Spectrogram data management method based on laboratory equipment

Abstract

The invention discloses a spectrogram data management method based on laboratory equipment, and relates to the technical field of medical data management, and the method comprises the steps: constructing a topological graph through a graph theory method, describing the incidence relation between different target drugs, collecting the drug component data of a target drug sample through the laboratory equipment, in combination with a traditional Chinese medicine compatibility theory and a formula standard, edges and weights between different nodes are initialized, the relation between attributes of the nodes and the edges in the topological graph is adjusted by comparing with a medicine component database in an existing pharmaceutical standard, and finally the verified and optimized topological graph is stored as standardized spectrogram data. According to the method, the relationship among the medicine components is visualized through a graph theory method, and the edges and the weights of the edges among the nodes in the topological graph are generated in combination with the attributes and interaction of the medicine components, so that researchers can visually check the action relationship among the components, and the research efficiency is improved. And the problems of information isolation and difficult query in traditional experimental data management are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pharmaceutical data management, and specifically to a spectral data management method based on laboratory equipment. Background Art

[0002] In the current fields of medicine and drug research and development, with the in-depth advancement of new drug research and development and the continuous development of traditional Chinese medicine research, a large amount of drug experiment data faces the problem of unified management. New drug research and development not only requires a large number of experimental operations, but also needs to strictly follow a series of standardized standards. At the same time, it also involves in-depth analysis and management of experimental data and drug ingredient information.

[0003] The uniqueness of traditional Chinese medicine research lies in its complex compatibility theory and multi-component, multi-index experimental characteristics. During the experiment, researchers need to frequently process a large amount of complex experimental data and materials, including ingredient information, medicinal properties, compatibility rules, and experimental results. There are significant correlations between these data, while the existing management methods usually store these data in isolation, lacking effective correlation analysis capabilities, resulting in difficulties for researchers to quickly query and comprehensively analyze during experimental design, data processing, and standard comparison, significantly increasing the experimental complexity and research and development cycle.

[0004] In addition, the existing laboratory management systems still have deficiencies 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 between drug ingredients or compatibility effects. In this case, not only does it affect the analysis efficiency of experimental data, but it is also prone to misinterpreting the interaction relationships between drug ingredients or missing key information, restricting the further development of traditional Chinese medicine modernization research.

[0005] Therefore, a new technical means that can integrate experimental data, standard specifications, and traditional Chinese medicine compatibility theory is needed to achieve efficient management and visualization analysis of drug ingredient data. Summary of the Invention

[0006] I) Technical Problems to be Solved The present invention provides a spectral data management method based on laboratory equipment, which can achieve efficient management and analysis of traditional Chinese medicine spectral data in the laboratory.

[0007] II) Technical Solutions To achieve the above object, the present invention provides the following technical solutions: A spectral data management method based on laboratory equipment, applicable to the management of traditional Chinese medicine data in the laboratory, includes: According to the target drugs under study and their medicinal ingredient characteristics, a topological graph is constructed using graph theory methods to describe the association relationships between different target drugs. Each of the said target drugs corresponds to a node in the topological graph. The drug ingredient data of each target drug is obtained through laboratory equipment and mapped into the corresponding node in the topological graph, and the attributes of the node are updated. The attributes of the node include drug ingredient type, functional characteristics, and medicinal effects. Analyze the relationships between different drug ingredients in combination with traditional Chinese medicine compatibility theory, generate the edges between different nodes in the topological graph, and set the weights of the edges according to the strength of the relationships between different drug ingredients. Among them, if the synergistic relationship between drug ingredients is strong, the weight is increased, and the higher the degree of synergy, the greater the weight value; if the antagonistic effect between drug ingredients is strong, the weight is decreased, and the stronger the degree of antagonism, the smaller the weight value until it becomes negative. Compare the drug ingredient data of each target drug with the drug ingredient database in the current pharmaceutical standards. By comparing the types, content ranges of the main ingredients, and the interactions between ingredients, verify whether the ingredients of the target drug meet the standard requirements, and adjust the attributes of the nodes and the relationships of the edges in the topological graph according to the verification results. Store the verified and optimized topological graph as standardized spectral data, and the spectral data includes complete node attributes and edge weight information.

[0008] Furthermore, in the construction of the topological graph using graph theory methods, each of the said nodes represents a target drug, and the edges between the nodes represent the relationships between different target drugs, including compatibility enhancement and mutual restraint relationships.

[0009] Furthermore, the drug ingredient data of each target drug is obtained through laboratory equipment, including chemical structure information, spectral characteristics, physicochemical characteristics, and medicinal effect function characteristics. After preprocessing the drug ingredient data, it is mapped into the corresponding nodes in the topological graph. The attributes of the node include drug ingredient type, functional characteristics, and medicinal effect characteristics. If new drug ingredients are discovered in the experiment, new nodes are created in the topological graph and corresponding attributes are added. If the drug ingredients already exist, the attributes of the nodes are updated, and the attributes of the nodes are adjusted by integrating new data.

[0010] Furthermore, the drug ingredient data of the target drug collected from the laboratory equipment and preprocessed is compared one by one with the drug ingredient database in the current pharmaceutical standards. The comparison process includes: Check whether the drug ingredients of the target drug are complete and whether there are non-standard ingredients. Compare and check whether the content of each ingredient is within the range specified by the standard. Based on the mechanism of action between drug ingredients recorded in the database, determine whether the combination of ingredients in the target drug is reasonable.

[0011] Furthermore, after comparing the target drug ingredient data with the drug ingredient database in the current pharmaceutical standards, the specific treatment for those that are determined to be non-compliant is as follows: If there is an important component missing as required by the standard, the nodes and edges related to the component are deleted in the topological graph; If the range of some drug ingredients is not within the guaranteed range, the corresponding node is marked as an abnormal node and its attributes are adjusted to reflect the unqualified status; If there are some combinations of components that are defined as having significant antagonistic effects in the database, the edge weights of the two in the topological graph are reduced to a negative value.

[0012] Furthermore, after comparison with the current standard database, the properties of the node are updated to reflect the status after drug component verification, specifically: If the mechanism of action between drug components does not meet the criteria or there is antagonism, the edges between the corresponding nodes are deleted; If the criteria show that there is a synergistic relationship between drug ingredients, new edges are added between nodes and given high weights; The weight of the edge is 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 reduced until it is negative.

[0013] Furthermore, according to the compatibility theory, the components extracted from the drug component data are analyzed in pairs to determine the complementary relationship, the mutually restraining relationship and the neutral relationship. In the constructed topological graph, the edges between the nodes are generated according to the analyzed combination relationship data. Specifically: Add positive association paths between nodes of complementary relationships, and mark the weight of the edge as a positive value, indicating the degree of enhancement; Add negative correlation paths between nodes in a counter-relationship, and mark the weight of the edge as a negative value to indicate the degree of inhibition; No edges are added between nodes with neutral relationships.

[0014] Furthermore, the verified and optimized topological graph is structured, its data is stored in a unified format, and the relationship between nodes and edges is directly stored as a graph structure, so that it constitutes standardized spectrum data.

[0015] III) Beneficial effects: Compared with the prior art, the invention has the following beneficial effects: By constructing a standardized topological graph model, the present invention integrates drug components and their attribute information into nodes, visualizes the relationships between drug components through graph theory methods, and generates the edges and their weights between nodes in the topological graph based on traditional Chinese medicine compatibility theory, combined with the attributes and interactions of drug components. Thus, researchers can intuitively view the interaction relationships, synergistic effects, and antagonistic effects between various components, reducing the problems of information isolation and difficult query in traditional experimental data management.

[0016] By comparing drug component data with a pharmaceutical standard database, the compliance of drug components is verified, and the node attributes and edge relationships in the topological graph are corrected according to the standards, ensuring that the data in the drug R & D process complies with current standards and avoiding risks brought by inconsistent or omitted standards. The optimized topological graph is stored as standardized spectral data. This visualization method improves the intuitiveness and accuracy of data analysis, helping researchers better understand the complex interactions between drug components. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a method for managing spectral data based on laboratory equipment provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for updating nodes of a topological graph according to spectral data obtained from experiments in a method for managing spectral data based on laboratory equipment provided by an embodiment of the present invention; Figure 3 It is a schematic diagram for updating corresponding edges of nodes with different associated attributes of a topological graph in a method for managing spectral data based on laboratory equipment provided by an embodiment of the present invention; Figure 4 It is a schematic diagram for constructing an initial topological graph according to traditional Chinese medicine compatibility theory in a method for managing spectral data based on laboratory equipment provided by an embodiment of the present invention; Figure 5 It is a schematic diagram for optimizing a topological graph after comparison with a current standard database in a method for managing spectral data based on laboratory equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention.

[0020] In addition, terms such as "first" and "second" are only used for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0021] It should be noted that, without conflict, the features in the embodiments of the present invention can be combined with each other.

[0022] In the current fields of medical and pharmaceutical research and development, with the continuous research and development of new drugs and the gradual in-depth study of traditional Chinese medicine, laboratory research work is facing more and more challenges. The research and development of new drugs not only require a large number of experiments but also need to follow a series of strict standards and specifications. The inventor of the present invention has found that existing laboratory management systems usually have difficulty providing sufficient visualization functions. Experimental personnel often can only view experimental results through data tables or basic reports, and are unable to intuitively understand the internal laws behind the experimental data or the relevant standards and methods. In such a situation, the analysis and evaluation of experimental data are not only inefficient but also prone to missing important information or misinterpreting data.

[0023] In the research of traditional Chinese medicine, many complex standards, experimental methods, reagent formulas and other materials are involved in the experimental process. These materials are usually isolated and difficult to quickly query and apply during the experiment. The correlation between different experimental steps, data indicators and standards is very important. Therefore, the lack of a system that can quickly retrieve relevant standards and materials will increase the difficulty of the experiment and extend the research and development cycle. In the process of new drug research and development, the evaluation of experimental data is not only the analysis of a single piece of data but also the comprehensive evaluation of data in relation to relevant standards and experimental methods. In the field of traditional Chinese medicine research, especially in the research of traditional Chinese medicine ingredients, a large amount of data such as chemical components, pharmacological effects, and clinical trials are involved. These data are often difficult to effectively combine in traditional laboratory management systems, resulting in the experimental results being difficult to accurately reflect the actual situation.

[0024] To solve the above various problems, in combination with Figures 1 to 5As shown, the present invention proposes a spectral data management method based on laboratory equipment. This method visualizes the relationships between drug components through graph theory methods, enabling researchers to intuitively view the interaction relationships, synergistic effects, and antagonistic effects between components. This visualization method greatly improves the intuitiveness and accuracy of data analysis, helping researchers better understand the complex interactions between drug components. In addition, by comparing the drug component data with the pharmaceutical standard database, it is verified whether the drug components meet the standards, and the node attributes and edge relationships in the topological graph are corrected according to the standards to ensure that the data in the drug R & D process conforms to the current standards, avoiding the risks brought by inconsistent or omitted standards.

[0025] Herein refer to Figure 1 , first, a data preparation stage is carried out, that is, S10: According to the target drug and its medicinal ingredient characteristics under study, a topological graph is constructed using graph theory methods to describe the association relationships between different target drugs; wherein, each target drug corresponds to a node in the topological graph, and the drug component data of each target drug is obtained through laboratory equipment and mapped into the corresponding node in the topological graph, and the attributes of the node are updated; the attributes of the node include drug component type, functional characteristics, and pharmacodynamic effects.

[0026] First, clarify the traditional Chinese medicine formula and its ingredients to be analyzed according to the research objective, combine the drug efficacy and properties, determine the research scope, and map the target ingredients to the nodes of the topological graph. In the theory of traditional Chinese medicine compatibility, there may be synergistic or antagonistic effects between different ingredients. For example, licorice is often used as a harmonizer of drug effects, and the combination of ephedra and cassia twig will enhance the sweating effect. The action mechanisms of these drug components are determined by their chemical compositions and properties, so it is necessary to clarify the relationships between the components through literature, experimental data, etc.

[0027] Graph theory methods are used to describe the structured relationships of target drug components, representing the interaction relationships between nodes with edges. The type and weight of the edges reflect the enhancing or inhibitory relationships in the compatibility theory, such as whether the compatibility of two components can synergistically enhance a certain efficacy, or whether there is a toxicity offset.

[0028] More specifically, each node represents a target drug component, and the node attributes include chemical category, drug property (cold, heat, warm, cool), and main efficacy (clearing heat, harmonizing, relieving cough). Each edge represents the relationship between two components, including the following types:

[0029] Synergistic relationship: such as the relationship between bupleurum and scutellaria baicalensis that enhances the antipyretic effect; Antagonistic relationship: such as the relationship between rhubarb and licorice that slows down the purgative effect; In some embodiments of the present invention, a neutralization relationship is also considered: such as the combination of licorice and bitter-cold drugs, which alleviates the irritation of bitter-cold drugs.

[0030] Using experimental data or pharmacological research materials, draw a preliminary topological graph, label the nodes and edges, and set the initial value of the edge weight to the default value (such as 1), which will be adjusted according to data analysis later.

[0031] Through the above steps, based on the compatibility theory and experimental verification, a drug component topological graph that can be dynamically adjusted later can be initially constructed. This topological graph not only clearly shows the interaction relationships between components, but also reflects the real efficacy compatibility mechanism through weight adjustment, providing technical support for the design and optimization of traditional Chinese medicine formulas.

[0032] Among them, drug component data is collected by modern chemical analysis equipment, which presents the component distribution and its characteristics in the target sample in the form of a graph. For example, mass spectrometers (MS) and nuclear magnetic resonance (NMR) spectrometers are used to detect molecular weight and structural information, while infrared spectrometers (IR) and ultraviolet-visible spectrophotometers are used for molecular vibration or light absorption characteristic analysis.

[0033] Specifically, each target drug sample is appropriately processed, such as extraction, concentration or dilution, to ensure moderate signal intensity during equipment detection without overloading. According to different equipment requirements, a suitable carrier or solvent, such as acetonitrile, water or methanol, is selected to dissolve the target drug sample. Subsequently, the equipment sets scanning parameters, such as the scanning range, resolution and ionization mode of the mass spectrometer, to collect the full drug component data of the target drug. Among them, a large number of signals are generated after the target drug sample passes through the equipment and are recorded as one-dimensional or two-dimensional spectrograms, representing information such as the mass, vibration frequency, and light absorption of the components.

[0034] It should be noted that since there may be background noise and interference signals in the drug component data, such as instrument electronic noise, solvent peaks, and environmental clutter, the target signals can be retained and these irrelevant background interferences can be removed through denoising algorithms to improve clarity and accuracy. In some embodiments, the denoising process includes using the moving average method to smooth the collected mass spectrogram to reduce the influence of random noise, and correcting the baseline through polynomial fitting to eliminate the baseline offset problem caused by equipment drift or sample solvent.

[0035] Regarding the characteristic peaks in the mass spectrometry of the above-mentioned target drug, which correspond to the key characteristics of the drug components, 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 components can be further characterized. Regarding peak detection, in some embodiments, the first derivative method or the gradient change method can be used to detect the local extreme points in the spectrum and determine the potential peak positions. It should be noted that false peaks, that is, meaningless peaks caused by noise or instrument distortion, need to be excluded. Regarding peak fitting, in some embodiments, the detected peaks are fitted, and common 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).

[0036] Spectrum matching is to compare the characteristics of the collected spectrum with the standard spectrum in the drug component database. By comparing characteristics such as mass-to-charge ratio and peak area, the specific components in the target drug sample can be accurately identified. Specifically, using the spectrum characteristics of known components stored in the database, including mass-to-charge ratio range, peak shape parameters, molecular formula, etc., and using matching algorithms, such as point-to-point matching or cosine similarity algorithm, to compare the characteristic values of the sample spectrum with the standard spectrum and calculate the matching degree. If the matching degree exceeds the threshold, it can be determined as this component.

[0037] After that, the component information in the drug component data is mapped to the nodes of the topological graph. Each node represents a target component, and the attributes of the node, such as component type and drug property characteristics, are updated according to the recognition result.

[0038] More specifically, the topological graph is constructed based on graph theory, where each node represents the specific component of the target drug and each edge represents the interaction relationship between components. To ensure the dynamics and accuracy of the topological graph, it is necessary to match the specific components identified by experiments with the existing nodes.

[0039] Traverse all the existing nodes in the topological graph to find the nodes that match the identified components. The matching conditions include: component name and component characteristics (such as molecular formula, molecular weight). If a matching node is found, the attribute information of the newly identified component is merged with this node. If no matching node is found, a new node is created to represent this component.

[0040] The newly created node needs to be attached with the basic attribute information of the component, including: drug component type and drug property characteristics, which are mainly based on literature records or experimental data to label the drug property of the component (such as cold, heat, warm, cool).

[0041] Here, reference can be made to Figure 4 , taking the initial topological graph constructed by the following eight medicinal materials as an example. The construction principles include the attributes of each medicinal material (node): medicinal material name, functional characteristics, and drug property. Its initial node information includes:

[0042] a: Ginseng, Function: Tonifying qi and strengthening the spleen, Medicinal property: Warm; b: Atractylodes macrocephala, Function: Strengthening the spleen and drying dampness, Medicinal property: Warm; c: Coptis chinensis, Function: Clearing heat and drying dampness, Medicinal property: Cold; d: Licorice root, Function: Harmonizing all medicines, Medicinal property: Neutral; e: Poria cocos, Function: Strengthening the spleen and promoting diuresis, Medicinal property: Neutral; f: Angelica sinensis, Function: Nourishing blood and promoting blood circulation, Medicinal property: Warm; h: Rheum palmatum, Function: Clearing heat and detoxifying, Medicinal property: Cold; k: Bupleurum chinense, Function: Soothing the liver and relieving depression, Medicinal property: Slightly cold. Referring to the theory of traditional Chinese medicine compatibility, according to the synergistic and antagonistic relationships between different medicines, the constructed topological graph is as Figure 4 shown. The relationship of its edges and the initial weights are set according to the intensity of the synergistic or antagonistic relationship. For example, the synergistic relationship between ginseng and licorice root is relatively low, and the initial weight is set to +1. The intensity of the synergistic relationship between ginseng and angelica sinensis is higher than that between ginseng and licorice root, and the initial weight of the edge between ginseng and angelica sinensis is set to +2.

[0043] In addition, considering that the node attributes of drug components may be updated or optimized with the accumulation of experimental data. For example, the same component may obtain new pharmacological property data in different experiments, or correct the existing data errors. Therefore, the topological graph needs to support dynamic updating of node attributes to reflect the latest research results of the components.

[0044] More specifically, check whether there is the same component data in the node attributes. If there is the same data, fuse 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 the existing attributes (such as the range of medicinal property characteristics), give priority to the weighted average or the data with high credibility. It should be noted here that avoid duplicate or conflicting attribute information in the nodes, and for redundant attributes in the same node, use the weight algorithm or consistency check to screen the most reliable data.

[0045] The research on traditional Chinese medicine components is usually carried out dynamically. New experimental data may supplement or correct the existing component information. By fusing the new data, it can be ensured that the attributes of the nodes in the topological graph always reflect the latest research conclusions of the components. If the quality of the new experimental data is better than the original data, directly overwrite the old attribute values. If the experimental data comes from different sources, such as the results of multiple laboratories, fuse the data by the method of weighted average. For example, fuse the component concentration ranges of two groups of experiments, and the weights are assigned according to the precision of the experimental equipment. In addition, when the new experimental data supplements unrecorded medicinal property characteristics, add them to the nodes. If the existing medicinal property records conflict with the experimental data, determine the final attribute value through literature verification or comprehensive analysis of historical data.

[0046] After that, perform S20: Analyze the relationships between different drug components in combination with the theory of traditional Chinese medicine compatibility, generate the edges between different nodes in the topological graph, and set the weights of the edges according to the strength of the relationships between different drug components. Among them, if the synergistic relationship between drug components is strong, increase the weight, and the higher the degree of synergy, the greater the weight value; if the antagonistic effect between drug components is strong, decrease the weight, and the stronger the degree of antagonism, the smaller the weight value until it becomes negative.

[0047] It can be understood that regarding the relationships between traditional Chinese medicine components, such as compatibility enhancement and mutual restraint, they are represented by edge weights, and the edge weights reflect the strength of the relationships between the two components. New experimental data may reveal new relationships or change the strength of existing relationships. Therefore, it is necessary to dynamically adjust the edges and weights in the topological graph.

[0048] If new relationships between two components are revealed in the experimental data, such as they have an enhancing effect in a specific compatibility, add the corresponding edge in the topological graph and set the initial weight value. If the new experimental data indicates that the strength of an existing relationship has changed, such as the enhancing effect becomes more significant, adjust the edge weight. The rules for updating the edge weights include: if the new experiment verifies a stronger relationship, increase the weight; if the new data indicates a weakened relationship, appropriately decrease the weight. If the new data indicates that some relationships no longer hold, such as there is no actual interaction between the two components, remove the corresponding edge from the topological graph.

[0049] In summary, it can be understood that through node attribute update, edge weight adjustment, and dynamic maintenance, the topological graph always accurately reflects the drug components and their interaction relationships, providing reliable technical support for drug formula optimization and property research by combining experimental data and graph theory analysis.

[0050] After that, perform S30: Compare the drug component data of each target drug with the drug component database in the current pharmaceutical standard. By comparing the types, content ranges, 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 topological graph according to the verification results.

[0051] More specifically, sort out the standard component data information of the key components from the current pharmaceutical regulations and industry standards, including comparing the types, content ranges, and interactions between the main components. It should be noted that ensure that the database covers the common main components in the target drug samples, and mark the standard attributes of each component such as category, purity (content range), drug property, etc.

[0052] In some embodiments, compare the preprocessed drug component data with the standard spectra in the database one by one: Check whether the drug components of the target drug are complete and check whether there are non-standard components; Compare and check whether the content of each ingredient is within the range specified in the standard; Based on the mechanism of action between drug ingredients recorded in the database, determine whether the combination of ingredients in the target drug is reasonable.

[0053] The comparison results are classified and processed. For components that meet the standards, their attributes are recorded to update the corresponding nodes in the topology map. For components that do not meet the standards, they are marked as abnormal or unqualified, and the relevant data is eliminated in subsequent analysis.

[0054] According to pharmaceutical standards, update the properties of nodes in the topology map, including: Ingredient type: ensure that the ingredient classification is consistent with the standard. Medicinal properties: correct the medicinal properties description of the drug, and revise the medicinal properties label of the node in combination with the compatibility standard. And other attributes: such as technical indicators such as purity and effective dosage range.

[0055] If there are new ingredients that meet the standards, add them to the topology map as new nodes and establish associated paths with existing nodes. For ingredients that are eliminated because they do not meet the standards, delete or block their corresponding nodes and paths to ensure the real-time validity and accuracy of the topology map. Save the verified drug ingredient data and the adjusted results of the topology map to provide basic data support for subsequent experiments and analysis.

[0056] Here, we also take the initial topology map composed of the above eight medicinal materials as an example, and compare the drug component data obtained and pre-processed by laboratory equipment with the standard spectra in the database one by one to verify the compliance of the drug components and optimize the topology map. Figure 5 , among which, regarding ginseng (a) and angelica (f), the current standard stipulates that the synergistic effect of ginseng and angelica must be achieved through ginsenoside Rg1, but experimental data show that the Rg1 content in ginseng is lower than the standard, so the edges of ginseng (a) and angelica (f) are deleted. The antagonistic effect of licorice (d) and coptis (c) is mainly caused by glycyrrhizic acid and berberine, but experimental data show that the berberine content in coptis samples is low, so the weight of the edge is weakened from -1 to -0.5. The standard database suggests that Bupleurum (k) and Poria (e) have potential synergistic effects in soothing the liver and strengthening the spleen, and experimental data support this relationship, so the edges of Bupleurum (k) and Poria (e) are added, and their weights are set to +1.

[0057] Finally, S40 is performed: the verified and optimized topological graph is stored as standardized spectrum graph data, and the spectrum graph data includes complete node attributes and edge weight information.

[0058] First, extract the drug ingredient data and topological graph information, including extracting the verified drug ingredient information from the drug ingredient data, such as ingredient types, contents, characteristic peaks, and the matching results with the standards. Obtain the association relationships, medicinal property characteristics, and path information between nodes of the drug ingredients from the topological graph, comprehensively reflecting the structure and action mechanism of the formula.

[0059] In some embodiments, the spectral data is presented in a graphical manner to facilitate researchers' understanding of the relationships between drugs and their ingredients. For example, the length and color of the edges represent the type and strength of the relationships. Here, reference can be made to Figure 4 and Figure 5 .

[0060] After standardization, the spectral data can be stored long-term, supporting regular updates and expansions. Researchers can conveniently add new data or correct existing data to maintain the dynamic availability of the spectrum, and use the spectrum for various data analyses, such as analyzing whether the synergistic effect of the drug ingredients in a certain formula meets the expectations, or tracing the source of a certain ingredient and its role in other drugs through the spectrum.

[0061] In summary, this method organically combines experimental data, standards and specifications, and traditional Chinese medicine theory, constructs a systematic and dynamic spectral data management solution, provides technical support for the research and development of traditional Chinese medicine and laboratory management, and effectively improves the utilization rate of experimental data and the reliability of research results.

[0062] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. The patent protection scope of the present invention is subject to the claims. Any equivalent structural changes made by using the description and drawings of the present invention should be equally included in the protection scope of the present invention.

Claims

1. A spectrum data management method based on laboratory equipment, suitable for laboratory traditional Chinese medicine data management, characterized in that: include: According to the target drugs under study and their medicinal properties and composition characteristics, a topological map is constructed using graph theory methods to describe the association relationship between different target drugs; wherein each target drug corresponds to a node of the topological map, and the drug component data of each target drug is obtained through laboratory equipment, mapped to the node corresponding to the topological map, and the attributes of the node are updated; the attributes of the node include drug component type, functional characteristics and efficacy; Combined with the theory of Chinese medicine compatibility, the relationship between different drug components is analyzed to generate edges between different nodes in the topological graph, and the weight of the edge is set according to the strength of the relationship between different drug components; among them, the weight is increased if the synergistic relationship between drug components is strong, and the higher the degree of synergy, the greater the weight value; the weight is reduced if the antagonistic effect between drug components is strong, and the stronger the degree of antagonism, the smaller the weight value until it becomes a negative value; Compare the drug ingredient data of each target drug with the drug ingredient database in the current pharmaceutical standards, verify whether the ingredients of the target drug meet the standard requirements by comparing the types, content atmospheres and interactions between the main ingredients, and adjust the attributes of the nodes and the relationship of the edges in the topological graph according to the verification results; The verified and optimized topological graph is stored as standardized spectrum graph data, which contains complete node attributes and edge weight information.

2. A spectrum data management method based on laboratory equipment according to claim 1, characterized in that: The topological graph is constructed using graph theory methods, wherein each node represents a target drug, and the edges between nodes represent the relationships between different target drugs, including compatibility enhancement and mutual restriction relationships.

3. A spectrum data management method based on laboratory equipment according to claim 1, characterized in that: Obtain the drug component data of each target drug through laboratory equipment, including chemical structure information, spectral characteristics, physicochemical characteristics, and pharmacodynamic function characteristics, and map the drug component data to the corresponding nodes of the topological graph after preprocessing; The attributes of the nodes include drug ingredient type, functional characteristics, and efficacy characteristics; If a new drug ingredient is discovered during the experiment, a new node is created in the topological map and the corresponding attributes are attached; if the drug ingredient already exists, the attributes of the node are updated and adjusted by integrating the new data.

4. A spectrum data management method based on laboratory equipment according to claim 3, characterized in that: The drug component data of the target drug collected and pre-processed from the laboratory equipment is compared one by one with the drug component database in the current pharmaceutical standards. The comparison process includes: Verify the drug composition of the target drug is complete and check for any non-standard ingredients; Compare and check whether the content of each ingredient is within the range specified in the standard; Based on the mechanism of action between drug ingredients recorded in the database, determine whether the combination of ingredients in the target drug is reasonable.

5. A spectrum data management method based on laboratory equipment according to claim 4, characterized in that: After comparing the target drug ingredient data with the drug ingredient database in the current pharmaceutical standards, the specific treatment for those that are judged to be non-compliant is as follows: If there is an important component missing as required by the standard, the nodes and edges related to the component are deleted in the topological graph; If the range of some drug ingredients is not within the guaranteed range, the corresponding node is marked as an abnormal node and its attributes are adjusted to reflect the unqualified status; If there are some combinations of components that are defined as having significant antagonistic effects in the database, the edge weights of the two in the topological graph are reduced to a negative value.

6. A spectrum data management method based on laboratory equipment according to claim 5, characterized in that: After comparing with the current standard database, update the node attributes to reflect the status after drug component verification, specifically: If the mechanism of action between drug components does not meet the criteria or there is antagonism, the edges between the corresponding nodes are deleted; If the criteria show that there is a synergistic relationship between drug ingredients, new edges are added between nodes and given high weights; The weight of the edge is 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 reduced until it reaches a negative value.

7. The method for managing spectrum data based on laboratory equipment according to claim 1, characterized in that: According to the compatibility theory, the components extracted from the drug component data are analyzed in pairs to determine the complementary relationship, the mutually restraining relationship and the neutral relationship. In the constructed topological graph, the edges between the nodes are generated according to the analyzed combination relationship data. Specifically: Add positive association paths between nodes of complementary relationships, and mark the weight of the edge as a positive value, indicating the degree of enhancement; Add negative correlation paths between nodes in a counter-relationship, and mark the weight of the edge as a negative value to indicate the degree of inhibition; No edges are added between nodes with neutral relationships.

8. A spectrum data management method based on laboratory equipment according to any one of claims 1 to 7, characterized in that: The verified and optimized topological graph is structured, its data is stored in a unified format, and the relationship between nodes and edges is directly stored as a graph structure to form standardized spectral data.

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