1787results about "Cheminformatics programming languages" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention relates to the technical field of pollutant tracing, and discloses a method and a system for tracing new pollutants in various environmental media based on machine learning. The method comprises the following steps: collecting multivariate environment data to construct a pollutant fingerprint database; performing space-time correlation analysis on the fingerprint database and the environment data, and reconstructing a transmission path; converting the data, the fingerprint database and the path diagram into source node fusion to construct an analytical model, and forming a contribution rate matrix; and positioning the pollution source through the three-dimensional features based on the matrix. According to the method and the device, an intelligent decision-making system based on machine learning can be constructed by integrating multi-source heterogeneous data under a complex environment background, automatic, standardized and precise traceability of new pollutants is realized, and the traceability efficiency and accuracy are improved.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention provides a seawater parameter calibration system, and belongs to the technical field of seawater parameter measurement. A thermohaline depth device is used for constructing a three-dimensional structure model to identify a spring layer position, a multi-parameter water quality sensing device is used for collecting data to establish a correlation model, and a dissolved oxygen analysis device is combined with thermohaline data to calculate a correction coefficient; and the spectral analysis device extracts feature recognition chemical components. The method comprises the key steps that a parameter interaction compensation function and an OceanParamNet model are adopted to construct a seawater comprehensive parameter calibration model, original data are corrected, and abnormal values are detected. The system realizes layered storage and wireless transmission of data, dynamically adjusts sampling frequency and power consumption, periodically executes a self-calibration program to compensate aging drift of a sensor, realizes high-precision self-adaptive calibration of seawater parameters, and solves the technical problems of low precision and poor environmental adaptability of seawater multi-parameter collaborative calibration in the prior art.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention relates to the technical field of chemical material detection, and discloses an AI-based chemical material detection information library retrieval and standard spectrum comparison system. The system comprises a data acquisition module, a spectrum preprocessing module, a feature extraction module, a retrieval matching module and a dynamic comparison module. The data acquisition module uses a multispectral sensor to acquire real-time spectral data covering ultraviolet, visible light and infrared bands, and the real-time spectral data is denoised by the spectrum preprocessing module. The feature extraction module adopts a deep residual convolutional network to extract spectral fingerprint feature vectors. The retrieval matching module inputs the feature vectors into a graph structure retrieval model to screen candidate standard spectrums. And the dynamic comparison module compares the candidate standard spectrums in multiple dimensions based on an improved quantum particle swarm optimization algorithm to obtain an optimal result. The system can accurately acquire and process spectral data, quickly retrieve and accurately compare standard spectrums, improve the efficiency and accuracy of chemical material detection, and have wide application prospects in the fields of chemical material research, production quality control and the like.

The invention belongs to the technical field of water pollutant traceability, and particularly discloses a water quality fingerprint database analysis method based on feature recognition, which relates to the field of water pollutant traceability, and comprises the following steps: cross-comparing feature curved surface information of different upstream and downstream under feature peak whole database data and sorting to obtain pollutant features under different intensities; the pollution source type is identified according to the pollutant difference; sorting the characteristic curved surface information of the water body samples reflecting different nodes, calling a chemical characteristic analysis result, outputting a chemical characteristic sequence, and selecting a first sequence sampling point position of the chemical characteristic sequence corresponding to the spectral characteristics of the identified pollution source type as a pollution source; according to the method, whether the identification result is met or not is analyzed according to the biological characteristics of the pollution source, efficient source tracing is carried out on water pollutants, an accurate pollutant transmission path is output, meanwhile, the corresponding pollutant type is evaluated, the corresponding pollutant diffusion trend is given, and powerful data is provided for follow-up pollutant treatment.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

The invention provides an equipment corrosion thinning life prediction method and system, and relates to the technical field of life prediction, and the method comprises the steps: deploying edge computing nodes at a plurality of equipment sites to collect corrosion data, carrying out the encryption through employing a differential privacy algorithm, and constructing a federated learning network for model training; and the central server extracts prediction experience through knowledge distillation to form knowledge priori, the edge nodes predict the corrosion rate and the residual life based on real-time data, and the server generates a comprehensive evaluation report and continuously optimizes the model. According to the method, safe sharing and collaborative prediction of the equipment corrosion data are realized, the prediction precision is improved, and the data privacy is protected.

The invention discloses an intelligent preparation and coating method for lithium battery electrode slurry in the technical field of information, which comprises the following steps: acquiring particle size distribution, specific surface area and chemical composition attribute parameters of a lithium battery electrode slurry raw material, and establishing a quantitative relation model among the slurry component proportion, rheological property and stability through an autonomous learning algorithm; adjusting the component proportion by adopting a dynamic optimization algorithm to obtain a slurry formula meeting a target attribute; feature extraction and classification are performed on the constructed data set, a decision tree algorithm is adopted to train and generate optimization rules and judgment logic of slurry preparation process parameters, a preparation process knowledge base is formed, and autonomous optimization of the process parameters is realized; aiming at the coating quality problem, slurry preparation process parameters are traced and analyzed, collaborative optimization of a slurry formula, a preparation process and coating parameters is realized through a human-computer interaction interface, and the production efficiency and the yield are improved while the electrode performance is ensured.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention relates to the technical field of information, in particular to a method for detecting and monitoring the cleaning effect of a semiconductor wafer in real time in the technical field of semiconductor wafer manufacturing, which comprises the following steps: introducing a rapid chemical analysis technology, combining an electrochemical sensor and spectral analysis, monitoring metal ion residues on the surface of the wafer in real time, and shortening the detection period; instant feedback on a production line is realized; a real-time feedback control system is constructed, according to the output of the comprehensive evaluation model, the working parameters of the cleaning equipment are automatically adjusted, the cleaning process is optimized, and the usage amount of cleaning liquid and the environmental influence are reduced; an edge computing technology is integrated, data processing and analysis tasks are sunk to a cleaning equipment end, data transmission delay is reduced, and the response speed of real-time monitoring and feedback control is increased; a visual monitoring platform is developed, key parameters, detection results and prediction trends in the cleaning process are displayed in a graphical mode, and operators can conveniently master the cleaning state in real time and quickly respond to abnormal conditions.

The invention relates to a multi-agent-based material performance prediction and synthesis system, and the system comprises a multi-agent data enhancement module which is configured to be used for firstly disassembling a complex problem into a plurality of subtasks, and then constructing a fine tuning data set comprising Sub-CoQ question and answer pairs by starting multi-source parallel retrieval; the multi-expert debate module is configured to be used for simulating decision conflicts of different roles in material engineering and generating a direct preference optimization DPO data set through debate; the training and verification module is configured to be used for training and verifying a large model MatMind in the field of materials by utilizing supervised fine tuning SFT and reinforcement learning RLHF based on the fine tuning data set and the DPO data set; and the material performance prediction and synthesis module is configured to be used for realizing intelligent recommendation of a material performance prediction and synthesis process by importing input parameters into the large model MatMind.

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

The invention discloses a soil element spectrum detection analysis method and system based on machine learning, and belongs to the technical field of smart agriculture. Field soil element data, plant growth data and environment data are obtained, and an element mineralization release model, an element loss model, a plant absorption model and a pollution element influence model are constructed; the method comprises the following steps: evaluating element mineralization release amount, plant element absorption amount and influence of harmful elements on plant absorption, constructing a soil element content prediction model, introducing the mineralization release amount, element loss amount, plant absorption amount and pollution element influence into the soil element content prediction model to calculate future soil element content, and constructing a fertilization time prediction model. The future soil element content is introduced into the fertilization time prediction model to evaluate the fertilization time, the fertilization time is predicted by coupling the soil element dynamic evolution and plant growth absorption and combining the nutrient demand characteristics of the plant in different growth states, and the plant planting fertilization efficiency is improved.

The invention discloses an intelligent industrial wastewater treatment method based on edge calculation and multi-modal data fusion, which comprises the following steps: synchronously acquiring various data of an industrial wastewater treatment site through edge nodes, automatically adjusting and aligning the generation time of each data, and ensuring that the time error of different data is less than a set threshold value; organically fusing the image features, the chemical parameters and the component analysis report to generate pollutant description features; constructing a simplified simulation model on the edge equipment, and predicting the diffusion trend of the wastewater pollutants by using the simulation model based on the pollutant description characteristics; and when the network is interrupted, the edge equipment is automatically switched to a local mode, and the operation is continued by using the stored model and the wastewater data acquired in real time. According to the method, the simulation model is deployed through the edge equipment, so that the calculation amount can be greatly reduced, the diffusion trend of wastewater pollutants can be accurately predicted, effective treatment measures can be taken in time, and the stability and reliability of an industrial wastewater treatment system are improved.

The invention discloses a soil organic pollutant identification method and system based on combination of AI and high-throughput screening, and relates to the technical field of pollutant screening, and the method comprises the following steps: extracting a substance peak from high-resolution mass spectrum data of a soil sample; the method comprises the following steps: establishing an organic pollutant mass spectrum database, extracting spectrogram characteristics and structural characteristics of compounds in the organic pollutant mass spectrum database, establishing a molecular fingerprint prediction model, and establishing a mapping relation from a spectrogram to a structure; constructing a spectrogram vector prediction molecular fingerprint according to the extracted substance peaks, and retrieving by using an organic pollutant mass spectrum database to obtain candidate chemical structures; scoring the candidate chemical structures by predicting the molecular fingerprints, selecting the chemical structures meeting a preset standard, constructing a recognition basis according to the selected chemical structures, and obtaining an organic pollutant recognition result of the soil sample. According to the method, non-targeted intelligent analysis is carried out through cross fusion of artificial intelligence and mass spectrometry, and rapid and accurate identification of soil organic pollutants is realized.

The invention provides a detection method for evaluating the sintering quality of an artificial heat-conducting film. The method comprises the following steps: constructing a personalized database containing electrical conductivity data corresponding to the artificial heat-conducting film under different materials and different process conditions; identifying performance fluctuation parameters of different sintering production devices and the same sintering production device in different production periods by analyzing the conductivity data; the method comprises the following steps: arranging multiple types of sensors on artificial heat-conducting film sintering production equipment, and collecting key parameters in the sintering production equipment; fusing the multiple types of key parameters through a data fusion algorithm to obtain a fused data set; the sintering production equipment is adjusted according to the performance fluctuation parameters; and inputting the key parameters in the fusion data set into the sintering quality evaluation model, and comparing the key parameters with the standard parameters of the personalized database to obtain a comparison result. Through the quality evaluation model and the automatic feedback mechanism, intelligent adjustment of the sintering process is achieved, and the product quality and the production efficiency are improved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention relates to the field of white carbon black modifier production, and particularly discloses an activity monitoring and early warning method of a white carbon black modifier based on Internet of Things, which comprises the following steps: acquiring reaction temperature, catalyst dosage and equipment operation parameters in real time through an Internet of Things sensor, and constructing a parameter and efficiency mapping table to form a multi-dimensional process database; encoding the temperature, the stirring rate and the raw material ratio into chromosome genes by utilizing a genetic algorithm, and constructing a multi-target model by taking the bifunctional grafting rate as an optimization target and combining the equipment load rate and the energy consumption constraint; a high-adaptability parameter set is generated by adopting iterative optimization, and a three-level response mechanism of local fine tuning, population resetting and historical optimal parameter loading is realized in combination with a hierarchical early warning system. According to the method, the problems that the synthesis conditions of the bifunctional silane coupling agent cannot be accurately controlled and the continuous production data response is delayed in the traditional process are solved, and the activity dynamic optimization and green and efficient production of the white carbon black modifier are realized.

The invention relates to the technical field of ozone precursor traceability, in particular to an ozone precursor collaborative traceability method and system based on a knowledge graph, and the method comprises the following steps: obtaining precursor concentration change, recognizing an abnormal path, extracting path characteristics, carrying out standardized scoring, adjusting graph connection strength, and analyzing sequence offset to obtain collaborative nodes. And screening origin nodes in combination with time sequence meteorology to generate an origin node list. According to the method, pollution response channels are identified through precursor node concentration changes and path connection relations, focusing of key paths is enhanced, paths are scored based on multi-dimensional indexes, connection attributes are dynamically adjusted in combination with monitoring period ozone response intensity, map structure updating is achieved, and concentration response sequence offset is analyzed; according to the method, nodes with co-evolution characteristics are screened, the stable relation identification capability is improved, the path reasonability is evaluated by combining a release time sequence and meteorological conditions, the initial source positioning accuracy is improved, and the response speed and the identification precision of precursor tracing are integrally enhanced.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention provides a tunnel surrounding rock dynamic grading and blasting parameter optimization method and system, and relates to the technical field of data processing.The method comprises the steps that geological parameters of a current tunnel face are collected in real time through a multi-source sensing system deployed on the tunnel face; based on the acquired geological parameters, identifying a plurality of characteristic sampling points with geological representativeness in a tunnel face spatial domain; four non-coplanar feature sampling points are selected to construct an initial tetrahedron, the point, farthest from the surface of the current convex hull, in the remaining points is included in sequence through an iterative extension method, the surface of the convex hull is recalculated till all the feature sampling points are enveloped, and a convex polyhedron evaluation area boundary is formed. According to the invention, data full-process connection and function collaboration can be realized.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The invention belongs to the technical field of composite material design, and discloses a polyimide-based composite material design method and system based on experiment-machine learning collaborative optimization. The method comprises the following steps: S1, experimental database construction and machine learning performance modeling; S2, machine learning model construction and training; S3, model evaluation and performance index output; and S4, intelligent design of the polyimide-based composite material. The multi-objective performance collaborative optimization design and preparation of the polyimide-based ternary carbon heterostructure composite material are carried out by taking experimental data as a main material and machine learning as an auxiliary material. According to the method, an experiment-model-optimization closed-loop iterative design system is constructed based on an experiment-machine learning collaborative optimization method, the method has the advantages of high prediction precision, high optimization efficiency, good multi-target adaptability and the like, the development efficiency of the polyimide-based composite material is remarkably improved, and the development cost of the polyimide-based composite material is reduced. The method is suitable for intelligent design and large-scale application and popularization of the high-performance heat-conducting electromagnetic shielding material.

An on-chip mid-infrared micro-ring resonant cavity microfluidic spectrometer is integrated on a substrate material and comprises a spot size converter, an optical coupler, a microfluidic module, a micro-ring resonant cavity, a miniature thin-film resistance heater, a sequential control power supply module, an optical filtering system and a photoelectric detector array. Selective filtering is performed on an input optical signal, and the flow and flow velocity of liquid or gas to be detected are controlled by using the microfluidic module, so that selective absorption of spectral components of the liquid or gas to be detected is realized. In addition, the invention also discloses a mid-infrared integrated optical chip micro-fluidic multiphase detection device comprising the spectrometer and a detection method thereof, the device also comprises a pumping and reference light source module and an on-chip mid-infrared frequency comb generation module, a broadband frequency comb is generated as a light source, a photoelectric detector array is used for collecting signals, and the detection precision is improved. The detection and inversion of the type and concentration of multi-component liquid or gas to be detected are realized, and the on-chip, real-time and micro detection of multi-component substances is realized.

The invention discloses a rapid drug quality detection system, and relates to the technical field of drug quality detection. The system integrates a multi-modal fusion detection front end, comprises an image acquisition assembly, a spectral analysis component, a thermal imaging auxiliary module and an image recognition module, and realizes comprehensive detection of appearance, components and thermal state characteristics of drugs. Through microscopic imaging, light source accurate regulation and control and imaging task scheduling, the detection precision and efficiency are improved. The intelligent self-adaptive detection middle end is combined with a physical and chemical detection platform, and deep analysis is carried out on suspected problem drugs. And the cloud data processing and remote monitoring rear end ensures real-time uploading, intelligent analysis and remote operation and maintenance of data, and constructs a drug full-life-cycle traceability system.

The invention, which relates to the technical field of coal full-process supervision, discloses a coal full-process intelligent supervision system comprising a data acquisition module, a data processing module, an early warning module, an event recording module and a management evaluation module which are in electrical signal connection. According to the coal full-process intelligent supervision system, potential laws and features of data of coal quantity, quality, transportation and combustion links are deeply mined through a model trained by using a deep learning algorithm; in a coal quantity link, demand and loss conditions can be accurately predicted, and purchase and inventory strategies can be planned in advance; whether coal meets production requirements or not can be accurately judged in the coal quality link, and the risks of equipment damage and environmental pollution are effectively reduced; early warning delay in advance and reasonably evaluating loss in a transportation link, and optimizing transportation arrangement; the combustion efficiency and pollutant emission are predicted in the combustion link, energy conservation and emission reduction measures are guided to be implemented, and the refinement degree and decision-making efficiency of power plant operation management are comprehensively improved.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.