802results about "Cheminformatics data warehousing" 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 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 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 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 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.

The invention belongs to the technical field of metallurgical industry process intelligent control and prediction, and discloses a self-adaptive mixed intelligent prediction method and system for smelting endpoint parameters of an electric arc furnace. Acquiring smelting process data of the electric arc furnace; constructing a dual-drive prediction system comprising a mechanism model and a data drive model; calculating a decision coefficient of a prediction value and an actual measurement value of the theoretical model, and counting an effective historical data volume; constructing a machine learning prediction model, and selecting a modeling algorithm according to the effective historical data volume; selecting a hybrid prediction strategy based on the decision coefficient and the effective historical data volume; predicting and outputting an end point carbon content predicted value and an end point temperature predicted value according to a hybrid prediction strategy; predictive deviation threshold value judgment and execution control are conducted, and the electric arc power, the oxygen blowing flow, the feeding speed or the cooling water flow are adjusted. Accurate prediction and dynamic optimization control of the electric arc furnace end point parameters are achieved, and the smelting quality, the energy utilization rate and the production stability are remarkably improved.

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 relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention provides a full-laser multi-energy-field remanufacturing device and method, and relates to the technical field of laser cladding remanufacturing, and the full-laser multi-energy-field remanufacturing device comprises a laser system, an energy field auxiliary system, a powder conveying system, a monitoring system and an intelligent management and control system; the energy field auxiliary system comprises a magnetic field generation unit and an ultrasonic vibration unit; the monitoring system is used for collecting multi-source sensing data reflecting the state of the molten pool in real time; the intelligent management and control system comprises a machine learning digital twinning module used for acquiring historical process data and receiving and performing analysis and prediction based on multi-source sensing data and the historical process data so as to generate a dynamic cooperative control instruction; and the numerical control execution module is used for receiving the control instruction and performing real-time feedback adjustment on the technological parameters of the laser system and the technological parameters of the magnetic field generation unit, the ultrasonic vibration unit and the powder conveying system according to the control instruction. And real-time and dynamic collaborative feedback regulation and control on a plurality of process units such as laser, a magnetic field, an ultrasonic field and powder feeding are realized.

The invention relates to a digital intelligent regulation and control preparation method of a high-solid-waste low-carbon high-durability concrete connecting material, and solves the problems that an existing UHPC connecting material is difficult to balance high performance and low carbon, the solid waste resource utilization rate is low, a digital intelligent precise regulation and control means is lacked, and the cost is low. The method comprises the following steps: firstly, integrating data to construct a special database for the ultra-high performance concrete, establishing a raw material-process-performance mapping relation through multi-scale simulation, then optimizing target parameters through machine learning, preprocessing the raw materials, and finally obtaining the ultra-high performance concrete after the target parameters are optimized and the raw materials are preprocessed. And verifying the iterative model by using a high-throughput experiment, determining optimal process parameters, importing the optimal process parameters into a system, and executing stirring, forming and curing. The method has the following beneficial effects that high solid waste recycling and low-carbon emission reduction are achieved through digital intelligent regulation and control, high strength and high durability of materials are synchronously guaranteed, and the core application requirements of an assembly type structure are precisely met.

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease prescription data base construction method and system.The method comprises the steps that five types of data including ancient literatures, modern medical records, prescription data, traditional Chinese medicine resources and medical record manuscript images are accessed through an extensible adapter frame; a traditional Chinese medicine term service platform is constructed by using multi-source term resources, and standardized data is generated from the analyzed data; the method comprises the following steps: extracting diseases, syndromes, prescriptions and traditional Chinese medicine entities by adopting a rule engine and a domain self-adaptive entity recognition model, on the basis of a public traditional Chinese medicine standard knowledge graph, aggregating neighbor node information through GAT, and introducing a contrast learning optimization entity relationship classifier to obtain an enhanced entity vector and classify a semantic relationship; constructing a knowledge graph based on the extracted semantic relationship; and carrying out data quality and algorithm performance monitoring and tracing on data access analysis, standardization processing, knowledge extraction and graph construction. Uniform access and standardized integration of multi-source heterogeneous traditional Chinese medicine data are realized.

The invention belongs to the cross technical field of artificial intelligence and environmental protection engineering, particularly relates to a denitration catalyst microwave regeneration optimization method based on machine learning, and aims to solve the problems of inaccurate energy matching, large structural damage and low efficiency of a traditional regeneration method. The method comprises the following steps: constructing a catalyst multi-dimensional feature database, establishing a microwave regeneration multi-physics field coupling simulation model, and training an integrated learning prediction model to associate input features with a regeneration effect; in the regeneration process, infrared thermal imaging, microwave reflection and gas sensing equipment are integrated, temperature field, energy absorption and tail gas data are collected in real time, and microwave power, frequency and atmosphere parameters are dynamically regulated and controlled. Accurate energy release and closed-loop intelligent regulation and control are achieved, the activity recovery rate after regeneration exceeds 92%, the specific surface area retention rate exceeds 85%, the regeneration period is shortened to one third of that of a traditional method, energy consumption is reduced by 40%, full-life-cycle intelligent management of the catalyst is supported, and economical efficiency and sustainability of environmental protection equipment are remarkably improved.

The invention belongs to the field of artificial intelligence, particularly relates to a coal-fired power plant denitration catalyst full-life-cycle intelligent management system, and aims to solve the problems of information splitting, state evaluation lagging, inaccurate life prediction, subjective regeneration decision and the like. The system comprises catalyst identity identification, multi-source data fusion acquisition, operation state real-time evaluation, life attenuation dynamic prediction, regeneration decision intelligent optimization, disposal path closed-loop management and a full-period digital twin platform. The whole process tracking from delivery to disposal, health quantitative evaluation, residual life accurate prediction, regeneration opportunity and process intelligent decision and compliance disposal closed loop are realized, and the operation economy and environmental protection reliability of the denitration system are improved.

The invention provides a semiconductor epitaxial growth parameter inversion method, a semiconductor epitaxial growth parameter inversion system, a semiconductor epitaxial growth parameter detection device and a related product, and relates to the technical field of semiconductor detection. The gas phase transmittance of a detection light path is further obtained, quantitative compensation is carried out on the actually measured reflection spectrum in the semiconductor epitaxial growth process, the compensated target reflection spectrum is closer to the real optical response of a semiconductor epitaxial film, and therefore spectral shape distortion and system errors caused by gas phase absorption superposition are effectively weakened. And the accuracy and the stability of the inverted growth parameters are obviously improved. Furthermore, the quantitative compensation value can be adaptively updated according to the fluctuation of the working condition, the inversion deviation introduced by the drift of the working condition is reduced, the consistency and comparability of the process are improved, and more reliable end point control and parameter closed-loop optimization can be conveniently realized.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

The embodiment of the invention provides a construction method of an initial structure of a molecular system, a construction method of a molecular system structure, a computer program product, a computer system and a computer readable storage medium. The construction method of the initial structure of the molecular system comprises the following steps: setting a molecular object, obtaining a basic construction unit of the initial structure of the molecular system to be constructed, and obtaining molecular parameters corresponding to the molecular object; obtaining atomic charges according to a predetermined atomic charge calculation method; according to the SMILES character string of the basic construction unit, obtaining an atomic type identifier; carrying out three-dimensional stacking; obtaining a Dresing force field parameter of the stacked molecular system, and obtaining an LAMMPS format file of the structure of the stacked molecular system and the Dresing force field parameter of the stacked molecular system; and carrying out a relaxation process on the stacked molecular system to obtain an initial structure of the molecular system. By adopting the construction method to construct the initial structure of the molecular system, the construction accuracy and efficiency are relatively high.

The invention provides an agricultural land heavy metal pollutant pollution tracing method and system, and relates to the technical field of data processing method.The agricultural land heavy metal pollutant pollution tracing method comprises the steps that multi-source heterogeneous environment data of an agricultural land to be traced is acquired; constructing a heterogeneous graph; inputting node features of each node in the heterogeneous graph and edge indexes and edge attributes of each connection edge into a plurality of trained graph attention network models to obtain edge contribution vectors; polymerizing the plurality of edge contribution vectors to obtain the final contribution of each heavy metal pollutant; and heavy metal pollutant pollution tracing is carried out. According to the scheme, the heterogeneous graph containing the receptor nodes and the pollution source nodes is constructed, the multi-source heterogeneous environment data is mapped into the node features and the edge attributes, explicit structured expression of the pollution source-receptor relation is achieved, the model can learn the complex nonlinear mapping relation of pollution contributions end to end, and the method has the advantages of being high in practicability and easy to popularize. And the spatial accuracy and the chemical rationality of the traceability result can be improved.

The embodiment of the invention discloses a drug molecule discovery method and device, a medium and equipment, and the method comprises the steps: extracting an entity of an input text, recognizing the intention of the input text, and scheduling one or more processes in drug molecule discovery processes related to the entity according to an intention recognition result, so that a user only needs to give the input text, and the user experience is improved. The subsequent operation of the drug molecule discovery process can automatically schedule one or more processes in the drug molecule discovery processes related to the entity in the input text according to the intention result of the input text, so that a user is prevented from manually importing and exporting data between different processes, and the processing efficiency of drug molecule discovery is improved.

The invention discloses a simulated screening and process optimization method of an anhydrous hydrogen fluoride desulfurization adsorbent, and relates to the technical field of anhydrous hydrogen fluoride, and the method comprises the following steps: constructing a database, and establishing a basic screening library; constructing an intelligent screening engine, and performing force field parameter correction on the materials in the basic screening library to capture a high-polarity solvent effect; calculating and extracting adsorption selectivity, Henry coefficients and working capacity indexes, and screening out a primary candidate material set; a core candidate material is optimized; determining an optimal adsorbent model; the microscopic adsorption data of the optimal adsorbent model are converted into macroscopic process parameters, the macroscopic process parameters are imported into a macroscopic process simulator for prediction, and process optimization is completed. According to the method, the strong polar solvent effect is simulated by constructing the corrected force field environment, and high-throughput screening of the adsorption materials is achieved in combination with an active learning strategy; an electronic grade refinement and modification strategy is utilized to optimize adsorption sites, and microcosmic adsorption data and a macroscopic mass transfer model are coupled to realize accurate prediction and optimization of an adsorption process.

The invention provides a source analysis method embedded in atmospheric monitoring equipment. The method comprises the following steps: (1) data acquisition; (2) data preprocessing and feature extraction; (3) constructing a lightweight source component spectrum database; (4) performing online recursion analysis calculation; and (5) non-negative constraint and result output. According to the method, the problem of how to systematically and fundamentally solve multiple bottleneck problems caused by time lag, space limitation, huge equipment, lengthy flow and data distortion under the background that a traditional atmospheric particulate matter and gaseous pollutant source analysis system is bound by an off-line sampling-laboratory analysis mode for a long time is solved.

The invention discloses a raw material preparation optimization system, and relates to the technical field of smelting waste reutilization, the raw material preparation optimization system comprises an acquisition assembly, a processing assembly, a calculation assembly and a measure decision assembly, and the calculation assembly comprises a waste evaluation unit, a waste collaborative configuration unit and a comprehensive optimization analysis unit. According to the method, a raw material preparation optimization system is constructed through a progressive process of the waste evaluation unit, the collaborative configuration unit and the comprehensive optimization analysis unit, the waste evaluation unit integrates multi-dimensional factors to form an activation potential coefficient and quantify the available value of the waste, and the collaborative configuration unit constructs a benefit cost model to form a collaborative index, so that multi-dimensional balance is realized; and the comprehensive optimization unit integrates multiple factors to construct an integrated evaluation analysis model and output a global optimization index, so that the problems of single evaluation dimension, one-sided target optimization and experience dependence are solved, a quantitative tool is provided for blast furnace waste recovery, the resource utilization rate is increased, the environmental load is reduced, and the process stability is guaranteed.

The invention discloses an intelligent prediction method and system for the alkali metal slagging tendency of a biomass gas coupled coal-fired boiler, and relates to the field of slagging tendency prediction.The intelligent prediction method comprises the steps that flue gas temperature data of a target area of a convection heating surface of the boiler is obtained, and the ash content melting characteristic temperature and alkali metal element content data of mixed fuel entering the boiler are obtained; performing physically constrained ash temperature-liquid phase yield adaptive correlation calculation, calculating coupling adhesion force, generating equivalent adhesion strength distribution of particle surfaces, counting particle turbulence collision physical parameters based on the equivalent adhesion strength distribution of the particle surfaces, and judging effective collision adhesion efficiency based on energy competition. The method comprises the following steps: calculating the agglomeration growth rate of ash particles in a flow field, outputting the spatial growth configuration of ash particle aggregates, carrying out macroscopic mechanical characteristic analysis and bridging stability judgment on an accumulation body at the throat part of the dry bottom hopper according to the spatial growth configuration of the ash particle aggregates, and outputting the blockage risk level of the dry bottom hopper, thereby effectively solving the blockage problem of the dry bottom hopper.

The present disclosure relates to materials science using processing of crystallographic structure data and artificial intelligence, and more particularly to methods and apparatuses for estimating material properties from crystallographic descriptive data, wherein a computer-implemented method includes generating first data representing a crystal structure from crystallographic descriptive data; generating, in view of structural periodicity, second data representing an expanded supercell; converting the second data into input data as a four-dimensional tensor in which a first dimension corresponds to atom species and remaining dimensions correspond to coordinates of a discretized three-dimensional grid; supplying the input data to a neural network including convolutional layers and a self-attention mechanism to extract features; and estimating, from the extracted features, at least one material property of the material. Related apparatuses and non-transitory computer-readable media are also disclosed.

A method performed by at least one processor, includes receiving a query comprising at least one of a product, a reactant, a reagent, and a reaction condition; generating a query embedding vector corresponding to the query by inputting the query into a neural network model; extracting a candidate embedding vector from among a plurality of embedding vectors based on a similarity between the query embedding vector and the plurality of embedding vectors corresponding to reaction records stored in a database; and outputting a synthesis recipe corresponding to the query by retrieving a candidate reaction record corresponding to the candidate embedding vector in the database.

The invention discloses a multi-target joint optimized antibiotic molecule design system and method applied to the field of biological medicine, and the method comprises the steps: obtaining FabI enzyme and AcrAB-TolC efflux pump structure data, extracting double-target space and physicochemical characteristics, and constructing a joint pharmacophore model containing a FabI inhibition domain and an efflux pump blocking domain; screening candidate molecules meeting a double-domain space matching threshold, screening high-affinity double-target molecules through conformation sampling and free energy calculation, and forming a lead compound library through multi-layer filtration of metabolic stability, membrane penetrability, quantum chemical properties and synthesis feasibility; carrying out electron effect, chain length or heteroatom fine tuning on dominant molecules through in-vitro bacteriostasis and efflux inhibition experiment feedback, and carrying out iterative optimization until MIClt is met; 2 [mu] g / mL and the efflux inhibition rate is gt; according to the present invention, the antibacterial effect and the drug resistance inhibition ability are significantly improved, the molecular synthesizability and the biological safety are ensured, and the engineering design new path is provided for the Gram-negative bacterium drug resistance crisis.

An example method performed via a computing device for providing support for a mass spectrometry (MS) system includes obtaining from a mass spectrometry library (i) a ranked hit list of reference spectra corresponding to a set of fragmented spectra of an analyte acquired with the MS system; and (ii) a first set of metadata corresponding to the list of sorted hit items for the reference spectrum. The method also includes obtaining, from the MS system, a second set of metadata corresponding to the set of fragmented spectra. The method further includes evaluating an order of entries in the ranked hit item list with a machine learning model based on the first set of metadata and the second set of metadata.

The invention relates to the field of drug discovery and delivery collaboration, in particular to an autonomous evolutionary drug discovery and delivery collaboration method and system based on a large language model. The method comprises the following steps: aiming at a given biological target three-dimensional structure, generating a candidate molecular library which is complementary with a target pocket and gives consideration to druggability through an SE (3) isovariant hybrid generation model; according to a two-stage funnel type high-throughput virtual screening process, screening out the molecule with the highest comprehensive potential from the candidate molecule library; generating a customized delivery scheme through a three-stage process; in a digital twinborn model for simulating a real in-vivo tumor microenvironment, a targeted delivery process of a drug and carrier complex is simulated, and efficiency is evaluated; atomic-scale performance confirmation is carried out on a medicine, carrier and target point ternary system through molecular dynamics simulation. The invention is suitable for drug research and development.

The invention provides a sparse knowledge graph entity alignment method and system. The method comprises the following steps: acquiring structure data of chemical molecules, and identifying a minimum ring structure based on topological connection and bond energy distribution; clustering molecules with the same ring structure and the bond energy deviation smaller than a threshold value into a molecular group, extracting a shared minimum ring structure as a substructure node, creating a molecular node, and constructing a bidirectional correlation map of the molecules and the substructure node; extracting absorption peak data of the compound sample by utilizing spectral imaging, and generating an energy level spectrum; searching a substructure node combination corresponding to the homonymous molecule based on a map, and obtaining an embedded vector; and segmenting the energy level atlas into energy fragments according to a substructure path, extracting a distribution vector, performing multi-scale fusion on the distribution vector, a path feature and an embedded vector, and generating a composite feature vector to identify an equivalent entity and establish aligned connection. According to the method, high-precision alignment of the functional equivalent entities in the chemical sparse knowledge graph is realized.