Data asset warehouse construction method

Abstract

This application provides a method for constructing a data asset warehouse. The method includes: acquiring multi-source heterogeneous logistics data and performing semantic extraction and tagging processing to generate a set of logistics semantic features; based on a logistics domain ontology model, performing business relationship mapping and attribute modeling processing on the set of logistics semantic features to generate logistics data asset instances; performing multi-dimensional quality confidence assessment on the logistics data asset instances and performing weighted mapping processing on the assessment results to generate a dynamic value index characterizing the application potential of the assets; based on a storage strategy determined by the dynamic value index, persisting the logistics data asset instances to an asset storage architecture and simultaneously performing semantic alignment mapping with the logistics digital twin foundation to realize the construction of the data asset warehouse. This application improves the governance depth and application efficiency of data assets through semantic modeling and dynamic value assessment.

Description

Technical Field

[0001] This application relates to the field of logistics big data processing technology, and more specifically, to a method for constructing a data asset warehouse. Background Technology

[0002] With the rapid development of smart logistics, logistics companies generate massive amounts of multi-source heterogeneous data during their operations, including vehicle trajectories, warehouse inventory status, electronic waybill circulation, and various work orders generated by business systems. To unlock the potential value of this data, establishing an efficient data asset warehouse has become a core component of logistics digital transformation, aiming to achieve asset-based management and efficient data utilization.

[0003] Existing logistics data management solutions typically employ storage schemes based on data lakes or traditional relational databases. These schemes first extract raw data from various business systems using ETL (Extract, Transform, Load) tools; then, the data undergoes simple cleaning and format conversion before being uniformly stored in pre-defined database tables; finally, the application layer retrieves the data directly through SQL queries or reporting tools based on business needs.

[0004] However, this traditional approach has significant technical limitations. Due to the complexity of logistics business logic and the dispersed nature of data sources, traditional solutions lack in-depth analysis of the underlying business semantics, resulting in a clear semantic gap between the raw data and the actual business logic. Furthermore, existing solutions often assess data value based on static quality indicators, failing to dynamically quantify the asset potential of data according to real-time business intensity, leading to blind storage strategies and inefficient resource allocation. In addition, warehouse data often lags behind the real-time status of physical entities, lacking deep collaboration with the physical space state, making it difficult to achieve real-time evolution of data assets. Summary of the Invention

[0005] To alleviate the aforementioned technical problems, this application provides a method for constructing a data asset warehouse, which at least mitigates the aforementioned technical problems.

[0006] A method for constructing a data asset warehouse includes: Step 1, acquiring multi-source heterogeneous logistics data and performing semantic extraction and labeling processing on it to generate a set of logistics semantic features; Step 2, based on a logistics domain ontology model, performing business relationship mapping and attribute modeling processing on the set of logistics semantic features to generate logistics data asset instances; Step 3, performing multi-dimensional quality confidence assessment on the logistics data asset instances and performing weighted mapping processing on the assessment results to generate a dynamic value index characterizing the application potential of the assets; Step 4, based on the storage strategy determined by the dynamic value index, persisting the logistics data asset instances to the asset storage architecture and simultaneously performing semantic alignment mapping with the logistics digital twin foundation to realize the construction of the data asset warehouse.

[0007] Optionally, the specific implementation process of step 1 includes: performing feature deconstruction on the multi-source heterogeneous logistics data for the spatiotemporal trajectory flow and discrete business event flow of logistics to determine the primary feature loads with spatial displacement indicators and time state nodes; using semantic annotation operators in the logistics context to perform business intent anchoring processing based on context logic on the primary feature loads, thereby implementing the semantic extraction and determining logistics entities and circulation events; mapping the logistics entities and circulation events to a preset attribute description space to perform classification labeling, thereby completing the labeling processing and generating the logistics semantic feature set.

[0008] Optionally, the specific implementation process of step 2 includes: inputting the set of logistics semantic features into the logistics domain ontology model, identifying the implicit dependency relationship between the logistics entity and the circulation event through a semantic embedding algorithm, and constructing a logical topological connection, thereby performing the business relationship mapping and generating a logistics business association knowledge graph; using graph neural network operators to extract high-order structural features from the logistics business association knowledge graph, and performing attribute completion processing on the high-order structural features for the logistics business scenario, thereby performing the attribute modeling processing and generating the logistics data asset instance.

[0009] Optionally, the specific implementation process of step 3 includes: using the time-depreciation model and the source reliability matrix, performing confidence verification on the logistics data asset instance for data freshness and source authority, and calculating and generating a multi-dimensional quality score set; mapping the quality score set to a pre-set business benefit cost model, and performing a profit and loss assessment on the difficulty of data acquisition and the contribution to decision support, thereby generating the assessment result.

[0010] Optionally, the weighted mapping process in step 3 is as follows: identify the real-time intensity of the target business associated with the logistics data asset instance to determine the scenario association weight that characterizes the sensitivity of the current business to data accuracy requirements; use the scenario association weight as an input parameter to perform normalized weighted mapping on the quality score set in the evaluation result, thereby completing the weighted mapping process and generating the dynamic value index.

[0011] Optionally, the process of persisting to the asset storage architecture in step 4 includes: performing energy efficiency classification processing for the dynamic value index to meet real-time response requirements, matching the classification results to the first storage bit order composed of high-performance flash memory media or the second storage bit order composed of high-capacity mechanical media in the asset storage architecture; and based on the matching results, classifying and writing the logistics data asset instances into the corresponding storage bit order, thereby realizing the persistence to the asset storage architecture.

[0012] Optionally, the semantic alignment mapping process in step 4 includes: extracting the real-time virtual mirror state of the physical entity in the logistics digital twin base; performing time-series consistency-based feature fusion processing on the static business attributes carried by the logistics data asset instance and the real-time virtual mirror state to generate an asset twin synchronization feature body; and performing self-healing attribute correction processing for the data asset warehouse by comparing the deviation value between the asset twin synchronization feature body and the preset standard ontology construct, thereby implementing the semantic alignment mapping.

[0013] Optionally, after generating the logistics data asset instance, the method further includes: performing semantic conflict detection on the logistics data asset instance under different storage batches through the logistics business association knowledge graph to identify heterogeneous attribute contradiction features for the same logistics entity; performing logical arbitration processing based on business confidence on the heterogeneous attribute contradiction features to generate a unified standard asset fingerprint and update it to the data asset warehouse.

[0014] Optionally, the storage strategy determined based on the dynamic value index includes: real-time monitoring of the hotspot frequency changes of the logistics data asset instance being called by the application side, and performing attenuation mitigation calculation on the dynamic value index based on the hotspot frequency changes; in response to the calculation result reaching a preset hierarchical switching threshold, driving the corresponding asset to perform asynchronous cross-layer migration processing between the first storage order and the second storage order, so as to realize the dynamic adjustment of the storage strategy.

[0015] Optionally, the multi-source heterogeneous logistics data includes flow location payloads sensed by mobile terminals, unit identity payloads extracted by radio frequency identification terminals, and fulfillment link payloads synchronized by business systems.

[0016] This application provides a method for constructing a logistics scenario data asset warehouse based on ontology mapping and dynamic value quantification. It addresses the technical deficiencies in traditional logistics data management solutions, such as the lack of business semantics, the single dimension of value assessment, and the disconnect between data and physical entity status. By constructing a closed-loop technical link of "deep semantic processing - dynamic value anchoring - real-time twin alignment", it achieves efficient governance and energy-efficient allocation of logistics data assets.

[0017] First, by acquiring multi-source heterogeneous logistics data and performing semantic extraction and labeling, a set of logistics semantic features is generated, alleviating the technical deficiency of traditional solutions where raw data lacks business logic interpretability. Compared to traditional solutions that only perform simple format conversion, this application utilizes semantic extraction to extract logistics entities with physical attributes and circulation events with process attributes from messy trajectories, waybills, and work order flows. This process achieves a fundamental leap from "raw bit stream" to "business semantic stream," ensuring that subsequent asset construction can be anchored on a data foundation with industry logic, providing a highly reliable semantic foundation for eliminating logistics data silos.

[0018] Secondly, by performing business relationship mapping and attribute modeling based on an ontology model in the logistics domain to generate logistics data asset instances, this alleviates the problem that traditional database solutions cannot represent the complex dynamic topological relationships in logistics. This application no longer stores data points in isolation, but rather identifies the temporal logical relationships and spatial evolution topology between entities and events through ontology mapping, thereby constructing a logistics business relationship knowledge graph. This processing action, by introducing high-order logical connection features, aggregates fragmented feature sets into asset instances with complete business lifecycles, enabling the data warehouse to recreate the real logistics fulfillment process in a higher dimension, thus improving the business cohesion and knowledge level of the data assets.

[0019] Furthermore, by performing multi-dimensional quality confidence assessments on logistics data asset instances and generating a dynamic value index, this approach alleviates the technical shortcomings of traditional management methods, such as the difficulty in quantifying data value and the blind allocation of storage resources. This application innovatively introduces "scenario-related strength" as a weighting adjustment factor, non-linearly normalizing the technical quality (confidence) of the data itself with the real-time demand intensity on the business side to obtain this dynamic value index. This index objectively reflects the application potential of assets at specific business moments. Compared to traditional static and subjective assessment methods, the quantitative results provided by this solution are more scientific and accurate, providing a data-driven decision-making basis for subsequent differentiated storage scheduling.

[0020] Finally, persistent storage based on a dynamic value index and simultaneous semantic alignment mapping of the digital twin foundation alleviate the technical shortcomings of traditional warehouses, such as data lag and misalignment with physical space conditions. This application guides the flow of assets between storage locations of different energy efficiency levels through the value index, improving the response time of high-value, high-frequency assets. Simultaneously, real-time synchronization alignment with the virtual mirror state of the logistics digital twin foundation enables self-healing correction of data warehouse attributes. This collaborative mechanism based on "physical mirror driving logical assets" ensures a high degree of consistency between the constructs within the data asset warehouse and the physical entity state, thereby constructing a data asset foundation that can dynamically evolve with the physical environment and provides deterministic service guarantees. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a data asset warehouse construction method provided in this application embodiment.

[0022] Figure 2 This is a schematic diagram of a data asset warehouse construction device provided in an embodiment of this application.

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] like Figure 1 The illustration shows a data asset warehouse construction method provided in this application embodiment, comprising: Step 1, acquiring multi-source heterogeneous logistics data and performing semantic extraction and tagging processing on it to generate a logistics semantic feature set; Step 2, performing business relationship mapping and attribute modeling processing on the logistics semantic feature set based on a logistics domain ontology model to generate a logistics data asset instance; Step 3, performing multi-dimensional quality confidence assessment on the logistics data asset instance and performing weighted mapping processing on the assessment results to generate a dynamic value index characterizing the asset application potential; Step 4, persisting the logistics data asset instance to an asset storage architecture based on a storage strategy determined by the dynamic value index, and simultaneously performing semantic alignment mapping with the logistics digital twin base to realize the construction of the data asset warehouse.

[0025] Optionally, the specific implementation process of step 1 includes: performing feature deconstruction on the multi-source heterogeneous logistics data for the spatiotemporal trajectory flow and discrete business event flow of logistics to determine the primary feature loads with spatial displacement indicators and time state nodes; using semantic annotation operators in the logistics context to perform business intent anchoring processing based on context logic on the primary feature loads, thereby implementing the semantic extraction and determining logistics entities and circulation events; mapping the logistics entities and circulation events to a preset attribute description space to perform classification labeling, thereby completing the labeling processing and generating the logistics semantic feature set.

[0026] Preferably, in the specific technical implementation of step 1, the multi-source heterogeneous logistics data, consisting of location point sequences collected by sensors, status pulses read by RFID terminals, and fulfillment logs synchronized by the business system, is processed by streaming segmentation and protocol decoding on the accessed binary bit stream. This process identifies and divides the logistics spatiotemporal trajectory stream, which represents the continuous motion trajectory of physical objects, into a discrete business event stream, which represents the abrupt changes in business state characteristics. To this end, this application establishes the logical boundaries for data feature extraction. By dividing the chaotic signal sources into a trajectory dimension with dynamic characteristics and an event dimension with logical characteristics, a standardized input source is obtained for subsequent fine-grained feature deconstruction. This results in two types of data streams recording the basic physical manifestations of the logistics process, enabling subsequent processing actions to be anchored on the flow load with physical semantic consistency.

[0027] Preferably, in one scenario, deep feature deconstruction processing is performed on the logistics spatiotemporal trajectory flow and the discrete business event flow to extract the latitude and longitude changes and velocity vectors in the logistics spatiotemporal trajectory flow, and to calculate and generate spatial displacement indicators describing spatial location transfers. Simultaneously, timestamps and state machine identifiers of key actions are extracted from the discrete business event flow to identify and generate time state nodes describing the switching of fulfillment stages. Therefore, this processing method achieves a morphological evolution from raw sampled values ​​to component features with physical cohesion. By establishing these two types of spatiotemporally correlated quantitative parameters, the primary feature load characterizing the underlying physical state of logistics is finally determined. This load alleviates the technical deficiency of heterogeneous data being difficult to uniformly represent at the feature level, thereby obtaining a digital analysis object for subsequent execution intent perception and semantic injection.

[0028] Preferably, the aforementioned determined primary feature payloads are acquired to drive the built-in semantic annotation operator to perform logical mapping processing for business intent. During the generation process, the semantic annotation operator retrieves a pre-set logistics business rule template, aligns the acquired spatial displacement indicators with the time state nodes on the time axis, identifies the operational motivations behind trajectory segments, and thus completes the business intent anchoring process. This processing action, by introducing industry logic verification, achieves the transformation from "pure physical features" to "business meaning," thereby implementing the semantic extraction and further determining the logistics entity and the corresponding circulation event. These determined semantic objects record the core logic of logistics fulfillment, enabling the subsequent tagging process to closely align with real logistics operation scenarios and improving the depth of asset identification.

[0029] Preferably, the process involves sensing the identified logistics entities and circulation events, driving their built-in attribute description mapping interface to perform feature projection processing. Based on the category definitions of the logistics domain ontology model, the static inherent attributes of the logistics entities and the dynamic process attributes of the circulation events are identified, and these objects and their attribute features are mapped to a preset attribute description space for classification and labeling. In the specific technical implementation of this processing, each identified object is assigned a feature label with a unique semantic reference, thereby completing the labeling process. Therefore, this processing method establishes a structured data expression system with multi-dimensional profile features, alleviating the semantic fragmentation defects in traditional data management and improving the semantic completeness for building a data asset warehouse with deterministic logic.

[0030] Preferably, the feature labels generated in the attribute description space are extracted, and a feature aggregation algorithm is used to perform logical aggregation processing for a single business cycle. Multiple semantic labels associated with the same business primary key or the same entity are vectorized and concatenated to establish the co-occurrence probability and evolutionary relationship between the labels, thereby generating the final logistics semantic feature set. This formation process realizes the morphological evolution from scattered annotation to a systematic feature body, resulting in a logistics semantic feature set that not only contains independent indicators of entities and events, but also embodies the topological evolution logic of the entire logistics chain, thus verifying the validity of the initial feature deconstruction. This step provides high-quality, computable semantic payloads for subsequent steps of ontology mapping and asset attribute modeling.

[0031] In summary, this application first establishes a physical origin for the entire semantic extraction chain through feature deconstruction, and the generated primary feature payload improves the quality of data input. Next, semantic injection technology, through business intent anchoring, elevates data from physical indicators to business logic. Finally, the resulting logistics semantic feature set achieves deep coupling between intent labels and physical constructs. Therefore, this application achieves deterministic semantic annotation, alleviating the shortcomings of traditional database solutions that cannot perceive business context, and improving the information density and asset value potential during the data asset warehouse construction phase.

[0032] Optionally, the specific implementation process of step 2 includes: inputting the set of logistics semantic features into the logistics domain ontology model, identifying the implicit dependency relationship between the logistics entity and the circulation event through a semantic embedding algorithm, and constructing a logical topological connection, thereby performing the business relationship mapping and generating a logistics business association knowledge graph; using graph neural network operators to extract high-order structural features from the logistics business association knowledge graph, and performing attribute completion processing on the high-order structural features for the logistics business scenario, thereby performing the attribute modeling processing and generating the logistics data asset instance.

[0033] Preferably, in the specific technical implementation of step 2, the set of logistics semantic features generated in the preceding steps is obtained and injected into the preset logistics domain ontology model to perform semantic alignment processing. During the specific processing, the feature elements within the set are normalized and mapped according to the category hierarchy defined in the ontology model, identifying the standard conceptual attributes of feature items in the logistics context, thereby generating an ontology-aligned feature stream. To this end, this application establishes a unified business context for heterogeneous features, mitigating the risk of logical conflicts caused by differences in multi-source data descriptions, and obtaining a standardized feature carrier for subsequent deep semantic mining. This results in the pre-normalization of feature dimensions, enabling subsequent processing actions to be anchored on a data foundation with consistent business logic.

[0034] Preferably, in the specific technical processing step 2, the generated ontology alignment feature stream is extracted, and the built-in semantic embedding algorithm is driven to perform high-dimensional space vectorization projection processing. By calculating the association probability of feature words in a preset logistics corpus, the abstract feature terms are transformed into a multi-dimensional semantic vector space payload carrying rich semantic information. During this process, the spatial proximity of the logistics entity and the circulation event in the vector space is identified, and algebraic operations are used to quantify the semantic fit between them, thereby discovering the implicit dependencies hidden beneath the physical manifestations. This step transforms nonlinear business associations into computable algebraic indicators, improving the sensitivity to capturing implicit logic. The generated multi-dimensional semantic vector space payload provides the most accurate computational foundation possible for subsequently constructing complex topological connections.

[0035] Preferably, in one scenario, the generated multidimensional semantic vector spatial payload is sensed, driving the internal structured modeling module to execute the logical topology connection construction action between different business nodes. By analyzing the evolution pulses of the circulation events on the time axis and the spatial displacement trajectory of the logistics entities, the causal chains and collaborative constraints between entities and events are identified, thereby executing the business relationship mapping. In the specific technical implementation of the processing, a directed acyclic graph structure representing the business collaborative logic is established, and discrete semantic vectors are associated as a topological network with structured features, thereby generating the final logistics business association knowledge graph. This graph records the dynamic interaction relationships of the entire logistics chain, alleviating the technical defects of traditional database solutions in representing long-chain logistics dependencies, and establishing a global structural framework for asset governance.

[0036] Preferably, in step 2, the generated logistics business association knowledge graph is extracted, and its configured graph neural network operator is driven to perform convolutional extraction processing targeting deep topological features. By performing multi-layer neighborhood feature aggregation operations, the structural position and evolution weight of each node in the knowledge graph within the global network are perceived, thereby capturing the complex business patterns contained in the graph to generate the high-order structural features. Therefore, this processing method achieves a sublimation from local point attributes to global topological patterns, resulting in high-order structural features that not only include the original labels of entities but also incorporate their hub value and risk propagation characteristics in the logistics fulfillment network. By utilizing these deep structural loads, the ability to describe the evolution path of asset value is enhanced, providing a high-order feature-driven source for subsequent targeted attribute modeling.

[0037] Preferably, in the specific technical implementation of step 2, for the logistics business association knowledge graph constructed by the preceding steps, the input feature embedding layer configured inside the graph neural network operator is driven to perform initial feature vectorization processing. During the specific processing, the static attributes of the logistics entities corresponding to each node in the knowledge graph and the temporal dynamic features of the circulation events are extracted, and these heterogeneous payloads are mapped to a continuous vector space of a unified dimension to generate initial node feature embedding vectors. Therefore, this processing method converts the topological discrete signals in the graph into numerical operators that can be computed by the neural network, alleviating the deficiency that entity relationships in the logistics scenario are difficult to directly participate in algebraic operations. This results in the initial node feature embedding vectors, which establish a feature base for subsequent convolutional extraction of neighborhood features, improving the consistency of the underlying physical state representation at the algorithm level.

[0038] Preferably, the generated initial node feature embedding vector is obtained, driving the neighborhood information aggregation layer in the graph neural network operator to perform convolutional extraction processing for logistics topology connections. The implicit dependencies and logical topology connections between nodes in the graph are identified, and the first-order neighborhood node features of the current node are extracted according to a preset graph convolution kernel. Weight-based superposition operations are then performed to generate a neighborhood aggregation intermediate feature body. This processing simulates the physical proximity and business relevance in logistics flow, realizing the flow and convergence of information in the topological structure. Therefore, this processing method captures the collaborative constraints between nodes and their direct business stakeholders. The generated neighborhood aggregation intermediate feature body initially incorporates the environmental context features of the node in the local fulfillment network, providing preliminary feature indicators for subsequent identification of complex business patterns.

[0039] Preferably, in one scenario, the business weight attention layer integrated within the graph neural network operator is driven to perform importance measurement processing based on business intensity on the generated neighborhood aggregation intermediate feature body. By calculating the response frequency and decision cost of the circulation events corresponding to different connection edges in the entire logistics chain, non-negative attention scores are assigned to different topological connection paths, thereby determining the dynamic evolution weights describing the priority of business execution. This processing step realizes the technical evolution from "uniform aggregation" to "value-oriented aggregation". By injecting business importance into the convolution process, core link features that have a significant impact on decision-making can be preferentially retained, alleviating the technical deficiency of traditional algorithms that cannot distinguish between key logistics nodes and ordinary nodes. This allows the weight features to directly participate in subsequent feature reconstruction, improving the business orientation of the asset modeling results.

[0040] Preferably, the dynamically evolved weights generated above are sensed to drive the nonlinear topological activation layer in the graph neural network operator to perform manifold transformation processing for the feature distribution. The weighted neighborhood feature load is injected into a preset nonlinear activation operator to perform nonlinear distortion and dimensional compression of the feature space, thereby generating high-order logical activation features that characterize the complex motion patterns of nodes. Therefore, this processing method utilizes nonlinear transformation to capture the volatility and randomness present in the logistics fulfillment network, improving the sensitivity to extreme operational scenarios (such as supply chain disruptions or sudden traffic surges). The determined high-order logical activation features achieve a deep abstraction of the dynamic logistics process, enabling subsequent output actions to be anchored to feature forms with high robustness.

[0041] Preferably, the generated higher-order logical activation features are extracted and used to drive the multi-level perception layer stacked within the graph neural network operator to perform feature transfer and deepening processing across skip levels. Through the serial iteration of the multi-level network structure, each node can perceive remote topological information beyond the second or even third order, thereby identifying the node's structural position in the global network and determining the hub value that characterizes the asset hub's energy efficiency. Simultaneously, potential failure disturbance propagation paths in the topological links are identified, and risk propagation features describing the ability to influence anomalies are calculated and generated. This formation process realizes a transformation from "point features" to "whole network topology patterns," resulting in these higher-order loads that not only record the physical properties of entities but also their topological impact effectiveness in the logistics fulfillment chain, enhancing the asset warehouse's ability to describe evolutionary paths.

[0042] Preferably, all structural and activation features obtained from the above processing are summarized, and the feature fusion output layer in the graph neural network operator is driven to perform the final morphological aggregation processing, thereby ultimately generating the higher-order structural features. In the specific technical implementation of the generation, the hub value, the risk propagation feature, and the original label of the node are subjected to a tensor-based fusion operation, and an evolution phase determined by the temporal alignment operator is introduced to generate a structured feature vector containing multi-dimensional spatiotemporal information. This formation process represents the completion of the morphological closed loop of logistics data assets from "isolated information points" to "network collaborative assets".

[0043] In summary, in this application, the input feature embedding layer, neighborhood information aggregation layer, business weight attention layer, nonlinear topology activation layer, multi-level structure perception layer, and feature fusion output layer perform feature vectorization, convolution aggregation, weight allocation, activation transformation, topology recognition, and morphological aggregation. The embedding layer establishes the computational origin for the entire algorithm chain, while the aggregation layer improves the substantial mapping of physical topological relationships in the feature dimension. Next, dynamic adjustment of attention weights optimizes feature extraction accuracy using business semantics. Finally, the resulting high-order structural features achieve the ultimate portrait of logistics entities and their relationships. To this end, the global perception characteristics of graph learning compensate for the shortcomings of traditional database solutions in perceiving complex topological patterns, improving the information richness and governance efficiency in the data asset warehouse construction phase.

[0044] Preferably, the generated high-order structural features are sensed, and the logistics business scenario of the current target is identified. Attribute completion processing for asset profile completeness is performed. Association calculations for the logistics decision-making cost model are performed on the acquired structural features to deduce the performance attributes or compliance indicators that the asset should possess in a specific operational stage. This is used to implement attribute modeling processing, thereby generating an asset-based attribute carrier. In the specific technical implementation of generation, dynamically extracted graph structural features are non-linearly fused with a static industry business rule template to ensure that each generated attribute item is anchored to the actual operational boundary, thus obtaining the asset-based attribute carrier. This achieves the evolution of the original data into a form with complete asset profile features, improving the completeness and business relevance of data definition within the asset warehouse from a technical perspective.

[0045] Preferably, the generated asset attribute carrier is obtained, and the built-in asset establishment interface is driven to perform the final instantiation and encapsulation process, thereby determining the final generated logistics data asset instance. In the specific process of determination, the quantified attribute features are logically bound to the corresponding logistics entity hardware identifier, and asset version fingerprints and ownership metadata synchronized by the business system are introduced to generate an asset unit with a unique business identity. This formation process achieves a closed loop from an abstract attribute model to a digitally storable and business-accessible asset, thus obtaining the logistics data asset instance. This reverse verification of the correctness of the ontology mapping and graph neural network learning logic provides a high-quality analysis object for subsequent multi-dimensional quality confidence assessment and dynamic value index generation.

[0046] Optionally, the specific implementation process of step 3 includes: using the time-depreciation model and the source reliability matrix, performing confidence verification on the logistics data asset instance for data freshness and source authority, and calculating and generating a multi-dimensional quality score set; mapping the quality score set to a pre-set business benefit cost model, and performing a profit and loss assessment on the difficulty of data acquisition and the contribution to decision support, thereby generating the assessment result.

[0047] Preferably, in the specific technical implementation of step 3, the logistics data asset instance generated by the preceding steps is obtained, and the built-in time-domain analysis operator is driven to extract the business occurrence timestamp recorded in the instance. Simultaneously, the current system clock information is obtained to perform the confidence verification for the data freshness. During the verification process, the time difference between the business occurrence time and the current time is injected into the preset time-depreciation model. The confidence weight corresponding to this difference is calculated using an exponential decay algorithm, thereby generating a dynamic time-depreciation confidence load characterizing the immediacy of the data asset. Therefore, this application quantifies the value reduction effect of logistics data over time, alleviating the technical defects of traditional solutions that statically treat historical data and real-time data, leading to decision-making biases. This results in the dynamic time-depreciation confidence load recording the survival significance of the asset at the current simulation moment, enabling subsequent quality diversity to be anchored on a data foundation with time-depreciation authenticity.

[0048] Preferably, for the logistics data asset instance acquired above, the built-in source traceability module is synchronously driven to identify the data payload's acquisition terminal attributes and the business system identifier to which it belongs. This is used to perform a confidence verification of the source authority, retrieving a preset source reliability matrix. Based on the identified terminal type (e.g., high-precision positioning terminal or manual data entry terminal), the matrix row and column indexes are executed to obtain the preset reliability weighted score corresponding to that source, thereby determining the source authority weight value. Therefore, this processing method realizes the evolution from physical device attributes to data credibility indicators. By establishing this weight value, interference from dirty data caused by insufficient accuracy of front-end sensing devices or human input errors can be filtered out, improving the purity at the source level for building a high-quality data asset warehouse. The determined source authority weight value will participate as a core influencing factor in the subsequent comprehensive score superposition.

[0049] Preferably, the generated dynamic timeliness confidence load and source authority weight value are extracted, driving the internal quality feature aggregation operator to perform orthogonal weighted calculations for different dimensions. The timeliness load and authority weight are multiplied, and a verification remainder for data completeness and compliance is introduced to calculate and generate a multi-dimensional set of primary quality scores. In the specific technical implementation, this set includes quantitative scores describing the confidence of a single asset instance in four dimensions: time, space, source, and structure, thus completing the calculation and generation of the quality score set. This process achieves a deep integration from single-dimensional indicator detection to multi-dimensional quality profiling, alleviating the technical shortcomings of traditional logistics data assessment dimensions being isolated and unable to comprehensively represent asset quality, and providing the most accurate data input carrier possible for subsequent business profit and loss assessment.

[0050] Preferably, during the specific processing of the quality feature aggregation operator, the built-in data index interface is driven to retrieve the dynamic timeliness confidence load generated by the timeliness decay model in the previous steps, and the source authority weight value determined by the source reliability matrix. An initial feature alignment operation is performed on these two types of heterogeneous loads to identify the synchronization phase of each load on the same logistics operation time axis, thereby determining the initial quality feature vector describing the "trust foundation" of the data. Therefore, this processing method establishes the logical starting point for the evaluation dimension, alleviating the technical shortcoming of traditional solutions where timeliness and source reliability are not correlated. It obtains an input source with physical semantic consistency for subsequent deep aggregation, thus obtaining the feature vector that records the original credibility state of a single asset instance before entering the warehouse, enabling subsequent processing actions to be anchored on a feature base with cross-source verification capabilities.

[0051] Preferably, the generated initial quality feature vector is obtained, driving the orthogonalization projection module configured within the quality feature aggregation operator to perform orthogonalized weighted calculations for different dimensions. In the specific technical implementation, the independent evolution patterns of the time and source dimensions within the logistics context are identified, and spatial projection technology is used to map the originally intertwined feature loads into mutually independent orthogonal subspaces. To this end, this processing method eliminates collinearity interference between features, ensuring that confidence fluctuations caused by time decay and weight differences caused by equipment precision can be independently quantified, thereby generating orthogonal weighted feature loads. This load ensures the rigor of the evaluation logic, avoids the problem of inflated or undervalued quality scores due to overlapping evaluation factors, and enhances the purity of input data in subsequent profit and loss assessment stages.

[0052] Preferably, the multiplication operator internally configured with the generated orthogonal weighted feature load is used to perform a product operation on the confidence gain. The dynamic timeliness confidence load, representing data freshness, is used as a multiplier and algebraically multiplied with the source authority weight value, representing the authority of the data source, to obtain the core trust strength index reflecting the dual constraints of time, space, and source. This processing step achieves a transformation from "independent scores" to "comprehensive gains." By performing this non-linear product operation, the confidence level can be automatically amplified or converged (e.g., even if the source is extremely authoritative, if the timeliness is extremely poor, the final product will tend to be lower). This dynamic adjustment mechanism accurately characterizes the "fleeting" asset characteristics of logistics data, establishing core components for generating the final quality profile.

[0053] Preferably, in one scenario, the verification process of executing the quality feature aggregation operator is illustrated. The built-in integrity audit operator and compliance scanning operator are driven to perform detection on the original logistics data for incomplete fields, misaligned formats, and business logic conflicts to identify the generated verification residues. These residues are then subjected to a quantitative deduction operation on the data structure to identify the defect scores of asset instances in terms of structured representation, and these scores are injected into the previously generated core trust strength index. To this end, this processing method compensates for quality defects through a "residue correction" technique to obtain a corrected trust feature body. This feature body not only includes dynamic sensing of timeliness and origin but also incorporates verification results of the physical integrity of the data itself, alleviating the technical limitations of traditional solutions in characterizing data "structural defects."

[0054] Preferably, the modified trust feature body generated above is extracted, driving the multi-dimensional profile generation module in the quality feature aggregation operator to perform attribute segmentation processing for different technical aspects. For logistics business needs, the information entropy in the feature body is subjected to logical clustering based on four dimensions: time, space, origin, and structure. This is used to calculate and generate a quantitative score sequence describing the multi-dimensional confidence of a single asset instance, ultimately generating the primary quality score set. In the specific technical implementation of the generation, a normalization operator is used to map the original scores of each dimension to a unified scale interval, realizing the transformation from complex feature vectors to standardized score carriers. This determined set comprehensively records the multi-dimensional quality indicators of asset instances in the initial stage of entering the warehouse, establishing a digital standard for data asset quality.

[0055] In summary, this application, based on obtaining the initial load, performing orthogonalization calculations, performing quadrature operations, introducing verification remainders, performing dimensional segmentation, and generating a score set, provides a physical trust origin for the entire aggregation chain through the initial load acquisition process; the generated orthogonal features ensure the logical independence of the calculation process. Next, by performing quadrature operations targeting timeliness and origin, gain adjustment techniques are used to achieve a deep characterization of the core trust level. Finally, the resulting primary quality score set represents the ultimate evolution from scattered indicators to a structured profile. Therefore, this application achieves deterministic compensation for multidimensional aggregation, overcoming the shortcomings of isolated dimensions in traditional logistics data quality monitoring, and enhancing the decision-making credibility of the logistics data asset warehouse when performing profit and loss assessment and value classification.

[0056] Preferably, the generated quality score set is sensed, and the configured revenue mapping interface is driven to inject it into the preset business revenue cost model to perform inference processing on the value of logistics auxiliary decision-making. This process identifies the expected improvement probability of each component score in the quality score set for downstream logistics scheduling logic (e.g., route planning optimization or load allocation scheme adjustment), and identifies the potential marginal revenue that asset instances can bring when supporting business decisions, thereby generating the decision support contribution index that characterizes the core value of the asset. Therefore, this processing method maps abstract data quality to specific business contribution potential, thus achieving a fundamental leap from "availability" to "usefulness" of data assets. This ensures that the asset warehouse can selectively manage high-quality data that has a high pulling effect on business decisions, improving the asset operation level of the warehouse.

[0057] Preferably, in the specific technical processing step of step 3, the business benefit cost model is used to simultaneously perform quantitative analysis on the acquisition cost and maintenance cost of the logistics data asset instance to identify the sensor power consumption, link bandwidth occupation, and storage space overhead required for the asset instance. Combined with the confidence performance in the quality score set, a balance calculation is performed on the acquisition cost and application benefits to generate the data acquisition difficulty index. This step achieves a technical evaluation of the asset's "lifecycle cost." By establishing this index, inefficient assets with "extremely high acquisition costs but low confidence" can be identified, providing a basis for subsequent differentiated storage strategies to eliminate or downgrade them. Thus, the data acquisition difficulty index, together with the aforementioned contribution index, constitutes a logical carrier describing the overall picture of asset profit and loss.

[0058] Preferably, the generated decision support contribution index and data acquisition difficulty index are obtained, driving the built-in comprehensive evaluation engine to perform morphological merging processing targeting asset profit and loss characteristics, thereby ultimately generating the evaluation result. In the specific generation process, the contribution index is used as a positive incentive term, and the acquisition difficulty index as a negative constraint term. A non-linear normalization mapping is performed to generate a structured evaluation load containing asset confidence level, business contribution rating, and profit and loss balance score. This formation process enables logistics data to complete its final evolution into an "asset state" with value anchors, thus obtaining the evaluation result. This not only reversely verifies the accuracy of the time-depreciation model and reliability matrix but also directly serves as a core driving instruction in the generation of the dynamic value index in subsequent steps.

[0059] In summary, this application establishes a physical confidence origin for the entire evaluation chain through the verification of timeliness and source, and the generated quality score set enhances the objectivity of the evaluation input. Next, by introducing the business benefit-cost model and utilizing profit and loss analysis techniques, a logical alignment from "data technical indicators" to "asset economic value" is achieved. Finally, the resulting evaluation results achieve a deep coupling between quality characteristics and business value. Therefore, this application achieves determinism in dynamic profit and loss evaluation, alleviates the shortcomings of traditional solutions in quantifying the potential of data applications, and enhances the intelligence level of the logistics data asset warehouse in resource allocation and value alignment.

[0060] Optionally, the weighted mapping process in step 3 is as follows: identify the real-time intensity of the target business associated with the logistics data asset instance to determine the scenario association weight that characterizes the sensitivity of the current business to data accuracy requirements; use the scenario association weight as an input parameter to perform normalized weighted mapping on the quality score set in the evaluation result, thereby completing the weighted mapping process and generating the dynamic value index.

[0061] Preferably, in the specific technical implementation of step 3, the built-in business context parsing operator is driven to obtain the quality score set generated by the preceding steps, and real-time business association parsing processing is performed on the logistics data asset instances therein to identify the real-time intensity of the target business currently in which the asset load is located. In the specific technical implementation of the processing, the configured traffic monitoring interface is used to monitor the call frequency, query load, and urgency score of the downstream fulfillment links for the current asset instance. To this end, this application realizes the determination of the business anchor point of the asset, providing a dynamic business background incentive for the subsequent execution of differentiated value measurement, thereby obtaining the intensity component and realizing the logical alignment from static data evaluation to dynamic business perception, enabling subsequent processing actions to be anchored on the boundary of real business needs.

[0062] Preferably, in the specific technical implementation of step 3, the built-in business context parsing operator is driven to obtain the quality score set generated by the previous steps, and the associated logistics data asset instance is retrieved synchronously. In the specific technical implementation of the processing, based on the business primary key recorded in the asset instance, the temporal phase of the current asset load in the logistics business lifecycle is identified, thereby determining the asset's application activity window indicator. Therefore, this processing method injects "temporal dynamism" into static technical quality indicators, alleviating the technical deficiency of traditional solutions that only focus on data accuracy while ignoring the real-time business value of data. This indicator records the logical correlation between the data asset and the current operation moment, enabling subsequent processing actions to be anchored to a carrier with business timeliness.

[0063] Preferably, based on the identification of the aforementioned application activity window indicators, the traffic monitoring interface configured within the business context parsing operator is driven to capture and process real-time data request throughput for the downstream fulfillment process of the target. Real-time access pulses of subsystems such as logistics scheduling, route planning, or warehousing execution to the current asset load are identified, and the call frequency representing read density is calculated and generated. Simultaneously, the scale of concurrent tasks and data contained in the request messages are identified to determine the query load describing computational pressure. This processing step realizes the evolution from physical interface traffic to logical data heat. By establishing these quantitative indicators, core assets on business hotspot paths can be captured, providing background parameters for subsequent differentiated value measurement.

[0064] Preferably, in one scenario, the priority determination logic configured within the business context parsing operator is used to retrieve the current operational status KPIs (Key Performance Indicators) and preset deadline constraints of the downstream business chain. It identifies whether the business path to which the current asset instance belongs is in a timeout critical state or an abnormal fluctuation state, and calculates and generates an urgency score for the business chain describing the importance of that link. To this end, this processing method establishes the "decision weight background" of the data. The determined urgency score records the pressure on the data asset to support business continuity or compliance at a specific moment, alleviating the technical difficulty in traditional solutions of not being able to distinguish the degree of data dependence between routine operations and emergency support operations, and improving the flexibility of subsequent value assessment logic.

[0065] Preferably, the generated call frequency, query load, and urgency score of the business chain are obtained, driving the feature synthesis module integrated within the business context parsing operator to perform normalization and integration processing for multi-dimensional business pressure. Using a designed weight allocation matrix, the "heat" represented by call frequency, the "severity" represented by query load, and the "hardness" represented by urgency score are algebraically mapped to determine the real-time intensity of the target business currently under the asset load. This formation process achieves a sublimation from scattered business states to a unified intensity component representation. The determined real-time intensity of the target business not only includes monitoring results at the traffic level but also incorporates quantitative indicators of the importance of business logic, establishing a driving component for building a data asset system with deterministic business anchors.

[0066] Preferably, the generated call frequency, query load, and urgency score of the business chain are obtained, driving the feature synthesis module integrated within the business context parsing operator to call the designed weight allocation matrix, and performing normalization integration processing based on dimension alignment for multi-dimensional business pressure; in the specific technical implementation of the processing, the heat adjustment coefficient corresponding to the call frequency, the heavy adjustment coefficient corresponding to the query load, and the hardness adjustment coefficient corresponding to the urgency score of the business chain are matched based on the weight allocation matrix, and each adjustment coefficient is multiplied by the corresponding business pressure component to map the discrete physical monitoring value and logical score value to a unified linear dimension space, and then the real-time intensity of the target business is determined by performing algebraic weighted superposition operation. To address this, the approach establishes an algebraic transformation mechanism with business-aware adaptive capabilities. By defining the substantive influence of each evaluation dimension on the real-time resource utilization and decision-making contribution of the asset warehouse, it effectively alleviates the technical shortcomings of heterogeneous indicators being mutually exclusive and difficult to implement unified quantitative measurement in the context of logistics big data. The determined real-time intensity of the target business achieves a sublimation from a scattered business state to a higher-order comprehensive business load representation, providing a decision incentive source with business semantic consistency for subsequent steps to drive the dynamic value correction of the logistics data asset instance.

[0067] Preferably, in the technical processing of acquiring the real-time intensity of the target business, the weight allocation matrix retrieved by the feature synthesis module is constructed as a multidimensional algebraic transformation operator with dimensional alignment. Preferably, the rows in the weight allocation matrix represent preset business evaluation target dimensions, specifically including a "heat assessment dimension" representing the system response rate demand, a "heavy assessment dimension" representing the computing resource consumption demand, and a "hardness assessment dimension" representing the business performance risk demand; preferably, the columns in the weight allocation matrix represent business pressure components input by the preceding monitoring stage, specifically including the call frequency, the query load, and the urgency score of the business chain. This structural design establishes a mapping framework between input physical quantities and output evaluation dimensions, constructing a logical benchmark for eliminating dimensional differences between heterogeneous data.

[0068] Preferably, the element at the intersection of each row and each column in the weight allocation matrix is ​​defined as the corresponding heat adjustment coefficient, intensity adjustment coefficient, or hardness adjustment coefficient. Preferably, in the specific technical implementation of the processing, the elements in the first row record the contribution weight of the call frequency component to the "heat assessment dimension," the elements in the second row record the contribution weight of each pressure component to the "intensity assessment dimension," and so on. These intersecting elements are essentially sensitivity factors describing the transformation of physical pressure into business intensity, alleviating the technical deficiency in traditional solutions that cannot quantify the proportion of influence of different monitoring indicators on business support. The generated set of adjustment coefficients ensures that subsequent dot product operations can accurately capture the dynamic drift of the business background.

[0069] Preferably, the feature synthesis module drives the built-in matrix operation unit to obtain an input vector composed of real-time monitoring values ​​corresponding to each column, and performs a matrix dot product operation based on column vector alignment with the weight allocation matrix. During the specific operation, the call frequency is identified and multiplied with the adjustment coefficients of each row in the corresponding column, and the query load and the urgency score of the business chain are simultaneously identified and multiplied in the same way, thereby generating a multidimensional intensity intermediate load vector to be aggregated. This processing step achieves feature enhancement from "raw observation data" to "business evaluation components." By utilizing the linear transformation characteristics of matrix dot product, indicators with different physical meanings are successfully mapped to a unified quantitative scale, enabling subsequent synthesis actions to be anchored within a mathematically rigorous feature space.

[0070] Preferably, for the multi-dimensional intensity intermediate load vector generated above, the internally configured weighted accumulation operator is driven to perform normalization integration processing on the row dimension to achieve the normalization integration processing based on dimension alignment. In the specific technical implementation of the processing, the intensity contribution values ​​generated by different input components within the same row are algebraically superimposed to identify the comprehensive performance value of "heat", "severity" and "hardness" in the current business cycle. Then, by executing a nonlinear fusion algorithm, the real-time intensity of the target business is finally determined. This formation process realizes the evolution from a local pressure indicator to a global business environment indicator. The determined intensity component not only includes real-time characteristics at the traffic level, but also has profound business logic connotations due to the introduction of adjustment coefficients, establishing a core driving component for building a data asset warehouse with adaptive adjustment capabilities.

[0071] Preferably, there is a close causal and functional support relationship between the rows and columns of the weight allocation matrix determined above, as well as the adjustment coefficients at their intersections. The definition of the column establishes the data entry standard, and the definition of the row establishes the business orientation of asset governance. Then, by performing a dot product operation through the elements (adjustment coefficients) at the intersections, algebraic transformation technology is used to achieve "value allocation" of heterogeneous logistics pressure data. Finally, the resulting target business real-time intensity achieves deep coupling between technical indicators and application background. To this end, this application uses the determinism of matrix transformation to compensate for the shortcomings of strong subjectivity in business evaluation in traditional schemes.

[0072] Preferably, during the dynamic evolution of the logistics fulfillment scenario, the values ​​of the elements at the intersections in the weight allocation matrix are optimized through self-learning based on historical warehouse call logs. Preferably, by identifying the deviation between the actual call timeliness of asset instances and the preset heat level within a certain business cycle, the distribution ratio of each adjustment coefficient in the first row is dynamically fine-tuned. The technical essence of this processing action is to establish a self-healing weight update mechanism, thereby obtaining an updated matrix that ensures the real-time intensity of the target business can accurately and sensitively reflect changes in the underlying physical flow, avoiding inaccurate asset efficiency allocation caused by a rigid business model.

[0073] Preferably, the real-time intensity of the target business generated above is obtained and injected as a feedback incentive signal into the value mapping process for the logistics data asset instance. In the specific processing, this intensity component is used to dynamically adjust the quality score set obtained in the preceding steps, identifying the potential risks of low-quality data under high-intensity business conditions and the redundancy of high-quality data under low-intensity conditions, thereby determining the business anchor point of the asset. This formation process realizes the transformation of static quality data into dynamic asset value, resulting in an asset feature body with background incentives that records the substantive connotation of the data in the real application context, alleviating the logical disconnect problem of "data quality being out of sync with business needs" in traditional warehouse governance.

[0074] In summary, the call intensity and load identified through the traffic monitoring interface in this application provide a realistic physical load for this starting point. Next, by introducing an urgency score, semantic reasoning technology is used to achieve deep calibration of business weights. Finally, the resulting real-time intensity of the target business achieves a high-level unification of technical indicators and application requirements. Therefore, this application utilizes the determinism of dynamic business perception to compensate for the shortcomings of the blindness in traditional evaluation schemes, significantly enhancing the intelligence level of the logistics data asset warehouse in performing energy efficiency allocation and asset governance.

[0075] Preferably, the real-time intensity of the target business identified above is obtained, driving the internally configured sensitivity mapping model to perform quantitative judgment processing based on data accuracy requirements. This identifies the sensitivity coefficients of different logistics operation links (e.g., high-efficiency delivery addressing or long-cycle strategy sorting) to spatiotemporal location accuracy and state integrity, thereby determining the scenario association weights that characterize the sensitivity of the current business to data accuracy requirements. This step realizes the transformation of business value logic into technical weight parameters. The determined scenario association weights record the potential negative impact of data quality defects on the final decision-making efficiency under the current business scenario. Therefore, this processing method alleviates the technical defects of traditional solutions where the weight configuration is too fixed and cannot adapt to fluctuations in the logistics scenario, obtaining a definite adjustment load for subsequent execution of nonlinear weighted mapping.

[0076] Preferably, in a specific implementation of step 3, the determined scenario-related weights are extracted, and the internally integrated nonlinear mapping operator is simultaneously invoked to drive the quality score set generated above. Feature benchmark alignment is performed on each confidence index in the quality score set to generate an intermediate feature body of quality confidence to be weighted. In the specific technical implementation of the processing, the marginal benefit of confidence indicators of different dimensions (such as source credibility, time freshness, etc.) in the current target business scenario is identified. To this end, this processing method constructs a digital calculation bridge between technical indicators and business weights. By converting discrete quality scores into standardized feature units that can be adjusted by weights, the calculation bias caused by inconsistencies in the dimensions of different indicators is eliminated, improving the technical rigor of the evaluation logic.

[0077] Preferably, for the intermediate feature body of quality confidence generated above, the scenario association weights are used as core input parameters to drive the built-in nonlinear normalization engine to perform nonlinear normalized weighted calculations for the asset application potential. Logistic regression transformation or exponential enhancement operations are introduced during the calculation process, and the confidence score is asymmetrically amplified or reduced according to fluctuations in business intensity, thereby generating a normalized value representation set that characterizes the potential contribution of the asset's substantial value. This formation process achieves in-depth mining of the application value of data assets. By utilizing a nonlinear adjustment mechanism, the "value mutation" characteristics of key high-quality data at core business nodes can be captured, thereby improving the sensitivity of asset value quantification and obtaining the most accurate calculation results possible for the subsequent generation of global index indicators.

[0078] Preferably, the normalized value representation set generated above is sensed, driving the configured index aggregation operator to perform linear superposition and morphological synthesis processing on the multi-dimensional value components. This completes the weighted mapping processing, algebraically merging the weighted and corrected multi-dimensional value values ​​and introducing a profit and loss calibration score determined by asset maintenance expenses, thereby ultimately generating the dynamic value index. In the specific processing, complex evaluation conclusions are transformed into numerical payloads with a single metric dimension, realizing the morphological evolution from a multi-dimensional quality vector to a single value scalar. The determined dynamic value index comprehensively records the integrated application potential of asset instances in the current spatiotemporal phase, alleviating the technical deficiency of lacking quantitative tools for asset classification in logistics big data governance.

[0079] In summary, this application utilizes the identified target business real-time intensity, the determined scenario association weights, the generated quality score set, the constructed quality confidence intermediate feature body, and the finally generated dynamic value index. The real-time intensity sensing action provides the business-level driving signal for the entire mapping chain; the generated scenario association weights quantify the business signal into weight coefficients that the algorithm can process. Next, by performing nonlinear normalized weighted calculations, the objectivity of the value quantification results is improved through the deep integration of business and technology. Finally, the resulting dynamic value index achieves the ultimate encapsulation of underlying physical quality and upper-level business value. This application uses a dynamic weighted mapping mechanism to alleviate the shortcomings of traditional solutions in quantifying the value of data applications, enhancing the intelligence level of the logistics data asset warehouse in resource allocation and value alignment.

[0080] Optionally, the process of persisting to the asset storage architecture in step 4 includes: performing energy efficiency classification processing for the dynamic value index to meet real-time response requirements, matching the classification results to the first storage bit order composed of high-performance flash memory media or the second storage bit order composed of high-capacity mechanical media in the asset storage architecture; and based on the matching results, classifying and writing the logistics data asset instances into the corresponding storage bit order, thereby realizing the persistence to the asset storage architecture.

[0081] Preferably, in the specific technical implementation of step 4, the dynamic value index generated by the preceding evaluation stage is obtained, driving the built-in response demand analysis operator to extract the business urgency component and call frequency prediction component contained in the index load, so as to perform the extraction processing of the real-time response demand for the target. In the specific technical implementation of the processing, the lower limit of the response latency of the data asset in the future decision-making cycle is identified, and an access performance benchmark indicator describing the access speed threshold of the asset is generated. To this end, this application realizes the transformation of abstract value indicators into quantifiable physical performance indicators, alleviating the technical defects of the serious disconnect between storage resource allocation and business value in traditional solutions, obtaining a logical origin for subsequent execution of differentiated storage energy efficiency segmentation, thereby obtaining the access performance benchmark indicator that records the substantive requirements of the asset for the throughput capacity of the underlying media, enabling subsequent processing actions to be anchored on the performance boundary with business sensitivity.

[0082] Preferably, the generated access performance benchmark indicators are obtained, driving the internally configured energy efficiency matching engine to perform hierarchical mapping processing on the read / write bandwidth and random access latency of different storage media, thereby implementing the target-specific energy efficiency grading processing. During the specific processing, the algebraic relationship between the value weight of asset instances and the response benchmark is identified, classifying assets into extremely high response levels, high response levels, and normal response levels, thus generating corresponding storage energy efficiency grading results. Therefore, this processing method realizes a form of evolution from performance requirements to media capabilities. By establishing this grading result, different hardware service guarantee levels can be preset according to the differences in asset value, avoiding the problem of massive logistics data blindly occupying expensive storage resources, improving the energy efficiency ratio of warehouse storage costs, and obtaining digital grading credentials for subsequent media calibration.

[0083] Preferably, in one scenario, when implementing step 4, the generated storage energy efficiency classification results are extracted, and the built-in media calibration module is driven to perform topology addressing processing on the physical storage pools in the asset storage architecture. The classification results of extremely high or high response levels are matched to the first storage sequence composed of high-performance flash memory media in the asset storage architecture, and the classification results of normal response levels are matched to the second storage sequence composed of large-capacity mechanical media. Therefore, this processing method constructs a deterministic mapping carrier between logical value levels and physical storage media, alleviating the technical deficiency of delayed response of high-frequency value data in logistics under mechanical hard drive environments. This results in media sequence allocation instructions that clearly define the flow trajectory of assets at the physical hardware level, improving the ability of high-value assets to preferentially utilize the high concurrency characteristics of flash memory media and enhancing the determinism of asset retrieval.

[0084] Preferably, the generated logistics data asset instances are acquired synchronously, and the built-in semantic clustering operator is used to extract the business primary key identifier and logical association label carried by each instance. By calculating the co-occurrence frequency and semantic overlap of different asset instances in the logistics topology, a quantitative judgment process for business cohesion is performed to determine the semantic affinity score describing the logical proximity between assets. In the specific technical implementation of generation, discrete assets belonging to the same logistics route, the same transportation batch, or the same physical entity are identified, and the semantic affinity aggregation is performed accordingly. To this end, this processing method realizes the transformation from the representation of independent asset instances to logically related feature bodies, thereby obtaining semantic affinity feature clusters that record the cohesion of the business chain in the storage dimension. This alleviates the low query efficiency caused by the overly dispersed physical distribution of related data in traditional solutions and provides a structured data set for subsequent sequential writing.

[0085] Preferably, the generated semantic affinity feature clusters and corresponding media bit order allocation instructions are obtained, driving the built-in persistence control engine to perform data pumping and disk writing processing for the target storage space. During the specific processing, the logistics data asset instances are controlled to be sequentially written to the first or second storage bit order of the target according to the boundaries of the semantic clusters, thereby achieving persistence to the asset storage architecture. This process enables assets to evolve from volatile computing memory to a stable storage environment, resulting in a hierarchical storage data payload that achieves a three-in-one binding of logical value, business semantics, and physical media. By executing this affinity-based aggregated storage, the sequential read / write advantages of the media can be leveraged to significantly improve the retrieval speed of large batches of logistics assets, achieving business optimization at the physical storage level.

[0086] Preferably, the persistent asset states are aggregated, and the built-in consistency control operator obtains the feedback payload of the logistics digital twin foundation. Semantic alignment mapping is then performed between the stored assets and their twin counterparts. By comparing the real-time state deviations between the asset attributes in the warehouse and the twin entity, self-healing updates are executed for the discrepancies, ultimately achieving the construction of the target data asset warehouse. This process realizes the ultimate evolution from a static storage repository to a dynamically evolving warehouse. The determined data asset warehouse not only possesses energy efficiency guarantees for physical resources but also logical real-time business authenticity. Therefore, this approach alleviates the technical defect of "expiration upon entry" in logistics data warehouses, enabling warehouse assets to perform real-time synchronization and self-correction in response to fluctuations in the logistics operating environment.

[0087] In summary, this application establishes a performance baseline for the entire storage chain through the extraction of response requirements, execution of energy efficiency grading, matching of storage bit order, calculation of semantic affinity, execution of clustered persistence, and synchronization of twin alignment. The generated grading results quantify abstract value into hard criteria for media selection. Next, semantic affinity aggregation optimizes the physical data layout structure using business semantics. Finally, the resulting data asset warehouse achieves closed-loop management of the entire data asset lifecycle. Therefore, this application implements a coupling mechanism between dynamic value guidance and semantic affinity organization, alleviating the shortcomings of traditional storage solutions that cannot balance response speed and storage cost, and enhancing the governance depth and application efficiency of data assets in logistics scenarios.

[0088] Optionally, the semantic alignment mapping process in step 4 includes: extracting the real-time virtual mirror state of the physical entity in the logistics digital twin base; performing time-series consistency-based feature fusion processing on the static business attributes carried by the logistics data asset instance and the real-time virtual mirror state to generate an asset twin synchronization feature body; and performing self-healing attribute correction processing for the data asset warehouse by comparing the deviation value between the asset twin synchronization feature body and the preset standard ontology construct, thereby implementing the semantic alignment mapping.

[0089] Preferably, in the specific technical implementation of step 4, the built-in twin communication operator is driven to access the target logistics digital twin base, identify the digital mapping unit established in the base for each physical entity, and perform real-time load extraction for the mapping unit to extract multi-dimensional dynamic data including the instantaneous spatiotemporal coordinates of the entity, sensor sensing parameters, and operating condition identifiers, thereby generating the real-time state of the virtual mirror. To this end, this application establishes a dynamic connection point between the data asset warehouse and the physical world. By acquiring high-frequency evolving mirror data, it obtains the original physical state input load for subsequent cross-dimensional feature integration, thus obtaining the real-time state record of the latest properties of the physical object in physical space, enabling subsequent processing actions to be anchored on a data foundation with physical authenticity.

[0090] Preferably, the real-time state of the extracted virtual image is obtained, and the persistent control interface is synchronously driven to call the generated logistics data asset instance to perform association preparation for static and dynamic features. The static business attributes recorded in the instance are identified, and a time-series alignment plugin is used to perform phase matching between the static business rule information and the dynamic real-time state of the virtual image on a unified time scale, generating twin-linked data units to be fused. Therefore, this processing method realizes the evolution from discrete business logic and physical state to a unified spatiotemporal carrier. By establishing a time-stamped consistent association relationship, the data misalignment problem caused by inconsistent sampling frequencies between business semantics and physical representation in logistics asset management is alleviated, obtaining a standardized intermediate payload for deep fusion processing.

[0091] Preferably, in one scenario, when step 4 is specifically implemented, a feature crossover operator based on time-series consistency is executed on the generated twin-related data unit. This process injects the performance contract constraints contained in the static business attributes into the physical evolution trajectory corresponding to the real-time state of the virtual image. Dynamic allocation of attribute weights and feature overlay operations are then performed to generate the target asset twin synchronous feature body. Therefore, this processing method constructs a "digital life form" deeply coupled with physical trajectory and business logic, thereby achieving a real-time portrait representation of the entire lifecycle of logistics objects. This enhances the system's ability to perceive asset evolution trends and alleviates the technical shortcomings of traditional warehouse data being static, rigid, and unable to reflect the dynamic drift of physical objects.

[0092] Preferably, the internally configured deviation analysis engine extracts the generated asset twin synchronous feature body and synchronously retrieves the pre-configured standard ontology construct to perform projection processing on the ideal business logic model for the real-time evolution features recorded in the feature body. A distance metric algorithm is used to calculate the topological distance between the real-time features and the boundary of the ideal model, thereby determining the deviation value that characterizes the degree of data-model mismatch. This processing method achieves a quantitative judgment from current status sensing to logical deviation. By establishing this deviation value, it is possible to keenly capture business deviations (such as vehicle deviation or delivery delays) that occur during the physical fulfillment of asset instances, providing digital judgment evidence for subsequent targeted warehouse data repair and improving the rigor of asset governance logic.

[0093] Preferably, in the specific technical implementation of determining the deviation value, the deviation analysis engine acquires the asset twin synchronous feature body generated by the preceding steps and synchronously retrieves the pre-configured standard ontology construct. Dimensional alignment processing is performed on the dynamic spatiotemporal trajectory contained in the feature body and the ideal business path defined in the standard ontology construct to identify the mapping relationship between the two under a unified semantic coordinate system, thereby generating a semantic comparison matrix to be quantified. Thus, this application establishes a common logical reference system for "real-time status" and "business standards," alleviating the technical deficiency of being unable to directly compare data due to heterogeneity. The generated matrix records the real-time coordinates and logical state of the asset during its physical evolution, enabling subsequent processing actions to be anchored on a logically consistent data carrier.

[0094] Preferably, the generated semantic comparison matrix to be quantized is obtained, and the built-in projection operator is driven to perform projection processing on the real-time evolution features within it for the ideal business logic model. In the specific technical implementation of this processing, the feature volume carrying the real-time location and state payload is mapped onto the manifold space defined by the standard ontology construct, thereby identifying the ideal evolution trajectory projection image of the asset instance at the current business stage. This processing step realizes feature translation from the physical perception dimension to the business logic dimension. By establishing this projection image, the system can clearly identify the topological location and attribute distribution that the asset should be in the ideal state, obtaining a definite business benchmark point for subsequent calculation of geometric distance.

[0095] Preferably, in one scenario, in the technical implementation of the distance metric algorithm, the internally configured multidimensional spatial metric module performs calculations based on dynamic time warping and Manhattan distance superposition on the generated real-time evolution features and the ideal evolution trajectory projection image. It identifies the phase difference of the feature vectors on the time axis and the Euclidean offset in the business attribute space, and calculates and generates a multidimensional semantic distance tensor describing the topological distance between the real-time state and the ideal boundary. Therefore, this application constructs a topological differentiation quantification mechanism with business flexibility, which alleviates the technical problem of traditional threshold judgment methods being too rigid and unable to identify nonlinear business deviations by quantifying the "offset intensity" of assets in the performance chain.

[0096] Preferably, the generated multidimensional semantic distance tensor is sensed, driving the internally configured boundary threshold judgment logic to perform topological spacing calculation for the target. By identifying the contribution ratio of each component score in the tensor to the business performance risk, the multidimensional distance information is linearly weighted and aggregated to determine the topological spacing between the real-time features and the boundary of the ideal model. In the specific processing, a sensitivity factor determined by the correlation strength of the logistics scenario is introduced to identify whether a small spatiotemporal deviation has constituted a substantial breach of contract at the business level. This formation process realizes the evolution from an abstract tensor to a specific distance value. The generated topological spacing records the physical and logical degree to which the asset instance deviates from the preset performance path, ensuring the objectivity of subsequent deviation judgments.

[0097] Preferably, the calculated topological spacing is obtained, and the built-in deviation quantification engine is driven to perform normalization mapping processing for the degree of mismatch, thereby ultimately determining the deviation value of the target. In a specific technical implementation, the system injects the calculated spacing value into a preset logical mapping function, identifies the distribution phase of the spacing value within the business tolerance range, and generates a percentage load or graded identifier representing the degree of data and model mismatch to generate the deviation value. Based on this, this application transforms complex spatial geometric deviations into logical judgment indicators that can be directly recognized by the processor, realizing the quantitative judgment of logical deviation from current status sensing. The determined deviation value not only includes positional deviation information, but also covers deep logical mismatch characteristics such as state switching lag, establishing a digital trigger load for subsequent warehouse self-healing correction.

[0098] Preferably, the determined deviation value is sensed, and the type of business deviation indicated by the deviation value is identified (e.g., vehicle yaw caused by trajectory drift or delivery delay caused by state stagnation). This triggers the built-in self-healing interface to perform targeted judgment credential encapsulation processing. The deviation score, associated asset identifier, and corresponding spatiotemporal conflict characteristics are merged to generate a digital judgment credential with arbitration effect. This process enables logistics asset management to complete a closed-loop evolution from "perceiving anomalies" to "quantifying evidence." The generated credential achieves the technical solidification of business violations, effectively alleviating the problems of untimely business anomaly judgment and lack of quantitative tools in traditional solutions. The determined credential will directly guide the self-healing attribute correction of the data asset warehouse in subsequent steps.

[0099] In summary, the above scheme includes processing steps such as obtaining the comparison matrix, performing manifold projection, calculating the semantic distance tensor, determining the topological spacing, and generating deviation values. The projection process establishes a logical alignment benchmark for the entire measurement chain, while the execution of the distance measurement algorithm provides a geometric quantification method for this benchmark. Next, by introducing the calculation of the topological spacing, geometric analysis techniques are used to achieve a digital representation of the degree of business deviation. Finally, the resulting deviation value achieves the ultimate comparison and mapping between the physical status quo and the logical model. This series of coordinated actions leverages the determinism of manifold distance measurement to compensate for the monitoring shortcomings in the complex and ever-changing business trajectories of logistics scenarios, significantly enhancing the intelligence level of the logistics data asset warehouse in performing real-time correction and quality backtracking.

[0100] Preferably, by sensing the aforementioned determined deviation value, the built-in attribute repair module is driven to perform self-healing attribute correction processing on the associated instances stored in the data asset warehouse. This process identifies conflicting attribute items contained in the deviation value and utilizes the associated physical mirror features to perform confidence-based rewriting or incremental update actions on outdated attributes within the warehouse, thereby generating corrected real-time asset attribute copies. To this end, this processing method utilizes real-time feedback from the physical world to achieve automatic calibration in the logical world, thereby implementing the semantic alignment mapping. This self-healing mechanism eliminates logically corrupted data in the warehouse due to business interruptions or sensor malfunctions, enabling warehouse assets to perform closed-loop state regression as the physical environment evolves, thus improving the operational robustness of the data asset warehouse.

[0101] Preferably, the generated real-time asset attribute copies are aggregated, and the asset establishment interface is driven to perform a final consistency locking action on the persistent layer within the warehouse, thereby completing the construction of the target data asset warehouse. In the specific technical implementation, it is ensured that the corrected attribute items maintain a strong temporal correlation with the logical construct of the logistics digital twin foundation, and asset twin consistency indicators representing warehouse health are generated. This formation process achieves a closed-loop morphology from a single storage repository to a dynamically self-evolving warehouse. The determined data asset warehouse not only possesses deep semantics modeled ontology but also real-time performance highly synchronized with physical assets. This step utilizes the real-time feedback characteristics of digital twins to alleviate the timeliness deficiencies of traditional ETL processes.

[0102] In summary, the above scheme includes extracting the mirror state, performing feature fusion, generating a synchronization feature body, comparing with the standard conformation, calculating the deviation value, and performing self-healing correction. Among these, the extraction of the mirror state provides the physical origin for the entire alignment chain, and the generated asset twin synchronization feature body improves the dynamic cohesion of the data assets. Next, by performing quantitative calculations on the deviation value, feedback control technology is used to achieve real-time correction of warehouse attributes. Finally, the resulting data asset warehouse achieves the ultimate fusion of the logical model and the physical entity. Therefore, this application achieves deterministic twin synchronization, mitigating the vulnerability of data to failure in logistics scenarios and enhancing the application value of logistics big data asset warehouses in real-time monitoring, risk prediction, and efficient scheduling.

[0103] Optionally, after generating the logistics data asset instance, the method further includes: performing semantic conflict detection on the logistics data asset instance under different storage batches through the logistics business association knowledge graph to identify heterogeneous attribute contradiction features for the same logistics entity; performing logical arbitration processing based on business confidence on the heterogeneous attribute contradiction features to generate a unified standard asset fingerprint and update it to the data asset warehouse.

[0104] Preferably, after generating the logistics data asset instance, the built-in semantic consistency monitoring operator is driven to call the generated logistics business association knowledge graph to perform a full-domain scan processing on asset units spanning different storage batches. In the specific technical implementation of the processing, the topological connection strength and business path dependency indicators between nodes in the knowledge graph are extracted to establish the consistency of different batches of instances in the logical space, thereby generating an asset semantic association matrix to be audited. To this end, this application utilizes the structured constraint capabilities of the graph to incorporate data instances originally discretely distributed on the time axis into a unified logical reference plane for examination. This provides a spatiotemporally continuous comparative foundation for subsequent cross-source conflict analysis, resulting in a matrix that records multiple versions of the evolutionary state of the same logistics entity, enabling subsequent processing actions to be anchored to a data set with business associations.

[0105] Preferably, the generated asset semantic association matrix is ​​obtained, and the internally configured differential feature detection operator is driven to perform cross-comparison operations on multiple sets of asset attributes associated with the same physical entity in the matrix. This is to perform semantic conflict detection and identify differences in logistics status reported by different data sources (e.g., BeiDou positioning system and manual entry system) at the same time, detect whether there are location coordinate deviations or logical conflicts in the business state machine, and then identify and extract the heterogeneous attribute contradiction features of the target. This formation process realizes the evolution from attribute differences to logical contradictions. These identified contradiction features record the semantic mutual exclusion points of data in a multi-source heterogeneous environment, alleviate the technical defects of decision uncertainty caused by data version chaos in traditional solutions, and obtain a digital conflict load for subsequent logical arbitration.

[0106] Preferably, in one scenario, an exemplary implementation of the logical arbitration process drives a built-in confidence extraction interface to retrieve the corresponding business confidence score from the metadata carried by the logistics data asset instance. This identifies and generates the sensor accuracy level, the survival status of the acquisition terminal, and the authority level of the business system to which the attribute item belongs, thereby determining the source credibility weight vector of each contradictory attribute. In this step, the weights from different sources are normalized and mapped, and combined with the data freshness decay factor, a decision weight incentive load representing the authenticity of each candidate attribute is generated. This processing action realizes the transformation from subjective judgment to objective quantitative mapping based on hardware performance and business rules, ensuring that subsequent arbitration actions can be executed based on real physical indicators, and improving the rigor of data warehouse governance logic.

[0107] Preferably, the generated decision weight incentive load is sensed, and the identified heterogeneous attribute contradiction features are retrieved. This drives the internally integrated arbitration operator to perform value reconstruction processing for conflicting items. Using a designed weighted voting algorithm or a maximum confidence priority rule, algebraic competition operations are performed on contradictory attribute values, selecting the component with the highest confidence as the final truth value, thereby implementing the logical arbitration process. To this end, this application achieves automated correction and unification of multi-source contradictions using algorithmic logic. The generated post-arbitration attribute set eliminates noise interference in multi-source heterogeneous environments. This determined set provides a unique representation of the true state of the logistics entity, mitigating downstream scheduling failures caused by "data discrepancies" in the data warehouse and enhancing the business availability of asset instances.

[0108] Preferably, the generated post-arbitration attribute set is obtained, and the built-in digest algorithm operator is used to perform fingerprint mapping processing on the core business attributes after arbitration and the corresponding logistics entity identifiers. In the specific technical implementation of this processing, a hash function is used to perform irreversible feature mapping on the attribute sequence, and the semantic category code determined by the ontology model is simultaneously associated, thereby ultimately generating the unified standard asset fingerprint of the target. This formation process realizes the transformation of the asset from a dynamic conflict state to a stable standard state. The determined fingerprint serves as a globally unique technical index for the asset within the warehouse, not only recording the asset's ultimate logical state but also anchoring the asset's ownership and lifecycle. This step establishes a standardized expression paradigm for data assets, providing a definite digital carrier for subsequent global rapid asset retrieval and version backtracking.

[0109] Preferably, the unified standard asset fingerprint generated above is sensed, driving the built-in warehouse synchronization interface to perform incremental overwriting and directory index reconstruction actions for the persistent layer, thereby updating the data asset warehouse. In the specific technical implementation of the update, it is ensured that the updated asset fingerprint maintains strong real-time topological consistency with the aforementioned logistics business-related knowledge graph, and an asset warehouse state synchronization instruction representing a successful update is generated. This processing method achieves the physical implementation of conflict resolution results. This formation process reversely verifies the effectiveness of semantic conflict detection and logical arbitration mechanisms in improving the confidence of the asset warehouse. By executing this self-healing update mechanism, the determinism of logical arbitration mitigates the inherent instability of heterogeneous data sources.

[0110] In summary, the above solution involves several processing steps, including semantic conflict detection, identification of heterogeneous attribute contradictions, retrieval of business confidence levels, execution of logical arbitration, generation of asset fingerprints, and warehouse updates. The introduction of knowledge graphs provides structured support for the detection chain; the generated heterogeneous attribute contradiction features establish the target objects for conflict resolution. Next, the accuracy of truth value screening is improved by implementing quantitative weighted arbitration techniques based on confidence levels. Finally, the resulting unified standard asset fingerprint achieves unique reconstruction of warehouse assets. Therefore, this application realizes the redundancy of multi-source heterogeneous data, achieves value complementarity and calibration, alleviates the problem of logically dirty data caused by unreliable data sources in logistics scenarios, and improves the confidence performance of the data asset warehouse in decision support.

[0111] Optionally, the storage strategy determined based on the dynamic value index includes: real-time monitoring of the hotspot frequency changes of the logistics data asset instance being called by the application side, and performing attenuation mitigation calculation on the dynamic value index based on the hotspot frequency changes; in response to the calculation result reaching a preset hierarchical switching threshold, driving the corresponding asset to perform asynchronous cross-layer migration processing between the first storage order and the second storage order, so as to realize the dynamic adjustment of the storage strategy.

[0112] Preferably, in the specific implementation of step 4, the built-in response demand analysis operator is driven to obtain the dynamic value index generated by the previous steps, and feature stripping processing is performed on the business urgency component and call frequency prediction component contained in the index load. In the specific technical implementation of the processing, the lower limit of the response latency of the data asset in the future decision-making cycle is identified, and an access performance benchmark indicator describing the access speed threshold of the asset is generated. To this end, this application realizes the transformation of abstract value scalars into quantifiable physical performance indicators, alleviating the technical defects of traditional solutions where storage resource allocation is seriously out of sync with the real value of the business. Thus, the access performance benchmark indicator records the substantive demand of the asset for the throughput capacity of the underlying medium, enabling subsequent processing actions to be anchored on the performance boundary with business sensitivity.

[0113] Preferably, the generated access performance benchmark indicators are obtained, driving the internally configured energy efficiency matching engine to perform hierarchical mapping processing on the read / write bandwidth and random access latency of different storage media, thereby implementing the target-specific energy efficiency grading processing. During the specific processing, the algebraic relationship between the value weight of asset instances and the response benchmark is identified, and assets are divided into response energy efficiency levels corresponding to different service level protocols, thus generating storage energy efficiency grading results. This process realizes the evolution from performance requirements to hardware capabilities. By establishing this grading result, differentiated hardware service guarantee levels can be preset according to the differences in asset value, avoiding the problem of massive logistics data blindly occupying high-cost storage resources, improving the overall energy efficiency ratio of the warehouse, and obtaining digital grading credentials for subsequent media calibration.

[0114] Preferably, in one scenario, when implementing step 4, the generated storage energy efficiency classification results are extracted, and the built-in media calibration module is driven to perform spatial addressing processing on the physical storage pool in the asset storage architecture. The classification results for extremely high response levels are matched to the first storage sequence composed of high-performance flash memory media in the asset storage architecture, and the classification results for normal response levels are matched to the second storage sequence composed of large-capacity mechanical media. Therefore, this processing method constructs a deterministic mapping carrier between logical value levels and physical storage media, alleviating the technical deficiency of delayed response of high-frequency value data in logistics under low-speed storage environments. This results in media sequence allocation instructions that clearly define the flow trajectory of assets at the physical hardware level, improving the ability of high-value assets to preferentially utilize the high-concurrency characteristics of flash memory media and enhancing the determinism of asset retrieval.

[0115] Preferably, during the operational cycle after the assets are put into storage, the hotspot monitoring operator configured by the driver performs real-time call frequency tracking processing on the logistics data asset instances stored in different positions, capturing each read command issued by the application side, identifying the asset identifier and access timestamp associated with the command, and generating a real-time hotspot frequency feature describing the degree of attention received by the asset. To this end, this application implements a dynamic feedback loop to establish asset value, thereby obtaining this feature and realizing the sensing of asset utilization. This alleviates the technical deficiency of traditional static storage solutions in being unable to detect business hotspot drift. The real-time hotspot frequency feature will then serve as the core driving source, participating in subsequent calculations to mitigate the decay of the asset value index, thus improving the dynamic adjustment capability of the storage strategy.

[0116] Preferably, the generated real-time hotspot frequency characteristics are obtained, and the internal value correction engine is driven to perform attenuation mitigation calculations for asset application value. A pre-set nonlinear attenuation operator is introduced to identify the trend of hotspot frequency changes over time. Incremental correction or decremental mitigation is then performed on the original dynamic value index to generate a corrected dynamic value index. This processing method achieves a technical expression of the "dynamic value throughout the asset's lifecycle." By performing attenuation mitigation calculations, "overcooled assets" experiencing value decline due to decreased business demand, or "instantaneous hotspot assets" experiencing value leaps due to sudden business events, can be identified. This results in a corrected index that verifies the rationality of the storage strategy and provides a real-time judgment benchmark for cross-layer migration.

[0117] Preferably, the modified dynamic value index generated above is sensed, driving the built-in migration decision operator to perform logical judgment processing based on a preset hierarchical switching threshold. When the modified index score falls below the switching threshold due to a decrease in business activity, an asynchronous cross-layer migration instruction is generated to drive the asset downward; conversely, an upward migration instruction is generated. The built-in asynchronous migration module responds to this instruction, performing background data transfer between the first storage sequence and the second storage sequence during a window of low system load. This process realizes a closed-loop model from single-media persistence to dynamic scheduling throughout the asset's entire lifecycle. This adaptive adjustment mechanism alleviates the expansion pressure of high-performance storage media, enabling the data asset warehouse to ensure extremely high business response efficiency at a lower overall cost.

[0118] In summary, the above scheme involves processing steps such as extracting performance indicators, performing energy efficiency classification, matching storage bit order, monitoring hotspot frequencies, performing attenuation mitigation, and driving cross-layer migration. Among these, the demand extraction action establishes the performance origin for the entire storage link; the generated energy efficiency classification results quantify abstract value into hard criteria for media selection. Next, by performing hotspot monitoring and attenuation mitigation calculations, feedback control technology is used to achieve online correction of the storage strategy. Finally, the asynchronous cross-layer migration process achieves the ultimate mapping from logical value displacement to physical space displacement. Therefore, this application realizes a coupling mechanism between dynamic value guidance and media energy efficiency matching, alleviating the shortcomings of traditional logistics storage schemes in terms of resource utilization.

[0119] Optionally, the multi-source heterogeneous logistics data includes flow location payloads sensed by mobile terminals, unit identity payloads extracted by radio frequency identification terminals, and fulfillment link payloads synchronized by business systems.

[0120] Preferably, in the underlying data perception stage of the logistics scenario, the flow direction position load, representing the real-time displacement of physical objects, is collected by mobile communication terminals deployed on transfer nodes or transport vehicles. In the specific technical implementation of the processing, raw latitude and longitude coordinates and acceleration vectors from the Global Positioning System (GPS) or Inertial Measurement Unit (INS) are received in real time and encapsulated using a physical clock to generate the original spatiotemporal perception carrier. Therefore, this application establishes a digital representation of the logistics process in the physical space dimension, alleviating the problem of inconsistent coordinate systems caused by heterogeneous equipment in the data acquisition stage. This allows the flow direction position load to record the continuous movement indicators of logistics entities in geographic space, providing a high-frequency physical input source for subsequent path identification and spatiotemporal trajectory flow deconstruction.

[0121] Preferably, when identifying the physical identity of a logistics package or carrier unit, RFID terminals configured at the inbound, outbound, and conveyor belt nodes are activated to perform non-contact signal sensing on the electronic tags attached to the logistics entity. This extracts the electromagnetic pulse frequency reflected from the tag, decodes the globally unified electronic product code, and simultaneously senses signal strength indicators to generate a unique identifier describing the entity's identity attributes. This process represents an evolution from physical electromagnetic signals to digital logical identities. By acquiring the unit's identity payload, a globally unique asset index can be assigned to each physical package, mitigating the risk of lost contact due to manual scanning in large-scale logistics flows and improving the availability of definite entity anchors for subsequent semantic extraction.

[0122] Preferably, the discrete logical data generated by the logistics management platform, transportation management system, and warehousing operation system drives the built-in data integration interface to perform asynchronous synchronization processing on the fulfillment link load. During the synchronization process, order creation logs, sorting completion pulses, and delivery receipt indicators generated by the business system are captured. The recorded business primary keys and operational statuses are identified and extracted to generate a business state machine vector representing the fulfillment progress. This processing method realizes the process of pushing logistics business logic down from the management level to the data level. The determined fulfillment link load records key time state nodes and operational motivations in the logistics chain, ensuring that subsequent intention-anchoring actions closely align with actual business agreements and improving the logical cohesion of asset-based modeling.

[0123] In summary, this application utilizes the technical synergy among the three types of data to obtain the flow location payload sensed by the mobile terminal. This payload is then cross-correlationally analyzed with the unit identity payload extracted by the RFID terminal in the spatiotemporal dimension. By identifying the peak signal strength at a specific location, the dynamic trajectory payload is physically bound to the defined entity identity, thus establishing a dynamic spatiotemporal unit describing "a specific object at a specific location." This step achieves deep integration of the trajectory and identity dimensions, alleviating the data fragmentation defect of "seeing location but not entity" in logistics big data. This unit thus provides functional support between different sensor sources, establishing a physical foundation for the subsequent generation of primary feature payloads with a complete profile.

[0124] Preferably, the generated dynamic identity spatiotemporal units are extracted, and semantic-dimensional morphological alignment processing is performed using the fulfillment link load synchronized by the business system. In the specific technical implementation of this processing, it is determined whether the physical displacement trajectory of the current entity logically overlaps with the preset business operation steps. A time-series alignment operator is used to normalize and integrate location changes, identity transitions, and business state switching, ultimately generating the target's multi-source heterogeneous logistics data. This formation process realizes the evolution of previously isolated physical locations, hardware identifiers, and business logs into a multi-dimensional data stream with consistent semantics. The determined data set achieves three-dimensional association processing at the physical layer, device layer, and business layer, alleviating the technical defects of high integration difficulty and high governance costs caused by the mutual exclusion of data source properties.

[0125] In summary, this application involves processing steps including location sensing by a mobile terminal, identity extraction by a radio frequency identification terminal, synchronization of business systems, and multi-dimensional feature normalization and integration. The location sensing action provides spatial continuity support for the entire acquisition chain; the extraction of unit identity establishes the logical attribution of spatial points. Next, by introducing business logic from the fulfillment chain and utilizing business semantic guidance technology, the fundamental transformation of data from a "physical sensing state" to a "business data state" is achieved. Finally, the generated multi-source heterogeneous logistics data reverse-verifies the collaborative nature of the sensor front-end and directly participates as a core input payload in subsequent steps for feature deconstruction of the logistics spatiotemporal trajectory flow and discrete business event flow.

[0126] like Figure 2The data asset warehouse construction apparatus provided in this application embodiment includes: a semantic feature generation module, used to acquire multi-source heterogeneous logistics data and perform semantic extraction and labeling processing on it to generate a logistics semantic feature set; an asset modeling module, used to perform business relationship mapping and attribute modeling processing on the logistics semantic feature set based on a logistics domain ontology model to generate a logistics data asset instance; a value index generation module, used to perform multi-dimensional quality confidence assessment on the logistics data asset instance and perform weighted mapping processing on the assessment results to generate a dynamic value index characterizing the application potential of the asset; and an asset persistence module, used to persist the logistics data asset instance to an asset storage architecture based on a storage strategy determined by the dynamic value index, and simultaneously perform semantic alignment mapping with the logistics digital twin base to realize the construction of the data asset warehouse.

[0127] like Figure 3 As shown, an electronic device provided in an embodiment of this application includes a processor and a memory; the memory is used to store computer programs; the processor is used to execute the program stored in the memory to implement the steps of the data asset warehouse construction method described in the embodiment of this application.

[0128] Figures 2-3 For an exemplary explanation, please refer to the above. Figure 1 .

Claims

1. A method for constructing a data asset warehouse, characterized in that, include: Step 1: Obtain multi-source heterogeneous logistics data and perform semantic extraction and labeling processing on it to generate a set of logistics semantic features; Step 2: Based on the ontology model in the logistics domain, perform business relationship mapping and attribute modeling on the set of logistics semantic features to generate logistics data asset instances; Step 3: Perform a multi-dimensional quality confidence assessment on the logistics data asset instance, and perform weighted mapping processing on the assessment results to generate a dynamic value index that characterizes the application potential of the asset; Step 4: Based on the storage strategy determined by the dynamic value index, persist the logistics data asset instance to the asset storage architecture, and simultaneously execute the semantic alignment mapping with the logistics digital twin base to realize the construction of the data asset warehouse.

2. The data asset warehouse construction method according to claim 1, characterized in that, Step 1 includes: The multi-source heterogeneous logistics data is subjected to feature deconstruction targeting the spatiotemporal trajectory flow and discrete business event flow of logistics in order to determine the primary feature loads with spatial displacement indicators and time state nodes. Using semantic annotation operators in the context of logistics, business intent anchoring processing based on context logic is performed on the primary feature payload, thereby implementing semantic extraction and determining logistics entities and circulation events; The logistics entities and circulation events are mapped to a preset attribute description space for classification and labeling, thereby completing the tagging process and generating the set of logistics semantic features.

3. The data asset warehouse construction method according to claim 2, characterized in that, Step 2 includes: The set of logistics semantic features is input into the logistics domain ontology model. The implicit dependency relationship between the logistics entity and the circulation event is identified through the semantic embedding algorithm, and a logical topological connection is constructed to perform the business relationship mapping and generate a logistics business association knowledge graph. The high-order structural features in the logistics business association knowledge graph are extracted using graph neural network operators, and the high-order structural features are subjected to attribute completion processing for the logistics business scenario. This is used to perform attribute modeling processing and generate the logistics data asset instance.

4. The data asset warehouse construction method according to claim 1, characterized in that, Step 3 includes: Using the time-depreciation model and the source reliability matrix, the logistics data asset instance is subjected to confidence verification for data freshness and source authority in order to calculate and generate a set of multi-dimensional quality scores. The quality score set is mapped to a pre-set business benefit cost model, and a profit and loss assessment is performed on the difficulty of data acquisition and the contribution to decision support, thereby generating the assessment result.

5. A method for constructing a data asset warehouse according to claim 4, characterized in that, Step 3 includes: Identify the real-time intensity of the target business associated with the logistics data asset instance to determine the scenario association weight that characterizes the sensitivity of the current business to data accuracy requirements; Using the scenario association weights as input parameters, a normalized weighted mapping is performed on the quality score set in the evaluation results to complete the weighted mapping process and generate the dynamic value index.

6. The method for constructing a data asset warehouse according to claim 1, characterized in that, Step 4 includes: The dynamic value index is processed for energy efficiency classification based on real-time response requirements, and the classification results are matched to the first storage position in the asset storage architecture, which is composed of high-performance flash memory media, or the second storage position, which is composed of large-capacity mechanical media. Based on the matching results, the logistics data asset instances are categorized and written into the corresponding storage location, thereby achieving persistence to the asset storage architecture.

7. A method for constructing a data asset warehouse according to claim 1, characterized in that, Step 4 includes: Extract the real-time virtual mirror state of the physical entity in the logistics digital twin base, and perform time-series consistency-based feature fusion processing on the static business attributes carried by the logistics data asset instance and the real-time virtual mirror state to generate an asset twin synchronization feature body. By comparing the deviation values ​​between the asset twin synchronization feature and the preset standard ontology construct, a self-healing attribute correction process is performed for the data asset repository, thereby implementing the semantic alignment mapping.

8. A method for constructing a data asset warehouse according to claim 3, characterized in that, After generating the logistics data asset instance, the process also includes: The semantic conflict detection of the logistics data asset instances under different storage batches is performed through the logistics business association knowledge graph to identify heterogeneous attribute contradiction features for the same logistics entity. The contradictory features of the heterogeneous attributes are subjected to logical arbitration based on business confidence to generate a unified standard asset fingerprint and update it to the data asset warehouse.

9. A method for constructing a data asset warehouse according to claim 6, characterized in that, The storage strategy determined based on the dynamic value index includes: Real-time monitoring of the hotspot frequency changes of the logistics data asset instance being called by the application side, and performing attenuation mitigation calculation on the dynamic value index based on the hotspot frequency changes; In response to the calculation result reaching the preset hierarchical switching threshold, the corresponding asset is driven to perform asynchronous cross-layer migration processing between the first storage order and the second storage order, so as to realize the dynamic adjustment of the storage strategy.

10. A method for constructing a data asset warehouse according to claim 1, characterized in that, The multi-source heterogeneous logistics data includes flow location payloads sensed by mobile terminals, unit identity payloads extracted by radio frequency identification terminals, and fulfillment link payloads synchronized by business systems.

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