137 results about "Process knowledge" patented technology

The invention relates to an intelligent operation and maintenance question-answering system for cable manufacturing equipment, and belongs to the technical field of computer systems based on specific calculation models. The system comprises an edge data acquisition module, a predictive map construction module, a semantic perception module, a question-oriented reasoning module and a question and answer generation module. According to the system, multi-dimensional real-time data in the operation process of equipment is collected and structurally processed, a process knowledge graph is constructed in combination with industry knowledge, and the causal relationship and reasoning parameters in the graph are dynamically updated according to the data trend. A semantic perception module is used for recognizing the problem intention of a user, a semantic weight vector is formed to guide the reasoning process, and a problem-oriented reasoning module is made to execute joint reasoning on the basis of combining real-time data and a knowledge graph and generate an explanatory conclusion. Finally, operation and maintenance suggestions with high readability are output through a question and answer generation module, and the targets of equipment fault intelligent diagnosis, process optimization and man-machine efficient interaction are achieved.

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 provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

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 intelligent decision-making method and device for cross-modal data, equipment and a storage medium, and the method comprises the steps: carrying out the blockchain storage of collected multi-modal process data, and generating an on-chain hash certificate; performing cross-modal space-time alignment and feature fusion on the multi-modal process data according to the space-time metadata analyzed by the Hash certificate on the chain to generate a process knowledge graph; constructing an interactive teaching model according to the process knowledge graph; determining a multi-dimensional contribution measurement value according to the use data of the interactive teaching model; and based on the multi-dimensional contribution magnitude and the hierarchical smart contract architecture, determining the data access permission through a community voting mechanism. Through implementation of the scheme, the process knowledge graph and the interactive teaching model, the inheritor operation data and the material response data form a causal association model, a traceable decision basis is provided for accurate reproduction of an endangered process, and meanwhile, the intelligent contract can automatically execute a decision to ensure high efficiency and transparency of the decision process.

The invention relates to the technical field of process scheme generation, and discloses an intelligent scheme reasoning generation method based on a process knowledge graph, and the method comprises the steps: firstly collecting multi-source process data, constructing a knowledge element extraction system, and outputting standardized knowledge entries through semantic analysis; establishing a process knowledge graph, and when a scheme reasoning demand is detected, positioning a target knowledge node through a logic association algorithm and generating an association relationship label; meanwhile, constructing a historical scheme case information base, and dynamically recording reasoning to generate a record; establishing a multi-dimensional weight configuration model, calculating a scheme matching degree score, and generating a recommendation priority sequence; solving an optimal scheme reasoning result by adopting an improved ant colony algorithm; and finally, outputting a result through the interactive verification platform, tracking and feeding back. The method improves the efficiency and quality of process scheme generation, and is suitable for process scheme formulation in industrial production.

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 lithium battery K value real-time prediction method based on neural symbol reasoning and multi-modal learning, and the method specifically comprises the steps: S1, carrying out the coding of an industrial signal through a binary coding mode, converting an input voltage sequence into a binary sequence, and extracting a frequency domain feature through a wavelet transformation technology; s2, using a neural symbol inference engine to realize verifiable feature selection, and executing feature selection under logic constraints; s3, by means of a multi-head potential attention mechanism fusion process knowledge graph, a potential space projection matrix is constructed, and the number of attention heads is adjusted through a dynamic head number adjusting mechanism; and S4, completing online knowledge migration by utilizing a dynamic distillation expert system, and realizing knowledge transfer and model optimization by combining a hybrid expert architecture and an online distillation technology and applying an expert dynamic activation function and knowledge distillation loss. According to the method, advanced technologies such as neural symbol reasoning and multi-modal learning are fused, the prediction precision is high, the response delay is small, the energy consumption ratio is low, and the interpretability score is high.

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 provides a complex part-oriented multi-modal process knowledge graph construction method and system, and relates to the technical field of component process knowledge construction and management, and the method comprises the following steps: 1, constructing a complex part-based manufacturing process knowledge ontology model; 2, multi-mode manufacturing process data such as related process data, a three-dimensional MBD model and text data of product manufacturing are obtained, and data preprocessing is carried out; 3, training unstructured text data, and performing entity extraction and relation extraction through the trained practice recognition model and relation recognition model; 4, extracting text data according to a knowledge graph mode layer and performing data fusion based on MBD data; and 5, combining the mode layer and the data layer to construct a knowledge graph, storing the knowledge graph and visually displaying the knowledge graph. The text data and the three-dimensional MBD data are integrated, so that the comprehensiveness and the accuracy of the part process knowledge graph are improved, and a more comprehensive data basis is provided for process design support of products.

The invention discloses a product assembly process knowledge pushing method, system and equipment in the technical field of semantic recognition and knowledge graph construction, and the method comprises the steps: carrying out the construction of a knowledge graph through employing a trained Bert-BiLSTM-CRF model and an LLM-KE model according to the obtained product assembly process information, obtaining a first initial knowledge graph and a second initial knowledge graph; and screening the first initial knowledge graph and the second initial knowledge graph by using a structural rationality discrimination module SRDM, performing knowledge graph evaluation on the screened first initial knowledge graph and the screened second initial knowledge graph by using a trained LLM-KGE model, and constructing a product assembly process knowledge graph APKG-CP according to an evaluation result. And pushing assembly process knowledge based on Bayesian network intelligent reasoning. According to the method, knowledge pushing can be completed in different process design requirements, and the efficiency of man-machine collaborative operation is improved.

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 factory process parameter intelligent matching method and system based on a knowledge graph, and the method comprises the steps: obtaining a process information set, carrying out the knowledge association analysis of the process information set, and generating a process knowledge association structure; extracting a process correlation feature group from the process knowledge correlation structure, wherein the process correlation feature group comprises operation parameter correlation features and parameter collaboration features; executing a process parameter matching reasoning operation based on the process associated feature group, and generating a candidate parameter matching scheme of a target production stage; matching rationality verification is carried out on the candidate parameter matching scheme, a final process parameter matching result of the target production stage is generated, the production abnormity risk caused by new parameter configuration is reduced, and a reliable basis is provided for factory intelligent decision making.

The invention relates to the technical field of intelligent manufacturing, in particular to a precision part machining control method and system based on digital twinning, and the method comprises the steps: obtaining the working characteristics of machining equipment corresponding to a target precision part; transmitting the working characteristics to a hybrid digital twin model to obtain a stress distribution cloud picture and a cutter wear prediction value, and performing filtering fusion on the stress distribution cloud picture and the cutter wear prediction value to obtain fused prediction data; constructing a state space according to the working characteristics and the fused prediction data, and optimizing process parameters of the processing equipment according to the state space to obtain optimized process parameters; constructing a space-time sequence according to the state change rate characteristics when the process parameters are optimized and the optimized process parameters, and predicting processing error distribution according to the space-time sequence to generate an error compensation instruction; and aggregating the data through federal learning, generating a local process knowledge base, and controlling the process nodes according to the local process knowledge base and the block chain smart contract.

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 technical field of electrical digital data processing, in particular to a process standardization-oriented production knowledge automatic extraction and management system, which comprises a data acquisition and processing module for acquiring multi-modal time sequence data, performing preprocessing and feature extraction and generating a multi-modal feature sequence; the skill identification module is used for segmenting a skill primitive sequence and acquiring a key parameter vector of each skill primitive based on the multi-modal feature sequence, and identifying an operation intention based on the skill primitive sequence and the key parameter vector; the skill modeling module is used for constructing a common skill model and a personalized skill model of the process operation process based on the skill primitive sequence; the model evaluation module is used for defining multi-dimensional evaluation indexes and evaluating operation effects corresponding to the generality and personalized skill models; and the knowledge management module is used for constructing a structured process knowledge base supporting dynamic updating and complex query. According to the method, a data-to-knowledge conversion system is established, and a scientific and efficient process standardization process is realized.

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 provides an intelligent process file inspection method and system based on a knowledge graph, and the method comprises the steps: firstly constructing a structured process knowledge graph based on a Neo4j graph database, carrying out the modeling of related information in the forms of entities, attributes and relationships, and outputting the graph database; then performing word segmentation, feature extraction and semantic understanding on the process file by utilizing a natural language and a deep learning technology, outputting structured data and formulating a process file inspection rule; secondly, training a model for converting the natural language of the process file into a Cypher query statement by adopting the trained deep learning model, and outputting a Cypher query statement model; and finally, according to a process file and an inspection rule, the inspected problems are listed in a striped manner, and a user can carry out online modification based on the problems. The checking method and system provided by the invention are used for checking the correctness of the process file parameters and the existing potential problems, so that the quality of the process file is improved, the manual checking time is saved, and the checking efficiency is improved.

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.