CN117390499A - Be applied to multiple sample detecting system that food pesticide remained and detected - Google Patents
Be applied to multiple sample detecting system that food pesticide remained and detected Download PDFInfo
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Abstract
The invention relates to the technical field of pesticide residue detection, in particular to a multi-sample detection system applied to food pesticide residue detection. According to the invention, through using nondestructive imaging technology, particularly X-ray imaging and infrared imaging, preliminary detection can be performed with higher accuracy, the efficiency of data processing and analysis is greatly improved by the application of deep learning and pattern recognition technology, the system has the capability of trend prediction and risk assessment, comprehensive analysis can be performed based on historical and real-time data, a user is assisted in predicting future risks and making corresponding management strategies, and the introduction of an intelligent decision support module provides data-based strategy suggestions for the user and optimizes the decision process.
Description
Technical Field
The invention relates to the technical field of pesticide residue detection, in particular to a multi-sample detection system applied to food pesticide residue detection.
Background
Pesticide residue detection is a key technical field, and relates to the core problems of food safety and public health. The main purpose is to determine and quantify the pesticide residues in food samples, ensuring that these residues meet safety standards. Since pesticides are widely used in agricultural production for controlling pests and diseases, there are residues in the harvested agricultural products. The kind and concentration of the pesticide residue need to be strictly controlled to prevent negative effects on human health. The detection methods used in this technical field are various, including chromatography, mass spectrometry, biochemical methods, etc., and are aimed at accurately detecting, analyzing and evaluating the pesticide residue in food samples.
Wherein, the multi-sample detection system applied to food pesticide residue detection is a device or system specially designed for detecting and analyzing pesticide residues in food samples. The primary purpose of the system is to provide a quick, accurate and efficient way to evaluate pesticide residue levels in multiple food samples. Such detection is critical to ensure that the food meets safety standards, while also helping to prevent health problems due to pesticide residue overstepping. Generally, such systems achieve this goal by using advanced chemical analysis techniques, such as high performance liquid chromatography, gas chromatography, mass spectrometry, and the like. These techniques can accurately identify and quantify specific pesticide components in food samples to assess whether they meet food safety standards.
Conventional systems suffer from several key disadvantages, most of which generally rely on simpler detection techniques, limiting the accuracy and reliability of the detection results. The efficiency in processing and analyzing large amounts of data tends to be low, resulting in slow detection and analysis processes. Conventional systems lack trend analysis and risk assessment capabilities, which limit effectiveness in predicting future risk and developing management strategies. The lack of intelligent decision support functionality further limits the ability of users to utilize data in the decision process.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a multi-sample detection system applied to food pesticide residue detection.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the multi-sample detection system applied to food pesticide residue detection comprises a pesticide residue analysis module, a deep learning model module, a pattern recognition and classification module, a pesticide trend prediction module, a data fusion analysis module, a risk assessment module, an intelligent decision support module and an adaptive learning module;
the pesticide residue analysis module is based on a nondestructive imaging technology, performs preliminary detection of pesticide residues by adopting high-resolution X-ray and infrared imaging, performs preliminary analysis on the data, and generates preliminary detection data;
The deep learning model module is used for carrying out feature extraction and mode analysis by adopting a convolutional neural network technology based on preliminary detection data, training a model and generating deep learning features;
the pattern recognition and classification module classifies and recognizes pesticide residues by adopting a pattern recognition technology based on deep learning characteristics and generates a classification recognition report;
the pesticide trend prediction module is used for carrying out trend analysis and predictive modeling by adopting a cyclic neural network and long-term and short-term memory network technology based on the classification recognition report, verifying the accuracy of the model and generating trend prediction data;
the data fusion analysis module is used for comprehensively analyzing based on trend prediction data and combining environment and historical use data by adopting a data fusion technology to generate a fusion analysis report;
the risk assessment module is used for carrying out potential risk analysis by adopting a risk assessment technology based on the fusion analysis report, outputting an assessment result and generating a risk assessment report;
the intelligent decision support module provides intelligent decision support based on the risk assessment report, assists a user in making a management strategy and generates a decision scheme;
the adaptive learning module continuously learns and adapts to new data patterns based on the decision scheme, optimizes system performance, and updates the learning report to generate a learning improvement report.
As a further scheme of the invention, the trend prediction data is specifically trend change graphs, prediction model precision and trend intensity assessment, the deep learning features comprise feature vectors, pattern recognition matrixes and training effect assessment, the classification recognition reports comprise category labels, recognition accuracy and pattern matching degree, the fusion analysis reports comprise comprehensive data matrixes, analysis result summaries and influence factor ranks, the risk assessment reports are specifically risk classification, risk factor analysis and risk response schemes, the decision schemes are specifically strategy selection schemes, decision simulation effects and optimization schemes, and the learning improvement reports comprise performance optimization indexes, data update records and system efficiency assessment.
As a further scheme of the invention, the pesticide residue analysis module comprises an X-ray detection sub-module, an infrared imaging sub-module, a preliminary data analysis sub-module and an image storage sub-module;
the X-ray detection submodule performs sample scanning by adopting a digital image processing method based on an X-ray imaging technology to generate X-ray image data;
the infrared imaging sub-module adopts an infrared spectrum analysis technology to carry out depth analysis on chemical components of residues based on X-ray image data to generate infrared spectrum data;
The primary data analysis submodule adopts a data fusion and statistical analysis method to identify the characteristics and distribution of pesticide residues based on infrared spectrum data and generate a primary analysis result;
the image storage sub-module is used for safely storing images and data by adopting a database management technology based on the primary analysis result to generate primary detection data;
the digital image processing method comprises edge detection, image enhancement and noise filtering, the infrared spectrum analysis comprises absorption peak identification and spectrum analysis, the data fusion comprises multi-source data integration and statistical inference, and the database management comprises data compression, index creation and access control.
As a further scheme of the invention, the deep learning model module comprises a feature extraction sub-module, a model training sub-module, a model verification sub-module and a feature storage sub-module;
the feature extraction submodule extracts key features from the image by adopting a convolutional neural network technology based on the preliminary detection data to generate a feature data set;
the model training submodule optimizes and adjusts the model by adopting a deep learning training method based on the characteristic data set to generate a training model;
The model verification submodule is based on a training model, adopts a cross verification technology, tests the accuracy and stability of the model and generates a verification report;
the feature storage submodule adopts a data storage and management technology to store the trained model and the features thereof safely based on the verification report, and generates deep learning features;
the convolutional neural network comprises a hierarchical feature extraction and activation function, the deep learning training method comprises a back propagation algorithm and a loss function minimization, the cross validation is specifically a data set dividing and verifying cycle, and the data storage and management comprises data backup, secure encryption and data retrieval.
As a further scheme of the invention, the pattern recognition and classification module comprises a pattern analysis sub-module, a classification algorithm sub-module, a recognition verification sub-module and a report generation sub-module;
the pattern analysis submodule analyzes patterns and relativity of pesticide residues based on deep learning characteristics by adopting a data clustering and association rule analysis technology to generate a pattern analysis result;
the classifying algorithm submodule accurately classifies pesticide residues by adopting a support vector machine and a random forest algorithm based on a mode analysis result to generate a classifying result;
The recognition verification submodule verifies the accuracy and reliability of classification by adopting a cross verification and confusion matrix analysis technology based on the classification result to generate a verification result;
the report generation sub-module adopts report generation and data visualization technology to sort and display the classification identification information based on the verification result, and generates a classification identification report;
the data clustering comprises K-means clustering and hierarchical clustering, the association rule analysis comprises an Apriori algorithm and an FP-growth algorithm, the support vector machine comprises kernel function selection and hyperplane optimization, the random forest algorithm comprises decision tree generation and feature selection, the confusion matrix analysis is used for evaluating classification performance, the report generation comprises text editing and chart generation, and the data visualization comprises creation of a scatter diagram and a thermodynamic diagram.
As a further scheme of the invention, the pesticide trend prediction module comprises a trend analysis sub-module, a prediction modeling sub-module, a data simulation sub-module and a trend verification sub-module;
the trend analysis submodule analyzes the historical trend and the future trend of the pesticide residue based on the classification recognition report by adopting a time sequence analysis and trend line drawing technology to generate a trend analysis result;
The prediction modeling module is used for constructing a prediction model by adopting a cyclic neural network and a long-term and short-term memory network based on a trend analysis result;
the data simulation submodule carries out simulation experiments and scene analysis based on the prediction model, tests the prediction effect of the model under the differential situation, and generates a simulation result;
the trend verification sub-module adopts statistical verification and error analysis technology to evaluate the accuracy and reliability of the model based on the simulation result and generates trend prediction data;
the time series analysis comprises an autoregressive model and a moving average method, the trend line drawing comprises linear and nonlinear trend models, the cyclic neural network comprises a network structural design and feedback connection, the long-term memory network comprises a memory unit and a gating mechanism, the simulation experiment comprises virtual data generation and scene assumption setting, the scene analysis comprises sensitivity analysis and assumption verification, the statistical verification comprises p-value calculation and significance test, and the error analysis comprises mean square error and absolute error evaluation.
As a further scheme of the invention, the data fusion analysis module comprises a data integration sub-module, an analysis algorithm sub-module, a fusion verification sub-module and a report arrangement sub-module;
The data integration sub-module integrates environment and historical use data by adopting a data warehouse technology based on trend prediction data to generate an integrated data set;
the analysis algorithm submodule adopts a multivariate analysis method to carry out deep analysis based on the integrated data set to generate an analysis result;
the fusion verification sub-module adopts a model verification technology to evaluate the fusion effect and accuracy based on the analysis result, and generates a verification result;
the report sorting sub-module sorts and presents comprehensive analysis report matters by adopting a report writing technology based on the verification result, and generates a fusion analysis report;
the data warehouse is specifically data summarization and index optimization, the multivariate analysis method comprises principal component analysis and factor analysis, the model verification comprises cross verification and model comparison, and the report writing comprises structural design and content arrangement.
As a further scheme of the invention, the risk evaluation module comprises a risk analysis sub-module, an evaluation method sub-module, a result verification sub-module and a report output sub-module;
the risk analysis sub-module is used for identifying and evaluating the potential risk of pesticide residues by adopting a risk matrix technology based on the fusion analysis report to generate a risk analysis result;
The evaluation method submodule carries out risk quantification evaluation by adopting a Bayesian network technology based on a risk analysis result to generate an evaluation result;
the result verification sub-module verifies the accuracy and reliability of the evaluation by adopting a statistical test technology based on the evaluation result to generate a verification result;
the report output submodule adopts a report writing technology to compile a detailed risk assessment report based on the verification result, and generates a risk assessment report;
the risk matrix comprises risk grading and influence evaluation, the Bayesian network comprises network construction and probability inference, and the statistical test comprises hypothesis test and variance analysis.
As a further scheme of the invention, the intelligent decision support module comprises a strategy generation sub-module, a decision simulation sub-module, a scheme optimization sub-module and a support feedback sub-module;
the strategy generation sub-module is used for customizing a management strategy based on a risk assessment report by adopting a decision tree analysis method and generating a management strategy scheme;
the decision simulation sub-module simulates a decision scene by adopting a Monte Carlo simulation method based on a management strategy scheme and generates a decision simulation report;
The scheme optimizing submodule optimizes a decision scheme by adopting a genetic algorithm based on the decision simulation report and generates an optimized decision scheme;
the support feedback submodule collects user feedback based on the optimized decision scheme by adopting a feedback control method and generates an intelligent decision support report;
the decision tree analysis method comprises information gain calculation, optimal feature selection and tree construction, the Monte Carlo simulation method specifically refers to random sampling and probability distribution analysis, the genetic algorithm specifically refers to an optimization process based on natural selection, crossover and mutation, and the feedback control method comprises closed-loop system design and feedback signal processing.
As a further scheme of the invention, the self-adaptive learning module comprises a learning algorithm sub-module, a performance tuning sub-module, a data updating sub-module and a report updating sub-module;
the learning algorithm submodule carries out learning of a new data mode by adopting a machine learning method based on the intelligent decision support report and generates a preliminary learning improvement report;
the performance optimization submodule performs algorithm optimization based on the preliminary learning improvement report by adopting a performance optimization method and generates a performance optimization report;
The data updating sub-module updates system data by adopting a data synchronization method based on the performance optimization report and generates a data updating report;
the report updating sub-module updates a learning report and generates a learning improvement report by adopting a document automatic generation method based on the data updating report;
the machine learning method comprises supervised learning, unsupervised learning and reinforcement learning, the performance optimization method is specifically algorithm parameter adjustment and operation efficiency improvement, the data synchronization method comprises real-time data capturing and database synchronization, and the document automatic generation method is specifically report generation and automatic text synthesis based on templates.
Compared with the prior art, the invention has the advantages and positive effects that:
in the present invention, preliminary detection can be performed with higher accuracy by using a nondestructive imaging technique, specifically, X-ray and infrared imaging. The application of deep learning and pattern recognition techniques greatly improves the efficiency of data processing and analysis. The system has the capabilities of trend prediction and risk assessment, can perform comprehensive analysis based on historical and real-time data, and assists users in predicting future risks and formulating corresponding management strategies. The introduction of the intelligent decision support module provides policy suggestions based on data for users, and optimizes the decision process. The adaptive learning module of the system ensures that the system can continuously learn and adapt to new data modes, and the performance of the system is continuously optimized.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of the pesticide residue analysis module of the present invention;
FIG. 4 is a flow chart of a deep learning model module of the present invention;
FIG. 5 is a flow chart of a pattern recognition and classification module according to the present invention;
FIG. 6 is a flow chart of a pesticide trend prediction module of the present invention;
FIG. 7 is a flow chart of a data fusion analysis module according to the present invention;
FIG. 8 is a flow chart of a risk assessment module according to the present invention;
FIG. 9 is a flow chart of the intelligent decision support module of the present invention;
fig. 10 is a flowchart of an adaptive learning module according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Embodiment one: referring to fig. 1 to 2, a multi-sample detection system for detecting food pesticide residues includes a pesticide residue analysis module, a deep learning model module, a pattern recognition and classification module, a pesticide trend prediction module, a data fusion analysis module, a risk assessment module, an intelligent decision support module, and a self-adaptive learning module;
the pesticide residue analysis module is based on a nondestructive imaging technology, performs preliminary detection of pesticide residues by adopting high-resolution X-ray and infrared imaging, performs preliminary analysis on the data, and generates preliminary detection data;
the deep learning model module is used for carrying out feature extraction and mode analysis by adopting a convolutional neural network technology based on the preliminary detection data, training a model and generating deep learning features;
the pattern recognition and classification module classifies and recognizes pesticide residues by adopting a pattern recognition technology based on deep learning characteristics and generates a classification recognition report;
the pesticide trend prediction module is based on a classification recognition report, adopts a cyclic neural network and a long-term and short-term memory network technology to perform trend analysis and prediction modeling, verifies the accuracy of the model and generates trend prediction data;
The data fusion analysis module is used for carrying out comprehensive analysis by adopting a data fusion technology based on trend prediction data and combining environment and historical use data to generate a fusion analysis report;
the risk assessment module is used for carrying out potential risk analysis by adopting a risk assessment technology based on the fusion analysis report, outputting an assessment result and generating a risk assessment report;
the intelligent decision support module provides intelligent decision support based on the risk assessment report, assists a user in making a management strategy and generates a decision scheme;
the adaptive learning module continuously learns and adapts to new data patterns based on the decision scheme, optimizes system performance, and updates the learning report to generate a learning improvement report.
The trend prediction data specifically comprises a trend change chart, a prediction model precision and a trend intensity evaluation, the deep learning characteristics comprise feature vectors, a pattern recognition matrix and a training effect evaluation, the classification recognition report comprises a category label, recognition accuracy and pattern matching degree, the fusion analysis report comprises a comprehensive data matrix, an analysis result abstract and an influence factor ranking, the risk evaluation report specifically comprises a risk grade division, a risk factor analysis and risk handling scheme, the decision scheme specifically comprises a strategy selection scheme, a decision simulation effect and an optimization scheme, and the learning improvement report comprises a performance optimization index, a data updating record and a system efficiency evaluation.
Through the application of the pesticide residue analysis module, the system can rapidly and efficiently perform preliminary detection, the detection speed and efficiency are effectively improved, the detection cost is reduced, and powerful support is provided for rapid reaction and risk reduction. The introduction of the deep learning model module further improves the detection accuracy, and is beneficial to effectively distinguishing pesticide residues in different samples, so that false alarm and missing alarm are reduced, and the reliability of detection results is ensured. The application of the pattern recognition and classification module enhances the classification and recognition capability of pesticide residues, so that the system can more comprehensively know the pesticide residue condition in the sample, and is beneficial to food quality control and risk management. The adoption of the pesticide trend prediction module further improves the intelligent level of the system, so that the pesticide trend prediction module can predict the trend of pesticide residues, and is beneficial to taking necessary measures in advance so as to reduce potential risks. The data fusion analysis module comprehensively considers various data, provides more comprehensive risk analysis and assessment, and is beneficial to making more intelligent decisions. The risk assessment module provides clear risk assessment results for users, so that the users can better understand and cope with potential risks, and the feasibility of food safety management is improved. The intelligent decision support module provides powerful decision support for users to assist them in making scientific management strategies. The application of the self-adaptive learning module further improves the performance of the system, so that the system can continuously adapt to new data modes and situations, and high-level efficiency is maintained.
Referring to fig. 3, the pesticide residue analysis module includes an X-ray detection sub-module, an infrared imaging sub-module, a preliminary data analysis sub-module, and an image storage sub-module;
the X-ray detection submodule performs sample scanning by adopting a digital image processing method based on an X-ray imaging technology to generate X-ray image data;
the infrared imaging submodule adopts an infrared spectrum analysis technology to carry out depth analysis on chemical components of residues based on the X-ray image data so as to generate infrared spectrum data;
the primary data analysis submodule adopts a data fusion and statistical analysis method to identify the characteristics and distribution of pesticide residues based on infrared spectrum data and generate a primary analysis result;
the image storage sub-module is used for safely storing the images and the data by adopting a database management technology based on the primary analysis result to generate primary detection data;
the digital image processing method comprises edge detection, image enhancement and noise filtering, infrared spectrum analysis comprises absorption peak identification and spectrum analysis, data fusion comprises multi-source data integration and statistical inference, and database management specifically comprises data compression, index creation and access control.
The X-ray detection submodule is based on an X-ray imaging technology and adopts a digital image processing method to scan a sample. In this module, the sample is scanned by X-rays, generating X-ray image data. Digital image processing methods include edge detection, image enhancement, and noise filtering to improve image quality and accuracy.
The infrared imaging submodule adopts an infrared spectrum analysis technology to carry out depth analysis on chemical components of residues based on X-ray image data. In this module, infrared spectrum data of the sample is obtained by infrared spectrum analysis to identify chemical components of the pesticide residue. Infrared spectroscopic analysis includes absorption peak identification and spectroscopic analysis.
Based on infrared spectrum data, the primary data analysis sub-module adopts a data fusion and statistical analysis method to identify the characteristics and distribution of pesticide residues. In the module, data fusion is carried out on the infrared spectrum data, multi-source data are integrated, and statistical inference is carried out to generate a primary analysis result.
The image storage sub-module adopts a database management technology to safely store images and data based on the primary analysis result. In this module, the X-ray image data, the infrared spectrum data, and the preliminary analysis results are stored in a database to generate preliminary detection data. Database management includes data compression, index creation, and access control to ensure security and accessibility of data.
Referring to fig. 4, the deep learning model module includes a feature extraction sub-module, a model training sub-module, a model verification sub-module, and a feature storage sub-module;
The feature extraction submodule extracts key features from the image by adopting a convolutional neural network technology based on the preliminary detection data to generate a feature data set;
the model training submodule optimizes and adjusts the model by adopting a deep learning training method based on the characteristic data set to generate a training model;
the model verification sub-module is based on a training model, adopts a cross verification technology, tests the accuracy and stability of the model and generates a verification report;
the feature storage sub-module is used for safely storing the trained model and the features thereof by adopting a data storage and management technology based on the verification report, and generating deep learning features;
the convolutional neural network comprises a hierarchical feature extraction and activation function, the deep learning training method comprises a back propagation algorithm and a loss function minimization, cross verification is specifically a data set dividing and verifying cycle, and data storage and management comprise data backup, security encryption and data retrieval.
The feature extraction submodule adopts a convolutional neural network technology to extract key features from the image based on the preliminary detection data. The convolutional neural network includes hierarchical feature extraction and activation functions. In this module, the image is processed by a convolutional neural network, key features are extracted, and a feature dataset is generated.
The model training submodule optimizes and adjusts the model by adopting a deep learning training method based on the characteristic data set. The deep learning training method includes a back propagation algorithm and a loss function minimization. In this module, a deep learning model is trained using the feature dataset to generate a training model.
The model verification sub-module adopts a cross verification technology based on a training model to test the accuracy and stability of the model. Cross-validation includes data set partitioning and validation loops. In this module, cross-validation is used to evaluate the performance of the training model, generating a validation report.
The feature storage sub-module adopts a data storage and management technology based on the verification report to store the trained model and the features thereof safely. Data storage and management includes data backup, secure encryption, and data retrieval. In this module, the trained deep learning model and associated feature data are securely stored to generate deep learning features.
Referring to fig. 5, the pattern recognition and classification module includes a pattern analysis sub-module, a classification algorithm sub-module, a recognition verification sub-module, and a report generation sub-module;
the pattern analysis submodule analyzes patterns and relativity of pesticide residues based on deep learning characteristics by adopting a data clustering and association rule analysis technology to generate a pattern analysis result;
The classifying algorithm submodule accurately classifies pesticide residues by adopting a support vector machine and a random forest algorithm based on the mode analysis result to generate a classifying result;
the identification verification sub-module adopts a cross verification and confusion matrix analysis technology to verify the accuracy and reliability of classification based on the classification result and generate a verification result;
the report generation sub-module adopts report generation and data visualization technology to sort and display the classification identification information based on the verification result, and generates a classification identification report;
the data clustering comprises K mean clustering and hierarchical clustering, the association rule analysis comprises an Apriori algorithm and an FP-growth algorithm, the support vector machine comprises kernel function selection and hyperplane optimization, the random forest algorithm comprises decision tree generation and feature selection, the confusion matrix analysis is used for evaluating classification performance, the report generation comprises text editing and chart generation, and the data visualization comprises creation of a scatter diagram and a thermodynamic diagram.
The pattern analysis submodule adopts a data clustering and association rule analysis technology to analyze the pattern and the association of pesticide residues based on the deep learning characteristics. The data clustering comprises K-means clustering and hierarchical clustering, and the association rule analysis comprises an Apriori algorithm and an FP-growth algorithm. In this module, patterns and correlations of pesticide residues are identified by analysis of the deep learning features, generating pattern analysis results.
The classifying algorithm submodule accurately classifies pesticide residues by adopting a support vector machine and a random forest algorithm based on a mode analysis result. The support vector machine comprises kernel function selection and hyperplane optimization, and the random forest algorithm comprises decision tree generation and feature selection. In this module, the pattern analysis results are used to train a classification model, generating classification results.
The recognition verification sub-module adopts a cross verification and confusion matrix analysis technology to verify the accuracy and reliability of classification based on the classification result. Confusion matrix analysis is used to evaluate classification performance. In this module, the accuracy of the classification model is verified by cross-validation and confusion matrix analysis, generating a validation result.
And the report generation sub-module is used for sorting and displaying the classification identification information by adopting report generation and data visualization technology based on the verification result to generate a classification identification report. Report generation includes text editing and chart generation, and data visualization includes creation of scatter plots and thermodynamic diagrams. In this module, the generated classification identification information is presented in the form of a report for further pesticide residue analysis and decision making.
Referring to fig. 6, the pesticide trend prediction module includes a trend analysis sub-module, a prediction modeling sub-module, a data simulation sub-module, and a trend verification sub-module;
The trend analysis submodule analyzes the historical trend and the future trend of the pesticide residue by adopting a time sequence analysis and trend line drawing technology based on the classification recognition report to generate a trend analysis result;
the prediction modeling module is used for constructing a prediction model by adopting a cyclic neural network and a long-term and short-term memory network based on the trend analysis result;
the data simulation submodule carries out simulation experiments and scene analysis based on the prediction model, tests the prediction effect of the model under the differential situation, and generates a simulation result;
the trend verification sub-module adopts statistical verification and error analysis technology based on the simulation result to evaluate the accuracy and reliability of the model and generate trend prediction data;
the time sequence analysis comprises an autoregressive model and a moving average method, the trend line drawing comprises a linear trend model and a nonlinear trend model, the cyclic neural network comprises a network structural design and feedback connection, the long-term memory network comprises a memory unit and a gating mechanism, the simulation experiment comprises virtual data generation and scene assumption setting, the scene analysis comprises sensitivity analysis and assumption verification, the statistical verification comprises p-value calculation and significance test, and the error analysis comprises mean square error and absolute error assessment.
In the trend analysis sub-module, the target: analyzing the historical trend of pesticide residues and predicting the future trend.
The method comprises the following steps:
time series analysis: autoregressive model (AR) and Moving Average (MA) were used.
AR model: the predicted future value is based on the previous value.
MA model: future values are predicted using past error terms.
Trendline drawing technology: including linear and nonlinear models.
Linear model: a least squares method is used to fit the line.
Nonlinear model: polynomial regression, etc. may be employed.
Code example (time series analysis):
import pandas as pd；
import numpy as np；
import statsmodels.api as sm；
let # assume df be the DataFrame containing time and pesticide residue data;
df['datetime'] = pd.to_datetime(df['datetime'])；
df.set_index('datetime', inplace=True)；
# autoregressive model;
model_ar = sm.tsa.ARIMA(df['residue'], order=(1,0,0))；
results_ar = model_ar.fit()；
# a moving average model;
model_ma = sm.tsa.ARIMA(df['residue'], order=(0,0,1))；
results_ma = model_ma.fit()；
predicting #;
predictions_ar = results_ar.predict(start, end)；
predictions_ma = results_ma.predict(start, end)；
in the predictive modeling module, the target: and constructing a prediction model based on the trend analysis result.
The method comprises the following steps:
recurrent Neural Network (RNN): is suitable for processing time series data.
Long and short term memory network (LSTM): specially designed to avoid long-term dependence problems.
A memory unit: information is stored.
Gating mechanism: and controlling information flow.
Code example (LSTM model):
from keras.models import Sequential；
from keras.layers import LSTM, Dense；
model = Sequential()；
model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))；
model.add(LSTM(units=50))；
model.add(Dense(1))；
model.compile(optimizer='adam', loss='mean_squared_error')；
training a model;
model.fit(x_train, y_train, epochs=100, batch_size=32)；
in the data simulation sub-module, the target: the test model performs in different situations.
The method comprises the following steps:
Virtual data generation: data of different contexts is created.
Scene assumption setting: different test scenarios are set.
Sensitivity analysis and hypothesis testing: the model was examined for performance under different conditions.
In the trend verification sub-module, the target: the accuracy and reliability of the model is assessed.
The method comprises the following steps:
and (3) statistical verification: such as p-value calculations and significance testing.
Error analysis: including Mean Square Error (MSE) and absolute error.
Code example (error analysis):
from sklearn.metrics import mean_squared_error, mean_absolute_error；
# assume that predictors are the prediction result of the model, and y_true is the actual value;
mse = mean_squared_error(y_true, predictions)；
mae = mean_absolute_error(y_true, predictions)；
referring to fig. 7, the data fusion analysis module includes a data integration sub-module, an analysis algorithm sub-module, a fusion verification sub-module, and a report sorting sub-module;
the data integration sub-module integrates environment and historical use data by adopting a data warehouse technology based on trend prediction data to generate an integrated data set;
the analysis algorithm submodule adopts a multivariate analysis method to carry out deep analysis based on the integrated data set to generate an analysis result;
the fusion verification sub-module adopts a model verification technology to evaluate the fusion effect and accuracy based on the analysis result, and generates a verification result;
the report sorting sub-module sorts and presents comprehensive analysis report matters by adopting a report writing technology based on the verification result, and generates a fusion analysis report;
The data warehouse comprises data summarization and index optimization, the multivariate analysis method comprises principal component analysis and factor analysis, the model verification comprises cross verification and model comparison, and the report writing comprises structural design and content arrangement.
The data integration submodule aims to integrate trend prediction data into a consistent data set for deep analysis and fusion. By employing data warehouse technology, including data aggregation and index optimization, the environmental and historical usage data is integrated into a complete, efficient data source. This ensures the quality and availability of the data, providing a reliable basis for subsequent analysis.
The goal of the analysis algorithm sub-module is to analyze the integrated data deeply to find correlations and trends. By employing multivariate analysis methods, such as principal component analysis and factor analysis, the underlying patterns and relationships of the data are deeply explored, generating analysis results relating to the characteristics of the data. These results provide insight and guidance for data fusion.
The fusion verification sub-module aims at evaluating the effect and accuracy of data fusion and ensuring the quality of an integrated data set. Based on the analysis results, model verification techniques, such as cross-validation and model comparison, are employed to evaluate the fused dataset. This step ensures that the data fusion not only retains critical information, but also reduces redundancy and errors and improves the reliability of the data.
The report sorting sub-module is responsible for sorting the integrated data analysis results into comprehensive analysis reports for decision makers and stakeholders to view and understand. Report matters are arranged through report writing technology, including structural design and content arrangement, and include analysis results, fusion effect evaluation and suggestion. This report provides a clear data background and decision basis for decision making.
Referring to fig. 8, the risk assessment module includes a risk analysis sub-module, an evaluation method sub-module, a result verification sub-module, and a report output sub-module;
the risk analysis sub-module is used for identifying and evaluating potential risks of pesticide residues by adopting a risk matrix technology based on the fusion analysis report to generate a risk analysis result;
the evaluation method submodule carries out risk quantification evaluation by adopting a Bayesian network technology based on the risk analysis result to generate an evaluation result;
the result verification sub-module verifies the accuracy and reliability of the evaluation by adopting a statistical test technology based on the evaluation result to generate a verification result;
the report output sub-module adopts a report writing technology to compile a detailed risk assessment report based on the verification result, and generates a risk assessment report;
the risk matrix comprises risk grading and influence evaluation, the Bayesian network comprises network construction and probability inference, and the statistical test comprises hypothesis test and variance analysis.
The risk analysis sub-module aims at identifying and evaluating the potential risk of pesticide residues, reports based on fusion analysis and adopts a risk matrix technology. This step includes risk ranking and impact assessment to determine the extent of potential risk and related factors. The importance and the urgency of the pesticide residue problem are determined through risk analysis, and a risk analysis result is generated, so that a basis is provided for subsequent evaluation.
The evaluation method submodule is responsible for carrying out quantitative evaluation on risks, and based on risk analysis results, a Bayesian network technology is adopted. This includes network construction and probabilistic inference to build a risk model and estimate a probability distribution of risk. By this step, the potential risk is quantized to specific values, providing quantitative information about the risk of pesticide residue, which helps to more accurately understand the severity of the problem.
The result verification sub-module is used for verifying the accuracy and reliability of the evaluation, and based on the evaluation result, a statistical test technology is adopted. This includes hypothesis testing and analysis of variance, etc., to ensure that the assessment results are authentic. Through verification, the assessment process is supported by scientific and strict, so that the reliability of the assessment result is enhanced.
The report output sub-module is responsible for integrating the risk assessment results into a detailed risk assessment report. This includes integrating the risk analysis results, the assessment results, and the verification results to generate a comprehensive report. This report provides detailed information about the risk of pesticide residues to the decision maker to support his decision making process.
Referring to fig. 9, the intelligent decision support module includes a policy generation sub-module, a decision simulation sub-module, a scheme optimization sub-module, and a support feedback sub-module;
the strategy generation sub-module is based on the risk assessment report, adopts a decision tree analysis method to customize the management strategy and generates a management strategy scheme;
the decision simulation sub-module simulates a decision scene by adopting a Monte Carlo simulation method based on a management strategy scheme and generates a decision simulation report;
the scheme optimizing submodule optimizes a decision scheme by adopting a genetic algorithm based on the decision simulation report and generates an optimized decision scheme;
the support feedback submodule collects user feedback based on the optimized decision scheme by adopting a feedback control method and generates an intelligent decision support report;
the decision tree analysis method comprises information gain calculation, optimal feature selection and tree construction, the Monte Carlo simulation method specifically refers to random sampling and probability distribution analysis, the genetic algorithm specifically refers to an optimization process based on natural selection, crossover and mutation, and the feedback control method comprises closed-loop system design and feedback signal processing.
The policy generation sub-module aims at customizing the management policy scheme according to the data of the risk assessment report. Decision tree analysis, including information gain computation, optimal feature selection, and tree construction, is used to trade-off between different decision options to generate an optimal management strategy. The result of this step is a well-designed management strategy scheme that takes into account potential risks and opportunities.
The decision simulation submodule simulates various decision scenes by a Monte Carlo simulation method comprising random sampling and probability distribution analysis. These simulations take into account uncertainty factors, allowing the effect of the management policy to be evaluated under different conditions. The decision simulation report includes simulation results and risk assessment under various strategies, providing more information and insight for decision making.
The scheme optimization submodule finds the optimal decision scheme through natural selection, crossover, mutation and other optimization processes. This step is based on the results of decision simulation reports, aimed at maximizing benefit and reducing risk. An optimized decision scheme is generated, and more competitive choices are provided for decision makers.
The support feedback sub-module is intended to ensure that the actual effect of the decision is consistent with the expectations. This module includes a feedback control system designed to collect feedback and comments from the user. These feedback comes from the actual experience and observations of the user, helping to understand the actual impact of the decision scheme. By processing and analyzing these feedback, intelligent decision support reports are generated, providing improvement suggestions and summaries to support future decision-making processes.
Referring to fig. 10, the adaptive learning module includes a learning algorithm sub-module, a performance tuning sub-module, a data updating sub-module, and a report updating sub-module;
the learning algorithm sub-module adopts a machine learning method to learn a new data mode based on the intelligent decision support report and generates a preliminary learning improvement report;
the performance optimization submodule performs algorithm optimization based on the preliminary learning improvement report by adopting a performance optimization method and generates a performance optimization report;
the data updating sub-module updates system data by adopting a data synchronization method based on the performance optimization report and generates a data updating report;
the report updating sub-module updates the learning report and generates a learning improvement report by adopting a document automatic generation method based on the data updating report;
the machine learning method comprises supervised learning, unsupervised learning and reinforcement learning, the performance optimization method comprises algorithm parameter adjustment and operation efficiency improvement, the data synchronization method comprises real-time data capturing and database synchronization, and the document automatic generation method comprises report generation and automatic text synthesis based on templates.
The learning algorithm sub-module is intended to accommodate new data patterns through machine learning methods. This includes techniques such as supervised learning, unsupervised learning, and reinforcement learning to identify and understand new decision scenarios. Based on the intelligent decision support report, this sub-module generates a preliminary learning improvement report including identification of new data patterns and suggested learning improvement methods. This step ensures that the system can adapt to changing decision needs and circumstances in time.
The performance optimization submodule improves the efficiency and accuracy of the learning algorithm by adopting a performance optimization method. This includes techniques such as algorithm parameter adjustment and operational efficiency improvement to optimize the performance of the learning algorithm. Based on the preliminary learning improvement report, this sub-module generates a performance optimization report including an optimized algorithm performance index and a suggestion of an improvement method. This ensures that the learning algorithm can more effectively support the decision process when processing the data.
The data update sub-module uses data synchronization methods, including real-time data capture and database synchronization, to update the system's data. Based on the performance optimization report, this sub-module generates a data update report, including the results of the data synchronization and any potential data quality issues. This ensures that the data used by the system is always accurate and time-efficient to support efficient decisions.
The report update sub-module is responsible for ensuring that the learning report reflects the most current data and performance information. Automatic document generation methods are used, including template-based report generation and automatic text synthesis, to update the learning report. Based on the data update report, this sub-module generates a learning improvement report including the learning progress of the system and the suggested direction of improvement. This ensures that users of the decision support system are always able to obtain up-to-date learning and performance information to guide their decision making process.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (10)
1. Be applied to multiple sample detecting system that food pesticide remained and detect, its characterized in that: the system comprises a pesticide residue analysis module, a deep learning model module, a pattern recognition and classification module, a pesticide trend prediction module, a data fusion analysis module, a risk assessment module, an intelligent decision support module and a self-adaptive learning module;
the pesticide residue analysis module is based on a nondestructive imaging technology, performs preliminary detection of pesticide residues by adopting high-resolution X-ray and infrared imaging, performs preliminary analysis on the data, and generates preliminary detection data;
the deep learning model module is used for carrying out feature extraction and mode analysis by adopting a convolutional neural network technology based on preliminary detection data, training a model and generating deep learning features;
The pattern recognition and classification module classifies and recognizes pesticide residues by adopting a pattern recognition technology based on deep learning characteristics and generates a classification recognition report;
the pesticide trend prediction module is used for carrying out trend analysis and predictive modeling by adopting a cyclic neural network and long-term and short-term memory network technology based on the classification recognition report, verifying the accuracy of the model and generating trend prediction data;
the data fusion analysis module is used for comprehensively analyzing based on trend prediction data and combining environment and historical use data by adopting a data fusion technology to generate a fusion analysis report;
the risk assessment module is used for carrying out potential risk analysis by adopting a risk assessment technology based on the fusion analysis report, outputting an assessment result and generating a risk assessment report;
the intelligent decision support module provides intelligent decision support based on the risk assessment report, assists a user in making a management strategy and generates a decision scheme;
the adaptive learning module continuously learns and adapts to new data patterns based on the decision scheme, optimizes system performance, and updates the learning report to generate a learning improvement report.
2. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the trend prediction data specifically comprises a trend change chart, prediction model precision and trend intensity assessment, the deep learning features comprise feature vectors, pattern recognition matrixes and training effect assessment, the classification recognition reports comprise category labels, recognition accuracy and pattern matching degree, the fusion analysis report comprises a comprehensive data matrix, an analysis result abstract and an influence factor ranking, the risk assessment report specifically comprises risk class division, risk factor analysis and a risk response scheme, the decision scheme specifically comprises a strategy selection scheme, a decision simulation effect and an optimization scheme, and the learning improvement report comprises a performance optimization index, a data update record and a system efficiency assessment.
3. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the pesticide residue analysis module comprises an X-ray detection sub-module, an infrared imaging sub-module, a preliminary data analysis sub-module and an image storage sub-module;
the X-ray detection submodule performs sample scanning by adopting a digital image processing method based on an X-ray imaging technology to generate X-ray image data;
the infrared imaging sub-module adopts an infrared spectrum analysis technology to carry out depth analysis on chemical components of residues based on X-ray image data to generate infrared spectrum data;
the primary data analysis submodule adopts a data fusion and statistical analysis method to identify the characteristics and distribution of pesticide residues based on infrared spectrum data and generate a primary analysis result;
the image storage sub-module is used for safely storing images and data by adopting a database management technology based on the primary analysis result to generate primary detection data;
the digital image processing method comprises edge detection, image enhancement and noise filtering, the infrared spectrum analysis comprises absorption peak identification and spectrum analysis, the data fusion comprises multi-source data integration and statistical inference, and the database management comprises data compression, index creation and access control.
4. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the deep learning model module comprises a feature extraction sub-module, a model training sub-module, a model verification sub-module and a feature storage sub-module;
the feature extraction submodule extracts key features from the image by adopting a convolutional neural network technology based on the preliminary detection data to generate a feature data set;
the model training submodule optimizes and adjusts the model by adopting a deep learning training method based on the characteristic data set to generate a training model;
the model verification submodule is based on a training model, adopts a cross verification technology, tests the accuracy and stability of the model and generates a verification report;
the feature storage submodule adopts a data storage and management technology to store the trained model and the features thereof safely based on the verification report, and generates deep learning features;
the convolutional neural network comprises a hierarchical feature extraction and activation function, the deep learning training method comprises a back propagation algorithm and a loss function minimization, the cross validation is specifically a data set dividing and verifying cycle, and the data storage and management comprises data backup, secure encryption and data retrieval.
5. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the pattern recognition and classification module comprises a pattern analysis sub-module, a classification algorithm sub-module, a recognition verification sub-module and a report generation sub-module;
the pattern analysis submodule analyzes patterns and relativity of pesticide residues based on deep learning characteristics by adopting a data clustering and association rule analysis technology to generate a pattern analysis result;
the classifying algorithm submodule accurately classifies pesticide residues by adopting a support vector machine and a random forest algorithm based on a mode analysis result to generate a classifying result;
the recognition verification submodule verifies the accuracy and reliability of classification by adopting a cross verification and confusion matrix analysis technology based on the classification result to generate a verification result;
the report generation sub-module adopts report generation and data visualization technology to sort and display the classification identification information based on the verification result, and generates a classification identification report;
the data clustering comprises K-means clustering and hierarchical clustering, the association rule analysis comprises an Apriori algorithm and an FP-growth algorithm, the support vector machine comprises kernel function selection and hyperplane optimization, the random forest algorithm comprises decision tree generation and feature selection, the confusion matrix analysis is used for evaluating classification performance, the report generation comprises text editing and chart generation, and the data visualization comprises creation of a scatter diagram and a thermodynamic diagram.
6. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the pesticide trend prediction module comprises a trend analysis sub-module, a prediction modeling sub-module, a data simulation sub-module and a trend verification sub-module;
the trend analysis submodule analyzes the historical trend and the future trend of the pesticide residue based on the classification recognition report by adopting a time sequence analysis and trend line drawing technology to generate a trend analysis result;
the prediction modeling module is used for constructing a prediction model by adopting a cyclic neural network and a long-term and short-term memory network based on a trend analysis result;
the data simulation submodule carries out simulation experiments and scene analysis based on the prediction model, tests the prediction effect of the model under the differential situation, and generates a simulation result;
the trend verification sub-module adopts statistical verification and error analysis technology to evaluate the accuracy and reliability of the model based on the simulation result and generates trend prediction data;
the time series analysis comprises an autoregressive model and a moving average method, the trend line drawing comprises linear and nonlinear trend models, the cyclic neural network comprises a network structural design and feedback connection, the long-term memory network comprises a memory unit and a gating mechanism, the simulation experiment comprises virtual data generation and scene assumption setting, the scene analysis comprises sensitivity analysis and assumption verification, the statistical verification comprises p-value calculation and significance test, and the error analysis comprises mean square error and absolute error evaluation.
7. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the data fusion analysis module comprises a data integration sub-module, an analysis algorithm sub-module, a fusion verification sub-module and a report arrangement sub-module;
the data integration sub-module integrates environment and historical use data by adopting a data warehouse technology based on trend prediction data to generate an integrated data set;
the analysis algorithm submodule adopts a multivariate analysis method to carry out deep analysis based on the integrated data set to generate an analysis result;
the fusion verification sub-module adopts a model verification technology to evaluate the fusion effect and accuracy based on the analysis result, and generates a verification result;
the report sorting sub-module sorts and presents comprehensive analysis report matters by adopting a report writing technology based on the verification result, and generates a fusion analysis report;
the data warehouse is specifically data summarization and index optimization, the multivariate analysis method comprises principal component analysis and factor analysis, the model verification comprises cross verification and model comparison, and the report writing comprises structural design and content arrangement.
8. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the risk assessment module comprises a risk analysis sub-module, an evaluation method sub-module, a result verification sub-module and a report output sub-module;
The risk analysis sub-module is used for identifying and evaluating the potential risk of pesticide residues by adopting a risk matrix technology based on the fusion analysis report to generate a risk analysis result;
the evaluation method submodule carries out risk quantification evaluation by adopting a Bayesian network technology based on a risk analysis result to generate an evaluation result;
the result verification sub-module verifies the accuracy and reliability of the evaluation by adopting a statistical test technology based on the evaluation result to generate a verification result;
the report output submodule adopts a report writing technology to compile a detailed risk assessment report based on the verification result, and generates a risk assessment report;
the risk matrix comprises risk grading and influence evaluation, the Bayesian network comprises network construction and probability inference, and the statistical test comprises hypothesis test and variance analysis.
9. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the intelligent decision support module comprises a strategy generation sub-module, a decision simulation sub-module, a scheme optimization sub-module and a support feedback sub-module;
the strategy generation sub-module is used for customizing a management strategy based on a risk assessment report by adopting a decision tree analysis method and generating a management strategy scheme;
The decision simulation sub-module simulates a decision scene by adopting a Monte Carlo simulation method based on a management strategy scheme and generates a decision simulation report;
the scheme optimizing submodule optimizes a decision scheme by adopting a genetic algorithm based on the decision simulation report and generates an optimized decision scheme;
the support feedback submodule collects user feedback based on the optimized decision scheme by adopting a feedback control method and generates an intelligent decision support report;
the decision tree analysis method comprises information gain calculation, optimal feature selection and tree construction, the Monte Carlo simulation method specifically refers to random sampling and probability distribution analysis, the genetic algorithm specifically refers to an optimization process based on natural selection, crossover and mutation, and the feedback control method comprises closed-loop system design and feedback signal processing.
10. The multiple sample detection system for detecting pesticide residues in food according to claim 1, wherein: the self-adaptive learning module comprises a learning algorithm sub-module, a performance tuning sub-module, a data updating sub-module and a report updating sub-module;
the learning algorithm submodule carries out learning of a new data mode by adopting a machine learning method based on the intelligent decision support report and generates a preliminary learning improvement report;
The performance optimization submodule performs algorithm optimization based on the preliminary learning improvement report by adopting a performance optimization method and generates a performance optimization report;
the data updating sub-module updates system data by adopting a data synchronization method based on the performance optimization report and generates a data updating report;
the report updating sub-module updates a learning report and generates a learning improvement report by adopting a document automatic generation method based on the data updating report;
the machine learning method comprises supervised learning, unsupervised learning and reinforcement learning, the performance optimization method is specifically algorithm parameter adjustment and operation efficiency improvement, the data synchronization method comprises real-time data capturing and database synchronization, and the document automatic generation method is specifically report generation and automatic text synthesis based on templates.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112322497A (en) * | 2020-11-03 | 2021-02-05 | 蒙自海关综合技术中心 | Pomegranate pathogen epidemic trend evaluation method and system |
| CN117670378A (en) * | 2024-02-02 | 2024-03-08 | 烟台市食品药品检验检测中心(烟台市药品不良反应监测中心、烟台市粮油质量检测中心) | Food safety monitoring method and system based on big data |
| CN117764726A (en) * | 2024-02-22 | 2024-03-26 | 山东省国土空间数据和遥感技术研究院(山东省海域动态监视监测中心) | Real estate financial risk prevention and control method and system based on big data and artificial intelligence |
| CN117787510A (en) * | 2024-02-28 | 2024-03-29 | 青岛小蜂生物科技有限公司 | Optimization method of pesticide residue monitoring process based on time sequence predictive analysis |
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2023
- 2023-12-12 CN CN202311699319.3A patent/CN117390499A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112322497A (en) * | 2020-11-03 | 2021-02-05 | 蒙自海关综合技术中心 | Pomegranate pathogen epidemic trend evaluation method and system |
| CN117670378A (en) * | 2024-02-02 | 2024-03-08 | 烟台市食品药品检验检测中心(烟台市药品不良反应监测中心、烟台市粮油质量检测中心) | Food safety monitoring method and system based on big data |
| CN117670378B (en) * | 2024-02-02 | 2024-04-30 | 烟台市食品药品检验检测中心(烟台市药品不良反应监测中心、烟台市粮油质量检测中心) | Food safety monitoring method and system based on big data |
| CN117764726A (en) * | 2024-02-22 | 2024-03-26 | 山东省国土空间数据和遥感技术研究院(山东省海域动态监视监测中心) | Real estate financial risk prevention and control method and system based on big data and artificial intelligence |
| CN117787510A (en) * | 2024-02-28 | 2024-03-29 | 青岛小蜂生物科技有限公司 | Optimization method of pesticide residue monitoring process based on time sequence predictive analysis |
| CN117787510B (en) * | 2024-02-28 | 2024-05-03 | 青岛小蜂生物科技有限公司 | Optimization method of pesticide residue monitoring process based on time sequence predictive analysis |
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