Method and system for multiplexed antimicrobial formulation optimization of water-based standard

Abstract

The application provides a kind of water base standard multiple composite bacteriostatic agent formula optimization method and system, it is related to formula optimization field, including obtaining initial formula and carrying out multidimensional bacteriostatic effect test, the mapping relationship of antagonism-synergistic effect between components is constructed to obtain interaction atlas, based on the atlas, formula reconstruction is carried out, in mixed bacterial environment test and stability evaluation are carried out, and environmental adaptability model is constructed and component stability compensation adjustment is carried out.The application can effectively eliminate the antagonistic effect between bacteriostatic agent components, improve the bacteriostatic stability and environmental adaptability in mixed bacterial environment.

Description

TECHNICAL FIELD

[0001] The present application relates to formula optimization technology, and in particular to a multi-composite bacteriostatic agent formula optimization method and system for water matrix standards. BACKGROUND

[0002] As an important reference material in the fields of environmental monitoring, water quality detection, and biological medicine, the stability of water matrix standards directly affects the accuracy and reliability of detection results. To ensure the stability of water matrix standards during storage and use, composite bacteriostatic agents are usually added to prevent microbial contamination and reproduction.

[0003] With the advancement of water quality analysis technology and the increasing demand for detection, the application environment of water matrix standards is becoming increasingly complex and variable, and traditional single bacteriostatic agents have been difficult to meet the diversified bacteriostatic needs. Composite bacteriostatic agents have gradually become the main solution for the preservation of water matrix standards due to their synergistic bacteriostatic and broad-spectrum bacteriostatic advantages. However, the current formula design of composite bacteriostatic agents still faces many challenges, making it difficult to achieve efficient and stable bacteriostasis in complex environments.

[0004] The existing composite bacteriostatic agent formula design lacks systematic evaluation of the interaction between components, often using an empirical superposition method, which fails to identify and eliminate possible antagonistic effects between components, resulting in actual bacteriostatic effects lower than theoretical expectations, and even some components canceling each other out. Traditional composite bacteriostatic agent formula optimization is mostly evaluated under single bacterial species or ideal environmental conditions, ignoring the complex situation of multiple bacterial species coexistence and environmental condition changes in actual application, making the bacteriostatic agent unstable in actual application environment and unable to cope with the challenge of mixed contamination of multiple bacterial species. SUMMARY

[0005] The embodiments of the present application provide a multi-composite bacteriostatic agent formula optimization method and system for water matrix standards, which can solve the problems in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a multi-composite bacteriostatic agent formula optimization method for water matrix standards, comprising:

[0007] Obtaining an initial formula of a composite bacteriostatic agent to be optimized, performing multi-dimensional bacteriostatic effect testing on the initial formula, and obtaining multi-dimensional bacteriostatic effect data;

[0008] Constructing an antagonistic-synergistic effect mapping relationship between bacteriostatic components in the initial formula, identifying the component ratio combination that produces antagonistic effect by cross-comparison analysis of the multi-dimensional bacteriostatic effect data under different component ratio combinations, and obtaining a component interaction map;

[0009] Based on the component interaction map, the initial formula is reconfigured by antagonistic elimination, and a reconfigured formula is obtained. The reconfigured formula is tested for antibacterial effect in a mixed bacterial environment to obtain mixed bacterial antibacterial effect data.

[0010] The mixed bacterial antibacterial effect data is evaluated for stability, the antibacterial effect fluctuation characteristics of the reconfigured formula under different environmental conditions are identified, the environmental sensitive factors causing the antibacterial effect fluctuation are extracted, an environmental adaptability model of the antibacterial component is constructed, the antibacterial components in the reconfigured formula are adjusted for stability compensation based on the environmental adaptability model, and an optimized formula is obtained. The optimized formula is applied to the target antibacterial scene.

[0011] An initial formula of a composite antibacterial agent to be optimized is obtained, and multi-dimensional antibacterial effect data is obtained by testing the initial formula in multiple dimensions.

[0012] Each antibacterial component in the initial formula is subjected to pulsed concentration changes, and the antibacterial rate change trajectories corresponding to the concentration rising stage and the concentration falling stage of each antibacterial component are obtained.

[0013] The antibacterial rate change trajectories of each antibacterial component in the concentration rising stage and the concentration falling stage are compared, and the antibacterial action reversibility coefficient of each antibacterial component is calculated.

[0014] According to the antibacterial action reversibility coefficient, the antibacterial components in the initial formula are divided into irreversible damage type components and reversible inhibition type components.

[0015] The antibacterial rate, antibacterial persistence and synergistic coefficient of the irreversible damage type components and the reversible inhibition type components are determined respectively, and the multi-dimensional antibacterial effect data containing the action reversibility characteristics is obtained.

[0016] An antagonistic-synergistic effect mapping relationship between the antibacterial components in the initial formula is constructed, and by cross-comparison and analysis of the multi-dimensional antibacterial effect data under different component ratio combinations, component ratio combinations producing antagonistic effects are identified, and a component interaction map is obtained, including:

[0017] For each antibacterial component in the multi-dimensional antibacterial effect data, the antibacterial rate data corresponding to the current antibacterial component under different ratio combinations is extracted, a ratio-antibacterial rate response surface is constructed, and the ratio interval where the antibacterial rate appears non-monotonic change in the ratio-antibacterial rate response surface is identified and marked as the effect reversal ratio interval.

[0018] For the bacteriostatic component with the effect reverse ratio interval, extract the ratio data of other bacteriostatic components coexisting with the bacteriostatic component in the effect reverse ratio interval, calculate the correlation degree of the ratio data of other bacteriostatic components and the bacteriostatic component entering the effect reverse ratio interval, and screen other bacteriostatic components with correlation degree exceeding the reference correlation degree as the effect reverse trigger component;

[0019] The bacteriostatic component with the effect reverse ratio interval and the corresponding effect reverse trigger component form an antagonistic-synergistic conversion component pair, and the ratio value of each bacteriostatic component in the effect reverse ratio interval boundary of the antagonistic-synergistic conversion component pair is extracted as the effect conversion critical ratio;

[0020] The antagonistic-synergistic conversion component pair and the corresponding effect conversion critical ratio are mapped to the ratio coordinate space to construct a component interaction map.

[0021] Based on the component interaction map, the antagonistic elimination reconstruction is performed on the initial formula to obtain a reconstructed formula, and the bacteriostatic effect test of the reconstructed formula in a mixed bacterial environment is performed to obtain mixed bacterial bacteriostatic effect data, including:

[0022] From the component interaction map, the antagonistic-synergistic conversion component pair is extracted, the curvature change rate analysis of the antagonistic effect intensity value of the antagonistic-synergistic conversion component pair in the bacterial environment with time is performed, the time point with the curvature change rate exceeding the mutation determination threshold is identified as the antagonistic effect mutation time point, and the residual concentration values of each bacteriostatic component corresponding to the antagonistic effect mutation time point are obtained to form an antagonistic elimination concentration combination;

[0023] The initial formula is decomposed into a presequence formula unit containing a presequence bacteriostatic component and a postsequence formula unit containing a postsequence bacteriostatic component, the time interval reference value between the presequence formula unit and the postsequence formula unit is set as the time value corresponding to the antagonistic effect mutation time point, and the reconstructed formula is constructed;

[0024] The presequence formula unit is applied to the mixed bacterial culture system, and the decay time difference required for the concentration of the presequence bacteriostatic component to decay to the corresponding residual concentration value in the antagonistic elimination concentration combination is calculated based on the consumption rate of each target bacterial species to the presequence bacteriostatic component and the initial bacterial population proportion;

[0025] The decay time difference is added to the time interval reference value to obtain an adjusted application time interval, and the application of the postsequence formula unit is triggered at the time point corresponding to the adjusted application time interval to obtain the mixed bacterial bacteriostatic effect data.

[0026] The stability of the mixed bacterial bacteriostatic effect data is evaluated, the bacteriostatic effect fluctuation characteristics of the reconstructed formula under different environmental conditions are identified, the environmental sensitive factors causing the bacteriostatic effect fluctuation are extracted, and an environmental adaptability model of the bacteriostatic component is constructed, including:

[0027] The stability of the mixed bacterial strain inhibition effect data is evaluated, and multiple environmental dimensions are cross-analyzed under different environmental conditions. The fluctuation amplitude value of the inhibition effect of the reconstituted formula under each environmental condition is extracted as the inhibition effect fluctuation characteristic;

[0028] A nonlinear mapping relationship between environmental condition parameters and inhibition effect fluctuation characteristics is constructed. Environmental sensitivity factors are extracted through sensitivity quantitative analysis. The environmental disturbance response coefficients of each environmental sensitivity factor to each bacteriostatic component are calculated and normalized to generate an environmental adaptability evaluation matrix;

[0029] Based on the environmental adaptability evaluation matrix, environmental adaptability degradation components with environmental disturbance response coefficients exceeding the disturbance threshold are identified. Candidate replacement components with homologous bacteriostatic target points are screened from the bacteriostatic component library;

[0030] When the synergistic compatibility evaluation value of the candidate replacement component and the remaining bacteriostatic components meets the synergistic effect condition, the candidate replacement component replaces the environmental adaptability degradation component, and the environmental adaptability evaluation matrix is updated. When the stability verification is passed, the updated environmental adaptability evaluation matrix is used as the environmental adaptability model of the bacteriostatic component.

[0031] A nonlinear mapping relationship between environmental condition parameters and inhibition effect fluctuation characteristics is constructed. Environmental sensitivity factors are extracted through sensitivity quantitative analysis. The environmental disturbance response coefficients of each environmental sensitivity factor to each bacteriostatic component are calculated and normalized to generate an environmental adaptability evaluation matrix including:

[0032] The environmental condition parameters and the corresponding inhibition effect fluctuation characteristics under different environmental conditions are collected. The environmental condition parameters are used as independent variables, and the inhibition effect fluctuation characteristics are used as dependent variables to construct a sample data set. A nonlinear fitting algorithm is used to fit and train the sample data set to generate a nonlinear mapping relationship function;

[0033] The sensitivity gradient values are obtained by calculating the partial derivatives of each environmental condition parameter in the nonlinear mapping relationship function. The environmental condition parameters with absolute values greater than the sensitivity threshold are extracted as environmental sensitivity factors;

[0034] For each bacteriostatic component in the reconstituted formula, the inhibition effect change rate of the bacteriostatic component is measured under the disturbance conditions of each environmental sensitivity factor. The inhibition effect change rate and the disturbance amplitude of the corresponding environmental sensitivity factor are calculated to obtain the environmental disturbance response coefficient;

[0035] The environmental disturbance response coefficients of each bacteriostatic component under each environmental sensitivity factor are normalized by maximum and minimum values in the bacteriostatic component dimension. The normalized environmental disturbance response coefficients are organized in a row-column structure to generate an environmental adaptability evaluation matrix.

[0036] The stability compensation adjustment is performed on the bacteriostatic components in the reconstructed formula based on the environmental adaptability model to obtain an optimized formula.

[0037] The environmental condition parameters of the target bacteriostatic scene are acquired, and corresponding environmental sensitive factors are identified, the normalized environmental disturbance response coefficients of each bacteriostatic component under the corresponding environmental sensitive factors are extracted from the environmental adaptability model, the reciprocal of the normalized environmental disturbance response coefficients is taken as the stability compensation weight to perform weighted adjustment on the formula proportion of each bacteriostatic component to obtain a compensation adjustment proportion.

[0038] The formula proportion deviation value of the compensation adjustment proportion and the original formula proportion is calculated, and when the formula proportion deviation value exceeds the proportion adjustment threshold, the actual addition amount of the bacteriostatic component is updated according to the compensation adjustment proportion, and the updated actual addition amount of each bacteriostatic component is combined to form an optimized formula.

[0039] The optimized formula is prepared into a bacteriostatic agent solution according to the actual addition amount, and the application verification is completed by applying the bacteriostatic agent solution to the bacteria to be inhibited in a simulated environment consistent with the environmental condition parameters of the target bacteriostatic scene.

[0040] The second aspect of the embodiment of the present application provides a multiple composite bacteriostatic agent formula optimization system of a water-based standard substance, which comprises:

[0041] A first unit is configured to acquire an initial formula of a composite bacteriostatic agent to be optimized, and perform multidimensional bacteriostatic effect testing on the initial formula to obtain multidimensional bacteriostatic effect data.

[0042] A second unit is configured to construct an antagonistic-synergistic effect mapping relationship among bacteriostatic components in the initial formula, identify component proportion combinations that produce antagonistic effects by cross-comparison and analysis of the multidimensional bacteriostatic effect data under different component proportion combinations, and obtain a component interaction map.

[0043] A third unit is configured to reconstruct the initial formula based on the component interaction map to eliminate antagonism and obtain a reconstructed formula, and perform bacteriostatic effect testing on the reconstructed formula in a mixed environment of multiple bacteria to obtain mixed bacteria bacteriostatic effect data.

[0044] A fourth unit is configured to perform stability evaluation on the mixed bacteria bacteriostatic effect data, identify bacteriostatic effect fluctuation characteristics of the reconstructed formula under different environmental conditions, extract environmental sensitive factors that cause the bacteriostatic effect fluctuation, construct an environmental adaptability model of bacteriostatic components, perform stability compensation adjustment on the bacteriostatic components in the reconstructed formula based on the environmental adaptability model, and obtain an optimized formula.

[0045] The third aspect of the embodiment of the present application provides an electronic device, which comprises:

[0046] A processor;

[0047] a memory for storing processor-executable instructions;

[0048] wherein the processor is configured to invoke the instructions stored by the memory to perform the aforementioned method.

[0049] A fourth aspect of the embodiment of the present application provides a computer readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the aforementioned method.

[0050] In the embodiment, by establishing the antagonistic-synergistic effect mapping relationship between the bacteriostatic components, the precise optimization of the composite bacteriostatic agent formula is realized, the mutual antagonistic effect between the components is effectively eliminated, the overall bacteriostatic effect is improved, and the problem of poor effect caused by simple superposition of components in the traditional formula optimization process is overcome. The bacteriostatic effect test method in the mixed environment of multiple bacteria is more in line with the actual application scene, can comprehensively evaluate the actual bacteriostatic performance of the composite bacteriostatic agent in the complex microbial environment, and solves the technical problem of large difference between the single bacterial test result and the actual application effect. The environmental adaptability model of the bacteriostatic component is constructed, the environmental sensitive factors are identified and targeted adjustment is performed, the stability and adaptability of the composite bacteriostatic agent under different environmental conditions are significantly improved, the optimized formula can maintain reliable bacteriostatic effect under various complex conditions, and the shelf life of the water-based standard substance is prolonged. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 FIG. 1 is a flowchart of the method for optimizing the multiple composite bacteriostatic agent formula of the water-based standard substance according to the embodiment of the present application;

[0052] Figure 2 FIG. 2 is a flowchart of the dynamic optimization and verification process of the bacteriostatic agent formula according to the embodiment of the present application. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in a clear and complete manner with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0054] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0055] Figure 1A flowchart of a method for optimizing a multi-composite bacteriostatic agent formula of a water-based standard substance of an embodiment of the present application is shown in FIG. 1, which comprises the following steps: Figure 1

[0056] An initial formula of a composite bacteriostatic agent to be optimized is obtained, and multi-dimensional bacteriostatic effect testing is performed on the initial formula to obtain multi-dimensional bacteriostatic effect data.

[0057] An antagonistic-synergistic effect mapping relationship among bacteriostatic components in the initial formula is constructed, and cross-comparison analysis is performed on the multi-dimensional bacteriostatic effect data under different component ratio combinations to identify component ratio combinations that produce antagonistic effects, thereby obtaining a component interaction map.

[0058] Based on the component interaction map, the initial formula is reconstructed to eliminate antagonistic effects, thereby obtaining a reconstructed formula, and bacteriostatic effect testing is performed on the reconstructed formula in a mixed bacterial environment to obtain mixed bacterial bacteriostatic effect data.

[0059] The mixed bacterial bacteriostatic effect data is subjected to stability evaluation to identify bacteriostatic effect fluctuation characteristics of the reconstructed formula under different environmental conditions, environmental sensitive factors causing the bacteriostatic effect fluctuations are extracted, an environmental adaptability model of the bacteriostatic components is constructed, the bacteriostatic components in the reconstructed formula are subjected to stability compensation adjustment based on the environmental adaptability model, and an optimized formula is obtained. The optimized formula is applied to a target bacteriostatic scenario.

[0060] In an alternative embodiment, obtaining an initial formula of a composite bacteriostatic agent to be optimized and performing multi-dimensional bacteriostatic effect testing on the initial formula to obtain multi-dimensional bacteriostatic effect data comprises the following steps:

[0061] Pulse-type concentration changes are applied to each bacteriostatic component in the initial formula, and bacteriostatic rate change trajectories corresponding to the concentration rising stage and the concentration falling stage of each bacteriostatic component are obtained.

[0062] The bacteriostatic rate change trajectories of each bacteriostatic component in the concentration rising stage and the concentration falling stage are compared, and a bacteriostatic action reversibility coefficient of each bacteriostatic component is calculated.

[0063] According to the bacteriostatic action reversibility coefficient, the bacteriostatic components in the initial formula are divided into irreversible damage type components and reversible inhibition type components.

[0064] The bacteriostatic rate, bacteriostatic durability, and synergistic enhancement coefficient of the irreversible damage type components and the reversible inhibition type components are determined, respectively, and the multi-dimensional bacteriostatic effect data containing the action reversibility characteristics is obtained.

[0065] ​The embodiment obtains an initial formula of a composite bacteriostatic agent to be optimized, performs multidimensional bacteriostatic effect testing, and obtains multidimensional bacteriostatic effect data. The initial formula can include various bacteriostatic components such as quaternary ammonium salts, isothiazolinones, and aldehydes, which are usually mixed in a specific ratio to prepare a composite bacteriostatic agent for the preservation of water-based standard substances.

[0066] When the initial formula is subjected to multidimensional bacteriostatic effect testing, a pulse concentration variation method is used, that is, the concentration of each bacteriostatic component is sequentially increased and decreased within a specific time, and the corresponding bacteriostatic rate is measured. The bacteriostatic component to be tested can be added to the water-based standard substance, and the initial concentration is set to 50% of the minimum effective concentration of the component, and then the concentration is increased by 20% every 24 hours until it reaches 150% of the concentration of the component in the initial formula, and the bacteriostatic rate corresponding to each concentration point is recorded to obtain the bacteriostatic rate change trajectory in the concentration increasing stage; then the concentration of the bacteriostatic component is gradually reduced, and the same time interval and concentration variation amplitude are used until the initial concentration is reached, and the bacteriostatic rate change trajectory in the concentration decreasing stage is recorded.

[0067] For the calculation of the reversibility coefficient of bacteriostatic action, the bacteriostatic rate change trajectories in the concentration increasing stage and the concentration decreasing stage are compared. The specific method is to calculate the difference in bacteriostatic rate at the same concentration point in the increasing stage and the decreasing stage, and then average the differences of all test concentration points to obtain the average difference in bacteriostatic rate. The average difference is divided by the average bacteriostatic rate in the concentration increasing stage to obtain the reversibility coefficient of bacteriostatic action. The smaller the coefficient value, the stronger the reversibility of bacteriostatic action; the larger the coefficient value, the stronger the irreversibility of bacteriostatic action.

[0068] According to the calculated reversibility coefficient of bacteriostatic action, the bacteriostatic components in the initial formula are divided into irreversible damage type components and reversible inhibition type components. When the reversibility coefficient of bacteriostatic action is greater than a preset threshold value (usually set to 0.5), the component is determined to be an irreversible damage type component; when the coefficient is less than or equal to the preset threshold value, it is determined to be a reversible inhibition type component. Irreversible damage type components usually act by damaging microbial cell structures or key enzyme systems, such as aldehyde compounds; while reversible inhibition type components mainly act by temporarily inhibiting the growth and reproduction of microorganisms, such as certain quaternary ammonium salt compounds.

[0069] After determining the type of each bacteriostatic component, the bacteriostatic effect of the irreversible damage type component and the reversible inhibition type component is determined in detail. The bacteriostatic rate determination uses the standard plate count method, that is, standard microorganism strains are inoculated in water-based standard substances containing different concentrations of bacteriostatic agents, and the number of viable bacteria is counted after 24 hours of culture. The bacteriostatic rate is calculated by comparing with the control group. The bacteriostatic durability determination is by measuring the bacteriostatic rate every 7 days after the initial addition of bacteriostatic agents, and continuously measuring for 4 weeks to observe the attenuation of the bacteriostatic effect. The bacteriostatic durability coefficient is defined as the ratio of the bacteriostatic rate on the 28th day to the bacteriostatic rate on the 1st day.

[0070] The determination of synergistic coefficient is determined by comparing the bacteriostatic effect of composite use and single use. The specific method is to determine the minimum effective concentration (MEC) of two bacteriostatic components when used alone, and then to determine the minimum effective concentration when two components are used in a specific ratio, and to calculate the synergistic coefficient. The synergistic coefficient is equal to the sum of the minimum effective concentrations of each component when used alone divided by the sum of the concentrations of each component when used in combination. The coefficient greater than 1 indicates that there is a synergistic effect, and the greater the coefficient, the more significant the synergistic effect.

[0071] Based on the above multi-dimensional bacteriostatic effect data, a composite bacteriostatic agent formula optimization model can be constructed. The model takes the type, concentration, type of action (irreversible damage type or reversible inhibition type), bacteriostatic rate, bacteriostatic durability and synergistic coefficient of bacteriostatic components as input variables, and finds the optimal formula through a specific optimization algorithm. The optimization process focuses on the reasonable proportion of irreversible damage type components and reversible inhibition type components, and utilizes the complementary effect of different types of bacteriostatic agents to ensure high initial bacteriostatic effect and long-term stable preservation effect. The optimization algorithm is based on the idea of multi-objective optimization, considering multiple objectives such as maximizing bacteriostatic effect, minimizing dosage and cost, and maximizing preservation stability.

[0072] In the actual optimization process, the effective concentration range of each bacteriostatic component is first determined, and then combination experiments are designed within this range to test the comprehensive bacteriostatic effect under different proportions. According to the test results, the proportion of each component is adjusted to gradually approach the optimal formula. For irreversible damage type components, the initial bacteriostatic effect is focused on; for reversible inhibition type components, more attention is paid to their sustained bacteriostatic ability. In the adjustment process, special attention is paid to the synergistic effect between different types of components, especially the complementary effect between irreversible damage type components and reversible inhibition type components, and through reasonable collocation, the effect of "1+1>2" is achieved.

[0073] The multi-composite bacteriostatic agent formula optimization method significantly improves the preservation stability and service life of water-based standard substances. By distinguishing the action mechanism of bacteriostatic components, accurately measuring multi-dimensional bacteriostatic effect parameters, and fully utilizing the synergistic effect of different types of bacteriostatic agents, the maximization of bacteriostatic effect and the minimization of bacteriostatic agent dosage are realized. Overcoming the limitations of traditional formula optimization which only considers single indicators such as bacteriostatic rate, by introducing multi-dimensional evaluation indicators such as bacteriostatic action reversibility and bacteriostatic durability, the optimization results are more comprehensive and scientific. The optimized composite bacteriostatic agent not only has stronger bacteriostatic effect and longer durability, but also reduces the influence of bacteriostatic agents on the physical and chemical properties of water-based standards, improving the stability and accuracy of the standard substances

[0074] In an alternative embodiment, the antagonistic-synergistic effect mapping relationship between the bacteriostatic components in the initial formula is constructed, and the multi-dimensional bacteriostatic effect data under different component ratio combinations are cross-compared and analyzed to identify the component ratio combinations that produce antagonistic effects, and a component interaction map is obtained, including:

[0075] For each bacteriostatic component in the multi-dimensional bacteriostatic effect data, the corresponding bacteriostatic rate data of the current bacteriostatic component under different ratio combinations is extracted, a ratio-bacteriostatic rate response surface is constructed, and the ratio interval where the bacteriostatic rate appears non-monotonic change in the ratio-bacteriostatic rate response surface is identified, which is marked as the effect reversal ratio interval;

[0076] For the bacteriostatic components with the effect reversal ratio interval, the ratio data of other bacteriostatic components coexisting with the bacteriostatic component in the effect reversal ratio interval is extracted, the correlation degree of the ratio data of other bacteriostatic components and the entry of the bacteriostatic component into the effect reversal ratio interval is calculated, and other bacteriostatic components with correlation degree exceeding the reference correlation degree are selected as effect reversal trigger components;

[0077] The bacteriostatic components with the effect reversal ratio interval and the corresponding effect reversal trigger components form an antagonistic-synergistic conversion component pair, and the ratio values of each bacteriostatic component in the effect reversal ratio interval boundary of the antagonistic-synergistic conversion component pair are extracted as the effect conversion critical ratio;

[0078] The antagonistic-synergistic conversion component pair and the corresponding effect conversion critical ratio are mapped to the ratio coordinate space to construct a component interaction map.

[0079] Multi-dimensional bacteriostatic effect data refers to testing the inhibition effect of the same group of bacteriostatic component ratio combinations on different indicator bacteria and collecting the formed data set. For example, for a formula containing components A, B and C, samples are prepared according to different ratios such as A:B:C = 1:2:3, 2:1:3, 3:2:1, etc., and the bacteriostatic rates of Staphylococcus aureus, Escherichia coli and Candida albicans are tested to obtain the bacteriostatic rate data of each ratio combination for each indicator bacteria.

[0080] For each bacteriostatic component in the multi-dimensional bacteriostatic effect data, the bacteriostatic rate data of the current bacteriostatic component under different ratio combinations can be extracted. Taking component A as an example, its ratio is increased from 0.1% to 1.0% with an interval of 0.1%, while the total amount of other components remains unchanged, and the corresponding bacteriostatic rates are measured as 18%, 25%, 33%, 42%, 46%, 43%, 41%, 38%, 36%, and 34%, respectively. Through these data, a ratio-bacteriostatic rate response surface can be constructed, that is, a two-dimensional curve with the ratio of component A as the horizontal coordinate and the bacteriostatic rate as the vertical coordinate. Observing the response surface, it can be found that when the ratio of component A increases from 0.1% to 0.5%, the bacteriostatic rate shows an upward trend, from 18% to 46%; and when the ratio continues to increase to 1.0%, the bacteriostatic rate decreases to 34%. Therefore, it can be identified that 0.5%-1.0% is the effect reversal ratio interval, and within this interval, the bacteriostatic rate shows a downward trend with the increase of the ratio, which violates the monotonic relationship of "ratio increase-effect enhancement".

[0081] For component A with an effect reversal ratio interval, the ratio data of other bacteriostatic components B, C coexisting with it in the effect reversal ratio interval (0.5%-1.0%) need to be extracted. Assuming that in the 5 groups of experiments with the ratio of component A being 0.5%-1.0%, the ratios of component B are 0.3%, 0.4%, 0.5%, 0.6%, and 0.7%, and the ratios of component C are 0.2%, 0.2%, 0.2%, 0.2%, and 0.2%, respectively. By calculating the correlation degree of the ratio changes of components B and C with the entry of component A into the effect reversal interval, for example, by calculating the ratio of the ratio change rate to the bacteriostatic rate change rate, the correlation degree of component B is 0.85, and the correlation degree of component C is 0.12. If the reference correlation degree is set as 0.5, component B is screened as the effect reversal trigger component, and component C is excluded because its correlation degree is lower than the reference value.

[0082] The component A with an effect reversal ratio interval and the effect reversal trigger component B form an antagonistic-synergistic conversion component pair (A, B). The ratio values of the component pair at the boundaries of the effect reversal ratio interval are extracted as the effect conversion critical ratio, that is, when the ratio of component A is 0.5% and the ratio of component B is 0.4%, the bacteriostatic effect reaches the best, which is the synergistic effect region; when the ratio of component A exceeds 0.5% and the ratio of component B exceeds 0.4%, it enters the antagonistic effect region. Therefore, the effect conversion critical ratio is A:B=0.5%:0.4%.

[0083] The antagonistic-synergistic conversion component pair (A, B) and the corresponding effect conversion critical proportion (0.5%, 0.4%) are mapped to a two-dimensional proportion coordinate space with the proportion of component A as the horizontal coordinate and the proportion of component B as the vertical coordinate to construct a component interaction map. In the map, the area where the proportion of component A is lower than 0.5% and the proportion of component B is lower than 0.4% is identified as the synergistic effect area, and the area where the proportion of component A is higher than 0.5% and the proportion of component B is higher than 0.4% is identified as the antagonistic effect area. Similarly, if it is found in the experiment that there is also an effect reversal phenomenon between components B and C, and components A and C, the antagonistic-synergistic conversion component pairs (B, C) and (A, C) and their effect conversion critical proportions can be analyzed by the same method to be mapped to the proportion coordinate space to form a complete component interaction map.

[0084] After the component interaction map is constructed, the optimization and adjustment of the composite bacteriostatic agent formula can be guided accordingly. The principle of optimization and adjustment is to avoid the antagonistic effect area and to try to keep the proportions of each component in the high bacteriostatic rate area of the synergistic effect area. The specific operation is to adjust the proportions of each bacteriostatic component to be located in the synergistic effect area in the map and near the dense area of the bacteriostatic rate contour, i.e. the area where the bacteriostatic effect is not sensitive to the change of the proportion, so as to improve the stability and robustness of the formula. At the same time, considering the cost factors of each component, the proportion of high-cost components is appropriately reduced under the premise of ensuring the bacteriostatic effect to improve the cost performance.

[0085] The component interaction map constructed by the above method can provide a scientific basis for the design and optimization of bacteriostatic formulations, effectively avoid the antagonistic effect between components, maximize the synergistic effect, and thus improve the bacteriostatic effect of the product under the same cost, providing strong support for the performance optimization and cost control of related products. It overcomes the limitations of simply superimposing the effects of each component in traditional formula optimization, reveals the nonlinear interaction law between components, and especially identifies the critical conditions of antagonistic-synergistic effect conversion. The optimized composite bacteriostatic agent formula can effectively avoid the antagonistic area between components, maximize the synergistic effect, significantly improve the bacteriostatic effect, and prolong the bacteriostatic durability.

[0086] In an optional embodiment, based on the component interaction map, the initial formula is reconstructed to eliminate antagonism to obtain a reconstructed formula, and the reconstructed formula is tested for bacteriostatic effect in a mixed bacterial environment to obtain mixed bacterial bacteriostatic effect data, including:

[0087] The antagonistic-synergistic conversion component pair is extracted from the component interaction map, the curvature change rate of the antagonistic effect intensity value of the antagonistic-synergistic conversion component pair with time in the bacterial environment is analyzed, the time point at which the curvature change rate exceeds the mutation judgment threshold is identified as the antagonistic effect mutation time point, and the residual concentration values of each bacteriostatic component corresponding to the antagonistic effect mutation time point are obtained to form an antagonistic elimination concentration combination.

[0088] The initial formula is decomposed into a pre-formulation unit containing the pre-bacteriostatic component and a post-formulation unit containing the post-bacteriostatic component, and the time interval reference value between the pre-formulation unit and the post-formulation unit is set as the time value corresponding to the antagonistic effect mutation time point to constitute the reconstituted formula;

[0089] The pre-formulation unit is applied to the mixed bacterial culture system, and the decay time difference required for the pre-bacteriostatic component concentration to decay to the corresponding residual concentration value in the antagonistic elimination concentration combination is calculated based on the consumption rate of each target bacterial species to the pre-bacteriostatic component and the initial bacterial population proportion;

[0090] The decay time difference is added to the time interval reference value to obtain an adjusted application time interval, and the application of the post-formulation unit is triggered at the time point corresponding to the adjusted application time interval, and the mixed bacterial species bacteriostatic effect data is obtained.

[0091] First, the antagonistic-synergistic conversion component pair is extracted from the component interaction map. By analyzing the effect conversion critical curve in the component interaction map, the component pair with antagonistic effect is identified. For example, the complex bacteriostatic agent commonly used in water-based standard substances may contain quaternary ammonium salt, isothiazolinone and organic acid bacteriostatic components. Through component interaction map analysis, it is found that quaternary ammonium salt and isothiazolinone have antagonistic effect under certain proportion conditions, which can be determined as the antagonistic-synergistic conversion component pair.

[0092] When analyzing the curvature change rate of the antagonistic effect intensity value of the antagonistic-synergistic conversion component pair changing with time in the bacterial environment, a time sequence experiment needs to be designed. In specific implementation, the antagonistic-synergistic conversion component pair is added to the water-based standard substance containing target microorganisms (such as mixed bacterial species of Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa) according to the initial formula proportion, and the bacteriostatic rate is measured every 2 hours, and the monitoring is continued for 48 hours. The antagonistic effect intensity value is defined as the absolute value of the difference between the actually measured bacteriostatic rate and the theoretically expected bacteriostatic rate (the bacteriostatic rate assuming no antagonistic effect). The data points of the obtained antagonistic effect intensity value changing with time are plotted into a curve, and the curvature and curvature change rate at each time point of the curve are calculated. The curvature is calculated by three-point method, that is, selecting three adjacent data points on the time axis, determining a circle through these three points, and the reciprocal of the radius of the circle is the curvature at the point. The curvature change rate refers to the difference between the curvature values of two adjacent time points divided by the time interval.

[0093] The time point where the curvature change rate exceeds the mutation determination threshold is identified as the antagonistic effect mutation time point. The mutation determination threshold is usually set to 3 times the average value of the curvature change rate. When the curvature change rate at a certain time point exceeds the threshold, it indicates that the antagonistic effect has changed significantly at that time point, and it is marked as the antagonistic effect mutation time point. At the antagonistic effect mutation time point, the residual concentration of each bacteriostatic component is determined, and these residual concentration values constitute the antagonistic elimination concentration combination. The determination of the residual concentration of the bacteriostatic component can use analysis methods such as high-performance liquid chromatography or ultraviolet spectrophotometry.

[0094] The decomposition of the initial formula into a pre-sequential formula unit containing pre-sequential bacteriostatic components and a post-sequential formula unit containing post-sequential bacteriostatic components is a key step in realizing antagonistic elimination reconstruction. The principle of decomposition is to divide the two bacteriostatic components in the antagonistic-synergistic conversion component pair into the pre-sequential formula unit and the post-sequential formula unit, respectively. The pre-sequential formula unit contains bacteriostatic components that are not prone to produce antagonistic effects or have weak antagonistic effects, such as organic acids; the post-sequential formula unit contains bacteriostatic components that are prone to produce antagonism with the pre-sequential components, such as certain quaternary ammonium salts. The time interval reference value between the pre-sequential formula unit and the post-sequential formula unit is set as the time value corresponding to the antagonistic effect mutation time point, constituting the reconstruction formula. If the antagonistic effect mutation time point is 12 hours after adding the initial formula, the time interval reference value is set to 12 hours.

[0095] When applying the pre-sequential formula unit to the mixed bacterial culture system, the consumption rate of each target bacterial species to the pre-sequential bacteriostatic component and the initial proportion of the initial bacterial community need to be considered. The consumption rate determination method is to add the pre-sequential bacteriostatic component to the culture system of each single bacterial species, take samples at regular intervals to determine the residual concentration of the bacteriostatic component, and calculate the concentration reduction value per unit time as the consumption rate. In a mixed bacterial environment, the comprehensive consumption effect of each bacterium on the bacteriostatic component is the weighted sum of the consumption effects of each single bacterium, with the weight being the initial proportion of each bacterium in the mixed bacterial community. Based on the comprehensive consumption effect, the decay time difference required for the pre-sequential bacteriostatic component concentration to decay to the corresponding residual concentration value in the antagonistic elimination concentration combination is calculated.

[0096] The decay time difference is added to the time interval reference value to obtain the adjusted application time interval. For example, if the time interval reference value is 12 hours and the decay time difference is 2 hours, the adjusted application time interval is 14 hours. The application of the post-sequential formula unit is triggered at the time point corresponding to the adjusted application time interval, i.e., the post-sequential formula unit is added 14 hours after the pre-sequential formula unit. In practical applications, the addition of the post-sequential formula unit can be realized through an automatic dosing system, which automatically adds the post-sequential bacteriostatic component to the water-based standard according to the preset application time interval.

[0097] After the application of the pre- and post-sequencing formula units is completed, the change in the microbial population in the mixed microbial system and the bacteriostatic effect are continuously monitored to obtain mixed microbial bacteriostatic effect data. The bacteriostatic effect data include the number of viable bacteria, the bacteriostatic rate, the residual concentration of each bacteriostatic component, and the like at each time point. These data can be used to evaluate the bacteriostatic effect and persistence of the reconstituted formula and provide a basis for further optimization.

[0098] The timing optimization of the bacteriostatic components is achieved by antagonistic elimination reconstitution, which overcomes the component antagonism caused by traditional one-time addition of composite bacteriostatic agents, significantly improving the bacteriostatic efficiency and persistence. This method accurately calculates the optimal application time interval based on the antagonistic effect mutation time point and the consumption dynamics of the bacteriostatic components, so that the action of each bacteriostatic component in the microbial population reaches the optimal cooperation. The optimized composite bacteriostatic agent exhibits strong broad-spectrum bacteriostatic ability in various water-based standard materials, effectively inhibiting the growth of various microorganisms and prolonging the shelf life of the standard materials. At the same time, this method reduces the total amount of bacteriostatic agent used, reduces the interference with the physical and chemical properties of the water-based standard materials, and improves the stability and accuracy of the standard materials.

[0099] In an alternative embodiment, the mixed microbial bacteriostatic effect data is subjected to stability evaluation, the bacteriostatic effect fluctuation characteristics of the reconstituted formula under different environmental conditions are identified, the environmental sensitive factors causing the bacteriostatic effect fluctuation are extracted, and an environmental adaptability model of the bacteriostatic components is constructed, including:

[0100] The mixed microbial bacteriostatic effect data is subjected to stability evaluation, and the bacteriostatic effect fluctuation amplitude value of the reconstituted formula under different environmental conditions is extracted as the bacteriostatic effect fluctuation characteristic through multi-environmental dimension cross analysis;

[0101] A nonlinear mapping relationship between the environmental condition parameters and the bacteriostatic effect fluctuation characteristics is constructed, the environmental sensitive factors are extracted through sensitivity quantitative analysis, the environmental disturbance response coefficients of each environmental sensitive factor to each bacteriostatic component are calculated and normalized to generate an environmental adaptability evaluation matrix;

[0102] Based on the environmental adaptability evaluation matrix, environmental adaptability degradation components with environmental disturbance response coefficients exceeding the disturbance threshold are identified, and candidate replacement components with homologous bacteriostatic target points are selected from the bacteriostatic component library;

[0103] When the synergistic compatibility evaluation value of the candidate replacement component and the remaining bacteriostatic components meets the synergistic effect condition, the candidate replacement component replaces the environmental adaptability degradation component, and the updated environmental adaptability evaluation matrix is used as the environmental adaptability model of the bacteriostatic components when the stability verification is passed.

[0104] In the present embodiment, when evaluating the stability of the antibacterial effect data of the mixed bacterial species, multi-environmental dimension cross analysis under different environmental conditions is required. In practice, five key environmental parameters, such as temperature, pH, light intensity, ionic strength, and oxidation-reduction potential, are selected as environmental dimensions. For each environmental parameter, three levels are set, such as temperature set to 4°C, 25°C, and 40°C. While keeping other environmental parameters unchanged at standard conditions, the antibacterial effect of the reconstituted formula under different levels of each environmental parameter is tested. The antibacterial effect test uses standard plate counting method. The mixed bacterial species (such as mixed bacterial solution of Escherichia coli, Pseudomonas aeruginosa, and Aspergillus niger) are inoculated in the water-based standard containing the reconstituted formula, and cultured under the corresponding environmental conditions for 72 hours. The viable cell count is determined every 24 hours, and the antibacterial rate is calculated. The antibacterial effect fluctuation value is defined as the difference between the maximum and minimum antibacterial rates of the same reconstituted formula under different levels of the same environmental parameter divided by the maximum antibacterial rate. This value reflects the sensitivity of the reconstituted formula to changes in the specific environmental parameter. The antibacterial effect fluctuation value is calculated for each of the five environmental parameters to form a five-dimensional antibacterial effect fluctuation feature vector.

[0105] When constructing the nonlinear mapping relationship between environmental condition parameters and antibacterial effect fluctuation characteristics, a radial basis function network modeling method is used. The network consists of an input layer, a hidden layer, and an output layer. The input layer nodes correspond to the five environmental condition parameters, and the output layer nodes correspond to the antibacterial effect fluctuation feature vector. The number of hidden layer nodes is set to twice the number of input nodes, and the hidden layer activation function is selected as the Gaussian radial basis function. Batch gradient descent method is used to train the network, and the loss function is mean square error. The training data is the antibacterial effect test results under different environmental conditions. The trained network is the nonlinear mapping model between environmental condition parameters and antibacterial effect fluctuation characteristics.

[0106] The specific method of extracting environmental sensitive factors through sensitivity quantification analysis is as follows: for the trained nonlinear mapping model, fix other input parameters unchanged, and only change the input value of one environmental parameter, and observe the change amplitude of the output antibacterial effect fluctuation feature vector. The sensitivity of the environmental parameter is defined as the average value of the change amplitude of the antibacterial effect fluctuation feature vector when the value of the environmental parameter changes within the allowed range. The five environmental parameters are sorted by sensitivity, and the top three environmental parameters with the highest sensitivity are determined as the environmental sensitive factors. For example, through analysis, it may be found that temperature, pH, and ionic strength are the three most significant environmental sensitive factors affecting the antibacterial effect of the reconstituted formula.

[0107] The environmental disturbance response coefficient of each environmental sensitive factor to each bacteriostatic component is calculated by measuring the change of bacteriostatic effect of a single bacteriostatic component under different levels of environmental sensitive factors. The environmental disturbance response coefficient is defined as the reciprocal of the ratio of the bacteriostatic rate of the bacteriostatic component under the most unfavorable condition of the environmental sensitive factor to the most favorable condition. The greater the coefficient value, the more sensitive the bacteriostatic component is to the change of the environmental sensitive factor. The environmental disturbance response coefficients of each bacteriostatic component to the three environmental sensitive factors are calculated to form a 3 x n matrix, where n is the number of bacteriostatic components. In order to facilitate comparison between different environmental sensitive factors, the data in each row of the matrix is normalized, i.e. each row of data is divided by the maximum value of the row to obtain the normalized environmental adaptability evaluation matrix.

[0108] Based on the environmental adaptability evaluation matrix, environmental adaptability deterioration components with environmental disturbance response coefficients exceeding the disturbance threshold are identified. The disturbance threshold is usually set to 0.8, and when the environmental disturbance response coefficient of any environmental sensitive factor corresponding to a bacteriostatic component in the environmental adaptability evaluation matrix exceeds the disturbance threshold, the bacteriostatic component is determined to be an environmental adaptability deterioration component. When screening candidate replacement components with homologous bacteriostatic target points from the bacteriostatic component library, the mechanism database of bacteriostatic components needs to be consulted to find other bacteriostatic components with the same or similar bacteriostatic mechanism as the environmental adaptability deterioration components. The bacteriostatic component library contains common bacteriostatic agents, such as quaternary ammonium salts, isothiazolinones, organic acids, aldehydes, etc., and each category contains multiple specific compounds. When screening, priority is given to compounds of the same type as the environmental adaptability deterioration components but with better environmental adaptability.

[0109] The synergistic compatibility of candidate replacement components with the remaining bacteriostatic components is evaluated by a comparative experiment method. The candidate replacement component is mixed with each bacteriostatic component (except the environmental adaptability deterioration component) in the reconstituted formula at a ratio of 1:1, and the bacteriostatic rate of the mixture is measured to calculate the synergistic factor. The synergistic factor is equal to the actual bacteriostatic rate of the mixture divided by the arithmetic mean of the bacteriostatic rates of the two components used alone. The synergistic effect condition is that the synergistic factor is greater than 1.2, indicating that the candidate replacement component has good synergistic effect with the remaining bacteriostatic components. When the synergistic factor of the candidate replacement component with all the remaining bacteriostatic components meets the synergistic effect condition, the candidate replacement component replaces the environmental adaptability deterioration component to form a new reconstituted formula.

[0110] When updating the environmental adaptability evaluation matrix, the environmental disturbance response coefficients of each bacteriostatic component to each environmental sensitive factor in the newly reconstructed formula are recalculated according to the foregoing method, and normalization processing is performed to form the updated environmental adaptability evaluation matrix. The stability verification refers to testing the bacteriostatic effect of the updated reconstructed formula under different combinations of the foregoing five environmental parameters, calculating the fluctuation amplitude value of the bacteriostatic effect, and determining that the stability verification is passed when all the fluctuation amplitude values are less than a preset threshold value (usually 0.2). When the stability verification is passed, the updated environmental adaptability evaluation matrix is used as the environmental adaptability model of the bacteriostatic component to guide the further optimization and application of the composite bacteriostatic agent formula.

[0111] By constructing the environmental adaptability model of the bacteriostatic component, stable and efficient bacteriostasis of the composite bacteriostatic agent under variable environmental conditions is achieved. This method systematically analyzes the influence law of environmental factors on the bacteriostatic effect, accurately identifies the environmental sensitive factors, scientifically evaluates the environmental adaptability of the bacteriostatic component, and provides a theoretical basis and technical guidance for the optimization of the composite bacteriostatic agent formula. Through the directional replacement and synergistic compatibility evaluation of the environmental adaptability degradation component, it is ensured that the optimized formula has stable bacteriostatic effect under different environmental conditions. This method overcomes the limitation of traditional formula optimization which only considers the bacteriostatic effect under standard conditions, significantly improves the environmental adaptability and bacteriostatic stability of the composite bacteriostatic agent, and prolongs the effective preservation period of the water-based standard substance.

[0112] In an alternative embodiment, a nonlinear mapping relationship between environmental condition parameters and bacteriostatic effect fluctuation characteristics is constructed, environmental sensitive factors are extracted through sensitivity quantitative analysis, environmental disturbance response coefficients of each environmental sensitive factor to each bacteriostatic component are calculated and normalized to generate an environmental adaptability evaluation matrix, including:

[0113] The environmental condition parameters and the corresponding bacteriostatic effect fluctuation characteristics under different environmental conditions are collected, the environmental condition parameters are taken as independent variables, and the bacteriostatic effect fluctuation characteristics are taken as dependent variables to construct a sample data set, and a nonlinear fitting algorithm is used to fit and train the sample data set to generate a nonlinear mapping function;

[0114] The sensitivity gradient values are obtained by calculating the partial derivatives of each environmental condition parameter in the nonlinear mapping relationship function, and the environmental condition parameters with absolute values of the sensitivity gradient values greater than the sensitivity threshold value are extracted as the environmental sensitive factors;

[0115] For each bacteriostatic component in the reconstructed formula, the bacteriostatic effect change rate of the bacteriostatic component is measured under the disturbance of each environmental sensitive factor, and the environmental disturbance response coefficient is obtained by ratio calculation of the bacteriostatic effect change rate and the disturbance amplitude of the corresponding environmental sensitive factor;

[0116] The environmental disturbance response coefficients of each bacteriostatic component under each environmental sensitive factor are normalized by maximum and minimum values in the dimension of bacteriostatic component, and the normalized environmental disturbance response coefficients are organized in a row-column structure to generate an environmental adaptability evaluation matrix.

[0117] When collecting environmental condition parameters and corresponding bacteriostatic effect fluctuation characteristics under different environmental conditions, multiple sets of environmental condition experiments need to be designed. In specific implementation, temperature, pH value, ionic strength, dissolved oxygen content and light intensity are selected as environmental condition parameters, and the variation range and value interval of each parameter are set. For example, the temperature range is 4-40°C, and 8 levels are set with an interval of 5°C; the pH value range is 5.0-9.0, and 9 levels are set with an interval of 0.5; the ionic strength range is 0.01-0.5 mol / L, and 6 levels are set; the dissolved oxygen content range is 2-8 mg / L, and 4 levels are set; the light intensity range is 0-5000 lux, and 5 levels are set. Based on the orthogonal experimental design method, 30 representative environmental condition combinations are selected from these level combinations for experiments. Under each set of environmental conditions, the reconstituted formula is added to the water-based standard, inoculated with standard mixed bacteria (such as mixed bacteria of E. coli, S. aureus and P. aeruginosa), and cultured for 72 hours. The viable cell count is determined every 12 hours, and the bacteriostatic rate is calculated. The bacteriostatic effect fluctuation characteristic is defined as the standard deviation of the bacteriostatic rate within the observation period. The environmental condition parameters are used as independent variables, and the bacteriostatic effect fluctuation characteristic is used as the dependent variable to construct a sample data set containing 30 groups of data.

[0118] When fitting and training the sample data set by a nonlinear fitting algorithm to generate a nonlinear mapping relationship function, a nonlinear fitting method based on neural network is used. In specific implementation, a three-layer feedforward neural network is constructed, the input layer contains 5 nodes corresponding to 5 environmental condition parameters, the hidden layer contains 10 nodes, and the output layer contains 1 node corresponding to the bacteriostatic effect fluctuation characteristic. The hyperbolic tangent function is used as the activation function of the hidden layer, and the linear function is used as the activation function of the output layer. The Levenberg-Marquardt algorithm is used to train the neural network, the learning rate is set to 0.01, the maximum number of iterations is set to 1000, and the error target is set to 0.001. The sample data set is divided into a training set and a validation set in a ratio of 8:2, the training set is used to train the neural network, and the validation set is used to evaluate the training effect. After training, the neural network represents the nonlinear mapping relationship function between the environmental condition parameters and the bacteriostatic effect fluctuation characteristic.

[0119] The sensitivity gradient value of each environmental condition parameter in the nonlinear mapping relationship function is obtained by partial derivative calculation. In specific implementation, other environmental condition parameters are fixed at the intermediate level, and only one environmental condition parameter is changed slightly within its value range. The ratio of the change amount of the antibacterial effect fluctuation characteristic to the change amount of the environmental condition parameter is the sensitivity gradient at that point. Select multiple evenly distributed points within the parameter value range for calculation, and take the average value as the overall sensitivity gradient value of the environmental condition parameter. The absolute value of the sensitivity gradient value represents the influence degree of the environmental condition parameter on the antibacterial effect fluctuation. The greater the absolute value, the more significant the influence. Set the sensitivity threshold as the median of the absolute values of the sensitivity gradient values of all environmental condition parameters, and extract the environmental condition parameters with absolute values of the sensitivity gradient values greater than the sensitivity threshold as environmental sensitive factors. For example, through analysis, it may be found that temperature, pH value and ionic strength are the three environmental sensitive factors that have the greatest influence on the antibacterial effect fluctuation.

[0120] For each antibacterial component in the reconstructed formula, the change rate of the antibacterial effect of the antibacterial component is measured under the disturbance of each environmental sensitive factor. The quaternary ammonium salt, isothiazolinone and organic acid antibacterial components contained in the reconstructed formula are taken as the research object, and the change of the antibacterial effect of each component under the disturbance of the environmental sensitive factor is measured. In specific implementation, the minimum effective concentration of each antibacterial component is selected and added to the water-based standard substance, inoculated with standard strains, and cultured for 24 hours under standard environmental conditions and environmental sensitive factor disturbance conditions, respectively, to measure the antibacterial rate. The environmental sensitive factor disturbance condition is set to 1.5 times or 0.5 times of the standard condition. If the standard temperature is 25°C, the disturbance condition is 37.5°C or 12.5°C. The change rate of the antibacterial effect is defined as the difference between the antibacterial rate under the disturbance condition and the antibacterial rate under the standard condition divided by the antibacterial rate under the standard condition. The change rate of the antibacterial effect is calculated by the ratio of the change rate of the antibacterial effect to the disturbance amplitude (i.e. 0.5 or 0.5) of the corresponding environmental sensitive factor, to obtain the environmental disturbance response coefficient. The environmental disturbance response coefficient reflects the sensitivity of the antibacterial component to the change of the environmental sensitive factor. The greater the coefficient value, the more sensitive the antibacterial component is to the change of the environmental sensitive factor.

[0121] When normalizing the environmental disturbance response coefficients of each antimicrobial component under various environmental sensitive factors according to the antimicrobial component dimension, for each antimicrobial component, the maximum and minimum values ​​of its environmental disturbance response coefficients under all environmental sensitive factors are found. The minimum value is subtracted from each original environmental disturbance response coefficient, and then divided by the difference between the maximum and minimum values ​​to obtain the normalized environmental disturbance response coefficient, with a value range of 0 to 1. The normalized environmental disturbance response coefficients are organized in a row-determinant structure, where rows represent different antimicrobial components and columns represent different environmental sensitive factors, forming an environmental adaptability evaluation matrix. This matrix visually displays the sensitivity of each antimicrobial component to different environmental sensitive factors, facilitating the identification of antimicrobial components with poor environmental adaptability and enabling targeted formulation adjustments.

[0122] The environmental adaptability evaluation matrix provides a scientific basis for subsequent optimization of compound antibacterial agent formulations. By analyzing the numerical distribution in the matrix, antibacterial components with high environmental sensitivity, i.e., components with large environmental disturbance response coefficients, can be identified. These components are prone to unstable antibacterial effects when environmental conditions change. Simultaneously, the matrix also reveals which environmentally sensitive factors have the greatest impact on specific antibacterial components, providing direction for targeted adjustments. If a quaternary ammonium salt antibacterial component is found to be particularly sensitive to pH, adding a pH buffer or replacing it with a similar antibacterial agent with better pH adaptability can be considered.

[0123] By constructing a nonlinear mapping relationship between environmental condition parameters and the fluctuation characteristics of antibacterial effects, the environmental adaptability of composite antibacterial agents was accurately quantified and evaluated. This method, based on data-driven and machine learning techniques, overcomes the blindness and limitations of traditional empirical formulation optimization, establishing a systematic mapping model between environmental factors and antibacterial effects. Environmentally sensitive factors extracted through sensitivity quantification analysis accurately reflect key environmental parameters affecting antibacterial effects, avoiding information redundancy in the optimization process. The calculation and normalization of environmental disturbance response coefficients allow for intuitive comparison and evaluation of the environmental adaptability of different antibacterial components, providing a clear direction for formulation adjustments. The generated environmental adaptability evaluation matrix, as a scientific tool for optimizing composite antibacterial agent formulations, significantly improves optimization efficiency and accuracy, giving the optimized composite antibacterial agent stronger environmental adaptability and stability, meeting the long-term preservation requirements of water-based standards in complex and variable environments, and improving the reliability and application range of the standards.

[0124] like Figure 2 As shown, the dynamic optimization and verification process of the antibacterial agent formulation in this embodiment is illustrated.

[0125] In one optional implementation, the antibacterial component in the reconstructed formulation is adjusted for stability compensation based on an environmental adaptability model to obtain an optimized formulation. Applying the optimized formulation to the target antibacterial scenario includes:

[0126] The environmental condition parameters of the target antibacterial scenario are obtained and the corresponding environmental sensitive factors are identified. The normalized environmental disturbance response coefficients of each antibacterial component under the corresponding environmental sensitive factors are extracted from the environmental adaptability model. The reciprocal of the normalized environmental disturbance response coefficients is used as the stability compensation weight to adjust the formulation ratio of each antibacterial component to obtain the compensation adjustment ratio.

[0127] Calculate the formula ratio deviation between the compensation adjustment ratio and the original formula ratio. When the formula ratio deviation exceeds the ratio adjustment threshold, update the actual addition amount of the antibacterial component according to the compensation adjustment ratio. Combine the updated actual addition amounts of each antibacterial component to form an optimized formula.

[0128] The optimized formula was prepared into an antibacterial agent solution according to the actual addition amount. The antibacterial agent solution was applied to the bacteria to be inhibited in a simulated environment with environmental conditions consistent with the target antibacterial scenario to complete the application verification.

[0129] In implementing this invention, professional environmental monitoring equipment is first used to collect environmental condition parameters of the target antibacterial scene, including temperature, pH value, ionic strength, dissolved oxygen content, and light intensity. Temperature monitoring uses a precision thermometer with a measurement range of 0-50℃ and an accuracy of ±0.1℃; pH monitoring uses a miniature pH electrode with a measurement range of 3-10 and an accuracy of ±0.05; ionic strength is obtained through conductivity measurement and conversion; dissolved oxygen content is measured using a dissolved oxygen meter; and light intensity is measured using a photometer. Each parameter is measured multiple times at different locations in the target scene, and the average value is taken as the representative value of that parameter. The acquired environmental condition parameters are compared with the environmental sensitivity factors determined in the environmental adaptability model to identify the corresponding environmental sensitivity factors. For example, in practical applications, it may be found that the target antibacterial scene has a temperature of 35℃, a pH value of 8.5, and an ionic strength of 0.4 mol / L, and these three parameters happen to be the environmental sensitivity factors determined in the previous environmental adaptability model.

[0130] When extracting the normalized environmental disturbance response coefficients of each antimicrobial component under corresponding environmental sensitive factors from the environmental adaptability model, it is necessary to consult the previously constructed environmental adaptability evaluation matrix. This matrix contains the sensitivity of each antimicrobial component to different environmental sensitive factors, expressed as normalized environmental disturbance response coefficients. For example, for antimicrobial components such as quaternary ammonium salts, isothiazolinones, and organic acids contained in the reconstructed formulation, their normalized environmental disturbance response coefficients under the three environmental sensitive factors of temperature, pH, and ionic strength can be extracted from the matrix. The normalized environmental disturbance response coefficients of quaternary ammonium salts under temperature, pH, and ionic strength are 0.85, 0.76, and 0.68, respectively; those of isothiazolinones are 0.72, 0.88, and 0.56, respectively; and those of organic acids are 0.65, 0.59, and 0.78, respectively.

[0131] When the reciprocal of the normalized environmental disturbance response coefficient is used as the stability compensation weight to adjust the formulation proportion of each bacteriostatic component, the weighted average method is adopted. The specific method is to take the reciprocal of the normalized environmental disturbance response coefficient of each bacteriostatic component under each environmental sensitive factor to obtain the stability compensation weight of the component under each environmental sensitive factor. Then calculate the importance weight of each environmental sensitive factor, which is usually determined according to the deviation degree (difference from the standard condition) of the environmental sensitive factor in the target bacteriostatic scene. Multiply the stability compensation weight of the bacteriostatic component under each environmental sensitive factor by the importance weight of the corresponding environmental sensitive factor, and sum to obtain the comprehensive stability compensation weight of the bacteriostatic component. Multiply the comprehensive stability compensation weight by the proportion of the bacteriostatic component in the original formulation, and then normalize to obtain the compensation adjustment proportion.

[0132] When calculating the formulation proportion deviation value of the compensation adjustment proportion and the original formulation proportion, the percentage difference method is adopted. Subtract the original formulation proportion from the compensation adjustment proportion, divide by the original formulation proportion, and multiply by 100% to obtain the formulation proportion deviation value. The proportion adjustment threshold is usually set to 15%, and when the absolute value of the formulation proportion deviation value exceeds the proportion adjustment threshold, it indicates that the bacteriostatic component needs to be significantly adjusted. At this time, update the actual addition amount of the bacteriostatic component according to the compensation adjustment proportion; when the absolute value of the formulation proportion deviation value does not exceed the proportion adjustment threshold, keep the original addition amount unchanged. For example, if the compensation adjustment proportion of quaternary ammonium salt is 1.25 times the original proportion, the deviation value is 25%, which exceeds the threshold of 15%, so update the actual addition amount according to the compensation adjustment proportion; if the compensation adjustment proportion of isothiazolinone is 1.08 times the original proportion, the deviation value is 8%, which does not exceed the threshold, so keep the original addition amount unchanged. Combine the updated actual addition amounts of each bacteriostatic component to form an optimized formulation.

[0133] When the optimized formulation is prepared into a bacteriostatic agent solution, the solubility characteristics and compatibility of each bacteriostatic component need to be considered. The specific preparation method is to first prepare each bacteriostatic component into a suitable concentration stock solution, then accurately measure each component stock solution according to the calculated actual addition amount, dilute to the required volume, and fully stir to obtain a bacteriostatic agent solution. During the preparation process, pay attention to control the pH value and temperature of the solution to ensure that each component is fully dissolved and does not react adversely with each other.

[0134] When applying the bacteriostatic agent solution to the bacteria to be inhibited in the simulated environment consistent with the target bacteriostatic scene environmental condition parameters to complete the application verification, a simulated environment needs to be established. Specifically, a constant temperature incubator or a water bath device is set up in the laboratory to control the temperature, a buffer solution is used to control the pH value, an appropriate amount of inorganic salt is added to adjust the ionic strength, the dissolved oxygen content is controlled by oxygenation or sealing, and a light device is used to adjust the light intensity, so that each environmental parameter is consistent with the environmental condition parameters of the target bacteriostatic scene. In the simulated environment, common microbial strains in the target bacteriostatic scene, such as Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa, are inoculated into sterile water-based standard materials, and adjusted to an appropriate initial bacterial concentration (usually 10 5 -10 6 CFU / ml). The prepared bacteriostatic agent solution is added to the water-based standard material inoculated with microorganisms, so that each component of the bacteriostatic agent reaches the concentration determined in the optimized formula. The viable cell count is determined by sampling at regular intervals, the bacteriostasis rate is calculated, and the bacteriostatic effect and durability of the optimized formula are evaluated. At the same time, the original formula is set as a control group to compare the bacteriostatic effect difference between the optimized and unoptimized formulas.

[0135] Through the stability compensation adjustment based on the environmental adaptability model, the intelligent and precise optimization of the composite bacteriostatic agent formula is realized. This method adjusts the bacteriostatic components differently according to the specific environmental conditions of the target bacteriostatic scene, effectively improving the adaptability and stability of the composite bacteriostatic agent in the actual application environment. By using the reciprocal of the environmental disturbance response coefficient as the stability compensation weight, the stability contribution of each bacteriostatic component under specific environmental conditions is quantified scientifically, making the formula adjustment more precise and reasonable. The optimized formula significantly improves the bacteriostatic effect of the composite bacteriostatic agent in extreme environmental conditions such as high temperature, alkalinity, or high salt, and prolongs the effective preservation period of the water-based standard material. At the same time, this method ensures the effectiveness and reliability of the optimized formula in actual application through application verification in a simulated environment, greatly reducing the risk of formula optimization failure and resource waste, improving the quality stability and comparability of water-based standard materials, and providing a strong guarantee for precise measurement and quality control in related fields. The technical route of this method is clear, the operation is simple, and the application range is wide.

[0136] In a second aspect of the embodiments of the present application, a multi-composite bacteriostatic agent formula optimization system for water-based standard materials is provided, and the system comprises:

[0137] A first unit is used to obtain an initial formula of a composite bacteriostatic agent to be optimized, perform multi-dimensional bacteriostatic effect testing on the initial formula, and obtain multi-dimensional bacteriostatic effect data;

[0138] The second unit is configured to build a mapping relationship of antagonistic-synergistic effects between bacteriostatic components in the initial formula, identify component ratio combinations that produce antagonistic effects by cross-comparison and analysis of multidimensional bacteriostatic effect data under different component ratio combinations, and obtain a component interaction map;

[0139] The third unit is configured to eliminate antagonism of the initial formula based on the component interaction map, obtain a reconstructed formula, and test bacteriostatic effect of the reconstructed formula under a mixed bacterial environment to obtain mixed bacterial bacteriostatic effect data.

[0140] The fourth unit is configured to evaluate stability of the mixed bacterial bacteriostatic effect data, identify fluctuation characteristics of bacteriostatic effect of the reconstructed formula under different environmental conditions, extract environmental sensitive factors causing the fluctuation of bacteriostatic effect, build an environmental adaptability model of the bacteriostatic components, perform stability compensation adjustment on the bacteriostatic components in the reconstructed formula based on the environmental adaptability model, and obtain an optimized formula.

[0141] In a third aspect, an electronic device is provided, including:

[0142] a processor;

[0143] a memory for storing processor-executable instructions;

[0144] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0145] In a fourth aspect, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0146] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored therein, which are used to perform various aspects of the present application.

[0147] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the formulation of a multiple complex antimicrobial agent for water-based standards, characterized in that, The method comprises the following steps: obtaining an initial formula of a composite bacteriostatic agent to be optimized, performing multidimensional bacteriostatic effect testing on the initial formula, and obtaining multidimensional bacteriostatic effect data; constructing an antagonistic-synergistic effect mapping relationship between bacteriostatic components in the initial formula, identifying component ratio combinations that produce antagonistic effects by cross-comparison and analysis of multidimensional bacteriostatic effect data under different component ratio combinations, and obtaining a component interaction map; based on the component interaction map, eliminating antagonism and reconstructing the initial formula to obtain a reconstructed formula, performing bacteriostatic effect testing of the reconstructed formula in a mixed bacterial environment, and obtaining mixed bacterial bacteriostatic effect data; performing stability evaluation on the mixed bacterial bacteriostatic effect data, identifying bacteriostatic effect fluctuation characteristics of the reconstructed formula under different environmental conditions, extracting environmental sensitive factors causing the bacteriostatic effect fluctuation, constructing an environmental adaptability model of the bacteriostatic components, and based on the environmental adaptability model, performing stability compensation adjustment on the bacteriostatic components in the reconstructed formula to obtain an optimized formula, and applying the optimized formula to a target bacteriostatic scenario; performing stability evaluation on the mixed bacterial bacteriostatic effect data, identifying bacteriostatic effect fluctuation characteristics of the reconstructed formula under different environmental conditions, extracting environmental sensitive factors causing the bacteriostatic effect fluctuation, and constructing an environmental adaptability model of the bacteriostatic components comprises: performing stability evaluation on the mixed bacterial bacteriostatic effect data, performing multi-environmental dimension cross-analysis under different environmental conditions, and extracting the bacteriostatic effect fluctuation amplitude value of the reconstructed formula under each environmental condition as the bacteriostatic effect fluctuation characteristic; constructing a nonlinear mapping relationship between environmental condition parameters and bacteriostatic effect fluctuation characteristics, extracting environmental sensitive factors through sensitivity quantitative analysis, calculating the environmental disturbance response coefficients of each environmental sensitive factor to each bacteriostatic component, and normalizing to generate an environmental adaptability evaluation matrix; based on the environmental adaptability evaluation matrix, identifying environmental adaptability degradation components with environmental disturbance response coefficients exceeding a disturbance threshold, and screening candidate replacement components with homologous bacteriostatic target points from a bacteriostatic component library; when the synergistic compatibility evaluation value of the candidate replacement component and the remaining bacteriostatic components meets the synergistic enhancement condition, the candidate replacement component replaces the environmental adaptability degradation component, and the environmental adaptability evaluation matrix is updated; when the stability verification passes, the updated environmental adaptability evaluation matrix is used as the environmental adaptability model of the bacteriostatic components.

2. The method of claim 1, wherein, obtaining an initial formula of a composite bacteriostatic agent to be optimized, performing multidimensional bacteriostatic effect testing on the initial formula, and obtaining multidimensional bacteriostatic effect data comprises: applying pulse-type concentration changes to each bacteriostatic component in the initial formula, and obtaining the bacteriostatic rate change trajectories corresponding to the concentration rising stage and the concentration falling stage of each bacteriostatic component; comparing the bacteriostatic rate change trajectories of each bacteriostatic component in the concentration rising stage and the concentration falling stage, and calculating the bacteriostatic action reversibility coefficients of each bacteriostatic component; according to the bacteriostatic action reversibility coefficients, dividing the bacteriostatic components in the initial formula into irreversible damage type components and reversible inhibition type components; The bacteriostatic rate, bacteriostatic persistence and synergistic coefficient of each component are determined to obtain the multi-dimensional bacteriostatic effect data including the reversible characteristics.

3. The method of claim 1, wherein, The antagonistic-synergistic effect mapping relationship between the bacteriostatic components in the initial formula is constructed, and the multi-dimensional bacteriostatic effect data under different component ratio combinations are cross-compared and analyzed to identify the component ratio combinations that produce antagonistic effects, and obtain the component interaction map, including: For each bacteriostatic component in the multi-dimensional bacteriostatic effect data, the bacteriostatic rate data corresponding to the current bacteriostatic component under different ratio combinations are extracted, and a ratio-bacteriostatic rate response surface is constructed to identify the ratio interval in which the bacteriostatic rate appears non-monotonic change in the ratio-bacteriostatic rate response surface, which is marked as the effect reversal ratio interval; For the bacteriostatic component with the effect reversal ratio interval, the ratio data of other bacteriostatic components coexisting with the bacteriostatic component in the effect reversal ratio interval are extracted, and the correlation degree of the ratio data of other bacteriostatic components and the entry of the bacteriostatic component into the effect reversal ratio interval is calculated, and other bacteriostatic components with a correlation degree exceeding a reference correlation degree are selected as effect reversal trigger components; The bacteriostatic components with the effect reversal ratio interval and the corresponding effect reversal trigger components are combined into an antagonistic-synergistic conversion component pair, and the ratio values of each bacteriostatic component in the antagonistic-synergistic conversion component pair at the boundaries of the effect reversal ratio interval are extracted as the effect conversion critical ratio; The antagonistic-synergistic conversion component pair and the corresponding effect conversion critical ratio are mapped to the ratio coordinate space to construct the component interaction map.

4. The method of claim 1, wherein, Based on the component interaction map, the antagonistic elimination reconstruction of the initial formula is performed to obtain a reconstructed formula, and the bacteriostatic effect test of the reconstructed formula in a mixed bacterial environment is performed to obtain mixed bacterial bacteriostatic effect data, including: From the component interaction map, the antagonistic-synergistic conversion component pair is extracted, the curvature change rate of the antagonistic effect intensity value of the antagonistic-synergistic conversion component pair in the bacterial environment is analyzed, the time point at which the curvature change rate exceeds a mutation determination threshold is identified as an antagonistic effect mutation time point, and the residual concentration values of each bacteriostatic component corresponding to the antagonistic effect mutation time point are obtained to form an antagonistic elimination concentration combination; The initial formula is decomposed into a pre-formula unit containing pre-bacteriostatic components and a post-formula unit containing post-bacteriostatic components, and the time interval reference value between the pre-formula unit and the post-formula unit is set as the time value corresponding to the antagonistic effect mutation time point to form a reconstructed formula; The pre-formula unit is applied to the mixed bacterial culture system, and the decay time difference required for the concentration of the pre-bacteriostatic component to decay to the corresponding residual concentration value in the antagonistic elimination concentration combination is calculated based on the consumption rate of each target bacterial species to the pre-bacteriostatic component and the initial bacterial population proportion; The decay time difference is added to the time interval reference value to obtain an adjusted application time interval, and the application of the post-formula unit is triggered at the time point corresponding to the adjusted application time interval to obtain the mixed bacterial bacteriostatic effect data.

5. The method of claim 1, wherein, The nonlinear mapping relationship between the environmental condition parameters and the fluctuation characteristics of the bacteriostatic effect is constructed, the environmental sensitive factors are extracted through sensitivity quantitative analysis, the environmental disturbance response coefficients of each environmental sensitive factor to each bacteriostatic component are calculated and normalized to generate an environmental adaptability evaluation matrix, including: Collecting environmental condition parameters and corresponding fluctuation characteristics of bacteriostatic effect under different environmental conditions, taking the environmental condition parameters as independent variables and the fluctuation characteristics of bacteriostatic effect as dependent variables to construct a sample data set, fitting and training the sample data set by a nonlinear fitting algorithm to generate a nonlinear mapping relationship function; Calculating the sensitivity gradient values of each environmental condition parameter in the nonlinear mapping relationship function by partial derivative, and extracting the environmental condition parameters with absolute values greater than the sensitivity threshold as environmental sensitive factors; For each bacteriostatic component in the reconstructed formula, the bacteriostatic effect change rate of the bacteriostatic component under the disturbance of each environmental sensitive factor is measured, and the environmental disturbance response coefficient is obtained by ratio calculation of the bacteriostatic effect change rate and the disturbance amplitude of the corresponding environmental sensitive factor; The environmental disturbance response coefficients of each bacteriostatic component under each environmental sensitive factor are normalized by maximum and minimum values according to the dimension of bacteriostatic component, and the normalized environmental disturbance response coefficients are organized in a row-column structure to generate an environmental adaptability evaluation matrix.

6. The method of claim 1, wherein, Based on the environmental adaptability model, the bacteriostatic components in the reconstructed formula are adjusted for stability compensation to obtain an optimized formula, and the optimized formula is applied to the target bacteriostatic scene, including: Obtaining the environmental condition parameters of the target bacteriostatic scene and identifying the corresponding environmental sensitive factors, extracting the normalized environmental disturbance response coefficients of each bacteriostatic component under the corresponding environmental sensitive factors from the environmental adaptability model, and taking the inverse of the normalized environmental disturbance response coefficient as the stability compensation weight to adjust the formula proportion of each bacteriostatic component to obtain a compensation adjustment proportion; Calculating the formula proportion deviation value of the compensation adjustment proportion and the original formula proportion, and updating the actual amount of each bacteriostatic component according to the compensation adjustment proportion when the formula proportion deviation value exceeds the proportion adjustment threshold, and combining the updated actual amounts of each bacteriostatic component to form an optimized formula; The optimized formula is prepared into a bacteriostatic agent solution in a simulated environment with the same environmental condition parameters as the target bacteriostatic scene, and the bacteriostatic agent solution is applied to the bacteria to be inhibited to complete the application verification.

7. A multiplexed antimicrobial formulation optimization system for water-based standard, for implementing the method of any one of claims 1-6, characterized in that, Including: The first unit is used to obtain an initial formula of a composite bacteriostatic agent to be optimized, and to perform multidimensional bacteriostatic effect testing on the initial formula to obtain multidimensional bacteriostatic effect data; The second unit is used to construct an antagonistic-synergistic effect mapping relationship among bacteriostatic components in the initial formula, to identify component proportion combinations that produce antagonistic effects by cross-comparison analysis of multidimensional bacteriostatic effect data under different component proportion combinations, and to obtain a component interaction map; The third unit is used to reconstruct the initial formula based on the component interaction map to obtain a reconstructed formula, and to perform bacteriostatic effect testing on the reconstructed formula in a mixed bacterial environment to obtain mixed bacterial bacteriostatic effect data. The fourth unit is configured to perform stability evaluation on the mixed bacteria inhibition effect data, identify fluctuation characteristics of the inhibition effect of the reconstructed formula under different environmental conditions, extract environmental sensitive factors causing the fluctuation of the inhibition effect, construct an environmental adaptability model of the bacteriostatic component, perform stability compensation adjustment on the bacteriostatic component in the reconstructed formula based on the environmental adaptability model, and obtain an optimized formula. The optimized formula is applied to a target bacteriostatic scene.

8. An electronic device, comprising: Comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method of any one of claims 1 to 6.

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