Unstructured data processing method and system

Abstract

The present invention provides an unstructured data processing method and system, which relates to the field of natural language technology, including obtaining standardized feature vectors by tensor decomposition of unstructured data, constructing a self-organizing critical state feature learning network in an isolated computing space, constructing a feature interaction network based on quantum entanglement perception, establishing a hierarchical causal graph for feature screening, using multi-scale feature fusion to realize semantic feature vector generation, and establishing an intelligent protection mechanism. The present invention can effectively improve the accuracy of feature extraction, enhance the reliability of data processing, and improve the efficiency of system operation.

Description

Technical Field

[0001] The present invention relates to natural language technology, and in particular to an unstructured data processing method and system. Background Art

[0002] At present, unstructured data processing mainly adopts methods such as feature extraction and deep learning, which captures the feature information in the data by building a neural network model, and performs analysis tasks such as classification and clustering based on these features. The main problems of existing technologies are:

[0003] Existing unstructured data processing methods have limitations when dealing with long-distance dependencies, making it difficult to effectively capture the remote associations between data features, resulting in incomplete feature extraction and affecting the accuracy of subsequent analysis. In particular, when dealing with data containing complex temporal relationships, traditional methods often cannot accurately model the temporal dependencies between features.

[0004] The current feature fusion method lacks dynamic adaptive capabilities and cannot automatically adjust the coupling relationship between features according to changes in data features. The feature fusion process is relatively rigid and difficult to adapt to complex and changing data environments. At the same time, there is a lack of effective feedback mechanism in the feature optimization process, making it difficult to achieve continuous optimization and improvement of features.

[0005] Existing technologies are deficient in data security protection, especially when processing sensitive data, lacking a complete isolation computing mechanism and dynamic authorization strategy, which is prone to data leakage and security vulnerabilities. At the same time, the existing fault tolerance mechanism is relatively simple, and it is difficult to ensure the reliability and continuity of data processing when facing complex failure scenarios. Summary of the invention

[0006] The embodiments of the present invention provide an unstructured data processing method and system, which can solve the problems in the prior art.

[0007] According to a first aspect of the embodiments of the present invention,

[0008] Provides unstructured data processing methods, including:

[0009] Perform tensor decomposition on unstructured data to obtain standardized feature vectors, build a time-series correlation index network based on the standardized feature vectors, build an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery;

[0010] A self-organizing critical state feature learning network is constructed in the isolated computing space, the standardized feature vector is mapped to a critical phase transition point, the optimal learning state is identified through an energy fluctuation monitor based on the critical phase transition point, and the long-distance dependency relationship between the standardized feature vectors is captured; a quantum entanglement-aware feature interaction network is constructed according to the long-distance dependency relationship, quantum state coding is used to represent the feature vector correlation, and a quantum decoherence monitor and a quantum error correction encoder are set; a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer is constructed for the feature vectors in the feature interaction network, a confidence scoring mechanism is designed based on the hierarchical causal graph to perform noise filtering, a multi-level cache mechanism based on task importance is established, and a screening feature vector set is obtained through a multi-dimensional resource monitor;

[0011] A multi-scale feature fusion network is constructed based on the screened feature vector set, the coupling strength between features is dynamically adjusted using the principle of self-organizing critical state, a feature feedback enhancement mechanism is established through recursive neural computing units, a feature collaborative optimization system is constructed in combination with the theory of group intelligence emergence, and a semantic feature vector set is generated using dynamic weight self-calibration and multi-layer feature cross-recombination. An intelligent protection mechanism based on scenario evolution is established for the semantic feature vector set, and spatiotemporal fusion features are extracted through spatiotemporal sequence analysis, anomaly thresholds are calculated, and multi-dimensional correlation maps are constructed to design a multi-level dynamic authorization strategy.

[0012] In an optional embodiment,

[0013] The steps of constructing a self-organizing critical state feature learning network in the isolated computing space, mapping the standardized feature vector to a critical phase transition point, identifying an optimal learning state through an energy fluctuation monitor based on the critical phase transition point, and capturing the long-distance dependency relationship between the standardized feature vectors include:

[0014] A feature learning network based on a two-dimensional Ising model is constructed in an isolated computing space, the standardized feature vector is mapped to a spin state, the coupling coefficient between adjacent feature vectors is calculated, and the spin state of the feature learning network is updated; an order parameter fluctuation positioning method is designed for the feature learning network, and a critical phase transition point is obtained by order parameter calculation;

[0015] Construct multi-level energy fluctuation monitoring, extract local energy fluctuation intensity, construct local fluctuation distribution measurement based on Wasserstein distance, establish energy fluctuation correlation matrix, and use spectral clustering method to obtain energy fluctuation pattern; design adaptive threshold update strategy based on reinforcement learning according to the energy fluctuation pattern, calculate reward signal based on spatiotemporal correlation of energy fluctuation pattern, map energy fluctuation difference into time series reward function, establish state-value mapping network, input energy fluctuation parameter, energy fluctuation adaptive threshold parameter and energy fluctuation correlation matrix to generate state evaluation score; design combined evaluation index based on mutual information and conditional entropy, normalize the state evaluation score, use recursive dynamic programming algorithm to search optimal state transfer path in evaluation index space, and determine state convergence moment based on path cumulative gain; use state convergence moment as time anchor point of optimal learning state, output final optimal learning state through multi-scale calculation and state stability verification based on state confidence interval;

[0016] The long-distance dependency between the standardized feature vectors under the optimal learning state is mapped to the quantum walk framework, and the quantum evolution space is constructed. The long-distance transition is performed by adjusting the phase parameter in the evolution operator. The quantum coherence measurement is introduced and the quantum master equation is solved to track the coherence evolution. Based on the quantum state migration probability and the quantum parameter estimation information measurement, a characteristic dependency matrix with quantum coherence is constructed.

[0017] In an optional embodiment,

[0018] The steps of constructing a characteristic interaction network of quantum entanglement perception according to the long-distance dependency, using quantum state coding to represent the characteristic vector correlation, and setting a quantum decoherence monitor and a quantum error correction encoder include:

[0019] Obtain a feature dependency matrix corresponding to the long-distance dependency, construct a feature interaction network based on the feature dependency matrix, map the eigenvector correlation in the feature interaction network to a two-dimensional quantum state space, construct a quantum state density matrix set, and calculate the entanglement degree between the eigenvectors based on the quantum entanglement entropy; construct a total Hamiltonian of quantum bit-noise coupling based on the quantum state density matrix set, the total Hamiltonian includes a feature interaction system Hamiltonian, an environment Hamiltonian, and a coupling term between the feature interaction system and the environment;

[0020] The influence functional method is used to calculate the time evolution characteristics of the quantum state density matrix under the action of the total Hamiltonian, and the influence functional of the interaction between the quantum bit and the noise source is obtained; the influence functional is subjected to wavelet time-frequency analysis to extract the decoherence characteristics of multiple time scales; a state discriminator is constructed based on the decoherence characteristics, and the feature weights of the state discriminator are optimized by maximizing the inter-class distance and minimizing the intra-class distance to generate a decoherence threshold discrimination result;

[0021] A surface code lattice structure is constructed according to the decoherence threshold judgment result, and data quantum bits and measurement quantum bits are set in the surface code lattice structure; a stabilizer operator acting on the vertices and facets of the surface code lattice structure is constructed to detect error syndrome, and a minimum weight complete matching algorithm is used to determine the error position and perform a Pauli operator correction operation; and a surface code distance parameter is dynamically adjusted based on the intensity of the decoherence feature.

[0022] In an optional embodiment,

[0023] The steps of constructing a hierarchical causal graph including a macro business layer, a meso feature layer and a micro data layer for the feature vectors in the feature interaction network, designing a confidence scoring mechanism based on the hierarchical causal graph to filter noise, establishing a multi-level cache mechanism based on task importance, and obtaining a screening feature vector set through a multidimensional resource monitor include:

[0024] Decomposing the feature vector in the feature interaction network into a feature processing node set, extracting the temporal feature sequence and the spatial feature sequence from the feature processing node set to construct a business feature vector, performing principal component analysis on the business feature vector to obtain a compressed feature matrix, converting the compressed feature matrix into a compressed feature vector, extracting data features using an autoencoder, and constructing a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer;

[0025] A time series sample set of compressed feature vectors is collected within a fixed time window, and the time stability of the compressed feature vectors is evaluated based on an exponentially weighted sliding variance calculation method, wherein the exponentially weighted sliding variance calculation method uses a time decay weight to perform a weighted calculation on the deviation of the compressed feature vectors from the weighted mean to obtain a time stability score; an improved mutual correlation coefficient matrix is ​​constructed to evaluate the spatial correlation of the compressed feature vectors, wherein the improved mutual correlation coefficient matrix calculates the multi-order mutual correlation coefficients between the compressed feature vectors and the adjacent nodes of the hierarchical causal graph, and uses multi-order correlation weight coefficients to obtain a spatial correlation score; a mutual information calculation framework between the compressed feature vectors and the target performance indicators is constructed, and the business impact is obtained by calculating the standardized mutual information based on information entropy and joint entropy;

[0026] The temporal stability score, the spatial correlation score and the business impact are weightedly integrated to obtain a confidence score of the compressed feature vector, and noise filtering is performed on the compressed feature vector based on the confidence score;

[0027] A multi-level cache mechanism is constructed for the compressed feature vectors that have passed noise filtering, the access entropy is obtained by calculating the probability distribution of the access time interval of the compressed feature vectors, and the cache priority is calculated according to the access entropy, access frequency and confidence score of the compressed feature vectors; a write-through strategy is adopted to maintain the consistency of the multi-level cache, the prefetch probability is calculated based on the cache priority, and dynamic adjustment is performed by minimizing the weighted sum of the cache miss rate and the prefetch overhead; a multi-dimensional resource monitor is constructed, and a screening feature vector set is obtained based on the usage of computing resources, storage resources and network resources combined with the confidence score.

[0028] In an optional embodiment,

[0029] The steps of constructing a multi-scale feature fusion network based on the screened feature vector set, dynamically adjusting the coupling strength between features by using the principle of self-organizing critical state, establishing a feature feedback enhancement mechanism by using a recursive neural computing unit, and constructing a feature collaborative optimization system by combining the theory of group intelligence emergence, and generating a semantic feature vector set by using dynamic weight self-calibration and multi-layer feature cross-recombination include:

[0030] The screened feature vector set is divided into three layers: bottom layer, middle layer and top layer according to the feature granularity to construct a multi-scale feature fusion network, local sensitive hashing is used to cluster similar feature vectors in each layer to form feature clusters, a weighted undirected graph is constructed based on the mutual information between feature clusters, and the spectral clustering method is used to divide the weighted undirected graph into multiple subgraphs, and the subgraphs represent high-order semantic information;

[0031] Calculating the activity of the feature nodes in the weighted undirected graph, taking the average activity as an order parameter, taking the coupling strength as a control parameter, and optimizing the coupling strength objective function based on a gradient descent method, wherein the coupling strength objective function includes a deviation term between the average activity and a critical point and a regularization term;

[0032] Constructing a recursive neural computing unit, wherein the recursive neural computing unit includes a gating mechanism of a forget gate, an input gate, and an output gate, and the unit state and hidden state output are calculated through the gating mechanism to establish a feature feedback enhancement mechanism;

[0033] Define the swarm intelligence potential energy function to characterize the interaction relationship between features, calculate the probability distribution of feature states based on the hierarchical mean field method, and optimize the synergistic relationship between features by minimizing the free energy including the average energy term, relative entropy term and multi-scale regularization term;

[0034] Based on the synergistic relationship between the features, a multi-layer feature cross-attention mechanism is constructed to reorganize the features to obtain recombined features, wherein the multi-layer feature cross-attention mechanism includes a query matrix, a key-value matrix and multi-head attention calculation; the feature weight coefficients in the multi-layer feature cross-attention mechanism are dynamically adjusted according to the feature prediction error, and a weight update equation including a learning rate and a momentum coefficient is used for self-calibration; the recombined features are converted into a semantic feature vector set, and the semantic feature vector set includes high-order semantic information of the original features.

[0035] In an optional embodiment,

[0036] The steps of defining the swarm intelligence potential energy function to characterize the interaction relationship between features, calculating the probability distribution of feature states based on the hierarchical mean field method, and optimizing the synergistic relationship between features by minimizing the free energy including the average energy term, the relative entropy term, and the multi-scale regularization term include:

[0037] Constructing a swarm intelligence potential energy function, wherein the swarm intelligence potential energy function includes a feature coupling term, a feature self-action term, and a feature dynamic modulation term, wherein the feature dynamic modulation term is used to characterize the dynamic change characteristics of the feature over time, the feature coupling term is dynamically updated through a time-varying coupling coefficient, and the time-varying coupling coefficient is calculated using an attention mechanism, and the feature self-action term introduces a memory mechanism to dynamically regulate feature historical information through a memory state and a forgetting coefficient;

[0038] Based on the swarm intelligence potential energy function, a hierarchical mean field method is used to calculate the probability distribution of feature states, including dividing the feature set into multiple subsets according to the graph clustering results, adopting the conditional independence assumption for the features in each subset, modeling the distribution of each feature as a mixed Gaussian model, introducing an uncertainty quantification mechanism to calculate the uncertainty of the distribution, constructing an adaptive weight according to the uncertainty, and optimizing the mean vector and covariance matrix of the mixed Gaussian model using an iterative update formula that takes uncertainty into account;

[0039] Based on the probability distribution of characteristic states, a multiscale free energy function is constructed, wherein the multiscale free energy function includes an average energy term, a relative entropy term and a regularization term, wherein the average energy term characterizes the matching degree between the characteristic state distribution and the swarm intelligence potential energy function, the relative entropy term measures the difference between the characteristic state distribution and the prior distribution, the regularization term includes a sparsity regularization term, a smoothness regularization term and a diversity regularization term, wherein the sparsity regularization term is used to promote feature selection, the smoothness regularization term is used to ensure the continuity of distribution change, and the diversity regularization term is used to prevent feature collapse, and the multiscale free energy function is optimized by an adaptive momentum method, wherein the learning rate of the optimization process is dynamically adjusted according to the average uncertainty, and the optimal distribution parameters of the characteristic state are obtained through iterative optimization; based on the optimal distribution parameters, the synergistic effect strength between the features is calculated, wherein the synergistic effect strength is jointly determined by the optimal mean vector and covariance matrix of the characteristic distribution.

[0040] In an optional embodiment,

[0041] The steps of establishing an intelligent protection mechanism based on scenario evolution for the semantic feature vector set, extracting spatiotemporal fusion features through spatiotemporal sequence analysis, calculating anomaly thresholds and constructing multi-dimensional association graphs, and designing a multi-level dynamic authorization strategy include:

[0042] Acquire the time series information of the semantic feature vector, extract the forgetting feature, input feature and output feature from the time series information, obtain the hidden layer state through the synergistic effect of the forgetting feature, input feature and output feature, perform query-key value calculation on the hidden layer state of different time windows to obtain the spatiotemporal attention weight, and perform weighted aggregation on the hidden layer state based on the spatiotemporal attention weight to obtain the spatiotemporal fusion feature;

[0043] Based on the spatiotemporal fusion features, a feature probability density distribution is constructed, the mean and standard deviation of the feature distribution are calculated according to the feature probability density distribution, a dynamic threshold update strategy is designed in combination with the skewness information of the feature distribution, and the abnormality threshold is adaptively adjusted through an adjustable scaling factor, and the scaling factor changes dynamically with the skewness of the feature distribution;

[0044] A multidimensional association graph is constructed using the spatiotemporal fusion features and the anomaly threshold, wherein the edge weight of the multidimensional association graph is determined by the distance between feature vectors and the anomaly threshold difference, and the node features in the graph are dynamically updated to obtain the temporal attention weight, and the association relationship between the nodes is updated based on the temporal attention weight;

[0045] A multi-level dynamic authorization strategy is designed based on the multidimensional association graph, and the node importance score is calculated according to the node's degree centrality, betweenness centrality and closeness centrality. The nodes are divided into different levels according to the importance score, and an authorization policy matrix is ​​constructed for different levels. The elements of the authorization policy matrix are jointly determined by the hierarchical matching relationship, association strength and anomaly compatibility. The final authorization decision is generated based on the authorization policy matrix combined with the anomaly threshold.

[0046] According to a second aspect of the embodiments of the present invention,

[0047] Provide unstructured data processing system, including:

[0048] The first unit is used to perform tensor decomposition on unstructured data to obtain standardized feature vectors, construct a time-series correlation index network based on the standardized feature vectors, construct an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery;

[0049] The second unit is used to construct a self-organizing critical state feature learning network in the isolated computing space, map the standardized feature vector to the critical phase transition point, identify the optimal learning state through the energy fluctuation monitor based on the critical phase transition point, and capture the long-distance dependency relationship between the standardized feature vectors; construct a quantum entanglement-aware feature interaction network based on the long-distance dependency relationship, use quantum state coding to represent the feature vector correlation, and set a quantum decoherence monitor and a quantum error correction encoder; construct a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer for the feature vectors in the feature interaction network, design a confidence scoring mechanism based on the hierarchical causal graph to perform noise filtering, establish a multi-level cache mechanism based on task importance, and obtain a screening feature vector set through a multi-dimensional resource monitor;

[0050] The third unit is used to construct a multi-scale feature fusion network based on the screened feature vector set, dynamically adjust the coupling strength between features using the principle of self-organizing critical state, establish a feature feedback enhancement mechanism through a recursive neural computing unit, build a feature collaborative optimization system based on the theory of group intelligence emergence, and generate a semantic feature vector set using dynamic weight self-calibration and multi-layer feature cross-recombination; establish an intelligent protection mechanism based on scene evolution for the semantic feature vector set, extract spatiotemporal fusion features through spatiotemporal sequence analysis, calculate anomaly thresholds and construct a multi-dimensional correlation map, and design a multi-level dynamic authorization strategy.

[0051] According to a third aspect of the embodiments of the present invention,

[0052] An electronic device is provided, comprising:

[0053] processor;

[0054] a memory for storing processor-executable instructions;

[0055] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0056] A fourth aspect of the embodiments of the present invention is:

[0057] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0058] The present invention obtains standardized feature vectors by performing tensor decomposition on unstructured data, constructs a time-series correlation index network, establishes an isolated computing space and a three-level fault-tolerant protection mechanism in a trusted execution environment, and improves the security and reliability of data processing. At the same time, through the self-organizing critical state feature learning network and energy fluctuation monitor, it can effectively capture the long-distance dependency between feature vectors, improving the effect and accuracy of feature learning.

[0059] The present invention introduces a quantum entanglement-aware feature interaction network, uses quantum state coding to represent the correlation of feature vectors, and sets a quantum decoherence monitor and a quantum error correction encoder, which greatly improves the accuracy and efficiency of feature interaction. By constructing a hierarchical causal graph and designing a confidence scoring mechanism, effective noise filtering and feature screening are achieved. At the same time, the multi-level cache mechanism based on task importance and the multi-dimensional resource monitor further optimize the utilization of computing resources.

[0060] The present invention constructs a multi-scale feature fusion network, uses the principle of self-organizing critical state to dynamically adjust the coupling strength between features, establishes a feature feedback enhancement mechanism through a recursive neural computing unit, and constructs a feature collaborative optimization system in combination with the theory of group intelligence emergence, which significantly improves the effect and adaptability of feature fusion. Finally, by establishing an intelligent protection mechanism based on scene evolution, all-round protection of the semantic feature vector set is achieved, and the security and robustness of the system are improved. In general, the present invention has achieved significant performance improvement and innovation in unstructured data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a flow chart of an unstructured data processing method according to an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of the structure of an unstructured data processing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0065] Figure 1 FIG. 1 is a flow chart of a method for processing unstructured data according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0066] S1. Perform tensor decomposition on unstructured data to obtain standardized feature vectors, construct a time-series correlation index network based on the standardized feature vectors, build an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery;

[0067] S2. Construct a self-organizing critical state feature learning network in the isolated computing space, map the standardized feature vector to the critical phase transition point, identify the optimal learning state through the energy fluctuation monitor based on the critical phase transition point, and capture the long-distance dependency between the standardized feature vectors; construct a quantum entanglement-aware feature interaction network based on the long-distance dependency, use quantum state coding to represent the feature vector correlation, set up a quantum decoherence monitor and a quantum error correction encoder; construct a hierarchical causal graph including a macro business layer, a meso feature layer and a micro data layer for the feature vectors in the feature interaction network, design a confidence scoring mechanism based on the hierarchical causal graph to filter noise, establish a multi-level cache mechanism based on task importance, and obtain a set of filtered feature vectors through a multidimensional resource monitor;

[0068] S3. Construct a multi-scale feature fusion network based on the screened feature vector set, adopt the principle of self-organizing critical state to dynamically adjust the coupling strength between features, establish a feature feedback enhancement mechanism through recursive neural computing units, build a feature collaborative optimization system based on the theory of group intelligence emergence, and use dynamic weight self-calibration and multi-layer feature cross-recombination to generate a semantic feature vector set; establish an intelligent protection mechanism based on scene evolution for the semantic feature vector set, extract spatiotemporal fusion features through spatiotemporal sequence analysis, calculate the anomaly threshold and construct a multi-dimensional correlation map, and design a multi-level dynamic authorization strategy.

[0069] In an optional embodiment,

[0070] The steps of constructing a self-organizing critical state feature learning network in the isolated computing space, mapping the standardized feature vector to a critical phase transition point, identifying an optimal learning state through an energy fluctuation monitor based on the critical phase transition point, and capturing the long-distance dependency relationship between the standardized feature vectors include:

[0071] A feature learning network based on a two-dimensional Ising model is constructed in an isolated computing space, the standardized feature vector is mapped to a spin state, the coupling coefficient between adjacent feature vectors is calculated, and the spin state of the feature learning network is updated; an order parameter fluctuation positioning method is designed for the feature learning network, and a critical phase transition point is obtained by order parameter calculation;

[0072] Construct multi-level energy fluctuation monitoring, extract local energy fluctuation intensity, construct local fluctuation distribution measurement based on Wasserstein distance, establish energy fluctuation correlation matrix, and use spectral clustering method to obtain energy fluctuation pattern; design adaptive threshold update strategy based on reinforcement learning according to the energy fluctuation pattern, calculate reward signal based on spatiotemporal correlation of energy fluctuation pattern, map energy fluctuation difference into time series reward function, establish state-value mapping network, input energy fluctuation parameter, energy fluctuation adaptive threshold parameter and energy fluctuation correlation matrix to generate state evaluation score; design combined evaluation index based on mutual information and conditional entropy, normalize the state evaluation score, use recursive dynamic programming algorithm to search optimal state transfer path in evaluation index space, and determine state convergence moment based on path cumulative gain; use state convergence moment as time anchor point of optimal learning state, output final optimal learning state through multi-scale calculation and state stability verification based on state confidence interval;

[0073] The long-distance dependency between the standardized feature vectors under the optimal learning state is mapped to the quantum walk framework, and the quantum evolution space is constructed. The long-distance transition is performed by adjusting the phase parameter in the evolution operator. The quantum coherence measurement is introduced and the quantum master equation is solved to track the coherence evolution. Based on the quantum state migration probability and the quantum parameter estimation information measurement, a characteristic dependency matrix with quantum coherence is constructed.

[0074] Exemplarily, first, a feature learning network based on a two-dimensional Ising model is constructed in an isolated computing space. The standardized feature vectors are mapped to spin states, and each feature vector corresponds to a spin. For example, for a data set containing 100 features, a 10x10 two-dimensional Ising model grid can be constructed, and each grid point represents a feature. Then the coupling coefficient between adjacent feature vectors is calculated, and methods such as cosine similarity or Pearson correlation coefficient can be used. According to the calculated coupling coefficient, the spin state of the feature learning network is updated.

[0075] Then, a method for locating order parameter fluctuations is designed for the feature learning network. The critical phase transition point is obtained by calculating the fluctuation of order parameters (such as magnetization). This can be achieved through Monte Carlo simulation or other numerical methods. For example, the fluctuation of order parameters can be calculated at different temperatures. When the fluctuation reaches the maximum value, the corresponding temperature is the critical phase transition point.

[0076] Next, a multi-level energy fluctuation monitoring system is constructed. First, the local energy fluctuation intensity is extracted, which can be achieved by calculating the energy change of the local spin configuration. Then, a local fluctuation distribution metric is constructed based on the Wasserstein distance to compare the energy fluctuation distributions in different regions. An energy fluctuation correlation matrix is ​​established to record the correlation between energy fluctuations in different regions. The spectral clustering method is used to analyze the correlation matrix and obtain the energy fluctuation pattern.

[0077] According to the energy fluctuation pattern obtained, an adaptive threshold update strategy based on reinforcement learning is designed. First, the reward signal is calculated based on the spatiotemporal correlation of the energy fluctuation pattern. For example, the duration and spatial range of the energy fluctuation pattern can be used as the basis for the reward. The energy fluctuation difference is mapped to a time-series reward function, and methods such as exponential decay or linear mapping can be used. A state-value mapping network is established, and the input includes energy fluctuation parameters, energy fluctuation adaptive threshold parameters, and energy fluctuation association matrix to generate a state evaluation score.

[0078] Design a combined evaluation index based on mutual information and conditional entropy to normalize the state evaluation score. Use a recursive dynamic programming algorithm to search for the optimal state transfer path in the evaluation index space. Specifically, methods such as the Viterbi algorithm or A* search can be used. Determine the state convergence moment based on the path cumulative gain and use this moment as the time anchor point of the optimal learning state.

[0079] Through multi-scale calculation and state stability verification based on state confidence interval, the final optimal learning state is output. Multi-scale calculation can be achieved by repeating the above process at different time and space scales. State stability verification can be judged by calculating the change amplitude of the state within a certain time window.

[0080] Finally, the long-distance dependencies between the standardized feature vectors in the optimal learning state are mapped to the quantum walk framework. The quantum evolution space can be constructed using Hadamard gates or other quantum gates. Long-distance transitions are performed by adjusting the phase parameters in the evolution operator. For example, a phase rotation gate can be used to adjust the phase. Quantum coherence metrics such as von Neumann entropy or relative entropy are introduced, and the quantum master equation is solved to track the coherence evolution. A feature dependency matrix with quantum coherence is constructed based on the quantum state migration probability and quantum parameter estimation information metric.

[0081] The present invention realizes the effective capture of long-distance dependencies in complex data by constructing a self-organizing critical state feature learning network in an isolated computing space. By using the critical phenomenon theory in physics, the feature learning problem is mapped to the Ising model, thereby obtaining the best feature representation near the critical point. It can fully utilize the long-range correlation characteristics of the critical state system and improve the effect of feature learning.

[0082] The present invention introduces multi-level energy fluctuation monitoring and an adaptive threshold update strategy based on reinforcement learning to achieve fine control of the learning process. By analyzing the energy fluctuation pattern and designing a reasonable reward mechanism, the optimal learning state can be automatically found, avoiding the subjectivity and uncertainty of artificial parameter setting. This adaptive learning mechanism greatly improves the robustness and generalization ability of the algorithm.

[0083] This paper introduces the quantum walk framework into the modeling of feature dependencies and uses quantum coherence to describe the complex correlations between features, breaking through the limitations of traditional machine learning methods in processing high-dimensional complex data and better capturing nonlinear and non-local feature dependencies. Through quantum evolution and quantum measurement, it provides new perspectives and tools for feature learning, which is expected to bring breakthroughs in image recognition, natural language processing and other fields.

[0084] In an optional embodiment,

[0085] The steps of constructing a characteristic interaction network of quantum entanglement perception according to the long-distance dependency, using quantum state coding to represent the characteristic vector correlation, and setting a quantum decoherence monitor and a quantum error correction encoder include:

[0086] Obtain a feature dependency matrix corresponding to the long-distance dependency, construct a feature interaction network based on the feature dependency matrix, map the eigenvector correlation in the feature interaction network to a two-dimensional quantum state space, construct a quantum state density matrix set, and calculate the entanglement degree between the eigenvectors based on the quantum entanglement entropy; construct a total Hamiltonian of quantum bit-noise coupling based on the quantum state density matrix set, the total Hamiltonian includes a feature interaction system Hamiltonian, an environment Hamiltonian, and a coupling term between the feature interaction system and the environment;

[0087] The influence functional method is used to calculate the time evolution characteristics of the quantum state density matrix under the action of the total Hamiltonian, and the influence functional of the interaction between the quantum bit and the noise source is obtained; the influence functional is subjected to wavelet time-frequency analysis to extract the decoherence characteristics of multiple time scales; a state discriminator is constructed based on the decoherence characteristics, and the feature weights of the state discriminator are optimized by maximizing the inter-class distance and minimizing the intra-class distance to generate a decoherence threshold discrimination result;

[0088] A surface code lattice structure is constructed according to the decoherence threshold judgment result, and data quantum bits and measurement quantum bits are set in the surface code lattice structure; a stabilizer operator acting on the vertices and facets of the surface code lattice structure is constructed to detect error syndrome, and a minimum weight complete matching algorithm is used to determine the error position and perform a Pauli operator correction operation; and a surface code distance parameter is dynamically adjusted based on the intensity of the decoherence feature.

[0089] Exemplarily, first, obtain the feature dependency matrix corresponding to the long-distance dependency. In this step, a feature dependency matrix is ​​constructed by analyzing the interdependencies between the features of the input data. Each element of the matrix represents the degree of dependency between two features. For example, for a data set containing 100 features, a 100x100 feature dependency matrix can be constructed.

[0090] Based on the feature dependency matrix, a feature interaction network is constructed. In this network, each node represents a feature, and the connection strength between nodes is determined by the corresponding value in the feature dependency matrix. For example, if the dependency value between feature A and feature B is 0.8, then in the feature interaction network, the connection strength between these two nodes is set to 0.8.

[0091] Next, the correlation between the feature vectors in the feature interaction network is mapped to the two-dimensional quantum state space. This step uses quantum state encoding technology to convert the traditional feature correlation into quantum state representation. Specifically, the superposition state of quantum bits can be used to represent the correlation between features.

[0092] Then, a set of quantum state density matrices is constructed. For each pair of features, a corresponding quantum state density matrix is ​​constructed. These density matrices form a set for subsequent quantum entanglement calculation and decoherence analysis.

[0093] The degree of entanglement between feature vectors is calculated based on quantum entanglement entropy. Quantum entanglement entropy is an important indicator to measure the degree of entanglement of a quantum system. By calculating the entanglement entropy of the quantum state density matrix corresponding to each pair of features, the degree of entanglement between the features can be obtained. The higher the degree of entanglement, the stronger the correlation between the features.

[0094] The total Hamiltonian of quantum bit-noise coupling is constructed based on the set of quantum state density matrices. The total Hamiltonian consists of three parts: the characteristic interaction system Hamiltonian, the environmental Hamiltonian, and the coupling term between the characteristic interaction system and the environment. The characteristic interaction system Hamiltonian describes the interaction between the features, the environmental Hamiltonian represents the external noise source, and the coupling term reflects the mutual influence between the system and the environment.

[0095] The influence functional method is used to calculate the time evolution characteristics of the quantum state density matrix under the action of the total Hamiltonian. This step simulates the dynamic change process of the quantum system in a noisy environment. By solving the time evolution equation of the quantum state density matrix, the change law of the system state over time can be obtained.

[0096] Obtain the influence functional of the interaction between the quantum bit and the noise source. The influence functional describes the degree of influence of the noise on the quantum system. For example, a time-varying function can be obtained to represent the intensity of the noise's disturbance on the quantum state.

[0097] Perform wavelet time-frequency analysis on the influence functional to extract decoherence features at multiple time scales. Wavelet analysis can capture local features of signals at different time scales. By performing wavelet transform on the influence functional, the time distribution of different frequency components can be obtained, thereby extracting decoherence features at multiple scales.

[0098] Construct a state discriminator based on decoherence features. Using the extracted decoherence features, design a classifier to judge the state of the quantum system. This discriminator can be a model based on a machine learning algorithm, such as a support vector machine or a random forest.

[0099] The feature weights of the state discriminator are optimized by maximizing the inter-class distance and minimizing the intra-class distance. This step aims to improve the performance of the discriminator. By adjusting the weights of different decoherent features, the distinction between different states is maximized, while the difference within the same state is minimized.

[0100] Generate decoherence threshold discrimination results. Based on the optimized state discriminator, set a threshold to determine whether the system has serious decoherence. For example, the output probability of the discriminator greater than 0.8 can be regarded as significant decoherence.

[0101] The surface code lattice structure is constructed according to the decoherence threshold judgment result. The surface code is a commonly used quantum error correction code, and its structure is similar to the lattice on a two-dimensional plane. According to the decoherence judgment result, the structure of the surface code can be dynamically adjusted, such as increasing or decreasing the number of lattice points.

[0102] Data qubits and measurement qubits are set in the surface code lattice structure. Data qubits are used to store actual quantum information, while measurement qubits are used to detect errors. For example, in a 5x5 surface code structure, 13 data qubits and 12 measurement qubits can be set.

[0103] Construct stabilizer operators that act on the vertices and facets of the surface code lattice structure. The stabilizer operator is a special set of Pauli operators used to detect whether errors have occurred in the quantum state. For surface codes, two types of stabilizers can be defined: X-type stabilizers that act on vertices and Z-type stabilizers that act on facets.

[0104] Detecting error syndromes. By measuring the expected value of the stabilizer operator, we can obtain the error syndrome. The error syndrome is a binary string indicating which stabilizer measurements are abnormal.

[0105] A minimum weighted complete matching algorithm is used to determine the error locations and perform Pauli operator correction operations. Based on the detected error syndrome, a minimum weighted complete matching algorithm is used to infer the most likely error locations. Appropriate Pauli operators are then applied to these locations to correct the errors.

[0106] Dynamically adjust the surface code distance parameter based on the strength of the decoherence feature. The distance parameter of the surface code determines its error correction capability. Depending on the strength of the decoherence feature previously extracted, the distance of the surface code can be dynamically adjusted. For example, when strong decoherence is detected, increase the distance of the surface code to improve the error correction capability.

[0107] The present invention makes full use of the superposition and entanglement of quantum systems by mapping the correlation degree of feature vectors to the quantum state space, and can more effectively capture and represent the complex relationship between features. Compared with the traditional feature interaction network, this quantum representation method can handle more complex nonlinear relationships and improve the modeling ability of the model for long-distance dependencies. The present invention introduces a quantum decoherence monitor, which realizes accurate monitoring of the decoherence process of the quantum system through wavelet time-frequency analysis and multi-scale feature extraction. This dynamic monitoring mechanism enables the system to detect and respond to the influence of environmental noise in a timely manner, greatly improving the stability and reliability of the quantum computing process. The present invention adopts quantum error correction technology based on surface codes, and dynamically adjusts the error correction parameters according to the decoherence characteristics. This adaptive quantum error correction strategy can effectively resist various types of quantum noise while maintaining low resource overhead, significantly improving the fault tolerance and operating efficiency of quantum computing.

[0108] In an optional embodiment,

[0109] The steps of constructing a hierarchical causal graph including a macro business layer, a meso feature layer and a micro data layer for the feature vectors in the feature interaction network, designing a confidence scoring mechanism based on the hierarchical causal graph to filter noise, establishing a multi-level cache mechanism based on task importance, and obtaining a screening feature vector set through a multidimensional resource monitor include:

[0110] Decomposing the feature vector in the feature interaction network into a feature processing node set, extracting the temporal feature sequence and the spatial feature sequence from the feature processing node set to construct a business feature vector, performing principal component analysis on the business feature vector to obtain a compressed feature matrix, converting the compressed feature matrix into a compressed feature vector, extracting data features using an autoencoder, and constructing a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer;

[0111] A time series sample set of compressed feature vectors is collected within a fixed time window, and the time stability of the compressed feature vectors is evaluated based on an exponentially weighted sliding variance calculation method, wherein the exponentially weighted sliding variance calculation method uses a time decay weight to perform a weighted calculation on the deviation of the compressed feature vectors from the weighted mean to obtain a time stability score; an improved mutual correlation coefficient matrix is ​​constructed to evaluate the spatial correlation of the compressed feature vectors, wherein the improved mutual correlation coefficient matrix calculates the multi-order mutual correlation coefficients between the compressed feature vectors and the adjacent nodes of the hierarchical causal graph, and uses multi-order correlation weight coefficients to obtain a spatial correlation score; a mutual information calculation framework between the compressed feature vectors and the target performance indicators is constructed, and the business impact is obtained by calculating the standardized mutual information based on information entropy and joint entropy;

[0112] The temporal stability score, the spatial correlation score and the business impact are weightedly integrated to obtain a confidence score of the compressed feature vector, and noise filtering is performed on the compressed feature vector based on the confidence score;

[0113] A multi-level cache mechanism is constructed for the compressed feature vectors that have passed noise filtering, the access entropy is obtained by calculating the probability distribution of the access time interval of the compressed feature vectors, and the cache priority is calculated according to the access entropy, access frequency and confidence score of the compressed feature vectors; a write-through strategy is adopted to maintain the consistency of the multi-level cache, the prefetch probability is calculated based on the cache priority, and dynamic adjustment is performed by minimizing the weighted sum of the cache miss rate and the prefetch overhead; a multi-dimensional resource monitor is constructed, and a screening feature vector set is obtained based on the usage of computing resources, storage resources and network resources combined with the confidence score.

[0114] Exemplarily, first, the feature vectors in the feature interaction network are decomposed into a set of feature processing nodes. This step aims to decompose the complex feature interaction network into basic units that are easier to process. For example, for an e-commerce feature interaction network containing user behavior, product attributes, and transaction records, it can be decomposed into feature processing nodes such as user nodes, product nodes, and transaction nodes.

[0115] Next, we extract the temporal feature sequence and spatial feature sequence from the feature processing node set to construct a business feature vector. The temporal feature sequence can capture the pattern of feature changes over time, such as changes in the user's purchase frequency; the spatial feature sequence reflects the distribution of features in different dimensions, such as the sales of goods in different regions. By combining these two types of features, we can obtain a more comprehensive business feature vector.

[0116] Then, perform principal component analysis on the business feature vector to obtain a compressed feature matrix. The purpose of this step is to reduce the dimension of the feature and extract the most representative information. For example, for a business feature vector containing hundreds of features, it may only be necessary to retain the first few principal components that can explain 80% of the variance, thereby greatly reducing the amount of computation required for subsequent processing.

[0117] The compressed feature matrix is ​​converted into a compressed feature vector, and the data features are extracted using an autoencoder. The autoencoder can learn the internal representation of the data and further refine the feature information. For example, for user behavior data, the autoencoder can learn the user's potential interest patterns, not just the surface behavior characteristics.

[0118] Based on the feature information obtained from the above processing, a hierarchical causal graph is constructed, which includes a macro business layer, a meso feature layer, and a micro data layer. The macro business layer may include high-level indicators such as overall sales and user satisfaction; the meso feature layer may include user group characteristics, product category characteristics, etc.; the micro data layer includes specific user behavior, product attributes, and other raw data. This hierarchical structure helps to understand the causal relationship between features at different levels.

[0119] A time series sample set of compressed feature vectors is collected within a fixed time window, and the temporal stability of the compressed feature vectors is evaluated based on the exponentially weighted sliding variance calculation method. Specifically, a 30-day time window can be set, and the value of the feature vector can be calculated every day, with a higher weight given to recent data. For example, the weight of the most recent day may be 0.1, while the weight of 30 days ago may be only 0.001. By calculating the deviation of these weighted feature values ​​from their weighted mean, a temporal stability score can be obtained.

[0120] An improved mutual correlation coefficient matrix is ​​constructed to evaluate the spatial correlation of compressed feature vectors. This step takes into account the direct and indirect relationships between features. For example, in an e-commerce scenario, it is necessary to consider not only the direct relationship between user age and purchasing power, but also the indirect impact of user age on purchasing power through the intermediate variable of occupation. By calculating multi-order mutual correlation coefficients and assigning different weights, a more comprehensive spatial correlation score can be obtained.

[0121] A framework for calculating the mutual information between the compressed feature vector and the target performance indicator is constructed, and the standardized mutual information is calculated based on information entropy and joint entropy to obtain the business impact. For example, the mutual information between the user's browsing time feature and the final purchase conversion rate can be calculated to evaluate the impact of this feature on the business goal.

[0122] The time stability score, spatial correlation score, and business impact are weighted and fused to obtain the confidence score of the compressed feature vector. The weights of these three indicators can be adjusted according to the specific business scenario. For example, for a rapidly changing business, time stability may be more important; while for a business that emphasizes personalized recommendations, spatial correlation may be more important. Based on this comprehensive confidence score, the compressed feature vector can be effectively filtered for noise, eliminating those features that are unstable, have low correlation, or have little impact on the business.

[0123] A multi-level cache mechanism is constructed for the compressed feature vectors that have been filtered through noise. First, the probability distribution of the access time interval of the compressed feature vector is calculated to obtain the access entropy. For example, if a feature is frequently accessed at a fixed time point every day, then its access entropy is low; conversely, if the access pattern is very random, the access entropy is high. Combining the access entropy, access frequency, and the confidence score obtained above, the cache priority of each feature vector is calculated.

[0124] A write-through strategy is used to maintain multi-level cache consistency to ensure the real-time and accuracy of data. The prefetch probability is calculated based on the cache priority and dynamically adjusted by minimizing the weighted sum of the cache miss rate and the prefetch overhead. For example, for features with high priority, a higher prefetch probability may be set to load them into a faster cache layer in advance; while for features with lower priority, they may be loaded only when actually needed to save resources.

[0125] Finally, a multi-dimensional resource monitor is constructed to obtain a set of filtered feature vectors based on the usage of computing resources, storage resources, and network resources combined with confidence scores. For example, when computing resources are detected to be tight, features with lower computational complexity may be retained first; when storage resources are sufficient, more features may be allowed to be retained to improve model accuracy. Through this dynamic adjustment, a balance can be achieved between resource constraints and model performance.

[0126] The present invention can fully capture the complex relationship and causal structure between features by constructing a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer. This multi-level analysis method not only considers the direct correlation of features, but also discovers potential indirect effects, thereby improving the accuracy and interpretability of feature selection. This is of great significance for understanding business logic and optimizing decision-making processes.

[0127] The present invention designs a comprehensive confidence scoring mechanism that takes into account the temporal stability, spatial correlation, and business impact of features. This multi-dimensional evaluation method can effectively identify and filter out noise features, improving the stability and generalization ability of the model. Especially when processing large-scale data with high dimensions and high noise, this screening mechanism can significantly improve the efficiency and accuracy of the model.

[0128] The present invention introduces a multi-level cache mechanism based on task importance and a multi-dimensional resource monitor to achieve dynamic management of feature vectors and resource optimization. This design not only improves the response speed of the system, but also allows the feature set to be flexibly adjusted according to real-time resource conditions and business needs, thereby achieving efficient use of computing resources while ensuring model performance. This has important practical value for large-scale online systems that require real-time response.

[0129] In an optional embodiment,

[0130] The steps of constructing a multi-scale feature fusion network based on the screened feature vector set, dynamically adjusting the coupling strength between features by using the principle of self-organizing critical state, establishing a feature feedback enhancement mechanism by using a recursive neural computing unit, and constructing a feature collaborative optimization system by combining the theory of group intelligence emergence, and generating a semantic feature vector set by using dynamic weight self-calibration and multi-layer feature cross-recombination include:

[0131] The screened feature vector set is divided into three layers: bottom layer, middle layer and top layer according to the feature granularity to construct a multi-scale feature fusion network, local sensitive hashing is used to cluster similar feature vectors in each layer to form feature clusters, a weighted undirected graph is constructed based on the mutual information between feature clusters, and the spectral clustering method is used to divide the weighted undirected graph into multiple subgraphs, and the subgraphs represent high-order semantic information;

[0132] Calculating the activity of the feature nodes in the weighted undirected graph, taking the average activity as an order parameter, taking the coupling strength as a control parameter, and optimizing the coupling strength objective function based on a gradient descent method, wherein the coupling strength objective function includes a deviation term between the average activity and a critical point and a regularization term;

[0133] Constructing a recursive neural computing unit, wherein the recursive neural computing unit includes a gating mechanism of a forget gate, an input gate, and an output gate, and the unit state and hidden state output are calculated through the gating mechanism to establish a feature feedback enhancement mechanism;

[0134] Define the swarm intelligence potential energy function to characterize the interaction relationship between features, calculate the probability distribution of feature states based on the hierarchical mean field method, and optimize the synergistic relationship between features by minimizing the free energy including the average energy term, relative entropy term and multi-scale regularization term;

[0135] Based on the synergistic relationship between the features, a multi-layer feature cross-attention mechanism is constructed to reorganize the features to obtain recombined features, wherein the multi-layer feature cross-attention mechanism includes a query matrix, a key-value matrix and multi-head attention calculation; the feature weight coefficients in the multi-layer feature cross-attention mechanism are dynamically adjusted according to the feature prediction error, and a weight update equation including a learning rate and a momentum coefficient is used for self-calibration; the recombined features are converted into a semantic feature vector set, and the semantic feature vector set includes high-order semantic information of the original features.

[0136] Exemplarily, the construction of a multi-scale feature fusion network first performs hierarchical processing on the feature vector set. The bottom-level features contain basic information at the pixel level, such as edges and textures; the middle-level features contain structural information of the local area; and the top-level features contain global semantic information. The feature vectors of each layer are clustered using the local sensitive hashing algorithm. In the specific implementation, a random projection matrix is ​​used to map high-dimensional features to a low-dimensional space. The feature similarity is measured by the Hamming distance, and the feature vectors with a similarity higher than a preset threshold are classified into a feature cluster. A weighted undirected graph is constructed with feature clusters as nodes, and the edge weights between nodes are determined by the mutual information. The spectral clustering method is used to divide the weighted undirected graph into multiple subgraphs, each of which represents a high-order semantic concept.

[0137] In the dynamic adjustment of feature coupling strength, the activity of feature nodes is first calculated. The node activity is determined by the connection strength and interaction frequency between the node and other nodes. The average activity of all nodes is used as the order parameter of the system, and the system is maintained near the critical state by adjusting the coupling strength. The optimization of coupling strength adopts the gradient descent method with adaptive learning rate, and the coupling strength parameters are updated according to the gradient of the objective function in each iteration.

[0138] The feature feedback enhancement mechanism is implemented using a recursive neural computing unit. The forget gate controls the degree of retention of historical information, the input gate controls the degree of reception of current input information, and the output gate controls the degree of output of information. In specific implementation, the weight parameters of each gate are optimized by the back propagation algorithm. The unit state integrates historical information and current input, and the hidden state is used as the output of the unit for subsequent feature enhancement.

[0139] In the crowd intelligence emergence optimization system, the potential energy function uses the cosine similarity between feature vectors to measure the interaction strength between features. The probability distribution of feature states is calculated iteratively, and the feature states are updated each iteration until the system reaches a stable state. The influence of features of different scales is considered simultaneously during the optimization process, and the contribution of features of each scale is balanced through multi-scale regularization terms.

[0140] In the feature reorganization stage, a multi-layer feature cross-attention mechanism is constructed. The query matrix and key-value matrix are obtained by linear transformation of the input features. The multi-head attention mechanism projects the input features into multiple subspaces for parallel calculation. The dynamic adjustment of attention weights uses the gradient descent method with momentum, and the learning rate is adaptively adjusted according to the prediction error. Finally, the reorganized features are transformed nonlinearly to obtain a semantic feature vector set.

[0141] The present invention realizes adaptive organization and dynamic optimization of features through a multi-scale feature fusion network and the principle of self-organizing critical state, improves the accuracy and robustness of feature extraction, and enables the system to better adapt to the needs of different scenarios.

[0142] The present invention adopts recursive neural computing units and group intelligence emergence theory to establish a feedback mechanism and collaborative optimization system between features, enhances the expressive power of features, improves the efficiency of feature learning, and realizes the effective use of feature information.

[0143] The present invention realizes adaptive fusion and optimization of features through dynamic weight self-calibration and multi-layer feature cross-recombination, improves the semantic expression ability of feature vectors, enhances the generalization performance of the system, and improves the overall performance of the model.

[0144] In an optional embodiment,

[0145] The steps of defining the swarm intelligence potential energy function to characterize the interaction relationship between features, calculating the probability distribution of feature states based on the hierarchical mean field method, and optimizing the synergistic relationship between features by minimizing the free energy including the average energy term, the relative entropy term, and the multi-scale regularization term include:

[0146] Constructing a swarm intelligence potential energy function, wherein the swarm intelligence potential energy function includes a feature coupling term, a feature self-action term, and a feature dynamic modulation term, wherein the feature dynamic modulation term is used to characterize the dynamic change characteristics of the feature over time, the feature coupling term is dynamically updated through a time-varying coupling coefficient, and the time-varying coupling coefficient is calculated using an attention mechanism, and the feature self-action term introduces a memory mechanism to dynamically regulate feature historical information through a memory state and a forgetting coefficient;

[0147] Based on the swarm intelligence potential energy function, a hierarchical mean field method is used to calculate the probability distribution of feature states, including dividing the feature set into multiple subsets according to the graph clustering results, adopting the conditional independence assumption for the features in each subset, modeling the distribution of each feature as a mixed Gaussian model, introducing an uncertainty quantification mechanism to calculate the uncertainty of the distribution, constructing an adaptive weight according to the uncertainty, and optimizing the mean vector and covariance matrix of the mixed Gaussian model using an iterative update formula that takes uncertainty into account;

[0148] Based on the probability distribution of characteristic states, a multiscale free energy function is constructed, wherein the multiscale free energy function includes an average energy term, a relative entropy term and a regularization term, wherein the average energy term characterizes the matching degree between the characteristic state distribution and the swarm intelligence potential energy function, the relative entropy term measures the difference between the characteristic state distribution and the prior distribution, the regularization term includes a sparsity regularization term, a smoothness regularization term and a diversity regularization term, wherein the sparsity regularization term is used to promote feature selection, the smoothness regularization term is used to ensure the continuity of distribution change, and the diversity regularization term is used to prevent feature collapse, and the multiscale free energy function is optimized by an adaptive momentum method, wherein the learning rate of the optimization process is dynamically adjusted according to the average uncertainty, and the optimal distribution parameters of the characteristic state are obtained through iterative optimization; based on the optimal distribution parameters, the synergistic effect strength between the features is calculated, wherein the synergistic effect strength is jointly determined by the optimal mean vector and covariance matrix of the characteristic distribution.

[0149] Exemplarily, firstly, a swarm intelligence potential function is constructed to characterize the interaction relationship between features. The potential function contains three key components: the coupling term between features describes the dynamic correlation strength between features through the time-varying coupling coefficient, and the coupling coefficient is calculated based on the attention mechanism. Specifically, the query vector, key vector and value vector are obtained by linear transformation of the feature vector, and the attention weight is calculated by the similarity between the query vector and the key vector, and the updated feature representation is obtained by weighted combination with the value vector; the feature self-action term introduces a memory mechanism to maintain the temporal coherence of the feature, and the historical state information is maintained through the long-term and short-term memory unit. The memory state is dynamically updated according to the control of the forget gate, input gate and output gate according to the current input and historical state; the feature dynamic modulation term characterizes the characteristics of the feature changing over time, and the feature is modulated by the time embedding vector, and the embedding vector is generated by position encoding.

[0150] Then, the probability distribution of feature states is calculated based on the hierarchical mean field method. First, the feature set is divided into multiple subsets using the spectral clustering method. The affinity matrix is ​​constructed using cosine similarity during clustering, and the number of clusters with the largest feature gap is selected. The conditional independence assumption is adopted for the features in each subset, and the distribution of a single feature is modeled as a mixed Gaussian model with multiple components. In order to quantify the uncertainty of the distribution, the entropy value of each Gaussian component is calculated as an uncertainty indicator. Adaptive weights are constructed based on uncertainty, and the weights are negatively correlated with the uncertainty. When iteratively updating the parameters of the mixed Gaussian model, samples with higher uncertainty are given smaller weights, thereby reducing the impact of noise samples. Specifically, given a set of feature samples, the mean vector and covariance matrix of the Gaussian components are first randomly initialized, and then the responsibility calculation and parameter update steps are performed alternately, where the uncertainty weights of the samples are considered when updating the parameters.

[0151] Finally, a multi-scale free energy function is constructed and optimized. The function contains three terms: the average energy term calculates the expectation of the feature state distribution and the potential energy function, the relative entropy term measures the difference between the distribution and the prior distribution, and the regularization term includes three aspects: sparsity, smoothness, and diversity. The sparsity regularization term adopts a feature selection strategy based on group sparsity to group the features and enforce sparse constraints within the group; the smoothness regularization term requires that the distribution parameters at adjacent moments change smoothly; the diversity regularization term prevents feature collapse by maximizing the mutual information between different feature distributions. In the optimization process, the gradient descent method with momentum term is used, and the learning rate is adaptively adjusted according to the average uncertainty. The larger the uncertainty, the smaller the learning rate. After the optimization converges, the optimal distribution parameters of the feature state are obtained, and then the synergy strength between the features is calculated.

[0152] By introducing a swarm intelligence potential function with a dynamic coupling coefficient and a memory mechanism, the present invention can accurately capture the time-varying interaction relationship between features and improve the model's ability to express complex dynamic systems. The hierarchical mean field method and uncertainty quantification mechanism are adopted to achieve efficient calculation of feature state distribution and improve the robustness of the model to noise and abnormal samples. Based on multi-scale free energy optimization and adaptive learning strategies, multiple goals such as feature selection, smooth evolution and diversity maintenance are achieved while ensuring model convergence, significantly improving the generalization performance and interpretability of the model.

[0153] In an optional embodiment,

[0154] The steps of establishing an intelligent protection mechanism based on scenario evolution for the semantic feature vector set, extracting spatiotemporal fusion features through spatiotemporal sequence analysis, calculating anomaly thresholds and constructing multi-dimensional association graphs, and designing a multi-level dynamic authorization strategy include:

[0155] Acquire the time series information of the semantic feature vector, extract the forgetting feature, input feature and output feature from the time series information, obtain the hidden layer state through the synergistic effect of the forgetting feature, input feature and output feature, perform query-key value calculation on the hidden layer state of different time windows to obtain the spatiotemporal attention weight, and perform weighted aggregation on the hidden layer state based on the spatiotemporal attention weight to obtain the spatiotemporal fusion feature;

[0156] Based on the spatiotemporal fusion features, a feature probability density distribution is constructed, the mean and standard deviation of the feature distribution are calculated according to the feature probability density distribution, a dynamic threshold update strategy is designed in combination with the skewness information of the feature distribution, and the abnormality threshold is adaptively adjusted through an adjustable scaling factor, and the scaling factor changes dynamically with the skewness of the feature distribution;

[0157] A multidimensional association graph is constructed using the spatiotemporal fusion features and the anomaly threshold, wherein the edge weight of the multidimensional association graph is determined by the distance between feature vectors and the anomaly threshold difference, and the node features in the graph are dynamically updated to obtain the temporal attention weight, and the association relationship between the nodes is updated based on the temporal attention weight;

[0158] A multi-level dynamic authorization strategy is designed based on the multidimensional association graph, and the node importance score is calculated according to the node's degree centrality, betweenness centrality and closeness centrality. The nodes are divided into different levels according to the importance score, and an authorization policy matrix is ​​constructed for different levels. The elements of the authorization policy matrix are jointly determined by the hierarchical matching relationship, association strength and anomaly compatibility. The final authorization decision is generated based on the authorization policy matrix combined with the anomaly threshold.

[0159] Exemplarily, the intelligent protection mechanism based on scenario evolution first needs to obtain the time series information of the semantic feature vector. The forgotten features, input features and output features are extracted through a deep neural network. The forgotten features are implemented by a feature selection gating unit, which dynamically adjusts the feature retention ratio according to the historical state information. The input features are obtained through the feature extraction layer, which contains entity attribute information and context association information. The output features are generated by the prediction layer, which contains high-level semantic representations related to the target task. These three types of features are fused to obtain the hidden state. For a time window with a length of 24 hours, data is sampled every 1 hour to generate a hidden state sequence of 24 time steps. The correlation strength between different time steps is calculated through the query-key value attention mechanism to obtain the spatiotemporal attention weight matrix. The weight matrix is ​​used to perform weighted summation on the hidden state sequence, and finally obtain the spatiotemporal fusion feature that integrates the temporal dependency.

[0160] Based on the obtained spatiotemporal fusion features, the kernel density estimation method is used to construct the feature probability density distribution. The mean and standard deviation of the feature distribution are calculated through statistical analysis. Combined with the skewness information of the feature distribution, an adaptive threshold update strategy is designed. When the feature distribution is positively skewed, it means that there are fewer abnormal samples. At this time, the scaling factor is increased to improve the detection sensitivity; when the feature distribution is negatively skewed, it means that there are more abnormal samples. At this time, the scaling factor is reduced to reduce the false alarm rate. The scaling factor ranges from 0.5 to 2, the initial value is set to 1, and the step size of each update is 0.1.

[0161] A multidimensional association graph is constructed using spatiotemporal fusion features and dynamic anomaly thresholds. In the graph, nodes represent feature vectors, and edges represent the associations between nodes. The weight of the edge is determined by the Euclidean distance between feature vectors and the anomaly threshold difference. For any two nodes, if their feature distance is less than the preset threshold and the anomaly threshold difference is within the allowable range, a connection is established between them. The temporal attention weight of the node is calculated by sliding the time window, which reflects the importance of the node at different time steps. The edge weights between nodes are dynamically updated based on the temporal attention weight.

[0162] Design a multi-level dynamic authorization strategy based on the constructed multi-dimensional association graph. First, calculate the three indicators of degree centrality, betweenness centrality and closeness centrality of the node. Degree centrality reflects the number of direct connections of the node, betweenness centrality reflects the bridging role of the node, and closeness centrality reflects the average distance of the node to other nodes. The weighted sum of these three indicators is used to obtain the importance score of the node. According to the importance score, the nodes are divided into three levels: high, medium and low. Construct an authorization policy matrix, and the matrix element values ​​are determined by the node level matching relationship, association strength and anomaly compatibility. When two nodes are of similar level, high association strength and similar anomaly, a higher authorization level is assigned; otherwise, the authorization level is reduced. Finally, the authorization policy matrix is ​​combined with the anomaly threshold to generate a specific authorization decision result.

[0163] The present invention extracts spatiotemporal fusion features through spatiotemporal sequence analysis and attention mechanism, effectively capturing the dynamic evolution law of feature vectors changing with time and space, and improving the accuracy and robustness of feature representation; dynamically adjusting the anomaly threshold based on the skewness information of feature distribution, realizing adaptive optimization of detection sensitivity, and effectively reducing the false alarm rate while ensuring detection accuracy; adopting multi-dimensional association graphs and multi-level authorization strategies, realizing refined permission management based on node importance, improving the flexibility and security of the authorization mechanism, and effectively preventing unauthorized access and abnormal behavior.

[0164] In an optional embodiment,

[0165] The following steps are involved:

[0166] Unstructured data collection based on scalable APIs and data ingestion tools;

[0167] Use data lake technology to store unstructured data and build a catalog table to manage metadata information of unstructured data;

[0168] Use search engines to index unstructured data and provide query services;

[0169] Unstructured data analysis based on natural language processing and deep learning framework.

[0170] Exemplary data collection implementation method: implement an extensible API framework based on Java language, adopt SPI mechanism to support rapid expansion and docking with different systems; use Logstash tool to obtain data from multiple data sources in real time, convert and send to the designated storage location.

[0171] Data storage implementation method: Introduce the concept of directory table in the data lake to store metadata information of unstructured data; store the actual content of unstructured data (such as text, pictures, videos and other files) in the object storage OSS layer, and manage unstructured data in a unified manner through the directory table.

[0172] Data query implementation method: Enable the data synchronization function, use Spark to load data and import it into Elasticsearch; build a search service based on Elasticsearch to achieve horizontal expansion and complex query functions, and provide a query interface.

[0173] Data analysis implementation method: Use natural language processing technology to process text-based unstructured data; use the TensorFlow framework to build, train and deploy artificial intelligence models to achieve in-depth analysis of unstructured data.

[0174] The present invention can improve the ease of model publishing and API calling in machine learning, and enhance the automation of the overall process. It does not require code writing by oneself, and can be deployed and published by automatically generating configurations. It also has the functions of dynamic publishing and specified version publishing.

[0175] Figure 2 FIG. 1 is a schematic diagram of the structure of an unstructured data processing system according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0176] The first unit is used to perform tensor decomposition on unstructured data to obtain standardized feature vectors, construct a time-series correlation index network based on the standardized feature vectors, construct an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery;

[0177] The second unit is used to construct a self-organizing critical state feature learning network in the isolated computing space, map the standardized feature vector to the critical phase transition point, identify the optimal learning state through the energy fluctuation monitor based on the critical phase transition point, and capture the long-distance dependency relationship between the standardized feature vectors; construct a quantum entanglement-aware feature interaction network based on the long-distance dependency relationship, use quantum state coding to represent the feature vector correlation, and set a quantum decoherence monitor and a quantum error correction encoder; construct a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer for the feature vectors in the feature interaction network, design a confidence scoring mechanism based on the hierarchical causal graph to perform noise filtering, establish a multi-level cache mechanism based on task importance, and obtain a screening feature vector set through a multi-dimensional resource monitor;

[0178] The third unit is used to construct a multi-scale feature fusion network based on the screened feature vector set, dynamically adjust the coupling strength between features using the principle of self-organizing critical state, establish a feature feedback enhancement mechanism through a recursive neural computing unit, build a feature collaborative optimization system based on the theory of group intelligence emergence, and generate a semantic feature vector set using dynamic weight self-calibration and multi-layer feature cross-recombination; establish an intelligent protection mechanism based on scene evolution for the semantic feature vector set, extract spatiotemporal fusion features through spatiotemporal sequence analysis, calculate anomaly thresholds and construct a multi-dimensional correlation map, and design a multi-level dynamic authorization strategy.

[0179] According to a third aspect of the embodiments of the present invention,

[0180] An electronic device is provided, comprising:

[0181] processor;

[0182] a memory for storing processor-executable instructions;

[0183] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0184] A fourth aspect of the embodiments of the present invention is:

[0185] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0186] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing unstructured data, characterized in that: include: Perform tensor decomposition on unstructured data to obtain standardized feature vectors, build a time-series correlation index network based on the standardized feature vectors, build an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery; A self-organizing critical state feature learning network is constructed in the isolated computing space, the standardized feature vector is mapped to a critical phase transition point, the optimal learning state is identified through an energy fluctuation monitor based on the critical phase transition point, and the long-distance dependency relationship between the standardized feature vectors is captured; a quantum entanglement-aware feature interaction network is constructed according to the long-distance dependency relationship, quantum state coding is used to represent the feature vector correlation, and a quantum decoherence monitor and a quantum error correction encoder are set; a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer is constructed for the feature vectors in the feature interaction network, a confidence scoring mechanism is designed based on the hierarchical causal graph to perform noise filtering, a multi-level cache mechanism based on task importance is established, and a screening feature vector set is obtained through a multi-dimensional resource monitor; Based on the screened feature vector set, a multi-scale feature fusion network is constructed, the coupling strength between features is dynamically adjusted using the principle of self-organizing critical state, a feature feedback enhancement mechanism is established through a recursive neural computing unit, a feature collaborative optimization system is constructed in combination with the theory of group intelligence emergence, and a semantic feature vector set is generated using dynamic weight self-calibration and multi-layer feature cross-recombination; An intelligent protection mechanism based on scenario evolution is established for the semantic feature vector set. Through spatiotemporal sequence analysis, spatiotemporal fusion features are extracted, anomaly thresholds are calculated, and multi-dimensional correlation maps are constructed to design multi-level dynamic authorization strategies.

2. The method according to claim 1, characterized in that: The steps of constructing a self-organizing critical state feature learning network in the isolated computing space, mapping the standardized feature vector to a critical phase transition point, identifying an optimal learning state through an energy fluctuation monitor based on the critical phase transition point, and capturing the long-distance dependency relationship between the standardized feature vectors include: A feature learning network based on a two-dimensional Ising model is constructed in an isolated computing space, the standardized feature vector is mapped to a spin state, the coupling coefficient between adjacent feature vectors is calculated, and the spin state of the feature learning network is updated; an order parameter fluctuation positioning method is designed for the feature learning network, and a critical phase transition point is obtained by order parameter calculation; Construct multi-level energy fluctuation monitoring, extract local energy fluctuation intensity, construct local fluctuation distribution measurement based on Wasserstein distance, establish energy fluctuation correlation matrix, and use spectral clustering method to obtain energy fluctuation pattern; design adaptive threshold update strategy based on reinforcement learning according to the energy fluctuation pattern, calculate reward signal based on spatiotemporal correlation of energy fluctuation pattern, map energy fluctuation difference into time series reward function, establish state-value mapping network, input energy fluctuation parameter, energy fluctuation adaptive threshold parameter and energy fluctuation correlation matrix to generate state evaluation score; design combined evaluation index based on mutual information and conditional entropy, normalize the state evaluation score, use recursive dynamic programming algorithm to search optimal state transfer path in evaluation index space, and determine state convergence moment based on path cumulative gain; use state convergence moment as time anchor point of optimal learning state, output final optimal learning state through multi-scale calculation and state stability verification based on state confidence interval; The long-distance dependency between the standardized feature vectors under the optimal learning state is mapped to the quantum walk framework, and the quantum evolution space is constructed. The long-distance transition is performed by adjusting the phase parameter in the evolution operator. The quantum coherence measurement is introduced and the quantum master equation is solved to track the coherence evolution. Based on the quantum state migration probability and the quantum parameter estimation information measurement, a characteristic dependency matrix with quantum coherence is constructed.

3. The method according to claim 1, characterized in that The steps of constructing a characteristic interaction network of quantum entanglement perception according to the long-distance dependency, using quantum state coding to represent the characteristic vector correlation, and setting a quantum decoherence monitor and a quantum error correction encoder include: Obtain a feature dependency matrix corresponding to the long-distance dependency, construct a feature interaction network based on the feature dependency matrix, map the eigenvector correlation in the feature interaction network to a two-dimensional quantum state space, construct a quantum state density matrix set, and calculate the entanglement degree between the eigenvectors based on the quantum entanglement entropy; construct a total Hamiltonian of quantum bit-noise coupling based on the quantum state density matrix set, the total Hamiltonian includes a feature interaction system Hamiltonian, an environment Hamiltonian, and a coupling term between the feature interaction system and the environment; The influence functional method is used to calculate the time evolution characteristics of the quantum state density matrix under the action of the total Hamiltonian, and the influence functional of the interaction between the quantum bit and the noise source is obtained; the influence functional is subjected to wavelet time-frequency analysis to extract the decoherence characteristics of multiple time scales; a state discriminator is constructed based on the decoherence characteristics, and the feature weights of the state discriminator are optimized by maximizing the inter-class distance and minimizing the intra-class distance to generate a decoherence threshold discrimination result; A surface code lattice structure is constructed according to the decoherence threshold judgment result, and data quantum bits and measurement quantum bits are set in the surface code lattice structure; a stabilizer operator acting on the vertices and facets of the surface code lattice structure is constructed to detect error syndrome, and a minimum weight complete matching algorithm is used to determine the error position and perform a Pauli operator correction operation; and a surface code distance parameter is dynamically adjusted based on the intensity of the decoherence feature.

4. The method according to claim 1, characterized in that: The steps of constructing a hierarchical causal graph including a macro business layer, a meso feature layer and a micro data layer for the feature vectors in the feature interaction network, designing a confidence scoring mechanism based on the hierarchical causal graph to filter noise, establishing a multi-level cache mechanism based on task importance, and obtaining a screening feature vector set through a multidimensional resource monitor include: Decomposing the feature vector in the feature interaction network into a feature processing node set, extracting the temporal feature sequence and the spatial feature sequence from the feature processing node set to construct a business feature vector, performing principal component analysis on the business feature vector to obtain a compressed feature matrix, converting the compressed feature matrix into a compressed feature vector, extracting data features using an autoencoder, and constructing a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer; A time series sample set of compressed feature vectors is collected within a fixed time window, and the time stability of the compressed feature vectors is evaluated based on an exponentially weighted sliding variance calculation method, wherein the exponentially weighted sliding variance calculation method uses a time decay weight to perform a weighted calculation on the deviation of the compressed feature vectors from the weighted mean to obtain a time stability score; an improved mutual correlation coefficient matrix is ​​constructed to evaluate the spatial correlation of the compressed feature vectors, wherein the improved mutual correlation coefficient matrix calculates the multi-order mutual correlation coefficients between the compressed feature vectors and the adjacent nodes of the hierarchical causal graph, and uses multi-order correlation weight coefficients to obtain a spatial correlation score; a mutual information calculation framework between the compressed feature vectors and the target performance indicators is constructed, and the business impact is obtained by calculating the standardized mutual information based on information entropy and joint entropy; The temporal stability score, the spatial correlation score and the business impact are weightedly integrated to obtain a confidence score of the compressed feature vector, and noise filtering is performed on the compressed feature vector based on the confidence score; A multi-level cache mechanism is constructed for the compressed feature vectors that have passed noise filtering, the access entropy is obtained by calculating the probability distribution of the access time interval of the compressed feature vectors, and the cache priority is calculated according to the access entropy, access frequency and confidence score of the compressed feature vectors; a write-through strategy is adopted to maintain the consistency of the multi-level cache, the prefetch probability is calculated based on the cache priority, and dynamic adjustment is performed by minimizing the weighted sum of the cache miss rate and the prefetch overhead; a multi-dimensional resource monitor is constructed, and a screening feature vector set is obtained based on the usage of computing resources, storage resources and network resources combined with the confidence score.

5. The method according to claim 1, characterized in that The steps of constructing a multi-scale feature fusion network based on the screened feature vector set, dynamically adjusting the coupling strength between features by using the principle of self-organizing critical state, establishing a feature feedback enhancement mechanism by using a recursive neural computing unit, and constructing a feature collaborative optimization system by combining the theory of group intelligence emergence, and generating a semantic feature vector set by using dynamic weight self-calibration and multi-layer feature cross-recombination include: The screened feature vector set is divided into three layers: bottom layer, middle layer and top layer according to the feature granularity to construct a multi-scale feature fusion network, local sensitive hashing is used to cluster similar feature vectors in each layer to form feature clusters, a weighted undirected graph is constructed based on the mutual information between feature clusters, and the spectral clustering method is used to divide the weighted undirected graph into multiple subgraphs, and the subgraphs represent high-order semantic information; Calculating the activity of the feature nodes in the weighted undirected graph, taking the average activity as an order parameter, taking the coupling strength as a control parameter, and optimizing the coupling strength objective function based on a gradient descent method, wherein the coupling strength objective function includes a deviation term between the average activity and a critical point and a regularization term; Constructing a recursive neural computing unit, wherein the recursive neural computing unit includes a gating mechanism of a forget gate, an input gate, and an output gate, and the unit state and hidden state output are calculated through the gating mechanism to establish a feature feedback enhancement mechanism; Define the swarm intelligence potential energy function to characterize the interaction relationship between features, calculate the probability distribution of feature states based on the hierarchical mean field method, and optimize the synergistic relationship between features by minimizing the free energy including the average energy term, relative entropy term and multi-scale regularization term; Based on the synergistic relationship between the features, a multi-layer feature cross-attention mechanism is constructed to reorganize the features to obtain recombined features, wherein the multi-layer feature cross-attention mechanism includes a query matrix, a key-value matrix and multi-head attention calculation; the feature weight coefficients in the multi-layer feature cross-attention mechanism are dynamically adjusted according to the feature prediction error, and a weight update equation including a learning rate and a momentum coefficient is used for self-calibration; the recombined features are converted into a semantic feature vector set, and the semantic feature vector set includes high-order semantic information of the original features.

6. The method according to claim 5, characterized in that The steps of defining the swarm intelligence potential energy function to characterize the interaction relationship between features, calculating the probability distribution of feature states based on the hierarchical mean field method, and optimizing the synergistic relationship between features by minimizing the free energy including the average energy term, the relative entropy term, and the multi-scale regularization term include: Constructing a swarm intelligence potential energy function, wherein the swarm intelligence potential energy function includes a feature coupling term, a feature self-action term, and a feature dynamic modulation term, wherein the feature dynamic modulation term is used to characterize the dynamic change characteristics of the feature over time, the feature coupling term is dynamically updated through a time-varying coupling coefficient, and the time-varying coupling coefficient is calculated using an attention mechanism, and the feature self-action term introduces a memory mechanism to dynamically regulate feature historical information through a memory state and a forgetting coefficient; Based on the swarm intelligence potential energy function, a hierarchical mean field method is used to calculate the probability distribution of feature states, including dividing the feature set into multiple subsets according to the graph clustering results, adopting the conditional independence assumption for the features in each subset, modeling the distribution of each feature as a mixed Gaussian model, introducing an uncertainty quantification mechanism to calculate the uncertainty of the distribution, constructing an adaptive weight according to the uncertainty, and optimizing the mean vector and covariance matrix of the mixed Gaussian model using an iterative update formula that takes uncertainty into account; Based on the probability distribution of characteristic states, a multiscale free energy function is constructed, wherein the multiscale free energy function includes an average energy term, a relative entropy term and a regularization term, wherein the average energy term characterizes the matching degree between the characteristic state distribution and the swarm intelligence potential energy function, the relative entropy term measures the difference between the characteristic state distribution and the prior distribution, the regularization term includes a sparsity regularization term, a smoothness regularization term and a diversity regularization term, wherein the sparsity regularization term is used to promote feature selection, the smoothness regularization term is used to ensure the continuity of distribution change, and the diversity regularization term is used to prevent feature collapse, and the multiscale free energy function is optimized by an adaptive momentum method, wherein the learning rate of the optimization process is dynamically adjusted according to the average uncertainty, and the optimal distribution parameters of the characteristic state are obtained through iterative optimization; based on the optimal distribution parameters, the synergistic effect strength between the features is calculated, wherein the synergistic effect strength is jointly determined by the optimal mean vector and covariance matrix of the characteristic distribution.

7. The method according to claim 1, characterized in that The steps of establishing an intelligent protection mechanism based on scenario evolution for the semantic feature vector set, extracting spatiotemporal fusion features through spatiotemporal sequence analysis, calculating anomaly thresholds and constructing multi-dimensional association graphs, and designing a multi-level dynamic authorization strategy include: Acquire the time series information of the semantic feature vector, extract the forgetting feature, input feature and output feature from the time series information, obtain the hidden layer state through the synergistic effect of the forgetting feature, input feature and output feature, perform query-key value calculation on the hidden layer state of different time windows to obtain the spatiotemporal attention weight, and perform weighted aggregation on the hidden layer state based on the spatiotemporal attention weight to obtain the spatiotemporal fusion feature; Based on the spatiotemporal fusion features, a feature probability density distribution is constructed, the mean and standard deviation of the feature distribution are calculated according to the feature probability density distribution, a dynamic threshold update strategy is designed in combination with the skewness information of the feature distribution, and the abnormality threshold is adaptively adjusted through an adjustable scaling factor, and the scaling factor changes dynamically with the skewness of the feature distribution; A multidimensional association graph is constructed using the spatiotemporal fusion features and the anomaly threshold, wherein the edge weight of the multidimensional association graph is determined by the distance between feature vectors and the anomaly threshold difference, and the node features in the graph are dynamically updated to obtain the temporal attention weight, and the association relationship between the nodes is updated based on the temporal attention weight; A multi-level dynamic authorization strategy is designed based on the multidimensional association graph, and the node importance score is calculated according to the node's degree centrality, betweenness centrality and closeness centrality. The nodes are divided into different levels according to the importance score, and an authorization policy matrix is ​​constructed for different levels. The elements of the authorization policy matrix are jointly determined by the hierarchical matching relationship, association strength and anomaly compatibility. The final authorization decision is generated based on the authorization policy matrix combined with the anomaly threshold.

8. An unstructured data processing system, for implementing the method according to any one of claims 1 to 7, characterized in that: include: The first unit is used to perform tensor decomposition on unstructured data to obtain standardized feature vectors, construct a time-series correlation index network based on the standardized feature vectors, construct an isolated computing space in a trusted execution environment, and set up a three-level fault-tolerant protection mechanism including data backup, error detection, and state recovery; The second unit is used to construct a self-organizing critical state feature learning network in the isolated computing space, map the standardized feature vector to the critical phase transition point, identify the optimal learning state through the energy fluctuation monitor based on the critical phase transition point, and capture the long-distance dependency relationship between the standardized feature vectors; construct a quantum entanglement-aware feature interaction network based on the long-distance dependency relationship, use quantum state coding to represent the feature vector correlation, and set a quantum decoherence monitor and a quantum error correction encoder; construct a hierarchical causal graph including a macro business layer, a meso feature layer, and a micro data layer for the feature vectors in the feature interaction network, design a confidence scoring mechanism based on the hierarchical causal graph to perform noise filtering, establish a multi-level cache mechanism based on task importance, and obtain a screening feature vector set through a multi-dimensional resource monitor; The third unit is used to construct a multi-scale feature fusion network based on the screened feature vector set, dynamically adjust the coupling strength between features using the principle of self-organizing critical state, establish a feature feedback enhancement mechanism through a recursive neural computing unit, build a feature collaborative optimization system in combination with the theory of group intelligence emergence, and generate a semantic feature vector set using dynamic weight self-calibration and multi-layer feature cross-recombination; An intelligent protection mechanism based on scenario evolution is established for the semantic feature vector set. Through spatiotemporal sequence analysis, spatiotemporal fusion features are extracted, anomaly thresholds are calculated, and multi-dimensional correlation maps are constructed to design multi-level dynamic authorization strategies.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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