42073 results about "Information processing" patented technology

In a described embodiment, a system for information processing is provided including a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system further includes a cognitive reasoning module configured to evaluate the reasoning process of the language model, emulate cognitive functions including metacognitive processes, and generate an assessment based on an analysis of the received feedback, wherein the assessment includes classifying the one or more outputs into components, assigning quality scores for each component, and identifying an improvement corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive reasoning module for adjusting the reasoning process of the language model based on the assessment is provided. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated assessment until a performance threshold is met.

The invention relates to the technical field of information processing, in particular to a water conservancy slope ecological management method and system based on multi-source sensing data. The method comprises the following steps: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying and mapping data and water and soil loss monitoring data as original slope ecological environment data; performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; performing erosion-runoff coupling process simulation by using the standardized slope ecological environment data to obtain water and soil erosion dynamic parameters; performing water and soil loss trend prediction based on the water and soil erosion kinetic parameters, and generating water and soil loss risk assessment data; and designing an ecological management scheme based on the water and soil loss risk assessment data. By dynamically adjusting and optimizing the ecological management scheme, the ecological state of the slope is monitored in real time, and continuous improvement and self-adaptive optimization of ecological management of the water conservancy slope are achieved.

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses a knowledge base generation and retrieval method based on the RAG technology and an intelligent question answering system, and belongs to the technical field of natural language processing, and the knowledge base generation and retrieval method based on the RAG technology and the intelligent question answering system based on the RAG technology have the beneficial effects that the accuracy and the real-time performance of information processing are improved through efficient combination of retrieval and generation. High-dimensional vector representation is generated through text slicing and vectorization, a vocabulary index is constructed, and hierarchical retrieval and keyword matching are achieved; meanwhile, a question generation and question-question retrieval method is utilized, a result is optimized and generated in combination with a large language model (LLM), the reasoning ability of the large model is further improved through chain reasoning (COT) and a cue word optimization module, and it is ensured that the generated answer has high accuracy and logicality; the method can be widely applied to the fields of intelligent question answering, knowledge base construction, real-time information retrieval and the like.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention discloses a document information input method and system based on big data processing, and belongs to the technical field of document information processing. According to the method, deep risk feature screening is performed on document data by combining big data mining and a knowledge graph, potential sensitive elements can be accurately identified, risk categories and influence ranges are analyzed based on association rules, automatic early warning and real-time risk notification are realized, a dynamic rule knowledge base is adopted, multi-dimensional compliance verification is supported, and the risk assessment efficiency is improved. Service constraint conditions can be updated in real time, the flexibility and adaptability of rules are ensured, the accuracy and timeliness of compliance check are improved, pattern matching and multi-level check are performed on compliance data objects through an intelligent check process and a recheck rule set trained by big data, the preciseness and reliability of data check are ensured, and the data check efficiency is improved. Through comprehensive quantitative calculation of the business complexity, the risk coefficient and the timeliness priority, the task scheduling weight parameter is generated, the receipt auditing path is optimized, and the auditing efficiency is improved.

To enable operation of an application during execution of processing of another application.SOLUTION: A program according to one aspect of the present technique is a program that displays a window including arbitrary information that changes over time in superimposition on a part of a screen of a second application during execution of processing of a first application in background. For example, after uploading of a content in a storage is started, a progress display window including information indicating progress of the uploading of the content and information that changes according to a status of the uploading of the content is displayed in superimposition on a home screen. When an arbitrary application is selected from the home screen, a screen of the arbitrary application with the progress display window displayed in superimposition thereon is displayed instead of the home screen. The present technique can be applied to applications of mobile terminals such as smartphones.SELECTED DRAWING: Figure 4

The invention relates to the technical field of multi-modal information processing, and provides a deep semantic collaborative fusion method for heterogeneous multi-modal data. The invention provides a dynamic adaptive fusion framework aiming at the problems that a modal interaction mechanism is rigid and semantic modeling is shallow in the prior art. The method comprises the following steps: carrying out feature coding and alignment on text, audio and video modal data to generate unified-dimension single-modal representation; dynamic interaction is realized through an enhanced multi-head gating fusion module, and double-path features are generated; and carrying out cross-modal depth modeling on the basis of a stacked Transform encoder, and outputting final fusion semantics. Wherein the multi-head attention path calculates cross-modal mapping by taking a text as a query vector and taking an audio / video as a key value vector; the gating path generates a dynamic weight through cosine similarity and a learnable temperature parameter; and the dual-path adaptive fusion adopts a balance factor alpha weighted combination. According to the method, the multi-modal data fusion precision and the system robustness are improved, and the method is suitable for government affair service, man-machine interaction and other scenes.

The invention discloses an enterprise audit information extraction method based on artificial intelligence, and relates to the technical field of enterprise audit information processing, and the method comprises the steps: obtaining an audit original document from an enterprise, carrying out the multi-mode conversion processing, and generating a standard data set; performing deep semantic analysis on the standard data set, and constructing an audit knowledge graph; performing cross-modal alignment on the auditing knowledge graph, the image features, the table features and the time sequence features, and constructing a joint auditing analysis model; performing dynamic verification on an audit analysis result and a historical case library to form a dynamic evolution knowledge base; and generating a final audit document based on the updated knowledge base. Through cross-modal feature vector alignment and attention-driven clause applicability scoring, dynamic association analysis of financial anomaly and business pipeline fluctuation is realized, and the cross-modal clue discovery efficiency is improved.

The present disclosure provides a multi-agent system-based information processing method and a multi-agent system. The multi-agent system comprises multiple agents. The method is applied to a target agent, and the target agent is any agent among the multiple agents. The method comprises: determining information to be processed corresponding to a target agent; on the basis of an information processing model, when said information does not match a function corresponding to the target agent, determining an agent to which a function matching said information corresponds; and sending said information to the matching agent for processing. In the present disclosure, information to be processed can be quickly and accurately transferred between agents by utilizing the analytical capability of an information processing model, and is processed by a suitable agent, thereby effectively improving the processing effect of tasks.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

Systems, methods, and computer-readable for endpoint integration and mapping are disclosed. A system can include one or more processing circuits configured to identify endpoints and access information. The processing circuits can generate an object package corresponding to the endpoint by initiating the object package based on an identifier corresponding to an endpoint type of which the object package is structured and mapping the access information to an access scheme corresponding to formatted requests to access the endpoints for protection data. The processing circuits can perform an endpoint request by invoking the object package using at least one formatted request and receiving output data with a response to the endpoint request by a DCDSI system. The processing circuits can further update a distributed ledger or data source based on the output data.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

The invention relates to a data security processing method and system based on distributed storage, and relates to the technical field of computer information processing. The method comprises the following steps: cutting data into encryption fragments with a configurable number by adopting a dynamic fragmentation strategy, and generating a physically isolated dynamic check block in combination with a timestamp to realize tampering prevention; a dynamic threshold value is dynamically calculated based on the data sensitivity index and the node load, and the node is optimized through the reliability score for cooperative decryption; a database table is divided into independent marshalling storage according to main foreign key association, foreign key fields are encrypted by adopting cross keys, and cross-marshalling access needs to meet a multi-key threshold condition; an intelligent threat perception engine is constructed, access logs and threat intelligence are analyzed in real time, and key rotation, fragment replacement and joint defense response mechanisms are dynamically triggered. According to the invention, full life cycle protection of data is realized, and the problems of key leakage risk and cross-table association attack are effectively solved.

The invention relates to the technical field of data information processing, in particular to an artificial intelligence-based index layer model construction analysis method and system, and the method comprises the following steps: 1, multi-source data synchronization and quality evaluation; 2, service context analysis and subject domain intelligent division are carried out, and service document and data table field semantics are analyzed; 3, index demand intelligent analysis and calculation logic generation: analyzing index demands input by a user through a natural language processing technology, matching service entities in the knowledge graph, generating initial calculation logic and optimizing execution efficiency; and 4, dynamically updating the index data and verifying the consistency, executing the optimized computational logic to generate the index data, tracking the blood relationship of the data, triggering dynamic updating, and setting a threshold value according to a historical fluctuation range to carry out abnormal alarm. Through automatic and intelligent processing, the probability of human intervention and human errors is reduced, and the efficiency and accuracy of data processing are improved.

The invention belongs to the technical field of image processing, and discloses a multi-modal image fusion method and model based on global and local text perception, which are used for respectively processing global and local information of images by combining two visual language models, namely CLIP and BLIP, and realizing effective fusion of the images under complex scenes and severe weather conditions. The global text sensing module utilizes CLIP features to enhance the understanding of the model for the whole scene, and the local text sensing module utilizes BLIP features to improve the processing capability for local details. According to the double text perception mechanism, the model can more comprehensively utilize the advantages of the visual language model, and the problem that only simple text prompt is relied on or local details are excessively emphasized is avoided. The advantages of a visual language model can be fully utilized, global and local information processing is considered, and the adaptability and generalization performance of the model under complex scenes and severe weather conditions are improved.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

To provide an information processing device, an information processing method and a program that can visualize resident's needs to move and actual service conditions of public transportation.SOLUTION: An information processing device 100 comprises: a traffic data storage unit 132 which stores information on stops of public transportation; a movement data storage unit 133 which stores movement data including places of departure, routes and destinations; a reception unit 124 which receives visualization conditions; a totaling unit 125 which totals, for each of mesh sections of a map, departure / arrival counts of the movement data extracted from the movement data storage unit by the use of visualization conditions; and an output unit 126 which outputs a visualized map including stops, and outputs one or more mesh sections in different display modes according to departure / arrival counts.SELECTED DRAWING: Figure 1

The invention provides a first-aid information processing method and system based on multi-source data fusion. The method comprises the following steps of: performing deep association matching and modeling on an emergency event by utilizing a multi-source heterogeneous data stream, a geographic position, a timestamp and environmental perception data which are acquired in real time to obtain a dynamic emergency event situation model; according to the dynamic first-aid event situation model, a resource optimization algorithm is adopted to evaluate and select the most suitable first-aid resource, and a first-aid resource configuration scheme and instant guidance information are obtained; updating the state of the patient based on the first-aid resource allocation scheme and the instant guidance information, and obtaining a dynamically adjusted first-aid process and an optimized path scheme through geographic information system path planning analysis, first-aid strategy adjustment and resource allocation path optimization; and integrating various feedback data related to the dynamically adjusted emergency process and the optimized path scheme, carrying out post analysis, and generating an optimized guide. According to the technical scheme provided by the invention, the first-aid information processing efficiency is greatly improved.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

The invention discloses a video content semantic understanding and text description generation method based on deep learning, and relates to the technical field of multimedia information processing.The method comprises the steps that the semantic similarity of a text and a video frame is calculated through a CLIP model, related key frames are selected, and features are aggregated; respectively extracting audio, visual and semantic features; aligning different modal features by using self-attention, unifying dimensions of the LSTM, and then splicing and fusing; attention weights are calculated at a video level, a frame level and a channel level, and key information expression is enhanced; swin Transform encodes fusion features, and LSTM (Long Short Term Memory) decodes step by step to generate natural language description; and a text-video index database is constructed, and rapid retrieval is realized based on semantic similarity. According to the method, the mapping relation between the video features and the natural language is learned end to end through the deep learning model, dependence on a fixed template can be eliminated, and semantic description with various sentence patterns and coherent logic is generated.

The invention discloses a domain name information processing and displaying method based on multi-modal data fusion, and the method comprises the following steps: S1, collecting and preprocessing domain name multi-modal original data, and generating a preprocessed data sample set; s2, performing feature extraction on the preprocessed data sample set, and constructing a multi-modal fusion feature vector sequence; s3, constructing an initial Tab Net model, and outputting an initial domain name risk prediction result; s4, constructing a model performance evaluation objective function; s5, introducing a dragonfly optimization algorithm, and searching to obtain an optimal Tab Net parameter combination; and S6, based on the optimal Tab Net parameter combination, obtaining an optimized Tab Net model, and outputting a final domain name risk prediction result. And S7, based on the final domain name risk prediction result, constructing a domain name knowledge graph display structure, and generating an interactive graph display result. According to the method, a multi-modal feature extraction mechanism, a Tab Net depth model and a dragonfly optimization algorithm are fused, and intelligent prediction of domain name risks and interactive knowledge graph display optimization are realized.

The invention discloses a smart city traffic signal lamp management and control system, and relates to the technical field of traffic signal control. The system comprises a region division module, a traffic flow detection sensor, a region traffic flow analysis module, a signal lamp management and control strategy generation module, an inter-region signal lamp cooperative control module and a variable information sign and information processing and generation module. The system can accurately monitor and analyze urban traffic flow conditions in real time by comprehensively applying an advanced region division algorithm, a traffic flow detection technology and an accurate traffic flow state evaluation model, and can intelligently generate a signal lamp management and control strategy according to an analysis result, so that the traffic lamp management and control efficiency is improved. Comprising the steps that a congestion relieving dynamic timing algorithm is adopted in a congestion area, the number of times of signal lamp switching is reduced, a flow balance guiding timing algorithm is applied in an area with small flow, in addition, cooperative control over signal lamps between the areas can be achieved, and efficient connection and balanced distribution of traffic flow between the areas are ensured.

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

The invention relates to the technical field of medical information processing, in particular to a health data processing method and system based on a distributed ledger library, and the method comprises the steps: a node firstly links a health data abstract to obtain a differential privacy budget token and a reversible tensor hash key; then, carrying out encryption training on local model parameters by using the key, injecting Gaussian noise according to budget to generate a differential protection gradient, and carrying out uplink together with zero-knowledge proof; the account book end decrypts the threshold value, uses a structural equation model to deduce a causal correction matrix according to the reputation weight aggregation gradient, and automatically adds a budget and turns the key when the concept drifts and the budget is insufficient; the operation mechanism splices the aggregation parameters and local parameters, generates a gating vector in combination with a causal correction matrix and a reputation weight, outputs disease risk prediction, and only uploads prediction hash and error information; chain-level audible privacy protection, dynamic budget management and hybrid deviation suppression are realized, and the safety and accuracy of a cross-institution medical model are improved.

The invention discloses an administrative institution internal control information processing method and system based on big data analysis, and relates to the technical field of big data information processing. And furthermore, by capturing dynamic association characteristics generated by interaction of local contents in the business field and the information, internal control information cross-dimension semantic joint coding representation with context sensing capability is generated, and cross-dimension semantic interaction analysis between the business field and information content semantics is further realized by applying a chain reasoning model. The system can automatically identify potential logical association between the business field and the information content, so that the risk of data leakage can be greatly reduced, and the scientificity of desensitization processing can be improved, thereby enhancing the effectiveness and efficiency of internal control of administrative institutions.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.