A method and system for automatically diagnosing a reconciliation difference of intelligent orchestration

Abstract

The application discloses a kind of intelligent orchestration's account difference automatic diagnosis method and system, it is related to intelligent account checking technical field, including the following steps: obtaining cross-domain fund link semantic bill stream, constructs heterogeneous account flow self-adapting perception link, and generates uniform semantic bill view;Based on uniform semantic bill view, drive account checking kernel to execute semantic topology reconstruction and time sequence coupling, form difference characteristic fingerprint atlas;By executing evolution state diagram deduction and cross-link causal backtracking arrangement to difference characteristic fingerprint atlas, form multidimensional association traceability;Based on multidimensional association traceability, trigger decision engine to execute strategy self-evolution regulation and arrangement path back-annotation;The application is by constructing the uniform semantic bill view of cross-domain fund link, and is combined with semantic topology reconstruction and difference characteristic fingerprinting expression mechanism, solves the problem that data source is dispersed in traditional account checking process, semantic is not uniform, difference positioning relies on artificial experience and cannot be self-adaptively adjusted.

Description

Technical Field

[0001] This invention relates to the field of intelligent reconciliation technology, and more specifically, to an intelligently orchestrated method and system for automated diagnosis of reconciliation discrepancies. Background Technology

[0002] With the increasing digitalization and cross-institutional collaboration in financial services, the frequency of accounting data transfer between different systems and platforms has significantly increased, forming a multi-source, heterogeneous, and structurally diverse fund flow data system. In actual reconciliation processes, inconsistencies exist in field definitions, semantic expressions, and record granularity among various types of billing data, making direct matching and unified processing difficult. Simultaneously, the fund flow path exhibits a complex structure involving multiple links and nodes, making discrepancies dispersed and hidden, increasing verification difficulty. Furthermore, existing reconciliation methods largely rely on rule matching or manual checks, lacking systematic support for identifying abnormal paths and tracing the causes of discrepancies, making it difficult to establish clear causal relationships within complex flows. During the discrepancy processing stage, the lack of a coordinated control mechanism based on the overall flow structure leads to fragmented processing, hindering dynamic adjustments and closed-loop optimization for discrepancy paths. Therefore, how to achieve semantic unification, flow structure reconstruction, refined characterization of discrepancy features, and integrated source tracing and control in a multi-source accounting data environment has become a pressing technical challenge in the field of automated reconciliation diagnostics.

[0003] For example, the invention patent with publication number CN121258689A discloses an intelligent early warning analysis method for abnormal capital flows, which relates to the field of data processing technology. This method acquires transaction data of a target account within a continuous time interval; extracts semantic features, generating a set of capital semantic units based on the transaction initiator, recipient, and business type; calculates causal relationships between adjacent capital semantic units to determine the causal strength of the behavior; when the causal strength of the behavior is lower than a preset causal threshold, the corresponding time period is marked as a behavioral fault zone; the set of accounts within the behavioral fault zone undergoes context reconstruction; a deviation analysis is performed to identify capital flow patterns with abnormal aggregation or reverse flow characteristics; the abnormal threshold weights of the initial judgment model are self-learned and updated to obtain a corrected judgment model; subsequent transaction data is detected based on the corrected judgment model to generate intelligent early warning information for abnormal capital flows; this improves the autonomy and accuracy of intelligent early warning analysis for abnormal capital flows.

[0004] For example, the invention patent with publication number CN115456765A discloses a method and system for processing bank fund flow data. This includes acquiring bank transaction statements, standardizing and converting the statement data, and extracting key information. Based on a preset analysis model tool, the acquired statement data is analyzed from multiple dimensions to obtain analysis results. The data analysis includes at least three of the following: bank reconciliation analysis, fund direction analysis, transaction date pattern analysis, mutual fund transaction analysis, and fund transaction path analysis. Suspicious fund data obtained through data analysis is automatically generated and stored as query records. The system can provide tools for importing bank statements from various banks and automatically standardizes corresponding key information.

[0005] The above-disclosed technical solutions have at least the following technical problems:

[0006] Traditional reconciliation methods rely on rule matching, which makes it difficult to unify the semantics of multi-source heterogeneous data. Furthermore, they lack adaptive adjustment and path orchestration mechanisms, resulting in low processing efficiency. To address these issues, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent orchestration-based automated diagnosis method and system for reconciliation discrepancies. By constructing a unified semantic billing view across cross-domain funding links and combining semantic topology reconstruction and a fingerprinting mechanism for discrepancy features, the method solves the problems of scattered data sources, inconsistent semantics, and reliance on human experience for discrepancy location in the traditional reconciliation process, which cannot be adaptively adjusted.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method and system for automated diagnosis of reconciliation discrepancies using intelligent orchestration includes: acquiring semantic billing flows across cross-domain funding links, constructing adaptive perception links for heterogeneous accounting flows, and generating a unified semantic billing view; based on the unified semantic billing view, driving the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling to form a discrepancy feature fingerprint map; performing evolutionary state diagram deduction and cross-link causal backtracking orchestration on the discrepancy feature fingerprint map to form multi-dimensional correlation tracing; and based on multi-dimensional correlation tracing, triggering the decision engine to execute strategy self-evolutionary regulation and orchestration path back-injection.

[0010] In a preferred technical solution, the steps of acquiring the cross-domain capital flow semantic billing stream, constructing the heterogeneous accounting flow adaptive perception link, and generating a unified semantic billing view are as follows: Capital flow data is acquired and unified semantic regularization and expression mapping processing is performed to form a semantic billing stream; multi-granularity semantic tensor expansion processing is performed on the semantic billing stream to map the capital flow data into a high-dimensional semantic representation; cross-domain semantic folding and equivalent reconstruction processing is performed on the multi-dimensional semantic representation to form a standardized semantic unit set; a link emergence construction operation is performed based on the unified semantic expression space to generate a capital flow link structure; temporal phase weaving processing is performed on the capital flow link structure to form a dynamic accounting link; semantic field-driven reorganization processing is performed on the dynamic accounting link, while spatial aggregation of multi-source semantic associations is performed; and structured expression encapsulation and semantic projection processing are performed based on the dynamic accounting link to generate a unified semantic billing view.

[0011] In a preferred technical solution, the semantic field-driven reorganization process of the dynamic accounting link, while spatially aggregating multi-source semantic associations, is specifically as follows: The semantic fragments of nodes in the dynamic accounting link are fluidized and expanded, and the semantic transmission trajectory is unfolded along the link direction; the semantic flow fragments are subjected to flow direction traction modulation, and the overall path morphology is reshaped; resonance activation processing is triggered based on the semantic fragments, and resonance semantic clusters are formed in space; collaborative folding and rearrangement are performed on the resonance semantic clusters, and the original connection paths are rewritten; field slicing projection processing is implemented based on the resonance semantic clusters, and spatial layering is performed based on semantic features; in each semantic region, self-organizing fusion operation is performed on the semantic clusters, and redundant expression structures are compressed simultaneously; cross-domain flow bridge weaving is performed on the relationships between each semantic region to complete the topology reconstruction of the overall link.

[0012] In a preferred technical solution, the step of driving the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling based on a unified semantic billing view to form a differential feature fingerprint map is as follows: Semantic deconstruction processing is performed on the unified semantic billing view, and fields from different sources are uniformly mapped to standard semantic identifiers to form a semantic data standardization mapping set; semantic structure skeletonization and reorganization processing is performed on the semantic data standardization mapping set to form a semantic topology flow skeleton; temporal stream compression and embedding processing is performed on the semantic topology flow skeleton, and cross-source time expressions are uniformly projected onto the time evolution axis to form a temporal coupling projection sequence; based on the temporal coupling projection sequence... The system performs cross-domain semantic isomorphic alignment processing and semantic merging and compression on repetitive expression nodes to form a cross-domain semantic isomorphic fusion skeleton. It then performs deviation path activation labeling processing on the cross-domain semantic isomorphic fusion skeleton to form a difference path activation label chain. Based on the difference path activation label chain, it performs spatiotemporal coupling reconstruction processing to form a spatiotemporally consistent coupling link. Finally, it performs multi-scale difference aggregation projection processing on the spatiotemporally consistent coupling link and performs cross-path difference aggregation calculation to form a multi-scale difference feature aggregate. Finally, it performs structural fingerprinting encoding generation processing on the multi-scale difference feature aggregate and outputs it sequentially according to topological position to form a difference feature fingerprint map.

[0013] In a preferred technical solution, the semantic data standardization mapping set is subjected to semantic structure skeletonization and reorganization processing to form a semantic topology flow skeleton, specifically as follows: The semantic data standardization mapping set is subjected to flow path decoupling and expansion processing, and expanded along semantic dependency relationships to form several semantic flow fragment clusters; the semantic flow fragment clusters are subjected to connection traction weaving processing, and nodes within the fragments are adaptively rearranged to form a semantic flow weaving structure; based on the semantic flow weaving structure, reverse link backtracking and splicing processing is performed to form an extended semantic flow chain; the extended semantic flow chain is subjected to intersection node fusion and arrangement processing, and the cross paths are uniformly rewritten to evolve the multi-path structure into a continuous backbone semantic flow chain; the dependency nodes between different flow chains in the backbone semantic flow chain are interwoven and connected to form a semantic topology weaving body; the semantic topology weaving body is subjected to global flow order reorganization processing, and broken links in the links are filled and embedded; the semantic topology weaving body is subjected to structural encapsulation and expression regularization processing to form a semantic topology flow skeleton.

[0014] In a preferred technical solution, the step of forming a multidimensional correlation tracing by performing evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint spectrum is as follows: the differential feature fingerprint spectrum is decoupled and split to form a differential fingerprint segment sequence set; based on the differential fingerprint segment sequence set, state potential energy is characterized to form a state potential energy field band; the potential energy field band is subjected to potential energy flow evolution and shift to form a state flow trajectory network; by capturing the overlapping disturbance relationship between different flows in the state flow trajectory network, key intersection nodes that trigger linkage are extracted to form a disturbance coupling node cluster; source point back mapping is performed on the disturbance coupling node cluster, and the back path is hierarchically compressed and the nodes are aligned to form a source point mapping trajectory chain; multi-track interlacing arrangement processing is performed on the source point mapping trajectory chain, and non-consistent paths are reconstructed in parallel to form a multi-track interlaced tracing network; structured tracing mapping processing is performed on the multi-track interlaced tracing network to form a multidimensional correlation tracing structure.

[0015] In a preferred technical solution, the step of performing potential energy flow evolution and shifting on the state potential energy field band to form a state flow trajectory network specifically includes: deconstructing and dividing the state potential energy field band into potential energy hierarchical segments, and decomposing the continuous potential energy field band into multi-layer semantic potential energy segments to form a hierarchical potential energy distribution sequence; splitting a single potential energy unit in the hierarchical potential energy distribution sequence into multiple diffusion sub-units along its evolution trend to form a multi-source extended potential energy flow fragment set; performing cross-layer penetration mapping processing based on the potential energy flow fragment set, and generating path projection fragments in each level; A path-connecting weaving method is introduced between multi-layered mapping trajectories, and cross-layer nodes are rearranged to construct a potential energy flow path system. Perturbation drives are injected into local regions of the potential energy flow path system, and deflection paths are reconnected and combined to form path reorganization units. Global flow direction integration is performed based on the path reorganization units, and each branch path is uniformly sorted according to the potential energy evolution direction to form a continuous flow sequence. The continuous flow sequence is networked, and the connection relationships between path nodes are uniformly bound to form a state flow trajectory network.

[0016] In a preferred technical solution, the step of triggering the decision engine to execute self-evolutionary regulation and orchestration path back-injection based on multi-dimensional correlation tracing specifically includes: based on the multi-dimensional correlation tracing structure, converting discrete tracing information into semantic evolution fragments and performing deep regularization to generate a tracing semantic primitive field; performing a causal tension-driven rearrangement operation on the tracing semantic primitive field to form a causal tension evolution chain; implementing multi-path strategy mapping deduction on the causal tension evolution chain and characterizing the trajectory of the node response behavior in each path to form a multi-path strategy evolution mapping set; and based on the multi-path... The policy evolution mapping set segments and normalizes policy responses in different paths according to their evolution phase, outputting a coherent sequence of policy responses. An adaptive control flow injection operation is applied to this coherent sequence, forming control trajectories within the sequence to generate a policy control fluidized chain. A topology back-embedding weaving operation is performed on the policy control fluidized chain, while the original path structure is alternately rearranged and hierarchically interleaved to generate a path back-injection composite chain. Cross-layer cooperative emergence is performed on the path back-injection composite chain, and the cooperative paths are uniformly normalized and output, forming a policy orchestration back-injection network structure.

[0017] In a preferred embodiment, the process of applying an adaptive control flow injection operation to the coherent sequence of the strategy response and forming a control flow path within the sequence to generate a strategy control fluidized chain is as follows: The strategy response coherent sequence is decomposed and extracted for control factors, and then directional matching and allocation are performed to form a node-level multi-source control set; based on the multi-source control set, a causal feedback loop structure for each strategy node is constructed, and the preceding and following dependencies of the nodes are closed-loop connected and recombined to form a control flow channel; a superposition and fusion injection operation is performed on the control flow channel to form a composite control injection node sequence; feedback-driven fluidization expansion is performed on the composite control injection node sequence to form a continuous transmission chain segment; adsorption traction is applied to the connection relationship between adjacent nodes in the continuous transmission chain segment, and the diffusion range is dynamically adjusted according to the node connection density to form a control flow fragment; control flow fragments formed in different paths within the control flow fragment are spliced ​​together according to the node connection relationship to generate a continuous flow path; global fluidization regularization is performed on the control flow path, and the loop and diffusion path are fused to finally form a strategy control fluidized chain.

[0018] In a preferred technical solution, a system for an intelligent orchestration-based automated diagnosis method for reconciliation discrepancies is characterized by comprising a semantic perception module, a topology coupling module, a tracing and deduction module, and a strategy control module, with connections between the modules; the semantic perception module is used to acquire cross-domain financial link semantic billing flows, construct heterogeneous accounting flow adaptive perception links, and generate a unified semantic billing view; the topology coupling module is used to drive the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling based on the unified semantic billing view, forming a difference feature fingerprint map; the tracing and deduction module is used to perform evolutionary state diagram deduction and cross-link causal backtracking orchestration on the difference feature fingerprint map to form multi-dimensional correlation tracing; the strategy control module is used to trigger the decision engine to perform strategy self-evolutionary control and orchestration path back-injection based on multi-dimensional correlation tracing.

[0019] The technical effects and advantages of the intelligent orchestration-based automated diagnosis method and system for reconciliation discrepancies of this invention are as follows:

[0020] 1. This invention constructs a cross-domain capital flow semantic billing stream and performs adaptive perceptual link weaving on heterogeneous accounting streams. Based on field-level unified mapping and semantic regularization of multi-source accounting data, it generates a unified semantic billing view, which can achieve alignment processing of data from different sources at the structural and semantic levels, significantly improving the comparability and consistency of accounting data. At the same time, by performing semantic topology reconstruction and temporal coupling on the unified semantic billing view, discrete accounting nodes are rearranged at the path level according to the capital flow relationship and embedded into a unified time evolution axis, which can construct a semantic link structure with continuous flow characteristics, significantly improving the expression completeness of complex accounting links and the accuracy of reconciliation verification.

[0021] 2. This invention performs evolutionary state diagram deduction on the difference feature fingerprint spectrum. Based on the characterization of state potential energy and trajectory shift of structural offset, temporal offset and path anomaly features, it realizes multi-track expansion and dynamic association construction of difference evolution paths. It can clearly characterize the key nodes and path differentiation relationships in the difference development process, significantly improving the precision of anomaly identification and association analysis capabilities. At the same time, through cross-link causal backtracking orchestration and multi-dimensional association tracing, combined with strategy self-evolution control and orchestration path back-injection mechanism, it performs strategy-level response mapping and path reconstruction on the tracing results, which can realize dynamic adjustment and closed-loop optimization of the difference processing process, further improving the automation level and overall processing efficiency of reconciliation diagnosis. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the process for an automated diagnosis of reconciliation discrepancies using intelligent orchestration, as described in this invention.

[0023] Figure 2 This is a schematic diagram of the system structure of an intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to the present invention.

[0024] Specific technical solutions

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1, Figure 1 This invention presents an automated diagnostic method for reconciliation discrepancies using intelligent orchestration, comprising the following steps:

[0027] S1, obtain the semantic billing flow of cross-domain funding links, construct the heterogeneous accounting flow adaptive perception link, and generate a unified semantic billing view;

[0028] In this embodiment, a semantic billing flow of cross-domain funding links is obtained, an adaptive perception link of heterogeneous accounting flows is constructed, and a unified semantic billing view is generated, as detailed below:

[0029] By accessing cross-domain funding links, the system acquires funding link data (transaction data, account data, clearing and settlement records, channel data, status data, circulation data, and accounting change data), and performs unified semantic normalization and expression mapping processing to form a semantic billing flow. Specifically, the system splits transaction flow, account data, clearing and settlement records, and accounting change data into layers according to business semantics, performs semantic mapping conversion on field names, encoding methods, and data formats that differ across systems, and maps data with similar business meanings to consistent semantic labels. Then, it performs unified processing on time expressions, converting different time formats into unified time series identifiers and standardizing the order of transactions. Furthermore, it converts data with different precision, currencies, and measurement methods into a unified expression form, extracts association identifiers, uniformly encodes key fields such as order number, transaction number, and channel identifier, establishes association indexes, normalizes missing or redundant fields, removes invalid information, and completes necessary semantic fields. Finally, it combines various types of data according to a unified semantic structure to form a semantic billing flow with consistent field definitions, unified expression rules, and standardized association relationships.

[0030] Multi-granularity semantic tensor expansion processing is performed on the semantic billing stream to map the fund link data into a high-dimensional semantic representation. Specifically, transaction data, account data, time and status data are extracted and generated into independent semantic dimensions. For each semantic dimension, corresponding field combination features are extracted and vectorized to convert discrete fields into continuous semantic representations. On this basis, transaction, time, account and status change data are embedded into a unified multi-dimensional structure. The semantic information of the same transaction at different link positions is positionally encoded to give it link context identifiers in the tensor. Furthermore, transaction records with related relationships are adjacently bound in the tensor space to form a set of semantic representations with contextual association features. Finally, the original semantic billing stream is converted into a high-dimensional semantic representation structure containing multi-dimensional semantic features, link relationships and time information.

[0031] Perform cross-domain semantic folding and equivalent reconstruction on multidimensional semantic representations to converge heterogeneous expressions from different systems into a unified semantic representation space, forming a standardized set of semantic units.

[0032] This method utilizes a unified semantic expression space to perform emergent link construction. By identifying implicit associations and flow dependencies between semantic units, it generates a self-organizing fund flow link structure. Specifically, it scans the semantic units in the unified semantic expression space to extract potential association paths between transactions, accounts, and channels. Based on the identifier combinations and time sequence contained in the semantic units, it matches the flow continuity between different records, connecting semantic units with transitive relationships. For semantic units that cannot be directly associated through explicit identifiers, it infers associations based on the direction of amount change, time adjacency, and state change characteristics to establish implicit connections. After completing explicit and implicit connections, the semantic units are aggregated according to the connection relationships to form several initial link segments. Then, iterative expansion processing is performed on the link segments, merging segments with shared nodes or continuous flow relationships to gradually build a complete link structure. During the link construction process, the dependency relationships and path sequences between nodes are recorded simultaneously, thereby organizing the dispersed semantic units into a fund flow link structure with continuous flow relationships.

[0033] The fund flow link structure is subjected to time-series phase weaving processing, which interweaves and rearranges discrete time markers and state evolution trajectories to form a dynamic accounting link. Specifically, time markers and corresponding state identifiers are extracted from each node in the fund flow link structure, and time expressions from different sources are uniformly serialized. Each node is mapped to the same time axis and its relative order is marked. Then, the nodes are divided into phases on the time axis, and nodes in adjacent time intervals are assigned to the same phase segment and the corresponding state change information is recorded. Furthermore, for nodes with time overlap or discontinuity in the same link, they are re-embedded into the corresponding phase segment according to the actual flow order, and the nodes in each phase segment are connected in series according to the state evolution order, mapping the transaction state change process into a continuous trajectory. Finally, sequential connection relationships are established between different phase segments, and the original discrete node structure is reorganized into a dynamic accounting link with continuous time evolution characteristics.

[0034] Semantic field-driven reorganization processing is performed on the dynamic accounting link. By constructing a global semantic potential field, the link nodes are adaptively guided and topologically rearranged, while multi-source semantic associations are spatially aggregated.

[0035] Based on the restructured dynamic accounting chain, structured expression encapsulation and semantic projection processing are performed to uniformly extract and map the node relationships, flow paths, and state evolution information in the chain, generating a unified semantic billing view. Specifically, the restructured dynamic accounting chain is parsed to extract the transaction data, account data, time, and status data corresponding to each node. The relationship between the nodes before and after the fund flow is recorded according to the chain sequence. The state change process of each node in the same chain is unfolded in chronological order to form a continuous state sequence. Based on the above, the node data, path relationships, and continuous state sequences are mapped to fields according to a unified structure to construct a standardized expression format. Then, the same semantic fields in different chains are uniformly named and aligned in position. Subsequently, the multidimensional information in the chain is compressed and mapped into a fixed field combination in the unified expression view, finally forming a unified semantic billing view containing node relationships, flow paths, and state evolution information.

[0036] In this embodiment, cross-domain semantic folding and equivalent reconstruction are performed on the multidimensional semantic representation to converge heterogeneous representations from different systems into a unified semantic representation space, forming a standardized set of semantic units, as follows:

[0037] Based on semantic expression structure, a cross-domain semantic isomorphic field is constructed. By extracting the structural features and correlations of semantic fragments from different systems, isomorphic mapping trajectories are generated in a unified semantic space.

[0038] Based on the isomorphic mapping trajectory, semantic folding and convergence processing is performed on the semantic expression structure. Multiple expressions with homologous semantics are compressed and mapped into a unified semantic kernel unit. Specifically, the semantic segments in the semantic expression structure are grouped and labeled according to the isomorphic mapping trajectory. Semantic expressions that show corresponding relationships in the trajectory are divided into homologous sets. Then, the core field combinations and structural features of the semantic segments in the homologous sets are extracted, and the repeated and different expressions are identified. The different parts are aligned. Based on the alignment, the multiple semantic expressions in the homologous sets are compressed and merged. Fields that express the same business meaning are uniformly mapped to a single semantic identifier. The merged semantic segments are structurally regularized, and the original scattered field combinations are reorganized into a unified structural unit. During the processing, the mapping relationship between the original semantic segments is preserved and recorded as an internal association index. Finally, the compressed and regularized semantic segments are output as a unified semantic kernel unit, so that the expression of the same business meaning in different systems is consistent in structure and identifier.

[0039] Link context-driven information is introduced into the semantic kernel unit. By superimposing the relationship between preceding and following nodes and the temporal evolution relationship, a semantic evolution unit with context awareness is formed. Specifically, the position of the semantic kernel unit in the funding link is located, its corresponding preceding and following node data are extracted, and the connection relationship between nodes is obtained. Then, the transaction data, account data, and status data in the preceding and following nodes are correlated and parsed, and they are bound to the current semantic kernel unit. Further, the time stamps corresponding to each node are extracted, and the time order between the preceding and following nodes and the current node is arranged to construct a continuous temporal evolution sequence. On this basis, the relationship between preceding and following nodes and the time sequence information are superimposed into the semantic kernel unit, and the flow path and state change process between nodes are embedded into the same expression structure. Finally, the embedded semantic information is uniformly encapsulated to form a semantic evolution unit containing link context information.

[0040] Semantic field projection processing is performed on semantic evolution units to embed them into a unified semantic expression space. Specifically, this involves: uniformly encoding various semantics within the semantic evolution unit, transforming transaction data, account data, time and state data into mappable semantic representations; establishing corresponding spatial mapping rules for the encoded semantics, assigning different types of semantics to corresponding expression dimensions; further, performing projection mapping on the semantic evolution unit as a whole, embedding its internal multidimensional semantic information into specified positions in the unified semantic expression space; during the mapping process, constraining the spatial positions based on the relationships between semantics, ensuring that related semantic elements are distributed adjacently in space; finally, sequentially embedding the time series and state series within the same semantic evolution unit, and performing unified mapping processing on semantic evolution units from different links to ensure they have consistent expression structures and positional rules within the same semantic expression space.

[0041] A semantic aggregation potential field is constructed in a unified semantic expression space to enable self-organized convergence of semantic evolution units, allowing semantically similar units to form high-density clustering regions in the space. Specifically, the embedded semantic evolution units are partitioned into neighborhoods in the unified semantic expression space, and the semantic similarity and correlation between each unit are extracted. Then, based on the similarity relationship, an initial scope of action is assigned to each semantic evolution unit, and interaction relationships are established in the space. Subsequently, the correlation relationships between adjacent semantic evolution units are accumulated and superimposed to form local semantic aggregation regions. During the aggregation process, according to the semantic association path, units with similar semantic features are gradually moved towards the same region. The position of semantic evolution units in the aggregation region is adjusted to form a continuous distribution structure in the space. At the same time, the boundaries between different aggregation regions are distinguished to maintain the independence of each region. Finally, through continuous aggregation and adjustment, semantically similar units gradually form high-density clustering regions in the space.

[0042] Semantic compression and structural reshaping are performed on the clustered regions to merge the semantic units within the clustered regions into standardized semantic clusters. Specifically, the semantic units within the clustered regions are divided into sets, and the field structure and semantics corresponding to each semantic unit are extracted. Repeated and differential expressions are identified. Then, the repeated expressions are merged, and fields with the same semantics are uniformly mapped to a single expression node. Further, the differential expressions are structurally aligned, and fields from different sources but with consistent semantics are reorganized and arranged according to a unified rule. After alignment, the semantic units within the clustered regions are compressed to integrate multiple semantic units into a unified structural unit. During the integration process, the relationships between the original semantic units are preserved, and their internal connection order is recorded. Subsequently, the integrated structure is reshaped to convert it into a standardized field combination form. Finally, the semantic units within each clustered region are merged into a standardized semantic cluster with consistent structure and unified semantics.

[0043] The original multidimensional semantic representation is restructured based on semantic clusters. Each semantic cluster is rearranged according to a unified semantic topology to form a standardized set of semantic units. Specifically, the generated semantic clusters are indexed and labeled, and the node set and internal connections of each semantic cluster are extracted. Then, the semantic clusters are mapped back to their corresponding positions in the original multidimensional semantic representation, replacing the original scattered semantic unit structure. Based on the relationships between semantic clusters, a cluster-level connection network is constructed, recording the flow order and dependency relationships between each semantic cluster. Subsequently, the cluster-level connection relationships are expanded, converting the node-level connections in the original links into a semantic cluster-level connection structure. On this basis, the semantic clusters are sorted according to a unified semantic topology, and semantic clusters with sequential dependencies are arranged in a predetermined order and combined according to a unified structure to form a continuously expressed semantic chain structure. Finally, the reorganized set of semantic clusters is uniformly encapsulated to form a standardized set of semantic units with consistent structure and complete relationships.

[0044] In this embodiment, semantic field-driven reorganization processing is performed on the dynamic accounting link. By constructing a global semantic potential field, adaptive traction and topology rearrangement are performed on the link nodes. At the same time, multi-source semantic associations are spatially aggregated, as follows:

[0045] The semantic fragments of nodes in the dynamic accounting link are processed into a fluidized form, transforming the original discrete nodes into continuous semantic stream fragments, and unfolding the semantic transmission trajectory along the link direction.

[0046] The semantic stream segments are subjected to flow-direction modulation. The extension direction of each segment is dynamically offset according to the semantic transmission trend, and the overall path shape is reshaped. Specifically, the semantics, temporal order and node associations in the semantic stream segments are extracted, and the forward and backward connection directions of each segment in the link are analyzed. Then, based on the continuity between semantics, the extension direction of the semantic stream segments is initially calibrated, and the path positions with bifurcations, backtracking or interruption are identified. Subsequently, segments that deviate from the transmission trend are realigned according to their forward and backward associations, and the extension direction is adjusted to be consistent with the mainstream semantic path. During the modulation process, the original discontinuous or misaligned segments are re-embedded in the corresponding positions, and the bifurcated nodes in the path are sequentially reorganized. Furthermore, the discrete path segments are connected in series according to the unified extension direction. Finally, the semantic stream segments present a path shape with a consistent extension direction in space.

[0047] Based on the semantic fragments triggered during the flow process, resonance activation processing is performed to synchronously respond to fragments with similar semantic expressions, forming synchronously evolving resonance semantic clusters in space. Specifically, based on the semantic flow fragments, the semantic similarity between different fragments is compared and calculated one by one to select a set of fragments with highly consistent semantic expressions. Then, the selected semantic fragments are subjected to resonance labeling processing, which assigns them a unified resonance response identifier and synchronously activates their associated propagation paths during the semantic flow process. When any fragment undergoes a state change, other semantic fragments under the same resonance label are triggered to perform a linkage response, causing them to synchronously adjust in spatial position and connection relationship. During the synchronous response process, the flow direction and connection relationship of the resonance fragments are aligned, and the scattered fragments are re-merged according to the resonance relationship. Furthermore, the merged semantic fragments are subjected to synchronous sorting processing to ensure that they maintain a consistent evolution rhythm in terms of time series and spatial structure. Finally, the semantic fragments with resonance relationships are aggregated in space into synchronously evolving resonance semantic clusters.

[0048] A collaborative folding and rearrangement operation is performed on the resonant semantic clusters to compress and integrate the scattered semantic fragments according to the resonance relationship, while rewriting the original connection paths. Specifically, the semantic fragments in the resonant semantic clusters are parsed to extract their corresponding semantics, positions and original connection paths. Then, the semantic fragments are grouped and sorted according to the resonance relationship. Fragments with the same resonance identifier are arranged in chronological order and link order. Subsequently, fragments with multiple scattered positions are compressed into continuous expression units and their internal structures are uniformly encoded. During the folding process, repeated connection relationships between fragments are merged, redundant paths are removed, and key sequential connections are retained. Furthermore, the original node-based connection paths are converted into connection structures based on folding units, and the connection relationships between cross groups are reorganized so that connections are established between each folding unit according to the updated order. Finally, a compact and clearly connected rearranged semantic path structure is formed.

[0049] Based on the compressed and integrated resonant semantic clusters, field slicing projection processing is implemented, and spatial hierarchical arrangement is performed according to semantic features. Specifically, the semantic identifier set and internal structural features of the compressed and integrated semantic clusters are extracted and classified. Then, different semantic components contained in the same cluster are split into multiple sub-structural segments. Subsequently, each sub-structural segment is projected and mapped to the corresponding semantic region according to its semantic features. A spatial position identifier is assigned to each segment. During the projection process, segments with similar semantic features are arranged adjacently. Furthermore, their positions are distributed vertically according to the semantic hierarchy, so that the basic semantics and derived semantics form an ordered hierarchical structure. At the same time, the connection identifiers of segments with cross-regional relationships are retained to ensure their correspondence in space. Finally, a spatial arrangement result with a hierarchical structure is formed by dividing according to semantic features.

[0050] Within each semantic region, a self-organizing fusion operation is performed on the semantic clusters to overlay and fuse multi-source semantics and simultaneously compress redundant expression structures. Specifically, the semantic clusters within each semantic region are structurally parsed to extract their internal semantic units and connections, and the sources of multi-source semantics are traced and marked. Subsequently, semantic units with the same content are merged. During the merging process, multi-source semantics are overlaid and mapped, and fields from different sources but with consistent semantics are uniformly encoded and converted and merged into the same semantic expression unit. At the same time, repeated connections within the region are merged and compressed, multiple redundant paths are rewritten into a single connection structure, and key path identifiers are retained. Furthermore, the dispersed semantic units are reordered in a unified order. Finally, the semantic units after overlay and compression are repackaged into a unified structural expression, forming a set of semantic expressions with consistent structure within each semantic region.

[0051] Cross-domain flow bridge weaving is performed on the relationships between semantic regions, connecting and rearranging semantic clusters in different regions according to their flow trajectories to complete the topology reconstruction of the overall link. Specifically, this involves: extracting boundary connections and cross-regional association identifiers for semantic clusters in each semantic region, identifying semantic flow paths between different regions, then converting the connections between regions into traceable flow sequences, and locating and marking key connection points in the sequences. Based on this, flow bridge mapping is performed on the cross-domain connections between semantic clusters, connecting corresponding semantic clusters in different regions one by one according to their flow trajectories and rewriting their connection order. At the same time, path intersections and duplicate connections that occur during cross-regional connections are sorted out, and redundant connections are merged and compressed, retaining only continuous flow paths. Furthermore, the mapped cross-domain connection structure is rearranged as a whole, arranging clusters in each semantic region in series according to their flow order and simultaneously adjusting their connection directions. Finally, a continuous and interconnected connection structure is formed between different semantic regions, completing the topology reconstruction of the overall link.

[0052] S2, based on a unified semantic billing view, drives the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling to form a differential feature fingerprint map;

[0053] In this embodiment, based on a unified semantic billing view, the reconciliation verification kernel is driven to perform semantic topology reconstruction and temporal coupling to form a differential feature fingerprint map, as follows:

[0054] Perform semantic deconstruction processing on various accounting nodes in the unified semantic billing view to extract transaction data, account data and status data, and map fields from different sources to standard semantic identifiers to form a standardized semantic data mapping set.

[0055] The semantic data standardization mapping set is subjected to semantic structure skeletonization reorganization processing, and the discrete data is rearranged and connected according to the capital flow dependency relationship to form a semantic topological flow skeleton with continuous flow characteristics.

[0056] The semantic topology flow skeleton is subjected to temporal stream compression and embedding processing, and cross-source time representations are uniformly projected onto the temporal evolution main axis to form a temporally coupled projection sequence. Specifically, the time identifiers corresponding to each node in the semantic topology flow skeleton are extracted, and the time representations from different sources are uniformly converted into a format that transforms discrete time data into continuous sequence identifiers. Then, the node times are sorted within the path, and the temporal relationship between each node is marked. Subsequently, the time series in the same path are compressed, and the closely spaced continuous time segments are merged and mapped to form compact time representation segments. For nodes with cross-path associations, their time identifiers are projected onto a unified time axis. By aligning their relative positional relationships, the time series from different sources are kept in a consistent order on the same main axis. Furthermore, the time series are bound to the corresponding semantic nodes, so that each node has a clear positional identifier in the unified temporal evolution main axis. Finally, a temporally coupled projection sequence is formed that is arranged in path order and aligned across sources.

[0057] Based on the temporally coupled projection sequence, cross-domain semantic isomorphic alignment processing is performed, and semantic merging compression is applied to repetitive expression nodes to form a cross-domain semantic isomorphic fusion skeleton. Specifically, the semantic and structural attributes of each node in the temporally coupled projection sequence and its position marker on the time axis are extracted. Nodes from different accounting sources are compared item by item to identify semantically consistent or semantically equivalent node sets. Then, based on the identification results, isomorphic mapping relationships are established for equivalent nodes. Nodes from different sources but with consistent expressions are uniformly marked and aligned in chronological order. Subsequently, semantic merging processing is performed on the isomorphic node sets to compress and integrate nodes from multiple sources into a single expression unit, and its internal fields are uniformly encoded. During the merging process, the original connection relationships between nodes are synchronously adjusted, and scattered connection paths are merged into a unified path structure. Furthermore, the merged nodes are re-embedded into the original sequence structure, and the relationships between the preceding and following nodes are rearranged in a consistent manner, ultimately forming a cross-domain semantic isomorphic fusion skeleton with consistent expression and unified structure on the time axis.

[0058] The cross-domain semantic isomorphic fusion skeleton is subjected to deviation path activation identification processing, and non-standard flow connection relationships are structured and abnormal trajectory enhanced encoding is performed to form a differential path activation mark chain;

[0059] Based on the differential path activation marker chain, spatiotemporal coupling reconstruction processing is performed, and semantic connection relationships and time flow trajectories are bidirectionally bound and rearranged to form a spatiotemporally consistent coupled link body. Specifically, the semantic connection identifier and corresponding time identifier of each node in the differential path activation marker chain are extracted, and the connection relationship and time sequence are separated and parsed to form a set of structural relationships and a set of time flow. Then, based on the set of structural relationships, the connection paths between nodes are sorted out to determine the previous and subsequent dependencies of each node, and abnormal connection paths are marked and located. Further, based on the set of time flow, the time sequence of nodes is uniformly serialized to convert time expressions from different sources into standard sequences under the same time axis. Subsequently, a bidirectional binding operation is performed on the structural relationship and time sequence, matching each semantic connection relationship with the corresponding time node one by one and establishing a corresponding mapping index. On the basis of binding, the node order is rearranged to make the connection path order consistent with the time flow order. Finally, the rearranged structure is processed to be continuous, and broken paths are filled in, thereby forming a spatiotemporally consistent coupled link body in which semantic connection relationships and time flow trajectories are arranged in a consistent manner.

[0060] Multi-scale difference aggregation projection processing is performed on the spatiotemporally consistent coupled link body, and cross-path difference aggregation calculation is performed to form a multi-scale difference feature aggregate. Specifically, the nodes and their connecting paths in the spatiotemporally consistent coupled link body are hierarchically divided, and difference marker information at the node level, path level, and link level is extracted. Corresponding difference feature sets are established for each level. Then, the node-level difference marker information is positionally calibrated and mapped to the corresponding coordinate position in the unified expression space, and its distribution in the link is recorded. Further, the path-level difference marker information is processed by path expansion, and the difference markers on the same path are concatenated according to the connection order to form a path difference sequence. Subsequently, the difference information between different paths is aligned, and the difference features with the same semantic position are matched accordingly. On this basis, the difference features with the same position or the same type in the path difference sequence are merged, and duplicate differences are compressed. Finally, the link-level difference information is integrated with the node-level and path-level difference information to form a multi-level fusion structure, and the integrated difference information is projected into a unified expression form to form a multi-scale difference feature aggregate.

[0061] A structural fingerprinting encoding process is performed on the multi-scale differential feature aggregate, and the output is serialized according to topological position to form a differential feature fingerprint map. Specifically, the various differential data in the multi-scale differential feature aggregate are classified and analyzed, and structural offset features, temporal offset features, and path anomaly features are extracted respectively. Each type of feature is independently identified. Then, according to the feature's level and its positional relationship in the link, a corresponding topological position label is assigned to each differential feature, and a position index is established. Subsequently, different types of differential features are encoded and converted into a unified structured identifier sequence. The encoded content is then standardized and arranged. During the encoding process, multiple differential features existing at the same position are combined and recombined to integrate them into composite encoding units. Furthermore, each encoding unit is serialized and arranged according to the topological order in the link, so that it is output sequentially along the link direction. At the same time, repeated encoding sequences are compressed and merged to retain a unique expression form. Finally, a differential feature fingerprint map arranged in topological position order is formed.

[0062] In this embodiment, the semantic data standardized mapping set is subjected to semantic structure skeletonization and reorganization processing. Discrete data is rearranged and connected according to the dependency relationship of capital flow, forming a semantic topological flow skeleton with continuous flow characteristics, as follows:

[0063] The semantic units in the standardized semantic data mapping set are decoupled and expanded according to their flow context. The flow direction identifiers and related directions are extracted and expanded along the semantic dependency relationships to form several semantic flow fragment clusters. Specifically, the connection relationships between different semantic units in the standardized semantic data mapping set are analyzed item by item. According to the flow direction identifier in the semantic unit, the preceding and succeeding nodes in the flow of funds are located and the flow position in the overall link is identified. Then, the original composite connection relationship is decomposed into several basic connection pairs and arranged in order according to the flow direction. On this basis, the original aggregate structure is decomposed into a sequentially arranged semantic sequence and the expanded semantic sequence is divided into segments. Semantic units with continuous dependency relationships and consistent flow directions are divided into the same segment. Finally, the segments are aggregated and merged. Segments with similar structures and consistent flow directions are aggregated to form several semantic flow fragment clusters.

[0064] The semantic flow fragment clusters are subjected to connection traction weaving processing, and the nodes within the fragments are adaptively traction rearranged to form an initially connected semantic flow weaving structure. Specifically, based on the correspondence between the termination nodes and the start nodes between the fragments in the semantic flow fragment cluster, the fragments with connection conditions are initially connected and arranged. Subsequently, the nodes within the fragments are traction rearranged, and the original node arrangement is adjusted according to the dependency relationship and flow order between the nodes. During the rearrangement process, the positions of nodes that are intersecting or misaligned are corrected and re-embedded into the corresponding fragment positions. Furthermore, shared nodes or related nodes are cross-connected. At the same time, the order of the woven connection paths is sorted out, and finally an initial connected semantic flow weaving structure composed of multiple interwoven fragments is formed.

[0065] Based on the key nodes in the semantic flow weaving structure, a reverse link backtracking and merging process is performed. This involves recursively backtracking upstream semantic units along the direction of capital flow and embedding the backtracked paths into the existing structure to form an extended semantic flow chain. Specifically: Each node in the semantic flow weaving structure is screened, and nodes with multiple path connections or located at flow convergence points are extracted as key nodes, and their positions in the structure are marked. For each key node, its association identifier and preceding connection relationship are extracted to locate its direct upstream semantic unit. Subsequently, a layer-by-layer backtracking process is performed along the direction of capital flow, extracting the preceding connection relationship for each upstream node to form a progressive backtracking path. During the backtracking process, recurring nodes are identified and merged, and branching paths are recorded. Further, the backtracked paths are organized according to node order and matched with the existing semantic flow weaving structure. Successfully matched paths are embedded into their corresponding node positions, and the connection relationships are updated. Finally, the backtracked paths are integrated with the original structure to form the extended semantic flow chain.

[0066] The extended semantic flow network chain is processed by fusion and orchestration of converging nodes, and the intersecting paths are uniformly rewritten to evolve the multi-path structure into a continuous backbone semantic flow network chain. Specifically, the nodes in the semantic flow network structure are screened, and nodes with multi-path connections or in the flow convergence position are extracted as key nodes and their positions in the structure are marked. Then, for each key node, its association identifier and preceding connection relationship are extracted, and its direct upstream semantic unit is located. Subsequently, the preceding connection relationship of each upstream node is extracted to form a progressive backtracking path. During the backtracking process, repeated nodes are identified and merged, and branching paths are recorded. Furthermore, the progressive backtracking path is arranged according to the node order and matched with the existing semantic flow network structure. The successfully matched path is embedded and inserted into the corresponding node position, and the connection relationship is updated. Finally, the backtracking path is integrated with the original structure to form the extended semantic flow network chain.

[0067] Interweave and connect the dependent nodes between different flow chains in the main semantic flow chain to form a semantic topology weave with multi-link coupling;

[0068] A global flow order restructuring process is performed on the semantic topology weave, which rearranges the nodes according to the order of fund flow and inserts replacement links at broken points in the links. Specifically, according to the direction of fund flow, all nodes in the semantic topology weave are globally sorted, and nodes distributed in different paths are arranged according to a unified flow order to form a continuous sequence. Then, the positions of nodes with order conflicts or cross connections in the sorted node sequence are adjusted to satisfy the unidirectional flow relationship. On this basis, the broken path positions are identified, and the nodes before and after the broken point are located. Further, according to the association identifier of the broken point, nodes with connection conditions are extracted from other paths, embedded into the broken position, and new connection relationships are established. Finally, the paths after replacement are sorted and the connections are adjusted again to form a semantic topology weave structure that is uniformly arranged and connected according to the order of fund flow.

[0069] The completed semantic topology weave is subjected to structural encapsulation and expression regularization processing, which unifies the expression of node relationships, flow paths and dependent structures, forming a semantic topology flow skeleton with continuous flow characteristics.

[0070] In this embodiment, deviation path activation identification processing is performed on the cross-domain semantic isomorphic fusion skeleton, and non-standard flow connection relationships are structurally marked and abnormal trajectory enhanced encoding is performed to form a differential path activation mark chain, as follows:

[0071] Semantic flow direction parsing is performed on each connection path in the cross-domain semantic isomorphic fusion skeleton, and mainstream semantic trajectory identification is performed on the overall path structure to form a benchmark semantic flow trajectory set. Specifically, the dependency relationship between the preceding and following nodes in each connection path in the cross-domain semantic isomorphic fusion skeleton is parsed item by item. According to the connection order between each node, the nodes are orderly expanded along the path direction to form the corresponding path flow sequence. Then, the flow direction identifiers in the path flow sequence are uniformly processed to convert the flow direction expressions from different sources into a consistent direction label, and the flow relationship between nodes is standardized and arranged. Furthermore, the flow directions between multiple paths are compared, and paths with the same flow direction are merged to form several candidate main path sets. On this basis, the path with a large number of nodes and continuous connection relationship is extracted as the backbone path. Finally, the identified backbone paths are uniformly organized, and their node order and connection relationship are standardized to form a benchmark semantic flow trajectory set.

[0072] Identify the connection relationships between the baseline semantic flow trajectory set and the baseline trajectory that are offset in terms of flow direction, connection order and path shape, and perform polarization calibration on the offset paths to form a set of semantic deviation trajectories;

[0073] Trajectory break detection is performed on the semantic deviation trajectory set, and trajectory offset is recorded for the break nodes to form broken semantic trajectory segments. Specifically, the node sequence and corresponding connection relationship of each path in the semantic deviation trajectory set are extracted, and the nodes are arranged in order according to the path direction. Then, node pairs that cannot form a direct connection or have missing connections are marked. Subsequently, the position of the connection interruption is determined as the break node, and the semantic identifier and path position of the nodes before and after it are recorded. Further, the path where the break node is located is partially expanded, and the nodes before and after the break point are divided into different segments, and their corresponding node sequences are extracted. On this basis, the offset relationship between the actual connection path and the adjacent nodes is marked, and the offset direction and offset position are recorded in a structured way. Finally, the path containing the break node and its segments before and after it is re-expressed to form a broken semantic trajectory segment divided by multiple break positions.

[0074] Based on the semantic trajectory segment of the fracture, a flow compensation backtracking is performed, and the deviation relationship between the actual path and the expected path is expressed in a structured manner to form a trajectory offset enhancement unit. Specifically, the fracture position in the semantic trajectory segment of the fracture is located, and for each fracture node, its upstream node sequence is extracted layer by layer along the direction of capital flow to form a backtracking path set. At the same time, the forward path is expanded for the successor node of the fracture node to obtain its downstream node sequence. Then, the backtracking path and the forward path are matched and matched. Based on the node identifier and connection relationship, the expected connection order in the complete path is identified. Furthermore, the actual fracture path and the matched expected path are compared item by item to extract the node missing, connection misalignment or sequence offset, and the corresponding position is marked. On this basis, the difference relationship between the actual path and the expected path is recorded in a structured manner, and the deviation node, offset direction and corresponding path position are uniformly expressed. Finally, the deviation relationship is associated and integrated with the fracture node to form a trajectory offset enhancement unit.

[0075] Path density analysis is performed on the semantic deviation trajectory set and trajectory offset enhancement unit to form a path energy anomaly segment set. Specifically, the deviation nodes in the trajectory offset enhancement unit are mapped to specific node positions in each path. Then, the distribution between adjacent nodes is statistically analyzed, and areas with dense node distribution or large intervals are marked. Deviation nodes are superimposed on the path distribution structure. The frequency of deviation nodes in the same area is recorded and analyzed in combination with node distribution density. Based on this, the path is segmented. Areas with similar continuous node distribution characteristics and concentrated deviation nodes are divided into the same segment, while areas with sparse nodes but concentrated deviation nodes are also marked as independent segments. Finally, the node distribution and deviation distribution of each segment are uniformly organized to form a path energy anomaly segment set that includes node density changes and deviation clustering characteristics.

[0076] Aggregate activation is performed on the set of abnormal path energy segments, and the path structure is enhanced to form abnormal path activation units. Specifically, the boundaries of each segment in the abnormal path energy segments are marked, and the nodes in each segment are centrally identified. Then, nodes belonging to the same segment are uniformly assigned segment labels, and the connection relationships between nodes are renumbered. Subsequently, the original scattered connection relationships are organized according to the node distribution order, and the deviation nodes in the segment are enhanced and identified, their positions in the path and corresponding connection relationships are highlighted and distinguished from ordinary nodes. Finally, the nodes and connection relationships in the segment are uniformly encapsulated, and the structured expression results of each segment are collected to form abnormal path activation units.

[0077] Semantic deviation, trajectory breakage, and path energy in the abnormal path activation unit are merged and encoded, and then serialized and arranged according to the path topology to form a differential path activation tag chain.

[0078] S3, through evolutionary state diagram deduction and cross-link causal backtracking arrangement of differential feature fingerprint maps, forms a multi-dimensional correlation tracing;

[0079] In this embodiment, by performing evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint map, a multi-dimensional association tracing is formed, as detailed below:

[0080] The structural offset, temporal offset, and path anomaly features in the differential fingerprint spectrum are decoupled and split to form a differential fingerprint fragment sequence set. Specifically, the following steps are taken: First, the encoded sequence in the differential fingerprint spectrum is subjected to topological parsing. Continuous fingerprint links are divided into independent feature unit segments according to node position identifiers. Then, node connection breakpoints and topological deformation segments are extracted and separated from the original sequence to form structural difference segments. Next, the misaligned intervals in the time series are segmented according to the temporal offset identifiers. Time jumps, delays, or reverse-order nodes appearing in the same link are reclassified into temporal difference segments. At the same time, path anomaly identifiers are used to segment and extract abnormal jumps, discontinuous transitions, and cross-path connection relationships to form path anomaly segments. After the extraction of the three types of segments, each segment is serialized and arranged according to the topological order. A unified index identifier is introduced to rearrange and bind the sequential dependencies between segments. Finally, the structural difference segments, temporal difference segments, and path anomaly segments are aggregated into a unified differential fingerprint fragment sequence set.

[0081] Based on the differential fingerprint fragment sequence set, state potential energy is characterized. Each differential fragment is mapped to a state potential energy unit according to its topological position and evolution trend, forming a continuously distributed state potential energy field band. Specifically, the process is as follows: First, the position of each fragment in the differential fingerprint fragment sequence set is parsed to extract its node index and path segment position in the original semantic topology, and a corresponding topological coordinate identifier is assigned to each differential fragment accordingly. Then, the evolution trend of each differential fragment is trajectory identified, and its change direction and extension path are marked by combining its previous and subsequent relationships in the sequence. After completing the position and evolution marking, each differential fragment is mapped to an independent state potential energy unit and bound to its corresponding topological coordinate and evolution trajectory identifier. Furthermore, each state potential energy unit is continuously arranged, and a trajectory continuity relationship is established between adjacent units. At the same time, the positions of state potential energy units associated across paths are aligned and embedded into a unified spatial sequence. Finally, all state potential energy units are arranged in an orderly manner according to the topological structure and evolution trajectory to form a continuously distributed state potential energy field band.

[0082] The potential energy field is subjected to potential energy flow evolution and shift. Based on the potential energy gradient, each unit is migrated in flow direction and extended in path to form a multi-path evolution state flow trajectory network.

[0083] By capturing the overlapping disturbance relationships between different trajectories in the state trajector network, key intersection nodes that trigger linkage are extracted to form a cluster of disturbance coupling nodes;

[0084] A source-point back-projection is performed on the perturbation-coupled node cluster. Each node is projected backwards along its corresponding flow path, and the back-projection path is compressed hierarchically and aligned with the nodes to form a source-point mapped trajectory chain. Specifically, this involves: first, extracting the flow path identifier and its position number within the flow path for each node in the perturbation-coupled node cluster, and determining the flow path for each node accordingly; then, performing a backward traversal along the forward sequence of the flow path belonging to each node, backtracking from the current node to the upstream nodes, and recording the sequence of nodes passed through. During the backtracking process, duplicate nodes appearing at the same level are processed... The process involves merging and aligning nodes that are cross-level but semantically consistent, mapping them to a unified hierarchical position. Further, parallel branches are reorganized according to the flow path order, retaining key nodes and eliminating redundant path segments to make the path structure a compact sequence. After path compression, each node is assigned a unified hierarchical identifier and arranged sequentially according to the turnaround order. At the same time, the dependencies between different nodes are reconstructed and bound. Finally, the turnaround paths corresponding to each node are integrated and output to form a source point mapping trajectory chain organized by flow path affiliation and aligned with hierarchical structure.

[0085] The source point mapping trajectory chain is subjected to multi-track interleaving and orchestration processing, and non-consistent paths are reconstructed in parallel to form a multi-track interleaved source tracing network. Specifically, the trajectory in the source point mapping trajectory chain is first grouped and classified according to the flow trajectory identifier, and a correspondence index between the trajectories is established. Then, based on the co-occurrence position and time order of nodes in different trajectories, multiple trajectories are aligned segment by segment, and matching node segments are cross-embedded and arranged. For non-consistent paths with structural differences or broken connections in different trajectories, parallel expansion processing is performed based on the flow direction relationship between the preceding and following nodes, splitting them into independent sub-paths, and extending and reconstructing them separately. After the sub-path reconstruction is completed, the flow trajectory identifier is reintroduced into each path unit, and they are arranged hierarchically according to the topology. At the same time, the associated nodes across trajectories are mapped and bound to rebuild the connection relationship between paths. Finally, the structure of multiple interleaved and parallel reconstructed trajectories is integrated and output to form a multi-track interleaved source tracing network.

[0086] A structured source mapping process is performed on the multi-track interleaved source tracing network to uniformly project and express node associations, flow paths, and disturbance relationships, forming a multi-dimensional associated source tracing structure. Specifically, this involves: extracting the node identifier, flow path to which each node belongs, and its hierarchical position in the network; simultaneously resolving the connection relationships and interleaved path order between nodes; then, concatenating nodes within the same flow path into a path sequence according to the connection order, and recording the cross-association positions between paths; further, extracting disturbance relationships in the network, identifying node pairs with overlapping, branching, or abnormal connections as disturbance node pairs, and establishing corresponding disturbance relationship mappings; after completing the extraction of nodes, paths, and disturbance relationships, aligning and organizing the node, path sequences, and disturbance relationships, and assigning unified identifier codes; subsequently, serializing each path and its associated nodes, and embedding disturbance relationship markers into the sequence, ultimately forming a multi-dimensional associated source tracing structure containing node associations, flow paths, and disturbance relationships.

[0087] In this embodiment, the potential energy field band is subjected to potential energy flow evolution and shifting. Based on the potential energy gradient, the flow direction of each unit is migrated and the path is extended to form a multi-path evolution state flow trajectory network, as detailed below:

[0088] The potential energy units in the potential energy field band are deconstructed and divided into potential energy hierarchical segments, forming a hierarchical potential energy distribution sequence. Specifically, the topological position of each potential energy unit in the potential energy field band and its distribution order in the path are extracted first, and the continuity of the potential energy change trend between adjacent units is analyzed. Then, the continuously arranged potential energy units are segmented, with units with relatively gentle potential energy changes assigned to the same segment, while nodes with prominent changes are used as segment boundaries. After completing the initial segmentation, the potential energy units within each segment are hierarchically labeled. Based on their relative positions in the overall path, the segments are divided into different semantic levels, and a corresponding level identifier is assigned to each level. Furthermore, potential energy units that cross segments but have a relationship are aligned and mapped to the same level position. Finally, the semantic potential energy segments are sequentially arranged according to the hierarchical order, and the connection relationships between segments are rearranged and bound to form a hierarchical potential energy distribution sequence with a clear structure.

[0089] In the hierarchical potential energy distribution sequence, a single potential energy unit is split into multiple diffusion sub-units along its evolution trend, and each sub-unit is extended and promoted in different semantic directions. At the same time, adjacent potential energy units are adsorbed and fused during the diffusion process to form a multi-source extended potential energy flow fragment set.

[0090] Based on the potential energy flow fragment set, cross-layer penetration mapping processing is performed, and corresponding path projection fragments are generated in each layer. Specifically, according to the potential energy flow fragment set, the path structure of the fragment in the original layer is parsed to determine its node connection order and extension direction. Then, cross-layer mapping and positioning are performed on each potential energy flow fragment, projecting its key nodes to the corresponding positions in the target layer and establishing cross-layer node mapping relationships. After completing the node mapping, the same potential energy flow fragment is re-connected into continuous path units in the target layer. Furthermore, for nodes with cross-fragment overlap relationships, their positions in each layer are aligned and their connection relationships are uniformly adjusted. Finally, the path structures generated by each potential energy flow fragment in different layers are serialized and output to form a set of path projection fragments corresponding to multiple semantic spaces.

[0091] This paper introduces path-connecting weaving between multi-layer mapping trajectories, aligns the corresponding potential energy flow segments in each layer according to their topological positions, and performs a reordering of cross-layer nodes to construct a cross-layer linked potential energy flow path system. Specifically, it extracts the node sequence and topological position identifier of the potential energy flow segments in each layer, and establishes a cross-layer mapping index based on the correspondence of nodes in different layers. Then, based on the order of nodes in the original path, it aligns the corresponding nodes in different layers. After completing the node alignment, it establishes a vertical connection relationship between corresponding nodes in cross layers, and connects the nodes of the same potential energy flow segment in different layers one by one. Furthermore, it cross-weaves the horizontal path connection and the vertical connection. For nodes with connection conflicts or path overlaps, it adjusts the level of their connection relationship and redetermines their order of attachment. Finally, it organizes the woven path structure as a whole, unifying the cross-layer connection path with the intra-layer path to form a continuous and interconnected cross-layer linked potential energy flow path system.

[0092] A perturbation drive is injected into a local region of the potential energy flow path system, a directional offset is applied to some potential energy units, and the deflected paths are reconnected and combined to form a path reorganization unit with a branching structure. Specifically, the path nodes in the potential energy flow path system are first divided into regions, and local segments with dense nodes or intersecting connections are selected as perturbation areas. The flow direction identifiers and connection relationships of each potential energy unit in this region are extracted. Then, a directional offset command is applied to the selected potential energy units in the perturbation area, causing their original flow direction to deflect. Based on this, their subsequent connection nodes are re-determined. During the offset process, the subsequent nodes in the original path are decoupled and disconnected, and the offset potential energy units are pointed to the new connection nodes to form a new path extension direction. Furthermore, the multiple path branches generated by the offset are simultaneously expanded, and the nodes in different branch paths are rearranged according to the flow direction. After the path deflection is completed, the nodes in the original path that did not participate in the offset are retained and connected to the newly generated branch paths. Finally, the branch paths are integrated in a unified manner, and the branch nodes and their connection relationships are serialized to form a path reorganization unit with branch structure characteristics.

[0093] The global flow direction is integrated based on the path reorganization unit, and each branch path is sorted in a unified manner according to the potential energy evolution direction, thereby forming a coherent flow sequence in the overall structure. Specifically, based on the path reorganization unit, the potential energy evolution direction corresponding to each path is analyzed. Then, paths with inconsistent directions are reversed. After the direction is unified, paths with sequential connection are connected in order, and the nodes at the connection points are aligned. For paths with intersecting or parallel relationships, they are sorted in order. The main path and branch paths are arranged according to the potential energy change order, and the position level of each path in the overall structure is determined. Furthermore, the connection relationship between paths is supplemented, and broken nodes are connected. Finally, the integrated path is serialized and output, and each node is arranged in order according to the unified flow direction to form a coherent overall flow sequence structure.

[0094] The integrated continuous flow sequence is organized into a network, and the connection relationships between path nodes are uniformly bound, ultimately forming a state flow trajectory network with the characteristics of multi-path differentiation, cross-layer connection and disturbance branching.

[0095] S4, based on multi-dimensional correlation tracing, triggers the decision engine to execute strategies for self-evolutionary regulation and orchestration path back injection;

[0096] In this embodiment, based on multi-dimensional correlation tracing, the decision engine is triggered to execute self-evolutionary regulation and orchestration path back-injection strategies, as detailed below:

[0097] Based on a multidimensional associative tracing structure, discrete tracing information is transformed into continuous, malleable semantic evolution fragments, and then deeply regularized to generate a tracing semantic primitive field with evolutionary directionality. Specifically, this involves: extracting the node association data, path flow data, and perturbation relationships from the multidimensional associative tracing structure; and segmenting and expanding the original discrete structure according to the topological position and flow order of the nodes in the tracing network, transforming the nodes and their preceding and following dependencies into continuously arranged sequence fragments; and then uniformly mapping node attributes, path identifiers, and causal triggering information into standard language. The semantic units are classified and grouped together. Based on this, the semantic units are sequentially connected along the original flow path, and the semantic fragments that were originally scattered in different paths are reassembled into a continuous semantic evolution chain. Furthermore, the positions of semantic units with cross-related relationships are rearranged, and the dependency relationships between semantic units are rebounded. Finally, the semantic evolution chain segments that have been connected and rearranged are regulated as a whole and arranged in an orderly manner according to the topological order, thereby forming a source semantic primitive field with continuous extension characteristics and a clear evolution direction.

[0098] A causal tension-driven rearrangement operation is performed on the source semantic primitive field. By introducing causal coupling tension, the semantic primitives are distributed in a traction manner, forming an adaptively reconstructed causal tension evolution chain. Specifically, the causal association identifiers and upstream and downstream dependencies of each semantic primitive in the source semantic primitive field are extracted first, and initial causal connection pairs are constructed based on their preceding and following connection positions in the evolution sequence. Subsequently, causal coupling relationships are established between any semantic primitives that have direct or indirect associations. Based on their association strength and transmission direction, corresponding traction pointers are assigned to each primitive, and these pointers are embedded into the primitive structure. In the identification process, based on this, the primitives with mutual coupling are shifted along the traction direction, while the original positional relationships are dynamically relaxed and released. Furthermore, during the traction process, the semantic primitives with multiple couplings are subjected to multi-directional stretching, which is split into different traction paths and unfolded separately, while maintaining the causal continuity in each path. Subsequently, the connection order of the semantic primitives that have completed the traction distribution is re-established, the paths are rearranged according to the traction direction, and the broken or intersecting paths are reconnected and integrated, ultimately forming a causal tension evolution chain that is arranged in an orderly manner along the causal traction direction.

[0099] A multi-path strategy mapping deduction is performed on the causal tension evolution chain, and the response behavior of nodes in each path is characterized to form a multi-path strategy evolution mapping set. Specifically, based on the causal tension evolution chain, key branch nodes that can be differentiated are identified along the link structure. Then, starting from each branch node, the evolution chain is expanded in multiple paths, splitting the original single link into several parallel paths, while maintaining the continuity of the causal order in each path. During the multi-path expansion process, the response behavior of semantic primitives in each path is labeled one by one, and their status is recorded according to their position changes and previous and subsequent relationships in the link. The system identifies the migration trajectories and assigns a trajectory identifier to each node. Furthermore, it performs a horizontal comparison of semantic primitives at the same topological position in different paths, synchronously maps their response behaviors, and independently labels trajectory segments with differences. Then, it concatenates the node trajectories in each path according to causal order to form a complete path-level trajectory sequence and structurally marks the differentiation relationships between paths. Finally, it uniformly collects and regularizes all path trajectories, grouping them according to the path dimension to form a multi-path strategy evolution mapping set containing multiple evolutionary paths and their corresponding node response trajectories.

[0100] Based on the multi-path policy evolution map set, the policy responses in different paths are segmented and consolidated according to the evolution phase, and synchronous folding and alignment are performed on cross-path co-position policy nodes to output a coherent sequence of policy responses with a unified phase structure.

[0101] An adaptive control flow injection operation is applied to the coherent sequence of the policy response. The control flow field is constructed to inject directional flow into each policy node and form a continuously propagating control flow path within the sequence, generating a policy control fluidization chain.

[0102] A topology back-embedding weaving operation is performed on the strategy-controlled fluidized chain, while the original path structure is rearranged in an interlaced manner and interlaced at different levels to generate a path back-injection composite chain. Specifically, based on the strategy-controlled fluidized chain, its corresponding original path nodes are located synchronously. Then, based on the original path structure, the regulated strategy nodes are back-embedded along the original flow direction and inserted between the corresponding original path nodes, while maintaining the previous and subsequent dependencies. During the embedding process, nodes with multiple control paths are segmented and split, and different control branches are inserted into different levels of the original path, with independent level identifiers established for each branch. Furthermore, the connection relationships that are interrupted by the embedded nodes in the original path are rearranged, and the new nodes are reconnected with the original nodes to form an interlaced node sequence. Subsequently, interlacing integration is performed on the nodes in different levels, and the nodes from different control paths are vertically interwoven according to their flow order. Finally, the overall structure after embedding and rearranging is uniformly regulated and serialized according to the path topology order to form a path back-injection composite chain that includes the interlacing and fusion of the original path and the control path.

[0103] A cross-layer cooperative emergent reconstruction is performed on the path back-injection composite chain. This is achieved by uniformly activating the semantic coupling relationships between paths at different levels and uniformly regularizing the output of cooperative paths, forming a policy orchestration back-injection network structure with self-evolutionary capabilities. Specifically: First, the paths at each level in the path back-injection composite chain are parsed hierarchically, extracting the node sequences and their corresponding semantic identifiers within each level, and establishing a cross-layer node correspondence index. Then, nodes with consistent semantics or related relationships in different levels are matched and identified to determine cross-layer coupled node pairs, and a unified coupling label is established for them. Based on this, synchronous activation is performed on each coupled node pair. The process involves processing the data and simultaneously adjusting the connection relationships between nodes along the existing flow direction. Further, during the activation process, multi-level path segments are connected, bridging previously separate hierarchical paths through coupling nodes, and rearranging the bridging positions. Subsequently, all coupled and connected path structures are systematically organized, aligning different hierarchical paths according to a unified topological order and reconstructing the dependencies between nodes. Finally, the systematically organized path set is uniformly organized, integrating and outputting the connection relationships, hierarchical structures, and node sequences of each path to form a strategy orchestration and back-injection network structure with cross-layer linkage and continuous flow characteristics.

[0104] In this embodiment, an adaptive control flow injection operation is applied to the policy response coherent sequence. A control flow field is constructed to directionally inject each policy node, forming a continuously propagating control flow path within the sequence, thus generating a policy control fluidization chain, as detailed below:

[0105] The regulatory factors of each policy node in the policy response coherent sequence are decomposed and extracted. Based on the semantic attributes and response characteristics of the nodes, each regulation is matched and allocated in a targeted manner to form a node-level multi-source regulation set. Specifically, the semantics of each policy node in the policy response coherent sequence are analyzed, and the regulation data inside the node is split at the field level. Then, the split data is classified according to the source type, distinguished into behavioral regulation, path guidance and state regulation, and a unified identifier is assigned to each type of regulation. On this basis, for each policy node, according to its semantic attributes and response performance in the sequence, various types of regulation are matched and screened. The regulatory factors that are semantically consistent with the node or have a dependency relationship are targeted and aggregated. Furthermore, the regulatory factors from multiple sources are integrated into the corresponding nodes according to the category, and their internal order is normalized. Then, the regulation with duplication or conflict is merged and normalized, and its internal association relationship is re-established. Finally, the set of regulatory factors that have been matched and integrated is mapped one by one to the corresponding policy nodes to form a node-level multi-source regulation set.

[0106] Based on a multi-source control set, a causal feedback loop structure for each strategy node is constructed, and the dependencies between nodes are closed-loop connected and reorganized to form a cyclically transmitted control flow channel between nodes. Specifically, the initial unidirectional connection path is determined according to the order of nodes in the policy response coherence sequence in the node-level multi-source control factor set. Then, based on this unidirectional path, a reverse association mapping is established for each node to its upstream node, expanding the original forward-only connection relationship into a bidirectional connection structure. In this process, node pairs with shared control are preferentially closed-loop bound, and their forward connections and reverse mappings are merged to form a local closed-loop structure. Further, the closed-loop structures between multiple adjacent nodes are serially reorganized, splicing the originally scattered local closed loops according to the node order. Then, multi-loop nesting processing is performed on nodes with branch relationships, cross-connecting the closed-loop structures in different branch paths, and assigning an independent identifier to each closed-loop path. Finally, the node relationships after the closed-loop construction are generally regularized, thereby establishing a continuously transmitted control flow channel in the node sequence.

[0107] A superposition and fusion injection operation is performed on the control flow channel, embedding different types of controls layer by layer according to node matching relationships to form a composite control injection node sequence. Specifically, firstly, the channel position of each node in the control flow channel is analyzed to extract its hierarchical position and preceding and following connections in the closed-loop structure, and the control embedding order is determined accordingly. Subsequently, the various types of controls in the node-level multi-source control set are classified, and behavioral control, path guidance, and state regulation are constructed as independent control layers. A corresponding multi-layer embedding interface is established at each node. On this basis, controls from different sources but with consistent semantics are preferentially injected into the target node. The core layer of the nodes is embedded progressively towards adjacent nodes along the node connection direction. Furthermore, the dominant regulation is placed at the priority level, and the remaining regulation is superimposed in sequence according to the dependency relationship. An association index is established between the layers. Subsequently, the regulation factors that conflict or overlap are fused and their semantic identifiers and connection relationships are merged to form a unified expression unit, which is then re-embedded into the corresponding layer. Finally, the structure of each node after the layered embedding is serialized according to the path order of the regulation flow channel, and the transmission relationship between the nodes is reconstructed simultaneously, thereby forming a composite regulation injection node sequence containing a multi-layered regulation embedding structure and continuously distributed along the channel.

[0108] Feedback-driven fluidized expansion is performed on the composite regulation injection node sequence, cyclically propagating the regulation data embedded in the nodes along the causal feedback loop. Under the alternating action of forward diffusion and back feedback, a continuous transmission chain is formed. Specifically, loop identifier resolution is performed on each node in the composite regulation injection node sequence to extract its forward path in the causal feedback loop, and a node-level bidirectional transmission index is established. Then, starting from the regulation data embedded in the node, transmission is triggered node by node along the predetermined forward path, diffusing the regulation content of the current node to downstream nodes in the connection order. The data flow trajectory is recorded during the transmission process, and the data is recorded when the forward diffusion reaches the end of the path or... When the loop closes, feedback transmission is initiated based on the established reverse mapping relationship. The updated control data in the downstream node is transmitted back to the upstream node, and the original control data is overwritten or superimposed. Furthermore, in the alternation of forward diffusion and reverse feedback, the node state after each round of transmission is updated synchronously. At the same time, for nodes with multiple branch connections, parallel transmission is performed along each branch path, and the data from different paths are merged at the branch intersection. Finally, the nodes that have completed multiple rounds of cyclic transmission are reconnected in the order of the path, preserving the continuous transmission relationship, thereby forming a continuous transmission chain segment that extends stably along the causal feedback loop.

[0109] An adsorption-traction mechanism is applied to the connection relationships between adjacent nodes in a continuous transmission chain segment, and the diffusion range is dynamically adjusted according to the node connection density to form a locally self-growing regulated flow segment. Specifically, adjacent node pairs are scanned segment by segment along the continuous transmission chain segment, and the connection tightness between nodes is directly compared. Closely connected node pairs are pulled and converged in the same direction. Subsequently, during the traction process, the connection relationships that originally had gaps or jumps are shortened into direct connection structures. On this basis, the node diffusion range is differentially controlled according to the distribution density of each node in the chain segment. The expansion boundary is restricted in the node-concentrated area, while in the node-sparse area, it extends outward along the existing connection direction, gradually absorbing the adjacent nodes that are not included in the chain segment. Furthermore, during the expansion process, the newly included nodes are rearranged in order with the original chain segment nodes, ultimately forming a regulated flow segment that can sustainably expand and has gradually strengthened connections within a local range.

[0110] The controlled flow segments formed in different paths within the controlled flow segment are spliced ​​together according to the node connection relationship to generate a continuous and uninterrupted flow path.

[0111] The integrated control flow path is subjected to global fluidization regularization, and the loop and diffusion path are merged to form a strategy control fluidization chain with loop feedback, multi-source superposition and adaptive diffusion characteristics.

[0112] Example 2, Figure 2The present invention provides a system for an automated diagnostic method for reconciliation discrepancies using intelligent orchestration, comprising a semantic perception module, a topological coupling module, a source tracing and deduction module, and a strategy control module, with connections between the modules;

[0113] The semantic awareness module is used to acquire the semantic billing flow of cross-domain funding links, construct the heterogeneous accounting flow adaptive awareness link, and generate a unified semantic billing view;

[0114] The topology coupling module is used to drive the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling based on the unified semantic billing view, forming a differential feature fingerprint map;

[0115] The source tracing and deduction module is used to perform evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint map to form multi-dimensional correlation source tracing;

[0116] The strategy control module is used to trigger the decision engine to execute strategy self-evolution control and orchestration path back injection based on multi-dimensional correlation tracing.

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0118] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0120] The above description is merely a specific technical solution of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0121] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automated diagnosis of reconciliation discrepancies using intelligent orchestration, characterized in that, include: Obtain the semantic billing flow of cross-domain funding links, construct an adaptive perception link for heterogeneous accounting flows, and generate a unified semantic billing view; Based on a unified semantic billing view, the reconciliation and verification kernel is driven to perform semantic topology reconstruction and temporal coupling to form a differential feature fingerprint map. By performing evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint map, a multi-dimensional correlation tracing is formed; Based on multidimensional correlation tracing, the decision engine is triggered to execute strategies that self-evolve, regulate, and orchestrate path back-injection.

2. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 1, characterized in that, The process of obtaining the semantic billing flow of cross-domain funding links, constructing an adaptive perception link for heterogeneous accounting flows, and generating a unified semantic billing view is as follows: Acquire funding chain data and perform unified semantic normalization and expression mapping processing to form a semantic billing stream; Perform multi-granularity semantic tensor expansion processing on the semantic billing stream to map the fund flow data into a high-dimensional semantic representation; Perform cross-domain semantic folding and equivalent reconstruction on multidimensional semantic representations to form a standardized set of semantic units; Based on the unified semantic expression space, perform link emergence construction operations to generate a fund transfer link structure; Perform time-series phase weaving processing on the fund flow chain structure to form a dynamic accounting chain; Semantic field-driven reorganization processing is performed on the dynamic accounting chain, and multi-source semantic associations are spatially aggregated. Based on the dynamic accounting process, structured representation encapsulation and semantic projection processing are performed to generate a unified semantic billing view.

3. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 2, characterized in that, The process of performing semantic field-driven reorganization on the dynamic accounting chain, and spatially aggregating multi-source semantic associations, is as follows: The semantic fragments of nodes in the dynamic accounting chain are processed into a streaming form and the semantic transmission trajectory is unfolded along the chain direction. Perform flow direction modulation on semantic stream segments and complete the overall reshaping of the path morphology; Resonance activation processing is triggered based on semantic fragments, and resonance semantic clusters are formed in space; Perform collaborative folding and rearrangement on the resonant semantic clusters, and rewrite the original connection paths at the same time; Based on the resonant semantic clusters, field slicing projection processing is performed, and spatial layering is arranged according to semantic features; Within each semantic region, a self-organizing fusion operation is performed on the semantic clusters, and redundant representation structures are compressed simultaneously. Cross-domain flow bridge weaving is performed on the relationships between semantic regions to complete the overall link topology reconstruction.

4. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 3, characterized in that, The unified semantic billing view drives the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling, forming a differential feature fingerprint map, as detailed below: Perform semantic destructuring on the unified semantic billing view and map fields from different sources to standard semantic identifiers to form a standardized semantic data mapping set. The semantic data standardization mapping set is subjected to semantic structure skeletonization and reorganization processing to form a semantic topology flow skeleton; The semantic topology flow skeleton is subjected to temporal stream compression and embedding processing, and the cross-source temporal representations are uniformly projected onto the temporal evolution axis to form a temporally coupled projection sequence. Based on the temporally coupled projection sequence, cross-domain semantic isomorphic alignment processing is performed, and semantic merging compression is applied to repeated expression nodes to form a cross-domain semantic isomorphic fusion skeleton. Perform deviation path activation identification processing on the cross-domain semantic isomorphic fusion skeleton to form a differential path activation marker chain; Based on the difference path activation tag chain, perform spatiotemporal coupling reconstruction processing to form a spatiotemporally consistent coupled link body; Multi-scale differential aggregation projection processing is performed on the spatiotemporally consistent coupled link body, and cross-path differential aggregation calculation is performed to form a multi-scale differential feature aggregate body; The structural fingerprinting encoding process is performed on the multi-scale differential feature aggregates, and the output is serialized according to the topological position to form a differential feature fingerprint map.

5. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 4, characterized in that, The semantic data standardization mapping set is subjected to semantic structure skeletonization and reorganization processing to form a semantic topology flow skeleton, as detailed below: The semantic data standardization mapping set is subjected to flow context decoupling and expansion processing, and expanded along semantic dependency relationships to form several semantic flow context fragment clusters; Perform connection traction weaving on semantic flow fragment clusters and adaptively traction rearrange nodes within fragments to form a semantic flow weaving structure; Based on the semantic flow weaving structure, reverse link backtracking and splicing processing is performed to form an extended semantic flow chain; The extended semantic flow pulse chain is subjected to intersection node fusion and orchestration processing, and the cross paths are uniformly rewritten to evolve the multi-path structure into a continuous backbone semantic flow pulse chain. The dependent nodes between different flow chains in the main semantic flow chain are interwoven and connected to form a semantic topology weave; Perform global flow order reconstruction on the semantic topology weave and insert link replacements at break points in the links; The semantic topology weave is subjected to structural encapsulation and expression regularization processing to form a semantic topology flow skeleton.

6. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 5, characterized in that, The process involves performing evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint map to form a multi-dimensional association tracing, as detailed below: The differential fingerprint maps are decoupled and split to form a set of differential fingerprint fragment sequences; Based on the differential fingerprint fragment sequence set, the state potential energy is characterized to form a state potential energy field band; The potential energy field is subjected to potential energy flow evolution and shift, forming a state flow trajectory network; By capturing the overlapping disturbance relationships between different trajectories in the state trajector network, key intersection nodes that trigger linkage are extracted to form a cluster of disturbance coupling nodes; Perform source point back mapping on the perturbation-coupled node cluster, and perform hierarchical compression and node alignment on the back path to form a source point mapping trajectory chain; Multi-track interleaving orchestration is performed on the source point mapping trajectory chain, and non-consistent paths are reconstructed in parallel to form a multi-track interleaved source tracing network; A structured traceability mapping process is performed on the multi-track intertwined traceability network to form a multi-dimensional associated traceability structure.

7. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 6, characterized in that, The potential energy field band is subjected to potential energy flow evolution and shift to form a state flow trajectory network, as detailed below: The potential energy field band is deconstructed and divided into potential energy hierarchical segments, and the continuous potential energy field band is decomposed into multi-layer semantic potential energy segments to form a hierarchical potential energy distribution sequence. In the hierarchical potential energy distribution sequence, a single potential energy unit is split into multiple diffuse sub-units along its evolution trend to form a multi-source extended potential energy flow fragment set. Based on the potential energy flow fragment set, cross-layer penetration mapping processing is performed, and path projection fragments are generated in each layer. A path-connecting weaving system is introduced between multi-layer mapping trajectories, and cross-layer nodes are rearranged to construct a potential energy flow path system. A disturbance drive is injected into a local region of the potential energy flow path system, and the deflection paths are reconnected and combined to form a path reorganization unit; The global flow direction is integrated according to the path reorganization unit, and the branch paths are uniformly sorted according to the potential energy evolution direction to form a coherent flow sequence; The continuous flow sequence is organized into a network, and the connection relationships between path nodes are uniformly bound to form a state flow trajectory network.

8. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 7, characterized in that, The multi-dimensional correlation tracing triggers the decision engine's execution strategy to self-evolutionize and regulate its orchestration path back-injection, as detailed below: Based on the multidimensional association tracing structure, discrete tracing information is transformed into semantic evolution fragments and then deeply regularized to generate a tracing semantic primitive field. Perform a causal tension-driven rearrangement operation on the source semantic primitive field to form a causal tension evolution chain; A multi-path strategy mapping deduction is performed on the causal tension evolution chain, and the response behavior of nodes in each path is characterized to form a multi-path strategy evolution mapping set. Based on the multi-path policy evolution map set, the policy responses in different paths are segmented and consolidated according to the evolution phase, and a coherent sequence of policy responses is output. An adaptive control flow injection operation is applied to the coherent sequence of the policy response, and a control flow path is formed within the sequence to generate a policy control fluidization chain; Perform topology back-embedding weaving on the strategy-controlled fluidized chain, and simultaneously perform staggered rearrangement and hierarchical interleaving on the original path structure to generate a path back-injection composite chain. Cross-layer collaborative emergence is carried out on the path back-injection composite chain, and the collaborative paths are uniformly regularized and output to form a strategy orchestration back-injection network structure.

9. The intelligent orchestration-based automated diagnosis method for reconciliation discrepancies according to claim 8, characterized in that, The process of applying an adaptive control flow injection operation to the coherent sequence of the policy response and forming a control flow path within the sequence to generate a policy control fluidization chain is as follows: The regulatory factors are decomposed and extracted from the coherent sequence of the strategy response, and then targeted matching and allocation are performed to form a node-level multi-source regulatory set. Based on a multi-source control set, a causal feedback loop structure for each strategy node is constructed, and the dependencies between nodes are closed-loop connected and reorganized to form a control flow channel. A superimposed and fused injection operation is performed on the control flow channel to form a composite control injection node sequence; Feedback-driven fluidization expansion is performed on the composite regulation injection node sequence to form a continuous transmission chain segment; Adsorption traction is applied to the connection relationship between adjacent nodes in a continuous transmission chain segment, and the diffusion range is dynamically adjusted according to the node connection density to form a controlled flow segment. The controlled flow segments formed in different paths within the controlled flow segment are spliced ​​together according to the node connection relationship to generate a continuous and uninterrupted flow path. The flow path is globally fluidized and regulated, and the loop and diffusion path are merged to form a policy-regulated fluidization chain.

10. A system using an automated reconciliation discrepancy diagnosis method according to any one of claims 1-9, characterized in that, It includes a semantic perception module, a topology coupling module, a source tracing and deduction module, and a strategy control module, with connections between the modules; The semantic awareness module is used to acquire the semantic billing flow of cross-domain funding links, construct the heterogeneous accounting flow adaptive awareness link, and generate a unified semantic billing view; The topology coupling module is used to drive the reconciliation verification kernel to perform semantic topology reconstruction and temporal coupling based on the unified semantic billing view, forming a differential feature fingerprint map; The source tracing and deduction module is used to perform evolutionary state diagram deduction and cross-link causal backtracking arrangement on the differential feature fingerprint map to form multi-dimensional correlation source tracing; The strategy control module is used to trigger the decision engine to execute strategy self-evolution control and orchestration path back injection based on multi-dimensional correlation tracing.

Images