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CN-121999879-A - Microbial cultivation experimental data cloud processing system

CN121999879ACN 121999879 ACN121999879 ACN 121999879ACN-121999879-A

Abstract

The invention discloses a microbial culture experimental data cloud processing system, which relates to the technical field of microbial culture data processing, and comprises an experimental data acquisition module, a data processing module and a data processing module, wherein the experimental data acquisition module is used for generating a standardized microbial culture data set; the system comprises a growth rule mining module, an influence factor analysis module, an abnormality detection module, an experiment scheme recommendation module and a control module, wherein the growth rule mining module is used for carrying out time sequence trend analysis and core feature extraction, dividing a microorganism growth stage and extracting key parameters of each stage to generate a microorganism growth rule feature set, the influence factor analysis module is used for carrying out microorganism growth state influence factor analysis to obtain an influence factor analysis report, the abnormality detection module is used for outputting abnormality early warning and incentive analysis results, and the experiment scheme recommendation module is used for generating an optimal culture scheme suggestion for adapting to target microorganisms. The invention solves the technical problems of scattered processing of traditional microorganism culture data and inaccurate excavation of the growth rule by accurately excavating the core technology of microorganism growth rule, multi-factor associated modeling analysis synergistic effect, prospective abnormality detection, incentive positioning and intelligent generation of a culture scheme.

Inventors

  • ZHAO XIANMING

Assignees

  • 纽勤生物科技(上海)有限公司

Dates

Publication Date
20260508
Application Date
20251231

Claims (10)

  1. 1. The microbial cultivation experimental data cloud processing system is characterized by comprising an experimental data acquisition module, a growth rule mining module, an influence factor analysis module, an anomaly detection module and an experimental scheme recommendation module; The experiment data acquisition module is used for acquiring multisource experiment data and culture environment auxiliary data in the microorganism culture process and generating a standardized microorganism culture data set; The growth rule mining module is used for carrying out time sequence trend analysis and core feature extraction based on the standardized microorganism culture data set, dividing microorganism growth stages and extracting key parameters of each stage to generate a microorganism growth rule feature set; The influence factor analysis module is used for analyzing the influence factors of the growth states of the microorganisms based on the characteristic set of the growth rules of the microorganisms and obtaining an influence factor analysis report; the abnormality detection module is used for establishing a normal growth reference model based on the standardized microorganism culture data set and the microorganism growth rule characteristic set, reporting and positioning abnormality causes through influence factor analysis, and outputting abnormality early warning and cause analysis results; the experimental scheme recommendation module is used for generating an optimal culture scheme recommendation adapting to the target microorganism based on the influence factor analysis report and the incentive analysis result.
  2. 2. The microbial cultivation experiment data cloud processing system of claim 1, wherein the growth rule mining module comprises: The time sequence feature depth extraction unit is used for extracting time sequence data of three core growth indexes of thallus concentration, metabolite content and thallus activity from a standardized microorganism culture data set, analyzing local variation trend of the time sequence by adopting a sliding window method, extracting three dynamic features of trend slope, fluctuation amplitude and peak value feature, calculating four statistical features of mean value, variance, growth rate and accumulation amount of each core index by adopting a statistical analysis algorithm, and constructing a multi-dimensional time sequence feature matrix; The intelligent modeling unit for the growth stage is used for identifying inflection points and stage demarcation points of a growth curve by adopting a clustering algorithm based on a multi-dimensional time sequence feature matrix, automatically dividing four growth stages of a slow stage, a logarithmic stage, a stable stage and a decay stage, constructing a stage exclusive feature model aiming at each growth stage, and quantifying key parameters of each stage by adopting a Logistic growth model, wherein a model formula is as follows: , In the middle of Is that The concentration of the cells at the moment, For the maximum cell concentration in the stationary phase, In order to be a log-phase growth rate constant, For the time of the cultivation, the culture medium was used, The time corresponding to the inflection point of the growth curve, Is a natural constant; And establishing a connection relation model among all growth stages through a time sequence association algorithm, revealing triggering conditions and characteristic change rules of stage transition, and integrating to form a microorganism growth rule characteristic set containing stage characteristics, parameter thresholds and association rules.
  3. 3. The microbial cultivation experiment data cloud processing system of claim 2, wherein the growth rule mining module further comprises: The growth rule dynamic optimization unit is used for introducing a time attenuation factor, carrying out weighting treatment on the continuously acquired time sequence data, strengthening the influence weight of recent data on a growth rule model, updating a growth rule feature set based on a sliding time window, adapting the dynamic change of the growth state of microorganisms in real time, and mining the commonality and the specificity of the growth rule of the same type of microorganisms by comparing the growth rule feature sets of different batches of experiments.
  4. 4. The microbial cultivation experiment data cloud processing system of claim 3, wherein the influencing factor analysis module comprises: The multi-factor association modeling unit is used for selecting five key culture condition parameters of temperature, pH value, culture medium formula, inoculum size and dissolved oxygen concentration, establishing a mapping relation with a microorganism growth rule feature set, quantifying the association strength of each culture condition parameter and a growth key parameter by adopting a correlation analysis algorithm, wherein the growth key parameter comprises a growth rate, peak value concentration and stage duration, screening core influence factors, constructing a multi-factor interaction model, analyzing the synergy and antagonism effects among the core influence factors, and determining the comprehensive influence rule of different factor combinations on the growth state of microorganisms.
  5. 5. The microbial cultivation experiment data cloud processing system of claim 4, wherein the influencing factor analysis module further comprises: The optimal condition interval calibration unit is used for traversing the value range of core influence factors by adopting a gradient analysis algorithm based on a multi-factor correlation model, determining an optimal value interval when each factor acts independently, optimizing optimal parameter configuration of multi-factor combination by combining interaction rules to form an optimal condition combination considering single-factor optimization and multi-factor cooperation, verifying the reliability of the optimal condition interval by a saliency detection algorithm, marking interval boundaries and adjustment rules, supplementing condition adaptation suggestions under three culture targets of thallus proliferation, metabolite efficient extraction and strain directional domestication, and forming a complete influence factor analysis report.
  6. 6. The microbial cultivation experiment data cloud processing system of claim 5, wherein the anomaly detection module comprises: The reference model construction unit is used for constructing a normal growth reference model comprising a normal fluctuation range of key parameters of each growth stage and a time sequence variation trend threshold value based on a characteristic set of a microorganism growth rule and normal culture data of historical similar microorganisms, setting abnormal judgment thresholds of different culture stages by combining an optimal condition interval in an influence factor analysis report to form an abnormal judgment system with staged and index division, and configuring an updating mechanism for the reference model to continuously optimize model parameters through newly added normal experimental data.
  7. 7. The microbial cultivation experiment data cloud processing system of claim 6, wherein the anomaly detection module further comprises: The abnormality identification and inducement positioning unit is used for comparing the time sequence data of the current microorganism culture with a normal growth reference model in real time, calculating a deviation quantification value, triggering abnormality early warning when the deviation exceeds a preset threshold value, analyzing whether a core influence factor deviates from an optimal interval based on an influence factor analysis report and related culture condition parameter change data of an abnormality occurrence period, judging whether the abnormality is caused by stage transition abnormality by combining a growth stage connection relation model, and outputting abnormality early warning and inducement analysis results comprising abnormality grades, occurrence nodes, inducement details and emergency treatment suggestions, wherein the abnormality inducement types are environment parameter fluctuation, operation step deviation, medium component abnormality and inoculum size deviation.
  8. 8. The microbial cultivation experiment data cloud processing system of claim 7, wherein the anomaly detection module further comprises: The abnormal trend pre-judging unit is used for analyzing the change trend of the growth index by adopting a trend prediction algorithm based on the real-time sequence data of the standardized microorganism culture data set, comparing the trend prediction result with the expected trend of the normal growth reference model, identifying potential risks possibly causing the occurrence of the abnormality in the future, judging whether the potential risks are derived from the slow drift of the culture condition parameters by combining the influence factor analysis report, and outputting risk prompt and preventive measure suggestion in advance to realize the prospective early warning of the abnormality.
  9. 9. The microbial cultivation experiment data cloud processing system of claim 8, wherein the experiment protocol recommendation module comprises: The intelligent scheme generating unit is used for matching successful experimental cases consistent with the current microorganism types and culture targets from a cloud-stored historical experimental database through abnormal early warning and risk points and emergency treatment suggestions in the analysis results based on optimal condition combinations in influence factor analysis reports and condition adaptation suggestions under three types of culture targets, extracting core configuration information of culture medium component ratios, environment parameter staged control curves, inoculation operation specifications and process sampling nodes in the cases, determining culture medium control parameters and inoculation quantity by combining the optimal condition combinations, supplementing index judgment threshold values, sampling time intervals and data records of process monitoring, and integrating to form a microorganism culture scheme comprising culture condition parameters, operation step specifications, monitoring schemes and emergency pretreatment measures.
  10. 10. The microbial cultivation experiment data cloud processing system of claim 9, wherein the experiment protocol recommendation module further comprises: the scheme dynamic optimization unit is used for receiving updated standardized microorganism culture data sets and updated growth rule feature sets in real time in the microorganism culture process, comparing the actual execution effect of the current culture scheme with the difference of an expected growth target, adopting a gradient descent algorithm to adjust key parameters in the scheme aiming at the difference data, optimizing switching nodes and specific numerical values of control parameters of each growth stage, feeding the optimized scheme back to the historical experiment database, and updating the parameter weight of a scheme recommendation model.

Description

Microbial cultivation experimental data cloud processing system Technical Field The invention relates to the technical field of microorganism culture data processing, in particular to a microorganism culture experimental data cloud processing system. Background The microbial cultivation is a core link in the fields of bioengineering, medicine research and development, food fermentation and the like, the cultivation effect directly determines the success and failure of experiments and the industrialization efficiency, and the accurate treatment of experimental data, the deep excavation of growth rules and the scientific optimization of cultivation schemes are key to improving the cultivation quality. The current microbial culture experimental data processing has the following problems that data processing is distributed, a sensor, an instrument and manually recorded multi-source data are manually arranged in a multi-dependence manner, a standardized integration mechanism is lacked, data timing sequence confusion and correlation are poor, systematic analysis is difficult to support, growth rule mining is not accurate enough, a traditional method is divided into growth stages in a multi-dependence manner through experience, key parameters of the stages are not extracted through a quantitative model, real-time change of a microbial growth state cannot be adapted, cooperation and antagonism effects among multiple factors are ignored, a scientific optimal culture condition interval is difficult to determine, abnormal detection lag is mostly found after the abnormal growth is caused, prospective prejudgment on abnormal trend is lacked, and precise positioning inducement is not realized. How to solve the technical problem is a technical problem that a person skilled in the art needs to overcome. Disclosure of Invention In order to solve the technical problems, the technical scheme solves the problems by providing the microbial culture experimental data cloud processing system. In order to achieve the above purpose, the invention adopts the following technical scheme: The microbial culture experimental data cloud processing system comprises an experimental data acquisition module, a growth rule mining module, an influence factor analysis module, an anomaly detection module and an experimental scheme recommendation module; The experiment data acquisition module is used for acquiring multisource experiment data and culture environment auxiliary data in the microorganism culture process and generating a standardized microorganism culture data set; The growth rule mining module is electrically connected with the experimental data acquisition module and is used for carrying out time sequence trend analysis and core feature extraction based on the standardized microorganism culture data set, dividing the microorganism growth stage and extracting key parameters of each stage to generate a microorganism growth rule feature set; the influence factor analysis module is electrically connected with the growth rule mining module and is used for analyzing the influence factors of the growth states of the microorganisms based on the characteristics set of the growth rule of the microorganisms and obtaining an influence factor analysis report; The abnormal detection module is electrically connected with the experimental data acquisition module and the growth rule mining module and is used for establishing a normal growth reference model based on the standardized microorganism culture data set and the microorganism growth rule characteristic set, reporting and positioning abnormal causes through influence factor analysis, and outputting abnormal early warning and cause analysis results; The experimental scheme recommendation module is electrically connected with the influence factor analysis module and the abnormality detection module and is used for generating an optimal culture scheme suggestion adapting to the target microorganism based on the influence factor analysis report and the incentive analysis result. Preferably, the growth rule mining module includes: The time sequence feature depth extraction unit is used for extracting time sequence data of three core growth indexes of thallus concentration, metabolite content and thallus activity from a standardized microorganism culture data set, analyzing local variation trend of the time sequence by adopting a sliding window method, extracting three dynamic features of trend slope, fluctuation amplitude and peak value feature, calculating four statistical features of mean value, variance, growth rate and accumulation amount of each core index by adopting a statistical analysis algorithm, and constructing a multi-dimensional time sequence feature matrix; The intelligent modeling unit for the growth stage is used for identifying inflection points and stage demarcation points of a growth curve by adopting a clustering algorithm based on a multi-dimensional time sequence feature matri