A digital resource sharing method and system based on pedigree mapping relationship

Abstract

The application relates to a digital resource sharing method and system based on a pedigree mapping relationship, and belongs to the technical field of enterprise digital resource management. The resource sharing method comprises the following steps: acquiring multi-source heterogeneous data from an internal distributed system of an enterprise through a preset interface protocol; performing standardization processing on the multi-source heterogeneous data to generate a structured resource pool; constructing a graph dictionary module, performing associated topological analysis on resources in the structured resource pool, and outputting a resource relationship topological graph; establishing a pedigree relationship between resources and business scenarios based on a three-dimensional mapping model, generating a business process demand graph, a production stage demand graph and a product capacity matching graph respectively, and fusing the graphs to construct a visual digital resource capacity platform; and in response to a resource calling instruction input by a user, outputting a target resource identifier and an associated path. The application can realize integration and sharing of multi-source heterogeneous data inside and outside an enterprise, solve a data island problem, and improve the intelligent degree of a resource management system.

Description

Technical Field

[0001] This application relates to the field of enterprise digital resource management technology, and in particular to a digital resource sharing method and system based on genealogical mapping relationships. Background Technology

[0002] In recent years, with the rapid development of information technology, digital transformation has become a core strategy for global enterprises to enhance competitiveness, optimize resource allocation, and achieve sustainable development. Especially in industries such as manufacturing, services, and finance, enterprises are gradually digitizing traditional business processes and leveraging advanced technologies such as the Internet, the Internet of Things, big data, and artificial intelligence to promote intelligent management of various businesses. However, in the process of promoting digital transformation, enterprises also face an increasingly prominent challenge—the integration and sharing of multi-source heterogeneous data.

[0003] In practical applications, enterprises often use multiple information systems to handle various business needs. For example, personnel management typically relies on HR systems, business project management on project management systems, and customer relationship management on CRM systems. Furthermore, as business expands, the addition of external systems and partners diversifies data sources. Enterprises' various data sources not only include traditional structured data (such as tabular data in databases) but also semi-structured and unstructured data (such as documents, logs, reports, sensor data, etc.).

[0004] Currently, existing enterprise resource management systems (ERPs) often encounter information silos when processing different types of semi-structured and unstructured data across systems and domains due to inconsistent formats, varying storage methods, and a lack of effective correlation between data. Furthermore, common resource recommendation systems rely heavily on static rules or keyword-based matching, lacking the ability to reason about deep relationships and semantics between data. This results in poor resource matching efficiency and cross-database resource integration capabilities, limiting the utilization value of resources and failing to meet the rapidly evolving business needs of enterprises. Summary of the Invention

[0005] To achieve the integration and sharing of heterogeneous data from multiple sources within and outside the enterprise, solve the problem of data silos, and improve the intelligence level of the resource management system, this application provides a digital resource sharing method and system based on genealogical mapping relationships.

[0006] Firstly, this application provides a digital resource sharing method based on genealogical mapping relationships, employing the following technical solution:

[0007] A digital resource sharing method based on genealogical mapping relationships, the resource sharing method includes:

[0008] Obtain multi-source heterogeneous data from the enterprise's internal distributed system through a pre-defined interface protocol;

[0009] Based on a preset resource classification standard, the multi-source heterogeneous data is standardized to generate a structured resource pool.

[0010] A graph dictionary module is constructed to perform topological analysis on the resources in the structured resource pool and output a resource relationship topology graph.

[0011] Based on the structured resource pool and resource relationship topology, a genealogical relationship between resources and business scenarios is established based on a three-dimensional mapping model, generating a business process requirement map, a production stage requirement map, and a product capability matching map, respectively.

[0012] By integrating the aforementioned business process requirement map, production stage requirement map, and product capability matching map, a visualized digital resource capability platform is constructed.

[0013] In response to the user's input resource call command, the target resource identifier and associated path are output based on the visualized digital resource capability platform.

[0014] By adopting the above technical solutions, a unified genealogical mapping relationship is constructed to eliminate data silos. A structured resource pool is built using ontology semantic modeling, and a dynamic graph computing engine generates resource relationship topologies. Through the innovative introduction of a three-dimensional tensor genealogy, business needs, resource supply, and product capabilities are modeled within a unified mathematical framework. Multi-graph fusion and visual analysis enable a global perspective of enterprise digital resources, and finally, precise resource paths are output based on users' resource access requirements. This application achieves integrated management, standardized application, and value empowerment of digital resources, constructing a multi-level, multi-scenario enterprise-level digital resource capability platform that can provide strong support for enterprise digital transformation.

[0015] Secondly, this application provides a digital resource sharing system based on genealogical mapping relationships, employing the following technical solution:

[0016] A digital resource sharing system based on genealogical mapping relationships, the resource sharing system comprising:

[0017] The heterogeneous data acquisition module is used to acquire multi-source heterogeneous data from the enterprise's internal distributed system through a preset interface protocol;

[0018] The data processing module is used to standardize the multi-source heterogeneous data based on a preset resource classification standard to generate a structured resource pool.

[0019] The association topology parsing module is used to construct the graph dictionary module, perform association topology parsing on the resources in the structured resource pool, and output a resource relationship topology graph;

[0020] The multi-graph generation module is used to establish the genealogical relationship between resources and business scenarios based on the structured resource pool and resource relationship topology graph, and generate business process requirement graph, production stage requirement graph and product capability matching graph respectively.

[0021] The platform construction module is used to integrate the business process requirement map, production stage requirement map and product capability matching map to build a visualized digital resource capability platform.

[0022] The target resource identifier output module is used to respond to the resource call command input by the user and output the target resource identifier and associated path based on the visualized digital resource capability platform.

[0023] Thirdly, this application provides a computer device, which adopts the following technical solution:

[0024] A computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to perform the steps of the method as described in the first aspect.

[0025] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0026] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.

[0027] In summary, this application includes at least one of the following beneficial technical effects: dynamically integrating scattered multi-source heterogeneous data within an enterprise through a preset interface protocol to eliminate information silos; transforming raw data into a structured resource pool based on a unified classification standard to achieve standardization and normalization of resource descriptions; parsing the topological relationships between resources using a graph dictionary module to generate a visualized relationship network; accurately constructing three core graphs—business process requirements, production stage requirements, and product capability matching—based on a 3D mapping model to form a phylogenetic mapping relationship between resources and scenarios; integrating multi-dimensional graphs to build a visualization platform to achieve global visibility and dynamic management of resource status; and finally, accurately locating target resources and their associated paths based on user commands through an intelligent response mechanism.

[0028] This application significantly improves resource retrieval efficiency and enhances cross-scenario matching accuracy, building a digital resource hub for enterprises that combines agility, accuracy, and security, thereby fully empowering business collaborative innovation and the implementation of digital transformation strategies. Attached Figure Description

[0029] Figure 1 This is a first flowchart illustrating a digital resource sharing method based on genealogical mapping relationships, which is one embodiment of this application.

[0030] Figure 2 This is a schematic diagram of the structure of a three-dimensional mapping model according to one embodiment of this application.

[0031] Figure 3 This is a second flowchart illustrating a digital resource sharing method based on genealogical mapping relationships, which is one embodiment of this application.

[0032] Figure 4 This is a schematic diagram of the third process of a digital resource sharing method based on genealogical mapping relationship according to one embodiment of this application.

[0033] Figure 5 This is a schematic diagram of the fourth process of a digital resource sharing method based on genealogical mapping relationship according to one embodiment of this application.

[0034] Figure 6 This is a schematic diagram of the fifth process of a digital resource sharing method based on genealogical mapping relationship according to one embodiment of this application.

[0035] Figure 7 This is a schematic diagram of the sixth process of a digital resource sharing method based on genealogical mapping relationship according to one embodiment of this application.

[0036] Figure 8 This is a schematic diagram of the seventh process of a digital resource sharing method based on genealogical mapping relationship according to one embodiment of this application. Detailed Implementation

[0037] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0038] This application discloses a digital resource sharing method based on genealogical mapping relationships.

[0039] Reference Figure 1 A digital resource sharing method based on genealogical mapping relationships, the resource sharing method includes:

[0040] Step S101: Obtain multi-source heterogeneous data from the enterprise's internal distributed system through a preset interface protocol;

[0041] Among them, multi-source heterogeneous data includes, but is not limited to, personnel management data, case selection data, and business scenario project data; the main sources include the company's digital personnel management system, digital case selection system, and the "Top 100 Scenarios" system. The "Top 100 Scenarios" system is mainly used to centrally display the company's key business scenarios and successful implementation cases in the process of digital transformation.

[0042] Specifically, predefined interface protocols (such as RESTful APIs and JDBC direct connection protocols) are essentially a set of predefined communication rules and data structure conversion standards. A protocol parsing engine automatically identifies the data format of the source system (such as ERP, CRM, and document management systems). For example, when integrating with the "SAP HR system," the protocol adaptation layer converts the IDOC format to a standardized JSON-LD (Linked Data) structure, ensuring that organizational relationships in personnel management data can be parsed. For unstructured data (such as construction log text), a NLP entity recognition engine automatically extracts key entities (such as project numbers and equipment models) and injects metadata tags. The aggregation of multi-source heterogeneous data relies on a dynamic routing mechanism, automatically selecting the optimal transmission path based on the data source type (structured SQL tables, semi-structured MongoDB documents, unstructured PDFs) (e.g., Kafka message queues for real-time data transmission, FTP for batch document transmission). This process enables non-intrusive data integration between internal and external systems, avoiding disruptive modifications to the existing system architecture.

[0043] Understandably, building a unified data access plane transforms heterogeneous information scattered across human resources systems, such as personnel qualification data, innovative scoring data from case evaluation systems, and business scenario metadata from the "Top 100 Scenarios" system, into spatiotemporally aligned atomic data units, eliminating semantic ambiguity caused by system isolation at the source. For example, after protocol conversion, data from a tunnel monitoring device can have its timestamp, location coordinates, sensor type, and other fields consistently parsed across systems, providing standardized input for subsequent resource pool construction.

[0044] Step S102: Based on the preset resource classification standards, standardize the multi-source heterogeneous data to generate a structured resource pool;

[0045] Specifically, the predefined resource classification criteria are actually domain ontology models. For example, the business attribute dimension is defined as the OWL (Web Ontology Language) class hierarchy of "technology class - case class - tool class". The hierarchy dimension adopts the three-level concept system of "group → secondary unit → project" of SKOS (Simple Knowledge Organization System).

[0046] For example, when multi-source heterogeneous data is input, the ontology reasoning engine performs semantic alignment and instantiation: it automatically matches the "Innovation Case Application Form" field in the data unit to the benchmark library case resource, and maps the "Bridge Health Monitoring Case" label of that resource to the "Highway Domain Sub-library" (based on the LabelRank algorithm to identify text topic similarity). Knowledge base construction relies on concept graph extraction, such as extracting "Tunnel Support Technology" as a knowledge node from technical specification documents, and establishing "technology-material" association edges with the concrete mix design table.

[0047] In addition, the structured resource pool contains independent benchmark, knowledge, product and expert databases. The isolation of the four databases is achieved through resource partitioning namespaces. For example, the expert database uses domain type as the sharding key (such as hash value sharding) to ensure that water transport expert information is not mistakenly written into the construction domain database.

[0048] Step S103: Construct a graph dictionary module to perform association topology parsing on the resources in the structured resource pool and output a resource relationship topology graph;

[0049] Among them, the graph dictionary module is essentially a dynamic graph calculation system, the core of which includes: (1) Topology relationship mining: based on the attribute tags of resources in the resource pool (such as the "applicable strata" field of "shield construction method" in the knowledge base), the semantic distance between resources is calculated (using the TransE embedding model to minimize the spatial vector difference of the "method-equipment" triple); (2) Relationship weight quantizer: using the structural entropy algorithm to analyze the influence of resource nodes in the business context, for example, generating high-weight connection edges between the frequently called "monitoring and measurement software" and "tunnel convergence algorithm" (weight value = log(number of joint calls)). The topology graph generation depends on the dynamic graph convolutional network. When a new "diaphragm wall leakage treatment report" is added, the resource node is automatically embedded into the existing graph (edge ​​generation rule: if the text similarity with "foundation pit support specification" is >0.7, then an edge is built), and finally outputs a JSON graph structure with an adjacency matrix.

[0050] Understandably, the resource relationship topology graph transforms discrete resources into a globally connected semantic network. An example shows that when a user searches for "bridge crack repair," the system automatically expands the associated resources along the relationship edges in the topology graph, centered on the "crack repair technology" node (weighted order: repair material library → testing equipment manual → expert directory), upgrading the resource discovery process from keyword matching to semantic reasoning. Experimental data shows that this mechanism improves the recall rate of cross-database resource associations by 62%, and due to the dynamic embedding mechanism, newly added "AI crack recognition models" can automatically form association chains with existing cases and tools.

[0051] Step S104: Based on the structured resource pool and resource relationship topology, establish the genealogical relationship between resources and business scenarios based on the three-dimensional mapping model, and generate business process requirement map, production stage requirement map and product capability matching map respectively.

[0052] The 3D mapping model is a high-order tensor, divided into a demand side (X-axis and Y-axis) and a supply side (Z-axis). The X-axis and Y-axis accurately reflect the resource needs of various professional engineering projects within the company, while the Z-axis displays the company's existing and potential digital product resources.

[0053] Specifically, the business process requirement graph (X-dimensional) models the spatiotemporal relationships of the business process chain. For example, the construction phase is decomposed into a matrix of [process nodes × time slots], and the dependencies between nodes are calculated by Petri net transfer functions; for example, if the output delay of the "pile foundation inspection report" exceeds the threshold, the "construction pause" node alarm is triggered.

[0054] Production phase demand map (Y-dimensional): describes the capacity coverage of resources throughout the production cycle. Resource demand is predicted using a hidden Markov model; for example, the probability of needing to call the "flood control expert database" increases by 85% during typhoon season, and a resource-lifecycle alignment matrix is ​​constructed.

[0055] Product Capability Matching Map (Z-dimensional): Quantifies the fit between resources and scenarios. A collaborative filtering algorithm is introduced; for example, when a slope monitoring radar achieves a "displacement early warning accuracy > 90%" in multiple tunnel projects, its score factor on the Z-axis is increased. In the above formula, score is the final scoring factor, α is the weighting coefficient of the number of calls, and β is the weighting coefficient of successful cases.

[0056] Based on the above model architecture, 3D graph fusion relies on tensor chain decomposition. By decomposing high-order relations into products of low-rank kernel tensors, computational complexity is reduced. For example, a practical application of the 3D graph system in a bridge monitoring scenario shows that: the X-dimensional demand chain accurately locates the "post-rainstorm monitoring" sub-process; the Y-dimensional prediction requires calling "radar monitoring equipment" (weather forecast trigger probability threshold); and the Z-dimensional system automatically matches "millimeter-wave radar P12" (score > 97% of similar types) from the equipment library. Tensor operations on these three dimensions shorten the resource matching path length to an average of 2.1 hops, achieving a 3-fold efficiency improvement compared to traditional solutions.

[0057] Step S105: Integrate the business process requirement map, the production stage requirement map, and the product capability matching map to build a visualized digital resource capability platform;

[0058] The platform construction is essentially an integration of a graph visual analytics engine and a distributed computing framework.

[0059] Specifically, the fusion process employs multi-graph joint embedding, and the specific steps include:

[0060] (1) Calculate the optimal alignment matrix between the nodes of the demand-side graph (business process demand graph and production stage demand graph) and the resource nodes of the supply-side graph (product capability matching graph) by using Gromov-Wasserstein distance;

[0061] (2) Based on the D3.js engine, the optimal alignment matrix is ​​rendered into a three-dimensional force-directed graph. The node size in the graph is logarithmically scaled according to the resource call frequency, and the side width is linearly scaled according to the collaborative filtering score.

[0062] (3) When new business requirements are added (such as smart construction site monitoring requirements), locate the affected graph subgraphs and locally reconstruct the alignment matrix and rendering results. Specifically, you can call TensorFlow to reconstruct the local graph (only re-decompose the affected subgraphs) to avoid the resource consumption of global recalculation.

[0063] Step S106: In response to the resource call command input by the user, output the target resource identifier and associated path based on the visual digital resource capability platform.

[0064] Specifically, the command response can be based on a reinforcement learning-driven path planning algorithm. When a user initiates a resource request (such as "obtain a case study of coastal soft soil treatment"), the system can output a precise resource path through a deep reinforcement learning strategy. For example, in a port expansion and renovation project, if a user requests an "automated terminal scheduling solution", the system will locate the "automated terminal benchmark library" node in the topology map and automatically output [Port scheduling algorithm SDK] → [Equipment interface protocol] → [List of external experts] along a high-weight association path.

[0065] In the above implementation, a unified genealogical mapping relationship is constructed to eliminate data silos. A structured resource pool is built using ontology semantic modeling, and a dynamic graph computing engine is used to generate resource relationship topologies. By innovatively introducing a three-dimensional tensor genealogy, business needs, resource supply, and product capabilities are modeled under a unified mathematical framework. Global visibility into enterprise digital resources is achieved through multi-graph fusion and visual analysis. Finally, precise resource paths are output based on users' resource access requirements. This application realizes the integrated management, standardized application, and value empowerment of digital resources, constructing an enterprise-level digital resource capability platform covering multiple levels and scenarios, providing strong support for enterprise digital transformation.

[0066] Reference Figure 2The image shows a three-dimensional mapping model constructed according to one embodiment of this application. The three-dimensional structure consists of an X-axis, a Y-axis, and a Z-axis, each representing a different dimension. The X-axis focuses on mining specific business processes, and the nodes on the axis are connected to form lines, which can comprehensively cover the typical processes and key scenarios of all professional engineering projects; the Y-axis represents specific production nodes, and the node connections run through the entire life cycle from planning and design to operation and maintenance; the Z-axis represents specific digital products, and the node connections cover all valuable digital products of the company.

[0067] The demand side, constructed using the X and Y axes, accurately reflects the resource needs of various professional engineering projects within the company. The business map, based on the business processes displayed on the X-axis, comprehensively outlines and presents the specific stages and key requirements of project implementation. The demand map, combined with the production nodes on the Y-axis, identifies and depicts the changes and distribution of resource needs at each stage of the project lifecycle. The supply side, represented by the Z-axis, showcases the company's existing and potential digital product resources. A product map is formed by analyzing digital products along the Z-axis to clarify which products can meet the various needs of the demand side. Based on this, a value map is further formed, demonstrating how digital products create value in different demand scenarios, thereby maximizing resource utilization.

[0068] Reference Figure 3 As a further implementation of the digital resource sharing method, before the step of establishing the genealogical relationship between resources and business scenarios based on the three-dimensional mapping model, the following is also included:

[0069] Step S201: Construct a functional module theme library based on business scenario tags, and associate the resources in the structured resource pool with the corresponding functional modules;

[0070] The functional module theme library includes, but is not limited to, a resource intelligent search module, a digital activity module, a product sharing module, a digital course module, a communication community module, and a graph dictionary module;

[0071] Specifically, business scenario tags are the concrete carriers of domain ontology. For example, the scenario tag "bridge health monitoring" can be mapped to a set of functional requirements (including monitoring data call specifications, expert consultation agreements, etc.). When building a topic library, discrete resources in the structured resource pool can be forcibly categorized through semantic constraint binding algorithms.

[0072] For example, when resource attributes overlap with module definitions (such as a BIM model simultaneously satisfying the version management rules of the "Product Sharing Module" and the association resolution conditions of the "Graph Dictionary Module"), the system adopts a multi-instance allocation strategy based on a rule engine. For instance, the resource ID of the "Tunnel Settlement Prediction Algorithm" is simultaneously associated with both the Product Sharing Module (as a downloadable tool) and the Graph Dictionary Module (as a topology node). Resource copies share physical data blocks in the storage layer through pointer reuse mechanisms, avoiding redundant storage. The functional module theme library is essentially a virtual resource container, its boundaries dynamically limited by access control lists (ACLs): the community module only allows users with discussion permissions to write resource comments, while the product sharing module opens download channels to authenticated users. This design enables multi-dimensional logical partitioning of resources within a unified physical storage pool.

[0073] Understandably, this elevates enterprise digital resources from "data objects" to "service subjects." Taking a port group's practice as an example: when a user enters the "smart terminal" business scenario, the thematic library automatically activates related modules—the product sharing module pushes loading and unloading scheduling algorithm toolkits, the communication community module loads port automation discussion forums, and the graph dictionary module visualizes the topological relationships between algorithms and equipment. Resource call response time is reduced to milliseconds, and due to the modular isolation mechanism, the "port safety specifications" file in the knowledge base will not mistakenly flow into the digital course module.

[0074] Step S202: Based on the user query logs of the resource intelligent search module, output the query intent vector set through the natural language processing model, and use it as the input features of the business process requirement map and the production stage requirement map;

[0075] Specifically, this step is the process of latent semantic modeling of human-computer interaction intentions.

[0076] For example, based on the original statement in the user query log (such as "get coastal soft soil settlement cases"), semantic noise is first eliminated using a word space projection algorithm: stop words ("of" "get") are removed, professional terms are standardized ("soft soil" is unified as "weak foundation"), and abbreviations are completed ("BIM" is expanded to "Building Information Modeling"). Subsequently, a fine-tuned BERT model is used to perform intent distillation: after the model is trained on vertical domain corpora (historical case reports, engineering standard texts), the query statement is encoded into a 128-dimensional vector. Queries with similar semantics in the vector space are automatically clustered. The vector cosine similarity between "weak foundation treatment scheme" and "coastal roadbed reinforcement case" reaches 0.93, while that between "bridge crack repair" and only 0.21. This vector set serves as a driving factor for the business process requirement graph (X-axis)—when the business process node loads the "weak foundation construction" stage, the graph automatically retrieves resource nodes with a similarity > 0.8 and injects them into the execution chain.

[0077] Step S203: Calculate the popularity rating vector based on the resource download volume and user rating of the product sharing module, and use it as the input feature of the product capability matching map;

[0078] Among them, the popularity rating is a resource value quantification system guided by collective wisdom. (Resource download count d) r Represents the intensity of demand, user rating S r Quality recognition is reflected by a weighted harmonic function. Fusion. The impact of logarithmic function compression on download volume extremes (avoiding short-term bursts of downloads distorting long-term value), the rating coefficient focuses on user subjective evaluation, and the final generated popularity vector H=(h r1 ,h r2 ,...,h rm Input the product capability matching map (Z-axis) to trigger the resource sorting recalibration mechanism. For example, the initial score s of a tunnel monitoring software. r =4.2, when the weekly download volume surged to d after a major tunnel accident. r =152,h r The score jumped to 9.7, making it surpass the original top tool in the Z-axis ranking. This is essentially a dynamic anti-entropy process that breaks the Matthew effect in the cold start phase of resources, allowing newly launched high-quality tools (such as AI-based subsidence prediction models) to quickly enter the top of the recommendations.

[0079] Step S204: Optimize the edge weights of the resource relationship topology graph output by the graph dictionary module to generate a topological adjacency matrix, which serves as the input feature of the three-dimensional mapping model.

[0080] Among them, edge weight optimization is a resource relationship purification process based on graph structure entropy. The edge weight w of the original resource relationship topology graph... ij This reflects the strength of resource association (e.g., w=0.83 between "Shield Tunneling Machine Parameter Manual" and "Earth Pressure Balance Calculation Tool"), but two types of noise exist: low-weight edges (w<0.3) are mostly accidental associations (e.g., fake links generated by a user simultaneously downloading unrelated resources), while high-weight edges (w>0.9) may be overcoupled (e.g., repeated associations between different versions of the same case). The optimizer uses a double-threshold Gaussian filter: it performs a shearing transformation on edges with w<0.3 (w<0.3). new Edges with w > 0.7 are augmented with Gaussian enhancement (=0), and the resulting topological adjacency matrix suppresses topological noise while strengthening the critical path. This topological adjacency matrix serves as the skeleton input for the 3D mapping model, ensuring path accuracy during phylogenetic relationship modeling.

[0081] In the above implementation, resource service is achieved by encapsulating virtual containers of the topic library, and fuzzy business needs are transformed into graph-driven factors by using intent vector space mapping. An innovative dynamic anti-entropy mechanism for popularity scoring is designed to break the cold start dilemma of resources. Based on the dual threshold optimization of the topological adjacency matrix, the input of the genealogy modeling is purified, and finally a closed-loop technology chain of "business needs → resource activation → value quantification → relationship purification → genealogy fusion" is formed.

[0082] Reference Figure 4 As one implementation of step S104, the steps of establishing a genealogical relationship between resources and business scenarios based on a three-dimensional mapping model, and generating a business process requirement map, a production stage requirement map, and a product capability matching map respectively, include:

[0083] Step S301: Identify the dependencies between business process nodes in the first dimension, associate the business process node sequence in the first dimension, output the business process node chain, and obtain a business process requirement graph covering the entire business process.

[0084] Specifically, a complete business process requirement map is constructed by analyzing the dependencies between business process nodes. A business process node refers to the smallest unit of a specific business activity within an enterprise. For example, in the construction industry, "design review" or "construction drawing review" can both be considered nodes. Dependencies refer to the logical sequence or causal connection between nodes. For instance, the "design review" node must be completed before the "construction drawing review" node; otherwise, it will lead to resource scheduling conflicts.

[0085] When identifying these relationships, the system can employ graph theory algorithms (such as depth-first search or adjacency matrix calculation) to automatically traverse all nodes, detect input-output dependencies between nodes (e.g., the output of node A is the input of node B), and ensure that the sequence is acyclic and covers the entire process. The process of associating node sequences involves linking the identified nodes in chronological or logical order to form a business process chain, such as a linear sequence from "requirements gathering" to "solution design" and then to "implementation and execution." Upon output, the system transforms this chain into a business process requirement graph.

[0086] It's important to note that the business process requirement graph is a graphical data structure that uses nodes to represent business activities and edges to represent dependencies. Similar to a flowchart, it places greater emphasis on resource requirement mapping (e.g., labeling each node with the required resource type). The graph's construction leverages knowledge graph technology to visualize abstract business processes, ensuring that even without viewing the raw data, one can intuitively understand how resources support the business flow. The entire process relies on training with historical enterprise data, using machine learning models to predict unknown dependencies and avoid errors caused by human intervention. This business process requirement graph can automatically identify resource gaps (e.g., a lack of specific tools during construction), reducing manual inspection time, while ensuring that resource requirements align with enterprise strategy (e.g., prioritizing high-value business nodes), avoiding resource waste, and significantly improving the accuracy and efficiency of business resource scheduling.

[0087] For example, taking a highway construction project as an example: the system identifies the business process node sequence as "geological exploration → design scheme formulation → construction bidding → on-site construction → acceptance and delivery". Dependency analysis shows that "design scheme formulation" depends on the data output of "geological exploration". If this step is skipped, the "construction bidding" node cannot be initiated. After associating the sequences, the system outputs the business process chain, ultimately generating a graph: in the graph, the "geological exploration" node points to the "best exploration case" in the benchmark library, while the "on-site construction" node is associated with the "safety specification document" in the knowledge base, covering the entire process from planning to delivery. If a project skips "acceptance and delivery", the graph will automatically issue a warning that resources are not in a closed loop.

[0088] Step S302: Associate the production lifecycle node sequence in the second dimension, output the resource demand matrix of the production stage, and obtain the production stage demand map.

[0089] The production lifecycle nodes include the planning and design, construction, and operation and maintenance phases. This step focuses on time-dimensional modeling of the production lifecycle, constructing a demand map of production stages through a sequence of related nodes. Production lifecycle nodes represent the entire lifecycle of a product from concept to disposal, including planning and design (such as conceptual design and feasibility analysis), construction (such as material procurement and on-site construction), and operation and maintenance (such as equipment maintenance and performance monitoring). These node sequences are not simple lists, but dynamic associations based on time series analysis and state machine models.

[0090] Specifically, the system first defines standard attributes for each stage (e.g., the planning and design stage requires "innovative resources," and the construction stage requires "execution resources"). Then, it uses matrix operations (e.g., matrix transformation) to associate nodes according to their lifecycle order (e.g., planning and design → construction → operation and maintenance). Finally, it outputs a resource demand matrix for each production stage, which is a two-dimensional table or data grid. Rows represent resource types (e.g., human resources, equipment, knowledge), columns represent lifecycle stages, and cell values ​​represent the intensity of resource demand (e.g., weight values ​​of 0-1).

[0091] It's important to note that when this matrix is ​​transformed into a production phase demand map, visualization techniques (such as heatmaps or node link diagrams) are used. Each phase node in the map is linked to a resource demand value, allowing users to interact and query (e.g., hovering to view a detailed resource list for the construction phase). In principle, the matrix generation relies on enterprise operational data (such as historical project records), using regression analysis to predict changes in resource demand, ensuring the model adapts to different project scales.

[0092] For example, in the construction of a smart port, the production lifecycle sequence is "planning and design (wharf layout design) → construction (crane installation) → operation and maintenance (automated system monitoring)". The system-related sequence output matrix shows that the resource requirement for "crane installation" during the construction phase is 0.9 (high demand), while the requirement for "AI monitoring tools" during the operation and maintenance phase is 0.7. The final demand map is displayed as a heatmap, highlighting the "construction" node, linking it to "BIM modeling software" in the product library, and providing a warning that insufficient resources during the planning and design phase (such as a shortage of design software) will lead to downstream delays.

[0093] Step S303: Associate the digital product attribute sequence in the third dimension, and output the product capability scoring table by binding the matching weight value between the digital product and the business scenario to obtain the product capability matching map.

[0094] The core of this step is the quantification of the value of digital products and their adaptation to various scenarios, generating a product capability matching map through attribute sequence association. The attribute sequence of digital products refers to the sequence of key characteristic dimensions of the product (such as functionality, compatibility, and cost-effectiveness), for example, the "processing speed" or "user-friendliness" of a software tool.

[0095] Specifically, the system first defines a standard framework for attribute sequences (based on industry standards), using multidimensional scaling analysis (MDS) or principal component analysis (PCA) to map attributes to a numerical space (e.g., quantifying "processing speed" into a score of 1-10). When binding matching weight values, a weighted fusion algorithm is used: calculating the matching weight between digital products (e.g., AI analysis tools) and business scenarios (e.g., bridge monitoring). The weight values ​​are dynamically calculated based on historical usage data (download volume, user ratings) and scenario requirements (urgency, complexity). For example, the weight formula is: Weight = Functionality Score × 0.4 + Compatibility Score × 0.3 + Cost Score × 0.3). The output product capability score table is a structured dataset (e.g., JSON or CSV format), listing the attribute scores and overall weights for each product. Finally, it is converted into a product capability matching graph. Nodes in the graph represent products, edges represent matching relationships with business scenarios, and edge weights visualize the matching strength (e.g., line thickness). The entire process integrates machine learning (e.g., clustering algorithms) to automatically optimize weight allocation, ensuring the graph reflects real-time business needs.

[0096] For example, in a railway construction project, the digital product series includes "track fine-tuning software (high functionality)," "settlement prediction model (medium compatibility)," and "BIM collaboration platform (low cost)." When binding matching weights, the software has a weight of 0.85 with the "track laying" scenario (due to a functionality score of 9 / 10), and the model has a weight of 0.6 with the "geological monitoring" scenario. After outputting the scoring table, the graph shows the "track fine-tuning software" node connected to the "track laying" scenario node with a thick edge, indicating that this resource should be deployed first; if the cost attribute changes, the graph updates the weights in real time.

[0097] In the above implementation, the business process requirement graph (first dimension) ensures full-process resource coverage and avoids business interruptions; the production stage requirement graph (second dimension) optimizes lifecycle resource allocation and reduces waste; and the product capability matching graph (third dimension) achieves accurate dynamic matching and improves efficiency. By upgrading resource management from static storage to intelligent driving, resource retrieval time is shortened, matching accuracy is improved, and strategic decision-making is supported through graph visualization, thus providing a core engine for enterprise digital transformation.

[0098] It should be noted that the three-dimensional genealogical mapping mechanism in this application embodiment differs from the planar classification of traditional resource management systems. This mechanism establishes a multi-level mapping between resources and business from a spatial dimension. The business process dimension (X-axis) reveals the location of resources in specific work chains, the production life cycle dimension (Y-axis) reflects the evolution trajectory of resources over time, and the digital product dimension (Z-axis) quantifies the supply capacity characteristics of resources. This three-dimensional coupling model enables enterprises to see the value contribution rate of resources in specific business scenarios. For example, in the intelligent highway construction scenario, by locating BIM technology nodes on the X-axis, associating construction stages on the Y-axis, and calling model library resources on the Z-axis, a precise technical solution can ultimately be formed.

[0099] Reference Figure 5 As one implementation of step S105, the steps of constructing a visual digital resource capability platform include:

[0100] Step 401: Construct a four-level graph framework, including a full-industry panoramic graph layer, a single-industry professional graph layer, a single-stage resource graph layer, and an application scenario card layer;

[0101] Specifically, a panoramic modeling system for enterprise resources is established by defining a four-layer progressive topology. The full-industry panoramic graph layer adopts a directed weighted graph model (nodes represent industry sectors, and edge weights reflect the intensity of cross-industry resource dependence), mapping the global business chain and resource allocation relationships from the perspective of group strategy. The single-industry professional graph layer is based on a lifecycle state machine (e.g., planning and design → construction → operation and maintenance), decomposing the entire process of a single industry into discrete state nodes, with each node bound to a standardized resource requirement template for that stage. The single-stage resource graph layer introduces a process-level knowledge graph, extracting process nodes, resource entities, and their association rules in specific production scenarios (e.g., bridge pile foundation construction) through entity recognition algorithms, forming fine-grained matching paths. The application scenario card layer constructs a scenario template instantiation engine, transforming unstructured requirements such as business pain points and resource constraints into standardized field combinations (e.g., "process type: pile foundation inspection; matching tool: ultrasonic flaw detector"), achieving product-level dimensionality reduction mapping from high-dimensional business elements to digital resources. The four levels ensure data logic consistency through parent-child inheritance relationships (such as the "infrastructure" node of the entire industry layer being associated with the "highway construction" subgraph of the single industry layer), forming a complete decision-making chain from macro strategy to micro execution.

[0102] Understandably, this four-tiered graph architecture solves the problem of the disconnect between strategy and execution in traditional resource platforms, enabling enterprises to penetrate from a group perspective down to specific process resource allocation plans. The four-tiered structure provides a standardized container for subsequent 3D data fusion, avoiding cross-departmental data redundancy, while standardized templates reduce user understanding costs (e.g., scene card fields can be directly parsed by business personnel).

[0103] Step 402: Inject the business process node chain into the single-stage resource graph layer;

[0104] When the business process node chain (X-axis data) is injected into the single-stage resource graph layer, a node alignment algorithm is used to establish a strong association between the key steps in the business chain (such as "construction drawing review") and the entity nodes in the process graph. The core of this algorithm is to calculate the semantic similarity score (such as the similarity of the nodes "construction drawing review → design verification" being 0.92) and to establish a bidirectional index for nodes with a score > 0.8.

[0105] Step 403: Inject the production stage demand matrix into the single-industry professional map layer;

[0106] When the production stage demand matrix (Y-axis data) is injected into the single industry professional graph layer, the resource demand characteristics in the matrix (such as "concrete curing stage requires temperature and humidity monitoring accuracy of ±0.5℃") are converted into graph node attribute values, driving the resource adaptation engine to automatically match candidate solutions in the knowledge base (such as matching "intelligent temperature control sensor - type A").

[0107] Step 404: Bind the digital product attribute sequence to the nodes of the full industry panoramic map layer;

[0108] Among them, the digital product attribute sequence (Z-axis data) is bound to the nodes of the full industry panoramic map layer. The attribute fusion technology is used to compare the product technical parameters (such as response latency ≤50ms) with the business demand indicators of the industry nodes (such as "real-time monitoring scenario requires latency ≤100ms") with thresholds. If the conditions are met, a binding relationship is established and written into the map edge attributes (the edge type is marked as "technical support").

[0109] Understandably, by injecting three-dimensional data, precise anchoring of business needs and product capabilities can be achieved at the resource logic layer. For example, the X-axis business flow ensures that resource scheduling conforms to engineering process specifications, the Y-axis demand characteristics ensure that resource technical parameters meet production constraints, and the Z-axis product binding opens up the technology supply channel. The three form a closed-loop decision-making basis in the graph.

[0110] Step 405: Call the weight value data of the product capability matching map, and fill the scene card field in the application scene card layer based on the industry code of the single industry professional map layer, the process node identifier of the single stage resource map layer, and the industry segment node of the full industry panoramic map layer.

[0111] This involves using weighted data from the product capability matching graph (e.g., resource popularity score = download frequency × 0.6 + user score × 0.4), combined with industry codes from the single-industry professional graph layer (e.g., "Highway Engineering → LY-2023" identifies road type and version), process node identifiers from the single-stage resource graph layer (e.g., "Pile Foundation Testing → ZJ-008"), and industry sector nodes from the full-industry panoramic graph layer to construct multi-dimensional search conditions. The weight threshold filtering module generates a high-confidence resource list through a dynamic filtering algorithm (e.g., selecting only the top 20% of products by weight value).

[0112] Next, the scene card population engine transforms the above data into business-readable fields (such as "Recommended Product: Ultrasound Pro; Matching Degree: 92%; Applicable Process: ZJ-008") based on the field mapping rule base, and writes them into the card's predefined field slots. During the card generation process, the relationship with the parent graph is automatically inherited (such as a "Bridge Pile Foundation Inspection" scene card being associated with the single-industry layer "Highway Engineering" and the full-industry layer "Infrastructure Sector"), forming cross-level traceability.

[0113] Step 406: Deploy the graph dynamic update engine, monitor new resource streams at the data access interface, and trigger graph hierarchical updates based on the type of new resource.

[0114] Among them, deploying event listeners to capture new data streams from API interfaces in real time (such as adding "5G smart monitoring reagent" products) and identifying data types through resource classifiers (such as marking product attribute changes as Z-axis updates).

[0115] Specifically, the engine triggers hierarchical incremental updates based on data type. For example, for changes in business processes (X-axis), a subgraph recalculation algorithm is used to locally refresh affected nodes in a single-stage resource graph layer (e.g., "construction process optimization" leads to updates of related process nodes); for changes in production requirements (Y-axis), demand matrix iteration is triggered in a single-industry professional graph layer (e.g., expanding matrix dimensions when adding "environmental protection requirements" indicators); for changes in product data (Z-axis), a reassessment of binding relationships is performed across the entire industry layer (e.g., automatically creating binding edges between nodes when new product parameters meet existing requirements).

[0116] It should be noted that the update process employs an influence propagation constraint mechanism (e.g., defining the influence radius of a node as ≤3 hops), refreshing only the relevant subgraph to reduce computational overhead. The update results are synchronized to the scene card layer in real time, driving dynamic updates of card fields (e.g., automatically adding new products to the recommendation list when they meet the threshold).

[0117] Step 407: Integrate the four-level graph framework and graph dynamic update engine into the visual interactive interface to build a visual digital resource capability platform.

[0118] Specifically, the four - level atlas framework and the dynamic engine are encapsulated into a microservice architecture, and modules such as resource retrieval and permission control are docked through a unified data bus. The visual interaction interface adopts a hierarchical rendering technology: the entire industry layer presents a heat map of industry segments, the single - industry layer shows the life - cycle time axis, the single - stage layer generates a topological map of process nodes, and the scenario card layer is arranged in a card matrix. User operations (such as clicking on the "road construction" node) trigger a cross - level linkage response (automatically drilling down all process cards under this industry). The platform integrates a permission inheritance model (such as group users can view the entire industry layer, and project departments can only see the associated single - stage layer) to ensure data security and business focus.

[0119] In the above - mentioned implementation, a knowledge ontology of enterprise resources is constructed based on the four - level framework. The injection of three - dimensional data enables the precise transformation from business requirements to technical solutions. The encapsulation of scenario cards provides an out - of - the - box resource solution for the business side. The dynamic update engine ensures that the system continuously responds to business changes. Finally, the platform integrates the scattered digital resources of the enterprise into a decision - making system that runs through strategy - tactics - execution in a visual form, greatly reducing the resource retrieval cost, improving the cross - departmental resource reuse rate, and enhancing the long - term adaptability of the platform through a dynamic mechanism.

[0120] Refer to Figure 6 , as an implementation of step S405, the steps of calling the weight value data of the product - ability matching atlas and filling the scenario card fields in the application scenario card layer based on the industry code of the single - industry professional atlas layer, the process node identifier of the single - stage resource atlas layer, and the industry segment node of the entire - industry panoramic atlas layer include:

[0121] Step S5o1, call the weight - threshold screening result of the product - ability matching atlas, filter the candidate products with weight values lower than the preset weight threshold, and generate a list of filtered candidate products;

[0122] Among them, the product - ability matching atlas is a dynamic database for quantitatively evaluating product value. The weight - threshold screening depends on a preset algorithm model (such as weight value = download frequency × α+ user score × β). The essence of the preset weight threshold is a resource - value filtering line. For example, only products with a weight > 85 points are selected, dynamically intercepting low - efficiency resources (such as unpopular tools or unproven new products), and generating a list of filtered candidate products (a subset of high - value resources). The essence of this step is a resource - value distillation mechanism.

[0123] Step S502, receive the input of the industry code corresponding to the single - industry professional atlas layer;

[0124] Step S503, based on the industry code, retrieve the resource adaptation rule library corresponding to the industry code;

[0125] Among them, the industry code corresponding to the single-industry professional map layer is the industry digital identifier internally divided by the enterprise. The essence of this industry code is a business semantic tag, which is bound to the resource usage rules specific to that industry (such as the highway engineering rules requiring cards to include the "pile foundation deformation compensation algorithm" field). The system retrieves the resource adaptation rule base (a resource configuration logic base stored by industry classification, for example, the highway engineering rule base mandates that "pile foundation detection must be associated with an error correction algorithm"), which is actually mapping to the resource scheduling protocol of the corresponding industry through coding.

[0126] It is understandable that by transforming abstract business requirements into machine-recognizable configuration instructions, the misuse of cross-industry resources (such as misconfiguring water conservancy engineering tools into highway scenarios) can be prevented.

[0127] Step S504: Generate a solution card based on the filtered candidate product list and resource adaptation rule base;

[0128] The resource adaptation rule base defines the logic for generating card content. After the system parses the rules, it extracts compliant product instances from the candidate product list according to strong constraints and assembles them into a solution card prototype (semi-structured data container).

[0129] Step S505: Fill the solution card into the predefined field slots of the application scenario card layer to form a structured card instance;

[0130] The predefined field slots in the application scenario card layer are essentially standardized templates. The system fills the solution card prototype according to the slot semantics to form a structured card instance (a complete business object that can be parsed by the machine). This process relies on a field semantic alignment algorithm to ensure the consistency between business terms and template definitions (e.g., "error correction" corresponds to the standardized field "process optimization scheme").

[0131] Step S506: Associate the lifecycle stage code of the single-industry professional graph layer with the process node identifier of the single-stage resource graph layer;

[0132] The process node identifier (e.g., "ZJ-008") is essentially a digital label for a business atomic unit. Its encoding rules implicitly contain business attributes (e.g., the first two letters "ZJ" represent pile foundation inspection, and the number segment "008" identifies the specific inspection point). The system uses a business semantic parsing engine to deconstruct the syntactic structure of the identifier (e.g., separators, prefix codes), extract key business features (process type, physical location, etc.), and match them with the lifecycle model of the single-industry professional graph layer. The association logic relies on a decision tree rule base. For example, if the process node identifier contains the semantics of "pile foundation inspection" (determined through NLP keyword clustering), it is forcibly associated with the "construction stage" code; if the process contains the feature of "scheme comparison," it is associated with the "design stage" code.

[0133] Step S507: Associate the industry sector nodes of the full industry panoramic map layer through industry coding;

[0134] Among them, the industry sector nodes in the full industry panorama map layer are the root nodes of the group-level business classification tree. They store the metadata of all subordinate industries (including the industry code mapping table). The system can locate the target industry sector node by querying the distributed hash index of the panorama map using the industry code as the key value.

[0135] Step S508: Write the hierarchical traceability identifier into the structured card instance;

[0136] Among them, the hierarchical traceability identifier is generated based on the coding of industry sector nodes and life cycle stages.

[0137] Specifically, the hierarchical traceability identifier is a concatenated sequence of industry segment ID, industry ID, and process node ID. This identifier is the unique gridded coordinate of the resource in the panoramic business map.

[0138] Understandably, the application scenario card layer breaks down the data barriers between business levels, enabling any scenario card to be traced back to the group's strategic layer (panoramic map), business execution layer (single industry map), and process node layer (single stage map), thus achieving end-to-end transparent management of resource applications.

[0139] In the above implementation, a business-oriented resource screening framework is established from industry coding to rule base binding. High-value resources are screened using a weight threshold mechanism. Standardized solution cards are generated through a rule-product dual-drive model. Finally, global traceability of resource application is achieved by relying on a three-dimensional cascaded coordinate system (full industry layer / single industry layer / single stage layer).

[0140] Reference Figure 7 As one implementation of step S106, the step of responding to a user-input resource call command and outputting the target resource identifier and associated path based on the visual digital resource capability platform includes:

[0141] Step S601: Parse the semantics of the resource call instruction and identify the target resource type and business domain according to the preset resource classification standard;

[0142] Specifically, relying on named entity recognition and part-of-speech tagging mechanisms in natural language processing (NLP) technology, the entity objects in user input and their contextual business context are identified. Essentially, this is a translation process from natural language to digital business space.

[0143] For example, when a user inputs a command such as "Coastal Soft Foundation Processing Case", the semantic parsing engine decomposes the sentence structure using a bidirectional LSTM model: "Coastal" → geographical attribute label → mapping to water transport domain code; "Soft Foundation Processing" → technical action label → associated foundation processing node; "Case" → resource type label → pointing to benchmark library.

[0144] Next, a pre-defined resource classification standard serves as a translation dictionary, transforming discrete vocabulary into structured business coordinates. The system categorizes user-required resources into standard resource type categories and further maps them to their respective business domains (such as intelligent manufacturing and smart transportation), thereby determining the semantic boundaries and business objectives of resource calls.

[0145] Step S602: Determine the three-dimensional coordinates of the corresponding target resource node based on the single-industry professional map;

[0146] Among them, the three-dimensional coordinates include the position of the business process axis, the position of the production node axis, and the position of the resource type axis. In essence, it is a unique matrix address of the business scenario in the digital space, which solves the resource mismatch problem caused by semantic ambiguity in traditional retrieval.

[0147] Specifically, after defining the basic semantics of resources, the system further spatially locates resource nodes using a pre-constructed single-industry professional map. This map is constructed using a three-dimensional coordinate system, with the X-axis representing business process stages, the Y-axis representing production node locations, and the Z-axis representing resource type dimensions. In this model, the business process axis (X-axis) is typically encoded based on the entire industry lifecycle (e.g., design, R&D, production, operation and maintenance), the production node code (Y-axis) reflects the organizational relationships between different process nodes or application scenarios within the industry, and the resource type axis (Z-axis) reflects the technical attributes of the resource, such as document type, algorithm type, tool type, etc. By mapping target resource nodes to this three-dimensional coordinate space, the system achieves semantic positioning of resource space, thus providing the structural foundation for constructing paths and contextual relationships between resources.

[0148] Step S603: Extract all associated edges of the target resource node from the graph dictionary, calculate the weight value of each associated edge, filter associated edges below a preset threshold, and generate a set of high-weight associated paths.

[0149] Specifically, the edges in the graph represent semantic or operational path relationships between resources. The weights of associated edges can be calculated using a dynamic statistical model, with the formula: Weight = Call Frequency × α + User Rating × β. Call frequency represents the number of times the resource edge has been actually accessed in history, reflecting operational activity. User ratings, derived from user feedback mechanisms, measure the satisfaction and effectiveness of the resource path. α and β are empirical parameters, generally satisfying α + β = 1, and their values ​​can be dynamically adjusted through A / B testing and other methods to adapt to different business scenarios.

[0150] This calculation method integrates behavior frequency analysis and user subjective experience evaluation to ensure that the recommended paths are both operationally representative and reflect user preferences. After the calculation is completed, the system sorts all edges according to their weights and filters low-quality paths based on a set threshold (e.g., weight values ​​greater than 0.6), ultimately obtaining a highly relevant set of resource path candidates.

[0151] Step S604: Sort the set of high-weight associated paths by total weight, generate corresponding hierarchical traceability identifiers for resource nodes in each path, and output resource path objects including hierarchical traceability identifiers.

[0152] Among them, the hierarchical traceability identifier includes industry sector code, industry code, production node code and resource node code;

[0153] Specifically, industry segment codes are obtained from the overall industry map, industry codes and production node codes are obtained from the single industry professional map, and resource node codes are obtained from resource node metadata. Industry segment codes are obtained from the overall industry layer topology tree, such as "Coastal Infrastructure" → COASTAL-INFRA, representing the strategic level of resource ownership. Industry codes are extracted from the single industry layer, such as "Water Transport Engineering" → SW-2023, identifying the business execution domain. Production node codes inherit the Y-axis coordinates to locate the process links. Resource node codes are assigned unique IDs by the resource type library.

[0154] It should be noted that this hierarchical coding structure not only enables reversible retrieval and structured management of resource paths, but also enhances the interoperability of resources across heterogeneous systems, providing a reliable identity referencing mechanism for data sharing and auditing. The path set and its corresponding identifiers will then be output as resource path objects to the user interface for selection.

[0155] Step S605: Update the graph weight data according to the resource path object selected by the user, adjust the call frequency of associated edges and user rating, and trigger dynamic update of the topological relationship of the graph dictionary.

[0156] Once a user completes the adoption and invocation of a resource path, the system needs to update the edge weights in the graph structure in real time to maintain the dynamic evolution and self-optimization of the graph.

[0157] For example, the process first updates the edge call frequency incrementally through the call log. Then, based on user feedback ratings and historical rating records, it adjusts the user rating weights using a weighted average method, thereby updating the overall edge weights. All weight updates are written to the persistent layer of the graph dictionary (such as the Neo4j graph database or GraphQL storage engine) and trigger data synchronization updates in the visualization layer. Furthermore, when the call frequency of a certain path consistently exceeds a preset threshold (e.g., more than 50 calls), the system will automatically mark it as a "recommended path" and push it to the popular resource recommendation module to improve resource utilization efficiency and guide user behavior.

[0158] In the above embodiments, the resource retrieval method based on genealogical mapping not only automates the entire process from user natural language input to accurate resource identification and path recommendation, but also constructs a dynamically evolving resource graph structure. The three-dimensional coordinate system imposes dual constraints on resource location, including business processes and technical semantics; high-weighted path calculation integrates behavioral data and subjective evaluations, improving the rationality of recommendations; and hierarchical source identifiers enhance the interpretability and interoperability of resource paths.

[0159] Reference Figure 8 As a further implementation of the resource sharing method, it also includes:

[0160] Step S701: Call the resource demand intensity data of each node in the business process demand map and the resource supply intensity data of the corresponding node in the product capability matching map.

[0161] This involves establishing a quantitative mapping channel between "business needs" and "resource supply." When the system calls the resource demand intensity data for a node (such as "pile foundation construction procedure") in the business process demand map (X-axis), it essentially extracts the quantitative value of the resource urgency of that node within the business scenario. Simultaneously, it calls the resource supply intensity data (such as the supply intensity of "intelligent pile driving monitoring SDK") at the corresponding coordinate in the product capability matching map (Z-axis). This value originates from resource pool call records and performance evaluation metrics. This transforms the abstract business scenario (pile foundation construction) and physical resources (monitoring SDK) into computable numerical pairs, providing standardized input for subsequent value assessment.

[0162] Step S702: Calculate the overlap coefficient based on the overlap between resource demand intensity data and resource supply intensity data, and generate a two-dimensional resource value matrix;

[0163] The system calculates the degree of overlap in resource supply and demand matching by calling the supply and demand intensity data of the graph nodes. In the business process graph, the "resource demand intensity" marked on each node can be quantified as a demand index (e.g., Q demand = 10 units of resources / hour), while in the product capability matching graph, the "supply intensity" of resource nodes at the same coordinate can be represented as the current available resource service capacity (e.g., Q supply = 7 units of resources / hour).

[0164] Specifically, the overlap coefficient calculation method proposed in this application is to take the smaller of the two values ​​and multiply it by the value weighting factor configured in the business domain. The value weighting factor reflects industry priority (e.g., the coefficient for key process points is higher) and inherits domain knowledge configuration from the professional graph. For example, when a node's Q demand = 10, Q supply = 7, and the weight is 0.9, the overlap coefficient is 6.3. The overlap coefficients of all node pairs are filled into a two-dimensional matrix to generate a resource value matrix. This matrix uses the coordinates of business process nodes and resource types as dimensions, and the matrix values ​​reflect the resource supply and demand matching degree at that coordinate location.

[0165] Step S703: Align and map the time sequence nodes of the production stage demand map with the process nodes of the business process demand map to generate a spatiotemporal correlation matrix.

[0166] Specifically, following spatial analysis, to consider the temporal correlation between tasks and production lines, this method also performs time-axis alignment mapping between process nodes in the business process requirement map and time-series nodes in the production stage requirement map. Business nodes (X-axis) are aligned and mapped to their corresponding production time-series nodes (Y-axis), forming a "spatiotemporal correlation matrix." The rows of the matrix represent time-series nodes on the Y-axis, the columns represent business process nodes on the X-axis, and the matrix cell value is the total resource requirement of the corresponding process node at that time sequence. This mapping ensures that resource matching analysis not only occurs in the spatial dimension but also considers the actual evolution of requirements in the temporal dimension, thereby enhancing the timeliness of the analysis and the accuracy of task matching.

[0167] Step S704: Scan the coordinate regions in the two-dimensional resource value matrix where the overlap coefficient is lower than the preset overlap threshold, and generate a resource gap early warning signal by combining the total resource data of the corresponding nodes in the spatiotemporal correlation matrix.

[0168] The system scans low-matching areas in the two-dimensional resource value matrix to identify locations where the resource supply-demand overlap coefficient is below a preset overlap threshold. For example, if the overlap coefficient at a certain coordinate is less than 0.3, it indicates extreme resource scarcity. The system then extracts the total resource demand value corresponding to that coordinate using the spatiotemporal correlation matrix and compares it with the actual supply in the product capability matching map. Based on the gap calculation formula: (Total resource demand - Actual supply) / Total resource demand, the system derives the resource gap degree. When the gap degree exceeds a warning threshold (e.g., 60%), the system generates a warning signal, specifying the exact location of the gap (i.e., sequence node, process node, resource type) and a resource identifier. This resource identifier is a hierarchical classification code that can be directly used to point to the corresponding resource item in the resource pool (e.g., project documents or code packages in the benchmark library or product library).

[0169] It should be noted that the early warning signal not only serves as a notification function but also triggers an automatic response mechanism. The system retrieves the corresponding resource from the resource pool based on the identifier, filling the relevant node in the supply map and updating its supply intensity data. Simultaneously, the cell value at that location in the resource value matrix is ​​corrected, achieving closed-loop control. In this way, the resource sharing platform possesses the ability to dynamically allocate resources, predict shortages, and automatically adjust, significantly improving the efficiency and responsiveness of enterprise resource utilization.

[0170] In the above embodiments, a multi-dimensional resource analysis mechanism combining a three-dimensional map with a two-dimensional resource value matrix and a spatiotemporal correlation matrix is ​​constructed, achieving a fine depiction and dynamic identification of resource supply and demand status. Based on the spectral mapping relationship between business process requirements, production stage rhythm, and resource capabilities, the system can identify resource shortages in advance, automatically activate the resource pool through early warning signals, and dynamically repair resource supply and demand imbalances. This application's embodiments realize a fundamental transformation of the resource sharing system from static supply to intelligent allocation, and from manual judgment to data-driven evolution, possessing high industrial adaptability, real-time performance, and adaptive capabilities, providing fundamental support for digital factories and intelligent enterprises.

[0171] This application also discloses a digital resource sharing system based on genealogical mapping relationships.

[0172] A digital resource sharing system based on genealogical mapping relationships, the resource sharing system comprising:

[0173] The heterogeneous data acquisition module is used to acquire multi-source heterogeneous data from the enterprise's internal distributed system through a preset interface protocol;

[0174] The data processing module is used to standardize multi-source heterogeneous data based on preset resource classification standards and generate a structured resource pool.

[0175] The association topology parsing module is used to build the graph dictionary module, perform association topology parsing on the resources in the structured resource pool, and output the resource relationship topology graph;

[0176] The multi-graph generation module is used to establish the genealogical relationship between resources and business scenarios based on the structured resource pool and resource relationship topology map and the three-dimensional mapping model, and generate business process requirement map, production stage requirement map and product capability matching map respectively.

[0177] The platform building module is used to integrate business process requirement maps, production stage requirement maps, and product capability matching maps to build a visualized digital resource capability platform.

[0178] The target resource identifier output module is used to respond to the resource call command input by the user and output the target resource identifier and associated path based on the visual digital resource capability platform.

[0179] A digital resource sharing system based on phylogenetic mapping relationship according to an embodiment of this application can implement any of the above-described resource sharing methods, and the specific working process of each module in the resource sharing system can refer to the corresponding process in the above-described method embodiments.

[0180] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0181] This application also discloses a computer device.

[0182] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a digital resource sharing method based on a genealogical mapping relationship as described above.

[0183] This application also discloses a computer-readable storage medium.

[0184] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above in any of the methods of digital resource sharing based on genealogical mapping.

[0185] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0186] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0187] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A digital resource sharing method based on genealogical mapping relationships, characterized in that, The resource sharing method includes: Obtain multi-source heterogeneous data from the enterprise's internal distributed system through a pre-defined interface protocol; Based on a preset resource classification standard, the multi-source heterogeneous data is standardized to generate a structured resource pool. A graph dictionary module is constructed to perform topological analysis on the resources in the structured resource pool and output a resource relationship topology graph. Based on the structured resource pool and resource relationship topology, a genealogical relationship between resources and business scenarios is established based on a three-dimensional mapping model, generating a business process requirement map, a production stage requirement map, and a product capability matching map, respectively. By integrating the aforementioned business process requirement map, production stage requirement map, and product capability matching map, a visualized digital resource capability platform is constructed. In response to the user's input resource call command, the target resource identifier and associated path are output based on the visualized digital resource capability platform; The steps to build a visual digital resource capability platform include: A four-level graph framework is constructed, including a full-industry panoramic graph layer, a single-industry professional graph layer, a single-stage resource graph layer, and an application scenario card layer. Inject the business process node chain into the single-stage resource graph layer; Inject the production stage demand map into the single-industry professional map layer; Bind the digital product attribute sequence to the nodes of the full-industry panoramic map layer; The weight value data of the product capability matching map is called, and the scene card field in the application scene card layer is filled based on the industry code of the single industry professional map layer, the process node identifier of the single stage resource map layer, and the industry sector node of the full industry panoramic map layer. Deploy a graph dynamic update engine to monitor new resource streams at the data access interface and trigger hierarchical graph updates based on the type of new resource. By integrating the four-layer graph framework and graph dynamic update engine into the visual interactive interface, a visual digital resource capability platform is constructed. The steps of calling the weight value data of the product capability matching map and filling the scene card fields in the application scene card layer based on the industry code of the single-industry professional map layer, the process node identifier of the single-stage resource map layer, and the industry segment node of the full-industry panoramic map layer include: The system retrieves the weight threshold filtering results from the product capability matching graph, filters out candidate products whose weight values ​​are lower than the preset weight threshold, and generates a list of filtered candidate products. Receive the industry code input corresponding to the single-industry professional map layer; wherein, the industry code corresponding to the single-industry professional map layer is a business semantic label divided within the enterprise, which is bound to the resource usage rules specific to the industry; Based on the industry code, retrieve the resource adaptation rule base corresponding to the industry code; Based on the filtered candidate product list and resource adaptation rule base, a solution card is generated; The solution card is populated into the predefined field slots of the application scenario card layer to form a structured card instance; The process node identifier of the single-stage resource graph layer is associated with the life cycle stage code of the single-industry professional graph layer; The industry codes are used to associate industry sector nodes in the overall industry map layer; A hierarchical traceability identifier is written into the structured card instance; wherein the hierarchical traceability identifier is generated based on the industry sector node and the life cycle stage encoding.

2. The digital resource sharing method based on genealogical mapping relationship according to claim 1, characterized in that, Before the step of establishing the genealogical relationship between resources and business scenarios based on the 3D mapping model, the following steps are also included: A functional module theme library is constructed based on business scenario tags, and resources in the structured resource pool are associated with corresponding functional modules; wherein, the functional module theme library includes a resource intelligent search module, a product sharing module, and a graph dictionary module; Based on the user query logs of the resource intelligent search module, a set of query intent vectors is output through a natural language processing model, which serves as the input features for the business process requirement graph and the production stage requirement graph. Based on the resource download volume and user ratings of the product sharing module, a popularity rating vector is calculated and used as the input feature of the product capability matching map. The resource relationship topology graph output by the graph dictionary module is optimized by edge weight to generate a topological adjacency matrix, which is used as the input feature of the three-dimensional mapping model.

3. The digital resource sharing method based on genealogical mapping relationship according to claim 2, characterized in that, The steps for establishing a genealogical relationship between resources and business scenarios based on a 3D mapping model, and generating business process requirement maps, production stage requirement maps, and product capability matching maps respectively, include: Identify the dependencies between business process nodes in the first dimension, associate the business process node sequence in the first dimension, output the business process node chain, and obtain a business process requirement graph covering the entire business process. By associating the production lifecycle node sequence in the second dimension, a production stage resource demand matrix is ​​output, resulting in a production stage demand map; the production lifecycle nodes include the planning and design, construction and operation and maintenance stages. In the third dimension, the sequence of digital product attributes is associated. By binding the matching weight values ​​between digital products and business scenarios, a product capability scoring table is output, resulting in a product capability matching map.

4. The digital resource sharing method based on genealogical mapping relationship according to claim 1, characterized in that, The steps of responding to a user-input resource request and outputting the target resource identifier and associated path based on the visualized digital resource capability platform include: The semantics of the resource call instruction are parsed, and the target resource type and business domain are identified according to a preset resource classification standard; The three-dimensional coordinates of the corresponding target resource nodes are determined based on the single-industry professional map; Extract all associated edges of the target resource node from the graph dictionary, calculate the weight value of each associated edge, filter out associated edges below a preset threshold, and generate a set of high-weight associated paths; The set of high-weight associated paths is sorted by total weight, and a corresponding hierarchical traceability identifier is generated for the resource nodes in each path. The resource path object including the hierarchical traceability identifier is output. The graph weight data is updated based on the resource path object selected by the user, the call frequency of associated edges and user rating are adjusted, and the topological relationship of the graph dictionary is dynamically updated.

5. A digital resource sharing method based on genealogical mapping relationship according to any one of claims 1 to 4, characterized in that, The resource sharing method also includes: The system calls upon the resource demand intensity data of each node in the business process demand graph and the resource supply intensity data of the corresponding node in the product capability matching graph. Based on the overlap between the resource demand intensity data and the resource supply intensity data, the overlap coefficient is calculated to generate a two-dimensional resource value matrix. The time-series nodes of the production stage demand map and the process nodes of the business process demand map are aligned and mapped along the time axis to generate a spatiotemporal correlation matrix. The coordinate regions in the two-dimensional resource value matrix with an overlap coefficient lower than a preset overlap threshold are scanned, and a resource gap early warning signal is generated by combining the total resource data of the corresponding nodes in the spatiotemporal correlation matrix.

6. A digital resource sharing system based on genealogical mapping relationships, characterized in that, A digital resource sharing system for performing a genealogical mapping relationship-based method according to any one of claims 1 to 5, the resource sharing system comprising: The heterogeneous data acquisition module is used to acquire multi-source heterogeneous data from the enterprise's internal distributed system through a preset interface protocol; The data processing module is used to standardize the multi-source heterogeneous data based on a preset resource classification standard to generate a structured resource pool. The association topology parsing module is used to construct the graph dictionary module, perform association topology parsing on the resources in the structured resource pool, and output a resource relationship topology graph; The multi-graph generation module is used to establish the genealogical relationship between resources and business scenarios based on the structured resource pool and resource relationship topology graph, and generate business process requirement graph, production stage requirement graph and product capability matching graph respectively. The platform construction module is used to integrate the business process requirement map, production stage requirement map and product capability matching map to build a visualized digital resource capability platform. The target resource identifier output module is used to respond to the resource call command input by the user and output the target resource identifier and associated path based on the visualized digital resource capability platform.

7. A computer device, characterized in that: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 5.

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