151 results about "Decision making support systems" patented technology

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

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 intelligent medical multi-modal data analysis and nursing aid decision making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. Comprising the steps that a data acquisition module acquires a physiological time sequence and nursing logs to obtain a multi-modal original data set; the feature extraction module performs nonlinear dynamic analysis on the physiological data to generate a state manifold vector, and performs semantic embedding on a log to generate an intervention state tensor containing spatio-temporal features; the causal reasoning module constructs a dynamic causal map, maps vectors and tensors as nodes, and adjusts transmission weights by using the tensors to suppress non-pathological fluctuations; the decision-making module calculates dynamic risks and difference values based on atlas anti-fact deduction, and generates graded early warning and an optimal nursing path; the feedback module updates the atlas parameters according to the processing result. According to the system, the problem of high-frequency false alarm caused by daily nursing actions of traditional monitoring equipment is effectively solved, the alarm fatigue of medical staff is relieved, and focusing on real critical events is ensured.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.

The invention provides an AI doctor assistant agent medical information processing method based on voice interaction and large model driving, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining the audio information of a doctor-patient conversation and a voice instruction through a voice collection module, and converting the audio information into a text in real time based on a real-time voice conversion module, the intelligent transfer service module is used for carrying out intention classification on texts and distinguishing doctor-patient conversations and voice instructions, the AI medical big model intelligent agent service module is used for generating structured medical information for the doctor-patient conversations, the structured medical information is automatically recorded into the electronic medical record system, and the voice instructions are automatically recorded into the electronic medical record system. And analyzing and executing corresponding desktop operation through the instruction response module and the desktop analysis large model service module. The method can consider performance, cost and data security, improves the accuracy and credibility of medical information, is suitable for electronic medical record input, clinical decision support, system operation automation and other actual scenes, and can help medical institutions improve the diagnosis and treatment efficiency and quality.

The invention relates to the technical field of deep learning, in particular to a forage grass yield prediction model construction method based on deep learning, which comprises the steps of constructing a multi-source data fusion module, establishing a cold start mechanism, designing a multi-modal deep learning prediction network, integrating a physical constraint mechanism and constructing a management decision support system. A seasonal attribution analysis function is realized; in the prior art, a simple data superposition or static weighted fusion scheme is generally adopted, and inherent defects of deficiency, different scales and heterogeneity of multi-source data are difficult to process, so that the fusion feature quality is poor; according to the method, firstly, a data blank is accurately filled through an intelligent algorithm based on space-time continuity, then heterogeneous data is unified to a standard grid by using a multi-scale pyramid engine, and finally, deep fusion is performed through an attention mechanism for dynamically calculating importance of each data source; the integrity, the consistency and the information density of the input data are remarkably improved, and a solid and reliable data foundation is laid for subsequent accurate prediction.

The invention belongs to the cross technical field of artificial intelligence and decision support systems, and discloses an intelligent decision support method based on a dynamic knowledge graph and multi-modal fusion, which comprises the steps of 1, acquiring and preprocessing multi-source heterogeneous original data, 2, constructing and updating the dynamic knowledge graph, carrying out multi-modal fusion, outputting multi-modal fusion features, and 3, carrying out multi-modal fusion on the multi-modal fusion features. The method comprises the following steps of: 1, obtaining a multi-modal fusion feature and a knowledge graph embedding matrix, 2, receiving the multi-modal fusion feature and the knowledge graph embedding matrix, and outputting a candidate decision path set, decision probability distribution and a state value, 4, outputting dual uncertainty of each path by an uncertainty estimation module, 5, calculating a comprehensive reward of each path, 6, carrying out meta reinforcement learning and strategy optimization, and 7, carrying out multi-modal fusion. And step 7, outputting a final decision result, an uncertainty evaluation report and an interpretable reasoning path, and completing the decision. According to the method, the defects in the prior art are effectively overcome, and more accurate, reliable and explainable decision support is provided for a complex scene.

The invention discloses a digital asset value evaluation method and system for power dispatching. The method comprises the steps of firstly obtaining a digital asset set in a target scheduling scene, then determining internal attributes of the digital asset set based on a power system physical topology and an operation rule, and determining external value characteristics of the digital asset set based on a power market environment and a scheduling decision demand. Thirdly, value calculation is carried out on various digital assets by utilizing the model in combination with real-time and historical scheduling operation data, and a basic value score is output; and generating a comprehensive value evaluation result and a value label according to the score in combination with a preset rule and an adjustment factor. Finally, the evaluation result and the label are applied to a power dispatching auxiliary decision support system, and a quantitative basis is provided for dispatching plan optimization, data asset internal pricing and external transaction. According to the method, accurate, dynamic and business value evaluation of the power dispatching digital assets is realized, and value release and efficient utilization of data elements are effectively supported.

The invention relates to the technical field, in particular to a cerebral hemorrhage intelligent decision support system based on deep adaptive feature fusion, which comprises a multi-modal data standardization module, a cross-modal feature fusion module, a focus segmentation calculation module, a hemorrhage type identification module and an illness state grading output module. According to the method, deep association between fusion features and bleeding types is mined through an attention mechanism, related feature indexes are converted into standardized scores by referring to clinical common scoring standards, model attention weight distribution is optimized by combining actual prognosis result deviation, the suitability of illness state grading and the clinical scoring standards is improved, and the probability of illness state grading is lowered. An accurate grading result and prognosis prediction reference are provided for clinical treatment decision and rehabilitation intervention; effective data are screened from multiple types of brain images according to image quality related indexes, clinical text key information extraction and association labeling are combined, data integrity and consistency verification is carried out at the same time, and the standardization degree of multi-source data is improved.

The invention relates to the technical field of clinical decision support and medical data intelligent analysis, in particular to a critical patient individualized liquid resuscitation decision support system, which is characterized in that a data acquisition unit acquires pulse pressure variation waveform and multi-dimensional physiological data of each stroke volume; a volume benefit analysis channel of the dynamic decision-making unit segments a respiratory cycle, extracts pulse pressure fluctuation characteristics, outputs a per-stroke volume increase probability value through a machine learning classifier, an organ injury early warning channel calculates a blood lactic acid and creatinine clearance rate change rate, and generates an acute kidney injury risk sign in combination with inferior vena cava image verification; the execution control unit sends an infusion control or limit instruction according to the combined state; and the feedback optimization unit adjusts the weight of the model according to the actual value of blood lactic acid removal, and recalculates the probability value, so that individualized precise resuscitation, balance capacity benefit and organ protection are realized, and the treatment safety and effectiveness are improved.

The invention discloses a wind power project multi-dimensional marketing and decision support system based on a carbon transaction value, and belongs to the technical field of renewable energy sources and carbon transactions, and the system comprises a carbon footprint calculation module, a carbon emission reduction calculation module, a carbon asset value prediction module, a customer portrait analysis module, a carbon value narration module and a decision support module. The decision support module models a marketing process of a wind power project into a Markov decision process, self-adaptive optimization of a marketing strategy is realized by constructing a state space, an action space and a reward function and adopting a reinforcement learning algorithm, and the system can dynamically adjust strategy selection according to customer behavior feedback so as to improve the marketing efficiency. Self-adaptive marketing strategy suggestions are generated for different wind power projects and different types of customers, and the marketing conversion rate and the customer response satisfaction degree are remarkably improved.

The invention discloses a clinical decision support system fusing structured knowledge and generative intelligence. A guide map generation module is used for converting an unstructured authoritative clinical guide into a machine-readable and executable structured knowledge map; the medical record information extraction module is used for processing an unstructured medical record original text written by a clinician and extracting a clinical event sequence with a timestamp from the unstructured medical record original text; the decision reasoning module is used for receiving the structured knowledge graph and the clinical event sequence and driving a large language model to perform multi-step logical reasoning under the guidance of the structured knowledge graph so as to plan a diagnosis and treatment path; and the interactive display module is used for displaying the diagnosis and treatment path and the decision basis corresponding to each decision node on the diagnosis and treatment path in a visual mode. According to the method, the unstructured medical record information and the structured guide knowledge can be deeply fused, and normative and highly personalized decision support is provided for clinicians.

The invention discloses a proton adaptive radiotherapy triggering decision method and system based on multi-modal data quantitative evaluation. The method comprises the following steps: acquiring image data, dose data and clinical data, respectively calculating a dosimetry scoring function, an anatomical structure scoring function and a proton specificity scoring function based on the image data and the dose data, obtaining an enhanced core quantitative score through weighted geometric average fusion, and obtaining the enhanced core quantitative score; determining parts, stages and prognosis adjustment parameters based on clinical data, and fusing a BMI dynamic evaluation factor, a treatment response prediction factor and a multi-factor interaction factor to obtain an enhanced clinical background adjustment coefficient; and calculating a final decision score in combination with the time modulation factor and the risk prediction index based on the LSTM, and outputting a grading decision result. According to the method, objective quantitative evaluation of deep fusion of multi-modal data is realized, specific risks of proton therapy are accurately captured, personalized intelligent decision is supported, and the method has good clinical operability.

The invention relates to the technical field of medical artificial intelligence, in particular to a one-question multi-certificate task-oriented special retrieval embedding method, which comprises the following steps of S1, constructing a one-question multi-certificate training data set: systematically collecting authoritative medical guide document data from a plurality of authoritative channels, and establishing a corresponding document meta-information index table based on the collected data; analyzing a document chapter structure by adopting an OCR (Optical Character Recognition) algorithm in combination with font characteristics, a serial number structure and paragraph indentation and line feed rules, and constructing a chapter tree; inputting each structured TXT guide into a GPT-4 or other language model, adopting a customized prompt word Prompt to guide the structured TXT guide to complete multiple rounds of question and answer generation, and forming structured question and answer pairs Qamp in a uniform form; structure A; an automatic sampling mechanism is established, each time N documents are processed, the method can be seamlessly connected with an existing RAG framework, and rapid deployment and iteration in scenes such as electronic medical records and clinical decision support systems are facilitated.

The invention discloses a method for predicting civil aviation carbon emission based on civil aviation core element configuration. The method comprises the following steps: S1, constructing a Chinese civil aviation development scene in a predetermined time period; s2, solving spatial-temporal evolution configuration of civil aviation core elements in the predetermined time period under different scenes based on the Chinese civil aviation development scene in the predetermined time period; s3, on the basis of the civil aviation core element configuration result, civil aviation CO2 emissions under different scenes are measured and calculated through a bottom-up method, and the emission peak level and time are determined; and S4, comparing the element configuration and emission results of different scenes, and analyzing the application effect of the carbon emission reduction means. Through coupling of scene analysis and a decision support system, collaborative prediction of element configuration and carbon emission is realized. According to the embodiment of the invention, a technical method for deeply coupling core element configuration, multi-scenario analysis and carbon emission prediction can be constructed, so that the prediction accuracy of civil aviation carbon emission can be effectively improved.

An electric power spot transaction decision support system based on multi-dimensional market analysis relates to the field of electric power spot transaction, and comprises a display layer which supports complex interaction through a Web end, large-screen cockpit command center monitoring and small program mobile end access, and realizes interaction result multi-terminal output. The application layer is communicated with the display layer, comprises a market analysis module and is used for processing multi-dimensional external market data to generate standardized background data; a user side load prediction module generates a user load day-ahead prediction result through an ARIMAX model based on historical power consumption and external factors, predicts monthly power by combining multiple linear regression and other models, and dynamically allocates weights by adopting an error reciprocal normalization method; and the day-ahead price prediction module cleans and preprocesses data such as historical electricity prices, and outputs a result through multi-model price comparison evaluation. The bottom layer is integrated with a scheduling management system through a data interaction interface, is in butt joint with a power grid operation environment and supports efficient decision-making of energy business.

The invention discloses an enterprise internal acquisition cost optimization decision support system based on big data analysis. The system comprises a data aggregation and integration module, a purchase behavior feature extraction module, a cost driving factor deconstruction module, a supplier multi-dimensional evaluation module, a purchase strategy intelligent customization module, an enterprise purchase data warehouse and an enterprise purchase supervision terminal. The purchase strategy suitable for the enterprise is intelligently generated by integrating the purchase behavior characteristics, the cost driving factors and the supplier evaluation result, the purchase cost composition and the dynamic change can be accurately informed, scientific and reasonable purchase decision suggestions are provided for the enterprise, the purchase cost is effectively reduced, and the purchase efficiency is improved. And the execution effect of the purchasing strategy is monitored, evaluated and fed back in real time, so that each link of the purchasing process can be monitored in real time, potential problems can be found and solved in time, the purchasing efficiency and quality are improved, the optimization of the purchasing cost and the maximization of the overall benefit are realized, the purchasing supervision difficulty of an enterprise is remarkably reduced, and the intelligent level is high.

The invention relates to the technical field of anaesthesia machines, in particular to an anaesthesia machine control parameter optimization method and a clinical decision support system, and the method comprises the steps: setting a safety interval of each control parameter of an anaesthesia machine; observation parameters and action space are determined, a reward function is designed, and a reinforcement learning model is constructed and trained; inputting observation parameters at each moment in the operation of the patient into the strategy network of the trained reinforcement learning model to obtain probability distribution of control parameters at each moment, and determining the value of each control parameter according to the safety interval; and sending the obtained values of the control parameters to a microcontroller of the anaesthesia machine so as to complete updating of the control parameters. By automatically optimizing the control parameters of the anesthesia machine, the workload of an anesthetist is reduced, and the physiological parameters of a patient are maintained within an expected range.

The invention relates to the technical field of soil management, and discloses a multi-source data-based agricultural decision support system, which comprises a dialogue module, a land parcel module and a my module, and is characterized in that the dialogue module comprises trial questions, historical dialogues and voice questions and answers; the land parcel module comprises land parcel management, periphery matching, report generation and data analysis; the my module comprises members, fruit values, reward fruit values, account setting, privacy policies, message notification and system setting. According to the agricultural decision support system based on the multi-source data, scientific and rigorous investment decisions can be made based on detailed historical climate records, geographical environment characteristics, altitude differences, soil types, market trends and other factors of selected land parcels; meanwhile, information such as historical climate, real-time weather forecast, disaster early warning and minute-level rainfall prediction within the range of 3-6 kilometers around a specific land parcel can be timely acquired, and agricultural producers can flexibly arrange agricultural activities conveniently.

The invention relates to a patient decision support system and operating method based on self-care algorithms that evaluate patient activity for patients diagnosed with liver cirrhosis, which is intended to monitor and manage patients' health conditions, provides regular data tracking and personalised recommendations, tracks patients' daily and weekly activities, medication use, diet, and exercise routines, collects and evaluates the data, and presents user-specific recommendations, and which uses artificial intelligence and care algorithms to determine, on the basis of the patient's health data, which treatment and care pathways are the most appropriate.

A method for dose guidance for a radiotherapy system during a radiotherapy session includes the steps of: performing substantially real-time dose reconstruction to obtain a delivered radiation dose generated by a radiotherapy beam of the radiotherapy system in at least one volume; repeatedly estimating a remaining radiation dose based on an observed and / or simulated motion of the at least one volume; repeatedly estimating a final delivered radiation dose as a sum of the delivered radiation dose and the estimated remaining radiation dose; and providing dose guidance for a remaining part of the radiotherapy session based on the estimated final delivered radiation dose. A method of continuous estimation of final delivered radiation dose during a radiotherapy session and a decision support system for a radiotherapy system are also provided.

The invention provides an unmanned aerial vehicle cluster load resource planning method based on multi-attribute coding and parallel variable neighborhood search, which belongs to the technical field of unmanned aerial vehicle resource planning, and comprises the following steps: firstly, determining target characteristics and task requirements, and determining unmanned platforms and available resource attributes; calling a load configuration algorithm to generate a configuration scheme, adopting multi-attribute coding to represent the configuration scheme, preferentially constructing an initial scheme based on task requirements, then adopting a self-adaptive variable neighborhood multi-population parallel evolution method to solve, generating a task configuration scheme meeting an optimization target and constraint conditions, and uploading the task configuration scheme; determining whether to approve to use the scheme or not by using expert experience or a decision support system according to the feasibility and efficiency of the scheme and the integrating degree of the scheme and a target; starting from the rationality and economy of the load resource allocation method for the unmanned aerial vehicle cluster, a higher cost-effectiveness ratio is realized on the premise of ensuring the task efficiency.

This invention relates to the field of enterprise management technology, and in particular to an intelligent decision support system and method based on cognitive logic and contextualized semantics. The method acquires enterprise decision-making data through multi-source data monitoring and preprocesses it to generate cross-modal enterprise scenario cognitive information; analyzes cross-modal correlations between data, integrates cognitive logic reasoning rules and semantic understanding models, and constructs contextualized semantic decision fusion features; trains an enterprise decision quality assessment model based on historical cases to perceive the quality of the current decision-making scenario and outputs decision quality information; judges the execution effect in conjunction with expected goals, and if deviations from the goals are detected, an optimization mechanism is initiated, using a historical knowledge base and multi-objective optimization algorithms to generate candidate solutions and select the optimal solution, thereby achieving intelligent adjustment of the decision-making plan. This method integrates multi-source heterogeneous data and cognitive logic, enhancing the semantic understanding, dynamic evaluation, and adaptive optimization capabilities of the decision-making system, and improving the scientific rigor and real-time performance of enterprise decisions.

The invention discloses a production line real-time data decision support system based on cloud edge collaboration, and relates to the technical field of industrial Internet of Things and intelligent manufacturing, and the system comprises a plurality of edge intelligent nodes which are disposed at manufacturing resources on a production line in a distributed manner and are used for collecting multi-source heterogeneous real-time data and executing a local decision; and the cloud decision platform is used for executing global data analysis and strategy generation. According to the production line real-time data decision support system based on cloud edge collaboration, a traditional passive exception handling mode is converted into an active prediction and collaborative decision mode. The system can continuously perceive the state of the production line in real time, predict and locally intervene before the influence of potential problems is expanded, and quickly mobilize multiple resources to form a solution through an intelligent coordination mechanism when needed, so that the overall response time from the occurrence of an anomaly to the implementation of management and control is effectively shortened, and the management and control efficiency is improved. And the stability and continuity of production operation are improved.

The invention discloses a deep learning-based traditional Chinese medicine acupuncture decision support system and method, and relates to the technical field of traditional Chinese medicine auxiliary decision, and the method comprises the steps: collecting a tongue image, a face image and a wrist pulse wave signal, and carrying out the preprocessing; performing feature extraction on the preprocessed tongue picture image, the face image and the wrist pulse wave signal through a deep convolutional neural network and a long-short-term memory network to generate a multi-modal feature vector; and performing priority ranking and advantage analysis on the candidate acupuncture scheme set and the quantitative curative effect score of each scheme to obtain an advantage comparison result between the schemes, and determining a traditional Chinese medicine acupuncture recommendation scheme from the advantage comparison result between the schemes according to statistical difference test. According to the method, by generating the candidate acupuncture scheme set, dynamic combination of deep learning features and traditional Chinese medicine syndrome differentiation logic is realized, it is ensured that the generation of the acupuncture recommendation scheme is based on data driving and follows the traditional medical science, and the scientificity and personalized suitability of decision making are enhanced.

This invention proposes an automated construction organization design platform and its design method for water conservancy and hydropower projects. The platform includes a structured experience database system, a dynamic optimization calculation system for earthwork balance, an intelligent construction schedule generation system, an automated construction resource design system, and a 3D visualization and decision support system for the overall construction layout. The platform system uses the correlation data between building quantities and schedule time as a link to achieve data exchange and collaborative linkage. The design method includes: constructing and initializing the structured experience database system; automatically generating a construction schedule plan; outputting an earthwork balance scheme; automatically calculating and configuring labor force, work area, and facilities of each construction plant; generating a construction layout scheme and outputting it in 3D visualization. This invention realizes intelligent processing of the entire process of construction organization design calculation, resource supply planning, and site layout, significantly improving design efficiency and scheme accuracy, while also possessing good operability and system integration.

The invention provides an electronic case management system and method based on a medical platform. The system comprises a server side and a client side. The server side comprises a data storage module, a data processing module, a security management module and a decision support system; the client comprises a user interface, an input module and a communication module; the data storage module is used for storing electronic cases and medical information of patients; the data processing module is used for processing and analyzing data in the electronic case and providing diagnosis and treatment suggestions; the safety management module is used for ensuring the safety of data transmission and storage; collecting medical information and case data of the patient; the collected data are uploaded to a server side; processing and analyzing the data at the server side by using a feature extraction algorithm and a disease recognition algorithm; extracting key features from the cases by using a feature extraction algorithm; carrying out disease recognition on the features by using a disease recognition algorithm; an analysis result is fed back to medical staff and a patient; diagnosis and treatment suggestions and medication guidance are provided.