2203results about "Molecular design" 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 AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses a ligand information generation model training method and device and a ligand information generation method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring sample receptor information and sample ligand information, wherein the binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information is not less than a set affinity; denoising the reference noise data based on the sample receptor information through a to-be-trained neural network model to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model. The reference ligand information can be generated based on the reference receptor information through the ligand information generation model, and the binding affinity between the ligand described by the reference ligand information and the receptor described by the reference receptor information is high.

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 discloses a genomics-based Parkinson's disease drug target prediction model construction method, and relates to the technical field of drug research and development, and the method comprises the following steps: collecting genomics, transcriptomics and proteomics data related to Parkinson's disease patients, and carrying out quality control and standardization processing; through differential expression analysis and function enrichment, key genes and signal pathways related to Parkinson's disease are identified, and a potential drug target range is determined. By integrating genomics, transcriptomics and proteomics data of patients with Parkinson's disease, molecular mechanisms related to Parkinson's disease can be comprehensively analyzed, multi-target combination is optimized in combination with the ant colony algorithm, the limitation that a traditional single-target model is difficult to capture complex disease network comprehensiveness is effectively overcome, and the method is suitable for popularization and application. The accuracy of target spot prediction is remarkably improved, a more reliable action target spot is provided for drug research and development, and the failure rate of clinical tests is reduced.

The invention discloses a drug combination risk prediction method and device based on multi-source feature fusion and comparative learning, equipment and a medium, and relates to the technical field of drug combination prediction. The drug combination risk prediction method comprises the following steps: acquiring a drug data set; and obtaining a feature map according to the drug data set. And according to the feature map, carrying out adaptive feature extraction on the molecular map structure to obtain molecular map features of the medicine. And defining molecular map features as structural features. And inputting the molecular graph features into a multi-layer cascaded graph convolutional network, extracting high-order topological features in a drug network, and obtaining relation features. According to the drug data set, similarity features are calculated, and interactive embedding features are extracted; according to the structural features and the similarity features, potential correlation and differences between the features are mined through a comparative learning mechanism, and structural comparative learning features and similarity comparative learning features are obtained; and performing feature fusion and feature alignment on the features to obtain fused features. And predicting the drug combination risk according to the fusion features.

The invention discloses a multi-agent cooperation molecular optimization method based on large language model driving. The method comprises the following steps: S1, task initialization and input analysis; s2, constructing an intelligent agent cluster; s3, task decomposition and scheduling execution; s4, performing expert optimization and tool calling; s5, performing evaluation and feedback screening; and S6, multi-round optimization and result output: the scheduling agent adjusts an optimization strategy and redistributes tasks according to a feedback result of the step S5, drives the expert agent to execute a next round of optimization operation, circulates the steps until the evaluation agent judges that an optimization target is met, and outputs a final optimization molecule and related attribute information thereof. And outputting an optimized path and an intermediate result for tracing analysis. According to the method, multi-round optimization and evaluation feedback iteration of a molecular structure are realized by fusing the knowledge reasoning ability of a large language model and an efficient interaction mechanism between intelligent agents.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention relates to the technical field of drug research and development, and discloses a drug-like molecule screening system and method based on deep learning, and the system comprises a physical and chemical characteristic analysis module which is used for calculating molecular descriptors of compounds and generating a visual characteristic distribution chart; and the drug-like molecule screening module integrates a Lipinski rule, a Veber rule, a Ghose rule and a Muegge rule, and supports a user to customize a screening rule. Compound characteristics are comprehensively analyzed through the physical and chemical characteristic analysis module, flexible screening is achieved through the drug-like molecule screening module, toxicity risks are accurately recognized through the toxicity assessment module, the affinity assessment module is combined to assess the binding capacity in multiple ways, and the synthesis feasibility assessment module plans an efficient and low-cost synthesis path. The dynamic feedback optimization module continuously improves the system performance; all the modules work cooperatively, the problems of scattered tools, insufficient precision, low planning efficiency, optimization deficiency and the like in the prior art are effectively solved, the medicine research and development efficiency is greatly improved, and the research and development risk and cost are reduced.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing language machine learning model (LLM) as autonomous reasoners to navigate and execute multiple layers of a computerized bio-activity discovery pipeline of a tech-bio exploration system. In particular, the disclosed systems can utilize an LLM that learns to access one or more tech-bio exploration tools to execute one or more processes and / or tasks in a bio-activity discovery pipeline. For instance, the disclosed systems can provide an interactive query prompt interface to enable users to provide tech-bio queries (as prompts) and utilize the LLM with the prompts to execute one or more tasks in the bio-activity discovery pipeline to generate and / or retrieve bio-activity data for the query. Moreover, the disclosed systems can utilize one or more LLMs to autonomously utilize and / or interact with one or more tech-bio tools in the bio-activity discovery pipeline to generate and / or obtain bio-activity data.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention relates to the technical field related to artificial intelligence, in particular to an intelligent medical diagnosis engine based on an AI large model, which comprises an auxiliary diagnosis module for collecting data such as an electronic medical record and a medical image of a patient and predicting an auxiliary result by using an integrated learning method; the drug recommendation module is used for analyzing drug recommendation data by using an AI large model and predicting potential curative effects and side effects of new drugs; and the operation simulation module is used for constructing an operation environment and a human body model in combination with a virtual reality technology. A large amount of medical data can be quickly processed and analyzed through auxiliary diagnosis, the diagnosis time is shortened, gene, physiological and pathological multi-omics data of a patient can be integrated by reducing unnecessary invasive examination and misdiagnosis and drug recommendation, a personalized drug treatment scheme is provided for the patient, and the patient experience is improved. Comprehensive and accurate diagnosis basis and treatment suggestions are provided for doctors; by accurately simulating a real operation scene including mechanical and optical characteristics, a more real training experience is provided for doctors and medical students.

The invention discloses a drug interaction prediction method based on an interaction enhancement graph self-attention mechanism, and belongs to the technical field of drug interaction prediction, and the method comprises the steps: carrying out the modeling of a drug under a local view and a global view, so as to obtain a molecular graph and a motif graph; designing an interaction-enhanced graph Transform module, respectively updating node embedding in two views of the molecular graph through an interaction-enhanced attention mechanism, and fusing through a multi-channel self-adaptive fusion module; enabling the extracted features to pass through a feedforward neural network, a residual connection layer and a normalization layer to obtain a final molecular representation of a molecular graph and a motif graph; designing a dynamic interaction scaling prediction module, and calculating a DDI score; and constructing a drug interaction prediction model, designing a loss function, and optimizing model parameters. According to the invention, the accuracy and stability of DDI prediction can be significantly improved.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

A system is disclosed for encoding a data string of a first modality into a hierarchical tokenized representation for processing by a text-based deep neural network (DNN) trained on a second modality. The data string comprises multiple units, each having one or more attributes. Each attribute is represented in the tokenized string as a sequence of hierarchical tokens, with a first hierarchical token encoding one or more most significant bits and a subsequent hierarchical token encoding one or more less significant bits. The DNN processes the data string bidirectionally, across the sequence of units and within the token hierarchy, to select tokens that capture attribute information. The selected hierarchical tokens output by the DNN from a representation of the original data string that preserves attribute detail while enabling cross-modal processing using models trained on text.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention is applicable to the technical field of drug research and development, and provides a lead compound discovery method based on a large language model, which comprises the following steps: determining a target spot and constructing an initial compound library; generating fragments and forming an initial fragment library; calculating the frequency of occurrence of the fragments and the affinity contribution degree of the fragments to the target protein, and updating the fragment library; fragment sampling weight calculation and candidate fragment selection; the large language model generates molecules according to the candidate fragments; evaluating the quality of the generated molecules and updating the molecule library; and repeating the cycle until a preset iteration round number is reached. A fragment-based drug design method is combined with the large language model, rich domain knowledge of the large language model is used for replacing human experts to guide drug design, fragments are used as prompts to stimulate the internal potential of the large language model, the advantages of the two methods complement each other, exploration of the whole chemical space is completed, and the drug design efficiency is improved. The lead compound reaching the human expert level is designed in actual use, and a new technical means is provided for drug research and development.

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.

The invention provides a multi-modal protein design method, device and system and a storage medium thereof, and relates to the technical field of protein design and machine learning. The multi-modal protein design method comprises the following steps: acquiring original sequence information of a target protein; determining a mutable candidate region of the target protein according to the original sequence information; obtaining a candidate protein sequence recommendation set by utilizing a zero sample scoring model based on the mutable candidate region and the original sequence information; and performing clustering dimension reduction processing on the candidate protein sequence recommendation set according to a zero sample scoring model, and performing sampling screening according to hidden space representation to obtain a target mutant protein sequence. The multi-modal protein design method provided by the invention shows remarkable advantages in the aspects of high efficiency, multi-objective optimization ability, small sample adaptability, generalization ability, optimization efficiency, flexibility, expandability and the like, and can be widely applied to protein design and optimization in the fields of biomedicine, industrial catalysis, material science and the like.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

The invention provides a molecular attribute prediction method introducing domain knowledge, which belongs to the technical field of meta-learning, integrates molecular graph information and molecular chemical information through a two-channel feature coding architecture, fuses small sample learning of molecular graph structure and chemical domain knowledge, and optimizes the molecular attribute through dynamic feature fusion and task perception optimization mechanism. The problem of insufficient model generalization ability caused by data scarcity in molecular attribute prediction is solved, so that multi-modal molecular attribute prediction is realized, and the prediction precision and interpretability are improved.

The invention discloses a molecular structure characterization-based drug-drug interaction prediction method, which comprises the following steps of: acquiring drug molecule data and drug-drug interaction data, performing standardized preprocessing on the data, and constructing a drug molecule map according to the drug molecule data; inputting the drug molecule map into a model, extracting drug molecule multi-scale features through a multi-scale map convolutional network, and fusing the drug molecule multi-scale features through a dynamic attention mechanism to obtain drug molecule features; based on the drug molecular characteristics, through a full-connection neural network, learning the relationship between the drug molecular characteristics and the drug-drug interaction, and obtaining the prediction probability of the drug-drug interaction; and training the model by adopting a joint loss function according to the drug-drug interaction data and the prediction probability, and applying the trained model to drug-drug interaction prediction. And through the multi-scale image convolutional network, dynamic attention fusion and joint optimization, the prediction accuracy is significantly improved.

The invention relates to a generative molecule reverse design system based on reinforcement learning, which comprises a data set construction module, a multi-target performance prediction model establishment module, a pre-training module, a reward function construction module and an optimization module, and is characterized in that the data set construction module is used for constructing and screening to obtain a molecular structure performance data set; the multi-target performance prediction model establishment module is used for establishing a multi-target performance prediction model based on the constructed molecular structure performance data set; the pre-training module is used for pre-training a molecular generation model by using the screened molecular structure data; the reward function construction module is used for constructing a layered multi-target reward function; and the optimization module is used for rapidly evaluating key indexes by using a performance prediction model by adopting a reinforcement learning method, and carrying out optimization adjustment on the molecular generation model through a layered multi-target reward function. According to the invention, efficient and systematic reverse design of lithium metal negative electrode interface self-assembly molecules can be realized.

The invention provides a drug-target interaction prediction method and device based on a dynamic heterogeneous double-flow graph neural network, and belongs to the field of drug research and development. The method solves the problems of low prediction accuracy and weak generalization ability caused by insufficient graph structure construction and feature expression in the prior art, and comprises the following steps: constructing a graph structure according to feature data of drugs and targets, and dynamically adjusting weights of the graph structure and edges according to data change to obtain a dynamic heterogeneous graph; the double-flow graph neural network is utilized to process feature information of the medicine and the target spot at the same time, and a complex mode of medicine-target spot interaction is effectively captured; in combination with long-range dependency modeling and random walk feature learning, on the basis of heterogeneous graph convolutional network learning, processing a remote dependency relationship between a drug and a target spot, and capturing multi-hop information in an isomorphic network by using random walk to further optimize interaction prediction; designing a decoder based on matrix completion; training and optimizing the model; the method is applied to drug-target interaction prediction.

The invention relates to the field of computer-aided drug design (CADD) and molecular informatics, in particular to a multi-modal representation learning method based on cyclopeptide molecules, which is used for predicting cell membrane permeability of cyclopeptide. The method mainly comprises the following steps: (1) data collection: integrating cyclic peptide permeability data from a ChEMBL database, a CycPeptMPDB database and a CyclicPepeda database and patent literatures; (2) multi-modal learning: for different modal data, a deep learning model is adopted to extract feature representations of the data; the method comprises the following steps of: encoding an SMILES sequence by using ChemBERTa (ChemBERTa); using Vision Transform to extract molecular image features, and learning a molecular image structure and 3D coordinate information based on GNN; (3) multi-modal feature fusion: adopting a self-adaptive extensible fusion mechanism, integrating SMILES feature information into image, graph and 3D coordinate features through a cross-modal feature fusion mechanism, and splicing all modal features to obtain multi-modal molecular representation; and (4) permeability prediction: sending the multi-modal molecular representation into a full connection layer for regression prediction so as to evaluate the permeability of the cyclopeptide.

The invention provides a new drug molecule design method and device fusing convex optimization and evolutionary learning, and belongs to the field of biological information.The method comprises the steps that in a generative adversarial network, a generator is adopted to generate a small molecule sequence, and the generator is established by adding an attention mechanism model in a long-short-term memory network; evaluating the authenticity of the small molecule sequence by adopting a discriminator, wherein the discriminator is established by adopting a convolutional neural network improved based on convex optimization; updating the parameters of the generator by adopting a strategy gradient method, and updating the parameters of the discriminator by adopting a gradient descent method; and generating a final small molecule sequence by the generator corresponding to the final generator parameter. According to the method, the small molecules which are highly similar to original samples and have higher effectiveness and innovativeness can be generated, the diversity and quality of the generated molecules are improved, and particularly, the method has excellent performance in the aspect of generating molecules with specific functions.

The invention provides a whole-process intelligent management system for stem cell culture medium development, and belongs to the technical field of intelligent management. According to the method, artificial intelligence, big data analysis and knowledge graph technologies are fused, and the whole process from demand analysis, formula intelligent design and virtual simulation, small-scale culture verification and iterative optimization, pilot scale-up process optimization to quality compliance and tracing is covered. All the modules are coordinated through the central intelligent decision engine, the research and development efficiency of the culture medium can be remarkably improved, the development cost can be greatly reduced, and batch consistency and quality stability of the culture medium are ensured by reducing trial and error and optimizing in real time. Meanwhile, a full-chain electronic tracing function is provided, the requirements of laws and regulations for compliance are comprehensively met, and the problems that traditional research and development are low in efficiency, high in cost, unstable in batch and the like are effectively solved.