Food multiple rapid detection and early warning system

Abstract

The invention provides a food multiple rapid detection and early warning system, and relates to the field of food detection. The food multiple rapid detection and early warning system comprises a sample collection module used for automatically collecting food samples and sending the food samples into a detection module; the multiple detection module comprises a plurality of sensors and can detect various pollutants in the food at the same time; the data processing unit is used for receiving sensor data and analyzing and processing the sensor data through a built-in algorithm; the early warning and feedback module is used for automatically triggering an early warning mechanism and sending a detection result and early warning information to related managers and consumers when the pollutants are detected to exceed the standard; and the user interaction interface allows a user to check real-time detection data and historical records, and provides a system setting and maintenance interface at the same time. The system can rapidly complete detection of various pollutants and timely discover food safety problems, so that measures are rapidly taken, and the influence of food safety events is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of food detection, in particular to a food multiple rapid detection and early warning system. Background Art

[0002] In the food industry, ensuring food safety and quality is of vital importance. With the growing demand of consumers for healthy and safe food, food production and distribution companies are facing increasingly stringent regulatory requirements and market pressure. The complexity of the food supply chain has increased the difficulty of food safety management. From raw material procurement, production and processing to final sales, food may be contaminated at every link. Once a food safety incident occurs, due to the lack of timely and effective detection and early warning mechanisms, companies may face huge economic losses and damage to their brand reputation.

[0003] Traditional food safety testing methods usually rely on manual sampling, laboratory analysis and manual record-keeping. These methods are not only time-consuming but also susceptible to human error, making it difficult to meet the needs of fast, accurate and comprehensive testing.

[0004] In response to these challenges, in recent years, food detection technology is developing in the direction of automation, intelligence and integration. It is becoming increasingly important to develop systems that can quickly detect multiple pollutants and provide real-time warnings using advanced sensor technology, data processing algorithms and information technology. These systems can not only improve detection efficiency and accuracy, but also help companies to promptly discover and deal with food safety issues through real-time data analysis and early warning mechanisms, thereby protecting the health and safety of consumers while meeting regulatory requirements and market standards. However, existing food safety detection systems often focus on a single type of detection and lack the ability to comprehensively detect and analyze multiple pollutants. In addition, these systems may have deficiencies in user interaction, data management and early warning feedback, resulting in poor information transmission and delayed response. Therefore, technicians in this field provide a food multiple rapid detection and early warning system to solve the problems raised in the above background technology. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies in the prior art, the present invention provides a food multiple rapid detection and early warning system, which solves the problems in the prior system of lacking the ability to conduct comprehensive detection and analysis of multiple pollutants and deficiencies in data management and early warning feedback.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a food multiple rapid detection and early warning system, comprising:

[0009] A sample collection module, used to automatically collect food samples and send them to the detection module;

[0010] The multi-detection module contains multiple sensors that can simultaneously detect multiple contaminants in food, including heavy metals, pesticide residues, microorganisms and excessive additives;

[0011] A data processing unit, which is used to receive sensor data and analyze and process it through built-in algorithms;

[0012] Early warning and feedback module: when pollutants are detected exceeding the standard, the system will automatically trigger the early warning mechanism and send the test results and early warning information to relevant managers and consumers;

[0013] The user interaction interface allows users to view real-time detection data and historical records, and also provides a system setting and maintenance interface.

[0014] Preferably, the sample collection module comprises the following steps:

[0015] S1. Preparation before sample collection, ensure the cleaning and disinfection of collection tools, avoid cross contamination, and choose the appropriate sampling method according to the food type and testing requirements;

[0016] S2. Sample collection: For solid foods, use sterile sampling tools to evenly collect samples from different parts or batches to ensure that the samples are representative. For liquid foods, use sterile containers to directly collect samples from different depths and parts of the container. For semi-solid or viscous foods, use dedicated sampling tools to ensure that samples can be accurately taken out. The amount of samples collected should meet the minimum amount required for subsequent testing.

[0017] S3. Sample labeling and recording: number each sample collected, record the sampling time, location, food type, and sampling personnel information, and use barcodes or RFID tags to label the samples for subsequent tracking and management;

[0018] S4. Sample storage and transportation: according to the characteristics of food and testing requirements, the samples should be placed in appropriate storage conditions and special transport containers should be used to ensure that the samples are not contaminated and the conditions are stable during transportation;

[0019] S5. Sample registration and processing: After arriving at the testing laboratory, the samples are registered again, the sample information is verified to be correct, and the samples are pre-processed according to the testing requirements, such as homogenization, dilution, and extraction;

[0020] S6. Sample detection, sending the processed samples to the multiple detection module for analysis;

[0021] S7. Quality control, regular quality control checks on sampling tools and processes to ensure sampling accuracy and repeatability;

[0022] S8. Data recording and analysis: record the key data in all sampling steps and analyze them in the data processing unit to ensure the accuracy and reliability of the data.

[0023] Preferably, the multiple detection module comprises the following steps:

[0024] S1. Sample preparation: According to the test requirements, the collected samples are pre-treated as necessary, such as homogenization, dilution, concentration or extraction, to ensure that the samples are in a form suitable for various test methods;

[0025] S2. Sample distribution: Use an automatic or semi-automatic sample distribution system to evenly distribute the pre-treated samples to each test unit to ensure that the amount of sample obtained by each test unit is accurate, so as to ensure the repeatability and accuracy of the test results;

[0026] S3. Multi-parameter detection, using integrated multiple sensors or detection technologies to detect different pollutants in the sample, including optical detection technology, electrochemical sensors, biosensors, chromatography technology and mass spectrometry technology;

[0027] S4. Data acquisition and processing: automatically collect signals from each detection unit, perform preliminary processing through the data acquisition system, and use built-in algorithms and software to analyze the collected data, including signal correction, background subtraction, and quantitative calculation;

[0028] S5. Result integration and analysis: integrate the results from different testing units, conduct comprehensive analysis, and use data fusion technology to improve the accuracy and reliability of the test results;

[0029] S6. Real-time monitoring and early warning: real-time monitoring of test results. Once any pollutant exceeds the standard, the system will automatically trigger the early warning mechanism, which includes issuing an alarm, notifying management personnel and recording the exceeding-standard event;

[0030] S7. Quality control and calibration: regularly calibrate and perform quality control on the detection module to ensure the accuracy of the test results, and use standard substances or quality control samples to verify the system;

[0031] S8. Data storage and reporting, store the test results and related data in the database for easy query, analysis and report generation, and provide a user-friendly interface so that users can easily access test reports and historical data.

[0032] Preferably, the data processing unit comprises the following steps:

[0033] S1. Data receiving, receiving raw data signals from multiple detection modules;

[0034] S2. Data preprocessing, removing noise and interference, adjusting the signal baseline, ensuring data accuracy, and converting data into a standard range for easy comparison and analysis;

[0035] S3. Feature extraction, extracting feature values ​​related to pollutants based on detection technology;

[0036] S4. Quantitative analysis: Use a standard curve or quantitative formula to convert the characteristic value into pollutant concentration. The calculation formula is as follows:

[0037]

[0038] Where: C is the pollutant concentration, A is the absorbance or characteristic peak area, ∈ is the molar absorptivity, and l is the optical path length;

[0039] S5. Calibration and verification: Use standard samples of known concentration to calibrate the system and apply linear regression analysis to verify the accuracy of the test results. The formula is as follows:

[0040] y=mx+b

[0041] Among them, y is the predicted value, x is the actual value, m is the slope, and b is the intercept;

[0042] S6. Result analysis, comparing test results with food safety standards or thresholds, and applying algorithms for pattern recognition and trend analysis;

[0043] S7. Generate a report, organize the analysis results into a report, including information on pollutant types, concentrations, and whether they exceed the standard;

[0044] S8. Data storage and management, store the processed data in the database to facilitate subsequent query and historical data analysis.

[0045] Preferably, the early warning and feedback module comprises the following steps:

[0046] S1. Data reception, receiving the test result data after analysis and processing from the data processing unit;

[0047] S2. Threshold setting: setting warning thresholds for pollutant concentrations based on food safety standards, historical data or expert advice;

[0048] S3. Comparative analysis: compare the test results with the set threshold to determine whether there is an over-limit situation. The judgment formula is:

[0049] R≥T

[0050] If the detection result R is greater than or equal to the threshold T, an early warning is triggered;

[0051] S4. Anomaly detection, applying statistical process control to identify abnormal patterns or trends in the data, is calculated as follows: Calculate the moving average :

[0052]

[0053] Calculate the moving standard deviation S:

[0054]

[0055] Set control limits to detect whether data points are abnormal;

[0056] S5. Warning triggering: If the detection result exceeds the threshold, the system automatically triggers the warning mechanism, which includes: sending emails or SMS notifications to managers and relevant persons in charge, displaying warning information on the user interface, and sending warning signals to other systems through the API interface;

[0057] S6. Feedback collection: collect feedback from users or managers on the warning to confirm the accuracy of the warning and the measures taken;

[0058] S7. Recording and reporting: record detailed information of all warning events, including time, test results and measures taken, and generate warning reports regularly for analyzing the performance and improvement of the warning system;

[0059] S8. System update and optimization: make necessary adjustments and optimizations to the system based on the feedback and analysis results of the warning time.

[0060] Preferably, the user interaction interface comprises the following steps:

[0061] S1. Login verification, the user logs in to the system with the username and password;

[0062] S2. Main interface display,After logging in, the user first enters the main interface, which provides a system overview, including the latest test results, warning status, and system notifications;

[0063] S3. Real-time data viewing: users can view real-time or historical test data. The interface provides clear charts and lists to display the concentration and test time of different pollutants.

[0064] S4. Warning information processing: When the system triggers a warning, the user interface should immediately display the warning information, including detailed information of the pollutants exceeding the standard, possible impacts, and recommended response measures. Users can confirm the warning, mark it as processed, or add notes;

[0065] S5. Detailed report generation: users can generate and download detailed test reports, which should include test data, analysis results, charts and conclusions;

[0066] S6. System settings and configuration: users can access system settings to configure parameters, such as setting warning thresholds, managing user accounts, and adjusting notification preferences;

[0067] S7. Data export and sharing: users can export test data and reports for further analysis or share with other relevant personnel or systems;

[0068] S8. Help and support. The user interface should provide help documents, FAQs or options to contact technical support so that users can get help when they encounter problems.

[0069] S9. User feedback, provide a feedback mechanism to allow users to make suggestions or report problems with the system.

[0070] (III) Beneficial effects

[0071] The present invention provides a food multiple rapid detection and early warning system. It has the following beneficial effects:

[0072] 1. In the present invention, the system can quickly complete the detection of multiple pollutants, discover food safety problems in a timely manner, and thus take prompt measures to reduce the impact of food safety incidents.

[0073] 2. In the present invention, through the automated and integrated detection process, the detection efficiency is greatly improved, the manual operation is reduced, and the labor intensity and operation errors are reduced.

[0074] 3. In the present invention, the system can monitor the detection data in real time and issue an early warning immediately when an abnormal situation is detected, ensuring that food safety issues can be discovered and handled in a timely manner. The system can simultaneously detect multiple types of pollutants, including chemicals, microorganisms and heavy metals, and provide a comprehensive food safety assessment.

[0075] 4. In the present invention, the system records detailed test data and historical records, which is convenient for tracing the source and processing of food, and helps to quickly trace the source when a food safety incident occurs. It provides an intuitive and easy-to-use user interaction interface so that non-professionals can easily operate and understand the test results, which is convenient for daily management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the overall flow of the system of the present invention;

[0077] Figure 2 It is a specific flow chart of the sample collection module in the present invention;

[0078] Figure 3It is a schematic diagram of a specific process of the multiple detection unit in the present invention;

[0079] Figure 4 It is a specific flow chart of the data processing unit in the present invention;

[0080] Figure 5 It is a specific flow chart of the early warning and feedback module in the present invention;

[0081] Figure 6 It is a specific flow chart of the user interaction interface in the present invention. DETAILED DESCRIPTION

[0082] 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.

[0083] Embodiment 1:

[0084] like Figure 1-6 As shown, an embodiment of the present invention provides a food multiple rapid detection and early warning system, comprising:

[0085] A sample collection module, used to automatically collect food samples and send them to the detection module;

[0086] The multi-detection module contains multiple sensors that can simultaneously detect multiple contaminants in food, including heavy metals, pesticide residues, microorganisms and excessive additives;

[0087] A data processing unit, which is used to receive sensor data and analyze and process it through built-in algorithms;

[0088] Early warning and feedback module: when pollutants are detected exceeding the standard, the system will automatically trigger the early warning mechanism and send the test results and early warning information to relevant managers and consumers;

[0089] The user interaction interface allows users to view real-time detection data and historical records, and also provides a system setting and maintenance interface.

[0090] The sample collection module includes the following steps:

[0091] S1. Preparation before sample collection, ensure the cleaning and disinfection of collection tools, avoid cross contamination, and choose the appropriate sampling method according to the food type and testing requirements;

[0092] S2. Sample collection: For solid foods, use sterile sampling tools to evenly collect samples from different parts or batches to ensure that the samples are representative. For liquid foods, use sterile containers to directly collect samples from different depths and parts of the container. For semi-solid or viscous foods, use dedicated sampling tools to ensure that samples can be accurately taken out. The amount of samples collected should meet the minimum amount required for subsequent testing.

[0093] S3. Sample labeling and recording: number each sample collected, record the sampling time, location, food type, and sampling personnel information, and use barcodes or RFID tags to label the samples for subsequent tracking and management;

[0094] S4. Sample storage and transportation: according to the characteristics of food and testing requirements, the samples should be placed in appropriate storage conditions and special transport containers should be used to ensure that the samples are not contaminated and the conditions are stable during transportation;

[0095] S5. Sample registration and processing: After arriving at the testing laboratory, the samples are registered again, the sample information is verified to be correct, and the samples are pre-processed according to the testing requirements, such as homogenization, dilution, and extraction;

[0096] S6. Sample detection, sending the processed samples to the multiple detection module for analysis;

[0097] S7. Quality control, regular quality control checks on sampling tools and processes to ensure sampling accuracy and repeatability;

[0098] S8. Data recording and analysis: record the key data in all sampling steps and analyze them in the data processing unit to ensure the accuracy and reliability of the data.

[0099] The multiplex detection module includes the following steps:

[0100] S1. Sample preparation: According to the test requirements, the collected samples are pre-treated as necessary, such as homogenization, dilution, concentration or extraction, to ensure that the samples are in a form suitable for various test methods;

[0101] S2. Sample distribution: Use an automatic or semi-automatic sample distribution system to evenly distribute the pre-treated samples to each test unit to ensure that the amount of sample obtained by each test unit is accurate, so as to ensure the repeatability and accuracy of the test results;

[0102] S3. Multi-parameter detection, using integrated multiple sensors or detection technologies to detect different pollutants in the sample, including optical detection technology, electrochemical sensors, biosensors, chromatography technology and mass spectrometry technology;

[0103] S4. Data acquisition and processing: automatically collect signals from each detection unit, perform preliminary processing through the data acquisition system, and use built-in algorithms and software to analyze the collected data, including signal correction, background subtraction, and quantitative calculation;

[0104] S5. Result integration and analysis: integrate the results from different testing units, conduct comprehensive analysis, and use data fusion technology to improve the accuracy and reliability of the test results;

[0105] S6. Real-time monitoring and early warning: real-time monitoring of test results. Once any pollutant exceeds the standard, the system will automatically trigger the early warning mechanism, which includes issuing an alarm, notifying management personnel and recording the exceeding-standard event;

[0106] S7. Quality control and calibration: regularly calibrate and perform quality control on the detection module to ensure the accuracy of the test results, and use standard substances or quality control samples to verify the system;

[0107] S8. Data storage and reporting, store the test results and related data in the database for easy query, analysis and report generation, and provide a user-friendly interface so that users can easily access test reports and historical data.

[0108] The data processing unit includes the following steps:

[0109] S1. Data receiving, receiving raw data signals from multiple detection modules;

[0110] S2. Data preprocessing, removing noise and interference, adjusting the signal baseline, ensuring data accuracy, and converting data into a standard range for easy comparison and analysis;

[0111] S3. Feature extraction, extracting feature values ​​related to pollutants based on detection technology;

[0112] S4. Quantitative analysis: Use a standard curve or quantitative formula to convert the characteristic value into pollutant concentration. The calculation formula is as follows:

[0113]

[0114] Where: C is the pollutant concentration, A is the absorbance or characteristic peak area, ∈ is the molar absorptivity, and l is the optical path length;

[0115] S5. Calibration and verification: Use standard samples of known concentration to calibrate the system and apply linear regression analysis to verify the accuracy of the test results. The formula is as follows:

[0116] y=mx+b

[0117] Among them, y is the predicted value, x is the actual value, m is the slope, and b is the intercept;

[0118] S6. Result analysis, comparing test results with food safety standards or thresholds, and applying algorithms for pattern recognition and trend analysis;

[0119] S7. Generate a report, organize the analysis results into a report, including information on pollutant types, concentrations, and whether they exceed the standard;

[0120] S8. Data storage and management, store the processed data in the database to facilitate subsequent query and historical data analysis.

[0121] The early warning and feedback module includes the following steps:

[0122] S1. Data reception, receiving the test result data after analysis and processing from the data processing unit;

[0123] S2. Threshold setting: setting warning thresholds for pollutant concentrations based on food safety standards, historical data or expert advice;

[0124] S3. Comparative analysis: compare the test results with the set threshold to determine whether there is an over-limit situation. The judgment formula is:

[0125] R≥T

[0126] If the detection result R is greater than or equal to the threshold T, an early warning is triggered;

[0127] S4. Anomaly detection, applying statistical process control to identify abnormal patterns or trends in the data, is calculated as follows: Calculate the moving average

[0128]

[0129] Calculate the moving standard deviation S:

[0130]

[0131] Set control limits to detect whether data points are abnormal;

[0132] S5. Warning triggering: If the detection result exceeds the threshold, the system automatically triggers the warning mechanism, which includes: sending emails or SMS notifications to managers and relevant persons in charge, displaying warning information on the user interface, and sending warning signals to other systems through the API interface;

[0133] S6. Feedback collection: collect feedback from users or managers on the warning to confirm the accuracy of the warning and the measures taken;

[0134] S7. Recording and reporting: record detailed information of all warning events, including time, test results and measures taken, and generate warning reports regularly for analyzing the performance and improvement of the warning system;

[0135] S8. System update and optimization: make necessary adjustments and optimizations to the system based on the feedback and analysis results of the warning time.

[0136] The user interface includes the following steps:

[0137] S1. Login verification, the user logs in to the system with the username and password;

[0138] S2. Main interface display,After logging in, the user first enters the main interface, which provides a system overview, including the latest test results, warning status, and system notifications;

[0139] S3. Real-time data viewing: users can view real-time or historical test data. The interface provides clear charts and lists to display the concentration and test time of different pollutants.

[0140] S4. Warning information processing: When the system triggers a warning, the user interface should immediately display the warning information, including detailed information of the pollutants exceeding the standard, possible impacts, and recommended response measures. Users can confirm the warning, mark it as processed, or add notes;

[0141] S5. Detailed report generation: users can generate and download detailed test reports, which should include test data, analysis results, charts and conclusions;

[0142] S6. System settings and configuration: users can access system settings to configure parameters, such as setting warning thresholds, managing user accounts, and adjusting notification preferences;

[0143] S7. Data export and sharing: users can export test data and reports for further analysis or share with other relevant personnel or systems;

[0144] S8. Help and support. The user interface should provide help documents, FAQs or options to contact technical support so that users can get help when they encounter problems.

[0145] S9. User feedback, provide a feedback mechanism to allow users to make suggestions or report problems with the system.

[0146] 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 food multiple rapid detection and early warning system, characterized by: include: A sample collection module, used to automatically collect food samples and send them to the detection module; The multi-detection module contains multiple sensors that can simultaneously detect multiple contaminants in food, including heavy metals, pesticide residues, microorganisms and excessive additives; A data processing unit, which is used to receive sensor data and analyze and process it through built-in algorithms; Early warning and feedback module: when pollutants are detected exceeding the standard, the system will automatically trigger the early warning mechanism and send the test results and early warning information to relevant managers and consumers; The user interaction interface allows users to view real-time detection data and historical records, and also provides a system setting and maintenance interface.

2. A food multiple rapid detection and early warning system according to claim 1, characterized in that: The sample collection module comprises the following steps: S1. Preparation before sample collection, ensure the cleaning and disinfection of collection tools, avoid cross contamination, and choose the appropriate sampling method according to the food type and testing requirements; S2. Sample collection: For solid foods, use sterile sampling tools to evenly collect samples from different parts or batches to ensure that the samples are representative. For liquid foods, use sterile containers to directly collect samples from different depths and parts of the container. For semi-solid or viscous foods, use dedicated sampling tools to ensure that samples can be accurately taken out. The amount of samples collected should meet the minimum amount required for subsequent testing. S3. Sample labeling and recording: number each sample collected, record the sampling time, location, food type, and sampling personnel information, and use barcodes or RFID tags to label the samples for subsequent tracking and management; S4. Sample storage and transportation: according to the characteristics of food and testing requirements, the samples should be placed in appropriate storage conditions and special transport containers should be used to ensure that the samples are not contaminated and the conditions are stable during transportation; S5. Sample registration and processing: After arriving at the testing laboratory, the samples are registered again, the sample information is verified to be correct, and the samples are pre-processed according to the testing requirements, such as homogenization, dilution, and extraction; S6. Sample detection, sending the processed samples to the multiple detection module for analysis; S7. Quality control, regular quality control checks on sampling tools and processes to ensure sampling accuracy and repeatability; S8. Data recording and analysis: record the key data in all sampling steps and analyze them in the data processing unit to ensure the accuracy and reliability of the data.

3. A food multiple rapid detection and early warning system according to claim 1, characterized in that: The multiple detection module comprises the following steps: S1. Sample preparation: According to the test requirements, the collected samples are pre-treated as necessary, such as homogenization, dilution, concentration or extraction, to ensure that the samples are in a form suitable for various test methods; S2. Sample distribution: Use an automatic or semi-automatic sample distribution system to evenly distribute the pre-treated samples to each test unit to ensure that the amount of sample obtained by each test unit is accurate, so as to ensure the repeatability and accuracy of the test results; S3. Multi-parameter detection, using integrated multiple sensors or detection technologies to detect different pollutants in the sample, including optical detection technology, electrochemical sensors, biosensors, chromatography technology and mass spectrometry technology; S4. Data acquisition and processing: automatically acquire the signals of each detection unit, perform preliminary processing through the data acquisition system, and analyze the acquired data using built-in algorithms and software, including signal correction, background subtraction and quantitative calculation; S5. Result integration and analysis: integrate the results from different testing units, conduct comprehensive analysis, and use data fusion technology to improve the accuracy and reliability of the test results; S6. Real-time monitoring and early warning: real-time monitoring of test results. Once any pollutant exceeds the standard, the system will automatically trigger the early warning mechanism, which includes issuing an alarm, notifying management personnel and recording the exceeding-standard event; S7. Quality control and calibration: regularly calibrate and perform quality control on the detection module to ensure the accuracy of the test results, and use standard substances or quality control samples to verify the system; S8. Data storage and reporting, store the test results and related data in the database for easy query, analysis and report generation, and provide a user-friendly interface so that users can easily access test reports and historical data.

4. A food multiple rapid detection and early warning system according to claim 1, characterized in that: The data processing unit comprises the following steps: S1. Data receiving, receiving raw data signals from multiple detection modules; S2. Data preprocessing, removing noise and interference, adjusting the signal baseline, ensuring data accuracy, and converting data into a standard range for easy comparison and analysis; S3. Feature extraction, extracting feature values ​​related to pollutants based on detection technology; S4. Quantitative analysis: Use a standard curve or quantitative formula to convert the characteristic value into pollutant concentration. The calculation formula is as follows: Where: C is the pollutant concentration, A is the absorbance or characteristic peak area, ∈ is the molar absorptivity, and l is the optical path length; S5. Calibration and verification: Use standard samples of known concentration to calibrate the system and apply linear regression analysis to verify the accuracy of the test results. The formula is as follows: y=mx+b Among them, y is the predicted value, x is the actual value, m is the slope, and b is the intercept; S6. Result analysis, comparing test results with food safety standards or thresholds, and applying algorithms for pattern recognition and trend analysis; S7. Generate a report, organize the analysis results into a report, including information on pollutant types, concentrations, and whether they exceed the standard; S8. Data storage and management, store the processed data in the database to facilitate subsequent query and historical data analysis.

5. A food multiple rapid detection and early warning system according to claim 1, characterized in that: The early warning and feedback module includes the following steps: S1. Data reception, receiving the test result data after analysis and processing from the data processing unit; S2. Threshold setting: setting warning thresholds for pollutant concentrations based on food safety standards, historical data or expert advice; S3. Comparative analysis: compare the test results with the set threshold to determine whether there is an over-limit situation. The judgment formula is: R≥T If the detection result R is greater than or equal to the threshold T, an early warning is triggered; S4. Anomaly detection, applying statistical process control to identify abnormal patterns or trends in the data, is calculated as follows: Calculate the moving average Calculate the moving standard deviation S: Set control limits to detect whether data points are abnormal; S5. Warning triggering: If the detection result exceeds the threshold, the system automatically triggers the warning mechanism, which includes: sending emails or SMS notifications to managers and relevant persons in charge, displaying warning information on the user interface, and sending warning signals to other systems through the API interface; S6. Feedback collection: collect feedback from users or managers on the warning to confirm the accuracy of the warning and the measures taken; S7. Recording and reporting: record detailed information of all warning events, including time, test results and measures taken, and generate warning reports regularly for analyzing the performance and improvement of the warning system; S8. System update and optimization: make necessary adjustments and optimizations to the system based on the feedback and analysis results of the warning time.

6. A food multiple rapid detection and early warning system according to claim 1, characterized in that: The user interaction interface comprises the following steps: S1. Login verification, the user logs in to the system with the username and password; S2. Main interface display,After logging in, the user first enters the main interface, which provides a system overview, including the latest test results, warning status, and system notifications; S3. Real-time data viewing: users can view real-time or historical test data. The interface provides clear charts and lists to display the concentration and test time of different pollutants. S4. Warning information processing: When the system triggers a warning, the user interface should immediately display the warning information, including detailed information of the pollutants exceeding the standard, possible impacts, and recommended response measures. Users can confirm the warning, mark it as processed, or add notes; S5. Detailed report generation: users can generate and download detailed test reports, which should include test data, analysis results, charts and conclusions; S6. System settings and configuration: users can access system settings to configure parameters, such as setting warning thresholds, managing user accounts, and adjusting notification preferences; S7. Data export and sharing: users can export test data and reports for further analysis or share with other relevant personnel or systems; S8. Help and support. The user interface should provide help documents, FAQs or options to contact technical support so that users can get help when they encounter problems. S9. User feedback, provide a feedback mechanism to allow users to make suggestions or report problems with the system.

Images