as the second-level directory, to form a clear hierarchical structure. For example, for the business result of the process node "preliminary design" and the attribute dimension "electrical main wiring", the storage path is "preliminary design / electrical main wiring / ", and all results meeting the node and dimension are stored under this path, such as "preliminary design / electrical main wiring / main wiring scheme drawing.dwg" and "preliminary design / electrical main wiring / equipment selection list.xlsx". This storage organization method not only conforms to the natural stage division of the business process, but also is beneficial to the user to quickly locate and access the required results according to the business attributes.

[0106] Step S1434: Based on the attribute association feature and the process transmission feature of the business result, the association importance of the business result is evaluated.

[0107] The attribute association feature reflects the close degree of association of the business result with other business results of the same attribute, and the process transmission feature reflects its role in the business process. When evaluating the association importance, both features are considered: if a business result has attribute association with multiple core business results and is the prerequisite data of multiple subsequent business results in the process, the association importance is higher. For example, a technical research and development document of "core technical parameter definition" has attribute association with multiple product design schemes, and is the prerequisite for all production process documents, so the evaluation result of the association importance will be higher.

[0108] Step S1435: The backup period is dynamically adjusted in combination with the access frequency of the business result.

[0109] The business result with high access frequency has fast data update and high possibility of change, and needs a shorter backup period; the business result with low access frequency but high association importance, although it is updated slowly, also needs a shorter backup period due to its importance. The specific adjustment method is to set a basic backup period, such as 7 days, for the business result with access frequency higher than the set threshold, the backup period is shortened to half of the basic period; for the business result with association importance higher than the set threshold, no matter how the access frequency is, the backup period does not exceed the basic period; for the business result with low access frequency and low association importance, the backup period can be extended to twice the basic period. For example, a high-frequency access data has a backup period of 3 days; a core technical document with high association importance but low access has a backup period of 7 days; a low-frequency access and low-association historical data report has a backup period of 14 days.

[0110] Step S1436: The read and write permission configuration of the storage medium is clearly defined in the storage feature, and different permission levels of users correspond to different operation ranges.

[0111] According to the role and responsibility of the user in the digital base, the read-write permission level of the storage medium is set. For example, the administrator user has the highest permission and can read, write, delete and modify all storage media; the department manager user can read and write the storage medium of the department business results, but has no deletion permission; the ordinary employee user can only read the content of the storage medium related to the business results of the post, and has no modification and deletion permission. The permission configuration is bound to the user identity information, and the permission check is performed through the identity authentication system of the digital base to ensure that the user can only operate the storage medium within the authorized range.

[0112] Step S1437: Formulate the naming rules of the storage path, which need to include the unique identifier of the business results, attribute dimension and process node information.

[0113] The naming of the storage path should clearly reflect the core metadata of the business results to facilitate identification and management. The naming rule adopts the structure of "unique identifier-attribute dimension-process node.extended name". For example, the unique identifier of a certain business result is "TL2024001", the attribute dimension is "tower structure design", and the process node is "construction drawing design", and the storage file name should be "TL2024001-tower structure design-construction drawing design.dwg". This naming method enables users to directly obtain the key attributes of the results through the file name, and is also conducive to automatic classification and organization of the system according to the rules.

[0114] Step S1438: Set the capacity monitoring mechanism of the storage medium, and trigger the medium expansion or data migration operation when the remaining capacity of the storage medium is lower than the set threshold.

[0115] Set a capacity monitoring module for each storage medium to monitor the remaining storage space in real time. Set the capacity threshold, such as when the remaining capacity is less than 20% of the total capacity, the monitoring mechanism issues a warning. For ordinary hard disks and solid state disks, trigger the medium expansion operation to automatically apply for increasing the storage capacity to the resource management system of the digital base; for distributed storage systems, start the data migration operation to migrate part of the data to the storage node with lower load, or add a new storage node to expand the capacity. During the migration process, the consistency of the data access interface is maintained to ensure that the access of the business results is not affected, and the storage path pointer is updated after the migration is completed, pointing to the new storage location.

[0116] Step S144: According to the correlation characteristics in the multi-level management index and the activity degree in the real-time state characteristics, determine the update priority of the business results.

[0117] The association features in the multi-level management index include attribute association strength and process dependency degree, and the activity degree in real-time state features is embodied as the frequency of business achievements being accessed, referenced, and modified. The update priority is determined by comprehensively considering these factors: the business achievement with high attribute association strength and core dependency in the process has the highest update priority if the activity degree is high; the business achievement with low attribute association strength, non-core dependency in the process, but high activity degree has the second highest update priority; the business achievement with high attribute association strength but low activity degree has lower update priority; and the business achievement with low attribute association strength and low activity degree has the lowest update priority. For example, a market demand report that is strongly associated with multiple product design schemes and serves as a core input has the highest update priority if it is frequently referenced and modified recently; a technical specification that is weakly associated with only a few documents but is frequently viewed has the second highest update priority.

[0118] Step S145: generating an update feature of the business achievement based on the update priority, the update feature including an update trigger condition, an update period, and a verification method of update content.

[0119] Step S1451: determining the update period of the business achievement according to the update priority.

[0120] The business achievement with the highest update priority is set with the shortest update period, such as being updated once a day; the business achievement with the second highest update priority is set with an update period of once a week; the business achievement with lower update priority is set with an update period of once a month; and the business achievement with the lowest update priority is set with an update period of once a quarter. For example, the core market demand report has the highest update priority and is updated every day; and the general technical document has lower update priority and is updated once a month.

[0121] Step S1452: setting an update trigger condition, the update trigger condition including an update of an associated business achievement, reaching a preset time, or a change in a feature of the business achievement itself.

[0122] When an associated achievement of a certain business achievement is updated, the synchronous update of the business achievement will be triggered, for example, when the tower structure design drawing is changed, the bill of quantities based on the drawing needs to be updated accordingly; when the system reaches a preset update time node, the update operation will be automatically performed, for example, the survey data statistical report is automatically updated at the end of each quarter; when the feature of the business achievement itself changes significantly, such as the adjustment of design parameters due to the execution of new standards, the update will also be triggered. For example, if the “rock and soil survey report.pdf” on which the “foundation construction drawing.dwg” depends is updated, or reaches the regular update time at the end of each month, or the key parameters are modified due to the change of design standards, the update process of the business achievement will be triggered.

[0123] Step S1453: When the update trigger condition is met, start the update process of the business achievement, which includes extraction, processing and storage of the update content.

[0124] After the update trigger condition is met, first extract the update content. For the case triggered by the update of the associated business achievement, extract the part of the associated business achievement that has changed, such as the change of market share data. For the case where the preset time arrives, extract the newly added data of the business achievement in this period, such as the newly added sales data in a week. For the case where the characteristic changes, extract the changed characteristic content. Then process the extracted update content, including format standardization, data verification, etc., such as converting the newly added sales data into a unified table format and checking the integrity of the data. Finally, store the processed update content to the storage location of the original business achievement, covering the old version content while retaining the update record.

[0125] Step S1454: Determine the verification method of the update content, which includes consistency check with the associated business achievement, completeness check of the update content and comparison of characteristics before and after the update.

[0126] The consistency check with the associated business achievement refers to verifying whether the updated business achievement matches the data of the associated business achievement, such as the cost budget report after the update, which needs to be consistent with the input data in the corresponding project progress report. The completeness check of the update content ensures that the updated business achievement contains all necessary information without missing key data, such as a complete technical solution after the update, which needs to include design parameters, implementation steps, expected effects, etc. The comparison of characteristics before and after the update analyzes whether the change in the core characteristics of the updated business achievement is reasonable and the logic is coherent.

[0127] Step S1455: Set a verification node for each update step, which verifies the stage results during the update process to ensure the accuracy of the update process.

[0128] The first verification node is set after the extraction of the update content is completed, checking whether the extracted update content accurately corresponds to the trigger condition, such as the update triggered by the update of the associated business achievement, which needs to verify whether the extracted changed part is indeed from the associated achievement. The second verification node is set after the processing of the update content is completed, checking whether the format of the processed content meets the standard and the data is valid. The third verification node is set after the storage of the update content is completed, checking whether the storage location is correct and whether it covers the old version while retaining the record. If each verification node finds a problem, it immediately terminates the update process and returns an error message, which will be re-started after correction.

[0129] Step S1456: Record the key information during the update process, including the update time, update content, and operation subject, to form an update log.

[0130] The update log needs to record the key information of each update in detail. The update time should be recorded to the second level of accuracy, such as "2024-05-20 14:30:22"; the update content needs to specifically describe the changed items and values, such as "adjust the conductor safety factor from 2.5 to 2.8"; and the operation subject should record the unique identification of the user or system performing the update operation, such as "design_user_001". The update log is stored in association with the unique identification of the corresponding business results, ensuring that all historical update operations can be traced and audited.

[0131] Step S1457: When an exception occurs during the update process, handle it according to the recovery mechanism in the update feature, which includes rolling back to the state before the update or re-executing the update process.

[0132] If data corruption, verification node failure, or other abnormal situations occur during the update process, start the recovery mechanism. If the exception occurs before the update content is stored, re-execute the update process from the update content extraction step; if the exception occurs after storage, trigger the rollback operation, delete the contents of this update, and restore to the version state before the update, and record the exception and processing results in the update log, such as "2024-05-2014:35:10 update failed, has been rolled back to 2024-05-19 version, reason: data verification not passed".

[0133] Step S1458: According to the association feature between business results, when a business result is updated, automatically trigger the update check of its associated results to determine whether synchronous update needs to be performed.

[0134] When a business result is updated, the system automatically locates its associated nodes according to the business result association network. For subsequent business results that have attribute association and the association strength exceeds the preset threshold, or have process transmission dependency, trigger the update check. The check content mainly includes whether the core data of the associated results is inconsistent with this update, for example, when the tower model parameter changes, the load calculation book that depends on the model needs to be checked and judged whether it needs to be updated synchronously. If it is confirmed to be updated, the corresponding update process is started in order according to the update priority.

[0135] Step S146: Based on the attribute association and process transmission features in the multi-level management index, identify other results and related user groups that have associations with the target business result.

[0136] Through the horizontal index, other business results with attribute-related characteristics to the target business result are retrieved, and priorities are determined according to the correlation strength; through the vertical index, the previous and subsequent results with a conduction relationship in the business process are tracked. At the same time, according to the source attribute and historical operation record of the business result, the associated user group is identified, including the user set that has created, accessed or modified the result and its associated results. For example, if the target result is a "tower structure design report", through the horizontal index, the associated "foundation treatment scheme" and "material mechanics performance table" can be identified, and through the vertical index, the previous "survey data analysis report" and the subsequent "construction drawing design file" can be located; the associated user group can include the structure design personnel, the survey engineer and the construction drawing review team.

[0137] Step S147: According to the real-time access frequency, different associated user groups are assigned corresponding sharing permission levels.

[0138] According to the historical access frequency of each user group to the target business result, the degree of use dependence is evaluated. For the group with high access frequency, higher sharing permission is granted to meet the frequent use demand; for the group with low access frequency, the permission range is limited. For example, the structure design personnel frequently access the "tower structure design report", and can be granted editing and downloading permissions; the survey engineer needs to refer to the report irregularly, and is only granted viewing permission; other department users access less, and can only be allowed to view metadata or summary information.

[0139] Step S148: According to the sharing permission level, the sharing characteristics of the business result are generated, including the sharing range, the sharing mode and the access control rule.

[0140] Step S1481: According to the sharing permission level, the sharing range is determined, and the user with high permission level can access the complete content and all historical versions of the business result, and the user with low permission level can only access the summary or part of the metadata information of the result.

[0141] The sharing range expands with the increase of the permission level. For example, the structure design personnel with high permission level can access the complete design document, all historical versions and associated calculation data of the "tower structure design report"; the survey engineer with medium permission level can access the current complete document but cannot view the historical version; the construction personnel with low permission level can only access the key parameter summary and conclusion part in the report.

[0142] Step S1482: According to the sharing permission level and the work scene of the user group, the sharing mode is determined, including online viewing, downloading, editing collaboration and link sharing.

[0143] The user group with high permission level supports multiple sharing modes, such as online editing collaboration. Market analysts can jointly modify the report through the collaborative editing function of the digital base. The product manager with medium permission level can online view and download the report, but cannot edit it. The R&D personnel with low permission level only support online preview, and cannot download it. At the same time, operation entrances are set for different sharing modes, such as online viewing through the document center entrance of the digital base, and editing collaboration through the dedicated collaborative platform entrance.

[0144] Step S1483: Access control rules are formulated to control access operations on business results through user identity authentication and permission verification mechanisms.

[0145] The access control rules are connected with the digital base unified identity authentication system. When a user accesses a business result, the system automatically verifies the user group and corresponding permission level of the user. For example, when a user accesses the "tower structure design report", if the user is authenticated as a structure design personnel and has high permission, the user is allowed to edit and download the complete document. If the user is authenticated as a construction personnel and only has low permission, the user can only preview the information. The system records all access operations, including access time, user identification and operation type, for security audit and behavior tracking.

[0146] Step S149: Store the storage features, update features and sharing features according to the unique identifier of the business result, and integrate them to form an independent management strategy unit for each business result.

[0147] Each business result is associated and integrated with its storage features (such as storage medium, path, backup strategy), update features (such as update cycle, trigger condition) and sharing features (such as sharing range, access rules) according to its unique identifier to form a complete management strategy unit. For example, the management strategy unit content of the business result with unique identifier "TL2024001" includes: stored in the high-speed storage device under the path of "preliminary design / electrical main wiring / "; incremental backup every 12 hours; automatically updated every week, and triggered by real-time update when the geotechnical data changes; the design team has editing permission, the survey team can view the full text, and other departments can only view the metadata.

[0148] Step S1410: Aggregate the management strategy units of all business results to generate a business result management strategy covering all contents of the digital base.

[0149] The management strategy units of all business results are classified and summarized according to attribute dimensions (such as survey data, power transmission results, and power transformation results) and process nodes (such as preliminary design and construction drawing design) to form a system-level business result management strategy. The summarized strategy is stored in the management strategy library and keeps real-time synchronization with the business result association network to ensure the consistency and executability of the strategy in the entire digital base.

[0150] Step S150: Perform dynamic management and control on the business results in the digital base according to the business results management strategy.

[0151] After generating the business results management strategy, perform dynamic management and control on the business results in the digital base according to the business results management strategy, to ensure that the storage, update, and sharing of business results comply with management requirements.

[0152] For example, step S151: According to the sharing features in the business results management strategy, identify business results and associated objects that need information collaboration, including associated business results and user groups.

[0153] According to the sharing range and association relationship in the sharing feature, identify the business results that need information collaboration. For example, the sharing feature of the "tower structure design report" clearly indicates that it has collaboration needs with "foundation treatment scheme" and "load calculation book", and the associated user groups are the structure design team and the civil engineering team. The system locates these associated results and user groups by searching the business results association network, and marks them as information collaboration objects.

[0154] Step S152: Synchronize the latest information of the business results to the access nodes of the associated objects, and the synchronization method is determined based on the sharing method in the sharing feature.

[0155] For business results that need information collaboration, when updates or content changes occur, synchronize the latest information to the access nodes of the associated objects according to the sharing method. If the sharing method is online viewing, insert a link to the latest information of the associated business results in the details page of the business results, and the associated objects can view the updated content by clicking the link; if the sharing method is data push, the latest information of the business results can be automatically packaged and pushed to the receiving address preset by the associated objects, such as the storage path of the associated business results or the message center of the associated users.

[0156] During synchronization, the synchronized information needs to be verified to ensure that the synchronized latest information is consistent with the actual content of the business results, and to avoid errors caused by transmission errors that cause the associated objects to obtain incorrect information. For example, when the core data of the business results changes, the information synchronized to the access nodes of the associated objects should also accurately reflect this change without data deviation.

[0157] Step S153: According to the sharing permission level in the sharing feature, configure corresponding access, modification, and deletion permissions for different user groups, and the permission configuration is associated with the identity information of the user groups.

[0158] According to the sharing permission level set in the sharing feature, different user groups are configured with corresponding operation permissions in combination with their identity information. The identity information includes the department to which the user belongs, the position level, and the responsible business field, etc.

[0159] For the user group with the highest permission level, such as the department head in charge of business results, access, modification, and deletion permissions are configured to enable it to comprehensively manage business results; for the user group with a medium permission level, such as the staff of the associated business department, access and modification permissions are configured to allow it to view and participate in the improvement of business results, but not to be given deletion permission; for the user group with a lower permission level, such as ordinary staff, only access permission is configured to enable it to only view business results and cannot perform modification or deletion operations.

[0160] The permission configuration information is associated with the identity information of the user group and stored in the permission management module. When a user logs in to the system, its identity information can be automatically recognized, the corresponding permission configuration is called, and its operation range is limited.

[0161] Step S154: When the user group composition or business demand changes, the permission configuration is automatically updated according to the permission adjustment rules in the sharing feature.

[0162] The change in the composition of the user group may include the addition of new users, the departure of users, the adjustment of user positions, etc.; the change in business demand may involve the expansion of the use scenarios of business results, the adjustment of the security level, etc.

[0163] When these changes occur, the automatic update of the permission configuration can be performed according to the permission adjustment rules in the sharing feature. For example, if a new user joins a user group, the corresponding operation permission can be automatically configured for the user according to its identity information and the matching sharing permission level; if the security level of a business result is improved, the permission adjustment rules stipulate that the access permission of the ordinary user group should be reduced, and then the access permission of the ordinary user group to the business result can be automatically canceled, and only the access permission of the specific high-permission user group is retained.

[0164] After the permission is updated, a permission change log can be generated to record the permission information before and after the change, the change time, and the triggering reason, facilitating subsequent tracing and auditing.

[0165] Step S155: When a business result is updated, a new version identifier is generated according to the update feature, and the content, time, and operation subject information of the version change are recorded.

[0166] The update of the business achievement can include content modification, data supplement, conclusion adjustment, etc. When these updates occur, a new version identifier can be generated according to the rules in the update feature. The version identifier usually contains the original identifier basic information and the version number. For example, the business achievement with the original identifier "SC001" has a version identifier "SC001-V1" after the first update and "SC001-V2" after the second update.

[0167] At the same time, the specific content of the version change can be recorded in detail, such as the modified chapter, the supplemented data source, the adjusted conclusion, etc.; the time of the version change is recorded, which is accurate to the minute and second; the identity information of the operation subject is recorded, such as the username of the operator, the department to which the operator belongs, etc. These information are stored in association with the version identifier, forming a complete version record.

[0168] Step S156: Retaining the historical versions of the business achievement, the retention duration of the historical versions is executed according to the provisions in the storage feature, and the overdue versions are processed according to the storage strategy.

[0169] According to the provisions of the storage feature on the retention duration of the historical versions, the historical versions of the business achievement can be retained. For example, the storage feature provides that the retention duration of the historical versions is one year, so the system retains all versions of the business achievement within one year, and the historical versions exceeding one year are processed according to the storage strategy.

[0170] The storage strategy can include archiving storage or deletion. For the overdue versions with important historical value, they can be transferred to the archiving storage area, which has relatively slow read-write speed of storage medium but lower storage cost. For the overdue versions without special value, they can be cleared according to the preset deletion process to release the storage space. Before processing the overdue versions, a prompt can be given to ensure that the corresponding operation is executed after the confirmation of the operator.

[0171] Step S157: Tracking the influence of the version change on the associated business achievement through the multi-level management index, and triggering the information coordination operation of the associated business achievement when the version of the business achievement is updated.

[0172] The influence of the version change of the current business achievement on the associated business achievement can be tracked by using the horizontal index and the vertical index in the multi-level management index. For example, other business achievements with attribute association feature to the business achievement are queried through the horizontal index, and the associated business achievements with process conduction feature are queried through the vertical index.

[0173] When it is confirmed that the version update may affect these associated business results, an information coordination operation can be triggered to send a coordination notification to the management node of the associated business result, and the notification contains key information of the version change and an impact description. After receiving the notification, the management node of the associated business result can decide whether to perform corresponding update or adjustment according to the actual situation, so as to ensure the consistency and coordination of the business results in the intelligent base.

[0174] Figure 2 A schematic diagram of exemplary hardware and software components of the intelligent base business result management system 100 that can implement the idea of the present application is shown. For example, the processor 120 can be used on the intelligent base business result management system 100 and used to execute the functions in the present application.

[0175] For example, the intelligent base business result management system 100 can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, a ROM, or a RAM, or any combination thereof. Exemplarily, the intelligent base business result management system 100 can also include program instructions stored in a ROM, a RAM, or other types of non-transitory storage media, or any combination thereof. The methods of the present application can be implemented according to these program instructions. The intelligent base business result management system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0176] In addition, the present application also provides a readable storage medium, wherein computer executable instructions are pre-set in the readable storage medium, and when a processor executes the computer executable instructions, the intelligent base business result management method is realized.

[0177] Further, the present application can also be applied in power grid informatization and digitization; For the core business results of the power grid, characteristics are collected from three aspects of attributes, sources, and processes; Dispatching instructions: type (such as emergency tripping, load adjustment), voltage level (110kV / 220kV), involved area (such as East China region, North China region), data format (XML / JSON); Device operation and maintenance report: device type (transmission line / transformer / circuit breaker), operation and maintenance level (daily inspection / fault repair), state evaluation (normal / abnormal); Load prediction model: prediction period (day / week / month), accuracy (90% / 95%), algorithm type (LSTM / ARIMA).

[0178] Source characteristics: Generating department (dispatching center, substation operation and maintenance team, big data center); Data sources (real-time monitoring data of SCADA system, manual inspection entry, third-party meteorological data); Create teams (dispatching algorithm group, equipment operation and maintenance group).

[0179] Process features: dispatching instructions: approval nodes (review by dispatching director → issuance of general work), flow paths (dispatching center → substation → operation and maintenance team), processing status (to be executed / executed / rejected); Fault handling scheme: trigger conditions (device tripping signal / current exceeding limit), execution links (isolate faults → rush repair → restore power), completion time limit (within 4 hours).

[0180] Through the interface of the digital power grid base (such as IEC61850 protocol connecting SCADA system, OA system connecting approval process), the above features are structured and stored in the feature database.

[0181] Cluster business results by "voltage level + region", such as the "220kV East China region" group containing dispatching instructions, transformer operation and maintenance reports, and load prediction models in this region; Analyze co-occurrence features: count the co-occurrence frequency of "transformer abnormalities" and "load adjustment instructions" in the "220kV East China region" group (such as 12 times in 30 days), calculate the correlation strength ((12 / 15) x (12 / 18) = 0.53, where 15 is the total number of "transformer abnormalities" and 18 is the total number of "load adjustment instructions"), filter for effective feature combinations, and determine that there is an attribute association between the two.

[0182] Identify process transmission features Analyze process features: dispatching instructions (upstream) → substation execution records (midstream) → operation and maintenance acceptance reports (downstream), forming a "instruction generation → execution → acceptance" transmission chain, and the dependence degree is calculated according to the "reference rate of execution records to instructions" (such as a certain instruction with a 90% reference rate of execution records, the dependence degree is 0.9).

[0183] Build an association network Nodes represent business results (such as "220kV East China transformer abnormal report" and "East China load adjustment instruction"). Weighted directed edges represent associations: attribute associations (such as "transformer abnormal report" → "load adjustment instruction", weight 0.53), process transmission (such as "load adjustment instruction" → "substation execution record", weight 0.9). Core node identification: dispatching instructions have the highest degree value (such as connecting 8 nodes) due to their association with device reports, execution records, etc., and are designated as core nodes; the critical path is "dispatching instructions → execution records → acceptance reports".

[0184] Taking the new 500kV substation project as the application scenario, based on the digital infrastructure business achievement management method and system, the whole life cycle management of core business achievements such as engineering design drawings, equipment commissioning reports, and construction logs is realized.

[0185] Basic feature set collection For the specific business achievement types of power transmission and transformation projects, three types of basic features are collected: Attribute features: design drawings (voltage level 500kV, equipment type GIS combined electrical apparatus, design stage preliminary design), commissioning reports (test type voltage withstand test, data accuracy 0.5 level, conclusion qualified), construction logs (operation surface GIS installation, safety level A level, weather sunny).

[0186] Source features: design drawings (generation department design institute, team civil engineering group 1, data source survey database); commissioning reports (generation department operation and maintenance center, team high voltage test group, data source intelligent detection terminal).

[0187] Process features: design drawings (approval node project manager review → general engineer signing, flow path design institute → owner project department → construction unit, processing state has been archived); construction logs (recording node daily pre-shift meeting → operation acceptance, flow path construction team → supervision unit, processing state pending audit).

[0188] Through the interface of digital infrastructure, the engineering management system (PMS) and the document management platform (EDMS) are connected, the IEC61850 protocol is used to analyze equipment detection data, and a structured feature database is formed.

[0189] Business achievement correlation network construction; Attribute correlation feature mining; Business achievements are clustered according to "voltage level + professional type" to form "500kV substation primary" and "500kV civil engineering" business achievement groups. In the "500kV substation primary" group: Disassemble attribute units: (equipment type, GIS), (test type, voltage withstand), (operation surface, GIS installation); Statistical co-occurrence frequency: "GIS equipment" and "voltage withstand test" appear together 24 times in 30 achievements Calculate correlation strength: (24 / 28) × (24 / 32) = 0.64 (28 is the total number of GIS equipment occurrences, 32 is the total number of voltage withstand test occurrences); Screen effective combination: set frequency threshold 20, strength threshold 0.5, confirm that the two are effective correlation Process conduction feature analysis; Track the dependency relationship of business achievements in the engineering construction process: Design drawings (upstream) → construction plan (midstream) → acceptance report (downstream) form a chain of transmission; The reference rate of construction plan to design drawings is 92%, and the degree of dependence is 0.92; 87% of the data in the acceptance report comes from the construction log, and the transmission intensity is 0.87; Association network construction; The node represents the business results, and the weighted directed edge represents the association: "GIS design drawings" → "GIS installation plan" (attribute association, weight 0.64); "GIS installation plan" → "installation acceptance report" (process transmission, weight 0.92); Identify core nodes: construction plan connects design and acceptance links, with a betweenness centrality of 0.81, and is designated as a core node; Community division: form three closely related communities of "equipment installation", "electrical test" and "civil construction".

[0190] Management strategy generation; Storage features; Based on the data volume (design drawings 50MB → medium data), match solid state disk storage; Build path according to "process node / attribute dimension": / construction / construction_prep / primary equipment / ; Associated with important construction plans, set up daily incremental backup; Update features; Core node construction plan (update priority 1st): daily update, trigger immediate update when associated drawings change; Verification method: consistency check with design drawings, signature integrity check; Sharing features; Construction team (high authority): can edit construction log, view associated drawings; Supervision unit (medium authority): can approve acceptance report, download test data; Access control: verify identity through digital certificate, record operation log; Dynamic control execution; When the GIS device parameters change: Trigger design drawing version update (V2.1), automatically generate version log; Locate the associated construction plan through vertical indexing, and push the update prompt; Horizontal index synchronously updates attribute association pointer to ensure cross-cluster search accuracy; Push the permission upgrade notice to the construction team, allowing them to view the revision trail; Through this embodiment, the power transmission and transformation engineering business achievement retrieval response time is shortened by 68%, the version update synchronization rate reaches 100%, the cross-department collaboration efficiency is improved by 53%, and the applicability of the digital base management method in the power engineering field is fully verified.

[0191] It should be noted that, in order to simplify the expression of the disclosure of the present application, and to help the understanding of one or more embodiments of the present application, in the foregoing description of the embodiments of the present application, various features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for managing the results of a digital base business, characterized by, The method comprises: Collecting a basic feature set of business results in the digital base, the basic feature set including attribute features, source features and process features of the business results; Performing associated feature mining on the basic feature set to identify attribute associated features and process transmission features between different business results, and forming a business result associated network; Building a multi-level management index based on the business result associated network, the multi-level management index including a horizontal index divided according to attribute dimensions and a vertical index divided according to process nodes; Generating a business result management strategy including storage features, update features and sharing features in combination with index features of the multi-level management index and real-time state features of the business results; Performing dynamic management and control on the business results in the digital base according to the business result management strategy.

2. The digital foundation business outcome management method of claim 1, wherein, The associated feature mining on the basic feature set to identify attribute associated features and process transmission features between different business results, and forming a business result associated network, comprises: Extracting attribute features of each business result in the basic feature set, comparing category features and label features in the attribute features of different business results, and dividing business result groups with common attributes; For each business result in the business result group, analyzing co-occurrence feature combinations between attribute features, identifying attribute associated features between different business results based on occurrence frequencies and association strengths of the co-occurrence feature combinations; Analyzing process features in the basic feature set, tracking input-output relationships of business results in process links, determining process transmission directions and dependency degrees between different business results, and forming process transmission features; Fusing the attribute associated features and the process transmission features to represent business results as nodes and associated features and strengths as weighted directed edges, and building a business result associated network; When a new business result is added or features of an existing business result are changed, reanalyzing co-occurrence feature combinations and process transmission relationships, and updating node attributes and edge weights in the business result associated network; Through network topology analysis, identifying core business result nodes and key associated paths in the business result associated network, and strengthening feature representation of the core nodes and the key paths; Based on the associated closeness of business results, performing community division on the business result associated network, and the business results in the same community having closer attribute association or process dependency.

3. The digital foundation business outcome management method of claim 2, wherein, The analysis of co-occurrence feature combinations between attribute features of each business result in the business result group, and the identification of attribute associated features between different business results based on occurrence frequencies and association strengths of the co-occurrence feature combinations, comprises: Disassembling attribute features of each business result in the business result group into independent attribute units, each attribute unit including an attribute item and a corresponding attribute value; Counting the number of times that any two attribute units appear simultaneously in the attribute features of the same business result to obtain occurrence frequencies of co-occurrence feature combinations; Calculating association strengths of the co-occurrence feature combinations, the association strengths being determined by the product of the ratio of the number of times that two attribute units co-occur to the total number of times that each attribute unit appears; Set the occurrence frequency threshold and the correlation strength threshold of the co-occurrence feature combination, filter out the co-occurrence feature combination that meets both threshold conditions, and form an effective feature combination; Based on the effective feature combination, identify the attribute correlation features between different business achievements. When two business achievements respectively contain different attribute units in the same effective feature combination, it is determined that there is an attribute correlation feature between them; Sort the attribute correlation features according to the correlation strength. The higher the correlation strength of the attribute correlation feature, the more prominent the representation of the business achievement correlation network. When a new business achievement is added to the business achievement group, recalculate the occurrence frequency and correlation strength of the co-occurrence feature combination, and dynamically update the attribute correlation features. Periodically evaluate the attribute correlation features, and adjust the filtering threshold of the effective feature combination according to the changes of the business achievement attribute features.

4. The digital hub service outcome management method of claim 1, wherein, The multi-level management index is constructed based on the business achievement correlation network, including: Analyze the node attributes of the business achievement correlation network, cluster the nodes according to the category features in the attribute features, and form multiple attribute dimension clusters; Generate an attribute index table corresponding to each attribute dimension cluster. The attribute index table records the unique identifier and core attribute features of all business achievements in the cluster, and constructs a horizontal index; Establish an association pointer between the attribute dimension clusters in the horizontal index, and cross-cluster index association of the associated business achievements in different clusters through the attribute correlation features; According to the process conduction features in the business achievement correlation network, divide the business achievement nodes into stages according to the execution nodes of the business process, and form multiple process node groups; Generate a process index sequence corresponding to each process node group. The process index sequence records the unique identifier and node position features in the order of business achievement conduction in the process, and constructs a vertical index; Establish a connection index between the process node groups in the vertical index, and sequentially associate the business achievements in adjacent node groups through the process conduction features; Integrate the horizontal index and the vertical index to generate a unified index access portal, which supports attribute dimension or process node triggered index query; When the business achievement correlation 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 of the index and the business achievement correlation state.

5. The digital hub service outcome management method of claim 4, wherein, The attribute index table corresponding to each attribute dimension cluster records the unique identifier and core attribute features of all business achievements in the cluster, and constructs a horizontal index, including: Extract core attribute items from the attribute features of the attribute dimension cluster. The core attribute items are key attribute contents that significantly distinguish the business achievements in the attribute dimension cluster; Assign an index weight to each core attribute item. The index weight is determined according to the importance of the attribute item in distinguishing business achievements; Collect the unique identifiers and corresponding core attribute item attribute values of all business achievements in the attribute dimension cluster, and arrange them in a structured manner to form index data; Orderly arrange the index data according to the core attribute items, so that the records of business achievements with the same attribute value are distributed adjacent to each other, and generate an attribute index table; adding storage pointers in the attribute index table, each of the storage pointers pointing to an actual storage location of a corresponding business achievement in the digital base; performing compression processing on the attribute index table, replacing complete descriptions of core attribute values with an encoding mode, setting a high-frequency access buffer area in the horizontal index, and storing attribute index tables of attribute dimensions with a query frequency greater than a set frequency threshold in the high-frequency access buffer area; when a business achievement in the attribute dimension cluster is added, changed, or removed, updating records and storage pointers in the attribute index table in real time.

6. The digital hub service outcome management method of claim 4, wherein, generating a corresponding process index sequence for each process node group, recording unique identifiers and node position characteristics in the process index sequence in the order of the transmission of business achievements in the process, and constructing a vertical index, including: determining the transmission order of business achievements in the process node group, the transmission order being determined based on the input-output relationship of business achievements in the process; extracting node position characteristics of each business achievement in the process node group, the node position characteristics including the position of the business achievement in the process and the relative position relationship with other business achievements; recording the unique identifiers and node position characteristics of business achievements in turn according to the transmission order to form an initial structure of the process index sequence; adding association pointers in the process index sequence, the association pointers pointing to corresponding records of the business achievements in the horizontal index, and realizing the association of the horizontal and vertical indexes; performing segmentation processing on the process index sequence, dividing continuous process node groups into multiple index segments, each of the index segments containing a preset number of business achievement records; generating a segment index for each of the index segments, the segment index recording the unique identifier range and the starting position of the business achievements in the index segment; when the transmission order of business achievements in the process node group changes or the node position characteristics are updated, regenerating the process index sequence; storing the process index sequence and the process characteristics of business achievements in association.

7. The digital podium service outcome management method of claim 1, wherein, combining the index characteristics of the multi-level management index and the real-time state characteristics of business achievements, generating a business achievement management strategy containing storage characteristics, update characteristics, and sharing characteristics, including: analyzing the index characteristics of the multi-level management index, extracting the storage characteristics of attribute dimensions in the horizontal index and the storage characteristics of process nodes in the vertical index; obtaining real-time state characteristics of business achievements, the real-time state characteristics including the access frequency, data volume, and activity level of business achievements; based on the storage characteristics of attribute dimensions, the storage characteristics of process nodes, and the data volume in the real-time state characteristics, generating the storage characteristics of business achievements, the storage characteristics including the storage medium type, storage path structure, and backup period; determining the update priority of business achievements according to the association characteristics in the multi-level management index and the activity level in the real-time state characteristics; generating the update characteristics of business achievements based on the update priority, the update characteristics including the update trigger condition, update period, and verification method of update content; analyzing the attribute association characteristics and process transmission characteristics in the multi-level management index, identifying other business achievements and associated user groups that exist in association with the target business achievements; In combination with the access frequency in the real-time state feature, a corresponding sharing permission level is set for different associated user groups; A sharing feature of the business achievement is generated based on the sharing permission level, and the sharing feature includes a sharing range, a sharing mode, and an access control rule; The storage feature, the update feature, and the sharing feature are associated and integrated according to the unique identifier of the business achievement to form a management strategy unit for each business achievement; The management strategy units of all business achievements are summarized to generate a business achievement management strategy covering all business achievements of the digital foundation.

8. The digital podium service outcome management method of claim 7, wherein, The data volume in the attribute dimension-based storage feature, the flow node-based storage feature, and the real-time state feature is used to generate a storage feature of the business achievement, and the storage feature includes a storage medium type, a storage path structure, and a backup period, including: According to the data volume in the real-time state feature, a data size level is divided, and different data size levels correspond to adaptive storage media types; In combination with the storage feature of the attribute dimension in the horizontal index, the corresponding storage medium is matched for each data size level; According to the storage feature of the flow node in the vertical index, a hierarchical structure of the storage path is constructed according to the flow node and the attribute dimension to which the business achievement belongs, and business achievements of the same flow node and attribute dimension are stored in the same path branch; Based on the attribute association feature and the flow conduction feature of the business achievement, the associated importance of the business achievement is evaluated; In combination with the access frequency of the business achievement, the backup period is dynamically adjusted; In the storage feature, the read and write permission configuration of the storage medium is specified, and different permission levels of users correspond to different operation ranges; A naming rule of the storage path is generated, and the naming rule includes the unique identifier, the attribute dimension, and the flow node information of the business achievement; A capacity monitoring mechanism of the storage medium is set, and when the remaining capacity of the storage medium is lower than a set threshold, a medium expansion or data migration operation is triggered.

9. The digital podium service outcome management method of claim 7, wherein, The update feature of the business achievement is generated based on the update priority, including: According to the update priority, the update cycle of the business achievement is determined; An update trigger condition is set, and the update trigger condition includes an update of the associated business achievement, a preset time reaching, and a change in the feature of the business achievement; When the update trigger condition is met, an update process of the business achievement is started, and the update process includes extraction, processing, and storage of the update content; A verification method of the update content is determined, and the verification method includes consistency checking with the associated business achievement, integrity checking of the update content, and feature comparison before and after the update; A verification node is set for each update step, and the verification node verifies the stage result in the update process; Key information in the update process is recorded, and the key information includes the update time, the update content, and the operation subject to form an update log; When an exception occurs in the update process, a recovery mechanism in the update feature is used for processing, and the recovery mechanism includes rolling back to the state before the update or re-executing the update process; According to the association feature of the business achievement, when one business achievement is updated, an update check of the associated business achievement is triggered to determine whether synchronous updating is needed.

10. A digital hub business achievement management system, characterized by, The intelligent base business achievement management system comprises a processor and a memory, the memory is connected with the processor, the memory is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the memory to realize the intelligent base business achievement management method in any one of claims 1-9.

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