Edible forest product quality safety early warning method
Through real-time data collection and in-depth data analysis, a quality and safety warning model for edible forest products was constructed, which solved the problem that traditional detection methods were difficult to meet the needs of real-time monitoring and early warning, and achieved accurate early warning and real-time monitoring of the quality and safety of edible forest products, ensuring the Food safety.
Patent Information
- Application Number
- CN202411564204.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-05
- Publication Date
- 2025-02-14
AI Technical Summary
The traditional edible forest product quality and safety testing methods have large time and manpower consumption and long testing cycles, making it difficult to meet the needs of real-time monitoring and early warning, making it difficult to detect and deal with food safety incidents in a timely manner.
A method of quality and safety warning for edible forest products is adopted. By collecting soil physical and chemical data, soil environmental data and edible forest product data in real time, using in-depth data analysis and model training, a safety warning model is constructed, risk assessment and early warning is carried out, and combined with geographic information system Carry out spatial analysis and visualization, and establish a simulation system to predict food safety incidents.
Real-time monitoring and accurate warning of the quality and safety of edible forest products has been achieved, real-time and accuracy of food safety have been improved, and potential quality and safety hazards have been promptly discovered and dealt with, and public health has been guaranteed.
Smart Images
Figure CN119444531A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of food safety, and in particular to a quality safety early warning method for edible forest products. Background Art
[0002] With the accelerated development of globalization and industrialization, food safety has gradually become a major issue of concern to the international community. Among them, edible forest products are an important part of human diet, and their quality and safety are directly related to people's health and quality of life. However, in the current food safety supervision system, traditional edible forest product quality and safety testing methods often have significant limitations.
[0003] Traditional detection methods mostly rely on laboratory analysis, which not only takes a lot of time and manpower, but also has a long detection cycle, making it difficult to meet the needs of real-time monitoring and early warning of food safety. Especially in the current situation where food safety incidents occur frequently, once a problem occurs, it is often not discovered and handled in time, posing potential risks to people's health. Summary of the invention In view of the deficiencies of the prior art, the present invention provides a quality safety early warning method for edible forest products.
[0004] To achieve the above purpose, the present invention provides the following technical solution: the present invention.
[0005] Preferably, the soil physical and chemical data include soil pH value, organic matter content, and nutrient content; the soil environmental data include temperature, humidity, and rainfall; and the edible forest product data include product type, output, and quality.
[0006] Further preferably, the safety early warning model comprises the following steps: A. Feature extraction and selection: Extract characteristics or indicators related to the quality and safety of edible forest products from the original data; Use statistical methods (such as principal component analysis PCA, cluster analysis) or machine learning algorithms (such as random forests, deep belief networks) to select features and reduce dimensionality to simplify the model and improve prediction accuracy; B. Risk assessment and early warning model: Use decision trees, random forests, deep learning, and machine learning algorithms to build risk assessment models to evaluate and predict the quality and safety of edible forest products; Formulate early warning strategies and measures based on the assessment results and actual conditions; C. Geographic information matching and spatial analysis: Use geographic information system (GIS) technology to combine the collected data with geographic spatial information for spatial analysis and visualization; Through spatial analysis, areas or products that may have quality and safety risks can be identified to provide decision support for early warning and supervision; D. Early warning information release and feedback: Release the assessment results and early warning information to relevant departments and the public through appropriate channels and methods; Collect and analyze feedback information to continuously optimize and improve the early warning model; E. Simulation system: Establish a food safety incident simulation system, and use data on the biological characteristics of pathogenic bacteria, natural environmental impacts, and social environmental impacts to conduct spatiotemporal reasoning and prediction of food safety evolution trends and food safety incident epidemic risks; Through the simulation system, the implementation effect of various prevention and control measures can be quantitatively evaluated to provide a scientific basis for formulating and optimizing prevention and control strategies.
[0007] Again preferably, the analysis network construction includes the following functions: Spatial relationship modeling: Establish spatial relationship models between geographic spatial data to reveal the interactions and connections between different geographic entities; Data mining and analysis: By integrating multiple data analysis methods, including CCA, RDA, PCA, and cluster analysis methods, we conduct in-depth mining and analysis of geospatial data and product data to extract key information and patterns, which will be used to support the construction and update of early warning models; Decision support: Based on the analysis results, it can provide scientific basis and decision support for decision makers, determine key monitoring areas and formulate targeted prevention and control measures according to the analysis results; Visualization display: Powerful visualization function can display the analysis results in the form of charts and maps.
[0008] Preferably, the basic information monitoring includes the following data: Geospatial data: covers the spatial location information of edible forest product planting areas, such as longitude and latitude, topography, climate conditions, and soil types; Environmental monitoring data: including data on soil quality, water quality, and air quality environmental factors; Product data: basic information on the types, planting areas, yields, and growth cycles of edible forest products; Quality monitoring data: including test results of key indicators such as pesticide residues, heavy metal and toxin content, and nutrient content; Sampling information: including sampling location, sampling time, sampling method, and sampling batch; Geographic information matching: Through geographic information system (GIS) technology, geographic spatial data, environmental monitoring data, product data and quality monitoring data are matched and associated to form a complete data network.
[0009] Further preferably, the safety warning information is divided into the following levels: Blue warning information: involves some potential risks or problems that have been initially discovered but have not yet caused obvious harm or impact; Yellow warning information: It means that certain risks or problems have begun to emerge and certain measures need to be taken to deal with them; Orange warning information: indicates that there is a high food safety risk and urgent and comprehensive response measures are required; Red warning information: indicates that there is an extremely serious food safety risk and a comprehensive emergency response mechanism needs to be immediately activated.
[0010] Compared with the prior art, the present invention provides an edible forest product quality safety early warning method, which has the following beneficial effects: Real-time and accuracy: By collecting data sets related to the quality and safety of edible forest products in real time, including soil physical and chemical data, soil environmental data and edible forest product data, the real-time and accuracy of the data is ensured. This provides a basis for timely discovery and early warning of potential quality and safety risks.
[0011] Accurate early warning: Using deep data analysis and model training, this method can accurately identify potential quality and safety risks of edible forest products, and determine whether the sample to be predicted has quality and safety risks through a preset threshold. This accurate early warning helps improve the safety and reliability of edible forest products.
[0012] Spatial analysis and visualization: Combining geographic information system (GIS) technology, spatial analysis and visualization can more intuitively identify potential quality and safety hazards and provide decision support for early warning and supervision. This helps to formulate targeted prevention and control measures and improve supervision efficiency.
[0013] Model optimization and continuous improvement: By collecting and analyzing feedback information, the early warning model is continuously optimized and improved to continuously improve the accuracy and stability of the model. This continuous improvement mechanism ensures that the early warning method can adapt to the changing environment and conditions and maintain its effectiveness and reliability.
[0014] Multi-source data fusion: By collecting multi-dimensional data including soil physical and chemical data, soil environmental data, edible forest product data, etc., and forming a complete data network, this method can make full use of various data resources and improve the comprehensiveness and accuracy of early warning.
[0015] Grading and classification of early warning information: Classifying early warning information into different levels according to the degree of risk and classifying them according to the source will help relevant departments and the public take corresponding response measures according to the level and type of early warning. This grading and classification of early warning information mechanism will help to respond to food safety incidents more effectively and reduce losses and impacts.
[0016] Scientific decision support: By building analytical networks, simulation systems and other tools, this method can provide scientific basis and decision support for decision makers, helping them to more accurately judge the food safety situation and formulate more effective prevention and control strategies.
[0017] In summary, the edible forest product quality and safety early warning method realizes accurate early warning of edible forest product quality and safety through key technologies such as real-time data collection, in-depth data analysis and model training, and has beneficial effects such as real-time, accuracy, precision, spatial analysis visualization, model optimization and improvement, multi-source data fusion and scientific decision-making support. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 It is a schematic diagram of the framework structure of the present invention; DETAILED DESCRIPTION
[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0020] See also Figure 1 The working principle of the edible forest product quality safety early warning method of the present invention mainly revolves around the following key steps: (1) System initialization: Start the early warning system and perform necessary initialization operations to ensure that the system is in normal working condition; (2) Model initialization: Setting initial parameters and thresholds for the safety warning model. These parameters and thresholds are the basis for subsequent model training and warning judgment; (3) Data collection: Real-time collection of data sets related to the quality and safety of edible forest products, including soil physical and chemical data, soil environmental data, and edible forest product data. These data sets ensure the real-time and accuracy of the data through real-time sampling and detection functions; (4) Data analysis: Conduct in-depth analysis of the collected data sets, using methods such as CCA, RDA, PCA and cluster analysis to reveal the intrinsic relationships and patterns between the data, providing strong support for subsequent model training; (5) Model training: Use the analyzed data set to train the safety warning model. The goal of model training is to enable the model to accurately identify potential quality and safety hazards of edible forest products; (6) Data collection of samples to be predicted: Collect geographic information matching data of samples to be predicted, which includes analysis of network construction and basic information monitoring to ensure the integrity and accuracy of the data of samples to be predicted; (7) Threshold judgment: Input the spatial data of the sample to be predicted into the trained safety warning model. The model will judge whether there are quality and safety risks in the sample to be predicted based on the preset threshold; (8) Safety warning: If the spatial data of the sample to be predicted exceeds the set threshold, the system will trigger a safety warning and send warning information to relevant departments and personnel so that timely measures can be taken to deal with potential quality and safety hazards of edible forest products.
[0021] The entire early warning method achieves accurate early warning of the quality and safety of edible forest products through real-time data collection, in-depth data analysis and model training, which helps to improve the safety and reliability of edible forest products.
[0022] The preferred scheme of the above-mentioned edible forest product quality safety early warning method is: Data collection: Collect soil physical and chemical data (such as pH value, organic matter content, nutrient content), soil environmental data (such as temperature, humidity, rainfall) and edible forest product data (such as product type, output, quality).
[0023] Feature extraction and selection: Extract features or indicators related to the quality and safety of edible forest products from the raw data, and use statistical methods or machine learning algorithms to perform feature selection and dimensionality reduction to improve prediction accuracy.
[0024] Risk assessment and early warning model: Based on the extracted features, a risk assessment model is constructed using decision trees, random forests, deep learning and other algorithms to assess and predict the quality and safety of edible forest products. Early warning strategies and measures are formulated based on the assessment results.
[0025] Geographic information matching and spatial analysis: Through geographic information system (GIS) technology, data is combined with geographic spatial information to conduct spatial analysis and visual expression to identify possible quality and safety hazards.
[0026] Early warning information release and feedback: The assessment results and early warning information will be released through appropriate channels, and feedback information will be collected and analyzed to continuously optimize and improve the early warning model.
[0027] Simulation system: Establish a food safety incident simulation system, use multi-faceted data to predict food safety evolution trends and epidemic risks, and provide a scientific basis for formulating prevention and control strategies.
[0028] Further preferred solutions: Feature selection and dimensionality reduction: Statistical methods (such as PCA, cluster analysis) and machine learning algorithms (such as random forests, deep belief networks) are preferably used for feature selection and dimensionality reduction to simplify the model and improve prediction accuracy.
[0029] Risk assessment model: Prioritize decision trees, random forests, deep learning and other algorithms to build risk assessment models to improve the accuracy and stability of the models.
[0030] Geographic information matching and spatial analysis: Utilize GIS technology, combined with spatial analysis and visual expression, to more intuitively identify potential quality and safety hazards and provide decision support for early warning and supervision.
[0031] Analysis network construction: Through spatial relationship modeling, data mining and analysis, decision support and visualization functions, it provides scientific basis and decision support for the construction and updating of early warning models.
[0032] Basic information monitoring: Multi-dimensional data such as geospatial data, environmental monitoring data, product data, quality monitoring data and sampling information are preferably included to form a complete data network to support the operation and optimization of the early warning model.
[0033] Safety warning information classification: Warning information is divided into four levels: blue, yellow, orange and red according to the degree of risk, so that relevant departments and the public can take corresponding response measures according to the warning level.
[0034] Blue warning information: This usually refers to food safety information that may cause public concern and requires close attention from regulatory authorities to prevent the harm from expanding and becoming a hot topic of public opinion. It may involve some potential risks or problems that have been initially discovered but have not yet caused obvious harm or impact.
[0035] Yellow warning information: This type of information covers a wide range, spreads quickly, and has caused or may cause certain harmful consequences or social impacts on food safety. It may mean that certain risks or problems have begun to emerge and certain measures need to be taken to deal with them.
[0036] Orange warning information: This type of information covers a wide range, may generate industry systemic risks, spreads quickly, and has caused or may cause significant harm or social impact. It indicates that there is a high food safety risk and urgent and comprehensive response measures are required.
[0037] Red warning information: This is the highest level of warning information, involving multiple provinces or foreign countries, spreading very quickly, and has caused or may cause serious harm or social impact on food safety information. It indicates that there is an extremely serious food safety risk and a comprehensive emergency response mechanism needs to be activated immediately.
[0038] In addition, early warning information can also be classified according to the source, such as the instructions of superiors on food safety issues, food safety information assigned or supervised by superior authorities, food safety information reported by grassroots food safety regulatory agencies, food safety information discovered during daily supervision and inspection and risk monitoring, food safety information reported by relevant departments, and food safety information that is the focus of media attention.
[0039] The above classification is not absolute. The specific types and levels of warning information may be adjusted and modified based on specific food safety incidents and actual conditions.
[0040] Through the above preferred schemes, the edible forest product quality and safety early warning method proposed in this patent can better ensure the quality and safety of edible forest products and provide strong protection for public health.
[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for early warning of quality and safety of edible forest products, characterized in that: The following steps are involved: (1) Start: Start the early warning system and initialize the system; (2) Model initialization: setting the initial parameters and thresholds of the safety warning model; (3) Datasets: Datasets including soil physical and chemical data, soil environmental data, and edible forest product data are collected. The data sets have real-time sampling and detection functions to ensure the real-time and accuracy of the data; (4) Data analysis: Perform CCA (Canonical Correlation Analysis), RDA (Redundancy Analysis), principal component analysis (PCA) and clustering analysis on the collected data sets to reveal the intrinsic relationships and patterns between the data; (5) Training safety warning models based on data: Using the analyzed data to train safety warning models, so that they can identify potential quality and safety hazards; (6) Spatial data of samples to be predicted: Collect geographic information matching data of samples to be predicted, including analysis network construction and basic information monitoring; (7) Whether it exceeds the set threshold: Input the sample space data to be predicted into the safety early warning model to determine whether it exceeds the set threshold; (8) Conduct safety warning: If the sample space data to be predicted exceeds the set threshold, a safety warning is triggered and warning information is sent to relevant departments and personnel.
2. The edible forest product quality safety early warning method according to claim 1, characterized in that: The soil physical and chemical data include soil pH value, organic matter content, and nutrient element content; the soil environmental data include temperature, humidity, and rainfall; and the edible forest product data include product type, output, and quality.
3. The edible forest product quality safety early warning method according to claim 2, characterized in that: The safety early warning model includes the following steps: A. Feature extraction and selection: Extract characteristics or indicators related to the quality and safety of edible forest products from the original data; Use statistical methods (such as principal component analysis PCA, cluster analysis) or machine learning algorithms (such as random forests, deep belief networks) to select features and reduce dimensionality to simplify the model and improve prediction accuracy; B. Risk assessment and early warning model: Use decision trees, random forests, deep learning, and machine learning algorithms to build risk assessment models to evaluate and predict the quality and safety of edible forest products; Formulate early warning strategies and measures based on the assessment results and actual conditions; C. Geographic information matching and spatial analysis: Use geographic information system (GIS) technology to combine the collected data with geographic spatial information for spatial analysis and visualization; Through spatial analysis, areas or products that may have quality and safety risks can be identified to provide decision support for early warning and supervision; D. Early warning information release and feedback: Release the assessment results and early warning information to relevant departments and the public through appropriate channels and methods; Collect and analyze feedback information to continuously optimize and improve the early warning model; E. Simulation system: Establish a food safety incident simulation system, and use data on the biological characteristics of pathogenic bacteria, natural environmental impacts, and social environmental impacts to conduct spatiotemporal reasoning and prediction of food safety evolution trends and food safety incident epidemic risks; Through the simulation system, the implementation effect of various prevention and control measures can be quantitatively evaluated to provide a scientific basis for formulating and optimizing prevention and control strategies.
4. The edible forest product quality safety early warning method according to claim 3 is characterized in that: The analysis network construction includes the following functions: Spatial relationship modeling: Establish spatial relationship models between geographic spatial data to reveal the interactions and connections between different geographic entities; Data mining and analysis: By integrating multiple data analysis methods, including CCA, RDA, PCA, and cluster analysis methods, we conduct in-depth mining and analysis of geospatial data and product data to extract key information and patterns, which will be used to support the construction and update of early warning models; Decision support: Based on the analysis results, it can provide scientific basis and decision support for decision makers, determine key monitoring areas and formulate targeted prevention and control measures according to the analysis results; Visualization display: Powerful visualization function can display the analysis results in the form of charts and maps.
5. The edible forest product quality safety early warning method according to claim 4, characterized in that: The basic information monitoring includes the following data: Geospatial data: covers the spatial location information of edible forest product planting areas, such as longitude and latitude, topography, climate conditions, and soil types; Environmental monitoring data: including data on soil quality, water quality, and air quality environmental factors; Product data: basic information on the types, planting areas, yields, and growth cycles of edible forest products; Quality monitoring data: including test results of key indicators such as pesticide residues, heavy metal and toxin content, and nutrient content; Sampling information: including sampling location, sampling time, sampling method, and sampling batch; Geographic information matching: Through geographic information system (GIS) technology, geographic spatial data, environmental monitoring data, product data and quality monitoring data are matched and associated to form a complete data network.
6. The edible forest product quality safety early warning method according to claim 5, characterized in that: The security warning information is divided into the following levels: Blue warning information: involves some potential risks or problems that have been initially discovered but have not yet caused obvious harm or impact; Yellow warning information: It means that certain risks or problems have begun to emerge and certain measures need to be taken to deal with them; Orange warning information: indicates that there is a high food safety risk and urgent and comprehensive response measures are required; Red warning information: indicates that there is an extremely serious food safety risk and a comprehensive emergency response mechanism needs to be immediately activated.