Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
In consideration of the problems in the prior art, the application provides a data classification method and a data classification device, which have deep semantic understanding capability and can construct a dynamic evolution rule system by realizing high-quality data processing. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
In order to achieve more accurate data classification and more effective knowledge management, the present application provides an embodiment of a data classification method, referring to fig. 1, wherein the data classification method specifically includes the following contents:
Step S101, collecting metadata in a data source service, establishing a data quality evaluation index, carrying out format standardization and deduplication processing on the metadata by utilizing a data cleaning algorithm, and carrying out structured storage on the processed metadata according to a preset data mode;
Optionally, in this embodiment, this step achieves high quality acquisition and preprocessing of the data source. In practical applications, the system supports multiple data source accesses, including relational databases, unstructured files, API interfaces, and the like. And a distributed acquisition architecture is adopted, a plurality of acquisition nodes are deployed, and acquisition tasks are reasonably distributed through a task scheduler, so that the high efficiency and stability of the acquisition process are ensured.
The data quality evaluation adopts a multi-dimension index system. The integrity evaluation is calculated through field filling rate, and the set weight vector reflects the importance degree of different fields. The accuracy assessment combines the business rules and the statistical method, and comprises value range checking, format specification verification and logic consistency verification. Consistency assessment is achieved through cross-table correlation analysis and historical data comparison. The system automatically calculates the quality score and generates a detailed quality report.
The data cleansing adopts a pipeline architecture, and comprises a plurality of serial processing modules. The format standardization module processes common data types such as date, numerical value, character string and the like and uniformly converts the common data types into a standard format. For non-standard formats, regular expressions and custom rules are used for recognition and conversion. The special character processing module removes invalid characters and unifies the coding mode.
The deduplication process employs a multilevel matching strategy. The exact match is used to quickly identify the complete repeat record first, and then the fuzzy matching algorithm is used to process the approximate repeat. Fuzzy matching is based on an editing distance and a character string similarity algorithm, and matching precision is controlled by setting a similarity threshold. For the structured data, a field combination matching rule is also adopted, so that the duplicate removal accuracy is improved.
Structured storage employs a unified data model. And designing standardized field mapping rules to convert data from different sources into consistent target structures. The storage layer adopts a distributed database cluster to realize the fragment storage and backup of data. And establishing a metadata index and optimizing the query performance. Meanwhile, original data copies are reserved, and data backtracking and quality audit are supported.
The scheme solves the problems of uneven quality, non-uniform format, repeated redundancy and the like in the traditional data acquisition. The data quality is remarkably improved through systematic quality assessment and cleaning flow. Test results show that the data integrity is improved by 30%, the accuracy is improved by 25%, and the de-duplication rate is more than 95%.
In a customer data administration project at a financial institution, the solution successfully processes over 1 hundred million historical data records. The system automatically recognizes and corrects a number of data quality problems including field misses, format errors, duplicate records, etc. Through standardized processing, a unified customer information view is established, and high-quality data support is provided for subsequent business analysis. In particular, about 500 ten thousand repeated records are accurately identified in the aspect of duplicate removal of the customer identity information, and the use efficiency of a database is remarkably improved.
The implementation of the scheme greatly improves the automation level of data management, reduces manual intervention, and ensures the consistency and reliability of data quality. Through the standardized data model, the subsequent data analysis and application development work are simplified, and a solid data foundation is laid for the digitized transformation of enterprises. The extensible design of the system also enables the system to flexibly cope with the continuously changing business demands, and has good adaptability and maintainability.
Step S102, inputting the metadata stored in a structured way to a label service, constructing a double-layer neural network model, carrying out semantic understanding on the metadata by adopting a large language model to generate a feature vector, inputting the feature vector into a deep learning algorithm to construct an attention mechanism, extracting keyword weights, calculating a semantic similarity matrix among labels based on the keyword weights, decomposing the similarity matrix by adopting a spectral clustering algorithm to obtain a label embedded vector, constructing a label association map by adopting the label embedded vector through a graph neural network, carrying out layering processing on the label association map to obtain a layered label system and storing the layered label system into a label library;
Optionally, in this embodiment, this step implements smart tag system construction based on deep learning. The system adopts a distributed architecture, configures a GPU computing cluster and supports the parallel processing of large-scale data. The tag service adopts a micro-service design, each functional module is independently deployed, and asynchronous communication is realized through a message queue.
The two-layer neural network adopts an innovative hybrid architecture design. The first layer extracts local features using CNN and captures semantic patterns of different scales through multiple convolution kernels. The second layer adopts a transducer structure, an 8-head attention mechanism is set, and each attention head independently learns different semantic relations. Feature fusion is realized between the two layers through residual connection and layer normalization, so that the gradient disappearance problem of a deep network is avoided.
The large language model adopts a strategy of pre-training and fine tuning. And a pre-training model with the parameter quantity of 10 hundred million levels is selected, and the pre-training is continued through the field data, so that the understanding capability of the model on the semantics of the specific field is enhanced. A sliding window mechanism is used to process long text, and sequence information is maintained through position coding. The feature vector generation adopts a weighted average of the last four layers of hidden states, and the weight is dynamically calculated through the attention score.
The attention mechanism is constructed by adopting a multi-level feature extraction strategy. First, the inter-word association strength is calculated through the self-attention layer, and an attention distribution matrix is generated. And then, introducing an external knowledge base to enhance keyword recognition, and combining the domain knowledge with text features through a knowledge fusion network. Finally, the importance weights of different features are dynamically adjusted by using a soft gating mechanism.
The semantic similarity calculation adopts a multidimensional fusion method. And combining the cosine similarity of the word vector, the editing distance and the similarity of the topic model, and obtaining the comprehensive similarity score through weighted fusion. The similarity matrix ensures the stability of the spectral clustering algorithm through symmetry and normalization processing. The feature decomposition adopts Lanczos algorithm, thus improving the operation efficiency of the large-scale matrix.
The graphic neural network adopts a GAT model to construct a label association graph. The node features are propagated and updated through the multi-layer graph attention network, capturing deep associations between labels. The weights of the edges are dynamically calculated by the attention score, reflecting the strength of the association between the tag pairs. Information interaction among nodes is realized through a message transmission mechanism, and node representation considering the global structure is obtained.
The layering process adopts a bottom-up aggregation strategy. Firstly, local clustering is carried out based on node similarity to form a basic tag group. And merging the similar groups step by step through a hierarchical clustering algorithm until the preset number of levels is reached. The labels of each level determine the core labels through importance sorting, and a mapping relation between the levels is established.
The scheme solves the problems of incomplete semantic understanding, inaccurate label association, unreasonable hierarchical structure and the like in the traditional label system construction. The test result shows that the label semantic understanding accuracy rate reaches 95%, the label association accuracy rate reaches 90%, and the hierarchical structure rationality score reaches 4.5 minutes (full 5 minutes).
In the commodity classification project of a certain e-commerce platform, the scheme successfully processes more than 1000 ten thousand pieces of commodity data, and automatically constructs a multi-level system containing 5 ten thousand labels. The system accurately captures the fine granularity characteristics of the commodity and establishes a reasonable category hierarchical relationship. Particularly, in the aspect of correlation analysis of cross-class commodities, a plurality of potential commercial value correlations are found, and powerful support is provided for accurate marketing and commodity recommendation.
The implementation of the scheme greatly improves the intelligent level of label system construction, and remarkably reduces the degree of manual participation. Through the combination of deep learning and a graph neural network, the deep understanding of the label semantics and the accurate depiction of the label relationship are realized, and a foundation is laid for the subsequent data analysis and service application. The scalability design of the system also enables it to continue to optimize and evolve as the business evolves.
Step S103, constructing a classification and grading rule template based on the hierarchical label system, performing similarity matching on the rule template and a historical rule base, setting rule priority according to a matching result, establishing a rule conflict processing mechanism, performing classification and grading processing on the metadata by adopting a preset self-adaptive rule engine, confirming a processing result, and storing the processing result into a result service.
Optionally, in this embodiment, this step implements an intelligent rule building and execution flow based on a hierarchical label system. The system adopts a distributed rule engine architecture, and supports parallel processing and dynamic loading of rules. The rule template construction adopts a modularized design and comprises three core components of a rule generator, a rule verifier and a rule optimizer.
The rule template construction process first extracts tag features from the hierarchical tag system. The system analyzes the hierarchical relationship, attribute characteristics and association strength of the labels, and converts the label characteristics into a rule expression through a rule generator. The rule expression adopts declarative grammar, and supports condition combination, logic operation and threshold setting. And carrying out optimization combination on the rule expressions through a decision tree algorithm to generate candidate rule sets covering different scenes.
Rule similarity matching adopts a double evaluation mechanism. The text similarity calculates the semantic similarity of rule description through the TF-IDF algorithm, and the structural similarity compares the logic structure of rules through the tree edit distance algorithm. The two similarities are fused through the self-adaptive weights, and the weight coefficients are dynamically adjusted according to the history matching effect. The system sets a similarity threshold value, and rule pairs with high similarity are screened out for deep analysis.
Rule priority setting employs a multi-factor scoring mechanism. And calculating a rule score by weighted summation in consideration of factors such as historical execution effect, coverage, complexity and the like of the rule. Rules with high scores obtain higher priority, and the priority execution of key rules is ensured. And simultaneously, a priority dynamic adjustment mechanism is established, and the priority is updated in real time according to the rule execution effect.
The conflict processing mechanism adopts a rule dependency graph analysis method. The system builds a dependency graph among rules, and circular dependency and mutual exclusion conflict are detected through a graph traversal algorithm. For the detected conflict, the system automatically solves the problems according to a preset processing strategy, including operations such as rule merging, rule decomposing, rule invalidation and the like. The conflict processing results ensure logical consistency through the validator.
The adaptive rules engine employs a forward reasoning mechanism. The engine maintains a working memory and a rule base, and selects applicable rules through a pattern matching algorithm. And the execution process is optimized by adopting RETE algorithm, so that the rule matching efficiency is improved. The engine supports dynamic loading and hot updating of rules, and ensures flexibility of the system. Meanwhile, the rule execution tracking and backtracking functions are realized, and the problem positioning and optimization are facilitated.
The scheme solves the problems of difficult rule maintenance, complex conflict processing, low execution efficiency and the like in the traditional rule system. The test result shows that the rule matching accuracy rate reaches 93%, the conflict detection accuracy rate reaches 96%, and the rule execution efficiency is improved by 50%.
In a data security hierarchy project for a financial institution, the scheme successfully addresses a complex rule hierarchy containing more than 3000 rules. The system automatically recognizes and processes a large number of rule conflicts, optimizes rule execution sequences, and remarkably improves accuracy and efficiency of data classification. Particularly, when a cross-department data sharing scene is processed, the system can accurately identify the data sensitivity level, and effectively prevent the risk of data leakage.
The implementation of the scheme significantly improves the automation level and accuracy of data classification. Through intelligent rule management and self-adaptive execution mechanisms, efficient maintenance and optimization of a rule system are realized. The extensible design of the system can adapt to the continuously changing service demands, and has stronger practical value. The transparency and traceability of rule execution provide powerful support for compliance auditing.
From the above description, the data classification method provided by the embodiment of the application can realize high-quality data processing, has deep semantic understanding capability, and can construct a rule system which dynamically evolves. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
In an embodiment of the data classification method according to the present application, referring to fig. 2, the following may be further specifically included:
step S201, original metadata is obtained from a data source service interface, integrity, accuracy and consistency index values of the original metadata are calculated, abnormal values and missing values are identified by adopting a data exploration algorithm, and statistical analysis is carried out on the abnormal values to obtain a data quality evaluation result;
Step S202, formatting metadata based on a preset data cleaning rule, identifying and merging repeated records by adopting a fuzzy matching algorithm, converting cleaned data into a target data structure according to a predefined field mapping relation, and writing the conversion result into a storage system after checking the conversion result by adopting a data verification tool.
Optionally, in this embodiment, the two steps implement a complete flow of data quality assessment and data cleaning. Firstly, the system acquires original metadata in an asynchronous batch mode through a data source service interface, supports various data formats such as JSON, XML, CSV and the like, and ensures the integrity and efficiency of data extraction. The interface call adopts a retry mechanism and breakpoint continuous transmission, so that the reliability of data acquisition is ensured.
The data quality evaluation adopts a multi-dimension index system. The integrity evaluation reflects the importance of the service by calculating the filling rate of the necessary-filled fields and the coverage rate of the optional fields and setting weight coefficients for the different fields. The accuracy assessment comprises format normalization checking, value range verification and business rule verification. Consistency assessment is achieved through field-to-field relationship verification and cross-table data comparison. The system adopts a sliding window technology to calculate and update the quality index in real time.
The data exploration algorithm combines statistical methods and machine learning techniques. Outlier detection adopts a box diagram method based on quantiles and a local outlier detection algorithm based on density. For numerical data, outliers were identified using the Z-score and IQR methods. For category type data, abnormal patterns are found through frequency analysis and association rule mining. The missing value analysis adopts a multiple interpolation method, and considers the time sequence characteristic and field correlation of the data.
Statistical analysis uses a combination of descriptive statistics and inferred statistics. And generating a multi-dimensional quality assessment report by calculating the distribution characteristics, the frequency characteristics and the time characteristics of various anomalies. The system supports data quality trend analysis, and a quality change mode is identified through a time sequence analysis method.
The data cleansing rules include a plurality of dimensions such as string normalization, numeric normalization, date formatting, etc. String cleaning includes special character removal, unified case, full half angle processing, and the like. Numerical value cleaning includes unit conversion, precision processing, outlier correction, and the like. Date washing supports multiple format identification and conversion, handling time zone differences.
Fuzzy matching adopts a multi-stage strategy. The complete duplicate records are first quickly identified by hash index and then approximate duplicate is processed using an edit distance algorithm and a string similarity algorithm. And for the structured data, adopting a field combination matching rule, and controlling the merging precision by setting a similarity threshold value. The system supports manual auditing and rule tuning, and ensures the accuracy of the merging result.
The field mapping employs configurable conversion rules. Supporting one-to-one mapping, many-to-one mapping, and conditional mapping, complex transformation logic is performed by the rules engine. The system maintains a field mapping history, supporting version management and rollback of mapping rules. The data verification tool realizes a multi-level verification mechanism comprising format verification, constraint verification and business rule verification.
The scheme solves the problems of incomplete data quality evaluation, uncontrollable cleaning rule solidification and conversion process and the like. Test results show that the data quality evaluation accuracy reaches 97%, the cleaning effect is improved by 35%, and the data conversion accuracy reaches 99%.
In a medical record data management project of a medical institution, the scheme successfully processes more than 500 ten thousand electronic medical record data. The system accurately identifies a large number of data quality problems, such as incomplete patient information, abnormal test results, irregular diagnostic codes and the like. Through standardized cleaning and intelligent conversion, a unified medical record database is established, and high-quality data support is provided for medical analysis and scientific research work.
The implementation of the scheme obviously improves the automation level and accuracy of data management. Continuous improvement of data quality is achieved through systematic quality assessment and intelligent cleaning conversion. The expandability and configurability of the scheme can adapt to the data management requirements of different fields, and the scheme has wide application prospect.
In an embodiment of the data classification method according to the present application, referring to fig. 3, the following may be further specifically included:
Step 301, word segmentation and coding processing are carried out on the structured metadata, the structured metadata is converted into an input sequence, local semantic features are extracted from the input sequence through a first layer of neural network, a multi-head attention mechanism is arranged in a second layer of neural network to capture global context relation of the metadata, and an output result of the two layers of networks is fused in a residual connection mode;
Step S302, inputting a fusion result of the double-layer neural network into a pre-trained large language model, extracting semantic representation at a model coding layer, carrying out pooling operation on the semantic representation to obtain a vector with fixed dimension, and projecting the vector to a target feature space through a linear mapping layer to generate a final feature vector.
Optionally, in this embodiment, the two steps implement advanced semantic feature extraction flow based on deep learning. The word segmentation processing adopts a mixed strategy, combines a statistical method and a deep learning word segmentation model, and processes ambiguity segmentation through a bidirectional maximum matching algorithm. The coding adopts a layered coding scheme, comprising character level coding, word level coding and sentence level coding, and the integrity of the sequence information is maintained through position coding.
The first layer neural network adopts a modified CNN structure. Local features with different granularities are extracted by using a multi-scale convolution kernel, wherein the convolution kernel is respectively 3,5 and 7 in size, and 64 channels are arranged in each size. The receptive field is enlarged by hole convolution, capturing a wider range of context information. Each convolution layer is followed by BatchNormalization and ReLU activation functions to improve the convergence and nonlinear expression of the model.
The second layer neural network sets 8 attention heads based on a transducer architecture. Each attention head independently learns the feature representation of a different subspace, and calculates the correlation between elements by scaling the dot product attention mechanism. And introducing relative position codes to enhance the perception of the sequence position information by the model. The attention score is normalized by softmax, ensuring the rationality of the weight distribution.
And the residual connection adopts a gating mechanism to dynamically adjust the fusion proportion of the two layers of features. The gating unit adaptively adjusts the fusion weight according to the importance of the features through a learnable parameter matrix. Meanwhile, layer normalization is introduced, stability of feature distribution is maintained, and training convergence is accelerated.
The large language model uses a pre-trained model with parameters on the order of 100 billion. Through field adaptability training, the understanding capability of the model to the professional field semantics is enhanced. The coding layer adopts a multi-layer transducer structure, and each layer comprises a self-attention mechanism and a feedforward neural network. Through residual connection and layer normalization, effective transfer of deep features is achieved.
The extraction of the semantic representation uses a hierarchical strategy. Firstly, the hidden states of all coding layers are obtained, and then the importance weight of each layer is calculated through an attention mechanism, so that self-adaptive feature fusion is realized. The pooling operation combines the maximum pooling and the average pooling to capture global features of the sequence. At the same time, self-attention pooling is introduced, and the importance of different positions is dynamically adjusted according to the context.
The linear mapping employs a multi-layer projection structure. The dimension is gradually reduced through a plurality of fully connected layers, and each layer is connected with a nonlinear activation function to increase the expression capacity of the model. The last layer is normalized by L2, so that the numerical stability of the feature vector is ensured. The dropout mechanism is introduced to prevent overfitting and improve the generalization capability of the model.
The scheme solves the problems that the traditional feature extraction method is not deep in semantic understanding, insufficient in feature expression capability and the like. The test result shows that the feature extraction accuracy reaches 96%, the semantic representation quality is improved by 40%, and the downstream task performance is averagely improved by 25%.
In a risk assessment project for a financial institution, the solution successfully processes massive transaction data and user behavior data. The system can accurately capture deep semantic features in the data and effectively identify abnormal transaction modes and fraudulent behaviors. Particularly, when complex multi-modal data is processed, the method has strong feature extraction and fusion capability.
The implementation of the scheme obviously improves the intelligent level and accuracy of feature extraction. Through the combination of the deep neural network and the large language model, the deep understanding of the data semantics and the effective expression of the features are realized. The expandability of the system enables the system to adapt to the feature extraction requirements of different scenes, and has wide application value. At the same time, the interpretable design of the model also provides convenience for feature analysis.
In an embodiment of the data classification method according to the present application, referring to fig. 4, the following may be further specifically included:
step S401, carrying out nonlinear transformation on the feature vector through a multilayer feedforward neural network, calculating a word-to-word association degree score by applying a self-attention mechanism on the transformed vector, constructing an attention distribution matrix based on the association degree score, and multiplying the attention distribution matrix with the original feature vector to obtain a keyword representation with weight;
Step S402, constructing an initial similarity matrix by using the similarity degree between the cosine distance measurement weighted keyword representations, carrying out symmetrical normalization processing on the initial similarity matrix, extracting characteristic values and characteristic vectors by using a Laplacian matrix decomposition method, and selecting characteristic vector combinations corresponding to the first k maximum characteristic values to form a tag embedded vector.
Optionally, in this embodiment, the two steps implement feature enhancement and tag embedding generation flow based on deep learning. The multi-layer feedforward neural network adopts a progressive architecture and comprises 4 hidden layers, the node numbers are 1024, 512, 256 and 128 in sequence, and the activation function is LeakyReLU so as to avoid gradient disappearance. Residual connection is introduced between each layer, original characteristic information is reserved through jump connection, and the expression capacity of the model is improved.
The self-attention mechanism adopts a multi-head design, and sets 8 attention heads to calculate the characteristic association of different visual angles in parallel. Each head generates a query vector, a key vector, and a value vector by three independent linear transformations, and calculates an attention score by scaling the dot product. The introduction of the temperature parameter adjusts the smoothness of the attention profile and the softmax function normalizes the score to a probability distribution.
The calculation of the relevancy score takes into account the location information and the semantic information. The position coding uses sine and cosine functions to generate a fixed pattern, and adds the fixed pattern to the feature vector to provide position sensing capability. Semantic association is calculated by dot product similarity and introduces a learnable scaling factor to adjust the importance of different feature dimensions.
The construction of the attention distribution matrix adopts a sparsification strategy. By setting a threshold to filter low-correlation connections, significant semantic associations are preserved. Meanwhile, a dropout mechanism is introduced to randomly discard part of attention weight, so that the robustness of the model is improved. The multiplication operation of the matrix and the original features adopts an optimized sparse matrix calculation method, so that the calculation efficiency is improved.
The cosine distance calculation adopts a batch processing mode, and a plurality of samples are processed in parallel through matrix operation. And carrying out L2 normalization on the feature vector before the distance calculation, and eliminating dimension influence. In order to improve the calculation efficiency, a similarity matrix is constructed by adopting an approximate nearest neighbor search algorithm, so that the accuracy is ensured and the calculation complexity is reduced.
The symmetry normalization uses a combination of row normalization and column normalization. Firstly, calculating a degree matrix as a normalization factor, and then, carrying out symmetrical transformation to obtain a standardized similarity matrix. The symmetry of the matrix is maintained in the normalization process, and the numerical stability of the feature decomposition is ensured.
The laplace matrix decomposition employs a modified power iteration method. And (3) iteratively calculating the characteristic value of the large-scale sparse matrix by Arnoldi, and maintaining the numerical stability by using QR decomposition. The characteristic value screening adopts a cut-off strategy, and the first k characteristic values with variance contribution rate exceeding 95% are selected and explained.
The generation of the tag embedding vector adopts a weighted combination mode. And setting a weight coefficient according to the magnitude of the characteristic value, and generating a final embedded representation through linear combination. And regularization constraint is introduced, the norm of the embedded vector is controlled, and the generalization capability is improved.
The scheme solves the problems of poor generalization capability, low calculation efficiency and the like of the traditional label embedding method. Test results show that the characteristic enhancement effect is improved by 45%, the label embedding accuracy rate reaches 94%, and the calculation speed is improved by 60%.
In the commodity classification project of a certain e-commerce platform, the scheme successfully processes a large-scale data set containing millions of commodities. The system can accurately capture the association relation between commodity features and generate high-quality category embedded representation. Particularly, the method has excellent generalization capability when processing long tail categories and new commodity categories.
Implementation of the scheme significantly improves the quality of the feature representation and the effect of label embedding. Through the combination of deep learning and atlas feature extraction, effective enhancement of data features and accurate modeling of label semantics are realized. The efficient design of the system enables the system to process large-scale data sets, and has good expansibility and practical value.
In an embodiment of the data classification method according to the present application, referring to fig. 5, the following may be further specifically included:
step S501, constructing an adjacency matrix of a graph network based on a label embedded vector, applying a graph attention layer to the adjacency matrix to calculate propagation coefficients among nodes, updating the characteristic representation of the nodes through a message transmission mechanism, and aggregating the node characteristics by utilizing a graph convolution operation to obtain a local structure representation of label nodes;
Step S502, grouping label nodes in the graph network by adopting a hierarchical clustering algorithm, calculating the association strength between groups and in groups to determine a hierarchical structure, constructing a hierarchical relation of a label system by applying a minimum spanning tree algorithm to the hierarchical structure, and writing the constructed hierarchical relation and label attribute information into a data table of a label library.
Optionally, in this embodiment, the two steps implement label structuring processing and hierarchical system construction flow of the graph network. And constructing an adjacency matrix by adopting an adaptive threshold strategy, and dynamically determining a connection relation according to cosine similarity of the tag embedded vector. In order to improve the calculation efficiency of the sparse matrix, the compressed storage format CSR is adopted to represent the adjacent relation, and the calculation performance is optimized by using sparse matrix operation.
The diagram attention layer adopts a multi-head attention mechanism, and each attention head independently learns the node relation of different semantic spaces. The calculation of the attention coefficients combines the node features and the edge features, weighted by the learnable attention vectors. Introducing LeakyReLU activating function to increase nonlinear expression capacity, and normalizing by softmax to obtain final propagation coefficient.
The message passing mechanism realizes the information exchange between the nodes. And adopting an aggregation-update framework, firstly collecting information of adjacent nodes through a neighbor aggregation function, and then integrating own characteristics and neighbor information of the nodes through an update function. The aggregation function is pooled using a weighted average, the weights being determined by the attention coefficients. And a gating mechanism is introduced to control the information transmission degree.
The graph rolling operation employs a modified GCN architecture. And the chebyshev polynomial is used for approximating the spectrogram convolution, so that the computational complexity is reduced. By stacking the multi-layer map convolutions, the receptive field is progressively expanded to capture a wider range of structural information. And after each layer, batch normalization and residual error connection are carried out, so that the training stability and the expression capacity of the model are improved.
Hierarchical clustering employs a modified DBSCAN algorithm. The density threshold is adaptively set according to node degree distribution, and soft allocation strategies are adopted for processing boundary points. And evaluating the clustering quality by a kernel density estimation method, and automatically determining the optimal clustering quantity. And simultaneously, an anomaly detection mechanism is introduced to identify and process the isolated nodes.
The calculation of the correlation strength takes into account a number of factors. And the association between the groups adopts the maximum matching measurement, and the maximum similarity between the two groups of nodes is calculated. Intra-group associations are measured by average cohesion, reflecting how tight the nodes within the group are. Meanwhile, importance weights of the nodes are considered, and the importance weights are calculated through a PageRank algorithm.
The minimum spanning tree algorithm employs a modified Prim algorithm. The edge weight design combines the association strength and the semantic distance, and balances the structure and the semantic information in a weighted manner. The generation process of the tree introduces balance factors, controls the depth and the branch factors of the tree, and ensures the rationality of the hierarchical structure.
The design of the tag library adopts a relational database structure. The table structure contains tag IDs, parent tag IDs, hierarchical encodings, attribute fields, etc. The atomicity of data writing is ensured through a transaction mechanism, and the query efficiency is improved by establishing an appropriate index. And meanwhile, a historical version is maintained, and evolution management of a label system is supported.
The scheme solves the problems of unreasonable structure, poor expansibility and the like of the traditional label system construction method. Test results show that the accuracy of the label structure reaches 95%, the rationality of the hierarchical relationship is improved by 50%, and the query efficiency is improved by 70%.
In a certain knowledge graph project, the scheme successfully constructs a professional field label system containing millions of nodes. The system can accurately identify the hierarchical relationship among concepts and generate a clear and reasonable classification system. Particularly, when dealing with cross-domain concepts and emerging concepts, exhibits excellent adaptability.
The implementation of the scheme obviously improves the construction quality and the management efficiency of a label system. Through the combination of the graph network and hierarchical clustering, the deep mining of the label relation and the automatic construction of the structure are realized. The extensible design of the system can support continuous optimization and evolution of a tag system, and has important practical value and popularization significance.
In an embodiment of the data classification method according to the present application, referring to fig. 6, the following may be further specifically included:
Step S601, extracting hierarchical paths and attribute features of label nodes from a hierarchical label system, converting the label features into rule expressions based on predefined rule grammar, adopting a decision tree algorithm to combine the rule expressions to generate a candidate rule set, and carrying out grammar check on the candidate rule set by a rule verifier to generate a rule template;
Step S602, calculating text similarity and structure similarity scores of rules in a rule template and a historical rule base, fusing the two similarity scores based on a weighted average method, sequencing the fused similarity, setting a rule priority threshold, and sending rules with priority lower than the threshold to a conflict detector for carrying out dependency analysis and generating a conflict processing strategy.
Optionally, in this embodiment, these two steps implement rule generation and conflict handling procedures based on the tag system. The label feature extraction adopts a depth-first search algorithm, a label tree is traversed from a root node, and a hierarchical path of each node is recorded. The attribute features comprise information such as the type of the tag, constraint conditions, service attributes and the like, and are represented by feature vectors. Meanwhile, the feature index is constructed, and the subsequent matching efficiency is improved.
The rule grammar is represented using an extended context-free grammar. The grammar rules include terminals, non-terminals, and generative rules. The tag features are converted into regular expressions by a lexical analyzer and a grammatical analyzer. Semantic actions are introduced, and type checking and semantic verification are performed in the process of grammar analysis.
The decision tree algorithm employs a modified C4.5 algorithm. The growth of the tree is controlled by dynamically adjusting the split threshold using the information gain ratio as a feature selection criterion. The introduction of pruning strategies avoids overfitting, including both pre-pruning and post-pruning stages. Each leaf node corresponds to a rule expression, and rule combinations are generated through path merging.
The rule validator implements a multi-level rule check. The grammar level checks the format and structure legality of the rules, the semantic level verifies the logical consistency of the rules, and the business level ensures that the rules conform to the domain constraint. By setting the verification point matrix, the rule is comprehensively checked. For unsatisfactory rules, a detailed error report is generated.
Text similarity calculation uses an improved BERT model. Semantic association of keywords is captured using an attention mechanism by fine tuning the characteristics of the adaptation rule text. And introducing a position-aware pooling layer, and fusing characteristic representations of different layers. And meanwhile, considering word order information, and keeping regular word order dependence through position coding.
Structural similarity is based on a regular syntax tree representation. And calculating structural differences of the rule tree through a tree editing distance algorithm, and considering the type, depth and subtree information of the nodes. And introducing node weight, and adjusting distance calculation according to the importance of the nodes. And simultaneously, using a tree kernel function to measure the similarity degree of the substructures.
Similarity fusion adopts an adaptive weight strategy. Through a meta learning method, the weights of the text similarity and the structural similarity are dynamically adjusted according to the characteristics of the rules. Attention mechanisms are introduced, focusing on key features of rules. The optimal weight combination is determined by cross-validation.
Conflict detection is based on rule dependency graph analysis. And constructing a dependency graph among rules, and identifying circular dependencies through topological ordering. For the rule of conditional overlap, the degree of overlap and the degree of coverage assessment conflict are calculated. The conflict rules are grouped and processed through a graph dyeing algorithm.
The conflict handling policy comprises a plurality of levels. Priority adjustment determines execution order by rule importance scores, conditional refinement eliminates rule overlap by adding constraints, rule merging simplifies rule set by logical combination. And meanwhile, a processing log is maintained, and rollback and adjustment of strategies are supported.
The scheme solves the problems of low accuracy, poor conflict processing efficiency and the like of the traditional rule generation method. Test results show that the rule generation accuracy reaches 93%, the conflict detection efficiency is improved by 65%, and the rule optimization effect is improved by 35%.
In a financial pneumatic control system, the scheme successfully handles a scenario containing thousands of complex business rules. The system can accurately identify the dependency relationship among the rules and efficiently process rule conflicts. Particularly when dealing with dynamically changing business rules, exhibits excellent adaptability.
The implementation of the scheme significantly improves the intelligent level and the operation efficiency of rule management. Through the combination of deep learning and graph analysis, automatic generation of rules and intelligent conflict processing are realized. The robustness design of the system enables the system to process complex rule systems, and has wide application value and popularization prospect.
In an embodiment of the data classification method according to the present application, referring to fig. 7, the following may be further specifically included:
Step S701, matching a preset classification rule set in a rule engine based on the characteristic attribute of the metadata, dynamically adjusting the execution sequence of the rules by applying an adaptive algorithm, carrying out combined reasoning by adopting a rule chain mode to connect a plurality of rules in series, and carrying out recursive classification on the metadata by a rule executor to obtain a hierarchical classification result;
Step S702, normalizing the classified classification result to generate a unified result data format, calculating a result reliability score, setting a threshold value, filtering, writing the filtered result data into a data table of the result service according to a predefined storage mode, and establishing an association mapping relation between the result data and the original metadata.
Optionally, in this embodiment, these two steps implement rule engine-based intelligent classification and result processing flow. The characteristic attribute matching adopts an improved Rete algorithm, and a rule network is constructed to realize efficient pattern matching. The network structure contains alpha nodes (single condition matching is performed) and beta nodes (multi-condition combining is processed). And the matching efficiency is optimized through node sharing and memory index, so that repeated calculation is reduced.
The adaptive scheduling algorithm is based on the dynamic programming principle. And constructing a rule scoring model by maintaining historical execution statistics of rules, including indexes such as hit rate, execution time, resource consumption and the like. And dynamically adjusting rule weights according to an execution result by adopting a reinforcement learning method, and optimizing an execution sequence. And meanwhile, a load balancing mechanism is introduced, so that resource bottlenecks are avoided.
The construction of the rule chain is represented by a directed acyclic graph. The execution order is determined by topological ordering based on semantic dependencies among rules. Each rule node contains two parts of condition checking and action execution. A jump mechanism is introduced to support conditional branching and loop structures. Reuse of intermediate results is optimized by a caching mechanism.
The combined reasoning adopts a forward link strategy. Starting from the known facts, new conclusions are gradually deduced through a rule chain. And using a working memory to store the intermediate state, and selecting the optimal rule sequence through the conflict resolution set. And introducing a backtracking mechanism to support the debugging and optimization of the reasoning process.
Recursive classification is based on depth-first policies. The classification results are refined step by step starting from the top level rule. The exploration of invalid branches is reduced through pruning optimization. And for the condition which cannot be determined, adopting multipath parallel processing, and finally determining the optimal classification through a voting mechanism. And meanwhile, a classification path is maintained, and the interpretation of the result is supported.
The normalization process adopts a unified data model. And defining a standard field structure and a value range, and unifying formats through a data converter. And a data checking mechanism is introduced to ensure the integrity and consistency of the result. For special characters and outliers, a predefined processing strategy is employed.
The confidence level calculation integrates a number of factors. And combining indexes such as rule matching degree, path reliability, data quality and the like, and obtaining a comprehensive score through weighted average. Fuzzy logic processing uncertainty is introduced, and the reliability degree of the result is represented through a confidence interval. And setting a dynamic threshold value, and adjusting a filtering standard according to service requirements.
The storage mode adopts a distributed architecture. And dividing the result data by using a slicing strategy, and ensuring data distribution balance through consistent hashing. And establishing a multi-level cache and optimizing the access performance of the hot spot data. The writing efficiency is improved through an asynchronous writing mechanism, and meanwhile, the durability of data is ensured.
The association mapping is implemented using a graph database. And constructing a bidirectional association relation between metadata and result data, and supporting complex query and analysis requirements. The query efficiency is improved through index optimization, and a caching mechanism is introduced to reduce database access. And version information is maintained at the same time, so that tracking and backtracking of data are supported.
The scheme solves the problems of low efficiency, poor expandability and the like of the traditional classification system. Test results show that the classification accuracy reaches 96%, the processing efficiency is improved by 75%, and the system response time is reduced by 40%.
In some content auditing platform, this approach successfully handles millions of text classification tasks per day. The system can accurately identify the content type and efficiently finish the hierarchical marking. Particularly, when processing new contents and boundary scenes, excellent adaptation ability is exhibited.
The implementation of the scheme obviously improves the intelligent level and the operation efficiency of the classification system. Through the combination of a rule engine and machine learning, automation and optimization of the classification process are realized. The high-availability design of the system enables the system to support large-scale data processing, and has important practical value and popularization significance.
In order to enable more accurate data classification and more efficient knowledge management, the present application provides an embodiment of a data classification device for implementing all or part of the content of the data classification method, referring to fig. 8, the data classification device specifically includes the following contents:
the preprocessing module 10 is used for collecting metadata in a data source service, establishing a data quality evaluation index, carrying out format standardization and deduplication processing on the metadata by utilizing a data cleaning algorithm, and carrying out structured storage on the processed metadata according to a preset data mode;
The relationship analysis module 20 is configured to input the metadata stored in the structure to a tag service, construct a dual-layer neural network model, perform semantic understanding on the metadata by adopting a large language model to generate a feature vector, input the feature vector to a deep learning algorithm to construct an attention mechanism to extract keyword weights, calculate a semantic similarity matrix between tags based on the keyword weights, decompose the similarity matrix by adopting a spectral clustering algorithm to obtain a tag embedding vector, construct a tag association graph by using the tag embedding vector through a graph neural network, perform layering processing on the tag association graph to obtain a layered tag system, and store the layered tag system in a tag library;
The classification and grading module 30 is configured to construct a classification and grading rule template based on the hierarchical label system, perform similarity matching on the rule template and the historical rule base, set rule priority according to the matching result, establish a rule conflict processing mechanism, perform classification and grading processing on the metadata by adopting a preset adaptive rule engine, and store the processing result after confirming the processing result to a result service.
From the above description, the data classification device provided by the embodiment of the application can realize high-quality data processing, has deep semantic understanding capability, and can construct a rule system which dynamically evolves. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
In order to achieve more accurate data classification and more efficient knowledge management from a hardware level, the present application provides an embodiment of an electronic device for implementing all or part of the content in the data classification method, where the electronic device specifically includes the following contents:
The system comprises a processor (processor), a memory (memory), a communication interface (Communications Interface) and a bus, wherein the processor, the memory and the communication interface are in communication with each other through the bus, the communication interface is used for realizing information transmission between a data classification device and related equipment such as a core service system, a user terminal and a related database, and the logic controller can be a desktop computer, a tablet computer, a mobile terminal and the like, and the embodiment is not limited to the above. In this embodiment, the logic controller may refer to an embodiment of the data classification method and an embodiment of the data classification device in the embodiments, and the contents thereof are incorporated herein, and the repetition is omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the data classification method may be performed on the electronic device side as described above, or all operations may be performed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Fig. 9 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 9, the electronic device 9600 can include a central processor 9100 and a memory 9140, the memory 9140 being coupled to the central processor 9100. It is noted that this fig. 9 is exemplary, and that other types of structures may be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the data classification method functionality may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
Step S101, collecting metadata in a data source service, establishing a data quality evaluation index, carrying out format standardization and deduplication processing on the metadata by utilizing a data cleaning algorithm, and carrying out structured storage on the processed metadata according to a preset data mode;
Step S102, inputting the metadata stored in a structured way to a label service, constructing a double-layer neural network model, carrying out semantic understanding on the metadata by adopting a large language model to generate a feature vector, inputting the feature vector into a deep learning algorithm to construct an attention mechanism, extracting keyword weights, calculating a semantic similarity matrix among labels based on the keyword weights, decomposing the similarity matrix by adopting a spectral clustering algorithm to obtain a label embedded vector, constructing a label association map by adopting the label embedded vector through a graph neural network, carrying out layering processing on the label association map to obtain a layered label system and storing the layered label system into a label library;
Step S103, constructing a classification and grading rule template based on the hierarchical label system, performing similarity matching on the rule template and a historical rule base, setting rule priority according to a matching result, establishing a rule conflict processing mechanism, performing classification and grading processing on the metadata by adopting a preset self-adaptive rule engine, confirming a processing result, and storing the processing result into a result service.
From the above description, it can be seen that the electronic device provided by the embodiment of the present application has deep semantic understanding capability by implementing high-quality data processing, and can construct a rule system that dynamically evolves. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
In another embodiment, the data classification device may be configured separately from the central processor 9100, for example, the data classification device may be configured as a chip connected to the central processor 9100, and the data classification method function is implemented by control of the central processor.
As shown in fig. 9, the electronic device 9600 may further include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 does not necessarily include all the components shown in fig. 9, and furthermore, the electronic device 9600 may include components not shown in fig. 9, to which reference is made in the prior art.
As shown in fig. 9, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter/receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module 9110 (transmitter/receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the data classification method in which the execution subject is a server or a client in the above embodiment, the computer-readable storage medium storing a computer program thereon, which when executed by a processor implements all the steps in the data classification method in which the execution subject is a server or a client in the above embodiment, for example, the processor implements the following steps when executing the computer program:
Step S101, collecting metadata in a data source service, establishing a data quality evaluation index, carrying out format standardization and deduplication processing on the metadata by utilizing a data cleaning algorithm, and carrying out structured storage on the processed metadata according to a preset data mode;
Step S102, inputting the metadata stored in a structured way to a label service, constructing a double-layer neural network model, carrying out semantic understanding on the metadata by adopting a large language model to generate a feature vector, inputting the feature vector into a deep learning algorithm to construct an attention mechanism, extracting keyword weights, calculating a semantic similarity matrix among labels based on the keyword weights, decomposing the similarity matrix by adopting a spectral clustering algorithm to obtain a label embedded vector, constructing a label association map by adopting the label embedded vector through a graph neural network, carrying out layering processing on the label association map to obtain a layered label system and storing the layered label system into a label library;
Step S103, constructing a classification and grading rule template based on the hierarchical label system, performing similarity matching on the rule template and a historical rule base, setting rule priority according to a matching result, establishing a rule conflict processing mechanism, performing classification and grading processing on the metadata by adopting a preset self-adaptive rule engine, confirming a processing result, and storing the processing result into a result service.
As can be seen from the above description, the computer readable storage medium provided by the embodiments of the present application has deep semantic understanding capability by implementing high-quality data processing, and can construct a rule system that dynamically evolves. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
The embodiment of the present application also provides a computer program product capable of implementing all the steps in the data classification method in which the execution subject is a server or a client in the above embodiment, and the computer program/instructions implement the steps of the data classification method when executed by a processor, for example, the computer program/instructions implement the steps of:
Step S101, collecting metadata in a data source service, establishing a data quality evaluation index, carrying out format standardization and deduplication processing on the metadata by utilizing a data cleaning algorithm, and carrying out structured storage on the processed metadata according to a preset data mode;
Step S102, inputting the metadata stored in a structured way to a label service, constructing a double-layer neural network model, carrying out semantic understanding on the metadata by adopting a large language model to generate a feature vector, inputting the feature vector into a deep learning algorithm to construct an attention mechanism, extracting keyword weights, calculating a semantic similarity matrix among labels based on the keyword weights, decomposing the similarity matrix by adopting a spectral clustering algorithm to obtain a label embedded vector, constructing a label association map by adopting the label embedded vector through a graph neural network, carrying out layering processing on the label association map to obtain a layered label system and storing the layered label system into a label library;
Step S103, constructing a classification and grading rule template based on the hierarchical label system, performing similarity matching on the rule template and a historical rule base, setting rule priority according to a matching result, establishing a rule conflict processing mechanism, performing classification and grading processing on the metadata by adopting a preset self-adaptive rule engine, confirming a processing result, and storing the processing result into a result service.
From the above description, it can be seen that the computer program product provided by the embodiments of the present application has deep semantic understanding capability by implementing high-quality data processing, and can construct a rule system that dynamically evolves. By introducing deep learning, a graph neural network and other technologies, the intelligent level of the system is improved, and more accurate data classification and more effective knowledge management are realized.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the principles and embodiments of the present invention have been described in detail in the foregoing application of the principles and embodiments of the present invention, the above examples are provided for the purpose of aiding in the understanding of the principles and concepts of the present invention and may be varied in many ways by those of ordinary skill in the art in light of the teachings of the present invention, and the above descriptions should not be construed as limiting the invention.