A method for managing spectral data based on laboratory equipment

Abstract

This invention discloses a spectral data management method based on laboratory equipment, belonging to the field of pharmaceutical data management technology. It includes constructing a topological graph using graph theory to describe the relationships between different target drugs; collecting drug component data from target drug samples using laboratory equipment; initializing edges and weights between different nodes by combining traditional Chinese medicine compatibility theory and formulation standards; adjusting the attributes of nodes and edge relationships in the topological graph by comparing it with a drug component database in current pharmaceutical standards; and finally storing the verified and optimized topological graph as standardized spectral data. This invention visualizes the relationships between drug components using graph theory, generating edges and weights between nodes in the topological graph by combining the attributes and interactions of drug components. This allows researchers to intuitively view the interactions between components, reducing the problems of information isolation and query difficulties in traditional experimental data management.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical data management technology, specifically a method for managing spectral data based on laboratory equipment. Background Technology

[0002] In the current field of medical and drug development, with the deepening of new drug research and the continuous development of traditional Chinese medicine research, a large amount of drug experimental data faces the challenge of unified management. New drug development not only requires extensive experimental operations but also strict adherence to a series of standardized procedures, while also involving in-depth analysis and management of experimental data and drug component information.

[0003] The uniqueness of traditional Chinese medicine research lies in its complex compatibility theory and the experimental characteristics of multiple components and indicators. During the experiment, researchers need to frequently process a large amount of complex experimental data and information, including component information, medicinal properties, compatibility rules and experimental results. There are significant correlations between these data, but the existing management methods usually store these data in isolation and lack effective correlation analysis capabilities. This makes it difficult for researchers to quickly query and comprehensively analyze the data when designing experiments, processing data and comparing standards, which significantly increases the complexity of experiments and the research and development cycle.

[0004] Furthermore, existing laboratory management systems still have shortcomings in data visualization and correlation analysis. Specifically, researchers often can only view experimental results through data tables or static reports, lacking an intuitive way to understand the complex relationships or compatibility effects between drug components. In this situation, not only is the efficiency of experimental data analysis affected, but it also easily leads to misinterpretations of the interactions between drug components or omission of key information, thus limiting the further development of modern research on traditional Chinese medicine.

[0005] Therefore, a new technological approach is needed that can integrate experimental data, standards and norms, and traditional Chinese medicine compatibility theory to achieve efficient management and visual analysis of drug component data. Summary of the Invention

[0006] 1) Technical problems to be solved

[0007] This invention provides a method for managing spectral data based on laboratory equipment, which enables efficient management and analysis of spectral data of traditional Chinese medicine in the laboratory.

[0008] (ii) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for managing spectral data based on laboratory equipment, applicable to the management of traditional Chinese medicine data in laboratories, comprising:

[0010] Based on the target drugs and their pharmacological characteristics, a topology graph is constructed using graph theory to describe the relationships between different target drugs. Each target drug corresponds to a node in the topology graph. The drug component data of each target drug is acquired through laboratory equipment, mapped to the corresponding node in the topology graph, and the node's attributes are updated. The node's attributes include drug component type, functional characteristics, and efficacy.

[0011] The relationship between different drug components is analyzed by combining the theory of compatibility of traditional Chinese medicine, generating edges between different nodes in the topology graph, and setting the weight of the edges according to the strength of the relationship between different drug components; among them, the stronger the synergistic relationship between drug components, the greater the weight, and the higher the degree of synergy, the greater the weight value; the stronger the antagonistic effect between drug components, the smaller the weight value, and the higher the degree of antagonism, the smaller the weight value, until it becomes negative.

[0012] The drug component data of each target drug is compared with the drug component database in the current pharmaceutical standards. By comparing the types, contents, and interactions between the main components, the composition of the target drug is verified to meet the standard requirements. The attributes of the nodes and the relationships of the edges in the topology graph are adjusted according to the verification results.

[0013] The verified and optimized topology graph is stored as standardized spectral graph data, which includes complete node attributes and edge weight information.

[0014] Furthermore, the topological graph is constructed using graph theory methods, where each node represents a target drug, and the edges between nodes represent the relationships between different target drugs, including compatibility enhancement and mutual constraint relationships.

[0015] Furthermore, the drug component data of each target drug is acquired through laboratory equipment, including chemical structure information, spectral characteristics, physicochemical characteristics and pharmacodynamic functional characteristics. After preprocessing, the drug component data is mapped to the corresponding nodes in the topology graph.

[0016] The attributes of a node include the type of drug component, its functional characteristics, and its efficacy characteristics;

[0017] If a new drug component is discovered in the experiment, a new node is created in the topology graph and corresponding attributes are attached; if the drug component already exists, the node's attributes are updated, and the node's attributes are adjusted by incorporating new data.

[0018] Furthermore, the drug component data of the target drug, collected and preprocessed from laboratory equipment, will be compared one by one with the drug component database in the current pharmaceutical standards. The comparison process includes:

[0019] Verify that the drug composition of the target drug is complete and check for any non-standard components;

[0020] Compare and check whether the content of each component is within the range specified in the standard;

[0021] Based on the mechanisms of action between drug components recorded in the database, determine whether the combination of components in the target drug is reasonable.

[0022] Furthermore, after comparing the target drug ingredient data with the drug ingredient database in the current pharmaceutical standards, the specific handling for those determined to be non-compliant is as follows:

[0023] If any important components required by the standard are missing, then delete the nodes and edges related to that component in the topology graph.

[0024] If the range of certain drug components is outside the standard range, mark the corresponding node as an abnormal node and adjust its attributes to reflect the non-compliance status.

[0025] If certain component combinations are defined in the database as having a significant antagonistic effect, then reduce the edge weights of both components in the topological graph until they become negative.

[0026] Furthermore, after comparing with the existing standard database, the node attributes are updated to reflect the status after drug component verification. Specifically:

[0027] If the mechanism of action between drug components does not meet the standard or there is an antagonistic effect, then delete the edges between the corresponding nodes;

[0028] If the standard shows a synergistic relationship between drug components, then new edges are added between nodes and assigned high weights.

[0029] The weights of edges are adjusted based on the strength of the synergistic relationship or the significance of the antagonistic effect between components. If the synergistic relationship is strong, the weight is increased; if the antagonistic effect is strong, the weight is decreased until it becomes negative.

[0030] Furthermore, based on compatibility theory, the components extracted from the drug component data are analyzed in pairs to determine complementary, antagonistic, and neutral relationships. In the constructed topology graph, edges between the nodes are generated based on the analyzed combination relationship data. Specifically:

[0031] Add positive association paths between complementary nodes, and mark the weight of the edges with positive values ​​to indicate the degree of reinforcement;

[0032] Add negative association paths between nodes with a antagonistic relationship, and mark the weight of the edge as negative to indicate the degree of inhibition;

[0033] No edges are added between nodes with neutral relationships.

[0034] Furthermore, the verified and optimized topology graph is structured and its data is stored in a unified format. The relationships between nodes and edges are directly stored as a graph structure, thus forming standardized spectral data.

[0035] (iii) Beneficial effects:

[0036] Compared with the prior art, this invention has the following beneficial effects:

[0037] This invention integrates drug components and their attribute information into nodes by constructing a standardized topological graph model. It visualizes the relationships between drug components using graph theory. Based on the theory of traditional Chinese medicine compatibility, it generates edges and their weights between nodes in the topological graph by combining the attributes and interactions of drug components. This allows researchers to intuitively view the interactions, synergistic effects, and antagonistic effects between the components, reducing the problems of information isolation and difficulty in querying traditional experimental data management.

[0038] By comparing drug component data with pharmaceutical standard databases, the compliance of drug components is verified. Node attributes and edge relationships in the topology graph are corrected according to standards, ensuring that data during drug development conforms to current standards and avoiding risks arising from inconsistencies or omissions in standards. Storing the optimized topology graph as standardized spectral data enhances the intuitiveness and accuracy of data analysis, helping researchers better understand the complex interactions between drug components. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a spectral data management method based on laboratory equipment provided in an embodiment of the present invention.

[0040] Figure 2 The flowchart illustrates a method for updating nodes in a topology graph based on experimentally acquired spectral data in a spectral data management method for laboratory equipment, as provided in this embodiment of the invention.

[0041] Figure 3 In a spectral data management method based on laboratory equipment provided in an embodiment of the present invention, a schematic diagram of updating the corresponding edges of nodes with different association attributes in the topological graph is provided.

[0042] Figure 4 This is a schematic diagram illustrating the construction of an initial topology map based on the theory of traditional Chinese medicine compatibility in a spectral data management method based on laboratory equipment provided in an embodiment of the present invention.

[0043] Figure 5 This is a schematic diagram illustrating the optimization of the topology graph based on a comparison with an existing standard database, provided as an embodiment of the present invention, for a spectral data management method using laboratory equipment. Detailed Implementation

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0046] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0047] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0048] In the current field of medical and drug development, with the continuous development of new drugs and the deepening research on traditional Chinese medicine, laboratory research work faces increasing challenges. New drug development not only requires numerous experiments but also adherence to a series of rigorous standards and specifications. The inventors have discovered that existing laboratory management systems often lack sufficient visualization capabilities. Researchers frequently rely on data tables or basic reports to view experimental results, unable to intuitively understand the underlying patterns or related standards and methods behind the experimental data. In this situation, the analysis and evaluation of experimental data is not only inefficient but also prone to overlooking important information or misinterpreting the data.

[0049] In traditional Chinese medicine (TCM) research, experiments involve numerous complex standards, experimental methods, and reagent formulations. This information is often isolated and difficult to access and apply quickly during experiments. The correlation between different experimental steps, data indicators, and standards is crucial; therefore, the lack of a system for rapidly retrieving relevant standards and information increases the difficulty of experiments and prolongs the research and development cycle. In new drug development, the evaluation of experimental data is not merely the analysis of individual data points, but a comprehensive assessment of the data in relation to relevant standards and experimental methods. In TCM research, especially the study of TCM components, a large amount of data on chemical components, pharmacological effects, and clinical trials is involved. This data is often difficult to integrate effectively in traditional laboratory management systems, leading to experimental results that fail to accurately reflect the actual situation.

[0050] To solve the above problems, combined with Figures 1 to 5 As shown, this invention proposes a spectral data management method based on laboratory equipment. This method visualizes the relationships between drug components using graph theory, allowing researchers to intuitively view the interactions, synergistic effects, and antagonistic effects among the components. This visualization method significantly improves the intuitiveness and accuracy of data analysis, helping researchers better understand the complex interactions between drug components. Furthermore, by comparing drug component data with pharmaceutical standard databases, the conformity of drug components to standards is verified, and the node attributes and edge relationships in the topology graph are corrected according to the standards, ensuring that data during drug development complies with current standards and avoiding risks arising from inconsistencies or omissions in standards.

[0051] For reference here Figure 1 The first step is the data preparation stage, namely S10: Based on the target drugs and their pharmacological characteristics, a topology graph is constructed using graph theory to describe the relationships between different target drugs. Each target drug corresponds to a node in the topology graph. The drug component data of each target drug is obtained through laboratory equipment, mapped to the corresponding node in the topology graph, and the node attributes are updated. The node attributes include drug component type, functional characteristics, and efficacy.

[0052] First, based on the research objectives, identify the specific traditional Chinese medicine (TCM) formulas and their components to be analyzed. Combining the efficacy and properties of the drugs, define the research scope and map the target components to nodes in the topological graph. In TCM compatibility theory, different components may exhibit synergistic or antagonistic effects. For example, licorice is often used as a harmonizing agent for drug efficacy, while the combination of ephedra and cinnamon twig enhances diaphoretic effects. The mechanisms of action of these drug components are determined by their chemical composition and properties; therefore, it is necessary to clarify the relationships between components through literature review, experimental data, and other means.

[0053] Graph theory methods are used to describe the structured relationships between target drug components. The interactions between nodes are represented by edges. The type and weight of the edges reflect the enhancing or inhibiting relationships in compatibility theory. For example, whether the combination of two components can synergistically enhance a certain therapeutic effect, or whether there is toxicity offsetting.

[0054] More specifically, each node represents a target drug ingredient, and node attributes include chemical category, medicinal properties (cold, hot, warm, cool), and main effects (clearing heat, harmonizing, relieving cough). Each edge represents the relationship between two ingredients, including the following types:

[0055] Synergistic relationships: such as the relationship between Bupleurum chinense and Scutellaria baicalensis in enhancing antipyretic effects;

[0056] Antagonistic relationship: For example, rhubarb and licorice have a relationship that can reduce the laxative effect;

[0057] In some embodiments of the present invention, a neutralizing relationship is also considered: for example, the combination of licorice with bitter and cold drugs can mitigate the irritation of bitter and cold drugs.

[0058] Using experimental data or pharmacological research materials, draw a preliminary topology diagram, label the nodes and edges, and set the initial edge weights to the default values ​​(such as 1), which will be adjusted based on subsequent data analysis.

[0059] Through the above steps, a drug component topology map that can be dynamically adjusted can be initially constructed based on compatibility theory and experimental verification. This topology map not only clearly shows the interaction relationship between components, but also reflects the real drug efficacy compatibility mechanism through weight adjustment, providing technical support for the design and optimization of traditional Chinese medicine prescriptions.

[0060] The drug component data is acquired through modern chemical analysis equipment, which presents the distribution and characteristics of components in the target sample in graphical form. For example, mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectrometry are used to detect molecular weight and structural information, while infrared spectroscopy (IR) and ultraviolet-visible spectrophotometers are used for molecular vibrational or absorption property analysis.

[0061] Specifically, each target drug sample undergoes appropriate processing, such as extraction, concentration, or dilution, to ensure moderate signal intensity and prevent overload during equipment detection. Depending on the specific equipment requirements, suitable carriers or solvents, such as acetonitrile, water, or methanol, are selected to dissolve the target drug sample. Subsequently, the equipment is configured with scanning parameters, such as the mass spectrometry scanning range, resolution, and ionization mode, to acquire complete drug component data. The target drug sample generates a large amount of signal after passing through the equipment, which is recorded as one-dimensional or two-dimensional spectra, representing information such as the mass, vibrational frequency, and light absorption of the components.

[0062] It is important to note that drug component data may contain background noise and interfering signals, such as instrument electronic noise, solvent peaks, and environmental clutter. Therefore, denoising algorithms can preserve the target signal while removing these irrelevant background interferences, improving clarity and accuracy. In some embodiments, denoising processing includes smoothing the acquired mass spectrum using a moving average method to reduce the impact of random noise, and correcting the baseline using a polynomial fitting method to eliminate baseline shifts caused by equipment drift or sample solvents.

[0063] Regarding the characteristic peaks in the mass spectrum of the target drug, they correspond to key characteristics of the drug component, such as molecular weight or absorbance. By identifying the peaks and extracting their parameters, such as peak height, peak area, and mass-to-charge ratio, the properties and content of the target component can be further characterized. Regarding peak detection, in some embodiments, the first derivative method or gradient variation method can be used to detect local extrema in the spectrum and determine potential peak positions. It is important to note that false peaks, i.e., meaningless peaks caused by noise or instrument distortion, must be excluded. Regarding peak fitting, in some embodiments, the detected peaks are fitted. Commonly used algorithms include Gaussian fitting and Lorentz fitting to accurately determine the peak position (corresponding mass-to-charge ratio or wavelength) and peak height (signal intensity).

[0064] Spectral matching compares the acquired spectral features with standard spectra in a drug component database. By comparing features such as mass-to-charge ratio and peak area, the specific component in the target drug sample can be accurately identified. Specifically, using the spectral features of known components stored in the database, including mass-to-charge ratio range, peak shape parameters, and molecular formula, matching algorithms, such as point-to-point matching or cosine similarity algorithms, are used to compare the feature values ​​of the sample spectrum with the standard spectrum and calculate the matching degree. If the matching degree exceeds a threshold, the sample can be identified as the specified component.

[0065] Next, the component information in the drug component data is mapped to nodes in the topology graph. Each node represents a target component, and the node's attributes, such as component type and drug properties, are updated based on the identification results.

[0066] More specifically, the topology graph is constructed based on graph theory, where each node represents a specific component of the target drug, and each edge represents the interaction between components. To ensure the dynamism and accuracy of the topology graph, it is necessary to match the experimentally identified specific components with the existing nodes.

[0067] Traverse all existing nodes in the topology graph, searching for nodes that match the identified component. Matching criteria include: component name and component characteristics (such as molecular formula and molecular weight). If a matching node is found, merge the newly identified component's attribute information with that node. If no matching node is found, create a new node representing the component.

[0068] Newly created nodes need to include basic attribute information of the ingredients, including: drug ingredient type and medicinal properties, which are mainly based on literature records or experimental data, and the medicinal properties of the ingredients (such as cold, hot, warm, cool).

[0069] This can be used as a reference. Figure 4 Taking the following eight medicinal herbs as an example, the initial topology graph is constructed based on the following principles: the attributes of each herb (node): herb name, functional characteristics, and medicinal properties. The initial node information includes:

[0070] a: Ginseng, function: to replenish qi and strengthen the spleen, medicinal properties: warm;

[0071] b: Atractylodes macrocephala, function: strengthens the spleen and dries dampness, medicinal property: warm;

[0072] c: Coptis chinensis, function: clears heat and dries dampness, medicinal property: cold;

[0073] d: Licorice, function: harmonizes the effects of other herbs, medicinal property: neutral;

[0074] e: Poria cocos, function: strengthens the spleen and promotes diuresis, medicinal property: neutral;

[0075] f: Angelica sinensis, function: nourishes blood and promotes blood circulation, medicinal properties: warm;

[0076] h: Rhubarb, function: clearing heat and detoxifying, medicinal property: cold;

[0077] k: Bupleurum, function: soothes the liver and relieves depression, medicinal properties: slightly cold. Based on traditional Chinese medicine compatibility theory, a topological diagram was constructed according to the synergistic and antagonistic relationships between different drugs, as shown below. Figure 4 As shown, the relationships and initial weights of the edges are set according to the strength of the synergistic or antagonistic relationship. For example, the synergistic relationship between ginseng and licorice is relatively low, so the initial weight is set to +1. The synergistic relationship between ginseng and angelica is stronger than that between ginseng and licorice, so the initial weight of the edge between ginseng and angelica is set to +2.

[0078] Furthermore, considering that the node attributes of drug components may be updated or optimized as experimental data accumulates—for example, the same component may yield new pharmacological property data in different experiments, or existing data errors may be corrected—the topology graph needs to support dynamic updates of node attributes to reflect the latest research findings on the components.

[0079] More specifically, check if identical component data already exists in the node attributes. If identical data exists, merge the new experimental results and optimize the node attributes. The fusion rules include: If the new experimental data supplements unrecorded attributes, add them directly. If the new data updates existing attributes (such as the range of pharmacological characteristics), prioritize the weighted average or data with high confidence. It is important to avoid duplicate or conflicting attribute information in nodes. For redundant attributes in the same node, use weighted algorithms or consistency checks to filter the most reliable data.

[0080] Research on the components of traditional Chinese medicine (TCM) is typically dynamic. New experimental data may supplement or correct existing component information. By fusing new data, it can be ensured that the attributes of nodes in the topology graph always reflect the latest research findings on the components. If the quality of new experimental data is superior to that of existing data, the old attribute values ​​are directly overwritten. If the experimental data come from different sources, such as results from multiple laboratories, the data are fused using a weighted average method. For example, the concentration ranges of components from two sets of experiments can be fused, with weights allocated based on the precision of the experimental equipment. Furthermore, when new experimental data supplements unrecorded medicinal properties, these are added to the nodes. If existing medicinal property records conflict with experimental data, the final attribute values ​​are determined through literature verification or comprehensive analysis of historical data.

[0081] Next, S20 is performed: the relationship between different drug components is analyzed in combination with the theory of compatibility of traditional Chinese medicine, the edges between different nodes in the topology graph are generated, and the weights of the edges are set according to the strength of the relationship between different drug components; among them, the weight is increased when the synergistic relationship between drug components is strong, and the higher the degree of synergy, the larger the weight value; the weight is decreased when the antagonistic effect between drug components is strong, and the weight value is smaller when the degree of antagonism is strong, until it becomes a negative value.

[0082] This can be understood as representing the relationships between components of traditional Chinese medicine, such as synergistic enhancement and mutual restraint, through edge weights, which reflect the strength of the relationship between two components. New experimental data may reveal new relationships or change the strength of existing relationships, thus requiring dynamic adjustment of the edges and weights in the topological graph.

[0083] If experimental data reveals a new relationship between two components, such as a reinforcing effect in a specific pairing, then add the corresponding edge to the topology graph and set initial weight values. If new experimental data indicates a change in the strength of an existing relationship, such as a more significant reinforcing effect, then adjust the edge weights. The rules for updating edge weights include: if new experiments verify a stronger relationship, increase the weight; if new data indicates a weaker relationship, appropriately decrease the weight. If new data indicates that some relationships no longer hold, such as no actual interaction between the two components, then remove the corresponding edge from the topology graph.

[0084] In summary, it is understandable that by updating node attributes, adjusting edge weights, and maintaining dynamics, the topology graph can always accurately reflect the drug components and their interactions. Combined with experimental data and graph theory analysis, this provides reliable technical support for drug formulation optimization and pharmacological research.

[0085] Then proceed to S30: compare the drug component data of each target drug with the drug component database in the current pharmaceutical standards. By comparing the types, contents, and interactions between the main components, verify whether the components of the target drug meet the standard requirements, and adjust the attributes of the nodes and the relationships of the edges in the topology graph according to the verification results.

[0086] More specifically, standard component data for key ingredients is compiled from current pharmaceutical regulations and industry standards. This includes comparing the types, concentrations, and interactions between major components. It is important to ensure that the database covers common major components found in the target drug sample and labels the standardized attributes of each component, such as category, purity (concentration), and medicinal properties.

[0087] In some embodiments, the preprocessed drug component data is compared one by one with the standard spectra in the database:

[0088] Verify that the drug composition of the target drug is complete and check for any non-standard components;

[0089] Compare and check whether the content of each component is within the range specified in the standard;

[0090] Based on the mechanisms of action between drug components recorded in the database, determine whether the combination of components in the target drug is reasonable.

[0091] The comparison results are categorized. For components that meet the criteria, their attributes are recorded to update the corresponding nodes in the topology graph. For components that do not meet the criteria, they are marked as abnormal or unqualified, and their data are removed from subsequent analyses.

[0092] According to pharmaceutical standards, update the attributes of nodes in the topology graph, including: Ingredient type: ensure ingredient classification is consistent with the standard; Drug properties: correct the drug's property description and revise the node's property label in conjunction with compatibility standards; and other attributes: such as purity, effective dosage range, and other technical indicators.

[0093] If a new component meets the criteria, add it as a new node to the topology graph and establish a path linking it to existing nodes. For components that are removed due to non-compliance, delete or hide their corresponding nodes and paths to ensure the real-time validity and accuracy of the topology graph. Save the validated drug component data and the adjusted topology graph results to provide basic data support for subsequent experiments and analyses.

[0094] Taking the initial topology diagram composed of the aforementioned eight medicinal materials as an example, the drug component data, obtained and preprocessed using laboratory equipment, was compared one by one with the standard spectra in the database to verify the compliance of the drug components and optimize the topology diagram. See details for further information. Figure 5 Regarding ginseng (a) and angelica (f), the current standard stipulates that the synergistic effect of ginseng and angelica must be achieved through ginsenoside Rg1. However, experimental data shows that the Rg1 content in ginseng is lower than the standard. Therefore, the edge between ginseng (a) and angelica (f) is deleted. The antagonistic effect between licorice (d) and coptis (c) is mainly caused by glycyrrhizic acid and berberine. However, experimental data shows that the berberine content in coptis samples is low. Therefore, the weight of the edge is weakened and adjusted from -1 to -0.5. The standard database suggests that bupleurum (k) and poria (e) have a potential synergistic effect in soothing the liver and strengthening the spleen. Experimental data supports this relationship. Therefore, an edge between bupleurum (k) and poria (e) is added, and its weight is set to +1.

[0095] Finally, S40 is performed: the verified and optimized topology graph is stored as standardized spectral graph data, which contains complete node attributes and edge weight information.

[0096] First, drug component data and topology information are extracted. This includes extracting verified drug component information from the drug component data, including component type, content, characteristic peak values, and matching results with standards. The correlation relationships, pharmacodynamic characteristics, and inter-node path information of the drug components are obtained from the topology graph, comprehensively reflecting the structure and mechanism of action of the formulation.

[0097] In some embodiments, spectral data is displayed graphically to facilitate researchers' understanding of the relationships between drugs and their components. For example, the type and strength of the relationship can be represented by the length and color of the edges, as can be found in [reference needed]. Figure 4 and Figure 5 .

[0098] Standardized spectral data can be stored long-term, supporting regular updates and expansions. Researchers can easily add new data or correct existing data, maintaining the dynamic availability of the spectra. Spectra can be used for various data analyses, such as analyzing whether the synergistic effect of drug components in a formulation meets expectations, or tracing the source of a component and its role in other drugs.

[0099] In summary, this method organically combines experimental data, standards and norms, and traditional Chinese medicine theory to construct a systematic and dynamic spectral data management scheme, providing technical support for traditional Chinese medicine research and development and laboratory management, and effectively improving the utilization rate of experimental data and the reliability of research results.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of patent protection of the present invention shall be determined by the claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention shall also be included within the scope of protection of the present invention.

Claims

1. A method for managing spectral data based on laboratory equipment, applicable to the management of traditional Chinese medicine data in laboratories, characterized in that, include: Based on the target drugs and their pharmacological characteristics, a topological graph is constructed using graph theory to describe the relationships between different target drugs. Each target drug corresponds to a node in the topological graph. Drug component data for each target drug is acquired using laboratory equipment. This drug component data includes chemical structure information, spectral characteristics, physicochemical characteristics, and pharmacodynamic functional characteristics. After preprocessing, the drug component data is mapped to the corresponding node in the topological graph, and the node attributes are updated. The node attributes include drug component type, functional characteristics, and pharmacodynamics. The relationship between different drug components is analyzed by combining the theory of compatibility of traditional Chinese medicine, generating edges between different nodes in the topology graph, and setting the weight of the edges according to the strength of the relationship between different drug components; among them, the stronger the synergistic relationship between drug components, the greater the weight, and the higher the degree of synergy, the greater the weight value; the stronger the antagonistic effect between drug components, the smaller the weight value, and the higher the degree of antagonism, the smaller the weight value, until it becomes negative. The drug component data for each target drug is compared with the drug component database in current pharmaceutical standards. By comparing the types, content ranges, and interactions between the main components, the drug's components are verified to meet the standard requirements. Based on the verification results, the attributes of nodes and the relationships between edges in the topology graph are adjusted. If any important components required by the standard are missing, the nodes and edges related to those components are deleted from the topology graph. If the range of certain drug components is outside the standard range, their corresponding nodes are marked as abnormal nodes, and their attributes are adjusted to reflect the non-compliance status. If certain component combinations are defined in the database as having significant antagonistic effects, the edge weights of the two components in the topology graph are reduced until they become negative. If new drug components are discovered in the experiment, new nodes are created in the topology graph and corresponding attributes are added. If a drug component already exists, the node attributes are updated, and the node attributes are adjusted by integrating new data. The drug component data of the target drug, collected and preprocessed from laboratory equipment, will be compared one by one with the drug component database in the current pharmaceutical standards. The comparison process includes: verifying whether the drug components of the target drug are complete and checking for non-standard components; comparing and checking whether the content of each component is within the range specified by the standard; and judging whether the combination of components in the target drug is reasonable based on the mechanism of action between drug components recorded in the database. After comparing with the existing standard database, the attributes of the nodes are updated to reflect the status after the drug components are verified. If the mechanism of action between drug components does not conform to the standard or there is an antagonistic effect, the edges between the corresponding nodes are deleted. If the standard shows that there is a synergistic relationship between drug components, new edges are added between the nodes and given high weights. The weights of the edges are adjusted according to the strength of the synergistic relationship or the significance of the antagonistic effect between the components. If the synergistic relationship is strong, the weight is increased; if the antagonistic effect is strong, the weight is decreased until it becomes negative. The verified and optimized topology graph is stored as standardized spectral graph data, which includes complete node attributes and edge weight information.

2. The method for managing spectral data based on laboratory equipment according to claim 1, characterized in that, The topological graph is constructed using graph theory, where each node represents a target drug, and the edges between nodes represent the relationships between different target drugs, including compatibility enhancement and mutual constraint relationships.

3. The method for managing spectral data based on laboratory equipment according to claim 1, characterized in that, Based on compatibility theory, the components extracted from the drug ingredient data are analyzed in pairs to determine complementary, antagonistic, and neutral relationships. In the constructed topology graph, edges between the nodes are generated based on the analyzed combination relationship data. Specifically: Add positive association paths between complementary nodes, and mark the weight of the edges with positive values ​​to indicate the degree of reinforcement; Add negative association paths between nodes with a antagonistic relationship, and mark the weight of the edge as negative to indicate the degree of inhibition; No edges are added between nodes with neutral relationships.

4. A method for managing spectral data based on laboratory equipment according to any one of claims 1 to 3, characterized in that, The verified and optimized topological graph is structured and its data is stored in a unified format. The relationships between nodes and edges are directly stored as a graph structure, thus forming standardized spectral graph data.

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