Enterprise multi-modal data intelligent processing system fusing rag technology and intelligent processing method thereof

Abstract

This invention discloses an intelligent processing system and method for enterprise multimodal data integrating RAG technology. The invention relates to the field of enterprise-level multimodal data intelligent processing technology, comprising a sequentially connected multimodal unified semantic space construction module configured to process structured and unstructured data separately through a dynamic heterogeneous encoder, and outputting a unified semantic representation using a cross-modal adversarial alignment mechanism. This intelligent processing system for enterprise multimodal data integrating RAG technology solves the problem of fragmented enterprise multimodal data through dynamic adversarial semantic alignment and a step-by-step fusion mechanism. Adaptive convergence of cross-modal features in the latent space eliminates the semantic gap, and concept mapping and credibility arbitration based on ontology networks achieve deep association of heterogeneous data, enabling the accurate extraction and transformation of key information from unstructured data into structured knowledge, improving the accuracy of cross-modal association analysis and the reliability of decision-making.

Description

Technical Field

[0001] This invention relates to the field of enterprise-level multimodal data intelligent processing technology, specifically to an enterprise multimodal data intelligent processing system and its intelligent processing method that integrates RAG technology. Background Technology

[0002] Against the backdrop of accelerated digital transformation, the multimodal data generated by enterprise operations is growing rapidly, encompassing both structured data (such as tables and forms) and unstructured data (such as text documents, images, audio, and video). This heterogeneous data is scattered across various internal and external systems, making efficient integration and in-depth processing difficult with existing technologies. On the one hand, unstructured data, due to its diverse formats and complex semantics, presents challenges in extracting key information and transforming it into structured knowledge. On the other hand, insufficient exploration of the correlations between multimodal data prevents the full realization of data value, impacting knowledge sharing efficiency, intelligent retrieval accuracy, and the effectiveness of decision support. How to uniformly collect and standardize enterprise multimodal data, enabling automatic extraction, structured integration, and cross-modal correlation analysis of key information, has become a core technical challenge that enterprises urgently need to address to improve data utilization efficiency and strengthen knowledge management and decision-making capabilities. Summary of the Invention

[0003] To achieve the above objectives, the present invention provides the following technical solution: an enterprise multimodal data intelligent processing system integrating RAG technology, comprising sequentially connected components:

[0004] The multimodal unified semantic space construction module is configured to process structured and unstructured data separately through a dynamic heterogeneous encoder, and output a unified semantic representation by adopting a cross-modal adversarial alignment mechanism.

[0005] The knowledge fusion layer, connected to the building module, is configured to achieve feature-level, semantic-level, and decision-level fusion through a three-level fusion strategy.

[0006] The dual verification module, connected to the knowledge fusion layer, is configured to perform runtime cross-modal consistency verification and knowledge graph logic verification.

[0007] An enhanced RAG engine, connected to the dual verification module, is configured to generate a knowledge response or trigger a self-correction process based on the verification results.

[0008] Preferably, the cross-modal adversarial alignment mechanism includes:

[0009] The structured data encoder uses a graph neural network to parse the topological relationships between fields;

[0010] The unstructured data encoder contains semantic decomposition units that separate the basic semantics of the text from domain-specific concepts;

[0011] The adversarial discriminator forces the latent space vectors output by different modal encoders to satisfy semantic indistinguishability.

[0012] Preferably, the semantic decomposition unit includes:

[0013] The general semantic extraction branch is implemented through a pre-trained language model;

[0014] Domain concept separation branches are achieved through an attention mechanism constrained by enterprise ontology;

[0015] The outputs of the two branches are gated and fused before being input into the adversarial discriminator.

[0016] Preferably, the runtime cross-modal consistency check includes:

[0017] Construct a multimodal chain of evidence for the same entity;

[0018] Calculate intermodal conflict scores using preset conflict detection rules;

[0019] When the conflict score exceeds a preset threshold, the manual review process is activated.

[0020] Preferably, the knowledge graph logic verification includes: compiling enterprise business rules into a differentiable loss function layer, injecting the loss function layer into the knowledge graph inference process, and correcting data nodes that violate the rules through gradient backpropagation.

[0021] Preferably, the third-order fusion strategy includes:

[0022] Feature-level fusion employs a cross-modal attention weight matrix;

[0023] Semantic-level fusion performs concept mapping through the enterprise ontology network;

[0024] Decision-level fusion employs a neural symbolic reasoning engine;

[0025] The fusion weights at each stage are adaptively adjusted based on the information entropy value.

[0026] Preferably, the contradiction detection rules include: confidence difference detection between text statements and video sentiment tags, logical conflict detection between table values ​​and audio statements, and semantic consistency detection between image recognition results and text descriptions.

[0027] Preferably, the construction process of the differentiable loss function layer includes:

[0028] Parse the business rule expression into computation graph nodes;

[0029] Define differentiable penalty terms for the constraint relationships between nodes;

[0030] The penalty term is used in gradient calculation during neural network training.

[0031] Preferably, the self-correction process includes:

[0032] Location of erroneous data sources based on conflict scoring;

[0033] Invoke the adversarial discriminant to realign the semantic representation;

[0034] Update the affected triple relationships in the knowledge graph.

[0035] A method for intelligent processing of enterprise multimodal data integrating RAG technology, applied to an enterprise multimodal data intelligent processing system, includes the following steps:

[0036] Step S1: Generate a cross-modal unified semantic representation through adversarial training;

[0037] Step S2: Integrate heterogeneous data using a three-order fusion strategy;

[0038] Step S3: Perform dual verification of cross-modal consistency and rule logic on the fusion results;

[0039] Step S4: Correct the output knowledge or activation data based on the verification status.

[0040] This invention provides an enterprise multimodal data intelligent processing system and its intelligent processing method that integrates RAG technology. It has the following beneficial effects:

[0041] This intelligent enterprise multimodal data processing system and its intelligent processing method, which integrates RAG technology, solves the problem of fragmented multimodal data in enterprises through dynamic adversarial semantic alignment and a step-by-step fusion mechanism. Adaptive convergence of cross-modal features in the latent space eliminates the semantic gap, and concept mapping and credibility arbitration based on ontology networks achieve deep association of heterogeneous data. This enables the accurate extraction and transformation of key information from unstructured data into structured knowledge, improving the accuracy of cross-modal association analysis and the reliability of decision-making.

[0042] This enterprise multimodal data intelligent processing system and its intelligent processing methods, integrating RAG technology, rely on a dual verification and targeted correction engine to build a closed-loop knowledge governance system. The gradient backtracking mechanism of the rule computation graph enables error source localization, while sandbox fine-tuning and local graph update technologies ensure problem repair, avoiding the resource consumption of a system-wide rollback. The knowledge evolution pipeline driven by verification results continuously accumulates business rules and optimizes model parameters, forming a self-improving ecosystem for enterprise knowledge assets, ultimately enhancing the accuracy of intelligent retrieval, the timeliness of risk warnings, and the effectiveness of decision support. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall module interaction of the present invention;

[0044] Figure 2 This is a schematic diagram of the semantic tree matching process of the present invention;

[0045] Figure 3 This is a schematic diagram of the process of restarting adversarial training in the isolated sandbox of the present invention. Detailed Implementation

[0046] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figures 1 to 3 This invention provides a technical solution: an enterprise multimodal data intelligent processing system integrating RAG technology, comprising sequentially connected components:

[0048] The multimodal unified semantic space construction module is configured to process structured and unstructured data separately through a dynamic heterogeneous encoder, and output a unified semantic representation by adopting a cross-modal adversarial alignment mechanism.

[0049] The knowledge fusion layer, connected to the building module, is configured to achieve feature-level, semantic-level, and decision-level fusion through a three-level fusion strategy.

[0050] The dual verification module, connected to the knowledge fusion layer, is configured to perform runtime cross-modal consistency verification and knowledge graph logic verification.

[0051] The enhanced RAG engine connects to the dual verification module and is configured to generate knowledge responses or trigger self-correction processes based on verification results.

[0052] It should be further explained that, in the specific implementation process, after the enterprise's multi-source heterogeneous data is input into the system, it is first processed by the multimodal unified semantic space construction module, as follows:

[0053] For structured data, such as database tables, graph neural networks are used to parse the topological dependencies between fields, and time-aware units are used to capture the evolution patterns of historical data.

[0054] For unstructured data, a differential encoder is deployed: text data is separated into basic semantics and domain-specific concepts by a semantic decomposition unit, and the two types of features are fused through a gating mechanism; among them, basic semantics correspond to general vocabulary, and domain-specific concepts correspond to enterprise terminology;

[0055] Visual data is extracted by parallel convolutional branches to extract spatial static features and temporal dynamic features. The spatial static features include object shape / position, and the temporal dynamic features include video motion stream.

[0056] Audio data is processed through a dual-channel process to separate the acoustic fingerprint from the speech content text;

[0057] All modal encoder outputs are connected to a cross-modal adversarial discriminator. This component forces the latent space vectors of different modalities to satisfy semantic indistinguishability: when the text description "device malfunction" and the image of equipment vibration in the monitoring video are input at the same time, the discriminator makes the latent vectors of the two converge through adversarial training, eliminating the semantic gap caused by modal differences.

[0058] The unified semantic representation input knowledge fusion layer performs third-order fusion, including the following:

[0059] Feature-level fusion: Establishing relationships between original data through cross-modal attention weight matrices, such as automatically associating "sales growth" in financial report text with values ​​in sales system tables;

[0060] Semantic-level fusion: Concept mapping based on enterprise ontology network, such as mapping "insufficient capacity" to the "equipment utilization rate" node in the production knowledge graph;

[0061] Decision-level fusion: The neural symbolic reasoning engine combines rule execution knowledge deduction to trigger a "supply chain warning" if "order delivery is delayed" and "inventory is below the threshold".

[0062] The determinism of each modality is dynamically evaluated during the fusion process: when the information entropy of video data increases due to blurring during shooting, its fusion weight is automatically reduced, and the high deterministic information of audio conference recordings is given priority.

[0063] The fusion result enters the dual verification module, including the following process:

[0064] Runtime cross-modal verification: Construct a multimodal evidence chain for the same entity, such as product defect description text, production line monitoring video, and quality inspection report form, and verify it through preset contradiction detection rules: If the text declares "pass rate 100%" but the video shows a large number of defective products, the conflict score exceeds the threshold; when there is a logical conflict between the table value and the audio conference record, such as "zero inventory" corresponding to the statement "serious backlog", the manual review process is activated.

[0065] Knowledge graph logic verification: Business rules, such as "safety stock quantity ≥ monthly average sales × 1.5", are compiled into a differentiable loss function layer and monitored in real time during neural network inference: when the predicted value of the inventory node violates the rule, the loss function outputs a penalty gradient; upstream data nodes are corrected through backpropagation, such as correcting the inventory quantity that is visually recognized incorrectly.

[0066] The enhanced RAG engine responds based on the verification results, including:

[0067] If the dual verification passes, an interpretable report containing cross-modal correlations is generated, such as displaying video clips of defective products and highlighting the associated quality inspection data;

[0068] If the verification fails, a self-correction process is triggered: the source of the error is located based on the conflict score, such as determining that the video encoder output is abnormal; the adversarial discriminator is called again to align the semantic representation; and the affected triples in the knowledge graph are updated, such as associating the defective product with the correct production line node.

[0069] Cross-modal adversarial alignment mechanisms include:

[0070] The structured data encoder uses a graph neural network to parse the topological relationships between fields;

[0071] The unstructured data encoder contains semantic decomposition units that separate the basic semantics of the text from domain-specific concepts;

[0072] The adversarial discriminator forces the latent space vectors output by different modal encoders to satisfy semantic indistinguishability.

[0073] It should be further explained that, in the specific implementation process, when heterogeneous enterprise data is input into the system, the structured data is first processed by a graph neural network encoder. This encoder transforms the field relationships in the table into a topological graph structure, where each data field serves as a graph node, and the logical dependencies between fields are connected by edges. Graph convolution operations are used to capture cross-field association patterns. The structured data includes supply chain forms, and the logical dependencies between fields include "order quantity → production plan → raw material procurement." For data with timestamps, such as sales records, the time-series awareness unit synchronously analyzes historical fluctuation patterns to identify periodic characteristics, such as quarterly sales peaks, ensuring that dynamic business logic is accurately encoded.

[0074] Unstructured data processing employs differentiated coding paths, including:

[0075] Text data is input into the semantic decomposition unit, which extracts the basic semantic layer, such as the general meaning of "equipment", through a pre-trained language model. At the same time, it initiates a domain concept separation branch, which loads an enterprise ontology library, such as a manufacturing terminology table, and uses an attention mechanism to focus on domain-specific terms, such as "CNC lathe C-2000". The two types of semantics are dynamically weighted and integrated by a gating fusion unit: when "C-2000 spindle overheating" appears in the text, the general semantic branch identifies "overheating" as an abnormal state, while the domain branch locks "C-2000" as a specific equipment model. The gating unit enhances the domain concept weight to a dominant position based on the context.

[0076] Visual data enters a spatial-temporal dual-path processing channel: the spatial branch identifies static features, such as the integrity of the equipment appearance, through a convolutional network, while the temporal branch captures dynamic evolution, such as changes in conveyor belt speed, through 3D convolution. The two outputs are spliced ​​at the feature layer to preserve the spatiotemporal integrity, such as simultaneously presenting the rusted appearance and abnormal vibration of the equipment in a video.

[0077] The audio data is processed by a dual-channel encoder: the acoustic channel extracts spectral features, such as the soundprint pattern of mechanical noises, and the semantic channel transcribes the audio content into text and then performs keyword extraction, such as "bearing wear alarm", to avoid speech recognition errors interfering with semantic parsing.

[0078] The output vectors of each modal encoder are input to the adversarial discriminator to perform key alignment operations, including: the discriminator receives pairs of cross-modal vectors, such as a text description of equipment faults plus features from monitoring video clips; the encoder parameters are forcibly adjusted through an adversarial training strategy until the discriminator can no longer distinguish the modality from which the vectors originate; when the text description "motor temperature exceeds the limit" and the features of a high-temperature area in an infrared thermal image are input simultaneously, the discriminator continuously compares the similarity between the two types of vectors; the text encoder gradually adjusts its parameters to include temperature semantics in the vectors, while the video encoder simultaneously enhances its thermal image feature extraction capabilities; after multiple iterations, the text and video vectors point to the same semantic coordinates in the latent space, such as both mapping to the concept of "overheating fault";

[0079] The alignment process introduces semantic consistency constraints: if the video display device is at a low temperature but the text claims "high temperature", adversarial training will trigger an abnormal interruption to prevent incorrect alignment.

[0080] For special data conflict scenarios, the following measures are taken: When the quality of a certain modality is low, such as blurry video: adversarial training automatically reduces the weight of that modality and uses high-confidence modalities, such as clear audio alarms, as the dominant alignment benchmark; When there are reasonable differences in cross-modal semantics, such as a text report of "system shutdown" but a video showing that some parts are running: add constraint rules through the enterprise knowledge base to allow component-level semantics to coexist with the overall description and avoid excessive forced alignment.

[0081] Semantic decomposition units include:

[0082] The general semantic extraction branch is implemented through a pre-trained language model;

[0083] Domain concept separation branches are achieved through an attention mechanism constrained by enterprise ontology;

[0084] The outputs of the two branches are gated and fused before being input into the adversarial discriminator.

[0085] It should be further explained that, in the specific implementation process, after the text data enters the semantic decomposition unit, two processing branches are triggered in parallel:

[0086] The general semantic extraction branch loads the basic parameters of the pre-trained language model and parses the common meanings of words, such as "conduction" referring to a physical process in a general context. Simultaneously, the domain concept separation branch activates the enterprise ontology library, such as the electronics industry knowledge graph, and identifies domain-specific terms through a constrained attention mechanism, such as "conduction" in "signal conduction path," which specifically refers to a circuit characteristic. When the input is "chip heat dissipation conduction anomaly": the general branch associates "conduction" with thermodynamic concepts; the domain branch, based on the association path "chip → heat dissipation design specification" in the ontology library, locks "conduction" as a circuit heat dissipation performance indicator. Both types of semantics are output to the gating fusion unit, which dynamically allocates weights based on the context: when domain keywords such as "chip" and "circuit" are detected, the domain concept weight is elevated to a dominant position, ensuring that "conduction" is correctly parsed as an enterprise term rather than a physical concept.

[0087] The gating fusion unit employs an adaptive decision-making mechanism, including:

[0088] Typical scenario: When the density of domain terms in the text exceeds the threshold, such as more than 5 professional words per 100 words, the domain branch weight will be automatically increased to more than 80%;

[0089] Hybrid semantic scenarios: If cross-domain terms appear, such as "catheter" in medical equipment text, which refers to both instruments and fluid mechanics, the following judgment process is triggered: Search for adjacent words in the knowledge graph, such as "angiography catheter" vs. "cooling catheter"; if the adjacent words belong to the medical ontology, the domain semantics are used; if the adjacent words are industrial terms, the general semantics are retained.

[0090] Conflict resolution: When the outputs of two branches contradict each other, such as the general branch interpreting "encapsulation" as a verb while the domain branch treats it as a noun meaning "process", the interpretation with more related nodes in the knowledge graph is preferred.

[0091] Semantic disinfection is performed before the fusion result is input into the adversarial discriminator, including:

[0092] Stripping away generic semantic components irrelevant to the current enterprise context, such as filtering out economic terms from the manufacturing context in terms of "market transmission mechanism"; retaining core feature vectors of domain concepts, such as the proper noun vector purity of "wafer etching rate" exceeding 90%; and initiating an instant learning protocol for new terms not yet registered, such as "quantum annealing chip".

[0093] a) Temporarily store general semantic interpretations;

[0094] b) Initiate a term registration request to the knowledge base;

[0095] c) Update the domain branch parameters after obtaining the ontology definition.

[0096] Runtime cross-modal consistency checks include:

[0097] Construct a multimodal chain of evidence for the same entity;

[0098] Calculate intermodal conflict scores using preset conflict detection rules;

[0099] When the conflict score exceeds a preset threshold, the manual review process is activated.

[0100] It should be further explained that, in the specific implementation process, after the knowledge fusion layer outputs the multimodal fusion result, the system automatically constructs an evidence chain for the same business entity: retrieving copies of all modal data related to that entity from the enterprise data lake. For example, when handling the "Q3 North China Region Compressor Failure" event:

[0101] Textual evidence: The fault description in the maintenance work order is "the abnormal noise from the bearing continues to worsen";

[0102] Video evidence: Workshop monitoring video clips that were identified as having "vibration amplitude exceeding limits" by a spatial-temporal encoder;

[0103] Audio evidence: Spectrum analysis shows an abnormal harmonic at 2000Hz in the device's recording.

[0104] Structured evidence: Sensor tabular data showing temperature values ​​exceeding safe thresholds.

[0105] These pieces of evidence are input into the contradiction scoring matrix for logical verification, as follows:

[0106] Statement and sentiment consistency detection: If the text report states "the fault has been resolved" but the device is still shaking violently in the video, a "behavior-statement conflict" is triggered; audio sentiment analysis detects the panicked tone of the operator's emergency call, which forms a "sentiment-content conflict" with the text description of "routine maintenance";

[0107] Numerical and logical consistency detection: The table data shows "bearing temperature 65℃", but the engineer mentioned "over-temperature shutdown threshold 80℃" in the audio recording, indicating that the current state has not met the shutdown conditions; when the video analysis report shows "oil leakage" but the sensor pressure value is normal, "state-parameter conflict" is triggered.

[0108] Cross-modal semantic consistency detection: the difference in severity rating between the text description "minor oil leak" and the sprayed oil stains in the video; verification of the match between the "metal friction sound" feature of the audio spectrum and the text diagnosis of "bearing wear".

[0109] The conflict score calculation uses a multi-level decision-making rule, as follows:

[0110] Level 1 conflict: A single contradiction, such as a difference of 1 level in the severity rating of the fault between the video and the text, resulting in a low score;

[0111] Level 2 conflict: Contradictory key indicators, such as the text stating "normal temperature" but the table showing excessive temperature, score as neutral;

[0112] Level 3 conflict: Safety-related contradictions, such as a "shutdown" statement versus equipment operation in a video, will receive a high score.

[0113] When the cumulative score exceeds the preset threshold, such as more than 10 points for a medium-risk event or more than 5 points for a high-risk event, the system will perform the following steps:

[0114] A1) Freeze the current decision-making process;

[0115] B2) Mark conflicting sources of evidence;

[0116] C3) Push the complete chain of evidence to the manual review queue.

[0117] The flexible handling mechanisms for special scenarios include:

[0118] When some evidence is missing, such as no video recording: only existing inter-modal validation is performed, and missing items are not included in the scoring;

[0119] When evidence is stratified by timeliness, such as tables containing real-time data while text contains yesterday's reports: apply a decay factor to older evidence to reduce its weight;

[0120] When the conflict originates from a sensor malfunction: the device abnormality is confirmed by comparing historical data, and the calibration process is triggered directly without manual review.

[0121] Knowledge graph logic verification includes: compiling enterprise business rules into a differentiable loss function layer, injecting the loss function layer into the knowledge graph reasoning process, and correcting data nodes that violate the rules through gradient backpropagation.

[0122] It should be further explained that, in the specific implementation process, the enterprise business rule base is transformed into an executable computation graph structure by the rule compiler, including the following:

[0123] The text rule "Inventory quantity ≤ Order quantity × Safety factor" is broken down into three calculation nodes:

[0124] Multiply the order volume node by the safety factor constant to obtain the threshold node;

[0125] When the inventory level is less than or equal to the threshold level, a compliance judgment node is obtained.

[0126] Define a differentiable penalty term for the constraint relationship between nodes: when the inventory exceeds the threshold, the compliance judgment node outputs a gradient proportional to the deviation value;

[0127] This computational graph, as a differentiable loss function layer, is embedded in the knowledge graph reasoning path and updated synchronously with the neural network parameters. The enterprise business rule base includes production safety standards and financial compliance regulations.

[0128] Dynamic monitoring is performed during real-time data processing, as follows:

[0129] Forward propagation monitoring: When the supply chain graph infers the "raw material procurement plan", the system reads the node values ​​of "current inventory = 100 tons" and "quarterly order quantity = 80 tons"; calculates the threshold of 96 tons according to the rule "inventory limit = order quantity × 1.2"; if the system detects that the inventory quantity 100 > the threshold 96, the compliance judgment node outputs a positive gradient.

[0130] Backpropagation correction: The gradient traces back along the computation graph to the data source node. If the inventory data comes from visual recognition, such as shelf scanning, the gradient forces a correction to the recognition model parameters. If there are input errors in the order data, such as mistakenly writing 80 tons as 80 tons, the node is marked as requiring manual verification.

[0131] Multi-rule collaborative constraints: When the inventory rule and the financial rule "purchase amount ≤ budget amount" conflict, a joint loss function layer is constructed, and the priority of the rules is adjusted through the weight balancing module, such as relaxing the financial constraints when production is urgent.

[0132] Strategies for handling special scenarios include: when there are ambiguities in the rules, such as "appropriately increasing the safety stock": activate the historical decision analysis module to extract compliance threshold ranges under similar scenarios; and use the median of the range as a temporary calculation benchmark.

[0133] When the data source is unreliable, such as frequent sensor failures, a reliability decay factor is added to the corresponding node to trigger the cross-modal verification module to verify the authenticity of the data.

[0134] When rules need urgent updates, such as newly promulgated environmental regulations, the online hot update rule compiler can add retrospective markers to historical decisions that have already been affected.

[0135] Third-order fusion strategies include:

[0136] Feature-level fusion employs a cross-modal attention weight matrix;

[0137] Semantic-level fusion performs concept mapping through the enterprise ontology network;

[0138] Decision-level fusion employs a neural symbolic reasoning engine;

[0139] The fusion weights at each stage are adaptively adjusted based on the information entropy value.

[0140] It should be further explained that, in the specific implementation process, after multimodal data enters the knowledge fusion layer, its knowledge value is extracted step by step through a tiered fusion process. In the feature-level fusion stage, a cross-modal attention weight matrix dynamically establishes connections between the original data: when the number "Q3 shipments 1000 units" from the sales report is simultaneously input with the "full capacity" scene from the product launch video, the system automatically generates an attention heatmap, highlighting and associating the numerical units in the table with the production line scene in the video, forming a "data-scene" binding relationship. If, at this point, the quality inspection report audio contains the statement "yield rate decline," the attention weight immediately reduces the influence of the sales data, preventing biased decision-making.

[0141] Entering the semantic-level fusion stage, the enterprise ontology network acts as a conceptual hub to perform deep alignment: for text descriptions of "a surge in customer complaints," the ontology library activates relevant nodes, mapping them to business entities such as "after-sales service response time" and "product fault classification." When associated monitoring videos show high vacancy rates in customer service centers, the system automatically strengthens the weight of the "response time" node and weakens the "product fault" path, ensuring semantic focus on service shortcomings rather than quality defects. For emerging business concepts, such as "carbon credit trading," the ontology network initiates a gap detection mechanism: temporarily creating shadow nodes and linking with the industry knowledge base to complete the definition, avoiding semantic gaps.

[0142] Decision-level fusion is driven by a neural symbolic system, combining a rule engine and a learning model to generate actionable insights: When handling the "risk of cold chain disruption in East China" warning, the system simultaneously analyzes:

[0143] Structured data: Historical failure rate of temperature control sensors;

[0144] Video data: Scanned copies of maintenance records for transport vehicles;

[0145] Audio data: Driver safety training completion rate;

[0146] At the symbolic level, a business rule is injected: "If the failure rate > threshold and maintenance expires, a red alert is triggered." Simultaneously, a neural network analyzes the frequency of keywords in driver training recordings, such as the number of times "temperature recorder operation" is mentioned, dynamically adjusting the rule threshold. When the outputs of the two conflict, such as a rule-based red alert / model-suggested yellow alert, an entropy arbitration mechanism is activated: sensor data entropy is calculated to reflect the reliability of the monitoring system and assess the semantic certainty of the training recordings; data sources with lower entropy values ​​receive higher decision weight.

[0147] Dynamic weight adjustment is implemented throughout the entire process: When heavy rain causes increased blur in transportation videos, the system automatically performs the following operations:

[0148] Feature level: Reduce video attention weight and increase the proportion of GPS trajectory data;

[0149] Semantic level: Increase the association strength of the "weather impact" node;

[0150] Decision level: Add a temporary rule to the neural symbolic system: "Allow temperature control offset of +2℃ during heavy rain";

[0151] The weight configuration will automatically reset after the weather improves to avoid long-term deviations.

[0152] The contradiction detection rules include: confidence difference detection between text statements and video sentiment tags, logical conflict detection between table values ​​and audio statements, and semantic consistency detection between image recognition results and text descriptions. It should be further explained that, in the specific implementation, the confidence difference detection between text statements and video sentiment tags is achieved through a multi-level analysis framework. When the meeting transcript text claims "improved customer satisfaction," the system simultaneously retrieves the product demonstration video clip for sentiment decoding. First, facial expression features, such as the frequency of mouth corners raising and eyebrow extension, are extracted to generate a basic sentiment score; then, combined with body language analysis, such as clapping intensity and body leaning angle, a behavioral auxiliary score is generated. If the text statement is positive but the overall video sentiment score falls into the negative range, such as detecting frequent frowning and crossed arms, the system performs discrepancy tracing: checking the time difference between the text posting time and the video recording time to eliminate timeliness interference; analyzing the speaker's identity, such as comparing the sales director's statement with the customer representative's reaction; when negative emotions persist for more than one-third of the video length, it is determined to be a substantial conflict rather than a brief emotional fluctuation; if the customer smiles politely but the text is overly optimistic: identifying disguised emotions through micro-expression twitching detection and comparison with historical interactions.

[0153] Logical conflict detection between table values ​​and audio statements employs dynamic inference chain verification: For the audio statement "Q3 profit margin 30%" in the earnings call, it is linked in real-time to revenue / cost data in the financial system tables. The system constructs a mathematical verification model, including: extracting key values ​​from audio-to-text conversion; retrieving related indicators from the table, such as revenue A and cost B; calculating the actual profit margin C = (AB) / A; initiating conflict analysis when |C - 30%| exceeds the allowable deviation: checking for discrepancies in statistical methods; verifying data timeliness; and identifying special adjustments, such as the impact of one-time provisions.

[0154] If the contradiction persists after excluding explanatory factors, it is marked as "numerical authenticity questionable". For ambiguous statements, historical year-on-year data is automatically matched: if the increase is less than two standard deviations below the historical average, a contradiction alert is triggered.

[0155] The semantic consistency detection between image recognition results and text descriptions introduces a space-concept dual mapping mechanism: when detecting the text description "bearing intact" in the equipment maintenance report, key frames of the disassembly process video are analyzed simultaneously. The system performs the following operations:

[0156] Spatial matching: Locating the component region mentioned in the text within a video frame, such as aligning the bearing position using a CAD model;

[0157] Proof of concept: The image recognition results, such as ball bearing defects and corrosion area, are matched with text keywords, such as "intact" and "no abnormalities", using a semantic tree.

[0158] When a negative path appears in the component status tree, such as when there is a defect but the text is claimed to be intact, cross-modal evidence is initiated: retrieve audio records from the same device to determine if there is any abnormal noise; search sensor time-series data to determine if the vibration value exceeds the standard; and determine whether the text is false based on the strength of multi-source evidence.

[0159] The construction process of the differentiable loss function layer includes:

[0160] Parse the business rule expression into computation graph nodes;

[0161] Define differentiable penalty terms for the constraint relationships between nodes;

[0162] The penalty term is involved in gradient calculation during neural network training.

[0163] It should be further explained that, in the specific implementation process, after the enterprise's business rule text is input into the rule compiler, it undergoes multi-stage parsing and is transformed into an executable computation graph. Taking the safety production rule "Valves must be closed when the temperature of a pressure vessel is ≥100℃" as an example, the process is as follows:

[0164] Syntax tree decomposition: Identify entity nodes: "Pressure vessel temperature" and "Valve status"; Extract relational operators: "≥" and "Must be closed"; Parse constraint conditions: "100℃" is the critical threshold.

[0165] Computational graph construction: Temperature sensor data stream is connected to the "Pressure Vessel Temperature Node"; a "Threshold Comparison Node" is set to perform real-time judgment: if the temperature is ≥100℃, the deviation value is output; the "Valve Status Monitoring Node" is connected: when the deviation value is positive, a gradient proportional to the deviation is generated;

[0166] Differentiable penalty term definition: Under normal operating conditions, the gradient is zero when the valve is closed; when the temperature exceeds the limit and the valve is not closed, the gradient value = deviation value × risk coefficient, and the risk coefficient increases cumulatively with the duration of temperature exceedance; in emergency shutdown scenarios, if the temperature is >120℃, a step-by-step gradient amplification mechanism is activated.

[0167] After the computation graph is embedded into the reasoning path of the knowledge graph, it is dynamically monitored during the forward propagation of the neural network, as follows:

[0168] Compliance scenario: The valve opens at a temperature of 95℃ without any gradient being generated;

[0169] Critical scenario: Temperature reaches 102℃ but valve is not closed, output gradient value = 2 × base risk coefficient;

[0170] Emergency scenario: When the temperature rises to 125℃:

[0171] Sa) gradient value = 25 × (basic risk coefficient + cumulative duration coefficient);

[0172] Sb) Forcefully activate the equipment interlocking control interface;

[0173] Sc) marks the associated data node as being in a high-risk state.

[0174] Special handling for complex rules includes the following:

[0175] Fuzzy rules, such as "moderately reduce energy consumption": retrieve historical best practice data, i.e., the lowest energy consumption record under the same operating conditions; use ±10% of the benchmark value as the acceptable range; generate a gradual gradient for deviations outside the range;

[0176] Multiple rules are coupled, such as conflicts between safety rules and energy-saving rules: a dual-objective loss function layer is constructed, the gradient weighting coefficient of the safety rule is automatically increased to 3 times that of the energy-saving rule, and the conflict resolution record is fed back to the rule knowledge base to optimize the original clause;

[0177] Rule version iteration: online hot replacement of computation graph components, parallel operation of old and new rules during the transition period, and version compatibility tags for historical decisions.

[0178] The self-correction process includes:

[0179] Location of erroneous data sources based on conflict scoring;

[0180] Invoke the adversarial discriminant to realign the semantic representation;

[0181] Update the affected triple relationships in the knowledge graph.

[0182] It should be further explained that during the specific implementation process, when the dual verification module detects an irreconcilable contradiction, the system initiates a self-correction process. First, the system locates the erroneous data source using a conflict scoring matrix: Regarding the conflict between "out of stock in South China" in sales data and the warehouse video showing full stock, the system performs multi-dimensional tracing, including:

[0183] Modal reliability analysis: Check if the recent error rate of the video encoder exceeds the standard;

[0184] Data timeliness comparison: Confirm whether the inventory table is the latest version;

[0185] Transmission link diagnostics: Verify the integrity of data packets from the camera to the server;

[0186] If the video encoder error rate suddenly increases while the inventory table is in good working order, identify the video processing module as the source of the error.

[0187] Then, the adversarial discriminator is invoked to perform semantic realignment: extract conflicting data, namely: full warehouse video frames and out-of-stock text; restart adversarial training in an isolation sandbox; optimize video encoder parameters through small sample iterations: reduce the misjudgment weight caused by shelf shadows and enhance the ability to extract product stacking features; terminate the iteration when the text "out of stock" and the optimized video features recover to a reasonable range in latent space distance.

[0188] Finally, perform targeted updates to the knowledge graph, including the following four steps:

[0189] Step S01: Locate the affected nodes: a three-hop sub-graph centered on "South China Inventory", including nodes such as procurement plans and sales forecasts;

[0190] Step S02: Recalculate node relationships: Delete the "Emergency Replenishment" instruction generated due to video misjudgment; restore the "Sufficient Inventory" status label;

[0191] Step S03: Add data lineage marker: Tag the correction node with "VID-0925 correction"; record the error parameter version, i.e.: VideoEncoder_v2.3 defective version;

[0192] Step S04: Trigger related party synchronization: Send order cancellation suggestion to the purchasing system and open promotional quotas to the sales system.

[0193] Flexible responses to special scenarios include:

[0194] When multiple error sources are intertwined, a layered correction protocol is initiated, as follows:

[0195] Level 1 correction: Addressing the main issues, such as video coding defects;

[0196] Secondary correction: Handling derivative errors, such as purchase orders triggered by errors;

[0197] Level 3 correction: Update dependent models, such as sales forecasting algorithms;

[0198] When historical data is contaminated: enable the time machine mechanism, slice the data according to the time of the error, reprocess only the affected period, and retain the original data snapshot for auditing;

[0199] When critical decisions rely on erroneous data: Initiate an emergency recall, push the corrected version to managers who have received the error report, and highlight the changes and their scope of impact.

[0200] A method for intelligent processing of enterprise multimodal data integrating RAG technology, applied to an enterprise multimodal data intelligent processing system, includes the following steps:

[0201] Step S1: Generate a cross-modal unified semantic representation through adversarial training;

[0202] Step S2: Integrate heterogeneous data using a three-order fusion strategy;

[0203] Step S3: Perform dual verification of cross-modal consistency and rule logic on the fusion results;

[0204] Step S4: Correct the output knowledge or activation data based on the verification status.

[0205] It should be further explained that, in the specific implementation process, after the enterprise's multi-source data is input into the system, the first step is to generate a unified semantic representation across modalities: modal differences are dynamically eliminated through an adversarial training mechanism. When production line monitoring videos and equipment log text are input synchronously, the video encoder extracts the vibration amplitude features of the equipment, the text encoder parses the description of "bearing noise," and the adversarial discriminator forces the two types of features to converge to the same coordinates in the latent space. If the video image is blurry, causing unstable feature extraction, the system automatically reduces the modality weight and uses high-resolution infrared thermal imaging data as the primary alignment benchmark to ensure that the semantic representation of "temperature anomaly" is consistent across different modalities.

[0206] Entering the third-order fusion strategy execution phase, a tiered knowledge integration approach is adopted, including the following three levels of fusion:

[0207] Feature-level fusion: Establishing cross-modal correlation: Binding vibration sensor waveforms with the "frequency fluctuation" description in maintenance work orders through an attention matrix;

[0208] Semantic-level fusion: Connecting with enterprise ontology: Mapping "bearing failure" to the equipment health subtree of the knowledge graph, and associating it with historical maintenance records and supplier ratings;

[0209] Decision-level fusion: Coordinating rules and models: A business rule of "three consecutive vibration exceedances require shutdown" is injected, while a neural network analyzes the harmonic characteristics of the vibration spectrum. When the rule requires shutdown but the model determines it to be transient interference, entropy arbitration is initiated: Sensor data entropy is calculated to assess signal stability; historical interference event frequencies of similar equipment are retrieved; and the conclusion supported by low-entropy data is selected as the final decision.

[0210] The dual verification process provides a comprehensive review of the fusion results, as follows:

[0211] Cross-modal consistency verification: Compare the label on the equipment to be repaired with the disassembly video frames. If the text indicates "gear wear" but the video shows the gear teeth are intact, voiceprint evidence is activated. If the audio analysis detects periodic scraping sounds, the text record is deemed inaccurate.

[0212] Rule logic verification: The "maximum vibration velocity ≤ 5 mm / s" condition is transformed into a differentiable loss function. When the sensor data reaches 5.2 mm / s, the output gradient signal is traced back along the computation graph: if the data comes from a newly installed sensor, a calibration request is marked; if it is a model inference value, the vibration analysis algorithm parameters are corrected.

[0213] The final response phase is categorized based on the verification status: when verification is successful, an interpretable report is generated, which includes linking abnormal vibration data fragments, linking maintenance procedure clauses, and highlighting the shutdown decision basis tree.

[0214] When verification fails, activate self-correction: locate the source of the conflict as an OCR recognition error in the text recording module; retrain the character segmentation model in the sandbox to enhance the differentiation of similar character shapes; update the device health status node in the knowledge graph; and push the corrected maintenance suggestions to the mobile terminal.

[0215] It should be further explained that, in the specific implementation process, after the enterprise's multi-source heterogeneous data enters the system, it first undergoes cross-modal semantic unified processing: structured data is analyzed through graph neural networks to resolve the logical relationships between fields. For example, order volume, production plan, and raw material procurement volume are constructed into a topology graph to capture business chain dependencies. Unstructured data adopts a modal coding strategy: text data is processed through dual channels to separate basic semantics from domain-specific terms. When the word "conduction" appears, the general branch resolves the physical concept, while the domain branch is locked to circuit characteristics based on the enterprise knowledge base. Video data uses a spatial feature extractor to identify static attributes such as the integrity of the equipment's appearance, while a time-series analysis module captures dynamic evolution such as mechanical vibration patterns. Audio data is decomposed into acoustic fingerprints and speech content text to avoid recognition error interference. The output vectors of each modality are input into an adversarial discriminator for dynamic alignment. The discriminator continuously compares the similarity of features of different modalities and feeds back to the encoder to adjust parameters until the features can no longer distinguish the source modality. For example, when the text description of "bearing noise" and the vibration spectrum features converge to the same coordinates in the latent space, the alignment is considered successful. If the data quality of a certain modality is low, such as a blurry video, the system will automatically reduce its alignment weight and use the high-confidence modality as the dominant benchmark.

[0216] After semantic unification, the data enters the knowledge fusion layer for tiered integration: In the feature-level fusion stage, a cross-modal attention mechanism establishes connections between the original data, dynamically binding the shipment volume values ​​in the sales table with conveyor belt speed changes in the production line monitoring video to form a data scene mapping. In the semantic-level fusion stage, the enterprise ontology network is invoked for concept mapping. For surges in customer complaints, descriptions are automatically linked to after-sales service response time nodes, and the path weight is strengthened based on video evidence of customer service center vacancy rates. In the decision-level fusion stage, the neural symbolic system coordinates business rules and the learning model. When rules require shutdown but the model judges it as a temporary disturbance, a credibility arbitration mechanism is activated to analyze sensor data stability and historical interference patterns, selecting conclusions supported by low-credibility data. Throughout the process, dynamic impact adjustment is implemented; for example, during heavy rain, the impact of video data is reduced, and temperature control rule thresholds are temporarily relaxed, automatically resetting after the weather improves.

[0217] The fusion results undergo dual verification to ensure reliability: cross-modal consistency verification constructs a chain of evidence for the same entity, such as comparing keyframes of equipment maintenance text records with disassembly video. If the text indicates gear wear while the video shows the gear teeth are intact, the audio spectrum is retrieved to detect the presence of a scraping sound, thus determining the text to be inaccurate. Knowledge graph logic verification transforms business rules into an executable computational graph. For example, the over-temperature rule for pressure vessels is compiled into a constraint relationship between temperature nodes and valve status nodes. When over-temperature is detected and the valve is not closed, a correction signal proportional to the temperature deviation and duration is output. This signal is traced back along the computational graph to the data source; if it is a sensor, calibration requirements are marked; if it is a model, the identification parameters are optimized.

[0218] The system distributes responses based on verification status: Upon successful verification, an interpretable report is generated, linking multimodal evidence fragments and annotating the decision path tree, such as binding abnormal vibration data to maintenance procedure clauses. Upon verification failure, a targeted self-correction process is initiated. First, the source of the error is located using a conflict scoring matrix, such as identifying a recent surge in the video encoder's error rate. Second, the parameters of the defective module are fine-tuned in an isolated environment, such as optimizing the shelf shadow processing algorithm. Finally, the affected nodes in the knowledge graph are updated in a targeted manner, such as correcting inventory status tags and associated procurement plans, while simultaneously pushing change notifications to the business systems. For historical data contamination issues, time slicing technology is used to reprocess only the erroneous time period while retaining the original snapshot for audit traceability.

[0219] Cross-modal dynamic alignment overcomes the reliance on manual annotation and achieves adaptive convergence of modal semantics through adversarial training, eliminating information silos. A tiered fusion mechanism uses credibility arbitration instead of static weighting strategies, achieving a balance between rule rigidity and data flexibility to avoid decision-making blind spots. A dual-verification design enables cross-modal verification and rule logic to mutually corroborate each other, forming a closed loop for contradiction detection and improving risk coverage density. Targeted self-correction achieves error isolation through sandbox fine-tuning and local graph updates, avoiding system-wide rollback resource consumption while driving the continuous evolution of enterprise knowledge assets.

[0220] In cross-border supply chain crisis management, multilingual contract texts and port congestion videos undergo adversarial training to reach a semantic unification and fusion stage. This involves mapping delivery date clauses and ship location data, mapping typhoon warnings to a global shipping route risk subtree, coordinating diversion rules with port throughput capacity models, verifying consistency between satellite cloud imagery and meteorological texts, monitoring cost overruns through a loss function layer, and ultimately generating contingency plans with self-correcting historical wind speed impact coefficients. For production line yield false alarms, discrepancies between text reports and visual inspection pinpoint defects in the old character recognition system. After optimizing the differentiation of similar character shapes, knowledge graph nodes are updated, and a dual-verification mechanism is added to prevent recurrence.

[0221] It should be further explained that, in the specific implementation process, an enterprise multimodal data intelligent processing method integrating RAG technology includes the following steps:

[0222] Step S1: Multimodal data input and dynamic encoding: Receive heterogeneous data source input from enterprises. Structured data is parsed using graph neural networks to analyze field topological relationships. Unstructured data is processed in different modalities: Text is processed by performing domain semantic separation to extract proprietary terms. Video is processed in parallel to extract spatial static features and temporal dynamic features. Audio is processed by separating acoustic fingerprints and speech text.

[0223] Step S2: Cross-modal adversarial semantic alignment: Input the encoded features of each modality into the adversarial discriminator, and force the different modal features to converge in the latent space by iterative parameter adjustment. When the distance between the text description "bearing noise" and the vibration spectrum features exceeds a threshold, the discriminator feeds back a signal to optimize the fault semantic extraction capability of the text encoder and the mechanical motion resolution accuracy of the video encoder, until the cross-modal features can no longer distinguish the source.

[0224] Step S3: Step-by-step knowledge fusion: Feature-level fusion: Construct a cross-modal attention matrix to dynamically associate the original data, such as binding temperature sensor values ​​with infrared video hot zones; Semantic-level fusion: Call the enterprise ontology network to map business concepts and adjust node weights according to the strength of evidence, such as strengthening the "response delay" path in customer service vacancy rate videos; Decision-level fusion: The neural symbol system coordinates the rules and model outputs. When the rules require a shutdown and the model judges a brief interference, credibility arbitration is initiated, that is: the conclusion with high data stability and consistent historical handling is given priority.

[0225] Step S4: Dual verification execution: Cross-modal consistency verification: Construct a multi-evidence chain for the same entity. If there is a state conflict between the equipment maintenance text and the disassembly video, retrieve the audio spectrum to verify the existence of the scratching sound; Rule logic verification: Compile the business rules into a computation graph. When a violation is detected, output the gradient signal to trace back to the error source: If the sensor is abnormal, mark it for calibration; if the model is defective, correct the parameters.

[0226] Step S5: Verify the status diversion response: If the dual verification passes, generate an interpretable report containing multimodal correlation evidence; if the verification fails, activate the targeted correction engine: Locate the error source: Analyze the module error rate and data lineage; Sandbox fine-tuning: Optimize the parameters of the defective module with conflict data, such as retraining OCR character recognition; Graph-oriented update: Recalculate the affected subgraph based on node influence, such as correcting inventory status and associated procurement nodes.

[0227] Step S6: Knowledge Closed-Loop Evolution: Amendments are precipitated into rules: Frequent video shadow misjudgments are transformed into ontology library verification conditions; Model parameters are continuously optimized: Fine-tuned encoder parameters are synchronized to the online learning pipeline; Business system linkage: Inventory correction data is pushed to the ERP system, and false alarm analysis reports are sent to the quality system.

[0228] By employing dynamic adversarial semantic alignment and a tiered fusion mechanism, the problem of fragmented multimodal data in enterprises is addressed. Adaptive convergence of cross-modal features in the latent space eliminates the semantic gap, and concept mapping and credibility arbitration based on ontology networks achieve deep association of heterogeneous data. This enables the accurate extraction and transformation of key information from unstructured data into structured knowledge, improving the accuracy of cross-modal association analysis and the reliability of decision-making.

[0229] A closed-loop knowledge governance system is built upon a dual-validation and targeted correction engine. The gradient backtracking mechanism of the rule computation graph enables error source localization, while sandbox fine-tuning and local graph update technologies ensure problem fixing, avoiding the resource consumption of a system-wide rollback. The knowledge evolution pipeline driven by validation results continuously accumulates business rules and optimizes model parameters, forming a self-improving ecosystem for enterprise knowledge assets, ultimately enhancing the accuracy of intelligent retrieval, the timeliness of risk warnings, and the effectiveness of decision support.

[0230] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0231] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An enterprise multimodal data intelligent processing system integrating RAG technology, characterized in that, Including sequential connections: The multimodal unified semantic space construction module is configured to process structured and unstructured data separately through a dynamic heterogeneous encoder, and output a unified semantic representation by adopting a cross-modal adversarial alignment mechanism. The knowledge fusion layer, connected to the building module, is configured to achieve feature-level, semantic-level, and decision-level fusion through a three-level fusion strategy. The dual verification module, connected to the knowledge fusion layer, is configured to perform runtime cross-modal consistency verification and knowledge graph logic verification. An enhanced RAG engine, connected to the dual verification module, is configured to generate a knowledge response or trigger a self-correction process based on the verification results. The cross-modal adversarial alignment mechanism includes: The structured data encoder uses a graph neural network to parse the topological relationships between fields; The unstructured data encoder contains semantic decomposition units that separate the basic semantics of the text from domain-specific concepts; The adversarial discriminator forces the latent space vectors output by different modal encoders to satisfy semantic indistinguishability; The semantic decomposition unit includes: The general semantic extraction branch is implemented through a pre-trained language model; Domain concept separation branches are achieved through an attention mechanism constrained by enterprise ontology; The outputs of the two branches are gated and fused before being input into the adversarial discriminator.

2. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 1, characterized in that: The runtime cross-modal consistency check includes: Construct a multimodal chain of evidence for the same entity; Calculate intermodal conflict scores using preset conflict detection rules; When the conflict score exceeds a preset threshold, the manual review process is activated.

3. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 1, characterized in that: The knowledge graph logic verification includes: compiling enterprise business rules into a differentiable loss function layer, injecting the loss function layer into the knowledge graph reasoning process, and correcting data nodes that violate the rules through gradient backpropagation.

4. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 1, characterized in that: The third-order fusion strategy includes: Feature-level fusion employs a cross-modal attention weight matrix; Semantic-level fusion performs concept mapping through the enterprise ontology network; Decision-level fusion employs a neural symbolic reasoning engine; The fusion weights at each stage are adaptively adjusted based on the information entropy value.

5. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 2, characterized in that: The contradiction detection rules include: confidence difference detection between text statements and video sentiment tags, logical conflict detection between table values ​​and audio statements, and semantic consistency detection between image recognition results and text descriptions.

6. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 3, characterized in that: The construction process of the differentiable loss function layer includes: Parse the business rule expression into computation graph nodes; Define differentiable penalty terms for the constraint relationships between nodes; The penalty term is used in gradient calculation during neural network training.

7. The enterprise multimodal data intelligent processing system integrating RAG technology according to claim 1, characterized in that: The self-correction process includes: Location of erroneous data sources based on conflict scoring; Invoke the adversarial discriminant to realign the semantic representation; Update the affected triple relationships in the knowledge graph.

8. A method for intelligent processing of enterprise multimodal data integrating RAG technology, applied to any one of the systems of claims 1-7, characterized in that, Includes the following steps: Step S1: Generate a cross-modal unified semantic representation through adversarial training; Step S2: Integrate heterogeneous data using a three-order fusion strategy; Step S3: Perform dual verification of cross-modal consistency and rule logic on the fusion results; Step S4: Correct the output knowledge or activation data based on the verification status.

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