An artificial intelligence big data-based knowledge management system

Abstract

The application relates to the technical field of knowledge management and artificial intelligence, and discloses a knowledge management system based on artificial intelligence big data. The system comprises knowledge flow capturing, symbolic reasoning, neural embedding, strategy synthesis and strategy deployment components. The system generates structured knowledge through multi-modal data acquisition and cleaning; carries out symbolic reasoning by using predicate logic and a rule base, constructs a symbolic knowledge graph; generates low-dimensional vector representation by using a deep learning model; fuses the symbolic knowledge graph and the vector representation, generates a knowledge management strategy by using a strategy gradient algorithm; and asynchronously executes the strategy in a cloud environment. The scheme combines symbolic reasoning and neural network technology, ensures the explainability and logical rigor of the knowledge processing process, and improves the dynamic adaptability and decision accuracy of the system strategy generation.

Description

Technical Field

[0001] This invention relates to the fields of knowledge management and artificial intelligence technology, specifically a knowledge management system based on artificial intelligence and big data. Background Technology

[0002] Traditional knowledge management systems primarily rely on keyword matching, statistical relation extraction, or simple graph construction techniques. While these methods enable digital storage and basic retrieval of knowledge, their capabilities are typically limited to entity recognition and shallow relational linking when processing unstructured data. Existing technologies lack effective mechanisms for expressing and processing the complex logical structures, implicit causal chains, and conclusions requiring rigorous reasoning behind the data. Knowledge is stored in a flattened manner, failing to support advanced intelligent applications such as deep question answering and causal analysis that require rigorous logical deduction, resulting in the extraction of knowledge value remaining superficial.

[0003] In knowledge management and dynamic optimization, mainstream solutions fall into two categories: expert systems based on predefined rules and deep learning models that rely entirely on data-driven approaches. While rule engines offer transparent decision-making processes and interpretable results, their rule bases require meticulous manual construction and maintenance, making them ill-suited to the rapid evolution of knowledge content and severely lacking in flexibility, easily becoming rigid. Pure end-to-end neural network models, while possessing powerful pattern recognition and adaptive learning capabilities, suffer from opaque internal decision-making processes, and the generated strategies may deviate from established business rules or common-sense logic, posing risks to credibility and controllability. These limitations of both technical approaches mean that the generation of knowledge management strategies often requires a difficult trade-off between interpretability and flexibility.

[0004] Existing technical architectures generally treat the symbolic representation of knowledge and the sub-symbolic learning of neural networks as two independent processes. The construction and updating of the knowledge base are disconnected from the knowledge-based reasoning and decision-making processes, failing to form a unified framework capable of collaboratively handling explicit logical constraints and implicit semantic relationships. This fragmentation makes it difficult for the system to respond in real-time to changes in knowledge state and to generate intelligent policies that are both logically consistent and adaptively optimized. A new paradigm that deeply integrates symbolic reasoning and neural network learning is needed to address the core challenges in the integration of knowledge representation, reasoning, and policy generation. Summary of the Invention

[0005] The purpose of this invention is to provide a knowledge management system based on artificial intelligence and big data to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a knowledge management system based on artificial intelligence and big data, the system comprising:

[0007] A knowledge stream capture component is used to continuously collect raw knowledge streams from multimodal data sources, and to clean and standardize the raw knowledge streams in real time to generate structured knowledge data. The multimodal data sources include text streams, image streams, and speech streams.

[0008] The symbolic reasoning component is used to convert structured knowledge data into predicate logic expressions and apply a rule base for logical deduction to generate a symbolic knowledge graph, which includes entity predicates and relation constraints.

[0009] Neural embedding components are used to vectorize symbolic knowledge graphs using deep neural networks, producing low-dimensional knowledge embedding representations.

[0010] A strategy synthesis component is used to combine low-dimensional knowledge embedding representation and symbolic knowledge graph to generate a knowledge management strategy sequence through a policy gradient algorithm. The knowledge management strategy sequence includes operation commands and parameter adjustment instructions.

[0011] The strategy deployment component is used to asynchronously execute knowledge management strategy sequences in a cloud computing environment and output knowledge status change records.

[0012] Preferably, the knowledge flow capture component includes:

[0013] The data cleaning unit is used to filter noise and impute missing values ​​in the original knowledge stream to generate a clean data stream.

[0014] The format conversion unit is used to convert the cleaned data stream into a unified data format and add timestamps to generate time-series knowledge data.

[0015] The feature extraction unit is used to apply a convolutional autoencoder to perform feature dimensionality reduction on time series knowledge data and extract key feature vectors.

[0016] The entity recognition unit is used to match key feature vectors with a knowledge entity database to identify a set of candidate entities.

[0017] The entity verification unit is used to perform consistency checks on the candidate entity set based on the rule base and generate the final knowledge entity features.

[0018] Preferably, the symbolic reasoning component includes:

[0019] The logic transformation unit is used to map knowledge entity features into predicate logic atoms and generate initial logic formulas;

[0020] The rule application unit is used to load reasoning rules from the rule base, perform forward chain reasoning on the initial logical formula, generate an extended logical formula set, check for logical contradictions in the extended logical formula set, and apply conflict resolution strategies to generate a consistent logical conclusion.

[0021] The graph construction unit is used to convert consistent logical conclusions into a graph structure, where nodes represent entities and edges represent relationships, generating a symbolic knowledge graph; the symbolic knowledge graph is then sparsified by removing redundant nodes and edges to generate an optimized symbolic knowledge graph.

[0022] Preferably, the neural embedding component includes:

[0023] A dynamic embedding vector generation unit is used to extract the topological connection relationships between entities from the optimized symbolic knowledge graph and generate a weighted adjacency matrix; the adjacency matrix is ​​input into a graph attention network, and the association strength between entities is calculated through a multi-head attention mechanism to generate dynamically updated entity embedding vectors.

[0024] The temporal feature capture unit is used to perform one-dimensional convolution processing on the time-series data of entity states in the symbolic knowledge graph to extract temporal feature vectors.

[0025] The cross-modal fusion unit is used to perform feature cross-calculation on the dynamically updated entity embedding vector and the temporal feature vector to generate a fused feature representation;

[0026] The dimension reduction and compression unit is used to perform nonlinear dimension reduction processing on the fused feature representation through a variational autoencoder, reconstruct features in the latent space, and output a low-dimensional knowledge embedding representation.

[0027] Preferably, the strategy synthesis component includes:

[0028] The state encoding unit is used to convert low-dimensional knowledge embedding representations into input state vectors for the policy network;

[0029] The policy network unit is used to calculate the policy value of the input state vector using the policy network and generate the initial policy distribution.

[0030] The sampling unit is used to sample from the initial policy distribution to generate a sequence of candidate policies; the evaluation unit is used to evaluate the expected returns of the candidate policy sequences using a value network and generate policy scores.

[0031] The selection unit is used to select the optimal strategy sequence based on the strategy score and add exploratory noise to generate a knowledge management strategy sequence.

[0032] Preferably, the system further includes:

[0033] The anomaly detection component is used to collect knowledge state change records in real time during policy execution and extract performance indicator data; analyze the anomaly patterns in the performance indicator data and generate anomaly event reports.

[0034] Preferably, the anomaly detection component includes:

[0035] The data slicing unit is used to divide performance indicator data into multiple data slices according to time windows.

[0036] The feature calculation unit is used to calculate statistical features for each data slice, including mean, variance, and peak value;

[0037] The pattern matching unit is used to match statistical features with a historical abnormal pattern library to calculate an anomaly similarity score; the anomaly similarity score is compared with a dynamic threshold to generate an anomaly label;

[0038] The report generation unit is used to aggregate anomaly flags and corresponding data slices to generate anomaly event reports.

[0039] Preferably, the system further includes:

[0040] The strategy adjustment component is used to dynamically adjust the parameter adjustment instructions in the knowledge management strategy sequence based on abnormal event reports.

[0041] Preferably, the strategy adjustment component includes:

[0042] The parameter parsing unit is used to extract affected operation commands and parameter adjustment instructions from abnormal event reports;

[0043] The impact analysis unit is used to analyze the degree of impact of operation commands and parameter adjustment instructions on the knowledge state and generate impact weights.

[0044] The calculation unit is adjusted to recalculate the values ​​in the parameter adjustment instruction based on the influence weight, generating a corrected parameter adjustment instruction; the corrected parameter adjustment instruction is then injected into the knowledge management strategy sequence to generate an adjusted knowledge management strategy sequence.

[0045] Preferably, the system further includes:

[0046] The instruction translation component is used to identify hardware and software differences in different execution environments and convert knowledge management strategy sequences into native instruction sets supported by the target platform.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] By converting cleaned and standardized structured knowledge data into formalized predicate logic expressions and utilizing a predefined rule base for automated logical deduction, the system constructs a symbolic knowledge graph containing entity, attribute, and relational constraints. This establishes a rigorous mathematical and logical foundation for knowledge, enabling machines to perform step-by-step, verifiable symbolic reasoning. Based on the predicate logic deduction process, each conclusion stems from explicit premises and reasoning rules, forming a clear and auditable causal chain. This mechanism makes the system's reasoning process completely transparent; decision-making is no longer based on difficult-to-interpret numerical calculations, but on logical statements that conform to human thinking habits. This enhances the credibility of applications in high-risk fields such as medical diagnosis and financial risk control. The symbolic system checks the consistency of knowledge, proactively identifying and avoiding logical contradictions, ensuring the inherent rigor of the knowledge system.

[0049] Through the policy synthesis component, the explicit logical rules carried by the symbolic knowledge graph are synergistically utilized with the implicit semantic information contained in the low-dimensional vectors generated by the neural embedding component. The symbolic knowledge graph provides a structured and interpretable constraint framework, ensuring that all generated policy drafts first meet the basic requirements of logical consistency and domain rules, setting rigid boundaries for the reliability and security of the policies. The distributed representations learned by the neural embedding model from massive amounts of data capture the complex, non-linear statistical relationships and contextual semantics between knowledge elements, information that is often difficult to fully describe with limited explicit rules. The policy gradient algorithm explores and optimizes in this hybrid representation space, dynamically adjusting policy parameters. This synergistic mechanism enables the final generated knowledge management policy sequence to not only strictly adhere to the preset logical constraints, avoiding operations that violate common sense and rules, but also possess the flexibility and generalization ability to continuously learn from actual interaction data and adapt to dynamic environmental changes. Attached Figure Description

[0050] Figure 1 This is a schematic diagram illustrating the working principle of the knowledge management system based on artificial intelligence and big data as described in this invention.

[0051] Figure 2 A flowchart illustrating how the knowledge flow capture component works;

[0052] Figure 3 A flowchart illustrating how the strategy synthesis component works;

[0053] Figure 4 A scatter plot of low-dimensional mappings for neural embeddings;

[0054] Figure 5 Comparison chart showing the effects of adjusting parameters for the strategy. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 This invention provides a knowledge management system based on artificial intelligence and big data. The system includes: a knowledge flow capture component that continuously collects raw knowledge flows from multimodal data sources such as text, image, and speech streams, and performs real-time cleaning and format standardization on the raw knowledge flows to generate structured knowledge data; a symbolic reasoning component that converts the structured knowledge data into predicate logic expressions and applies a rule base for logical deduction to generate a symbolic knowledge graph containing entity predicates and relational constraints; a neural embedding component that uses a deep neural network to vectorize the symbolic knowledge graph, generating a low-dimensional knowledge embedding representation; a policy synthesis component that combines the low-dimensional knowledge embedding representation and the symbolic knowledge graph, and generates a knowledge management policy sequence including operation commands and parameter adjustment instructions through a policy gradient algorithm; and a policy deployment component that asynchronously executes the knowledge management policy sequence in a cloud computing environment and outputs knowledge state change records.

[0057] Example 1: See Figure 2 The knowledge flow capture component includes a data cleaning unit, a format conversion unit, a feature extraction unit, an entity recognition unit, and an entity verification unit. In specific implementation, the data cleaning unit performs noise filtering and missing value imputation on the original knowledge flow. Noise filtering uses a threshold processing method based on wavelet transform to remove high-frequency interference components. Missing value imputation uses a linear regression model to predict and fill in missing values ​​based on adjacent data points, thereby generating a cleaned data flow. The format conversion unit converts the cleaned data flow into a unified data format, which is defined as a JSON-LD structure to support semantic annotation and adds timestamps. The timestamps use the ISO8601 standard format to record the data acquisition time, generating time-series knowledge data. The feature extraction unit applies a convolutional autoencoder to perform feature dimensionality reduction on time-series knowledge data. The convolutional autoencoder consists of an encoder and a decoder. The encoder extracts spatial features through convolutional and pooling layers, while the decoder reconstructs the output through deconvolutional layers to extract key feature vectors. The entity recognition unit performs similarity matching between the key feature vectors and a knowledge entity database. The knowledge entity database stores predefined entity embeddings. Similarity matching uses cosine similarity to calculate the distance between vectors and identifies a set of candidate entities. The entity verification unit performs consistency checks on the candidate entity set based on a rule base. The rule base contains logical constraints and domain rules. The consistency check verifies whether entity attributes and relationships conform to the rule base definition and generates the final knowledge entity features.

[0058] The symbolic reasoning component comprises a logic transformation unit, a rule application unit, and a graph construction unit. In its implementation, the logic transformation unit maps knowledge entity features to predicate logic atoms, which represent entity-attribute relationships as triples and generate initial logic formulas. The rule application unit loads reasoning rules from a rule base. These rules define logical derivation paths using Horn clauses and perform forward chain reasoning on the initial logic formulas. Forward chain reasoning generates new facts through iterative rule application, producing an extended set of logic formulas. Logical contradictions within the extended set are checked using resolution principles for conflict resolution, and a conflict resolution strategy is applied to generate a consistent logical conclusion. This conflict resolution strategy selects the optimal derivation path based on priority ranking. The graph construction unit converts the consistent logical conclusions into a graph structure, where nodes represent entities and edges represent relationships, generating a symbolic knowledge graph. This graph is then sparsified by calculating node degree centrality and edge weight thresholds to remove redundant nodes and edges, resulting in an optimized symbolic knowledge graph.

[0059] In some embodiments, the convolutional autoencoder applied to the feature extraction unit performs feature reconstruction using the following formula:

[0060]

[0061] in: Indicates the reconstruction loss. Indicates the number of samples. This represents the first [item] in the input time series knowledge data. Data points, This represents the reconstructed data points output by the decoder. In practice, the convolutional autoencoder optimizes the feature extraction process by minimizing the reconstruction loss, thereby ensuring that key feature vectors retain the main information of the original data.

[0062] It is understandable that the noise filtering and missing value imputation processes of the data cleaning unit rely on real-time data stream processing frameworks, such as Apache Kafka or similar platforms, to achieve efficient data preprocessing. In some embodiments, the similarity matching process of the entity recognition unit can integrate an approximate nearest neighbor search algorithm, such as a method based on locality-sensitive hashing, to accelerate the query of large-scale knowledge entity databases. When integrating an approximate nearest neighbor search algorithm into the similarity matching process of the entity recognition unit, a specific implementation involves using a locality-sensitive hashing method to establish a hash index structure for predefined entity embedding representations in the knowledge entity database. By designing a set of hash functions, high-dimensional key feature vectors are mapped to a low-dimensional signature space, so that semantically similar vectors are assigned to the same hash bucket with a high probability. When a new key feature vector is input, the system first calculates its hash value using a locality-sensitive hashing function, quickly retrieves a subset of candidate entities within the corresponding hash bucket, and then performs an accurate cosine similarity calculation on this subset. This significantly reduces the computational overhead of full database scanning while maintaining matching accuracy, adapting to the efficient query requirements of large-scale knowledge entity databases. Optionally, the forward chain reasoning of the rule application unit can be combined with parallel computing technology to distribute the reasoning task to multiple processing cores, thereby improving the efficiency of logical deduction. It can be understood that the sparsity processing of the graph construction unit can dynamically adjust the threshold based on the graph density index, thus balancing the complexity and expressive power of the symbolic knowledge graph. Optionally, the predicate logic atomic mapping process of the logic transformation unit can support multiple logic languages, such as first-order logic or descriptive logic, to adapt to the knowledge representation needs of different domains.

[0063] Example 2: See Figure 3The neural embedding component includes a dynamic embedding vector generation unit, a temporal feature capture unit, a cross-modal fusion unit, and a dimensionality reduction and compression unit. In specific implementation, the dynamic embedding vector generation unit extracts the topological connection relationships between entities from the optimized symbolic knowledge graph. The topological connection relationships are determined by analyzing the edge attributes and directionality between nodes, generating a weighted adjacency matrix. The adjacency matrix is ​​then input into a graph attention network, which contains multiple graph attention layers. The multi-head attention mechanism is used to calculate the association strength between entities. The association strength is calculated by weighting based on the similarity of node features and edge features, generating dynamically updated entity embedding vectors. The temporal feature capture unit performs one-dimensional convolution processing on the time-series data of entity states in the symbolic knowledge graph. The one-dimensional convolution processing uses multiple convolution kernels of different sizes to capture patterns at different time scales and extract temporal feature vectors. The cross-modal fusion unit performs feature cross-computation on the dynamically updated entity embedding vector and the temporal feature vector. The feature cross-computation is implemented by combining outer product operation with a fully connected layer to generate a fused feature representation. The dimensionality reduction and compression unit performs non-linear dimensionality reduction processing on the fused feature representation through a variational autoencoder. The variational autoencoder includes an encoder network and a decoder network. The encoder network maps the fused feature representation to the mean and variance parameters of the latent space. The decoder network samples and reconstructs the feature representation from the latent space and outputs a low-dimensional knowledge embedding representation.

[0064] The policy synthesis component includes a state encoding unit, a policy network unit, a sampling unit, an evaluation unit, and a selection unit. In specific implementation, the state encoding unit converts the low-dimensional knowledge embedding representation into an input state vector for the policy network. The state encoding unit performs dimensionality transformation and feature enhancement on the low-dimensional knowledge embedding representation through fully connected layers and activation functions. The policy network unit uses the policy network to calculate policy values ​​on the input state vector. The policy network adopts a multilayer perceptron structure and calculates the probability distribution of each possible action through forward propagation to generate an initial policy distribution. The sampling unit samples from the initial policy distribution to generate candidate policy sequences. The sampling process randomly selects actions from the action probability distribution based on a multinomial sampling method. The evaluation unit uses a value network to evaluate the expected reward of the candidate policy sequences. The value network receives the state vector and the candidate policy sequences as input and calculates the state-action value function through forward propagation to generate a policy score. The selection unit selects the optimal policy sequence based on the policy score. The selection process adopts a greedy policy to select the sequence with the highest score from the candidate policy sequences and adds exploration noise to generate a knowledge management policy sequence. The exploration noise is implemented by adding Gaussian random perturbations to the policy parameters.

[0065] In some embodiments, the multi-head attention mechanism of a graph attention network calculates the attention coefficient using the following formula:

[0066]

[0067] in: Indicates the first Nodes in each attention head For nodes Attention coefficient and Representing nodes respectively and nodes The input feature vector, This represents a trainable weight matrix. The parameter vector representing the attention mechanism. This represents the transpose operation of a vector. Represents a node The set of neighboring nodes, This represents a vector concatenation operation. This represents a linear rectified activation function with leakage. In practice, the graph attention network generates the final dynamically updated entity embedding vector by aggregating the outputs of multiple attention heads.

[0068] It is understandable that the one-dimensional convolutional processing of the temporal feature capture unit can be configured with convolutional kernels of different lengths to adapt to time series patterns of different frequencies. In some embodiments, the feature cross-computation of the cross-modal fusion unit can introduce a gating mechanism to control the fusion ratio of different modal features through a sigmoid function. Optionally, the variational autoencoder of the dimensionality reduction compression unit employs a reparameterization technique during training, sampling from a standard normal distribution and combining the mean and variance of the encoder output to generate a latent representation. It is understandable that the policy value computation of the policy network unit can combine a softmax function to transform the network output into a legitimate probability distribution. Optionally, the value network of the evaluation unit can adopt a deep Q-network architecture, updating network parameters through temporal difference learning to improve the accuracy of policy scoring.

[0069] See Figure 4This diagram focuses on the dimensionality reduction and compression unit of the neural embedding component, visually presenting the low-dimensional embedding representation of the symbolic knowledge graph after vectorization. From a technical perspective, the neural embedding component first extracts entity topological connections from the optimized symbolic knowledge graph using a graph attention network, generating dynamic entity embedding vectors. Then, it combines the one-dimensional convolutional processing results of the entity state time-series data from the temporal feature capture unit with feature cross-computation via a cross-modal fusion unit. Finally, it performs nonlinear dimensionality reduction through a variational autoencoder, obtaining the distribution of this two-dimensional embedding space. From a technical verification perspective, the point distribution in the two-dimensional space in the diagram verifies the effectiveness of the dimensionality reduction process: high-dimensional knowledge features are successfully mapped to a low-dimensional latent space while preserving the structural information of the original data. This provides a computable low-dimensional knowledge representation for the subsequent policy synthesis component, supporting the technical goal of generating low-dimensional knowledge embedding representations. The chart presents the relationship between low-dimensional embedding dimension 1 and low-dimensional embedding dimension 2 in the form of scatter points. The distribution pattern of the gray-scale scatter points intuitively reflects the topological relationship of knowledge entities in low-dimensional space. It is a typical visualization verification of the effective transformation from high-dimensional to low-dimensional in the neural embedding link, which strongly supports the technical effectiveness of neural embedding components in knowledge vectorization and dimensionality reduction.

[0070] Example 3: The anomaly detection component collects knowledge state change records in real time during policy execution and extracts performance index data; it analyzes abnormal patterns in the performance index data and generates anomaly event reports. The anomaly detection component includes a data slicing unit, a feature calculation unit, a pattern matching unit, and a report generation unit. In specific implementation, the data slicing unit divides the performance index data into time windows. The time windows use a fixed-length sliding window mechanism to segment the data. The window length is configured according to the system sampling frequency and detection sensitivity requirements, generating multiple data slices. The feature calculation unit calculates statistical features for each data slice. The statistical features include mean, variance, and peak value. The mean reflects the central tendency of the data slice, the variance describes the degree of data fluctuation, and the peak value characterizes the sharpness of the data distribution. The feature calculation unit updates the statistical feature values ​​using an incremental calculation method to reduce the computational load. The pattern matching unit matches statistical features with a historical anomaly pattern database, which stores feature vectors of previously labeled anomaly patterns. The matching process is achieved by calculating the Euclidean distance between the current statistical feature vector and the historical anomaly pattern feature vector, and calculating anomaly similarity scores. The anomaly similarity scores are compared with dynamic thresholds, which are automatically adjusted based on the percentiles of historical normal data distribution, to generate anomaly markers. The report generation unit aggregates anomaly markers and corresponding data slices to generate anomaly event reports. The anomaly event reports record the time of occurrence, duration, scope of impact, and characteristic indicators of the anomaly in a structured data format.

[0071] In some embodiments, the anomaly similarity score of the pattern matching unit is calculated using the following formula:

[0072]

[0073] in: Indicates the abnormal similarity score. Indicates the number of feature dimensions. Indicates the first The weight coefficients of each feature, This indicates the first data slice. One statistical characteristic value, This represents the first corresponding pattern in the historical anomaly pattern library. Each feature value. In practice, the pattern matching unit uses this formula to quantify the degree of deviation between the current data slice and historical anomaly patterns; the higher the anomaly similarity score, the greater the probability of an anomaly.

[0074] It is understandable that the time window segmentation of the data slicing unit can support an overlapping window mode, achieving continuous monitoring by setting the window step size to be smaller than the window length. In some embodiments, the peak value calculation of the feature calculation unit can be combined with the kurtosis coefficient algorithm, quantifying the distribution pattern through the ratio of the fourth central moment to the square of the standard deviation. When implementing the kurtosis coefficient algorithm in the feature calculation unit, the specific method includes calculating the fourth central moment for each data point within the data slice. The fourth central moment is obtained by averaging the fourth power of the deviation of the data value from the mean. At the same time, the square of the standard deviation, i.e., the variance, is calculated, which is the average of the squares of the deviation of the data value from the mean. The kurtosis coefficient algorithm quantifies the kurtosis of the data distribution by dividing the fourth central moment by the variance. This ratio reflects the deviation of the distribution pattern from the normal distribution. A positive value indicates that the distribution has sharper peaks and thicker tails, while a negative value indicates that the distribution is flatter, thus providing more refined distribution pattern features for anomaly detection. This calculation process is integrated into the statistical feature pipeline of the feature calculation unit, ensuring that peak indicators can capture potential abnormal patterns in data slices and, in conjunction with mean and variance features, enhance the anomaly identification capability of the pattern matching unit. Optionally, the historical anomaly pattern library update mechanism of the pattern matching unit can adopt an incremental learning approach, adding newly confirmed anomaly pattern feature vectors to the pattern library. It can be understood that the structured data format of the report generation unit can be expanded to include an anomaly level field, classifying different severity levels based on the anomaly similarity score range. Optionally, the dynamic threshold adjustment process can combine an exponentially weighted moving average method to adaptively update the threshold boundary based on recent normal data characteristics.

[0075] Example 4: The strategy adjustment component dynamically adjusts the parameter adjustment instructions in the knowledge management strategy sequence based on the anomaly event report. In specific implementation, the strategy adjustment component includes a parameter parsing unit, an impact analysis unit, and an adjustment calculation unit. The parameter parsing unit extracts the affected operation commands and parameter adjustment instructions from the anomaly event report. The anomaly event report stores the anomaly type, timestamp, and associated strategy identifier in JSON format. The parameter parsing unit identifies the operation command field and parameter adjustment instruction field in the report through a pattern matching algorithm and converts the extracted instructions into an internal data structure. The impact analysis unit analyzes the degree of influence of the operation commands and parameter adjustment instructions on the knowledge state. The degree of influence is quantified by calculating the cosine similarity of the knowledge state vector before and after the instruction execution, generating an impact weight. The impact weight is a floating-point number between 0 and 1. The adjustment calculation unit recalculates the values ​​in the parameter adjustment instructions based on the impact weight. The recalculation process uses a weighted adjustment algorithm to perform a linear transformation combining the original parameter values ​​and the impact weight to generate the corrected parameter adjustment instructions. The corrected parameter adjustment instructions are injected into the knowledge management strategy sequence. The injection process involves replacing the instruction data at the corresponding positions in the original strategy sequence to generate the adjusted knowledge management strategy sequence.

[0076] In some embodiments, the weighted adjustment algorithm for adjusting the computing unit uses the following formula to calculate the correction parameter value:

[0077]

[0078] in: This indicates the corrected parameter value. This indicates the parameter value in the original parameter adjustment command. Indicates the adjustment factor. This represents the influence weight affecting the generation of the analysis unit. In practical implementation, the adjustment coefficient... The system is dynamically configured based on the operating environment and the type of exception, and the specific values ​​are obtained by querying the preset adjustment coefficient mapping table.

[0079] It is understood that, referring to Table 1, the pattern matching algorithm of the parameter parsing unit can support regular expression matching to handle abnormal event reports with different structures. In some embodiments, the cosine similarity calculation of the influence analysis unit can be combined with the TF-IDF weighting method to assign different importance to different dimensions of the knowledge state vector. Optionally, the linear transformation process of the adjustment calculation unit can introduce a boundary check mechanism to ensure that the corrected parameter values ​​do not exceed the legal range allowed by the system. It is understood that the internal data structure of the parameter parsing unit can be defined in key-value pair form, which facilitates quick access and modification by subsequent units. Optionally, the influence weight quantification process can adopt a multi-factor comprehensive evaluation method to integrate factors such as the severity of the anomaly and the frequency of policy execution.

[0080] Table 1: Parameter Adjustment Command Correction Table

[0081]

[0082] See Figure 5 This chart focuses on the strategy adjustment component, visually presenting a comparison of execution performance scores before and after parameter adjustments in the knowledge management strategy sequence. From a technical perspective, the strategy adjustment component first extracts affected parameter adjustment instructions from anomaly reports through the parameter parsing unit. Then, the impact analysis unit quantifies the degree of influence of these instructions on the knowledge state. Finally, the adjustment calculation unit corrects parameter values ​​based on a weighted adjustment algorithm, achieving strategy optimization. From a data performance and technical verification perspective, the chart uses parameter type as the horizontal axis and execution performance score as the vertical axis, with two lines representing the differences in performance before and after adjustment. This significant score improvement verifies the technical goal of the strategy adjustment component in dynamically optimizing parameters based on anomaly reports. Through precise parameter correction, the knowledge management strategy better aligns with business needs and changes in knowledge state during execution. The line comparison structure clearly demonstrates the performance gain after each parameter adjustment, serving as a typical visual verification of the strategy adjustment component's effectiveness and adaptability in ensuring the knowledge management strategy, strongly supporting the technical effectiveness of dynamically adjusting parameter adjustment instructions to optimize the knowledge management strategy.

[0083] Example 5: The instruction conversion component is used to identify hardware and software differences in different execution environments and convert the knowledge management strategy sequence into a local instruction set supported by the target platform. In specific implementation, the instruction conversion component includes an environment detection unit and an instruction mapping unit. The environment detection unit reads hardware configuration information and software environment variables through a system call interface. The hardware configuration information includes the central processing unit architecture, memory size, and storage device type. The software environment variables include the operating system version number, runtime library version, and application programming interface support level, generating an environment feature descriptor. The instruction mapping unit queries a predefined instruction mapping rule base based on the environment feature descriptor. The instruction mapping rule base stores the mapping relationship between the operation commands in the knowledge management strategy sequence and the target platform's local instruction set. The mapping relationship is defined based on platform-specific constraints and performance optimization requirements, converting the operation commands and parameter adjustment instructions in the knowledge management strategy sequence into machine code or script instructions executable by the target platform, generating a local instruction set.

[0084] In some embodiments, the system call interface of the environment detection unit includes accessing platform-specific registers to read the processor identifier and calling operating system functions to obtain memory page size and cache configuration. The instruction mapping rule base of the instruction mapping unit is organized in a hierarchical structure, including a platform abstraction layer and an instruction translation layer. The platform abstraction layer defines the mapping from general operations to platform-specific operations, and the instruction translation layer handles parameter format adjustment and instruction sequence optimization. In a specific implementation, the instruction mapping unit uses the following formula to calculate instruction translation efficiency:

[0085]

[0086] in: Indicates instruction conversion efficiency. Indicates the number of conversion instructions. Indicates the first The bit width of an instruction. Indicates the first The frequency of instruction execution, Indicates the number of registers on the target platform. This indicates memory access latency. Instruction translation efficiency is used to evaluate the performance of the translated instruction set.

[0087] It is understood that the environment detection unit can be extended to support virtualization environment detection, obtaining virtual hardware characteristics by querying the virtual machine monitoring program interface. In some embodiments, the instruction mapping rule base update mechanism supports online synchronization, downloading the latest mapping rules from the central repository to adapt to the new platform. Optionally, the instruction conversion process can integrate an instruction scheduling algorithm to reorder the converted instructions to maximize pipeline utilization. It is understood that the generation process of environment feature descriptors can include verification and calculation to ensure the integrity and consistency of hardware and software information. Optionally, the platform abstraction layer of the instruction mapping unit can define an intermediate representation format, converting the knowledge management strategy sequence into platform-independent code before mapping it to the local instruction set.

[0088] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A knowledge management system based on artificial intelligence and big data, characterized in that, The system includes: A knowledge stream capture component is used to continuously collect raw knowledge streams from multimodal data sources, and to clean and standardize the raw knowledge streams in real time to generate structured knowledge data. The multimodal data sources include text streams, image streams, and speech streams. The symbolic reasoning component is used to convert structured knowledge data into predicate logic expressions and apply a rule base for logical deduction to generate a symbolic knowledge graph, which includes entity predicates and relation constraints. Neural embedding components are used to vectorize symbolic knowledge graphs using deep neural networks, producing low-dimensional knowledge embedding representations. A strategy synthesis component is used to combine low-dimensional knowledge embedding representation and symbolic knowledge graph to generate a knowledge management strategy sequence through a policy gradient algorithm. The knowledge management strategy sequence includes operation commands and parameter adjustment instructions. The strategy deployment component is used to asynchronously execute knowledge management strategy sequences in a cloud computing environment and output knowledge status change records; The strategy synthesis component includes: The state encoding unit is used to convert low-dimensional knowledge embedding representations into input state vectors for the policy network; The policy network unit is used to calculate the policy value of the input state vector using the policy network and generate the initial policy distribution. A sampling unit is used to sample from the initial policy distribution to generate a sequence of candidate policies; The evaluation unit is used to evaluate the expected returns of candidate policy sequences using a value network and generate policy scores. The selection unit is used to select the optimal strategy sequence based on the strategy score and add exploratory noise to generate a knowledge management strategy sequence; The system also includes: An anomaly detection component is used to collect knowledge state change records in real time during policy execution and extract performance indicator data; analyze anomaly patterns in the performance indicator data and generate anomaly event reports. The anomaly detection component includes: The data slicing unit is used to divide performance indicator data into multiple data slices according to time windows. The feature calculation unit is used to calculate statistical features for each data slice, including mean, variance, and peak value; The pattern matching unit is used to match statistical features with a historical abnormal pattern library to calculate an anomaly similarity score; the anomaly similarity score is compared with a dynamic threshold to generate an anomaly label; The report generation unit is used to aggregate anomaly flags and corresponding data slices to generate anomaly event reports.

2. The knowledge management system based on artificial intelligence and big data according to claim 1, characterized in that, The knowledge flow capture component includes: The data cleaning unit is used to filter noise and impute missing values ​​in the original knowledge stream to generate a clean data stream. The format conversion unit is used to convert the cleaned data stream into a unified data format and add timestamps to generate time-series knowledge data. The feature extraction unit is used to apply a convolutional autoencoder to perform feature dimensionality reduction on time series knowledge data and extract key feature vectors. The entity recognition unit is used to match key feature vectors with a knowledge entity database to identify a set of candidate entities. The entity verification unit is used to perform consistency checks on the candidate entity set based on the rule base and generate the final knowledge entity features.

3. A knowledge management system based on artificial intelligence and big data according to claim 2, characterized in that, The symbolic reasoning component includes: The logic transformation unit is used to map knowledge entity features into predicate logic atoms and generate initial logic formulas; The rule application unit is used to load reasoning rules from the rule base, perform forward chain reasoning on the initial logical formula, generate an extended logical formula set, check for logical contradictions in the extended logical formula set, and apply conflict resolution strategies to generate a consistent logical conclusion. The graph construction unit is used to convert consistent logical conclusions into a graph structure, where nodes represent entities and edges represent relationships, generating a symbolic knowledge graph; the symbolic knowledge graph is then sparsified by removing redundant nodes and edges to generate an optimized symbolic knowledge graph.

4. A knowledge management system based on artificial intelligence and big data according to claim 3, characterized in that, The neural embedding component includes: A dynamic embedding vector generation unit is used to extract the topological connection relationships between entities from the optimized symbolic knowledge graph and generate a weighted adjacency matrix; the adjacency matrix is ​​input into a graph attention network, and the association strength between entities is calculated through a multi-head attention mechanism to generate dynamically updated entity embedding vectors. The temporal feature capture unit is used to perform one-dimensional convolution processing on the time-series data of entity states in the symbolic knowledge graph to extract temporal feature vectors. The cross-modal fusion unit is used to perform feature cross-calculation on the dynamically updated entity embedding vector and the temporal feature vector to generate a fused feature representation; The dimension reduction and compression unit is used to perform nonlinear dimension reduction processing on the fused feature representation through a variational autoencoder, reconstruct features in the latent space, and output a low-dimensional knowledge embedding representation.

5. A knowledge management system based on artificial intelligence and big data according to claim 1, characterized in that, The system also includes: The strategy adjustment component is used to dynamically adjust the parameter adjustment instructions in the knowledge management strategy sequence based on abnormal event reports.

6. A knowledge management system based on artificial intelligence and big data according to claim 5, characterized in that, The strategy adjustment component includes: The parameter parsing unit is used to extract affected operation commands and parameter adjustment instructions from abnormal event reports; The impact analysis unit is used to analyze the degree of impact of operation commands and parameter adjustment instructions on the knowledge state and generate impact weights. The calculation unit is adjusted to recalculate the values ​​in the parameter adjustment instruction based on the influence weight, generating a corrected parameter adjustment instruction; the corrected parameter adjustment instruction is then injected into the knowledge management strategy sequence to generate an adjusted knowledge management strategy sequence.

7. A knowledge management system based on artificial intelligence and big data according to claim 1, characterized in that, The system also includes an instruction conversion component, used to identify hardware and software differences in different execution environments and convert knowledge management strategy sequences into local instruction sets supported by the target platform.

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