845 results about "Data treatment" patented technology

The invention discloses an intensive care unit patient early warning method based on clinical data, and relates to the technical field of medical data processing. Comprising the following steps: collecting patient data in real time, and performing multi-modal data fusion; performing real-time analysis on the breathing waveform, extracting morphological characteristics in the waveform, and identifying subtle abnormal changes in the respiratory system; based on historical data and real-time data of the patient, constructing a time sequence model, and predicting whether the patient has a potential risk; and dynamically adjusting an early warning threshold value and a strategy based on the change trend of the historical data and the real-time data. According to the invention, the level of risk management of the patient in the intensive care unit is greatly improved, precise identification of subtle abnormity of the patient is ensured, and effective intervention can be carried out before a problem occurs.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention discloses a multi-parameter monitoring data analysis method, and particularly relates to the technical field of medical monitoring, and the method comprises the following steps: obtaining a plurality of preset physiological parameters, carrying out the classification analysis, and dividing the parameters into a correlation physiological parameter set and an independence physiological parameter set; carrying out joint credibility analysis on the relevance physiological parameter set, identifying reliability hidden dangers, and obtaining a consistency quality index through overall trend consistency analysis; independent credibility analysis is carried out on the independent physiological parameters, and an accurate quality index is obtained through accuracy analysis; based on the quality indexes, the physiological parameter groups are divided into high-quality data and low-quality data, and the low-quality data are recollected to improve the data quality; through the universality and flexibility of the method, the method can adapt to different medical scenes, stable monitoring data quality guarantee is provided, clinical risks caused by data quality problems are effectively reduced, and monitoring data processing is more scientific and efficient.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention discloses a geological mineral exploration data extraction method and system, and relates to the technical field of geological mineral exploration data extraction, and the method comprises the steps: carrying out the data collection of a target region through a multi-source data collection method, obtaining satellite remote sensing data, unmanned aerial vehicle aerial survey data and ground geophysical prospecting data, and obtaining an original data set; preprocessing the collected data by adopting a preliminary data processing method, removing noise and correcting errors to obtain a high-quality data set; integrating data from different sources in the high-quality data set by adopting a weighted fusion algorithm to generate a comprehensive database and obtain a comprehensive data set; performing high-resolution geophysical exploration on the fused comprehensive data set by adopting a geophysical exploration technology to obtain underground structure information; analyzing the exploration data by adopting a geophysical inversion technology, and identifying the position of a potential ore body to obtain an ore body distribution map; the method is based on the comprehensive data set, underground structure information and the ore body distribution diagram.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention discloses a new-generation medical equipment integration engine, which comprises an equipment data standardization module used for automatically identifying and converting various equipment data formats; the equipment data standardization module can process various medical equipment data such as HL7, DICOM and MODBUS through a built-in multi-protocol analyzer and convert the medical equipment data into a uniform standard format, so that the consistency and interoperability of the data are ensured; the real-time data processing engine module is used for collecting, transmitting and processing the data of the medical equipment in real time; the intelligent analysis and decision support module is used for performing intelligent analysis on the data from the medical equipment through deep learning and time sequence analysis technologies; a micro-service architecture design module; and a multi-level security mechanism module. The device data can be deeply analyzed and predicted, the accuracy of diagnosis and treatment decisions can be improved, early warning can be achieved, and the safety of patients is ensured.

The invention relates to the technical field of medical data processing, in particular to an intelligent decision-making system and method for multi-modal medical data dynamic fusion, and the system comprises a data collection module which is used for collecting multi-modal medical data; the data preprocessing module is used for preprocessing the collected multi-modal medical data; the multi-modal fusion and decision generation module is used for constructing a cascade learning framework comprising a plurality of subtask models; the dynamic decision adjustment module is used for setting cascade priorities among different modes in different diagnosis and treatment scenes; and the closed-loop optimization module is used for constructing a closed-loop process in combination with a national diagnosis and treatment guide and real clinical data. According to the method, in different diagnosis and treatment scenes, the system can automatically adjust the cascade priority of each modal data according to the illness state stage, so that the characterization capability of the decision model on the complex illness state is remarkably improved, the limitation of traditional static fusion is broken through by the dynamic fusion mechanism, and the accuracy of diagnosis and treatment suggestions and the scene adaptability are remarkably enhanced.

The invention relates to the technical field of disaster prediction and early warning, in particular to a rural water supply project drought disaster prediction and early warning method and system. The method comprises the following steps: acquiring a hydrological data set; performing data preprocessing on the hydrological data set to generate a hydrological core processing data set; performing data feature extraction on the hydrological core processing data set to generate a hydrological feature data set; quantifying the hydrological characteristic data set to generate a multi-water-source drought early warning threshold calculation algorithm; constructing a multi-dimensional interaction model by using a multi-water-source drought early warning threshold calculation algorithm in combination with the hydrological characteristic data set, and generating a multi-source hydrological drought early warning and prediction model; therefore, by optimizing hydrological data processing and early warning threshold calculation, the problem of inaccurate prediction caused by inconsistent multi-source data in a traditional early warning system is solved, and the accuracy and reliability of early warning of drought disasters are greatly improved.

The invention discloses a medical image intelligent diagnosis method and system based on deep learning, and the method comprises the steps: obtaining multi-modal medical image data through a medical image collection device, carrying out the data preprocessing, and forming a standardized image data set; constructing a deep learning diagnosis model based on multi-scale feature fusion, inputting the standardized image data set into the deep learning diagnosis model for transfer learning training, and optimizing model parameters by adopting a dynamic weight adjustment strategy; verifying the trained deep learning diagnosis model through an integrated learning framework, generating a diagnosis confidence score, and performing probability calibration on a diagnosis result in combination with a Bayesian optimization algorithm to obtain an optimized diagnosis model; and inputting medical image data to be diagnosed, and outputting a pathological classification result. The problems that in the prior art, multi-modal medical image data processing is insufficient, model optimization strategies are insufficient, and diagnosis confidence coefficient calibration methods are insufficient are solved.

The invention discloses a geological mapping method based on unmanned aerial vehicle three-dimensional modeling, and relates to the technical field of geological survey and intelligent path planning, and the method comprises the steps: S1, extracting an initial feature data set from geological information through constructing a three-dimensional space data processing frame, decomposing the original data into a plurality of sub-region data sets, obtaining a preliminary classification geological information set, and carrying out the calculation of the preliminary classification geological information set; s2, analyzing abnormal values and boundary information in the sub-region data set according to the preliminarily classified geological information set, and determining a potential key geological feature point position set, and S3, matching the point position set with geological environment parameters according to the key geological feature point position set, recalibrating the point positions if the matching degree is lower than a preset threshold value, and if the matching degree is lower than the preset threshold value, recalibrating the point positions if the matching degree is lower than the preset threshold value. An optimized geologic characteristic distribution diagram is obtained; according to the geological mapping method based on unmanned aerial vehicle three-dimensional modeling, the accuracy and efficiency of geological survey in a complex terrain environment are effectively improved, and comprehensive technical support is provided for field geological survey.

The invention discloses a heart failure treatment aid decision generation system based on multi-modal data fusion, and the system comprises a data collection module which is used for collecting the multi-modal data of a patient, and the multi-modal data comprises structured data, unstructured data and medical image data; the data processing module is used for carrying out standardization, quantization and vectorization processing on the multi-modal data; the knowledge graph construction module is used for constructing a knowledge graph of heart failure treatment, and the knowledge graph comprises a disease entity, a pathological feature, a treatment scheme and an association relationship thereof; the reasoning module is used for generating a personalized treatment decision based on the knowledge graph and the patient data; and the treatment scheme generation module is used for dynamically adjusting and outputting a personalized treatment scheme in combination with the real-time state data of the patient. The problems that multi-modal data are difficult to fuse and real-time disease change is difficult to dynamically adjust in heart failure diagnosis and treatment are solved, accurate diagnosis and personalized treatment are realized through knowledge graph reasoning and a dynamic correction mechanism, and the diagnosis and treatment efficiency and accuracy are remarkably improved.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention relates to the technical field of drainage basin water environment management, in particular to a construction method of a water pollution source emission list. The method comprises the following steps: performing classification and account subdivision on different types of pollution sources based on pollution source sub-categories, pollution production modes, treatment technologies and pollution discharge paths to obtain a pollution source classification and classification system; a multi-source data fusion strategy and a sparse data processing strategy are utilized to carry out parameter fine value processing on data in the pollution source grading and classification system, and calculation parameters of different types of pollution sources are determined; performing modeling characterization on the calculation parameters of the different types of pollution sources according to a pollutant generation and emission mechanism to obtain the river entering quantity of different types of pollutants of the different types of pollution sources, and constructing a water pollution source emission list based on the river entering quantity. Therefore, the problems of difficulty in full-aperture refined estimation of the pollution source emission list, relatively low space-time precision of the list and the like are solved, and refined calculation of pollution source emission is realized.

The invention relates to the technical field of soil monitoring and data analysis, and discloses a soil carbon and nitrogen content dynamic prediction method based on deep learning. The method comprises the following steps: acquiring soil monitoring data from an environment monitoring platform, performing dimension reduction by using a multi-layer perceptron model to obtain core features, and dividing a dynamic monitoring data set according to the core features; taking the data set as input, and constructing an initial prediction model by using a time convolutional network; and constructing a meteorological factor library and an influence map, replacing an initial model time node, and obtaining a climatic factor node prediction model through cross validation. And performing regression fitting and cross validation verification by using a Gaussian process, and constructing a soil dynamic prediction model. According to the method, through multi-step data processing and model construction, the influence of soil data characteristics and meteorological factors is effectively mined, the dynamic change of the soil carbon and nitrogen content can be accurately predicted, and powerful support is provided for the fields of precision agriculture, environmental protection and the like.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention provides a special education resource management system. The special education resource management system comprises a data analysis and evaluation module, a learning support tool module, a teacher and assistant management module, a resource allocation and scheduling module, a student demand evaluation module, a resource database and a user interface module. The data analysis and evaluation module is responsible for monitoring and evaluating the use effect of resources in real time and providing accurate data support for decision makers, and the data analysis and evaluation module has a data acquisition and monitoring function, a data processing and cleaning function and a data analysis and evaluation function. Wherein a data acquisition part in the data acquisition and monitoring function is used for integrating a plurality of data sources. The special education resource management system provided by the invention has the advantages that various resources can be monitored and scheduled in real time, accurate resource configuration and evaluation analysis are provided, and then the problems existing in current special education resource management can be effectively solved.

The invention provides a sewage treatment detection method and system with a diagnosis function, and the method comprises the steps: firstly receiving multi-source detection data which is uploaded by each treatment unit in a sewage treatment process and comprises sewage pollutant component data, treatment equipment operation parameter data and flora activity data in a reaction tank; performing cross-unit association mapping processing on the multi-source detection data to generate a substance transformation association network among processing units, constructing a dynamic balance model of the sewage treatment system based on the network, inputting the multi-source detection data acquired in real time into the model, analyzing abnormal association nodes deviating from a balance state and corresponding abnormal influence factors, and determining the abnormal association nodes and the corresponding abnormal influence factors; and finally, generating a diagnosis report containing an abnormal processing path and an equipment regulation and control instruction according to an analysis result, thereby accurately positioning a problem source of the sewage treatment system, timely adjusting equipment parameters to recover system balance, and improving the processing efficiency and stability.

The invention belongs to the technical field of farmland tail water pollution load analysis and evaluation, particularly relates to a farmland tail water pollution load analysis and evaluation method, and aims to overcome the obvious defects of an existing monitoring and evaluation method in the aspects of monitoring point layout, index coverage, data processing, analysis depth and the like. In order to solve the problems that farmland tail water pollution load analysis and evaluation cannot meet accurate and dynamic farmland tail water pollution load analysis and evaluation requirements and pollution prevention and control decisions are difficult to effectively support in the prior art, the invention provides the following scheme: the method comprises the following steps: S1, carrying out multi-source acquisition on farmland information data, and carrying out fusion processing; s2, setting a fixed sampling unit and a movable sampling unit according to farmland information setting; according to the method, multi-source data such as farmland basic information, agricultural production activity data, meteorological data and water quality monitoring data are comprehensively considered, and comprehensive and accurate quantification and evaluation of farmland tail water pollution load are realized through a data preprocessing and fusion technology.

The invention relates to the technical field of medical data processing, in particular to a multi-modal medical data classification method, model and equipment and a storage medium. The method comprises the following steps: firstly, carrying out coding alignment processing on original multi-modal medical data to obtain aligned multi-modal features, then converting the aligned multi-modal features into graph data, extracting common features among multiple modals and specific features of each modal, and finally, carrying out multi-modal feature extraction. Finally, the original multi-modal medical data are classified according to the common features and the specific features, the common features reflect the relevance between the multi-modal data, and the specific features can reflect the specificity of modals, so that accurate classification of the multi-modal medical data can be achieved by comprehensively utilizing the common features and the specific features.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention belongs to the technical field of data processing, and discloses a construction method and system based on a medical examination medical knowledge base and a medium. Comprising the steps that a basic medical data set is collected, data cleaning is conducted, and a structured medical data set is output; constructing a multistage medical dictionary and establishing semantic mapping with the structured medical data set to generate a standard medical data set; carrying out medical related entity extraction on the standard medical data set and establishing a semantic relationship of each medical entity to obtain a medical entity set; performing semantic alignment on the medical entity set to obtain a semantic alignment entity set; constructing a medical examination knowledge rule base and constructing an initial medical examination knowledge graph in combination with the semantic alignment entity set; performing entity association extension on the initial medical examination knowledge graph, and outputting an enhanced medical examination knowledge graph; real-time clinical data are collected, and the medical examination knowledge graph is updated and enhanced based on the real-time clinical data to obtain the medical examination knowledge base, so that convenience is provided for medical decision making and intelligent reasoning diagnosis.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The invention relates to the technical field of condition prediction, and particularly discloses a condition score prediction method for Alzheimer's disease, which comprises the following steps: S1, acquiring longitudinal multi-time-point MRI image data and corresponding clinical score data of a target subject; s2, processing to obtain a feature matrix of the brain region and brain region data and clinical scores at different time points; s3, performing sparse representation on the feature matrix by using a dictionary learning algorithm to obtain a sparse representation matrix; s4, constructing an SLSTM, taking the sparse representation matrixes of the baseline moment and the M06 and M12 time points as input, and training a model to predict clinical scores of subsequent time points M18, M24 and M36; according to the method, sparse representation is performed on MRI image data through dictionary learning, redundant features are removed, features highly related to disease change are extracted, and a long-term dependency relationship in a longitudinal time sequence is captured in combination with a double-layer stacked LSTM structure, so that the model can more accurately predict clinical scores of future time points.

The invention discloses an intelligent sewage treatment system and method based on data analysis, and belongs to the technical field of sewage treatment.The intelligent sewage treatment system comprises a sewage treatment equipment monitoring module for obtaining associated data generated by sewage equipment in the sewage treatment process; the sewage monitoring module is used for acquiring water quality parameters in the sewage treatment process; the data processing terminal is used for generating a sewage treatment condition value according to the acquired associated data and the water quality parameters, so that the sewage treatment equipment is correspondingly optimized and adjusted. According to the method, the data generated in the working process of the sewage treatment equipment and the water quality parameters of the sewage in the sewage treatment process are obtained and comprehensively analyzed to generate the sewage treatment condition value, and whether the currently set parameters of the sewage treatment equipment are qualified or not is judged according to the sewage treatment condition value; and if the sewage is unqualified, a corresponding optimization adjustment strategy is generated to carry out corresponding optimization treatment, so that the sewage treatment efficiency can be ensured and the operation cost can be effectively saved during sewage treatment.

The invention relates to the technical field of financial data processing, in particular to a financial data automatic extraction and articulation processing system and method, and the system comprises a financial data obtaining module which is used for obtaining financial image data, audio data and bank API data; and the financial data processing module adopts OCR (Optical Character Recognition), Whisper voice conversion and bank API (Application Program Interface) paging query technologies to carry out standardization processing on the data and extract financial data through an anomaly detection mechanism. And the financial data sorting module classifies and stores the data to different financial databases by adopting a dynamic routing and four-way distribution strategy based on format verification and priority marks. And the financial data articulation module performs verification and difference processing on the data through a cross-library articulation rule to generate a financial analysis report. And the financial data display module monitors the financial analysis result by adopting a data snapshot and rollback mechanism, so that automatic extraction, articulation and visualization of the financial data are realized, and the accuracy and compliance of the financial data are improved.

The invention discloses an AI-driven disease diagnosis and curative effect monitoring analysis system, which comprises a data processing module used for collecting multi-source diagnosis and treatment data and constructing a multi-modal sample set; the diagnosis prediction module is used for generating a diagnosis label, a diagnosis confidence value and a prediction curative effect trend through a multi-task learning model; the curative effect comparison module is used for collecting actual curative effect data, constructing a curative effect observation sequence and generating a curative effect deviation sequence based on the curative effect observation sequence and the predicted curative effect trend; the credibility evaluation module is used for executing credibility review based on the curative effect deviation sequence and the diagnosis confidence value; and the path evolution module is used for recording continuous multi-round diagnosis correction results, generating a jump type diagnosis path and updating a diagnosis and treatment data chain. The dynamic closed-loop verification mechanism between the multi-mode diagnosis and treatment data and the predicted curative effect trend improves the accuracy of disease diagnosis, the reliability of curative effect prediction and the adaptability of the diagnosis and treatment process.

The invention provides a field-level data governance method and system based on a data blood relationship, and relates to the technical field of data processing, and the method comprises the steps: obtaining upstream data and downstream data of each field in to-be-processed data; taking the upstream data and the downstream data as sample data to construct a data blood relationship map; determining the upstream and downstream blood relationship among the fields; determining a basic credibility score, a timeliness weight score and a rule verification score of each field; constructing a credibility model; inputting the sample data into a credibility model for training; acquiring real-time sample data of the to-be-processed data; inputting real-time sample data into the trained credibility model, and outputting final credibility; performing data treatment on the to-be-processed data according to the final credibility; and marking a data treatment result as sample data, and inputting the sample data into the credibility model again for training. According to the method, refined management of the data can be realized, and the data quality and availability are improved.