147 results about "GiST" patented technology

The invention discloses a corrugated carton production management system and method, and relates to the technical field of corrugated carton production, the system comprises a production scheduling module, a material tracing module, an equipment cooperation module, a multi-mode detection module and a cost control module, and solves the problem that supply and demand relationships, production efficiency and quality management and control are not coordinated; according to the technical key points, an order ID is coded into an integer chromosome, sequential crossover and reverse mutation operation are applied, a fuzzy comprehensive evaluation model is constructed by combining Hash chain table and local hill climbing method optimization, and factors are quantified through a trapezoidal membership function, so that the order delivery punctuality rate and the equipment utilization rate are improved, and the mold replacement cost is reduced; the real-time production cost is collected and calculated through Kafka and Flink, an operation cost method model is constructed to maintain a standard cost database, abnormity is detected through a CEP engine, the cost hyper-branched risk is predicted through an LSTM-Transform hybrid neural network, a thermodynamic diagram is generated through a cache result to assist decision making, the cost accounting delay time is shortened, and the hyper-branched processing efficiency is improved.

The invention relates to a data lake-based industry templated modeling and dynamic access control method, which comprises the following steps of: acquiring multi-source heterogeneous data of a data center in real time, and performing data verification, key field extraction and metadata extraction through an analyzer; according to the data classification and grading key points, grading protection is carried out on the currently obtained data matching industry grading and classification template, and the protection grade is dynamically adjusted according to the data dynamic access control rule, the sensitivity and the service importance; performing threat detection and risk prediction on the abnormal behavior data by using a machine learning algorithm; constructing a distributed intelligent data lake storage architecture, and storing the currently acquired data into a data lake in a layered manner according to a classification result; and displaying the security situation and the early warning information through a visual interface. The invention also relates to a corresponding system. By adopting the industry templated modeling and dynamic access control method and system based on the data lake, the limitation of traditional data lake modeling is effectively solved.

The invention discloses an artificial intelligence model traceability and tamper-proofing method and system based on block chain enhancement, and relates to the technical field of artificial intelligence model traceability and tamper-proofing. According to the technical key points, the method comprises the following steps: carrying out simulation attack by utilizing penetration testing to obtain multi-type data containing security vulnerability data; preprocessing the multi-type data; constructing an anomaly detection and attack recognition model based on a variable parameter neural network, and training the anomaly detection and attack recognition model based on the preprocessed data; performing Merkle tree encryption on the preprocessed data and model parameters obtained by training, encoding model prediction output, embedding the encoded model prediction output into a Merkle tree structure, and completing recording on a block chain through an intelligent contract; and for a to-be-evaluated artificial intelligence model, calling the global root hash value on the block chain for verification. According to the invention, the whole process of the artificial intelligence model from data acquisition to reasoning output is verifiable, traceable and tamper-proof.

The invention provides an electronic medical record connotation quality control method and system based on a large language model, and the method comprises the steps: carrying out the modal classification of a whole-process medical record of a patient, and uniformly converting the whole-process medical record into a corresponding text modal; performing hierarchical division on the whole-process medical record of the text mode to form a medical record content tree; according to different nodes of the medical record content tree, retrieving a quality control rule set corresponding to the nodes of the medical record content tree, and retrieving a corresponding diagnosis and treatment knowledge set; for each quality control rule in the quality control rule set, if a plurality of medical record content tree nodes are needed, establishing a corresponding context set, constructing a quality control cue word list, inputting cue words into the connotation quality control big language model, judging whether a quality control problem exists in the cue words or not by the connotation quality control big language model, and proposing a modification suggestion; and checking and revising the output result to obtain a final quality control result. According to the method, key point missing, description conflicts and deep diagnosis and treatment logic contradictions in medical records can be recognized and corrected, and the accuracy and stability of quality control results are improved.

The invention discloses a safe evacuation path planning method under a strong wind condition of island engineering. According to the technical scheme, the method is characterized by comprising the steps that firstly, the physical space of an engineering site is discretized into a plurality of grid units, and a computable graph structure is constructed; then, acquiring multi-source dangerous area information generated in real time, and quantifying the dangerous area information into a dynamic risk value of each grid unit; then, acquiring the real-time position of the constructor as a starting point, and presetting an emergency exit as an ending point; and finally, on the constructed risk grid map, a heuristic search algorithm such as A * is adopted, and the geometric distance and the dynamic risk value between grid units are fused by a cost function of the heuristic search algorithm, so that an optimal evacuation path considering safety and efficiency is quickly searched. According to the method, the safety risk is quantified and fused into the path optimization process, so that the quick response to the dynamic environment and the real-time re-planning of the path are realized, and the efficiency and safety of personnel evacuation under the emergency condition are remarkably improved.

The invention provides a joint retrieval method and system based on knowledge enhancement, a medium and a terminal, and ensures that a retrieval result is not limited to text segments with similar literal surfaces through a structured knowledge base, a domain label knowledge base and a multi-source heterogeneous knowledge graph. And instead, an organic knowledge whole integrating direct clauses, related law articles, judgment key items and internal rules is formed, so that the generated reply has a rigorous legal basis and a complete business context, and claim settlement disputes and legal risks caused by lack of key knowledge association are reduced. And the compliance control is changed from lagging manual inspection to real-time beforehand system built-in, so that the compliance vacuum period caused by policy update lagging is reduced, and the supervision punishment and reputation risk of an enterprise is reduced. The historical versions are reserved, and the version replacement chain is established, so that explanation based on historical contexts is provided for insurance policy life cycle query crossing different time points, and the credibility and accuracy of customer services are enhanced.

The invention discloses a book data processing and intelligent service system based on artificial intelligence, and relates to the technical field of artificial intelligence and digital library crossing, and the technical scheme is characterized in that a multi-format document intelligent conversion and regular cleaning mechanism is adopted, a multi-level metadata extraction algorithm is combined, and a structured knowledge base is accurately constructed; vector semantic retrieval, BM25 keyword retrieval and a cross encoder reordering model are creatively fused, and high-precision context retrieval is achieved through dynamic weight configuration and locality sensitive hash de-duplication. A parallel data processing architecture supporting GPU acceleration is constructed based on a Chroma vector database, the knowledge response capability of a local large model is enhanced in combination with an RAG technology, and verifiable standardized content is generated. The system is compatible with a Linux / Windows platform and containerized deployment, has hundred million-level data throughput efficiency, can be widely applied to the fields of intelligent libraries, knowledge questions and answers and personalized recommendation, and remarkably improves the retrieval accuracy of book data and the intellectualization level of knowledge services.

The invention discloses a sintering batching optimization scheme, and relates to the technical field of sintering ore batching control, and the technical key points are as follows: a random forest proxy model is used to predict the performance of finished ore, and a nonlinear mapping relationship between an independent variable and a dependent variable is used as a target function; carrying out Pareto optimal solution set search through non-dominated sorting and crowding distance calculation by adopting an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; calculating the weight of each target index by using an entropy weight method based on a Pareto solution set; weight is determined from a Pareto solution set generated by an NSGA-II algorithm, an optimal batching scheme closest to an ideal solution and far away from a negative ideal solution is screened out through a TOPSIS method, and multi-target comprehensive weighing is achieved; according to the method, sintering batching optimization based on finished ore performance feedback is realized, dynamic optimization of a batching plan can be effectively guided, the sinter quality control level is improved, the production cost is reduced, and technical support is provided for efficient and stable operation of a blast furnace ironmaking process.

The invention discloses an overlapping community detection method, system and device and a medium, and particularly relates to the technical field of community detection, and the technical key points are as follows: extracting node information data from a pre-constructed adjacent matrix of an overlapping community, and inputting the node information data into a point mutual information function to calculate and obtain point mutual information; obtaining a superpoint mutual information matrix by combining the point mutual information with the hypergraph structure; constructing a target function by using the super-point mutual information matrix and a pre-constructed three-factor illegal matrix decomposition optimization model based on graph regularization; decomposing the super-point mutual information matrix in the objective function to obtain an indication matrix, and solving the indication matrix by using an alternating iteration method to obtain an optimal solution of the indication matrix; and detecting the overlapping community based on the optimal solution of the indication matrix to obtain an overlapping community detection result.

The invention relates to the technical field of document management, and relates to a legal document key point automatic extraction and association method and system.The new legal document data of a target case is obtained, a preset document fingerprint algorithm is used for automatically comparing the new legal document data with a historical reference document, differential text data fragments are extracted, and the text data fragments are extracted; the method comprises the following steps of: performing deep semantic analysis on different text data fragments to generate candidate triple data containing entities and relationships, intelligently mapping the candidate triple data into a pre-constructed case knowledge graph, quickly positioning graph anchor nodes with data change, and then, performing data change on the basis of change types of the different text data fragments. And executing atomic updating operation on the map anchor node, including updating the dynamic mark of the node state attribute, and finally generating the change source node data with the latest state identifier. The problems that the current legal case data updating depends on manpower, is low in efficiency and is easy to make mistakes are effectively solved.

The invention discloses an engineering document generation method and system based on a logic structure tree and LLM and a medium. The method comprises the steps that original data are analyzed to form an initial text segment set X; calling LLM to generate an initial technical draft based on X, extracting a logic structure, and constructing an initial logic structure tree T; maintaining a search queue, for leaf nodes in the queue, acquiring context information related to semantics from X through a retrieval enhancement mechanism, after same-level brother node deduplication and global deduplication judgment, calling LLM to expand the nodes to generate child nodes, optimizing text description, and recursively enriching a tree structure; and finally, based on the X and the optimized tree, calling LLM to generate a title, an abstract and a background, generating content, details and key point chapters through traversal, and combining into a complete engineering document. The system comprises a document analysis module, a knowledge base module, a retrieval enhancement module, a logic structure tree maintenance module and a document generation module. The method is high in automation degree, and the generated document is clear in structure, complete in content and high in specialty.

The invention discloses a ground monitoring data identification and association method, system and device, and a storage medium, and the key points of the technical scheme are that the method comprises the steps: receiving the current monitoring data of a current object and the identification data of a to-be-associated object; encoding the current object according to a preset encoding scheme and the current monitoring data to obtain an identifier of the current object; determining an identifier of the to-be-associated object according to the identification data of the to-be-associated object; and according to the identifier of the current object and the identifier of the to-be-associated object, establishing an association relationship between the current object and the to-be-associated object. Unification and association of the same objects in different monitoring tasks are ensured, and dynamic monitoring of the objects is facilitated.

The invention discloses a document generation method and device based on multi-agent collaboration and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of receiving a natural language research theme input by a user; inputting the research theme into a preset planning agent to generate a keyword set for executing network search according to the research theme; inputting the keyword set into a preset search agent, and executing a plurality of times of network search by utilizing a network search tool to obtain a plurality of key point type abstracts; and inputting the research theme and the plurality of key point type abstracts into a writing agent to generate a structured research report, and outputting the structured research report to a user interface.

The invention relates to the field of park power management, and discloses a park power intelligent management method and system, and the key points of the technical scheme are that S1, a full-dimensional collection network is constructed, and data collection is carried out; s2, carrying out preprocessing and tagging processing on the collected data, and constructing a data association network; s3, based on the data association network, screening key data objects, integrating historical power data, environmental data and associated data, and training an intelligent prediction model group by adopting a machine learning algorithm; s4, performing real-time monitoring on a data object by using the intelligent prediction model group, and identifying power operation abnormity through dynamic threshold setting, real-time data comparison and abnormity type judgment; s5, performing graded early warning according to the abnormal type and severity; s6, performing intelligent fault diagnosis on the abnormal data object, generating a disposal scheme and tracking a disposal process; and S7, optimizing the data association network, the prediction model and the early warning response flow according to the application result of the intelligent prediction model group.

The invention discloses a porosity real-time prediction method and system based on alkane gas and carbon isotope characteristics thereof and a storage medium, relates to the technical field of oil and gas reservoir development, and aims to solve the problem of insufficient precision in a heterogeneous reservoir due to neglect of dynamic characteristics of alkane gas carbon isotope in an existing method. The method is characterized by comprising the following steps: S100, collecting alkane gas concentration and carbon isotope data thereof in real time, and performing time-frequency decomposition on the data to obtain characteristic data on different time and frequency scales; s200, performing multi-scale feature extraction on the data to obtain multi-scale features; s300, performing feature fusion on the multi-scale features; s400, constructing a GNN network model, introducing an adaptive attention mechanism, residual connection, jump aggregation and time sequence dynamic fusion, constructing a graph structure by taking a reservoir unit as a node, and mapping a fusion feature to a graph node attribute; and S500, constructing a reservoir space structure based on the GNN network model, and combining physical constraint optimization to realize real-time prediction of the porosity.

The invention discloses a GIS fault intelligent identification and visual positioning method. According to the technical scheme, the method comprises the steps that S1, a multi-source data collection platform is built, S2, an adaptive wavelet packet decomposition algorithm is adopted, and an initial point cloud is obtained; s3, constructing a GIS equipment three-dimensional point cloud topology model based on the initial point cloud; s4, setting a correlation degree threshold, screening effective candidate areas, and preliminarily determining a defect position range; and S5, identifying a fault type by adopting an adaptive quantum particle swarm optimization-graph attention network model, and outputting a fault identification result and a three-dimensional visual positioning report. The method is mainly used for fault feature extraction, defect type identification, accurate positioning and visual operation and maintenance analysis of gas insulated switchgear of various voltage classes.

The invention relates to the technical field of medical data management, and discloses a GIST special disease database intelligent management system, which comprises a multi-source data acquisition module, a standardized processing module, an intelligent analysis module and a security sharing module, the multi-source data acquisition module is in communication connection with a hospital information system H IS, a laboratory information system L IS, an image archiving and communication system PACS and a patient end application, and is used for acquiring electronic medical records, inspection reports, image data and patient active report data; a standardized data model is built in the standardized processing module, and the standardized processing module is used for performing field mapping and format conversion on the acquired multi-modal data; the intelligent analysis module comprises a machine learning algorithm engine. According to the medical data management system, the multi-source data acquisition module is in communication connection with the hospital information system and the laboratory system, so that the problem that most traditional medical data management systems can only acquire single-source or single-mode data, and consequently disease characteristics cannot be comprehensively reflected is solved.

The invention discloses a fusion method and device based on multi-source water conservancy data, equipment and a medium, and relates to the technical field of water conservancy data processing, and the technical scheme is characterized in that the method comprises the steps: collecting water conservancy original data, carrying out the classified storage of the water conservancy original data according to a water conservancy data specification, and obtaining a water conservancy classification data set; preprocessing the water conservancy classification data set to obtain a preprocessed water conservancy classification data set, combining similar data of multiple sources, and carrying out missing compensation processing on the similar data to obtain a fused similar data set; and carrying out hierarchical modeling on the fused data sets of the same kind by utilizing distributed processing and a multi-bin hierarchical architecture design to obtain a standardized unified water conservancy data mart. The system has the advantages of high-efficiency water conservancy data acquisition, high-quality similar data fusion, high-standard heterogeneous data fusion, high-availability water conservancy data service and the like.

The invention discloses a federated learning fault diagnosis method and a federated learning fault diagnosis system for multi-source unbalanced data of a harmonic reducer, relates to a harmonic reducer fault diagnosis technology, and aims to solve the problem of low diagnosis accuracy caused by unbalanced sample numbers of different fault categories of the harmonic reducer of an industrial robot and limited single-source signal acquisition information. The method is technically characterized by comprising the following steps of: performing wavelet transform on multi-source signals of different users to construct a time-frequency graph data set; carrying out equalization processing on the unbalanced data set by utilizing an improved data enhancement method; an effective channel attention mechanism is introduced, and the output of a residual branch is weighted through a learnable weight, so that the adaptability of the model to different residual information and the extraction capability of the model to data key features are enhanced; the method comprises the following steps: mining complementary information among multi-source signals through an improved multi-mode variational auto-encoder to perform feature fusion, and constructing a multi-user personalized local model; and the server aggregates local model parameters and updates the model, and guarantees user island privacy data through federal learning, thereby performing fault diagnosis on the harmonic reducer under the multi-source unbalanced data. A harmonic reducer signal acquisition experiment platform is established for verification, the characteristics of multi-source unbalanced data can be effectively extracted by the method, information fusion is realized, the average fault diagnosis accuracy is 98.8%, and the performance is superior to that of the compared method.

The invention discloses a deep learning-based cross-department government affair data security sharing and analysis method, and relates to the technical field of data sharing and analysis. Comprising the following steps: acquiring multi-source data across departments, converting the multi-source data into standard data through data cleaning verification, full-amount acquisition implementation and data type unified conversion processing in sequence, and performing distributed storage; constructing a knowledge graph, performing semantic analysis on the data stored in a distributed manner, then analyzing the data, calculating data levels, and constructing a multi-level retrieval directory corresponding to the data levels; the technical key points are as follows: hidden association between entities is mined through a knowledge graph, deep learning is performed to perceive risks in real time, and compared with a passive storage mode in the prior art, upgrading of data from only sharing to safe use is realized, so that sharing efficiency can be guaranteed, and the depth and security of data analysis can be improved; the method meets the requirements of cross-department collaborative decision making, is good in use effect, and has a good use prospect.

The application discloses an abstracted geological exploration database form generation method and system, and relates to the technical field of computers.The technical scheme is as follows: a geological object table is established by taking geological objects as indexes;corresponding data source fields are configured in the geological object table, and an exploration object field is configured; data sources, exploration objects and corresponding geological information are associated, and additional independent fields are configured to record geometric and attribute information of the same geological object collected by different exploration means; a data entry subtable is formed by extracting field combinations associated with exploration means from one or more geological object tables; and the geological object table is connected with a data output interface of an exploration database.The application takes results as a guide, takes geological objects as initial indexes, and takes data obtained by exploration means as data sources of each record in the geological object table, so that the data is associated and stored, the repetition of fields and forms is reduced, and the development and operation efficiency of the database is improved.

This invention discloses a method and system for extracting and analyzing key points of legal documents based on a large model, relating to the field of natural language processing technology. The invention generates a semantic parsing candidate graph based on legal semantic nodes and semantic relationships, calculates the matching scores of candidate paths in the semantic parsing candidate graph, summarizes the matching scores of all candidate paths in the semantic parsing candidate graph, and calculates the topological consistency index of the semantic parsing candidate graph. It identifies semantic conflict units and calculates initial conflict weights, and calculates the overall conflict impact of the semantic parsing candidate graph. Based on the topological consistency index, the basic semantic confidence of the semantic parsing candidate graph, and the overall conflict impact, it calculates a comprehensive score for the semantic parsing candidate graph and selects the optimal result, comprehensively optimizing and dynamically adjusting the legal semantic parsing results, significantly improving the accuracy, reliability, and intelligence level of legal text parsing.

This invention discloses a method and system for cross-domain fault diagnosis of rolling bearings with unknown inter-domain data label relationships. It relates to the field of rolling bearing fault diagnosis technology and aims to solve the problem of low fault diagnosis accuracy in existing transfer learning methods due to unknown data label relationships between the source and target domains. The key technical points of this invention include: constructing a comparative general domain adaptation model; introducing a BYOL network to mine the unique structure of the target domain data and proposing an entropy separation strategy to reject unknown class samples; simultaneously, designing a source class weighting mechanism to improve the transfer semantic enhancement method, assigning different class-level weights to the source classes, so that the feature distributions of the two domains are better aligned in the shared label space, further constructing a fault diagnosis model; and performing fault diagnosis through the trained fault diagnosis model. This invention exhibits superior fault diagnosis accuracy under different operating conditions.

The present invention belongs to the technical field of engineering structure analysis and optimization, and discloses a method for predicting vehicle body structural performance based on heterogeneous graph neural networks. The technical points include: sampling the structural samples required by the data set in the design domain to be predicted, using heterogeneous graph representation methods and numerical analysis methods to establish a heterogeneous graph data set; defining the HGNN design space with the HGT layer as the core; using part of the heterogeneous graph data set, controlled random search method and grid search method to construct the optimal HGNN model; using the entire heterogeneous graph data set and the optimal training configuration parameters to complete the training of the optimal HGNN model; using the trained HGNN model to predict the performance of any structural sample in the design domain. The present invention is not only applicable to homogeneous structures corresponding to homogeneous graphs, but also to more widely distributed structures corresponding to heterogeneous graphs, which greatly improves the versatility of the method and enhances the scalability of the method without losing the scope of application.

The invention discloses a rolling bearing fault diagnosis method and system based on comparison decoupling single-source domain generalization, relates to the field of rolling bearing fault diagnosis, and aims to solve the problem that in the prior art, a method for effectively solving the problem that the model generalization performance is reduced due to insufficient diversity of single-source data in bearing fault diagnosis does not exist. The method is technically characterized by comprising the following steps: step 1, adopting rolling bearing time domain data, and performing two data enhancement processing on the rolling bearing time domain data to obtain two groups of enhanced data; step 2, the model construction is based on a causal decoupling network and a comparative learning BYOL framework, the causal decoupling network is used for extracting causal features and non-causal features of data, and the rank of the causal features is constrained through adaptive threshold weighted nuclear norm regularization; the contrast learning BYOL framework is used for pulling in causal features extracted from the two groups of enhanced data to obtain causal domain invariant features of each sample; step 3, using a prototype Gaussian triple loss function to constrain causal domain invariant features, and improving intra-class compactness and inter-class separability of training samples; and step 4, finally inputting a target domain sample to the trained comparison decoupling network model to realize diagnosis.

The invention provides a key point information generation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, natural language processing, intelligent documents and the like. The method comprises the steps of generating a segmentation outline according to document contents of paragraphs in a to-be-processed document; according to preset theme information and the segmentation outline, paragraphs in the to-be-processed document are aggregated, and multiple aggregated theme contents are obtained; the theme content is segmented according to preset sub-themes, multiple segmented sub-theme content is obtained, and the sub-themes are sub-classes of themes obtained by dividing the theme information; and generating key point information for the to-be-processed document according to the information of the paragraphs in the multiple sub-theme contents. According to the method, the generation time of the document key point information is shortened, and the generation efficiency of the key point information is improved.

The application discloses a kind of based on deep learning's cross-department government affair data security sharing and analysis method, it is related to data sharing and analysis technical field;Including the following steps: cross-department acquisition multi-source data, and in turn after data cleaning verification, full volume acquisition implementation and data type uniform conversion processing, after conversion into standard data, distributed storage is carried out;Knowledge graph is constructed, and the data of distributed storage is analyzed after semantic analysis, calculates data level, and constructs the multistage search directory corresponding to data level;Its technical key points are: through knowledge graph mining hidden association between entities, deep learning real-time risk perception, compared with the passive storage mode of prior art, realize the upgrade of data from only can share to safe use, can guarantee sharing efficiency, also can improve the depth and security of data analysis, meet cross-department collaborative decision-making needs use effect is good, with good use prospect.

The invention relates to the technical field of data processing, and discloses a public data quality evaluation method and device, and the key point of the technical scheme is that the method comprises the following steps: S1, in response to a data quality evaluation instruction, collecting public data, and carrying out the standardization processing to obtain standard data; s2, carrying out block chain evidence storage on the standard data; s3, performing quality detection on the data through an intelligent detection algorithm to obtain a detection result; s4, performing data quality evaluation according to the detection result to obtain an evaluation result; and S5, performing feedback adjustment on the intelligent detection algorithm according to an evaluation result, breaking through a traditional evaluation mode based on a single standard, comprehensively considering various data quality indexes, and providing more comprehensive data quality analysis.

The invention discloses a point cloud registration method and system based on parallel plane group features, and relates to the technical field of registration algorithms, and the technical scheme is characterized in that the method comprises the steps: obtaining preprocessed two-point cloud data; extracting a triad structure descriptor from the preprocessed two-point cloud data, and obtaining a triad structure descriptor of a source point cloud and a triad structure descriptor of a target cloud; kD tree scanning matching is constructed, the most similar triad structure descriptor in the target point cloud is inquired for each triad structure descriptor in the source point cloud, point cloud transformation is calculated, and optimal initial matching is obtained; and performing multi-objective optimization on the optimal initial matching, and screening out the optimal transformation. The registration method mainly points to high-level structure information hidden in a scene structure, and a descriptor formed by plane groups greatly reduces the information calculation amount and the registration transformation number needing to be screened.

This invention discloses a quality traceability method based on collaborative management of the waterproof material supply chain, relating to the field of waterproof material technology. The key technical points include the following steps: acquiring raw data from the waterproof material supply chain and dividing the raw data into several data blocks; determining the relationships between data blocks and extracting quality content from the data blocks to generate data feature values; generating profile index factors for the data blocks based on data feature values, identifiers, dynamic index codes, and adjacent tags; comparing the material status information of the profile index factors according to quality evaluation criteria and analyzing the material status information to obtain quality profile labels for the profile data blocks; obtaining quality profile verification labels for the data blocks based on quality compliance, status deviation, and quality feature information; and outputting quality traceability results based on the quality profile verification labels and quality profile labels. The effect is to improve the efficiency of quality control in the supply chain.