A construction method for an integrated model for bacterial detection

Abstract

The present invention relates to the technical field of data model construction, and in particular to a method for constructing an integrated model for bacteria detection. The method comprises the following steps: obtaining a raw bacterial data set; performing data denoising on the raw bacterial data set to obtain bacteria data to be detected; pre-classifying the bacteria data to be detected to obtain pre-classified bacterial data; performing data batch identification on the pre-classified bacterial data to obtain batch data to be detected; performing batch deviation detection on the batch data to be detected to obtain bacteria batch deviation data; and performing batch correction on the bacteria data to be detected based on the bacteria batch deviation data to obtain bacteria correction data. The present invention enhances the generalization capability of the integrated model for bacteria detection through data processing technology, feature mapping technology, intelligent feedback technology, and adaptive adjustment technology, so as to provide a rapid detection response to the bacteria data to be detected, thereby improving the efficiency and accuracy of bacteria detection.

Description

Technical Field

[0001] The present invention relates to the technical field of data model construction, and in particular to a method for constructing an integrated model for bacteria detection. Background Art

[0002] Early bacterial detection relied on traditional culture and biochemical identification methods, which, while mature, were time-consuming. With the development of molecular biology, bacterial identification techniques based on DNA sequence analysis have gradually emerged, providing more accurate bacterial classification information. The emergence of microfluidics has revolutionized bacterial detection. By integrating microfluidic control and multiple optoelectronic sensing monitoring modules, microfluidics enables dynamic monitoring of bacteria and their interactions. In recent years, with the development of artificial intelligence, machine learning and deep learning algorithms have been widely used in bacterial detection, particularly in the processing and analysis of bacterial detection data. Specifically, through feature selection and model training, integrated bacterial detection models are constructed to identify bacteria from high-dimensional, complex bacterial population data. However, existing integrated bacterial detection models have low generalization capabilities for complex bacterial detection data, making it difficult to achieve rapid detection responses, resulting in low bacterial detection efficiency and accuracy. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for constructing an integrated model for bacterial detection to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing an integrated model for bacteria detection is provided, the method comprising the following steps:

[0005] Step S1: obtaining a bacterial original data set; performing data denoising on the bacterial original data set to obtain bacterial data to be detected; performing pre-classification on the bacterial data to be detected to obtain pre-classified bacterial data; performing data batch identification on the pre-classified bacterial data to obtain batch data to be detected; performing batch deviation detection on the batch data to be detected to obtain bacterial batch deviation data; performing batch correction on the bacterial data to be detected based on the bacterial batch deviation data to obtain bacterial correction data;

[0006] Step S2: performing bacterial characteristic identification on the pre-classified bacterial data according to the bacterial correction data to obtain bacterial characteristic data; performing bacterial behavior pattern determination on the bacterial characteristic data to obtain bacterial behavior pattern data; performing bacterial type characteristic identification on the pre-classified bacterial data according to the bacterial behavior pattern data to generate type characteristic data; performing type characteristic mapping on the bacterial data to be detected based on the type characteristic data to obtain bacterial type mapping data;

[0007] Step S3: constructing a bacteria detection integrated model for the bacteria to be detected data based on the bacteria correction data and the bacteria type mapping data to obtain a bacteria detection integrated pre-model; enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; performing adaptive generalization adjustment on the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data; performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model;

[0008] Step S4: Perform performance evaluation on the bacteria detection integrated training model to generate training model performance evaluation data; perform intelligent iterative optimization on the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model; perform bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

[0009] The present invention significantly improves the quality and accuracy of the original bacterial data set through data denoising, ensuring the effectiveness of subsequent analysis; pre-classification processing allows bacterial data to be grouped according to specific characteristics; data batch identification ensures that data from different batches can be considered independently, thereby reducing the impact of batch differences on analysis results; batch deviation detection further reveals the specific differences between batches, providing a basis for batch correction; batch correction guided by bacterial batch deviation data effectively reduces batch variation and improves data consistency and reliability. The use of bacterial correction data makes bacterial characteristic identification more accurate and provides accurate basic data for subsequent bacterial behavior pattern analysis; the determination of bacterial behavior patterns clarifies the biological characteristics and potential behavioral laws of bacteria; the generation of type characteristic data provides clear standards for bacterial classification, making bacterial type characteristic identification more accurate. The combination of bacterial correction data and bacterial type mapping data provides a data foundation for constructing a bacterial detection integrated model, ensuring the accuracy and reliability of the model; the generalization ability of the integrated model is enhanced, improving the model's detection ability for unknown data, thereby enhancing the model's practicality; adaptive generalization adjustment further optimizes the model's generalization performance, ensuring that the model can maintain stable performance on different data sets; rapid detection response integrated training of the bacterial detection integrated pre-model enables the model to quickly adapt to new data, improving the model's response speed and detection efficiency. Performance evaluation provides an objective evaluation standard for the bacterial detection integrated training model, ensuring the optimization and improvement points of the model; intelligent iterative optimization based on performance evaluation data further improves the performance of the model, ensuring the efficiency and accuracy of the model in practical applications; ultimately, the application of the bacterial detection integrated model achieves accurate detection of bacterial data to be detected, generates detailed bacterial detection reports, and provides strong support for bacterial identification, classification, and research. Therefore, the present invention enhances the generalization ability of the bacterial detection integrated model through data processing technology, feature mapping technology, intelligent feedback technology, and adaptive adjustment technology, enabling rapid detection response to bacterial data to be detected, thereby improving bacterial detection efficiency and accuracy.

[0010] Preferably, step S2 includes the following steps:

[0011] Step S21: applying multi-parameter statistics to the bacterial correction data to obtain bacterial statistical data; extracting bacterial biomarkers from the bacterial statistical data to obtain bacterial biomarker data; and identifying bacterial characteristics of the pre-classified bacterial data based on the bacterial biomarker data to obtain bacterial characteristic data;

[0012] Step S22: grouping the bacterial characteristic data to obtain bacterial characteristic grouping data; identifying bacterial populations based on the bacterial characteristic grouping data to obtain bacterial population data; performing behavioral classification on the bacterial population data to generate bacterial behavior classification data; determining behavioral patterns of the bacterial characteristic data based on the bacterial behavior classification data to obtain bacterial behavior pattern data;

[0013] Step S23: extracting multi-modal standard rules from the bacterial behavior pattern data to generate bacterial multi-modal standard rules; comparing the pre-classified bacterial data with the pattern rules one by one according to the bacterial multi-modal standard rules to obtain bacterial key feature data; and identifying bacterial type features from the bacterial key feature data to generate type feature data;

[0014] Step S24: Associating the bacteria data to be detected with bacteria types through the type feature data to obtain bacteria type association data; quantifying the degree of association of the bacteria type association data to generate type association quantification data; assigning type labels to the bacteria data to be detected based on the type association quantification data to obtain labels of the data to be detected; performing type feature mapping on the labels of the data to be detected based on the type feature data to obtain bacteria type mapping data.

[0015] The present invention can reveal the statistical laws and distribution characteristics in the data by applying multi-parameter statistics to bacterial correction data; extract bacterial biomarkers from bacterial statistical data to clearly identify bacterial data with biologically significant indicators; use bacterial biomarker data to perform characteristic recognition on pre-classified bacterial data to accurately identify the characteristics of bacteria; group bacterial characteristic data to classify bacteria with similar characteristics; identify bacterial populations based on bacterial characteristic grouping data to divide bacteria into different bacterial populations; further, perform performance behavior classification on bacterial population data to understand the behavioral characteristics of different bacterial populations; determine behavioral patterns of bacterial characteristic data based on bacterial behavioral classification data , which can reveal the behavioral laws of bacteria; the extraction of bacterial behavior pattern data can be used to obtain multimodal standard rules; the pre-classified bacterial data are compared one by one according to the bacterial multimodal standard rules to clearly identify the key characteristics of bacteria; the bacterial type of the bacterial data to be detected is associated with the type feature data, so that the bacterial type of the bacterial data to be detected is associated with the feature type; the association degree of the bacterial type association data is quantified, and a quantitative evaluation of the bacterial type association is provided; the type label is assigned to the bacterial data to be detected according to the type association quantification data, so as to realize the rapid identification of the bacterial type; the type feature mapping of the data label to be detected is performed based on the type feature data, which provides an intuitive mapping for the classification of bacteria.

[0016] Preferably, step S3 includes the following steps:

[0017] Step S31: performing data uniform normalization on the bacteria correction data to obtain bacteria uniform correction data; performing mapping enhancement on the bacteria type mapping data to generate type mapping enhancement data; demarcating the bacteria to-be-detected sample groups based on the bacteria uniform correction data and the type mapping enhancement data to generate to-be-detected sample group data; performing type feature mapping on the to-be-detected sample group data based on the type mapping enhancement data to generate type feature mapping data; performing type feature label extraction on the type feature mapping data to generate type feature label data;

[0018] Step S32: performing bacterial colony feature recognition on the bacterial uniformity correction data to obtain bacterial colony feature data; performing colony distribution detection on the bacterial colony feature data to obtain bacterial colony distribution data; performing colony type count on the type feature corresponding data using the bacterial colony distribution data to obtain colony type quantity data; performing colony number label extraction on the colony type quantity data to generate colony number label data;

[0019] Step S33: performing a bacterial growth status analysis on the bacterial colony characteristic data to obtain bacterial growth status data; extracting colony status labels from the bacterial growth status data to generate colony status label data; constructing a bacterial detection integrated model for the bacteria to be detected data based on the type characteristic label data, colony quantity label data, and colony status label data to obtain a bacterial detection integrated pre-model;

[0020] Step S34: enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; and adaptively generalizing the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data;

[0021] Step S35: Performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model.

[0022] The present invention enhances the consistency and comparability of bacterial correction data through data uniform normalization, providing a standardized basis for subsequent analysis; mapping enhancement processing improves the discrimination of bacterial type mapping data, making the identification of bacterial types more accurate; combining bacterial uniform correction data and type mapping enhancement data, the bacterial samples to be detected are grouped to ensure the accuracy of sample classification; type feature correspondence is performed on the group data of the sample to be detected according to the type mapping enhancement data, so that the type mapping enhancement data establishes a direct connection with the group data of the sample to be detected, providing a basis for feature extraction; type feature label extraction further clarifies the key attributes of bacterial samples; bacterial colony feature recognition improves the ability to recognize bacterial colony morphology, and colony distribution detection clarifies the spatial distribution of bacterial colonies, providing a basis for counting the number of colony types; Species count enables quantification of the species and quantity of different colonies, and colony quantity label extraction clarifies the quantitative characteristics of the colonies; bacterial growth status analysis helps to evaluate bacterial growth, and colony status label extraction provides key information on colony growth status for subsequent model construction, thereby enhancing the model's ability to identify bacterial growth; type feature label data, colony quantity label data, and colony status label data are integrated to form a bacterial detection framework; the generalization ability of the bacterial detection integrated pre-model is enhanced, which improves the model's adaptability to unknown data and detection accuracy; adaptive generalization adjustment further optimizes the model's generalization performance, ensuring the model's stability and reliability on different data sets; rapid detection response integrated training enables the bacterial detection integrated training model to efficiently respond to new detection needs, improving the model's application efficiency and practicality.

[0023] Preferably, step S33 includes the following steps:

[0024] Step S331: performing colony contour recognition on the bacterial colony characteristic data to generate bacterial colony contour data; performing morphological measurement on the bacterial colony contour data to obtain bacterial morphological data;

[0025] Step S332: performing a colony two-dimensional morphological image scan on the bacterial colony characteristic data based on the bacterial morphological data to generate a colony two-dimensional morphological image; and performing a colony three-dimensional morphological reconstruction on the colony two-dimensional morphological image to obtain three-dimensional colony morphological data;

[0026] Step S333: performing bacterial volume detection on the three-dimensional colony morphology data to obtain bacterial volume data; performing bacterial division identification on the three-dimensional colony morphology data to obtain bacterial division status data; determining bacterial growth status of the bacteria to be detected data based on the bacterial division status data and the bacterial volume data to generate bacterial growth status data; performing colony status label extraction on the bacterial growth status data to generate colony status label data;

[0027] Step S334: Using the type feature label data, colony number label data, and colony status label data as a bacteria detection integrated model classifier component to obtain a model classifier component; inputting the to-be-detected sample group data into the model classifier component to obtain detection sample classification data;

[0028] Step S335: Perform detection integration and fusion on the to-be-detected sample group data according to the detection sample classification data to obtain a bacteria detection integrated pre-model.

[0029] The present invention accurately depicts the external features of bacterial colonies through colony contour recognition; performs morphological measurement on bacterial colony contour data to further refine the morphological features of bacterial colonies for subsequent identification and differentiation of different bacterial types; performs two-dimensional morphological image scanning using bacterial morphological data to intuitively display the planar features of the colony, providing necessary image information for further three-dimensional morphological reconstruction; reconstructs the three-dimensional morphology of the colony from the two-dimensional morphological image of the colony, enabling a more comprehensive understanding of the growth status of the colony; performs volume detection on the three-dimensional colony morphological data to quantify the volume of the bacterial colony, providing volume parameters for evaluating the growth status of the colony; identifies the bacterial division state, clarifies the bacterial reproduction state; combines bacterial division with the The state and volume data can accurately judge the growth state of bacteria and then generate bacterial growth state data; the colony state label data is extracted from the bacterial growth state data to provide a clear classification basis for subsequent bacterial detection; the type feature label data, colony number label data and colony state label data are integrated, and different types of data are used to improve the classification accuracy of the model; the group data of the sample to be tested is input into the model classifier component to achieve accurate classification of the sample; the group data of the sample to be tested is integrated and fused according to the classification data of the test sample to construct an integrated pre-model for bacterial detection; by integrating multiple test results, the reliability and stability of the test are improved, and a pre-model is provided for the final bacterial detection.

[0030] Preferably, step S34 includes the following steps:

[0031] Step S341: performing classification feature layer determination on the bacteria detection integrated pre-model to obtain classification feature layer information; performing feature structure recognition on the classification feature layer information to obtain classification feature structure data; performing feature structure slicing on the model classifier component based on the classification feature structure data to generate feature structure slicing data; performing type range segmentation on the type feature label data based on the feature structure slicing data to obtain type segmentation data;

[0032] Step S342: performing colony magnitude gradient grouping on the colony quantity label data according to the type segmentation data to obtain colony magnitude gradient data; performing colony state cycle determination on the colony state label data according to the colony magnitude gradient data to obtain colony state cycle data;

[0033] Step S343: performing multi-dimensional feature fusion on the type segmentation data, colony magnitude gradient data, and colony state cycle data to generate multi-dimensional feature fusion data; performing feature autoencoding compression on the multi-dimensional feature fusion data to obtain multi-dimensional feature compression data;

[0034] Step S344: performing multi-scale feature recognition on the multi-dimensional feature compression data to generate multi-scale feature data; performing detection label weight extraction on the model classifier component to obtain detection weight data; performing feature weight mapping on the detection weight data using the multi-scale feature data to generate multi-scale detection weight data; performing dynamic weight generalization enhancement on the bacteria detection integrated pre-model using the multi-scale detection weight data to obtain model generalization enhancement measures;

[0035] Step S345: adjusting the detection priority of the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain priority adjustment data; and performing adaptive learning rate optimization on the priority adjustment data to obtain adaptive generalization adjustment data.

[0036] The present invention determines the classification feature layer of the integrated pre-model for bacterial detection and clarifies the key information used for classification in the model, thereby improving the accuracy of classification; identifies the structure of the classification feature layer information and can understand the relationship between features; performs feature structure slicing on the model classifier component based on the classification feature structure data, and can refine the type feature label data, thereby achieving accurate subdivision of the type range; utilizes the type subdivision data to perform gradient grouping on the colony quantity label data, and can effectively classify the colony quantity according to the magnitude; determines the colony state cycle according to the colony magnitude gradient data, which helps to grasp the growth and change of the colony; multi-dimensionally integrates the type subdivision data, the colony magnitude gradient data and the colony state cycle data It can integrate feature information from different sources to form comprehensive multi-dimensional feature data; reduce data dimensions and improve data processing efficiency through feature autoencoding compression; perform multi-scale recognition on multi-dimensional feature compressed data to extract feature information at different levels, thereby improving the model's recognition ability for complex data; extract detection label weights to help identify key features in the model; optimize the model's weight distribution through feature weight mapping, thereby enhancing the model's generalization ability; adjust the detection priority based on the model's generalization enhancement measures, optimize the model's detection process, and improve detection efficiency; further improve the model's generalization performance through adaptive learning rate optimization, so that the model can better adapt to new bacteria data to be detected.

[0037] Preferably, step S345 includes the following steps:

[0038] Step S3451: performing non-linear feature screening on the multi-scale detection weight data according to the model generalization enhancement measure to generate non-linear feature screening data; performing detection contribution calculation on the detection sample classification data according to the non-linear feature screening data to obtain detection contribution data;

[0039] Step S3452: performing contribution level classification on the detection contribution data to obtain contribution level level data; performing detection weight ranking on the detection weight data based on the contribution level level data to generate weight ranking data; performing detection priority weighted calculation on the weight ranking data to obtain detection priority weighted data;

[0040] Step S3453: performing multi-scale priority assignment on the detection weight data according to the detection priority weighted data and the multi-scale feature data to obtain multi-scale priority assignment information; performing detection priority transformation on the multi-scale detection weight data based on the multi-scale priority assignment information to obtain priority adjustment data;

[0041] Step S3454: performing dynamic feature sensitivity detection on the priority adjustment data to obtain feature sensitivity data; performing sensitivity level classification on the weight ranking data according to the feature sensitivity data to obtain weight sensitivity level data; performing weight feature learning rate calculation on the weight sensitivity level data to generate feature learning rate data;

[0042] Step S3455: dynamically dividing the feature learning rate data according to the detection priority weighted data to obtain learning rate division data; and adaptively optimizing the priority adjustment data according to the dynamic learning rate division to obtain adaptive generalization adjustment data.

[0043] The present invention identifies nonlinear features that have a significant impact on the classification of detection samples through nonlinear feature screening, thereby improving the classification accuracy of the model; detection contribution measurement can quantify the contribution of each feature to the classification result; contribution level division helps to distinguish the contribution of different features to the detection result, thereby sorting the detection weights; through weighted calculation, reasonable priorities are assigned to detection samples to ensure that key samples are given priority; multi-scale priority assignment can reasonably allocate detection weights according to the importance of features, and priority transformation ensures that the detection process can be dynamically adjusted according to the importance of features, thereby optimizing detection efficiency; dynamic feature sensitivity detection enables the identification of features that are most sensitive to detection results, and sensitivity level division further refines the sensitivity of features; weighted feature learning rate calculation assigns appropriate learning rates to features of different sensitivities to optimize the learning process of the model; dynamic division of learning rates ensures that feature learning rate data can be reasonably allocated according to detection priority weighted data, and adaptive learning rate optimization further improves the generalization ability and learning efficiency of the model.

[0044] Preferably, step S35 includes the following steps:

[0045] Step S351: performing single feature matching on the model classifier component according to the adaptive generalization adjustment data to generate single feature matching data; performing multi-scale feature association matching on the to-be-detected sample group data based on the single feature matching data to obtain multi-scale feature association data;

[0046] Step S352: performing feature detection interconnection on the multi-dimensional feature fusion data according to the multi-scale feature association data to obtain detection feature interconnection data; performing label parameter extraction on the detection feature interconnection data to generate interconnection label parameters;

[0047] Step S353: performing detection priority interconnection on the detection priority weighted data based on the interconnection tag parameters to obtain detection priority interconnection data; performing detection contribution increment on the detection priority interconnection data to obtain detection contribution increment data; and re-ranking the weight ranking data according to the detection contribution increment data to generate weight ranking response data.

[0048] Step S354: Perform a rapid learning rate response on the learning rate division data according to the weight sorting response data to obtain the learning rate rapid response data; perform a rapid pre-detection on the sample group data to be detected through the learning rate rapid response data to obtain the sample pre-detection data; input the sample pre-detection data into the bacterial detection integrated pre-model for rapid detection response training to obtain the bacterial detection integrated training model.

[0049] The present invention uses adaptive generalization to adjust data for single feature matching of model classifier components, ensuring the precise correspondence between features and models and improving the accuracy of matching; multi-scale feature association matching further enhances the correlation between the sample group data to be detected and the model; feature detection interconnection realizes the deep integration of multi-dimensional feature fusion data through multi-scale feature association data, and enhances the mutual connection between features; label parameter extraction provides key parameter information for feature detection, which helps to optimize the detection process; detection priority interconnection ensures that the detection process can be dynamically adjusted according to the interconnected label parameters, and improves the priority management of detection; the detection contribution increment reflects the new contribution of the feature to the detection result, and the generation of weighted ranking response data helps to adjust the detection weight to adapt to new detection requirements; the generation of learning rate rapid response data speeds up the adjustment speed of the learning rate and improves the model's adaptability to new data; rapid pre-detection provides preliminary detection results for the sample group data to be detected. After inputting the bacterial detection integrated pre-model, a bacterial detection integrated training model is finally formed through rapid detection response training, which improves the detection efficiency and accuracy of the model.

[0050] Preferably, step S4 includes the following steps:

[0051] Step S41: Divide the to-be-detected sample group data into a test set and a validation set to obtain a to-be-detected sample test set and a to-be-detected sample validation set;

[0052] Step S42: Input the test set of samples to be detected into the bacteria detection integrated training model to calculate the detection accuracy and generate test set accuracy data; input the test set of samples to be detected into the bacteria detection integrated training model to record the recall rate and generate test set recall data; integrate the test set accuracy data and the test set recall data to obtain test set performance data;

[0053] Step S43: Input the verification set of samples to be tested into the bacteria detection integrated training model to calculate the detection accuracy and generate verification set accuracy data; input the verification set of samples to be tested into the bacteria detection integrated training model to record the recall rate and generate verification set recall data; integrate the verification set accuracy data and the verification set recall data to obtain verification set performance data;

[0054] Step S44: performing a performance comparison between the test set performance data and the validation set performance data to generate training model performance evaluation data; iteratively optimizing the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model;

[0055] Step S45: performing bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

[0056] The present invention ensures the comprehensiveness and accuracy of model evaluation by dividing the sample group data to be detected into a test set and a validation set, and using them for performance evaluation and verification of the model respectively; the test set is input into the bacteria detection integrated training model, the accuracy and recall rate are calculated, and the test set performance data is generated; this helps to evaluate the performance of the model in practical applications and ensure the accuracy and comprehensiveness of the model's detection results; the validation set is input into the bacteria detection integrated training model, the accuracy and recall rate are also calculated, and the validation set performance data is generated, which helps to further confirm the stability and reliability of the model; by comparing the performance data of the test set and the validation set, the performance of the model can be comprehensively evaluated, providing a basis for the iterative optimization of the model; the model is optimized according to the performance evaluation data, thereby improving the accuracy and stability of the model; the optimized bacteria detection integrated model is used to detect the bacteria data to be detected, and a bacteria detection report is generated, ensuring the accuracy and reliability of the detection results.

[0057] Preferably, step S44 includes the following steps:

[0058] Step S441: performing similarity measurement on the test set performance data and the validation set performance data to generate performance similarity data; performing similarity deviation calculation on the performance similarity data to obtain performance similarity deviation data; performing detection response time difference calculation on the test set performance data and the validation set performance data to obtain response time difference data;

[0059] Step S442: performing a performance evaluation on the bacteria detection integrated training model using the performance similarity deviation data and the response time difference data to generate training model performance evaluation data;

[0060] Step S443: performing anomaly detection feedback on the bacteria detection integrated training model based on the training model performance evaluation data to obtain anomaly detection feedback data; performing an association parameter improvement operation on the multi-scale feature association data based on the anomaly detection feedback data to generate association parameter improvement data;

[0061] Step S444: performing error back propagation on the performance similarity deviation data based on the association parameter improvement data to obtain error back propagation data; performing loss function back propagation on the detection priority weighted data based on the error back propagation data to obtain loss function back propagation data; performing regularization constraints on the bacteria detection ensemble training model based on the loss function back propagation data to obtain regularization constraint data;

[0062] Step S445: performing multi-task collaborative training on the sample pre-detection data according to the regularized constraint data to obtain multi-task collaborative data; iteratively optimizing the bacteria detection integrated training model according to the multi-task collaborative data to obtain a bacteria detection integrated model.

[0063] The present invention evaluates the performance consistency of the model on different data sets by measuring the similarity between the test set performance data and the validation set performance data, and calculates the similarity deviation; at the same time, the detection response time difference is calculated to clarify the response of the model on different data sets; the performance similarity deviation data and the response time difference data are combined to evaluate the performance of the bacteria detection integrated training model, comprehensively evaluate the accuracy and efficiency of the model, generate comprehensive performance evaluation data, and provide a basis for model optimization; perform anomaly detection feedback based on the performance evaluation data to identify the abnormal performance of the model under specific circumstances; and perform correlation parameter improvement operations on multi-scale feature correlation data to enhance the model's ability to identify abnormal situations; adjust model parameters through error back propagation to reduce performance deviation; back propagation of loss function helps to optimize detection weights and improve the prediction accuracy of the model; the application of regularization constraints prevents model overfitting and ensures that the model has good generalization ability; multi-task collaborative training improves the performance of the model on different tasks and enhances the generalization ability of the model; iteratively optimizes the bacteria detection integrated training model to further improve the performance of the model, making it more stable and reliable.

[0064] Preferably, step S444 includes the following steps:

[0065] Step S4441: determining the parameter improvement range of the associated parameter improvement data to obtain associated parameter range data; calculating the deviation degree of the performance similarity deviation data to obtain performance deviation degree data;

[0066] Step S4442: performing range-degree mapping on the associated parameter range data and the performance deviation degree data to generate deviation parameter mapping data; performing error impact identification on the deviation parameter mapping data to obtain error impact data;

[0067] Step S4443: performing error back propagation on the multi-scale feature association data according to the error influence data to obtain error back propagation data; performing detection network layer extraction on the bacteria detection integrated training model to obtain detection network layer data; performing loss function calculation layer by layer on the detection network layer data according to the detection priority weighted data to obtain partial derivatives of the loss function;

[0068] Step S4444: The partial derivative of the loss function is reversely transmitted to the detection network layer to obtain the loss function back-propagation data; the regularization function is determined according to the anomaly detection feedback data to obtain the regularization function; the regularization function is added to the loss function back-propagation data to generate the back-propagation function information; the bacteria detection integrated training model is regularized and constrained according to the back-propagation function information to obtain the regularization constraint data.

[0069] By determining the range of associated parameter improvement, the present invention can accurately control the amplitude of parameter adjustment and ensure the stability of model optimization; the measurement of the degree of performance deviation can quantify the degree of deviation of model performance; the associated parameter range is mapped with the degree of performance deviation to accurately identify and adjust the key parameters in the model; error impact identification further refines the specific influencing factors of model performance deviation; error back propagation uses error impact data to adjust multi-scale feature correlation data, which can reduce the error of model detection; the extraction of detection network layer provides structured data for in-depth analysis of the model; the loss function is calculated layer by layer to ensure that each layer of the model can be properly optimized; the back propagation of the partial derivative of the loss function enables the network layer of the model to be effectively adjusted according to the error; the regularization function is determined and added to the loss function to enhance the model's resistance to overfitting; the application of regularization constraints further improves the generalization ability of the model and ensures that the model can maintain stable performance on different data sets. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic flow chart of the steps of a method for constructing an integrated model for bacterial detection;

[0071] Figure 2for Figure 1 Detailed implementation steps of step S3 in FIG.

[0072] Figure 3 for Figure 2 Detailed implementation steps of step S33 are shown in the flowchart;

[0073] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0074] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0076] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figures 1 to 3 A method for constructing an integrated model for bacteria detection, comprising the following steps:

[0078] Step S1: obtaining a bacterial original data set; performing data denoising on the bacterial original data set to obtain bacterial data to be detected; performing pre-classification on the bacterial data to be detected to obtain pre-classified bacterial data; performing data batch identification on the pre-classified bacterial data to obtain batch data to be detected; performing batch deviation detection on the batch data to be detected to obtain bacterial batch deviation data; performing batch correction on the bacterial data to be detected based on the bacterial batch deviation data to obtain bacterial correction data;

[0079] Step S2: performing bacterial characteristic identification on the pre-classified bacterial data according to the bacterial correction data to obtain bacterial characteristic data; performing bacterial behavior pattern determination on the bacterial characteristic data to obtain bacterial behavior pattern data; performing bacterial type characteristic identification on the pre-classified bacterial data according to the bacterial behavior pattern data to generate type characteristic data; performing type characteristic mapping on the bacterial data to be detected based on the type characteristic data to obtain bacterial type mapping data;

[0080] Step S3: constructing a bacteria detection integrated model for the bacteria to be detected data based on the bacteria correction data and the bacteria type mapping data to obtain a bacteria detection integrated pre-model; enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; performing adaptive generalization adjustment on the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data; performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model;

[0081] Step S4: Perform performance evaluation on the bacteria detection integrated training model to generate training model performance evaluation data; perform intelligent iterative optimization on the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model; perform bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

[0082] The present invention significantly improves the quality and accuracy of the original bacterial data set through data denoising, ensuring the effectiveness of subsequent analysis; pre-classification processing allows bacterial data to be grouped according to specific characteristics; data batch identification ensures that data from different batches can be considered independently, thereby reducing the impact of batch differences on analysis results; batch deviation detection further reveals the specific differences between batches, providing a basis for batch correction; batch correction guided by bacterial batch deviation data effectively reduces batch variation and improves data consistency and reliability. The use of bacterial correction data makes bacterial characteristic identification more accurate and provides accurate basic data for subsequent bacterial behavior pattern analysis; the determination of bacterial behavior patterns clarifies the biological characteristics and potential behavioral laws of bacteria; the generation of type characteristic data provides clear standards for bacterial classification, making bacterial type characteristic identification more accurate. The combination of bacterial correction data and bacterial type mapping data provides a data foundation for constructing a bacterial detection integrated model, ensuring the accuracy and reliability of the model; the generalization ability of the integrated model is enhanced, improving the model's detection ability for unknown data, thereby enhancing the model's practicality; adaptive generalization adjustment further optimizes the model's generalization performance, ensuring that the model can maintain stable performance on different data sets; rapid detection response integrated training of the bacterial detection integrated pre-model enables the model to quickly adapt to new data, improving the model's response speed and detection efficiency. Performance evaluation provides an objective evaluation standard for the bacterial detection integrated training model, ensuring the optimization and improvement points of the model; intelligent iterative optimization based on performance evaluation data further improves the performance of the model, ensuring the efficiency and accuracy of the model in practical applications; ultimately, the application of the bacterial detection integrated model achieves accurate detection of bacterial data to be detected, generates detailed bacterial detection reports, and provides strong support for bacterial identification, classification, and research. Therefore, the present invention enhances the generalization ability of the bacterial detection integrated model through data processing technology, feature mapping technology, intelligent feedback technology, and adaptive adjustment technology, enabling rapid detection response to bacterial data to be detected, thereby improving bacterial detection efficiency and accuracy.

[0083] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for constructing an integrated model for bacteria detection according to the present invention. In this example, the method for constructing an integrated model for bacteria detection includes the following steps:

[0084] Step S1: obtaining a bacterial original data set; performing data denoising on the bacterial original data set to obtain bacterial data to be detected; performing pre-classification on the bacterial data to be detected to obtain pre-classified bacterial data; performing data batch identification on the pre-classified bacterial data to obtain batch data to be detected; performing batch deviation detection on the batch data to be detected to obtain bacterial batch deviation data; performing batch correction on the bacterial data to be detected based on the bacterial batch deviation data to obtain bacterial correction data;

[0085] In an embodiment of the present invention, bacterial samples are obtained by laboratory culture or field sampling, and DNA is extracted and sequenced from the samples using high-throughput sequencing technology, such as the Illumina platform, to obtain a bacterial original data set; the bacterial original data set is quality controlled, and low-quality sequences are detected and removed using tools such as FastQC; the sequences are trimmed using software such as Trimmomatic to remove adapter sequences, low-quality tails, and sequences containing N to obtain bacterial data to be detected; the processed data are operated using QIIME or Mothur software, specifically including the selection of OTUs and preliminary classification of bacterial species to obtain pre-classified bacterial data; statistical methods such as ANOVA or t-SNE are used to specifically count the batch effects of the batch data to be detected, and batch deviations are detected by comparing statistical indicators of samples between different batches, such as α diversity and β diversity; a batch correction algorithm, such as SWARM or Harmony, is applied to correct the data to eliminate the batch effect; the corrected data are statistically analyzed again to verify the removal of the batch effect; the bacterial data to be detected are batch corrected according to the bacterial batch deviation data to obtain bacterial corrected data.

[0086] Step S2: performing bacterial characteristic identification on the pre-classified bacterial data according to the bacterial correction data to obtain bacterial characteristic data; performing bacterial behavior pattern determination on the bacterial characteristic data to obtain bacterial behavior pattern data; performing bacterial type characteristic identification on the pre-classified bacterial data according to the bacterial behavior pattern data to generate type characteristic data; performing type characteristic mapping on the bacterial data to be detected based on the type characteristic data to obtain bacterial type mapping data;

[0087] In an embodiment of the present invention, bacterial species annotation is performed using bioinformatics analysis software, such as MEGAN or Krona; machine learning algorithms, such as random forests or support vector machines, are applied to analyze bacterial gene sequences to identify their biological characteristics and biological markers; bacterial characteristic data are dynamically monitored through time series analysis, such as principal component analysis (PCA), to determine their bacterial behavior patterns; bacterial simulation techniques, such as cellular automaton simulation technology, are used to simulate the growth and spread of bacteria under different environmental conditions; multi-locus sequence typing (MLST) or whole genome sequencing (WGS) technology is used to identify bacterial type characteristics of pre-classified bacterial data, and identify the molecular typing and evolutionary relationships of bacteria; specific genes between different bacterial types are identified through comparative genomics analysis; type characteristic data are homology analyzed using bioinformatics tools, such as BLAST or Mauve; and the data to be detected are compared and mapped with known bacterial types to obtain bacterial type mapping data.

[0088] Step S3: constructing a bacteria detection integrated model for the bacteria to be detected data based on the bacteria correction data and the bacteria type mapping data to obtain a bacteria detection integrated pre-model; enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; performing adaptive generalization adjustment on the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data; performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model;

[0089] In an embodiment of the present invention, a machine learning algorithm, such as a random forest or a gradient boosting machine (GBM), is used to combine bacterial correction data and bacterial type mapping data to construct an integrated pre-model for bacterial detection; a feature selection technique, such as principal component analysis (PCA) or an autoencoder, is used to extract the features that are most helpful for bacterial detection from the data, reduce the model complexity, and improve the detection performance; data enhancement techniques, such as rotation, scaling, and color transformation, are used to transform the training data, specifically performing enhancement operations such as rotation (20 degrees), scaling (ratio of 1.2), and color transformation (saturation increased by 20%) on the training data set; a meta-learning strategy is used to train on multiple different bacterial data sets, specifically on 5 different bacterial Meta-training was performed on the dataset, and K-fold cross-validation (K=5) was used for each dataset to enable the model to quickly adapt to new bacterial detection tasks; a multi-task learning framework was used to simultaneously perform learning and training on multiple bacterial detection tasks. Specifically, the model's performance on three tasks, bacterial detection, bacterial counting, and antibiotic resistance prediction, was trained simultaneously to enhance the model's generalization ability to new data; online learning technology was applied to perform rapid detection response ensemble training on the bacterial detection ensemble pre-model based on adaptive generalization adjustment data. Specifically, the gradient boosting machine (GBM) was selected as the base model, 100 trees were set, the depth of each tree was 6, the minimum number of leaf node samples was 10, and the learning rate was 0.1, thus obtaining the bacterial detection ensemble training model.

[0090] Step S4: Perform performance evaluation on the bacteria detection integrated training model to generate training model performance evaluation data; perform intelligent iterative optimization on the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model; perform bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

[0091] In an embodiment of the present invention, a cross-validation method, such as k-fold cross-validation, is used to evaluate the performance of an integrated training model for bacterial detection; performance evaluation indicators, such as accuracy, recall, F1 score, and area under the ROC curve (AUC), are applied to generate training model performance evaluation data; based on the performance evaluation data, deficiencies in model performance are identified, such as low accuracy on specific bacterial categories; intelligent optimization algorithms, such as a bacterial foraging optimization algorithm, are applied to adjust model parameters, for example, if the detection accuracy is low, the depth of the model detection type is adjusted; the generalization ability of the model is enhanced; based on the model generalization enhancement measures, the performance of the model on new data is monitored in real time and adaptive adjustments are made; a meta-learning strategy is used to enable the model to quickly adapt to new bacterial samples; an online learning algorithm is applied to enable the model to update its parameters in real time to adapt to new bacterial samples; and a model distillation technique is used to compress the knowledge of multiple pre-trained bacterial detection models into a smaller model to improve the reasoning speed and efficiency of the model.

[0092] Preferably, step S2 includes the following steps:

[0093] Step S21: applying multi-parameter statistics to the bacterial correction data to obtain bacterial statistical data; extracting bacterial biomarkers from the bacterial statistical data to obtain bacterial biomarker data; and identifying bacterial characteristics of the pre-classified bacterial data based on the bacterial biomarker data to obtain bacterial characteristic data;

[0094] Step S22: grouping the bacterial characteristic data to obtain bacterial characteristic grouping data; identifying bacterial populations based on the bacterial characteristic grouping data to obtain bacterial population data; performing behavioral classification on the bacterial population data to generate bacterial behavior classification data; determining behavioral patterns of the bacterial characteristic data based on the bacterial behavior classification data to obtain bacterial behavior pattern data;

[0095] Step S23: extracting multi-modal standard rules from the bacterial behavior pattern data to generate bacterial multi-modal standard rules; comparing the pre-classified bacterial data with the pattern rules one by one according to the bacterial multi-modal standard rules to obtain bacterial key feature data; and identifying bacterial type features from the bacterial key feature data to generate type feature data;

[0096] Step S24: Associating the bacteria data to be detected with bacteria types through the type feature data to obtain bacteria type association data; quantifying the degree of association of the bacteria type association data to generate type association quantification data; assigning type labels to the bacteria data to be detected based on the type association quantification data to obtain labels of the data to be detected; performing type feature mapping on the labels of the data to be detected based on the type feature data to obtain bacteria type mapping data.

[0097] In an embodiment of the present invention, statistical software is used to apply multi-parameter statistics to bacterial correction data to calculate statistical quantities such as means and variances between different samples; chemical analysis methods, such as gas chromatography-mass spectrometry (GC-MS), are used to qualitatively and quantitatively analyze components such as fatty acids and proteins in bacterial samples; specifically, characteristic chemical components are identified as bacterial biomarkers based on information such as retention time and mass-to-charge ratio; the extracted biomarker data is compared and analyzed with pre-classified bacterial data, and pattern recognition algorithms, such as principal component analysis (PCA) and cluster analysis, are used to group bacteria with similar biomarker characteristics into one category to obtain bacterial characteristic data. Based on the similarity of bacterial characteristic data, cluster analysis methods such as K-means or hierarchical clustering are used to divide bacterial characteristic data into different groups. The grouped bacterial characteristic data are analyzed to identify bacterial groups with common characteristics. The boundaries of bacterial groups are determined by calculating the similarity within the groups and the dissimilarity between the groups. Behavioral divisions are performed based on behavioral characteristics such as growth characteristics and metabolic types of the bacterial groups. The behavioral division data and characteristic data of the bacteria are combined to determine the bacterial behavioral patterns. The bacterial behavioral pattern data are analyzed to extract standard rules for bacterial behavioral characteristics. The extracted standard rules are compared with pre-classified bacterial data to identify key bacterial characteristics that meet the rules. The identified key bacterial characteristics are classified to determine the bacterial type characteristics. The bacterial data to be tested is compared with known type characteristic data to find the most similar type characteristics. The similarity between the data to be tested and the known type characteristic data is calculated to quantify the degree of association. Based on the similarity calculation results, the bacterial data to be tested is assigned corresponding type labels. The type labels of the data to be tested are mapped with the type characteristic data to obtain bacterial type mapping data.

[0098] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0099] Step S31: performing data uniform normalization on the bacteria correction data to obtain bacteria uniform correction data; performing mapping enhancement on the bacteria type mapping data to generate type mapping enhancement data; demarcating the bacteria to-be-detected sample groups based on the bacteria uniform correction data and the type mapping enhancement data to generate to-be-detected sample group data; performing type feature mapping on the to-be-detected sample group data based on the type mapping enhancement data to generate type feature mapping data; performing type feature label extraction on the type feature mapping data to generate type feature label data;

[0100] Step S32: performing bacterial colony feature recognition on the bacterial uniformity correction data to obtain bacterial colony feature data; performing colony distribution detection on the bacterial colony feature data to obtain bacterial colony distribution data; performing colony type count on the type feature corresponding data using the bacterial colony distribution data to obtain colony type quantity data; performing colony number label extraction on the colony type quantity data to generate colony number label data;

[0101] Step S33: performing a bacterial growth status analysis on the bacterial colony characteristic data to obtain bacterial growth status data; extracting colony status labels from the bacterial growth status data to generate colony status label data; constructing a bacterial detection integrated model for the bacteria to be detected data based on the type characteristic label data, colony quantity label data, and colony status label data to obtain a bacterial detection integrated pre-model;

[0102] Step S34: enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; and adaptively generalizing the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data;

[0103] Step S35: Performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model.

[0104] In an embodiment of the present invention, a normalization algorithm, such as minimum-maximum normalization or Z-score normalization, is used to scale the data to a uniform range, such as [0, 1], to eliminate the influence of different dimensions and obtain uniformly corrected bacterial data; data enhancement techniques, such as rotation, flipping, scaling, etc., are used to expand the bacterial type mapping data to generate type mapping enhanced data; the normalized bacterial correction data is combined with the enhanced type mapping data, and the bacterial data to be detected is divided into sample groups through cluster analysis or decision tree methods to generate sample group data to be detected; a pattern recognition algorithm, such as a support vector machine (SVM), is used to match and identify type features of the sample group data to be detected; based on the type feature matching, the most representative type feature labels are extracted through feature extraction techniques, such as principal component analysis (PCA). Use image processing techniques, such as edge detection and morphological operations, to identify the morphological characteristics of bacterial colonies; use spatial analysis methods, such as the nearest neighbor index, to analyze the distribution of colonies; combine colony distribution data and type feature corresponding data, use counters to count the number of different colony types; generate corresponding colony quantity labels based on the number of colony types; use time series analysis methods, such as growth curve analysis, to detect the growth status of bacteria; extract the growth status labels of colonies based on the growth status analysis results; integrate type feature label data, colony quantity label data and colony status label data, and use machine learning frameworks, such as random forests, specifically, set 100 decision trees; each tree The maximum depth of the proposed method is 10, and no pruning is performed. At each decision node, a feature subset is randomly selected to select the best splitting attribute. A bootstrap sampling method is used to extract samples from the original training set to train each tree. In this way, an integrated pre-model for bacterial detection is constructed. The generalization ability of the integrated pre-model for bacterial detection is enhanced through regularization techniques, such as L1 or L2 regularization, to obtain model generalization enhancement measures. The integrated pre-model for bacterial detection is adaptively generalized according to the model generalization enhancement measures, specifically by dynamically adjusting the model parameters to achieve adaptive generalization. The data after the model adaptive generalization adjustment is used for rapid iterative training to obtain an integrated training model for bacterial detection.

[0105] As an example of the present invention, refer to Figure 3 As shown, in this example, step S33 includes:

[0106] Step S331: performing colony contour recognition on the bacterial colony characteristic data to generate bacterial colony contour data; performing morphological measurement on the bacterial colony contour data to obtain bacterial morphological data;

[0107] Step S332: performing a colony two-dimensional morphological image scan on the bacterial colony characteristic data based on the bacterial morphological data to generate a colony two-dimensional morphological image; and performing a colony three-dimensional morphological reconstruction on the colony two-dimensional morphological image to obtain three-dimensional colony morphological data;

[0108] Step S333: performing bacterial volume detection on the three-dimensional colony morphology data to obtain bacterial volume data; performing bacterial division identification on the three-dimensional colony morphology data to obtain bacterial division status data; determining bacterial growth status of the bacteria to be detected data based on the bacterial division status data and the bacterial volume data to generate bacterial growth status data; performing colony status label extraction on the bacterial growth status data to generate colony status label data;

[0109] Step S334: Using the type feature label data, colony number label data, and colony status label data as a bacteria detection integrated model classifier component to obtain a model classifier component; inputting the to-be-detected sample group data into the model classifier component to obtain detection sample classification data;

[0110] Step S335: Perform detection integration and fusion on the to-be-detected sample group data according to the detection sample classification data to obtain a bacteria detection integrated pre-model.

[0111] In the embodiment of the present invention, image processing technology is used to identify the outline of the colony through threshold segmentation and edge detection algorithm (such as Canny algorithm) to generate bacterial colony outline data; morphological analysis is performed on the identified colony outline, including calculating the area, perimeter, shape index, etc. of the colony to obtain bacterial morphological data; scanning technology, such as confocal microscopy, is used to obtain a two-dimensional morphological image of the bacterial colony; a three-dimensional reconstruction algorithm, such as two-photon microscopy or optical coherence tomography technology, is used to reconstruct the three-dimensional morphological data of the colony from the two-dimensional image; the volume calculation formula is used to determine the volume of the bacteria through the three-dimensional morphological data; the three-dimensional morphological data is analyzed and the image is used to obtain the bacterial morphological data. Image processing technology is used to identify the patterns and stages of bacterial division to obtain bacterial division status data; combining the division status and volume data, a pattern recognition algorithm is used to determine the growth status of bacteria to generate bacterial growth status data; based on the growth status data, colony status labels representing different growth stages are extracted to generate colony status label data; the extracted various label data are integrated into the machine learning model as feature input to obtain the model classifier component; the data of the sample to be tested is input into the classifier component to obtain the classification data of the test sample; the classification data is fused using an integrated learning method, such as a random forest or a gradient boosting machine, to obtain an integrated pre-model for bacterial detection.

[0112] Preferably, step S34 includes the following steps:

[0113] Step S341: performing classification feature layer determination on the bacteria detection integrated pre-model to obtain classification feature layer information; performing feature structure recognition on the classification feature layer information to obtain classification feature structure data; performing feature structure slicing on the model classifier component based on the classification feature structure data to generate feature structure slicing data; performing type range segmentation on the type feature label data based on the feature structure slicing data to obtain type segmentation data;

[0114] Step S342: performing colony magnitude gradient grouping on the colony quantity label data according to the type segmentation data to obtain colony magnitude gradient data; performing colony state cycle determination on the colony state label data according to the colony magnitude gradient data to obtain colony state cycle data;

[0115] Step S343: performing multi-dimensional feature fusion on the type segmentation data, colony magnitude gradient data, and colony state cycle data to generate multi-dimensional feature fusion data; performing feature autoencoding compression on the multi-dimensional feature fusion data to obtain multi-dimensional feature compression data;

[0116] Step S344: performing multi-scale feature recognition on the multi-dimensional feature compression data to generate multi-scale feature data; performing detection label weight extraction on the model classifier component to obtain detection weight data; performing feature weight mapping on the detection weight data using the multi-scale feature data to generate multi-scale detection weight data; performing dynamic weight generalization enhancement on the bacteria detection integrated pre-model using the multi-scale detection weight data to obtain model generalization enhancement measures;

[0117] Step S345: adjusting the detection priority of the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain priority adjustment data; and performing adaptive learning rate optimization on the priority adjustment data to obtain adaptive generalization adjustment data.

[0118] In an embodiment of the present invention, a machine learning algorithm, such as a random forest, is used to evaluate the feature importance of the model and determine the feature layer information that contributes most to the classification; a feature selection method, such as recursive feature elimination (RFE), is used to identify the most representative feature structure and obtain classification feature structure data; a feature extraction technique, such as principal component analysis (PCA), is used to perform dimensionality reduction processing on the classification feature structure data and generate feature structure slice data; a clustering algorithm, such as K-means, is used to segment the type feature label data according to the feature structure slice data to obtain type segmentation data. Based on the frequency of type features in the type segmentation data, the quantile method is used to divide the colony quantity label data into different magnitude gradients to obtain colony magnitude gradient data; the colony magnitude gradient data and time series data are combined to determine the periodic changes in the colony state through a periodic analysis method to obtain colony state cycle data; the type segmentation data, colony magnitude gradient data and colony state cycle data are combined to perform multi-dimensional feature fusion to generate multi-dimensional feature fusion data; an autoencoder is used to compress the multi-dimensional feature fusion data to extract more representative features to obtain multi-dimensional feature compression data; multi-scale analysis methods, such as wavelet transform, are applied to the multi-dimensional feature compression data to identify features at different scales to generate multi-scale feature data; In the model classifier component, the weight of each detection label is determined by the gradient ascent method during the training process to obtain the detection weight data; the multi-scale feature data is combined with the detection weight data, and the feature weight mapping is performed through the weighted average method to generate multi-scale detection weight data; based on the multi-scale detection weight data, the weight of the model is dynamically adjusted to enhance the generalization ability of the model for unseen samples, and the model generalization enhancement measures are obtained; based on the model generalization enhancement measures, the detection priority of different features is adjusted to reflect their contribution to the final classification decision, and the priority adjustment data is obtained; using an adaptive learning rate algorithm, such as the Adam optimizer, the learning rate of the model is dynamically adjusted according to the priority adjustment data to obtain adaptive generalization adjustment data.

[0119] Preferably, step S345 includes the following steps:

[0120] Step S3451: performing non-linear feature screening on the multi-scale detection weight data according to the model generalization enhancement measure to generate non-linear feature screening data; performing detection contribution calculation on the detection sample classification data according to the non-linear feature screening data to obtain detection contribution data;

[0121] Step S3452: performing contribution level classification on the detection contribution data to obtain contribution level level data; performing detection weight ranking on the detection weight data based on the contribution level level data to generate weight ranking data; performing detection priority weighted calculation on the weight ranking data to obtain detection priority weighted data;

[0122] Step S3453: performing multi-scale priority assignment on the detection weight data according to the detection priority weighted data and the multi-scale feature data to obtain multi-scale priority assignment information; performing detection priority transformation on the multi-scale detection weight data based on the multi-scale priority assignment information to obtain priority adjustment data;

[0123] Step S3454: performing dynamic feature sensitivity detection on the priority adjustment data to obtain feature sensitivity data; performing sensitivity level classification on the weight ranking data according to the feature sensitivity data to obtain weight sensitivity level data; performing weight feature learning rate calculation on the weight sensitivity level data to generate feature learning rate data;

[0124] Step S3455: dynamically dividing the feature learning rate data according to the detection priority weighted data to obtain learning rate division data; and adaptively optimizing the priority adjustment data according to the dynamic learning rate division to obtain adaptive generalization adjustment data.

[0125] In an embodiment of the present invention, a nonlinear feature screening technology is used, such as using kernel principal component analysis (KernelPCA) or autoencoders, to perform feature conversion and dimensionality reduction on multi-scale detection weight data, and screen out the nonlinear features that best represent the data structure; by calculating the degree of influence of each feature on the model prediction result, for example, using the feature importance score in random forest or the gradient-based feature importance method, the contribution of nonlinear feature screening data to the detection sample classification data is evaluated to generate detection contribution data; using hierarchical clustering or K-means clustering algorithm, the features are divided into different contribution levels according to the detection contribution data to obtain contribution level data; according to the contribution level data, the detection weight data is sorted to generate weight ranking data; using a weighted summation method, according to the feature weights in the weight ranking data and their corresponding contributions, the detection priority weighted data is calculated; combining the detection priority weighted data and the multi-scale feature data, multi-scale priority assignment is performed on each feature to generate multi-scale priority assignment information; according to the multi-scale priority assignment information, the multi-scale detection weight data is adjusted. According to the weight value in the data, the priority of different features in the detection is reflected to obtain priority adjustment data; the gradient information of the model or the variance analysis of the features is used to perform feature sensitivity detection on the priority adjustment data to obtain feature sensitivity data; according to the feature sensitivity data, the threshold segmentation or similarity measurement method is used to divide the weight sorting data into sensitivity levels to obtain weight sensitivity level data; an adaptive learning rate algorithm is used, such as the learning rate scheduling strategy in AdaBoost or the gradient descent method, to calculate the learning rate of the feature according to the weight sensitivity level data to generate feature learning rate data; combined with the detection priority weighted data and the feature learning rate data, the learning rate of each feature is dynamically adjusted to optimize the convergence speed and classification performance of the model to obtain learning rate partition data; according to the learning rate partition data, the priority adjustment data is adaptively optimized in learning rate. Specifically, Adam is selected as the optimizer and the initial learning rate is set to 0.001. During the training process, the Adam optimizer will automatically adjust the learning rate of each parameter according to the gradient information, thereby obtaining adaptive generalization adjustment data, and finally completing the dynamic adjustment of feature weights in the entire model training process.

[0126] Preferably, step S35 includes the following steps:

[0127] Step S351: performing single feature matching on the model classifier component according to the adaptive generalization adjustment data to generate single feature matching data; performing multi-scale feature association matching on the to-be-detected sample group data based on the single feature matching data to obtain multi-scale feature association data;

[0128] Step S352: performing feature detection interconnection on the multi-dimensional feature fusion data according to the multi-scale feature association data to obtain detection feature interconnection data; performing label parameter extraction on the detection feature interconnection data to generate interconnection label parameters;

[0129] Step S353: performing detection priority interconnection on the detection priority weighted data based on the interconnection tag parameters to obtain detection priority interconnection data; performing detection contribution increment on the detection priority interconnection data to obtain detection contribution increment data; and re-ranking the weight ranking data according to the detection contribution increment data to generate weight ranking response data.

[0130] Step S354: Perform a rapid learning rate response on the learning rate division data according to the weight sorting response data to obtain the learning rate rapid response data; perform a rapid pre-detection on the sample group data to be detected through the learning rate rapid response data to obtain the sample pre-detection data; input the sample pre-detection data into the bacterial detection integrated pre-model for rapid detection response training to obtain the bacterial detection integrated training model.

[0131] In an embodiment of the present invention, the gradient information of the model and the feature importance score are used to determine the contribution of each feature to the model; the most influential features are screened out through feature selection algorithms, such as recursive feature elimination (RFE), to generate single feature matching data; the single feature matching data is compared with feature data of different scales, and specifically correlation analysis or pattern recognition technology, such as K-nearest neighbor (KNN) algorithm, is used to identify and match multi-scale features to obtain multi-scale feature association data; feature fusion technology, such as multi-scale feature fusion network, is used to combine multi-scale feature association data with multi-dimensional feature fusion data to achieve interconnection between features and obtain detection feature interconnection data; the detection feature interconnection data is analyzed through supervised learning algorithms, such as support vector machines (SVM), to extract key label parameters for classification decisions and generate interconnection label parameters; a decision tree algorithm is used to perform feature determination on the interconnection label parameters and detection priority weighted data, specifically to determine the priorities of different features and obtain detection priority interconnection data. Combined with the detection priority interconnected data and the prediction results of the model, the incremental contribution of each feature to the final decision is calculated to obtain the incremental detection contribution data; based on the incremental detection contribution data, the weight of each feature in the weight ranking data is re-evaluated to generate weight ranking response data; based on the weight ranking response data, an adaptive learning rate algorithm, such as AdaBoost, is used to dynamically adjust the learning rate of each feature to obtain learning rate fast response data; the learning rate fast response data is used to perform preliminary detection on the sample group data to be detected, and bacterial samples are quickly screened out to obtain sample pre-detection data; the sample pre-detection data is input into the bacterial detection integrated pre-model, and rapid iterative training is performed to optimize the model parameters to obtain the bacterial detection integrated training model; according to the model generalization enhancement measures, the detection priority in the detection integrated pre-model is adjusted to optimize the decision path of the model to obtain priority adjustment data; based on the priority adjustment data and the feature learning rate data, an adaptive learning rate algorithm, such as AdaGrad, is used to optimize the learning rate of the model to obtain adaptive generalization adjustment data.

[0132] Preferably, step S4 includes the following steps:

[0133] Step S41: Divide the to-be-detected sample group data into a test set and a validation set to obtain a to-be-detected sample test set and a to-be-detected sample validation set;

[0134] Step S42: Input the test set of samples to be detected into the bacteria detection integrated training model to calculate the detection accuracy and generate test set accuracy data; input the test set of samples to be detected into the bacteria detection integrated training model to record the recall rate and generate test set recall data; integrate the test set accuracy data and the test set recall data to obtain test set performance data;

[0135] Step S43: Input the verification set of samples to be tested into the bacteria detection integrated training model to calculate the detection accuracy and generate verification set accuracy data; input the verification set of samples to be tested into the bacteria detection integrated training model to record the recall rate and generate verification set recall data; integrate the verification set accuracy data and the verification set recall data to obtain verification set performance data;

[0136] Step S44: performing a performance comparison between the test set performance data and the validation set performance data to generate training model performance evaluation data; iteratively optimizing the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model;

[0137] Step S45: performing bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

[0138] In an embodiment of the present invention, a data segmentation technology is used, such as simple random sampling or stratified sampling, to divide the data of the sample group to be tested into a test set of the sample to be tested and a verification set of the sample to be tested; specifically, the data set is divided according to a certain ratio, such as 70% as a training set, 15% as a verification set, and 15% as a test set; the test set is tested using an integrated training model for bacterial detection, and the accuracy is calculated by comparing the test results with the actual labels to generate test set accuracy data; similarly, the test set is tested using an integrated training model for bacterial detection, the recall rate is calculated, and the test set recall rate data is generated; and the test set accuracy data and the test set recall rate data are integrated using data fusion technology to obtain test set performance data. Use the same method to calculate the accuracy of the validation set and generate validation set accuracy data; record the recall rate of the validation set and generate validation set recall data; integrate the accuracy and recall rate data to obtain validation set performance data; compare the performance data of the test set and validation set, evaluate the performance of the model on different data sets, and generate training model performance evaluation data; adjust the model parameters according to the performance evaluation data. Specifically, if the model shows overfitting (good performance on the training set and poor performance on the test set), reduce the overfitting by adjusting the learning rate or adding dropout and other techniques; if the model underfits (both the training set and the test set perform poorly), increase the model complexity or provide more training data to obtain a bacterial detection integrated model; perform bacterial detection on the data to be detected based on the bacterial detection integrated model to obtain a bacterial detection report.

[0139] Preferably, step S44 includes the following steps:

[0140] Step S441: performing similarity measurement on the test set performance data and the validation set performance data to generate performance similarity data; performing similarity deviation calculation on the performance similarity data to obtain performance similarity deviation data; performing detection response time difference calculation on the test set performance data and the validation set performance data to obtain response time difference data;

[0141] Step S442: performing a performance evaluation on the bacteria detection integrated training model using the performance similarity deviation data and the response time difference data to generate training model performance evaluation data;

[0142] Step S443: performing anomaly detection feedback on the bacteria detection integrated training model based on the training model performance evaluation data to obtain anomaly detection feedback data; performing an association parameter improvement operation on the multi-scale feature association data based on the anomaly detection feedback data to generate association parameter improvement data;

[0143] Step S444: performing error back propagation on the performance similarity deviation data based on the association parameter improvement data to obtain error back propagation data; performing loss function back propagation on the detection priority weighted data based on the error back propagation data to obtain loss function back propagation data; performing regularization constraints on the bacteria detection ensemble training model based on the loss function back propagation data to obtain regularization constraint data;

[0144] Step S445: performing multi-task collaborative training on the sample pre-detection data according to the regularized constraint data to obtain multi-task collaborative data; iteratively optimizing the bacteria detection integrated training model according to the multi-task collaborative data to obtain a bacteria detection integrated model.

[0145] In an embodiment of the present invention, a measurement method such as cosine similarity or Euclidean distance is used to calculate the similarity between the test set performance data and the validation set performance data; the concept of deviation in statistics is used to calculate the absolute deviation and relative deviation of the performance similarity; by comparing the response time of the test set and the validation set, the difference between the two is calculated to obtain the response time difference data; the performance similarity deviation data and the response time difference data are combined, and a comprehensive evaluation method such as the weighted average method is used to generate training model performance evaluation data; based on the performance evaluation data, anomaly detection is performed using anomaly detection algorithms such as DBSCAN, and anomaly detection feedback data is generated; according to the anomaly detection feedback data, the association parameters of the multi-scale feature association data are adjusted to specifically improve the accuracy and robustness of the model; the error back propagation algorithm is used to improve the data adjustment error according to the association parameters, and the error is back propagated to the model parameters to obtain the error back propagation data; the error back propagation data is applied to the loss function, and the gradient of the parameters is calculated by the chain rule to achieve The loss function is back-propagated to obtain the loss function back-propagation data; based on the loss function back-propagation data, a regularization term, such as L1 or L2 regularization, is introduced to perform regularization constraints on the bacteria detection integrated training model to obtain regularized constrained data; according to the regularized constrained data, a multi-task learning strategy is adopted to perform multi-task collaborative training. Specifically, a neural network comprising shared layers and task-specific layers is constructed, wherein the shared layers (such as the first few convolutional layers) are responsible for extracting common features, such as edges and textures in bacterial images; each task has its own specific layer, and task-related features are further extracted based on the shared layers. For example, for bacterial classification tasks, deeper convolutional layers are required to identify the morphological characteristics of bacteria; and for bacterial counting tasks, fully connected layers are required to process spatial information; finally, multi-task collaborative data are obtained; using the multi-task collaborative data, the bacteria detection integrated training model is iteratively optimized. Specifically, through multiple iterative training, the parameters of the shared layers and task-specific layers are continuously updated to obtain the final bacteria detection integrated model.

[0146] Preferably, step S444 includes the following steps:

[0147] Step S4441: determining the parameter improvement range of the associated parameter improvement data to obtain associated parameter range data; calculating the deviation degree of the performance similarity deviation data to obtain performance deviation degree data;

[0148] Step S4442: performing range-degree mapping on the associated parameter range data and the performance deviation degree data to generate deviation parameter mapping data; performing error impact identification on the deviation parameter mapping data to obtain error impact data;

[0149] Step S4443: performing error back propagation on the multi-scale feature association data according to the error influence data to obtain error back propagation data; performing detection network layer extraction on the bacteria detection integrated training model to obtain detection network layer data; performing loss function calculation layer by layer on the detection network layer data according to the detection priority weighted data to obtain partial derivatives of the loss function;

[0150] Step S4444: The partial derivative of the loss function is reversely transmitted to the detection network layer to obtain the loss function back-propagation data; the regularization function is determined according to the anomaly detection feedback data to obtain the regularization function; the regularization function is added to the loss function back-propagation data to generate the back-propagation function information; the bacteria detection integrated training model is regularized and constrained according to the back-propagation function information to obtain the regularization constraint data.

[0151] In an embodiment of the present invention, by analyzing the changing trend and distribution of multi-scale feature association data, the adjustable range of the association parameter is specifically determined; by using statistical analysis methods, such as calculating the minimum value, maximum value and standard deviation of the parameter, the association parameter range data is obtained; by using the deviation analysis method in statistics, such as the root mean square deviation (RMSD), the deviation degree of the performance similarity deviation data is calculated to obtain the performance deviation degree data; the association parameter range data is compared with the performance deviation degree data, and a mapping algorithm, such as nearest neighbor mapping or multivariate linear regression, is used to generate deviation parameter mapping data; using pattern recognition technology, such as support vector machine (SVM) or decision tree, the deviation parameter mapping data is analyzed to identify errors that have a significant impact on model performance and obtain error impact data; based on the error impact data, the error back propagation algorithm, such as the chain rule, is used to adjust the multi-scale feature association data to obtain error back propagation data; by analyzing the structure of the bacterial detection integrated training model, the key detection network layer is identified to obtain the detection network layer data; in combination with the detection priority weighted data, a loss function, such as cross entropy loss or mean square error loss, is applied to the detection network layer data to perform layer-by-layer analysis. Calculate and obtain the partial derivative of the loss function; use the gradient descent method to reverse the partial derivative of the loss function to the detection network layer, update the network parameters, and obtain the loss function back-propagation data; based on the anomaly detection feedback, select a suitable regularization function; if the model shows signs of overfitting, select L1 regularization (Lasso) or L2 regularization (Ridge) to reduce the model complexity; L1 regularization promotes the sparsity of model weights by adding the L1 norm (sum of absolute values) of the weights to the loss function, which helps feature selection; L2 regularization promotes the sparsity of model weights by adding the L2 norm (sum of squares) of the weights ) to limit the size of the model weights to avoid overfitting; thereby obtaining a regularization function; adding the regularization function to the back-propagation data of the loss function to generate back-propagation function information; applying the back-propagation function information, regularizing the bacteria detection integrated training model, optimizing the model performance, and obtaining regularized constrained data; utilizing the regularized constrained data, multi-task collaborative training is performed on the sample pre-detection data, such as using a multi-task learning framework to obtain multi-task collaborative data; based on the multi-task collaborative data, the bacteria detection integrated training model is iteratively optimized, the model parameters are adjusted, and the bacteria detection integrated model is obtained.

[0152] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0153] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing an integrated model for bacterial detection, characterized in that: The following steps are involved: Step S1: obtaining a bacterial original data set; performing data denoising on the bacterial original data set to obtain bacterial data to be detected; performing pre-classification on the bacterial data to be detected to obtain pre-classified bacterial data; performing data batch identification on the pre-classified bacterial data to obtain batch data to be detected; performing batch deviation detection on the batch data to be detected to obtain bacterial batch deviation data; performing batch correction on the bacterial data to be detected based on the bacterial batch deviation data to obtain bacterial correction data; Step S2: performing bacterial characteristic identification on the pre-classified bacterial data according to the bacterial correction data to obtain bacterial characteristic data; performing bacterial behavior pattern determination on the bacterial characteristic data to obtain bacterial behavior pattern data; performing bacterial type characteristic identification on the pre-classified bacterial data according to the bacterial behavior pattern data to generate type characteristic data; performing type characteristic mapping on the bacterial data to be detected based on the type characteristic data to obtain bacterial type mapping data; Step S3: constructing a bacteria detection integrated model for the bacteria to be detected data based on the bacteria correction data and the bacteria type mapping data to obtain a bacteria detection integrated pre-model; enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; performing adaptive generalization adjustment on the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data; performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model; Step S4: Perform performance evaluation on the bacteria detection integrated training model to generate training model performance evaluation data; perform intelligent iterative optimization on the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model; perform bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

2. The method for constructing an integrated model for bacteria detection according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: applying multi-parameter statistics to the bacterial correction data to obtain bacterial statistical data; extracting bacterial biomarkers from the bacterial statistical data to obtain bacterial biomarker data; and identifying bacterial characteristics of the pre-classified bacterial data based on the bacterial biomarker data to obtain bacterial characteristic data; Step S22: grouping the bacterial characteristic data to obtain bacterial characteristic grouping data; identifying bacterial populations based on the bacterial characteristic grouping data to obtain bacterial population data; performing behavioral classification on the bacterial population data to generate bacterial behavior classification data; determining behavioral patterns of the bacterial characteristic data based on the bacterial behavior classification data to obtain bacterial behavior pattern data; Step S23: extracting multi-modal standard rules from the bacterial behavior pattern data to generate bacterial multi-modal standard rules; comparing the pre-classified bacterial data with the pattern rules one by one according to the bacterial multi-modal standard rules to obtain bacterial key feature data; and identifying bacterial type features from the bacterial key feature data to generate type feature data; Step S24: Associating the bacteria data to be detected with bacteria types through the type feature data to obtain bacteria type association data; quantifying the degree of association of the bacteria type association data to generate type association quantification data; assigning type labels to the bacteria data to be detected based on the type association quantification data to obtain labels of the data to be detected; performing type feature mapping on the labels of the data to be detected based on the type feature data to obtain bacteria type mapping data.

3. The method for constructing an integrated model for bacteria detection according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing data uniform normalization on the bacteria correction data to obtain bacteria uniform correction data; performing mapping enhancement on the bacteria type mapping data to generate type mapping enhancement data; demarcating the bacteria to-be-detected sample groups based on the bacteria uniform correction data and the type mapping enhancement data to generate to-be-detected sample group data; performing type feature mapping on the to-be-detected sample group data based on the type mapping enhancement data to generate type feature mapping data; performing type feature label extraction on the type feature mapping data to generate type feature label data; Step S32: performing bacterial colony feature recognition on the bacterial uniformity correction data to obtain bacterial colony feature data; performing colony distribution detection on the bacterial colony feature data to obtain bacterial colony distribution data; performing colony type count on the type feature corresponding data using the bacterial colony distribution data to obtain colony type quantity data; performing colony number label extraction on the colony type quantity data to generate colony number label data; Step S33: performing a bacterial growth status analysis on the bacterial colony characteristic data to obtain bacterial growth status data; extracting colony status labels from the bacterial growth status data to generate colony status label data; constructing a bacterial detection integrated model for the bacteria to be detected data based on the type characteristic label data, colony quantity label data, and colony status label data to obtain a bacterial detection integrated pre-model; Step S34: enhancing the generalization capability of the bacteria detection integrated pre-model to obtain a model generalization enhancement measure; and adaptively generalizing the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain adaptive generalization adjustment data; Step S35: Performing rapid detection response integrated training on the bacteria detection integrated pre-model according to the adaptive generalization adjustment data to obtain a bacteria detection integrated training model.

4. The method for constructing an integrated model for bacteria detection according to claim 3, characterized in that: Step S33 includes the following steps: Step S331: performing colony contour recognition on the bacterial colony characteristic data to generate bacterial colony contour data; performing morphological measurement on the bacterial colony contour data to obtain bacterial morphological data; Step S332: performing a colony two-dimensional morphological image scan on the bacterial colony characteristic data based on the bacterial morphological data to generate a colony two-dimensional morphological image; and performing a colony three-dimensional morphological reconstruction on the colony two-dimensional morphological image to obtain three-dimensional colony morphological data; Step S333: performing bacterial volume detection on the three-dimensional colony morphology data to obtain bacterial volume data; performing bacterial division identification on the three-dimensional colony morphology data to obtain bacterial division status data; determining bacterial growth status of the bacteria to be detected data based on the bacterial division status data and the bacterial volume data to generate bacterial growth status data; performing colony status label extraction on the bacterial growth status data to generate colony status label data; Step S334: Using the type feature label data, colony number label data, and colony status label data as a bacteria detection integrated model classifier component to obtain a model classifier component; inputting the to-be-detected sample group data into the model classifier component to obtain detection sample classification data; Step S335: Perform detection integration and fusion on the to-be-detected sample group data according to the detection sample classification data to obtain a bacteria detection integrated pre-model.

5. The method for constructing an integrated model for bacteria detection according to claim 3, characterized in that: Step S34 includes the following steps: Step S341: performing classification feature layer determination on the bacteria detection integrated pre-model to obtain classification feature layer information; performing feature structure recognition on the classification feature layer information to obtain classification feature structure data; performing feature structure slicing on the model classifier component based on the classification feature structure data to generate feature structure slicing data; performing type range segmentation on the type feature label data based on the feature structure slicing data to obtain type segmentation data; Step S342: performing colony magnitude gradient grouping on the colony quantity label data according to the type segmentation data to obtain colony magnitude gradient data; performing colony state cycle determination on the colony state label data according to the colony magnitude gradient data to obtain colony state cycle data; Step S343: performing multi-dimensional feature fusion on the type segmentation data, colony magnitude gradient data, and colony state cycle data to generate multi-dimensional feature fusion data; performing feature autoencoding compression on the multi-dimensional feature fusion data to obtain multi-dimensional feature compression data; Step S344: performing multi-scale feature recognition on the multi-dimensional feature compression data to generate multi-scale feature data; performing detection label weight extraction on the model classifier component to obtain detection weight data; performing feature weight mapping on the detection weight data using the multi-scale feature data to generate multi-scale detection weight data; performing dynamic weight generalization enhancement on the bacteria detection integrated pre-model using the multi-scale detection weight data to obtain model generalization enhancement measures; Step S345: adjusting the detection priority of the bacteria detection integrated pre-model according to the model generalization enhancement measure to obtain priority adjustment data; and performing adaptive learning rate optimization on the priority adjustment data to obtain adaptive generalization adjustment data.

6. The method for constructing an integrated model for bacteria detection according to claim 5, characterized in that: Step S345 includes the following steps: Step S3451: performing non-linear feature screening on the multi-scale detection weight data according to the model generalization enhancement measure to generate non-linear feature screening data; performing detection contribution calculation on the detection sample classification data according to the non-linear feature screening data to obtain detection contribution data; Step S3452: performing contribution level classification on the detection contribution data to obtain contribution level level data; performing detection weight ranking on the detection weight data based on the contribution level level data to generate weight ranking data; performing detection priority weighted calculation on the weight ranking data to obtain detection priority weighted data; Step S3453: performing multi-scale priority assignment on the detection weight data according to the detection priority weighted data and the multi-scale feature data to obtain multi-scale priority assignment information; performing detection priority transformation on the multi-scale detection weight data based on the multi-scale priority assignment information to obtain priority adjustment data; Step S3454: performing dynamic feature sensitivity detection on the priority adjustment data to obtain feature sensitivity data; performing sensitivity level classification on the weight ranking data according to the feature sensitivity data to obtain weight sensitivity level data; performing weight feature learning rate calculation on the weight sensitivity level data to generate feature learning rate data; Step S3455: dynamically dividing the feature learning rate data according to the detection priority weighted data to obtain learning rate division data; and adaptively optimizing the priority adjustment data according to the dynamic learning rate division to obtain adaptive generalization adjustment data.

7. The method for constructing an integrated model for bacteria detection according to claim 3, characterized in that: Step S35 includes the following steps: Step S351: performing single feature matching on the model classifier component according to the adaptive generalization adjustment data to generate single feature matching data; performing multi-scale feature association matching on the to-be-detected sample group data based on the single feature matching data to obtain multi-scale feature association data; Step S352: performing feature detection interconnection on the multi-dimensional feature fusion data according to the multi-scale feature association data to obtain detection feature interconnection data; performing label parameter extraction on the detection feature interconnection data to generate interconnection label parameters; Step S353: performing detection priority interconnection on the detection priority weighted data based on the interconnection tag parameters to obtain detection priority interconnection data; performing detection contribution increment on the detection priority interconnection data to obtain detection contribution increment data; and re-ranking the weight ranking data according to the detection contribution increment data to generate weight ranking response data. Step S354: Perform a rapid learning rate response on the learning rate division data according to the weight sorting response data to obtain the learning rate rapid response data; perform a rapid pre-detection on the sample group data to be detected through the learning rate rapid response data to obtain the sample pre-detection data; input the sample pre-detection data into the bacterial detection integrated pre-model for rapid detection response training to obtain the bacterial detection integrated training model.

8. The method for constructing an integrated model for bacteria detection according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Divide the to-be-detected sample group data into a test set and a validation set to obtain a to-be-detected sample test set and a to-be-detected sample validation set; Step S42: Input the test set of samples to be detected into the bacteria detection integrated training model to calculate the detection accuracy and generate test set accuracy data; input the test set of samples to be detected into the bacteria detection integrated training model to record the recall rate and generate test set recall data; integrate the test set accuracy data and the test set recall data to obtain test set performance data; Step S43: Input the verification set of samples to be tested into the bacteria detection integrated training model to calculate the detection accuracy and generate verification set accuracy data; input the verification set of samples to be tested into the bacteria detection integrated training model to record the recall rate and generate verification set recall data; integrate the verification set accuracy data and the verification set recall data to obtain verification set performance data; Step S44: performing a performance comparison between the test set performance data and the validation set performance data to generate training model performance evaluation data; iteratively optimizing the bacteria detection integrated training model based on the training model performance evaluation data to obtain a bacteria detection integrated model; Step S45: performing bacteria detection on the bacteria to be detected data based on the bacteria detection integrated model to obtain a bacteria detection report.

9. The method for constructing an integrated model for bacteria detection according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: performing similarity measurement on the test set performance data and the validation set performance data to generate performance similarity data; performing similarity deviation calculation on the performance similarity data to obtain performance similarity deviation data; performing detection response time difference calculation on the test set performance data and the validation set performance data to obtain response time difference data; Step S442: performing a performance evaluation on the bacteria detection integrated training model using the performance similarity deviation data and the response time difference data to generate training model performance evaluation data; Step S443: performing anomaly detection feedback on the bacteria detection integrated training model based on the training model performance evaluation data to obtain anomaly detection feedback data; performing an association parameter improvement operation on the multi-scale feature association data based on the anomaly detection feedback data to generate association parameter improvement data; Step S444: performing error back propagation on the performance similarity deviation data based on the association parameter improvement data to obtain error back propagation data; performing loss function back propagation on the detection priority weighted data based on the error back propagation data to obtain loss function back propagation data; performing regularization constraints on the bacteria detection ensemble training model based on the loss function back propagation data to obtain regularization constraint data; Step S445: performing multi-task collaborative training on the sample pre-detection data according to the regularized constraint data to obtain multi-task collaborative data; iteratively optimizing the bacteria detection integrated training model according to the multi-task collaborative data to obtain a bacteria detection integrated model.

10. The method for constructing an integrated model for bacteria detection according to claim 9, characterized in that: Step S444 includes the following steps: Step S4441: determining the parameter improvement range of the associated parameter improvement data to obtain associated parameter range data; calculating the deviation degree of the performance similarity deviation data to obtain performance deviation degree data; Step S4442: performing range-degree mapping on the associated parameter range data and the performance deviation degree data to generate deviation parameter mapping data; performing error impact identification on the deviation parameter mapping data to obtain error impact data; Step S4443: performing error back propagation on the multi-scale feature association data according to the error influence data to obtain error back propagation data; performing detection network layer extraction on the bacteria detection integrated training model to obtain detection network layer data; performing loss function calculation layer by layer on the detection network layer data according to the detection priority weighted data to obtain partial derivatives of the loss function; Step S4444: The partial derivative of the loss function is reversely transmitted to the detection network layer to obtain the loss function back-propagation data; the regularization function is determined according to the anomaly detection feedback data to obtain the regularization function; the regularization function is added to the loss function back-propagation data to generate the back-propagation function information; the bacteria detection integrated training model is regularized and constrained according to the back-propagation function information to obtain the regularization constraint data.

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