Unstructured data processing system based on artificial intelligence

Abstract

The embodiment of the invention provides an unstructured data processing system based on artificial intelligence, and the unstructured data processing system based on artificial intelligence comprises multi-modal original data obtained through a distributed probe array which comprises a text scanning unit, an image perception unit and an audio and video synchronization unit; the characteristic smelting center is connected with the probe array and is provided with an anti-decoupling module and a cross-modal attention grafting module; the evolutionary engine is dynamically connected with the smelting center and comprises a periodic self-checking neural architecture searcher and a knowledge incubator based on analogy reasoning; and the system outputs a 128-dimensional composite feature code with a timestamp. Through the cross-modal fusion technology of the feature melting center, the semantic understanding depth which cannot be achieved by a traditional single-modal system is achieved, a dynamic evolution engine ensures that the processing precision is continuously optimized along with time, and the common industry problem of model performance degradation is overcome.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of equipment operation and maintenance technology, and in particular to an unstructured data processing system based on artificial intelligence. Background Technology

[0002] Currently, the field of unstructured data processing faces three major technical bottlenecks: First, traditional systems employ static processing architectures, which cannot adapt to the temporal evolution of data characteristics, causing processing models to quickly become ineffective as business grows. Second, cross-modal association relies on manual rule configuration, achieving an accuracy of less than 60% in scenarios such as video semantic parsing and mixed text and image document processing. Third, existing metadata management systems lack evolutionary capabilities and cannot automatically identify emerging data patterns. Mainstream solutions on the market, such as single-modal processing frameworks based on TensorFlow or semantic analysis systems using fixed knowledge graphs, have failed to address the aforementioned dynamic adaptation issues. This technology fundamentally changes this technical predicament by constructing a feature furnace with a memory-prediction mechanism.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, embodiments of this specification provide an artificial intelligence-based unstructured data processing system to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, an artificial intelligence-based unstructured data processing system is provided, comprising: Multimodal raw data is obtained through a distributed probe array, which includes a text scanning unit, an image perception unit, and an audio-visual synchronization unit. The feature melting center connecting the probe array has an adversarial decoupling module and a cross-modal attention grafting module; The dynamic connection to the smelting center's evolutionary engine includes a periodically self-checking neural architecture searcher and an analogical reasoning-based knowledge incubator; The system outputs a 128-dimensional composite feature code with a timestamp.

[0006] In one possible implementation, when the text scanning unit performs semantic tomography, it simultaneously generates a semantic depth map and a contextual offset, and the resolution matrix output by the image perception unit contains spatial frequency distribution features.

[0007] In one possible implementation, the adversarial decoupling module generates a feature purity index when it operates, which is jointly determined by the interlayer gradient of the semantic depth map and the harmonic components of the spatial frequency distribution features.

[0008] In one possible implementation, the fused features output by the cross-modal attention grafting module satisfy: For any combination of modes, the grafting weight is:

[0009] Where α and β are modal adjustment coefficients, which are obtained by transforming the characteristic purity index using the hyperbolic tangent function; The semantic relevance of the current modality is extracted from the convolutional kernel response value of the semantic depth graph; This is the maximum relevance benchmark value recorded by the system; The cross-modal distance is represented by the Euclidean space mapping module of the feature melting center; This is the preset maximum distance threshold.

[0010] In one possible implementation, the neural architecture searcher generates architecture optimization instructions every 72 hours while simultaneously calculating the entropy values ​​of historical decision paths:

[0011] in The entropy value represents the probability of the i-th processing paradigm appearing in the most recent period T, where n is the total number of identified paradigm types, and T is 240 consecutive processing periods. The entropy value is used to adjust the analogical reasoning intensity of the knowledge incubator.

[0012] In one possible implementation, the modality adjustment coefficients α and β are updated according to the following principle: when the system continuously processes the same type of data for more than a preset threshold, the α value decays in proportion to the inverse of the entropy value, and the β value increases linearly with the standard deviation of the fused features.

[0013] In one possible implementation, the generation process of the composite feature code includes a timing mark injection step, which converts the acquisition time difference of the probe array into the phase offset of the feature vector.

[0014] In one possible implementation, the compensation value for the phase offset is dynamically adjusted by the evolutionary engine based on the ambient clock jitter, with the compensation magnitude inversely proportional to the clock stability score recorded by the neural architecture searcher.

[0015] In one possible implementation, the calculation process of the mutation entropy value includes an abnormal event filtering mechanism. When the processing time of a single operation exceeds 1 / 24 of the period T, the pi value of the corresponding time period is not included in the entropy value statistics.

[0016] In one possible implementation, new rules output by the knowledge incubator must be reverse-verified by the feature melting center. Rules with a verification success rate of less than 80% trigger an emergency refactoring process for the neural architecture searcher.

[0017] This specification provides an AI-based unstructured data processing system, comprising: multimodal raw data acquired through a distributed probe array, the probe array including a text scanning unit, an image perception unit, and an audio / video synchronization unit; a feature fusion center connected to the probe array, the fusion center having an adversarial decoupling module and a cross-modal attention grafting module; an evolutionary engine dynamically connected to the fusion center, the engine including a periodically self-checking neural architecture searcher and a knowledge incubator based on analogical reasoning; and a system outputting a 128-dimensional composite feature code with a timestamp. Through the cross-modal fusion technology of the feature fusion center, a semantic understanding depth unattainable by traditional single-modal systems is achieved. The dynamic evolutionary engine ensures continuous optimization of processing accuracy over time, overcoming the industry-wide problem of model performance degradation. Attached Figure Description

[0018] Figure 1 This is a system schematic diagram of an artificial intelligence-based unstructured data processing system provided in one embodiment of this specification. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] This specification provides an artificial intelligence-based unstructured data processing system, which will be described in detail in the following embodiments.

[0023] See Figure 1 , Figure 1 The diagram illustrates a system schematic of an artificial intelligence-based unstructured data processing system according to an embodiment of this specification. Specifically, it includes multimodal raw data acquired through a distributed probe array, which comprises a text scanning unit, an image perception unit, and an audio / video synchronization unit; a feature melting center connected to the probe array, which has an adversarial decoupling module and a cross-modal attention grafting module; an evolutionary engine dynamically connected to the melting center, which includes a periodically self-checking neural architecture searcher and a knowledge incubator based on analogical reasoning; and a system output of a 128-dimensional composite feature code with a timestamp.

[0024] The distributed probe array refers to a heterogeneous sensor cluster deployed at the data source terminal, used to capture raw data streams such as text, images, audio, and video in real time. The text scanning unit can recognize character encoding and semantic structure, extracting deep language features across languages. The image perception unit is used to analyze the topological relationships of visual elements and can adapt to input sources of different resolutions. The audio-visual synchronization unit can align audio-visual temporal information to establish a cross-modal spatiotemporal correlation index. The feature fusion center can uniformly map heterogeneous features to a high-dimensional space to achieve multimodal semantic fusion. The adversarial decoupling module can separate the essential features of the data from scene noise, improving the purity of feature extraction. The cross-modal attention grafting module is used to establish dynamic weight associations between modalities, optimizing feature combination efficiency. The evolutionary engine continuously optimizes the system's processing capabilities, enabling autonomous architecture upgrades. The neural architecture searcher can periodically evaluate the network structure and automatically generate optimal connection schemes. The knowledge incubator transforms processing experience into decision rules, enhancing the system's reasoning ability. The composite feature code encapsulates multi-dimensional semantic information, uniquely identifying the complete features of a data object.

[0025] As a concrete example: This system is deployed in a smart city project to process traffic monitoring data. The text scanning unit identifies license plate information in real time, the image perception unit analyzes vehicle movement trajectories, and the audio-visual synchronization unit correlates siren sounds with emergency vehicle locations. The feature melting center completes multi-source data alignment in milliseconds, generating a 128-dimensional feature code containing vehicle behavior patterns. The evolutionary engine automatically optimizes the image recognition model weekly, continuously reducing the license plate misidentification rate from its initial value. When a new drone inspection video source is added, the system autonomously establishes a new processing channel within an hour, requiring no manual intervention.

[0026] This system significantly improves the processing efficiency of unstructured data. Distributed probes ensure comprehensive collection of multi-source data, while feature fusion guarantees accurate correlation of cross-modal information. A dynamic evolution mechanism keeps the system in optimal performance, and composite feature codes provide rich semantic interfaces for downstream applications. Compared to traditional solutions, this system has significant advantages in data fusion depth, processing timeliness, and environmental adaptability, making it particularly suitable for rapidly changing business scenarios.

[0027] In one possible implementation, when the text scanning unit performs semantic tomography, it simultaneously generates a semantic depth map and a contextual offset, and the resolution matrix output by the image perception unit contains spatial frequency distribution features.

[0028] Among them, semantic tomography refers to a hierarchical analysis technique for text structure, used to peel away surface semantics and latent semantics layer by layer; semantic depth maps can map the abstraction gradient of words and quantify the cognitive distance between concepts; context offset is used to measure the degree of deviation between text units and preset scenarios and can identify unconventional expression patterns; resolution matrix can encapsulate multi-scale features of images and is used to coordinate the extraction of visual information of different granularities; spatial frequency distribution features can describe the periodicity of image components and optimize the targeting of feature extraction.

[0029] As a concrete example: In a medical report analysis scenario, the text scanning unit performs semantic tomography on CT diagnostic reports, generating a semantic depth map containing a three-layer structure of "lesion description - clinical indications - diagnostic conclusion". When a significant deviation is found between the description of "calcification" and the typical radiological context, the system automatically triggers a review mechanism. Simultaneously, the image perception unit processes CT images, coordinating macroscopic anatomical structure recognition and microscopic calcification detection through a resolution matrix, while the spatial frequency analysis module accurately distinguishes the characteristic differences between vascular wall calcification and tumor calcification.

[0030] This technical solution achieves collaborative optimization of text and image analysis. Semantic tomography overcomes the planar limitations of traditional text processing, while depth map construction visualizes conceptual relationships. Contextual offset monitoring enhances tolerance for unconventional expressions in specialized fields. The innovative application of resolution matrices allows image processing to consider both macroscopic and microscopic features, and spatial frequency analysis provides a new quantitative dimension for medical image diagnosis. The entire mechanism significantly improves the accuracy and efficiency of multimodal medical data analysis.

[0031] In one possible implementation, the adversarial decoupling module generates a feature purity index when it operates, which is jointly determined by the interlayer gradient of the semantic depth map and the harmonic components of the spatial frequency distribution features.

[0032] Among them, the feature purity index can be used as a comprehensive indicator to quantify the feature separation effect and is used to evaluate the extraction quality of the essential features of the data; the interlayer gradient of the semantic depth map can reflect the intensity of concept transitions between different abstraction levels and can identify the mutation points of semantic structure; the harmonic components of the spatial frequency distribution features are used to describe the cooperative relationship of the periodic features of the image and can suppress irregular noise interference.

[0033] As a concrete example: In a financial risk control scenario, when the adversarial decoupling module processes multimodal customer data: the semantic depth map generated by the text scanning unit shows an abnormal gradient in the description of "large transfer" at the behavioral and intent layers, and the spatial frequency analysis of the ID photo captured by the image perception unit reveals abnormal harmonic components. Based on this, the system calculates a feature purity index below the threshold (0.85), triggering a risk review process. Ultimately, the system identifies a dual fraud feature in this case: deliberate obfuscation of the text description and tampering with the ID image.

[0034] This technical solution enhances the reliability of feature decoupling through innovative index design. The feature purity index enables unified quality assessment across modalities, inter-layer gradient analysis provides a quantitative basis for semantic anomaly detection, and harmonic component optimization improves the stability of image feature extraction. The synergistic effect of these three elements significantly enhances the system's feature discrimination capability in complex scenarios, providing a new technical paradigm for multimodal data analysis in high-risk domains.

[0035] In one possible implementation, the fused features output by the cross-modal attention grafting module satisfy: For any combination of modes, the grafting weight is:

[0036] Where α and β are modal adjustment coefficients, which are obtained by transforming the characteristic purity index using the hyperbolic tangent function; The semantic relevance of the current modality is extracted from the convolutional kernel response value of the semantic depth graph; This is the maximum relevance benchmark value recorded by the system; The cross-modal distance is represented by the Euclidean space mapping module of the feature melting center; This is the preset maximum distance threshold.

[0037] In one possible implementation, the neural architecture searcher generates architecture optimization instructions every 72 hours while simultaneously calculating the entropy values ​​of historical decision paths:

[0038] in The entropy value represents the probability of the i-th processing paradigm appearing in the most recent period T, where n is the total number of identified paradigm types, and T is 240 consecutive processing periods. The entropy value is used to adjust the analogical reasoning intensity of the knowledge incubator.

[0039] In one possible implementation, the modality adjustment coefficients α and β are updated according to the following principle: when the system continuously processes the same type of data for more than a preset threshold, the α value decays in proportion to the inverse of the entropy value, and the β value increases linearly with the standard deviation of the fused features.

[0040] Among them, the modality adjustment coefficient α can be a dynamic parameter that controls the influence of the main modality and is used to balance the contribution weights of different data modalities; the modality adjustment coefficient β can adjust the compensation intensity of the auxiliary modality and optimize the synergistic effect of cross-modal features; the preset threshold is used to determine the continuous stability of data processing and can trigger the parameter adaptive adjustment mechanism; the variation entropy value can quantify the degree of abnormal fluctuation in data distribution and is used to evaluate the reliability of modal features; the standard deviation of the fused features can reflect the dispersion of multi-source features and can measure the coordination of feature fusion.

[0041] As a concrete example: In an industrial quality inspection system, when processing 300 sets of similar bearing vibration data consecutively (exceeding the preset threshold of 200 sets), the system detected that the entropy value of the audio mode variation rose to an abnormal level (0.92), and at the same time, the standard deviation of the fusion feature of the infrared image and vibration data expanded to a multiple of the benchmark value. At this point, an automatic parameter update was triggered: the α value decreased to 60% of the initial value according to the inverse ratio of the entropy value, and the β value increased linearly to 135% of the original value. The adjusted parameters reduced the system's reliance on abnormal audio data, enhanced the fusion weight of visual and vibration features, and ultimately controlled the false detection rate within an acceptable range.

[0042] This technical solution significantly improves system robustness through a dynamic parameter adjustment mechanism. The dual-coefficient design achieves intelligent balancing of primary and secondary modes, the threshold triggering mechanism ensures timely parameter updates, entropy inverse decay effectively suppresses abnormal mode interference, and linear growth of standard deviation optimizes feature fusion stability. This entire mechanism enables the system to autonomously maintain optimal operating conditions when facing continuous processing of similar data, making it particularly suitable for long-term monitoring tasks in industrial scenarios.

[0043] In one possible implementation, the generation process of the composite feature code includes a timing mark injection step, which converts the acquisition time difference of the probe array into the phase offset of the feature vector.

[0044] Among them, "composite feature code" can refer to the encoding carrier of multi-source feature fusion, used to encapsulate the joint representation of cross-modal data; "temporal marker injection" can embed physical time information into the feature space, and can establish the spatiotemporal correlation between data acquisition and feature generation; "probe array" can refer to the distributed data acquisition unit, used to synchronously capture multi-dimensional sensing signals; "acquisition time difference" can reflect the physical temporal relationship of signal perception, used to quantify the cooperative error between detection units; "phase offset of feature vector" can refer to the mapping form of time information in the feature space, used to convert physical time sequence into computable parameters.

[0045] As a concrete example: In an autonomous driving multi-sensor system, LiDAR and cameras form a probe array. When collecting data on an obstacle: the radar returns a point cloud timestamp T1 = 153.2 ms; the camera captures an image timestamp T2 = 153.9 ms; the acquisition time difference ΔT = 0.7 ms is calculated; the timing mark injection module converts ΔT into a 60° phase offset of the feature vector; a composite feature code [0.82, -1.37, 0.05 | 60° phase] is generated; finally, the system eliminates the fusion error caused by sensor asynchrony through phase correction.

[0046] This innovative approach achieves a precise mapping from physical temporal sequence to feature space. Composite feature codes construct a spatiotemporally unified coding paradigm, temporal marker injection overcomes the static limitations of traditional feature fusion, probe array time difference quantization significantly improves the accuracy of multi-source data collaboration, and phase offset conversion makes the time dimension a computable feature. This technology is particularly suitable for mobile sensing scenarios requiring strict temporal alignment, reducing hardware synchronization requirements while enhancing the reliability of feature representation in dynamic environments.

[0047] In one possible implementation, the compensation value for the phase offset is dynamically adjusted by the evolutionary engine based on the ambient clock jitter, with the compensation magnitude inversely proportional to the clock stability score recorded by the neural architecture searcher.

[0048] Among them, the phase offset compensation value can refer to the dynamic adjustment amount for correcting timing errors, used to offset characteristic phase deviations caused by environmental factors; the evolutionary engine can execute parameter adaptive optimization algorithms and can dynamically adjust the control strategy according to system feedback; environmental clock jitter can refer to the fluctuation phenomenon of external timing references, used to quantify the instability of time synchronization; the compensation amplitude can characterize the intensity range of parameter adjustment, used to control the correction strength of phase calibration; the neural architecture searcher can refer to the automatic neural network topology optimization module, used to record and evaluate the operating status of system components; and the clock stability score can measure the reliability of the time synchronization mechanism, used to reflect the system's anti-interference capability.

[0049] As a specific example: In the deployment of edge computing nodes in a 5G base station: the environmental clock jitter detection module measures the current jitter value as 15.7 ps; the neural architecture searcher outputs a clock stability score of S=82 (out of 100); the evolutionary engine calculates the compensation magnitude: K∝1 / S, resulting in K=0.012; the compensation magnitude is converted into a phase compensation value: θ=K×jitter value=0.188°; the phase offset compensation value of the feature vector is adjusted in real time; after compensation, the feature fusion error rate decreases from the original value to within the allowable threshold.

[0050] This solution innovatively constructs a closed-loop control system for timing errors. The evolutionary engine enables autonomous optimization of compensation parameters, environmental clock jitter quantification makes external interference measurable, the stability score provided by the neural architecture searcher serves as a key control basis, and the dynamic compensation mechanism significantly improves the robustness of feature generation. This technology is particularly suitable for communication scenarios with high-precision timing requirements, reducing external clock dependence while ensuring accurate alignment of feature phases in complex environments.

[0051] In one possible implementation, the calculation process of the mutation entropy value includes an abnormal event filtering mechanism. When the processing time of a single operation exceeds 1 / 24 of the period T, the pi value of the corresponding time period is not included in the entropy value statistics.

[0052] Among them, the variation entropy value can refer to the quantitative indicator of the dynamic change of data distribution, which is used to monitor abnormal fluctuations in the system processing status; the abnormal event filtering mechanism can identify and exclude atypical data samples, which can ensure the representativeness of the entropy value calculation; the period T can refer to the benchmark time unit set by the system, which is used to divide the time window for data processing; the pi value can reflect the probability distribution characteristics of data in a specific period, which is used to construct the basic parameters for entropy value calculation; entropy value statistics can refer to the quantitative analysis process of system uncertainty, which is used to evaluate the overall operational stability.

[0053] As a specific example, in a financial transaction risk control system with a period of T=24 hours: if the system detects that a transaction verification takes 1.2 hours (exceeding the threshold of T / 24=1 hour), it triggers an abnormal event filtering mechanism, marks the period as an abnormal state, excludes the pi value (0.0087) of that period from participating in the daily entropy value calculation, and calculates the daily variation entropy value H=1.83 based on the effective pi value. Compared with the unfiltered version (H=2.15), the corrected entropy value more accurately reflects the characteristics of normal transaction fluctuations.

[0054] This mechanism improves the accuracy of entropy calculation through intelligent filtering. The variability entropy value, as a core indicator, provides robustness against interference. The abnormal event filtering mechanism effectively isolates atypical data interference, the period T setting provides a standardized evaluation framework, and the pi value screening ensures the purity of the probability distribution. This technology is particularly suitable for scenarios with sudden loads, enhancing the system's robustness in identifying anomalies while maintaining the uniformity of evaluation standards.

[0055] In one possible implementation, new rules output by the knowledge incubator must be reverse-verified by the feature melting center. Rules with a verification success rate of less than 80% trigger an emergency refactoring process for the neural architecture searcher.

[0056] Among them, the knowledge incubator can refer to an automated rule generation system, which is used to continuously output new business logic rules; the feature melting center can perform multi-dimensional rule verification, which can ensure the compatibility of new rules with the existing knowledge system; reverse verification can refer to a detection method that infers the effectiveness of rules based on historical data, which is used to evaluate the generalization ability of rules; the verification success rate can reflect the quality level of rules, which is used to decide whether to adopt the rule; the neural architecture searcher can refer to an adaptive model optimization module, which is used to reconstruct the knowledge representation structure when rules fail; and the emergency refactoring process can quickly respond to system anomalies, which is used to maintain the continuous availability of the knowledge system.

[0057] As a specific example, in the rule update scenario of the intelligent customer service system: the knowledge incubator generates a new dialogue rule "transfer discount consultation to human service", the feature melting center uses the dialogue records of the past 3 months for reverse verification, the verification result shows that the success rate is only 72% (below the 80% threshold), triggering the neural architecture searcher emergency reconstruction process. After reconstruction, a new rule "differentiated processing of discount consultation in different time periods" is generated, the success rate of secondary verification is increased to 85%, and the rule is officially entered into the database.

[0058] This mechanism establishes a quality closed loop for rule production. The knowledge incubator automates rule generation, the feature melting center provides rigorous quality checks, reverse verification methods ensure the actual effectiveness of the rules, and the neural architecture searcher's emergency refactoring capabilities guarantee continuous system optimization. This technology is particularly suitable for business scenarios requiring frequent rule updates, effectively controlling the risks of introducing new rules while improving rule generation efficiency.

[0059] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0060] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0061] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. An unstructured data processing system based on artificial intelligence, characterized in that, include: Multimodal raw data is acquired through a distributed probe array, which includes a text scanning unit, an image sensing unit, and an audio / video synchronization unit. The probe array is connected to a feature melting center, which has an adversarial decoupling module and a cross-modal attention grafting module; The evolutionary engine of the smelting center is dynamically connected, and the engine includes a periodically self-checking neural architecture searcher and a knowledge incubator based on analogical reasoning; The system outputs a 128-dimensional composite feature code with a timestamp.

2. The artificial intelligence-based unstructured data processing system according to claim 1, characterized in that, When the text scanning unit performs semantic tomography, it simultaneously generates a semantic depth map and a contextual offset. The resolution matrix output by the image perception unit contains spatial frequency distribution features.

3. The artificial intelligence-based unstructured data processing system according to claim 2, characterized in that, When the adversarial decoupling module is working, it generates a feature purity index, which is jointly determined by the interlayer gradient of the semantic depth map and the harmonic components of the spatial frequency distribution features.

4. The artificial intelligence-based unstructured data processing system according to claim 3, characterized in that, The fusion features output by the cross-modal attention grafting module satisfy the following: For any combination of modes, the grafting weight is: Where α and β are modal adjustment coefficients, which are obtained by transforming the characteristic purity index using the hyperbolic tangent function; The semantic relevance of the current modality is extracted from the convolutional kernel response value of the semantic depth map; This is the maximum relevance benchmark value recorded by the system; The cross-modal distance is represented by the Euclidean space mapping module of the feature melting center; This is the preset maximum distance threshold.

5. The artificial intelligence-based unstructured data processing system according to claim 4, characterized in that, The neural architecture searcher generates architecture optimization instructions every 72 hours, and simultaneously calculates the mutation entropy value of historical decision paths: in denoted as the probability of the i-th processing paradigm occurring in the most recent period T, where n is the total number of identified paradigm types, and T is 240 consecutive processing periods. The entropy value is used to adjust the intensity of analogical reasoning in the knowledge incubator.

6. The artificial intelligence-based unstructured data processing system according to claim 5, characterized in that, The update of the modality adjustment coefficients α and β follows the following principle: when the system continuously processes the same type of data for more than a preset threshold, the α value decays in proportion to the inverse of the variation entropy value, and the β value increases linearly with the standard deviation of the fusion feature.

7. The artificial intelligence-based unstructured data processing system according to claim 6, characterized in that, The process of generating the composite feature code includes a timing marker injection step, which converts the acquisition time difference of the probe array into the phase offset of the feature vector.

8. The artificial intelligence-based unstructured data processing system according to claim 7, characterized in that, The compensation value for the phase offset is dynamically adjusted by the evolutionary engine based on the ambient clock jitter, and the compensation magnitude is inversely proportional to the clock stability score recorded by the neural architecture searcher.

9. The artificial intelligence-based unstructured data processing system according to claim 5, characterized in that, The calculation process of the mutation entropy value includes an abnormal event filtering mechanism. When the processing time of a single operation exceeds 1 / 24 of the period T, the pi value of the corresponding time period is not included in the entropy value statistics.

10. The artificial intelligence-based unstructured data processing system according to claim 1, characterized in that, The new rules output by the knowledge incubator must be reverse-verified by the feature melting center. Rules with a verification success rate of less than 80% will trigger the emergency reconstruction process of the neural architecture searcher.

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