104 results about "Process knowledge" patented technology

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention discloses an automobile wire harness process rule automatic matching method based on a knowledge graph, and the method comprises the following steps: collecting wire harness design data, and carrying out the standardization processing; analyzing the process rule base, extracting key attribute fields and generating a process rule metadata set; semantic modeling and structured fusion are carried out, and a process knowledge graph is constructed; performing semantic association analysis, causal constraint fusion and feasibility judgment processing by utilizing a semantic retrieval enhancement module; carrying out provable retrieval, risk assessment and conflict resolution based on the candidate process rule set; converting the target process rule set into a process instruction, and driving a design system to perform synchronous updating and rule labeling; and updating the process knowledge graph based on system feedback data, and outputting an optimized process verification report and updating a design version. The method is based on the knowledge graph and the semantic causal fusion technology, intelligent matching of the wire harness process rules is achieved, and the method has the advantages of being high in matching precision, high in interpretability and capable of achieving self-adaptive optimization.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

The invention relates to an intelligent process research and development system and method based on AI model closed-loop iteration, and belongs to the field of intelligent manufacturing and artificial intelligence. In order to solve the problems that in existing process research and development, due to the fact that links of knowledge analysis, model optimization and experimental verification are separated, the efficiency is low, and manual experience is highly relied on, the method comprises the steps that unstructured process knowledge is automatically analyzed into machine executable scripts, and new process parameters are recommended according to experimental data through a process optimization model. The core of the method is that the system automatically feeds back verification results of new parameters, updates the verification results to a data set, and drives a model to carry out next round of iterative training, so that an automatic closed loop from knowledge to the model to an experiment is constructed, and continuous self-evolution and efficient convergence of process research and development are realized.

The invention provides a life cycle evaluation automatic modeling method based on a large language model, and relates to the technical field of life cycle evaluation, and the method comprises the steps: extracting the multi-modal information of texts, tables, images and the like from the multi-source data of academic literatures, reports and the like, constructing an instruction data set in combination with LCA process knowledge, carrying out the instruction fine tuning of a base large model, and carrying out the automatic modeling of the life cycle evaluation. And obtaining the LCA field large language model. And generating a standardized prompt by utilizing a prompt word optimizer, driving a model to generate a product LCA model framework, filling list data, and perfecting process data in combination with semantic search and a correction mechanism. And intelligent connection recommendation among unit processes is realized by calculating the semantic similarity of input and output streams. And finally, the verified structured data is output to LCA modeling software, an automatic modeling process is completed, and the modeling efficiency and normalization are improved.

The invention discloses an assembly process knowledge graph construction and updating method based on graph attention network mapping, and the method mainly comprises the following steps: standardization system design construction, data collection, data classification, multi-modal data preprocessing, multi-source information extraction and graph node feature initialization. Semantic modeling and vector space mapping driven by a graph attention network, knowledge storage, construction of an assembly process knowledge graph by using stored knowledge data, and semantic complementation and dynamic updating of a process information model. Through the unique self-attention mechanism of the GAT, the weight can be dynamically distributed for the relationship among different information nodes, so that knowledge reasoning and expression are more accurately carried out, and the problems of knowledge fragmentation, management rigidity, semantic understanding deficiency, low knowledge utilization efficiency and the like in the prior art are systematically solved.

The invention discloses a method and a system for generating a manufacturing process disturbance knowledge graph, and a storage medium. The method comprises the following steps: constructing a mode layer of the knowledge graph; inputting the SMT manufacturing process disturbance data set into the trained entity recognition model for processing to obtain an entity in the SMT manufacturing process disturbance data set; extracting the context of the entity to obtain an attribute corresponding to the entity and a relationship between the entities; fusing entities in the SMT manufacturing process disturbance data set, attributes corresponding to the entities and relationships between the entities based on the mode layer, and constructing a first SMT manufacturing process disturbance knowledge graph based on a Neo4j database; performing relation completion and attribute completion on the SMT manufacturing process disturbance basic knowledge graph to obtain a final SMT manufacturing process disturbance knowledge graph; according to the method for generating the disturbance knowledge graph in the manufacturing process, system modeling and efficient utilization of bad process knowledge are realized.

The invention discloses a manufacturing industry process parameter optimization method based on a knowledge graph, and belongs to the field of manufacturing industry intellectualization. The technical problems that in the prior art, parameter optimization depends on artificial experience, systematic management is lacked, intelligent reasoning is difficult to conduct, and knowledge retrieval precision is low are solved. The method comprises the steps of firstly collecting and preprocessing manufacturing industry process data; then, constructing a semantic vector embedding model, and converting the process description into vector representation by adopting a pre-trained bidirectional encoder; defining a process knowledge ontology model, and establishing a parameter association relationship through semantic coding to form a knowledge graph; storing the coded knowledge vector into a distributed database and establishing a retrieval index; user requirements are received, and parameter matching is carried out through cosine similarity calculation; optimizing the matching parameters based on the knowledge graph association relationship; and finally collecting feedback to continuously update the knowledge graph. Through continuous optimization of the feedback learning mechanism, the precision and efficiency of technological parameter optimization in the manufacturing industry are improved.

The invention provides a discrete manufacturing production line process decision and optimization method and system based on digital twinning, and relates to the field of production intellectualization and digitalization, and the method mainly comprises the steps: constructing a high-fidelity digital twinning model corresponding to a physical production line through the combination of mechanism modeling and data-driven modeling; establishing a virtual-real consistency evaluation index system, and realizing deviation identification and dynamic consistency maintenance of the digital twinborn model; production disturbance, equipment state change and process deviation information are sensed in real time; a part-process-equipment-quality multi-dimensional correlation model is constructed; an improved stochastic gradient descent algorithm is introduced to carry out dynamic updating and convergence control on intelligent agent strategy network parameters, and a knowledge base self-evolution updating mechanism is constructed; according to the method, dynamic updating and consistency maintaining of the twinborn model are achieved, the dynamic response capability of the digital twinborn model under the dynamic operation condition is remarkably improved, and the systematicness and reusability level of process knowledge are effectively improved.

The invention provides an automatic temperature control method and system for an environment-friendly brick roasting kiln, and the method comprises the steps: constructing a thermotechnical process knowledge graph skeleton, associating a thermodynamic law with an actual process variable, dynamically correcting a causal structure through multi-batch production data, and forming a multi-version causal path set through combining anti-fact deduction, context causal branches and graph labels; based on a causal atlas and a reinforcement learning mechanism, an intelligent temperature control strategy network is designed, action-causal chain-effect collaborative interpretation is realized, control suggestions with physical feasibility and process context self-adaption are generated, and continuous optimization is performed through closed-loop feedback. And the dynamic collaborative optimization capability of temperature control intellectualization and product quality gain is enhanced.

The invention discloses a method and system for adjusting ceramic atomization core preparation parameters based on a knowledge graph, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: obtaining a preparation parameter sequence and finished product performance detection data of a current batch of ceramic atomization cores, and extracting process parameter features and performance deviation indexes; inputting the process parameter characteristics and the performance deviation indexes into a pre-constructed process knowledge graph; executing reverse influence propagation reasoning, determining a source preparation parameter node having the highest correlation degree with the performance deviation index, and outputting an abnormal parameter traceability path; calling a corresponding multi-target performance constraint sub-graph in the process knowledge graph to generate a candidate preparation parameter combination; and providing the candidate preparation parameter combination for an operation terminal, converting an interactive feedback operation aiming at the candidate preparation parameter combination into a structured triple, and writing the structured triple into a process knowledge graph so as to update the semantic relationship weight between the nodes. According to the method, the yield stability and the intelligent regulation and control level of ceramic atomization core preparation are remarkably improved.

The invention provides a method for constructing a knowledge graph based on complex structural parts. According to the method, feature parameters are fused in a hidden layer through a neural network model, and a high-precision classification result is output to define a knowledge graph category; defining and standardizing a process knowledge concept from a data source to form a mode layer of a tree structure; extracting entities from technical documents and historical regulations in combination with a bottom-up method to construct a data layer; multi-dimensional mapping of a mode layer and a data layer is realized through a Neo4j graph database, and a visual knowledge graph containing a process knowledge relationship is generated; and finally, generating a process route of the complex structural part by using the semantic association network in the map. According to the method, the knowledge graph is constructed on the basis of the complex structural parts, scattered technical documents, expert experience and historical data are integrated, a unified knowledge network is formed, data islands are effectively broken, the structured level, relevance and reuse efficiency of complex structural part process knowledge are enhanced, and the machining efficiency of the complex structural parts is effectively improved.

The invention relates to the field of intelligent manufacturing and digital process planning, in particular to a knowledge-driven automatic generation method for a part machining process and a related product, and the method comprises the steps: constructing a machining-process-oriented knowledge graph, and coupling the knowledge graph with a large knowledge model to form a feedback closed loop; extracting process knowledge units from the multi-source data, and performing quantitative scoring and credibility screening on the process knowledge units based on a preset knowledge evaluation algorithm to obtain a high-reliability process knowledge data set; performing fine-grained attribute relation extraction on the process knowledge in the process knowledge data set, structuring an extraction result, and dynamically updating the extraction result to the knowledge graph to realize incremental updating of the knowledge; through collaborative innovation of four core links of efficient and automatic knowledge acquisition, structured and refined process knowledge expression and dynamic extension and real-time updating capability generation, the technical bottleneck in traditional process planning is solved.

The invention discloses a user self-filling washing requirement and standard process difference verification method, and particularly relates to the technical field of life, and the method comprises the steps: obtaining a corresponding standard process parameter vector from a clothes feature vector through a standard process retrieval algorithm based on a process knowledge base, constructing a knowledge base secondary retrieval index, and traversing the process knowledge base; and obtaining a candidate set meeting the constraint, screening out a record with the highest score, and obtaining a recommendation process and a security boundary. The method is realized based on a standard parameter vector, a rule engine and a statistical learning method, can be conveniently integrated into an existing internet clothes washing platform or a traditional clothes washing management system, can be flexibly expanded according to standard process libraries of different enterprises, and is easy to integrate and expand.

The invention relates to the technical field of industrial automation control, and discloses an intelligent parameter decision-making and self-adaptive regulation and control method for workpiece machining. Constructing a product specification database, a processing parameter database and a mapping knowledge base for storing historical success data pairs and an adjustment rule set; after a new specification requirement is received, matching the most similar historical specification through a weighted Euclidean distance to obtain an initial parameter, if trial production is unqualified, calculating a differentiation vector and calling a rule to correct the parameter, and if the trial production is still unqualified, predicting and optimizing the parameter by adopting a Gaussian process regression model; and triggering manual intervention when the three times of trial production are all unqualified, automatically generating a new rule to update the knowledge base after success, and forming a knowledge closed loop. Parameter regulation and control are converted into a data-driven intelligent self-adaptive process from manual trial and error, so that the debugging time of new specification products is shortened, waste products and energy consumption are reduced, and digital inheritance and continuous learning of process knowledge are realized.

The invention relates to an industrial quality prediction method based on priori knowledge constraint graph convolution, and the method comprises the steps: collecting multivariable time series data containing quality variables and process variables, deeply mining the Granger causality between the variables based on the multivariable time series data, preliminarily obtaining a directed information transfer matrix, and carrying out the deep mining of the Granger causality between the variables; combining a prior sub-process knowledge mask matrix to dynamically adjust and refine an information transfer relationship between variables so as to generate a dynamic adjacency matrix; and designing a multi-head space-time diagram convolution long-short-term memory network based on the dynamic adjacency matrix to learn long-short-term space-time characteristics. According to the method, correlation between a quality variable and a process variable is effectively mined by adopting a Granger causal relationship based on constraint priori knowledge, and a long-short term dependency relationship is captured by utilizing a multi-head space-time diagram convolution long-short term memory network, so that the accuracy of quality prediction of an industrial system is improved.

The invention provides an enterprise management process intelligent optimization system and method based on a knowledge graph, and the method comprises the steps: mapping an entity dependency relationship between graph structure nodes in an enterprise management process into the knowledge graph, and generating a process knowledge graph of enterprise management; determining time sequence influence characteristics of content update in the process knowledge graph through the dynamic attenuation factor of each management process sub-graph; calculating the data variation amplitude and the information entropy weight of each graph structure node, and further performing amplitude weighting on each data variation amplitude according to each information entropy weight to obtain the structure saliency of each management process sub-graph; and determining an aging evaluation index of each graph structure node according to the time sequence influence characteristics and each structure saliency, and carrying out batch updating on the enterprise management process based on each aging evaluation index. Based on the scheme, fusion updating of data value and data timeliness in enterprise process management can be realized, so that the execution efficiency of an enterprise business process can be improved.

The invention discloses a clothing process knowledge base management system, and relates to the technical field of computer application, the clothing process knowledge base management system comprises a database and a user interface, and is characterized by further comprising a deep semantic extraction module used for identifying and extracting process entities and semantic association relationships among the process entities from unstructured process documents; the input end of the knowledge graph construction module is in communication connection with the output end of the deep semantic extraction module, and the knowledge graph construction module is used for receiving the incidence relation between the process entities and the semantics. According to the garment process knowledge base management system, organic integration and deep association of dispersed process data are realized by constructing the knowledge graph which dynamically reflects the process logic relationship, the problems of knowledge isolation and retrieval passivity in a traditional system are effectively solved, and the utilization efficiency of process knowledge and the credibility of decision support are improved. The system can automatically generate a complete set of process schemes meeting production requirements, and manual trial and error and experience dependence are reduced.

The invention discloses a large model-based document handling flow knowledge base construction and multi-round memory question and answer method, which comprises the following steps of: respectively acquiring region information associated with document handling items and flow key information corresponding to various document handling items to form a target document; performing knowledge segmentation on data in the target document, performing vectorization processing on segmented contents by adopting an open source semantic vector and a reordering model, storing a result into a local vector database to construct a file handling flow knowledge base, and configuring a target function; an original retrieval question input by a user is processed in combination with the constructed multi-round memory question and answer intelligent model and the document handling flow knowledge base, reply content is output in a point division mode, and the reply content is automatically associated with a direct link corresponding to a document handling item and a document handling flow chart; and updating the file handling process knowledge base based on question feedback and executing a new round of memory questions and answers.

The invention relates to the technical field of intelligent management of brewing processes, and discloses an informatization intelligent management system and method for a brewing process of Luzhou-flavor liquor. The system comprises a brewing data multi-dimensional analysis module, a brewing track construction module, a process knowledge deconstruction module and a brewing parameter dynamic recombination module. The system performs acquisition, cleaning and dimension separation on multi-source heterogeneous data of a whole brewing process, and constructs a dynamic brewing track reflecting a single-batch complete life cycle through time sequence alignment. And further, the continuous process is deconstructed into discrete nodes based on the process knowledge graph, the incidence relation of multi-source data characteristics in the nodes is analyzed, and a new composite process parameter group is dynamically aggregated. According to the method, the problems that brewing data are isolated and dispersed, a batch panoramic view cannot be formed, process parameters are rigid, and the process internal state is difficult to deeply represent are solved, and full-link digital tracing of the brewing process and process state deep cognition based on data fusion are realized.

A blockchain-based knowledge-sharing intelligent customer service AI response accuracy optimization system, belonging to the fields of blockchain and artificial intelligence technology, is disclosed. The system includes a multi-source knowledge acquisition unit, a blockchain knowledge sharing unit, a knowledge quality assessment unit, an AI response optimization unit, and an interactive feedback and iteration unit. The multi-source knowledge acquisition unit collects customer service knowledge data from different departments, business lines, and application scenarios. The blockchain knowledge sharing unit receives pre-processed knowledge data and constructs a decentralized knowledge-sharing network based on distributed ledger technology. The knowledge quality assessment unit performs multi-dimensional quality assessments of the knowledge stored in the blockchain. The AI ​​response optimization unit trains and optimizes the intelligent customer service AI model based on high-quality knowledge samples. The interactive feedback and iteration unit collects user feedback from the intelligent customer service system. All units operate collaboratively to form a closed-loop mechanism.

This invention belongs to the interdisciplinary fields of industrial internet, knowledge engineering, and intelligent mining. It proposes a method and system for knowledge decoupling and dynamic collaborative reasoning throughout the entire mineral processing process. It constructs a static domain ontology covering geology, mining, and beneficiation, integrates multi-source heterogeneous data, responds to mining loading events, matches target ore body segments based on spatial coordinates, creates ore unit instances with attached geological attributes and established traceability associations, and updates their status in real time as the ore flows, associating them with current process equipment to form a dynamic flow knowledge graph. Based on the geological attributes and location information in the graph, it uses spatiotemporal mapping rules to predict the estimated time for the ore to reach downstream equipment, generates process adjustment instructions based on preset reasoning rules, and sends them to the control system during the buffer period. This invention achieves dynamic collaboration and intelligent control throughout the entire process from geological source to beneficiation terminal, effectively improving the accuracy and efficiency of mineral processing.

The invention discloses a process knowledge graph intelligent construction method based on cross-domain transfer learning, and the method comprises the following steps: constructing an ontology data set of a target process domain, obtaining a source domain data set and a target domain data set, analyzing the entity label of the source domain data set and the entity label of the target domain data set, and obtaining the entity label of the target domain data set; constructing a mapping relationship between the two data set entity tags; pre-training and adjusting the deep learning model by using the source field data set, and generating a process information extraction model adaptive to the target process field; and inputting the ontology data set into a process information extraction model, automatically executing entity recognition and relation extraction through the process information extraction model, forming a triple, and storing the triple into a graph database to construct a process knowledge graph. Therefore, the deep learning model, the transfer learning strategy, the knowledge graph construction process and the closed-loop feedback mechanism are combined, and efficient, automatic and sustainable extraction and management of the process knowledge are achieved.

The invention discloses a manufacturing process knowledge multi-path retrieval question and answer method based on knowledge graph enhancement. The method comprises the following steps: firstly, performing knowledge structured extraction on a manufacturing process document; constructing a multi-modal index and a knowledge graph; intelligent analysis of the query intention is carried out; then, multi-path mixed retrieval of knowledge graph enhancement is carried out; then depth reordering and context enhancement are carried out; and finally completing answer generation based on the enhanced context. According to the method, the defect that a traditional analysis mode is insufficient in understanding of a complex document structure is overcome, a solid structured foundation is laid for cross-document complex technology reasoning, and the reasoning ability and the answering accuracy of the system are remarkably improved.

The invention relates to the field of coal bed gas exploration, and particularly discloses a coal bed gas multi-source heterogeneous data fusion and management method based on a knowledge graph, which establishes a unified semantic framework by constructing a coal bed gas field ontology model and definitely defining entity types of geology, wells, engineering, data and the like as well as attributes and relationships thereof. Structured knowledge is extracted from multi-source heterogeneous data based on the framework, knowledge fusion is achieved through entity alignment and conflict resolution, and finally the processed knowledge is stored in a graph database to construct a coal bed gas knowledge graph. Based on the knowledge graph, intelligent query, visual correlation analysis, decision support and other services can be provided. According to the method, semantic-level deep fusion of coal bed gas multi-source heterogeneous data is realized through a knowledge graph technology, a data island is broken through, and a unified knowledge network is constructed; the analysis and mining capability of the complex incidence relation is enhanced through a graph structure expression mode, and multi-hop query and causal inference are supported.

The invention relates to the technical field of building information processing, and discloses a building interior design and construction integrated platform based on a BIM technology, and the platform can analyze a semantic design intention label preset in a building information model, and converts the semantic design intention label into a formalized engineering constraint condition; based on the engineering constraint condition and the construction process knowledge graph, generating a final-version construction instruction packet through multi-dimensional simulation rehearsal; in the construction process, the platform collects on-site actual execution data, and compares and analyzes the actual execution data with the plan data in the instruction packet to identify systematic deviation; and finally, according to the identified systematic deviation, the platform updates the construction technology knowledge graph under a man-machine collaborative auditing mechanism. According to the method, a data closed loop from the design intention to the construction practice and then to the core knowledge base is constructed, the technical problems that design and construction are disjointed and the plan performability is low are solved, and self-evolution and continuous optimization of a construction knowledge system are achieved.

The invention discloses an intelligent process generation method and device based on knowledge graph embedding similarity matching, and belongs to the technical field of intelligent manufacturing process planning, and the method comprises the steps: constructing an assembly process knowledge graph; an assembly process sub-graph is extracted with the process as the center and serialized; the method comprises the following steps of: obtaining node-level embedding through a twin Transform encoder with shared parameters; obtaining graph-level embedding through batch embedding splicing and a pooling layer; performing bilinear interaction on an assembly subject, a relationship and an object by using an assembly element interaction neural tensor network, and extracting deep semantic features; outputting a similarity score of the two sub-graphs; retrieving the most similar process node based on the score; and executing shortest path search on the directed acyclic graph to complement missing nodes, and generating a target assembly process path. According to the method, rapid matching and automatic generation of different and semantically similar processes are realized, and the process reuse rate and the generation efficiency in a multi-variety, small-batch and rapid iterative manufacturing environment are remarkably improved.

This invention discloses a method, system, and device for pushing product assembly process knowledge in the field of semantic recognition and knowledge graph construction. The method includes: constructing a knowledge graph based on acquired product assembly process information using a trained Bert-BiLSTM-CRF model and an LLM-KE model to obtain a first initial knowledge graph and a second initial knowledge graph; filtering the first and second initial knowledge graphs using a structural rationality judgment module (SRDM); evaluating the filtered first and second initial knowledge graphs using a trained LLM-KGE model; constructing a product assembly process knowledge graph (APKG-CP) based on the evaluation results; and pushing assembly process knowledge based on Bayesian network intelligent reasoning. This invention can complete knowledge pushing under different process design requirements, improving the efficiency of human-machine collaborative work.

The present application relates to the field of intelligent manufacturing and digital process planning, and particularly relates to a knowledge-driven automatic generation method for part machining process and related products, which comprises constructing a knowledge graph for machining process, coupling the knowledge graph with a knowledge big model to form a feedback closed loop, extracting process knowledge units from multi-source data, quantitatively scoring and credibility screening the process knowledge units based on a preset knowledge evaluation algorithm to obtain a high-reliability process knowledge dataset, performing fine-grained attribute relationship extraction on the process knowledge in the process knowledge dataset, and dynamically updating the structured extraction results to the knowledge graph to realize incremental updating of knowledge. The present application solves the technical bottleneck in traditional process planning through the synergistic innovation of the four core links of efficient and automatic knowledge acquisition, structured and refined process knowledge expression, and dynamic expansion and real-time updating capability.