301 results about "Data view" patented technology

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal content compliance auditing method, device, equipment and medium, and the method comprises the steps: receiving a multi-modal content set, and generating to-be-audited content; converting the multi-source content into unified semantic representation through an analysis unit; calling the judgment model based on the auditing strategy library and the configured auditing engine to perform semantic comparison, and generating an auditing processing result; outputting an abnormal level result according to the processing result; a rectification suggestion is generated and pushed based on the abnormal level result, and feedback data is received at the same time; recording feedback data and auditing tracking records, and generating a compliance data view and an auditing conclusion; and updating the auditing strategy library and the configured auditing engine according to the feedback data. According to the method, cross-modal check is realized through unified semantic representation, the check flexibility is improved in combination with a strategy library and a configuration engine, rectification and feedback are driven by using an abnormal level result, and the accuracy and efficiency of compliance check are improved.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention relates to the technical field of government affair information management, in particular to a cloud-based government affair information management method, which comprises the following steps of: S1, adding a sensitivity label for each piece of government affair data in a cloud storage layer; s2, extracting data from the government affair database and generating a dynamic data view with an authority mark; s3, establishing a mapping relation table between routing keys and data source addresses; s4, acquiring a requester department function code, a business scene code and a target data identifier; s5, matching a target data source, and based on the authority mark in the dynamic data view, executing a desensitization operation on the sensitive field; and S6, if the genetic marker bit of the original data is true, superposing the data security classification in the original tag and the current security classification to form a new tag. According to the method, by constructing the sensitivity label and the dynamic authority control mechanism, safe inheritance and accurate access of government affair data in cross-department circulation are achieved, and the safety and compliance of data management are remarkably improved.

The invention belongs to the technical field of office park electric load prediction, and relates to an office park electric load prediction and scheduling method for a novel electric power system. The method comprises the steps that S1, multi-source data fusion and panoramic view construction are carried out, historical load data of a park are collected, and park operation data, meteorological data and a special event calendar are obtained; s2, data preprocessing and feature directional extraction; s3, building and cooperating short-term, medium-term and long-term prediction models on the basis of multi-time scale prediction of model cooperation to form a hierarchical prediction system; s4, optimizing and evaluating a prediction result, and performing uncertainty evaluation on the prediction result in the step S3; and S5, generating and executing a hierarchical scheduling instruction based on hierarchical scheduling decision and execution of multi-objective optimization. According to the method, the problem of full-chain link splitting is solved, panoramic data view, collaborative prediction model, quantitative risk assessment and multi-target optimization scheduling are realized, prediction accuracy and scheduling flexibility are improved, and active participation in energy management of the office park is supported.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention belongs to the technical field of cross-data island consensus, and particularly relates to a cross-data island consensus verification method and system based on credibility scoring. According to the invention, by dynamically evaluating the trust degree of each data island and combining network state fluctuation, real-time verification and abnormal early warning of a consensus result are realized, the stability and safety of cross-data island cooperation are effectively improved, the data consistency is ensured, the data risk caused by network fluctuation is reduced, the self-adaptive capability of the system is enhanced, and the method is suitable for large-scale popularization and application. In the consensus process, historical interaction events and a current cooperation state of a data island are considered, efficient alignment of cross-island features is achieved through a gradient exchange mechanism, a standardized consensus data view is generated, in addition, self-adaptive adjustment of a consensus verification rule is ensured by pre-deploying a trusted execution environment, and the consensus verification efficiency is improved. And the consensus accuracy and efficiency are further improved.

The invention relates to the technical field of computer programming, in particular to a method for realizing data binding and code generation based on a Qt plug-in technology, which comprises the following steps: developing a Qt Creator extension plug-in, realizing dynamic loading and registration, analyzing a database file through the plug-in, generating a visual data view, and providing a visual binding interface through the steps. The method comprises the following steps: establishing a binding relationship between an interface control and a data source by supporting a dragging operation, automatically generating a JSON data model, compiling to generate a binding code according to the JSON data model, registering, and finally realizing code increment updating through file hash value comparison; according to the method, through the IDE integrated graphical interface, dragging connection of the control and the data field is supported, manual writing of repetitive codes is avoided, change is detected through the file hash value, only influenced codes are regenerated, compiling time is shortened, development efficiency is remarkably improved, and labor cost is reduced.

The invention provides a data lake construction method and system based on multi-source distributed data, and relates to the field of data lake construction. The method comprises the following steps: firstly, deploying a data acquisition agent at each node of a distributed data source and establishing a distributed collaborative mechanism, and performing structured analysis on data through the data acquisition agent to obtain data characteristics and establish a relationship between fields; and then semantic description information is constructed, and an RDF triple is generated based on the information and stored in a source end node. And then inputting the RDF triple into an ontology registration service for processing to generate a distributed ontology, and analyzing a corresponding relationship between the RDF triple and concepts in the distributed ontology to obtain a semantic equivalence rule and a mapping rule. And finally, organizing the distributed data sources into a unified data view according to the rules, establishing a distributed index, configuring an access permission, and finally constructing a data lake containing the unified data view, the distributed index and the access permission. According to the method, the performance bottleneck problem caused by centralized processing is avoided.

The invention relates to the technical field of big data, in particular to a warehouse management analysis method and system based on big data and a medium, comprising heterogeneous data real-time acquisition, stream batch fusion processing, AI intelligent decision making, digital twinborn verification and hierarchical storage scheduling. Compared with the prior art in which a distributed data acquisition and delay batch processing scheme is adopted, which leads to serious data islands and lagging of decision basis, the scheme adopts an edge protocol conversion gateway and flow batch fusion processing architecture, uniformly accesses real-time data flow of industrial equipment through MQTT, captures service system changes in combination with DebeziumCDC, realizes millisecond data integration through Kafka, and realizes real-time data flow access through MQTT. Dynamic feature calculation and deep model training are synchronously completed by means of a Flink-Spark hybrid engine; the method has the advantages of total-factor real-time mapping and cross-domain data collaboration, gets through information barriers of an equipment layer, a business layer and an environment layer, and provides millisecond-level fresh global data views for storage decisions.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention relates to the technical field of invoice risk control management, and discloses an invoice risk control management method and system based on multi-source data linkage. According to the method, multi-source data of the whole invoice business process is collected in real time through a multi-source data fusion gateway. And performing streaming ETL processing and encryption integration on the multi-source data to generate an encrypted unified data view. And based on the view, constructing an association hypergraph by utilizing a dynamic graph association technology, analyzing an entity relationship by virtue of a graph neural network, and identifying false invoice making gang and repeated financing risks. And calculating a dynamic risk score by adopting a risk quantification and traceability technology, and outputting a risk level, a responsibility node identifier and a capital flow thermodynamic diagram. When the risk score exceeds a threshold value, risk gradient information is safely shared among the group headquarters, the molecular companies and the tax-bank institutions, and linkage processing actions such as invoice freezing, red character notification or credit adjustment are triggered. According to a manual reexamination result, the risk model is finely adjusted through a knowledge self-updating technology, and the rule base is hot-updated.

The invention provides a building intelligent operation and maintenance agent cluster construction method and equipment, and the method comprises the steps: carrying out the integration and cleaning of building data, and obtaining a cleaned and repaired data view; on the basis of the cleaned and repaired data view, a Model Context Protocol protocol is adopted to construct a unified tool calling interface; constructing the following representative operation and maintenance agents, including a building supervisor leader agent and other expert agents; based on an A2A protocol, a building supervisor leader agent and other expert agents are enabled to automatically discover, distribute, cooperate, pass back and trace back through a standardized interface to form a decoupling and safe multi-agent collaborative operation and maintenance mechanism; according to the method, the building operation and maintenance response speed and the task processing efficiency can be remarkably improved, and manual intervention and misjudgment are reduced; on the premise of ensuring comfort and safety, energy consumption and sudden failure occurrence frequency are effectively reduced.

The invention discloses a three-dimensional visualization head and neck tumor preoperative imaging system based on deep learning. The method comprises the following steps: image analysis and preprocessing; carrying out image denoising and spatial-temporal feature extraction based on a deep learning convolutional neural network architecture, and introducing a u-Net model to realize image segmentation and semantic annotation; performing three-dimensional reconstruction and surface rendering by adopting a Marking Cubes algorithm, and realizing visual interaction image presentation and personalized operation by combining volume rendering and surface rendering; and data is output through multiple ports, and data viewing and interactive operation of a web terminal and a mobile terminal are supported. The method has the advantages that according to the technical scheme, through fusion of a three-dimensional attention mechanism and multi-phase data, the tumor segmentation Dice coefficient reaches 0.92 + / -0.03, and the method is superior to a traditional threshold segmentation method.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention discloses an inspection intelligent analysis method and system for power marketing, and the method comprises the steps: building a mapping relation between power data and business data in a target marketing region according to the power supply distribution information of the target marketing region, and obtaining a fusion data set; performing semantic mapping on the fused data set to obtain a power marketing data view; obtaining a rule between every two pieces of data in the electricity marketing data view to identify an abnormal service; determining target business data corresponding to the abnormal business, and obtaining an abnormal business analysis result based on a pre-constructed marketing inspection full-business model; generating a problem tracking work order according to the abnormal service analysis result, and matching a corresponding work order processing flow to obtain a problem processing work order; extracting an abnormal service processing opinion in the problem processing work order to predict a rectification effect of an abnormal service; and an inspection analysis report of the abnormal service is generated based on the rectification effect and the abnormal service processing opinion, so that the efficiency and accuracy of inspection analysis are improved.

The embodiment of the invention discloses an intelligent number asking method and system based on dynamic feedback, a storage medium and electronic equipment. The method comprises the steps that a subject domain and a semantic model are constructed, a data view is established, data mapping is configured, and a natural language problem and a standardized index are analyzed; generating an SQL (Structured Query Language) logical expression based on the Few-shot example and the large model, and carrying out semantic correction on the SQL logical expression; sQL query is executed, dynamic feedback storage is carried out based on an execution result, and SQL generation parameters are updated by utilizing a self-adaptive learning mechanism. According to the method, through semantic modeling and data mapping, precise alignment of a natural language and a database structure is achieved; few-shot examples and large model generation are combined with semantic correction rules, so that the accuracy and stability of SQL generation are improved; and a continuously optimized intelligent number-asking closed loop is constructed by executing a feedback and adaptive learning mechanism, so that the self-learning and evolution capabilities of the system are improved.

The invention relates to the technical field of software, and discloses a nursing shift change system based on homologous heterogeneous data fusion and a large language model, and the system comprises a shift change generation module which comprises a first generation unit and a second generation unit, calling a large language model to generate global summary information of nursing shift change; and the second generation unit is used for calling a large language model to generate corresponding detailed handover information only based on the original data of the key event. And the report synthesis module is used for integrating the global summary information and the detailed handover information according to a preset structure to generate a nursing handover report. According to the method, the panoramic data view of the patient is constructed based on the unified time axis, so that structured data and unstructured data which are originally dispersed in different systems can be organized and called in the same semantic space, and a consistent data basis is provided for subsequent data analysis and processing.

The invention relates to the technical field of information, and discloses a hotel resource digital management method and system and a storage medium. The method comprises the following steps: constructing a unified semantic data model; multi-source data fusion and real-time state perception visualization are realized; cross-department collaborative arrangement is carried out; and intelligent decision support and optimization are realized. According to the invention, a comprehensive digital management platform based on a unified semantic data model, multi-source data fusion, real-time state perception, cross-department collaborative workflow arrangement and intelligent decision support and optimization is constructed. According to the platform, global visualization and real-time dynamic management of each core resource of the hotel are realized, and the operation efficiency and the fine management level of the hotel are remarkably improved. The departments of guest rooms, catering, conference rooms, spare part inventory, human resources, finance and the like do not combat each other, but perform cooperative work based on a unified data view.

The invention discloses an electroencephalogram signal clustering method based on a Mama framework and comparative learning, and belongs to the crossing field of engineering application and information science, and the method comprises the following steps: collecting and preprocessing label-free electroencephalogram data; the method comprises the following steps of: constructing a Mama-based feature extractor, dividing an input electroencephalogram signal into a plurality of slices by adopting an electroencephalogram signal slice embedding strategy so as to obtain fine-grained local information, and modeling a potential context relationship by utilizing a slice perception scanning mechanism; the method comprises the following steps: constructing an original data view and an enhanced data view, and respectively inputting samples of the two data views into two Mamba feature extractors with shared network parameters for feature embedding; processing the feature pairs of the two views using an instance projector, constructing weighted instance level contrast learning wherein the distance in the original data space provides weight information; processing feature pairs of the two views by using a clustering projector, and constructing a clustering distribution discrimination contrast learning branch and a semantic perception contrast learning branch; and finally, performing joint training by using the comparative learning branches, and outputting a high-quality clustering result by ensuring feature, clustering distribution and semantic consistency.

The invention relates to a data-in-channel-based cross-source data joint query method, which comprises the following steps of: S1, constructing a data-in-channel access strategy model, and automatically generating a most suitable access mode; s2, constructing a power data knowledge graph, and analyzing a dependency relationship, a traceability path and a blood relationship among the data by using a graph neural network; s3, seamless access is achieved through natural language understanding, natural language query translation is adopted, and the natural language query of the user is converted into a corresponding SQL statement; s4, according to the SQL statements, similarity is calculated through the embedded vectors, user problems and data models are matched, and according to historical query modes and service requirements, a virtual data view is generated; s5, based on the generated virtual data view, sensitive information fields possibly contained in the data set are recognized through a natural language model and regular pattern recognition, and automatic labeling and classification are carried out. According to the method, the technical threshold of data fusion and sharing is remarkably reduced, and the intelligence of a data platform system in the power industry is improved.

The invention discloses a digital operation management platform oriented to enterprise transformation and upgrading and a construction method thereof, and belongs to the field of enterprise digital transformation. The platform comprises a front-end system cluster which comprises a safety management module, a production management module, an asset management module, a material management module, a collaborative office module, a production real-time module, a public foundation module, a system management module and a data governance module. The construction method comprises the following steps: S1, constructing a unified data interface architecture in a data governance server, accessing a multi-source heterogeneous data source and caching an original data packet; s2, cleaning and converting data based on the unique field identifier, and generating a structured data table; s3, transmitting the data to a data fusion center node, performing partition storage, deconstructing semantics, and generating an index metadata set; s4, constructing a multi-path service route in the data service distribution server, and generating a static index table and a dynamic index flow; and S5, pushing the data to a front-end system cluster through an interface service layer, and calling the data by a user terminal to realize index-driven data view construction. The method has the beneficial effects that the data integration efficiency and the operation intelligence level are improved.

The invention discloses a credit rating system based on multi-source data fusion and an enterprise knowledge graph. The credit rating system comprises the following modules: a data acquisition module, a data preprocessing module, a data fusion module, a knowledge graph construction module and a credit rating module. The data collection module is configured to collect enterprise-related data from a plurality of heterogeneous data sources. By integrating multi-source data fusion and an enterprise knowledge graph technology, comprehensive and dynamic evaluation of enterprise credit is realized, the system can acquire data from multiple channels and construct a unified enterprise data view through preprocessing and fusion, and the enterprise knowledge graph constructed on the basis not only contains attributes of enterprises, but also can construct a unified enterprise data view. Various relationships between enterprises are also disclosed, enabling credit rating to take into account associated risks.

The invention discloses a lithium battery remaining service life prediction method based on multimode collaborative compressed sensing and IDMamba, and relates to the technical field of batteries. According to the method, a multi-mode cooperative compressed sensing (MCS) method and an IDMamba neural network model method are adopted, the advantage of data denoising is achieved, the MCS is adopted for processing sequence data, mapping the data to a low-dimensional measurement space, removing redundant information and noise and then conducting data reconstruction, in the process, a self-attention mechanism is added to pay more attention to key features, and the accuracy of data reconstruction is improved. And the stability is improved through layer normalization, so that redundant components of the original data can be removed, a clearer data view is provided, and the nature of the data can be better understood.

The invention discloses a cross-platform teaching tool data collaboration and personalized resource recommendation system based on an MCP protocol, and belongs to the technical field of education. According to the system, a multi-platform teaching tool is in unified butt joint through the MCP protocol adaptation layer, and the problem of data splitting is solved; the data co-processing layer cleans and fuses multi-tool data, generates a teacher / student / parent data view and stores an evidence through a block chain; the personalized recommendation engine generates role-based resource recommendation based on a teaching knowledge graph and multi-dimensional behavior analysis; the user interaction layer supports multi-terminal access, and the data security module guarantees compliance. According to the system, the cross-platform data collaboration efficiency is improved by 70% or above, the resource recommendation matching degree reaches 92%, the system is suitable for K12 education, higher education and vocational education scenes, teaching tool data barriers are effectively broken through, and the teaching data utilization rate and the resource recommendation accuracy are improved.

The invention provides a graphical monitoring dynamic refreshing method and device which are applied to the technical field of information, an interceptor is arranged in a persistent layer service, a change event of a database can be captured in real time, then transaction message middleware is used for pushing the change event to a global Actor, the global Actor is responsible for maintaining a full-amount data view, and the overall data view is used for updating the full-amount data view. According to the method, the local Actor filters out customized data according to needs, personalized monitoring requirements of different users are met, accurate pushing of incremental data is achieved through Socket service, only data concerned by the users are transmitted, unnecessary data transmission is remarkably reduced, pressure of a database and a network is greatly reduced, and user experience is improved. The millisecond-level customized dynamic refreshing of the monitoring data is realized, the real-time performance and the accuracy of the monitoring system are remarkably improved, and the strict requirement of wharf production operation on real-time monitoring is met.

The invention relates to an intelligent drug management method and system in a drug clinical test process, and the method comprises the steps: obtaining a clinical test scheme, determining a drug clinical test standard according to the clinical test scheme, searching potential subjects, and transmitting a recommendation list to the potential subjects; in the clinical test process, acquiring multi-dimensional clinical test information of a subject corresponding to the target drug, extracting target data associated with the safety of the subject corresponding to the clinical test scheme in the multi-dimensional clinical test information, acquiring risk index data through the target data, and performing risk assessment; after a clinical test is finished, visit information is configured according to a clinical test scheme, the visit information comprises a visit base line, a visit window, a visit period and visit examination items, a visit schedule table is generated based on the visit information, remote monitoring is further carried out under the condition of being not affected by area and time factors, and the visit schedule table is displayed on the basis of all doctor-seeing data of a subject. A subject panoramic data view is formed.