WO2018192418A1 - 基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台及侦测报告自动生成方法 - Google Patents

基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台及侦测报告自动生成方法 Download PDF

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WO2018192418A1
WO2018192418A1 PCT/CN2018/082954 CN2018082954W WO2018192418A1 WO 2018192418 A1 WO2018192418 A1 WO 2018192418A1 CN 2018082954 W CN2018082954 W CN 2018082954W WO 2018192418 A1 WO2018192418 A1 WO 2018192418A1
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data
database
pesticide residue
analysis
report
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French (fr)
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庞国芳
陈谊
范春林
邹小波
孙悦红
常巧英
侯堃
方冰
白若镔
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中国检验检疫科学研究院
北京工商大学
北京合众恒星检测科技有限公司
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Priority to US16/311,594 priority Critical patent/US11256723B2/en
Publication of WO2018192418A1 publication Critical patent/WO2018192418A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7206—Mass spectrometers interfaced to gas chromatograph
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8804—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 automated systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers

Definitions

  • the invention relates to an online traceability warning method for pesticide residues of edible agricultural products, and particularly relates to a pesticide residue detection data platform construction and automatic detection method based on high resolution mass spectrometry, internet and data science ternary fusion technology, belonging to the Internet, High-resolution mass spectrometry and data science ternary cross-border fusion technology.
  • the test data is mainly expressed by data tables and a few statistical charts. Not only is the generation slow, the timeliness is poor, and the cold digital figures are difficult to understand and lack the role of timely management and early warning.
  • massive analysis data has been generated, which poses a challenge to traditional data statistical analysis methods, and it is urgent to establish new big data collection, transmission, statistics and intelligence. analysis system.
  • new methods and methods have been provided for the multi-dimensional expression, sharing and analysis of pesticide residue detection big data.
  • the pesticide residue test report provides real-time online services for the traceability of pesticide residues, risk assessment, and scientific management and use of pesticides. So far, such methods and systems have not been reported.
  • the invention designs high-resolution mass spectrometry, internet, data science three-dimensional cross-border fusion technology, constructs a pesticide residue detection data platform, and proposes a method for automatically generating detection reports.
  • a number of more than 1,200 pesticide residues commonly used in different types of fruit and vegetable worlds across the country have been tested for circulation throughout the year on the Internet-based Alliance Laboratories, and the collection and construction of pesticide residue detection data is large.
  • the database is automatically generated by intelligent management and analysis of the data.
  • the "pesticide residue detection data platform construction and automatic detection report generation method based on high resolution mass spectrometry, internet, data science ternary fusion technology” proposed by the present invention includes: 1 establishing an alliance laboratory and a pesticide residue detection standard method; 2 establishing an alliance Laboratory test results database and four basic data sub-libraries; 3 establishment of pesticide residue data collection system and 4 establishment of pesticide residue data intelligent analysis system four parts.
  • the first part establishes an alliance laboratory and a standard method for detecting pesticide residues.
  • the establishment of the alliance laboratory refers to the establishment of several standard laboratories throughout the country, in accordance with the five unified standard operations (uniform sampling method, unified sample preparation method, unified detection method, unified format data upload, unified format statistical analysis report), closed
  • the operation will carry out cycle detection of pesticide residues in fruits and vegetables throughout the country.
  • the pesticide residue data detection method uses liquid chromatography-quadrupole-time-of-flight mass spectrometry and gas chromatography-quadrupole-time-of-flight mass spectrometry to detect pesticide residues in fruits and vegetables, and obtains relevant pesticide residue original data;
  • the second part establishes an alliance laboratory test result database and four basic data sub-libraries.
  • the database of the test results of the alliance laboratory includes the name of the pesticide, the name of the agricultural product, the sampling time, the sampling location, the detection method, the testing unit, etc.; the four basic data sub-libraries are the multi-country MRL standard database, the agricultural product classification database, the pesticide information database and the geographic information.
  • the multi-country MRL standard database mainly includes China MRL, Hong Kong MRL, US MRL, EU MRL, Japanese MRL, CAC MRL, and related MRL standards of 241,527, including pesticides, agricultural products, maximum allowable residue, and standard-setting countries;
  • the agricultural product category database mainly includes Chinese classification, Hong Kong classification, US classification, EU classification, Japanese classification, and CAC classification standards, including the names of agricultural products, primary classification, secondary classification information, and tertiary classification.
  • the pesticide information database contains basic information.
  • toxicity information including the name of all pesticides detected, CAS code, toxicity intensity, whether metabolites and their metabolic precursors, whether it is a standard ban; geographic information database coverage Place Geographical area, including full address provincial administrative divisions of all the sampling point belongs, prefecture-level administrative divisions, county-level administrative divisions and so on.
  • the third part establishes a three-layer architecture of the data acquisition system based on "browser/web server/database server", including data acquisition, data preprocessing, pollution level judgment, and data storage module.
  • the browser layer is located in the client of the local affiliate lab, and is the interface for the user to access the system;
  • the web server layer is located in the data center, and is responsible for accessing the database and executing pre-processing logic;
  • the database server is located in the data center and is responsible for storing and managing pesticide residues. Class data.
  • the functions of each module of the collection system (1) the data acquisition module is responsible for obtaining the pesticide residue detection result reported by each alliance laboratory; (2) the data preprocessing module is responsible for processing the reported detection data, including the determination of the reported data.
  • the pollution level judgment module is responsible for determining the pollution level according to the MRL of each country (or regional organization); (4) the data storage module is responsible for the final result record. Save to the database.
  • the fourth part of the establishment of the data intelligent analysis system mainly realizes that the detection result database and the four basic data sub-libraries are related to each other and interconnected; and the cross-analysis of sampling points, pesticides, agricultural products and pollution levels is realized according to the statistical analysis model.
  • the system is also based on the three-tier architecture of "browser/web server/database server", including parameter setting module, single item analysis module, comprehensive analysis module, report generation module, schedule generation module, and early warning report module.
  • the browser layer is located in the client of the local federation laboratory, which is the interface of the user accessing the system, setting statistical parameters and downloading statistical results; the web server layer is also located in the data center, is responsible for accessing the database and performing various statistical analysis logic; the database server Located in the data center, responsible for storing and managing all types of pesticide residue data.
  • each module of the data intelligent analysis system (1) the parameter setting module is responsible for providing the user with the parameter setting interface and channel; (2) the single item analysis module is responsible for completing 18 individual item statistics functions; (3) the comprehensive analysis module is responsible for Based on the results of the single analysis, 5 comprehensive analysis is completed; (4) the report generation module is responsible for forming the analysis report with the analysis results; (5) the schedule generation module is responsible for generating various statistical reports; (6) the early warning report module, according to The analysis results give an early warning prompt.
  • the platform construction and automatic detection method for detecting reports provide an efficient and accurate data analysis platform for analyzing and warning pesticide residue data in various regions of China.
  • the alliance laboratory and pesticide residue detection standard method fully guarantee the unity, integrity, accuracy, safety and reliability of the data; the alliance laboratory test result database and the four basic data sub-libraries are established as pesticide residue detection data.
  • the analysis and pollution level determination provide the basis; the proposed pesticide residue data collection system realizes the automatic uploading of the test results, data pre-processing and pollution level determination, and establishes the national pesticide residue detection result database; the proposed pesticide residue data intelligent analysis system
  • the detection of the original database and the four basic data sub-libraries are interconnected, interconnected, and the multi-item pesticide residue data is single-item and comprehensive statistical analysis, which realizes the automation of the graphical report generation report. Achieved the "one-click download" of a pesticide residue in a province and city.
  • the detection report was automatically generated in 30 minutes, which is impossible to achieve by traditional statistical methods.
  • the test report formed by the invention not only has high accuracy, high speed, and many judgment standards, but also has flexible statistical range and various analysis methods. It realizes the automation of online data collection, result determination, statistical analysis and report production, which greatly improves the depth, precision and work efficiency of data analysis. It has extremely important practical significance and commercial promotion value.
  • Figure 9 measures the safety level of samples with reference to MRL standards from multiple countries or international organizations.
  • Figure 10 shows the toxicity classification and proportion of pesticides
  • the Internet-based national pesticide residue detection big data technology platform is shown in Figure 1. It mainly consists of four parts: 1 Internet-based nationwide more than 30 alliance laboratories; 2 Alliance laboratory test result database and four basic data sub-libraries (Multinational MRL standard database, agricultural product category database, pesticide basic information database, geographic information database); 3 pesticide residue data collection system and 4 pesticide residue intelligent analysis system, the latter two parts constitute the data processing center.
  • the working principle is as follows: distributed in the alliance laboratories across the country, the original results of pesticide residue detection are reported to the collection system through the Internet at its client, as shown in Figure 2; the acquisition system through data acquisition, information supplement, derivative merger, toxicity Analysis and reference to multi-country MRL standards for pollution level determination, formation of results records, stored in the test results database; intelligent analysis system according to user conditions set, read data, and then according to statistical analysis models, statistical analysis, generate charts , draw comprehensive conclusions, test reports, and return the analysis results to the client of the Alliance Lab for viewing and downloading, as shown in Figure 1.
  • Table 1 shows the construction of more than 30 affiliated laboratories in China and the four basic data sub-libraries (multi-country MRL standard database, agricultural product category database, pesticide basic information database, geographic information database), and proposed based on “multinational MRL standard – agricultural product classification”
  • the data-associated storage and query model of “more than a thousand kinds of pesticide properties” has realized the related access and invocation of the basic data of pesticide residues, which provides a standard basis for the determination of pesticide residue detection results.
  • the design of pesticide residue data collection system is shown in Figure 2, and a national pesticide residue detection result database is constructed.
  • the proposed data fusion and processing model of “data acquisition-information supplement-derivative combination-ban drug treatment-polluting level determination” has realized rapid online collection and fusion of pesticide multi-residue test results data, and reference to multi-country pesticide residue limits.
  • the accurate determination of the standard (MRL) has enabled the dynamic addition and real-time updating of the database of pesticide residue detection results, providing scientific data for national food safety decision-making.
  • the pesticide residue detection data acquisition system adopts a browser/server based three-layer architecture, and each alliance laboratory operates according to five unified specifications (uniform sampling, unified sample preparation, unified detection method, unified format).
  • the intelligent analysis system includes a presentation layer, a business layer, an access layer and a data layer; the data layer is composed of a detection result database and four basic data sub-libraries and related
  • the file consists of providing a database and a file service; the access layer accesses the data in the database through the database access component and provides the data to the business layer; the business layer implements statistical analysis of the sampling point, the pesticide, and the pollution level according to the statistical analysis model.
  • the presentation layer implements intelligent analysis reports of various types of texts that are required by several conditions of the customer.
  • the invention establishes an online customization mode, which allows users to independently select and filter statistical data to highlight interest data or key data; support user customized report types and ranges, and improve data display and big data analysis capabilities.
  • the automatic statistics of 20 pesticide residue indicators from agricultural products, pesticides, regions, multinational MRLs, etc. have been realized, as shown in Table 2. It includes 31 different forms of forms and 38 different types of maps automatically generated, as well as comprehensive evaluation of statistical results and automatic generation of warning information. In the end, “one-click download” is realized, and an illustrated pesticide residue detection report is automatically generated in 30 minutes, as shown in Figure 4.
  • Figure 4 shows the results of multi-disciplinary cross-multiple technology integration of high-resolution mass spectrometry, Internet and data science ternary cross-border fusion technology for pesticide residue big data, realizing “one-click download”, and an illustrated report was completed within 30 minutes.
  • the pesticide residue detection report reflects the regularity of 20 pesticide residues in 18 types of more than 150 kinds of fruits and vegetables sold in 31 provincial capitals/municipalities across the country. See Table 2.
  • the download parameters of the pesticide residue detection report are shown in Figure 5.
  • the sampling period type can be arbitrarily selected, and one or more administrative areas can be arbitrarily selected (a national-large-provincial-prefecture-prefecture-district-level five-level structure can be realized).
  • Figure 6, select the type of detection equipment, choose to export the report body or schedule; the local version of the report text is divided into five chapters of the catalog, see Figure 7, such as reflecting the detection rate of pesticide residues in fruits and vegetables sold in 31 provincial capitals/municipalities, see Figure 8.
  • a pie chart reflecting the safety level of the detected samples reflects the classification and proportion of pesticide toxicity detected, as shown in Figure 10, and a number of histograms for the analysis of exceptional samples exceeding the standard (see Figure 11).
  • the report schedule contains 20 schedules that record the raw results of the test and detailed statistics from the detection of the distribution of pesticide residues, the level of pollution, and the excess of each MRL standard.
  • the number of words in a report varies from tens of thousands to hundreds of thousands, from the text to the attached table, and the pictures and texts can be realized in one-minute download within 30 minutes, which greatly improves the mass.
  • the automated reporting system enables custom extension development of reporting structures and content.
  • Pesticide residue detection result database has been deposited in 10 league laboratories in the country 31 provincial capitals/municipalities (including 284 districts and counties) 638 sampling points more than 140 kinds of fruits and vegetables 22368 batch samples, 13.74 million detection Data, 145 million high-resolution mass spectrometry, and a 25 million-word pesticide residue detection report.
  • China's pesticide residue limit standards face the challenge of small quantity and low level.
  • China's MRL standard has only 2233 frequency corresponding to the standard, accounting for 22.7%, which is the lowest among all the six MRL standards. , far below the level that the EU and Japan can fully cover.

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Abstract

一种基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台及侦测报告自动生成方法，包括联盟实验室、联盟实验室检测结果数据库和四个基础数据子库、数据采集系统、数据智能分析系统；分布各地的联盟实验室在其客户端将农残的检测原始结果通过Internet上报至采集系统；采集系统对数据获取、信息补充、衍生物合并、毒性分析、参照多国MRL标准进行污染等级判定形成结果记录，存入检测结果数据库；智能分析系统根据用户的条件设定、读取数据、根据统计分析模型进行各项统计分析，生成图表，得出综合结论，将分析结果返回联盟实验室的客户端。实现了"一键下载"某地区农残图文并茂侦测报告，是传统方法不可能实现的。

Description

基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台及侦测报告自动生成方法 技术领域
本发明涉及一种食用农产品农药残留在线溯源预警方法，特指一种基于高分辨质谱、互联网和数据科学三元融合技术的农药残留侦测数据平台构建及侦测报告自动生成方法，属于互联网、高分辨质谱和数据科学三元跨界融合技术。
背景技术
目前，由质监部门发布的农药残留情况检测报告中，检测数据主要由数据表格和少数统计图表来进行表达。不但生成慢、时效性差，而且冷冰冰的数字民众很难看懂，缺乏及时管理与预警的作用。另外，鉴于非靶标农药残留侦测技术的高度数字化、信息化和自动化的实现，产生了海量分析数据，向传统数据统计分析方法提出了挑战，急需建立新的大数据采集、传送、统计和智能分析系统。近年来，随着电子信息技术与互联网技术的发展，为农药残留检测大数据的多维表达、共享和分析提供了新的手段和方法。
如何基于互联网，利用先进的高分辨率质谱技术与数据科学跨界融合，构建农药残留侦测数据平台，实现食用农产品农药残留数据的及时采集，管理和智能分析，并在短时间内自动生成相关农药残留检测报告，为农药残留追根溯源、风险评估、农药的科学管理与使用，提供实时在线服务。到目前为止，这类方法和系统未见报道。
发明内容
本发明设计了高分辨质谱、互联网、数据科学三元跨界融合技术，构建农药残留侦测数据平台，提出了侦测报告自动生成的方法。对以互联网为基础建立的多个分布在全国各地的联盟实验室，对全国不同种类水果蔬菜世界常用1200多种农药残留实施一年四季循环检测，并对农药残留侦测数据进行采集、构建大数据库，并通过对数据的智能管理与分析，实现报告自动生成。
本发明提出的“基于高分辨质谱、互联网、数据科学三元融合技术的农药残留侦测数据平台构建及侦测报告自动生成方法”包括①建立联盟实验室与农药残 留检测标准方法；②建立联盟实验室检测结果数据库和四个基础数据子库；③建立农药残留数据采集系统和④建立农药残留数据智能分析系统四大部分。
所述第一部分建立联盟实验室与农药残留检测标准方法。所述的建立联盟实验室是指在全国建立若干个标准实验室，按照五统一规范操作(统一采样方法、统一制样方法、统一检测方法、统一格式数据上传，统一格式统计分析报告)，封闭运行，对全国市售果蔬农药残留进行一年四季循环侦测。所述的农药残留数据检测方法是利用液相色谱-四极杆-飞行时间质谱和气相色谱-四极杆-飞行时间质谱技术对水果蔬菜农药残留进行检测，可获得相关农药残留原始数据；
所述第二部分建立联盟实验室检测结果数据库和四个基础数据子库。其中联盟实验室检测结果数据库中包含农药名称、农产品名称、采样时间、采样地点、检测方法、检测单位等；四个基础数据子库为多国MRL标准数据库、农产品分类数据库、农药信息数据库和地理信息数据库，其中多国MRL标准数据库主要有中国MRL、香港MRL、美国MRL、欧盟MRL、日本MRL、CAC MRL，相关MRL标准241527条，包括所针对的农药、农产品、允许最大残留量、标准制定国家；农产品种类数据库主要包含中国分类、香港分类、美国分类、欧盟分类、日本分类、CAC分类标准，具体包括农产品的名称、一级分类、二级分类信息、三级分类等信息；农药信息数据库包含基本信息、毒性信息、功能信息、化学成份、禁用信息、衍生物信息，具体包括所有检出农药的名称、CAS码、毒性强度、是否代谢产物及其代谢前身、是否为标准禁用；地理信息数据库覆盖所需的地域范围，包括所有采样点所属的省级行政区划、地级行政区划、县级行政区划等详细地址。
所述的第三部分建立数据采集系统基于“浏览器/Web服务器/数据库服务器”的三层架构，包括数据获取、数据预处理、污染等级判断、数据存储模块。其中浏览器层位于各地联盟实验室的客户端中，是用户访问系统的界面；Web服务器层位于数据中心，负责访问数据库和执行预处理逻辑；数据库服务器位于数据中心，负责存储和管理农药残留各类数据。所述采集系统各模块的功能：(1)数据获取模块负责获取各联盟实验室上报的农药残留检测结果；(2)数据预处理模块负责对上报的检测数据进行处理，包括对上报数据的判定，对农药、地域和农产品分类等信息的补充、分类、合并；(3)污染等级判断模块负责根据各国(或地区组织)的MRL进行污染等级判定；(4)数据存储模块负责最后形成结果记录 存入数据库。
所述的第四部分建立数据智能分析系统主要实现检测结果数据库和四个基础数据子库相互关联、互联互通；根据统计分析模型实现采样点、农药、农产品、污染等级多维度的交叉分析。该系统同样基于“浏览器/Web服务器/数据库服务器”的三层架构，包括参数设置模块、单项分析模块、综合分析模块、报告生成模块、附表生成模块、预警报告模块。其中浏览器层位于各地联盟实验室的客户端中，是用户访问系统的界面，设置统计参数、下载统计结果；Web服务器层也位于数据中心，负责访问数据库和执行各类统计分析逻辑；数据库服务器位于数据中心，负责存储和管理农药残留各类数据。所述数据智能分析系统各模块的功能：(1)参数设置模块负责为用户提供参数设置的界面和通道；(2)单项分析模块负责完成18项单项统计功能；(3)综合分析模块负责在单项分析结果的基础上完成5项综合分析；(4)报告生成模块负责将分析结果形成图文并茂的检测报告；(5)附表生成模块负责生成各类统计报表；(6)预警报告模块，根据分析结果给出预警提示。
本发明的有益效果：
本发明提供的平台构建及侦测报告自动生成方法为我国各地区农药残留数据的分析与预警提供了高效精准的数据分析平台。其中联盟实验室与农药残留检测标准方法充分保障数据的统一性、完整性、准确性、安全性和可靠性；联盟实验室检测结果数据库和四个基础数据子库的建立为农药残留侦测数据的分析和污染等级判定提供依据；提出的农药残留数据采集系统实现了检测结果的自动上传、数据预处理和污染等级判定，建立了国家农药残留侦测结果数据库；提出的农药残留数据智能分析系统实现了检测原始数据库和四个基础数据子库相互关联、互联互通，多维农药残留数据的单项和综合统计分析，实现了图文并茂的检查结果报告生成的自动化。实现了“一键下载”一个省市的农药残留图文并茂侦测报告30分钟自动生成，这是传统统计方法不可能实现的。
应用本发明形成的检测报告与现有人工报告相比，不但准确性高、速度快、判定标准多，且统计范围灵活、分析方法多样。实现了在线数据采集、结果判定、统计分析和报告制作的自动化，大大提高了数据分析的深度、精准度和工作效率，具有极其重要的现实意义和商业推广价值。
附图说明
图1全国互联网农药残留侦测数据分析平台
图2农药残留侦测数据采集系统
图3农药残留侦测数据智能分析系统
图4自动化生成的农药残留侦测报告
图5农药残留侦测报告自动导出参数选择界面
图6农药残留侦测报告行政区域5级树形结构
图7农药残留侦测报告目录
图8 31省会/直辖市在2012-2015年间市售水果蔬菜农药残留检出率
图9参照多国或国际组织MRL标准衡量样品安全水平
图10检出农药的毒性分类和占比
图11超过CAC-MRL农药品种及频次
具体实施方式
下面结合附图和实施例对本发明作进一步说明。
构建基于Internet的全国农药残留侦测大数据技术平台如图1所示，主要包括四部分：①基于互联网的全国30多个联盟实验室；②联盟实验室检测结果数据库和四个基础数据子库(多国MRL标准数据库、农产品种类数据库、农药基础信息数据库、地理信息数据库)；③农药残留数据采集系统和④农药残留智能分析系统，后两部分构成数据处理中心。其工作原理如下：分布在全国各地的联盟实验室，在其客户端将农药残留检测原始结果通过Internet上报至采集系统，见图2；采集系统通过对数据获取、信息补充、衍生物合并、毒性分析、参照多国MRL标准进行污染等级判定、形成结果记录，存入检测结果数据库；智能分析系统根据用户的条件设定、读取数据、然后逐一根据统计分析模型，进行各项统计分析，生成图表，得出综合结论、检测报告，将分析结果返回给联盟实验室的客户端，供查看和下载，见图1。
表1为构建全国30多个联盟实验室检测原始数据与四大基础数据子库(多国MRL标准数据库、农产品种类数据库、农药基础信息数据库、地理信息数据库)，提出基于“多国MRL标准—农产品分类—千余种农药特性”的数据关联存储与查询模型，实现了农药残留基础数据的关联存取与调用，为农药残留侦测结果的判定提供了标准依据。
表1.构建五大基础数据库，为多个联盟实验室农药残留侦测结果的定性判定提供科学依据
Figure PCTCN2018082954-appb-000001
设计农药残留数据采集系统如图2所示，构建国家农药残留侦测结果数据库。提出的“数据获取-信息补充-衍生物合并-禁药处理-污染等级判定”的数据融合与处理模型，实现了对农药多残留检测结果数据进行快速在线采集、融合、以及参照多国农药残留限量标准(MRL)的精准判定，实现了农药残留侦测结果数据库的动态添加与实时更新，为国家食品安全决策提供了科学数据。如图2所示，所述农药残留侦测数据采集系统，采用基于浏览器/服务器的三层架构，各联盟实验室按五统一规范操作(统一采样、统一制样、统一检测方法、统一格式数据上传，统一格式统计分析报告)，封闭运行，利用液相色谱-四极杆-飞行时间质谱和气相色谱-四极杆-飞行时间质谱技术，上报一年四季循环侦测的农药残留检测数据，充分保障数据的统一性、完整性、准确性、安全性和可靠性。采用ASP.NET技术，获取侦测结果原始数据，对农药、地域和农产品分类信息进行补充；进行衍生物合并、农药毒性分类处理；根据各国或地区组织的MRL进行污染等级判定；形成结果记录并存入侦测结果数据库。
建立农药残留侦测数据智能分析系统，如图3所示，智能分析系统包括展现层、业务层、访问层和数据层；所述数据层由侦测结果数据库和四大基础数据子库以及相关文件组成，提供数据库和文件服务；所述访问层通过数据库访问组件访问数据库中的数据并提供给业务层；所述业务层则根据统计分析模型实现采样点、农药、污染等级多维度的统计分析；展现层则实现按客户若干个条件要求的各类文图并茂的智能分析报告。本发明建立了在线定制模式，支持用户自主选择和过滤统计数据以凸显兴趣数据或关键数据；支持用户定制报告类型和范围，提高数据展示和大数据分析能力。实现了从农产品、农药、地域、多国MRL等多维度进行的20项农药残留指标的自动统计，见表2。其中包括31项不同形式的表格和38幅不同类型的图自动生成，以及根据统计结果的综合评价和预警信息的自动生成。最终实现“一键下载”，一本图文并茂的农药残留侦测报告30分钟 自动生成，如图4所示。
图4显示了高分辨质谱、互联网和数据科学三元跨界融合技术农药残留大数据多学科交叉多元技术融合的成果，实现了“一键下载”，一份图文并茂的侦测报告30分钟内完成。农药残留侦测报告，反映了全国31个省会/直辖市市售18类150多种水果蔬菜20项农药残留的规律性特征，见表2。
表2.目前农药残留大数据统计分析发现20项规律性特征
Figure PCTCN2018082954-appb-000002
农药残留侦测结果报告下载参数见图5，可任意选择采样期间类型，任意选择一个或多个行政区域(可实现全国-大区-省级-地市级-区县级5级架构)见图6，选择检测设备类型，选择导出报告正文或附表；地方版报告正文共分5章目录，见图7，比如反映31省会/直辖市市售水果蔬菜农药残留检出率，见图8，反映侦测样品安全水平的饼图，见图9，反映检出农药毒性分类和占比，见图10，用于特例样品超标情况分析的多例柱状图(见图11)等。报告附表包含20个附表，记录了检测原始结果，并从检出农残浓度分布、污染等级以及超过各MRL标准的情况进行了详细统计。
根据数据量不同，一份报告字数从几万到几十万不等，从正文到附表，图文并茂，均可在30分钟内实现“一键下载”，一次性成型，极大地提高了对海量农 残数据进行分析报告的能力。同时，自动报告系统还可实现报告结构和内容的定制扩展开发。
分析报告实例：农药残留侦测结果数据库，现已存入全国10个联盟实验室31省会/直辖市(含284个区县)638个采样点140多种水果蔬菜22368批样品，1374万条侦测数据，高分辨质谱1.45亿张，并形成2500万字的农药残留侦测报告。
初步查清了我国31省会/直辖市市售水果蔬菜农药残留“家底”，见图8和表3和表4。通过2016年京津冀市售水果蔬菜农药残留的再次普查，基本重现了2012-2015年对31省会/直辖市水果蔬菜普查发现的农药残留基本态势。
表3. 31省会/直辖市市售水果蔬菜农药残留“家底”(2012-2015)
Figure PCTCN2018082954-appb-000003
表4.京津冀市售水果蔬菜农药残留“家底”(2016年度)
Figure PCTCN2018082954-appb-000004
从表3可以看出，31省会/直辖市2012-2015年22368例样品，共检出农药517种(其中93种是两种技术共检的)，检出频次45866频次。从表4可以看出，2016年度京津冀10190例样品，共检出农药227种，19558频次。不论2012-2015年31省会/直辖市，还是2016年度京津冀普查大数据分析发现，我国市售水果蔬菜质量安全水平有基本保障，参照中国MRL标准衡量合格率均在97％以上，但农药残留问题依然严重。大数据统计分析发现：①高剧毒农药(如：克百威、水氨硫磷、杀扑磷)和禁用农药(如：甲拌磷、灭线磷)仍常有检出，检出频次占总检出频次的5.5％；②有大约2.9％的样品中农药残留超标；③单例样品检出农药残留超过10种的约占0.7％；④单类果蔬检出农药残留大多在30种以上，最多的近100种；⑤日常常吃的水果(葡萄、苹果、梨和桃)和蔬菜(芹菜、番茄、黄瓜和甜椒)检出率高，超标也多，见表5和表6；⑥与世界先进国家相比我国农药残留限量标准(MRLs)面临数量少、水平低的挑战。以2016年度京津 冀筛查GC-Q-TOF/MS检出9834频次农药残留为例，我国MRL标准有对应标准的仅有2233频次，占比22.7％，是所有6大MRL标准中最低的，远低于欧盟和日本可以全覆盖的水平。
表5. 4种水果(葡萄、苹果、梨、桃)和4种蔬菜(芹菜、番茄、黄瓜、甜椒)农药残留检测结果
Figure PCTCN2018082954-appb-000005
表6. 4种水果(葡萄、苹果、梨、桃)和4种蔬菜(芹菜、番茄、黄瓜、甜椒)三类残留限量标准MRL分析
Figure PCTCN2018082954-appb-000006
上文所列出的一系列的详细说明仅仅是针对本发明的可行性实施方式的具体说明，它们并非用以限制本发明的保护范围，凡未脱离本发明技艺精神所作的等效实施方式或变更均应包含在本发明的保护范围之内。

Claims (13)

  1. 基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，包括联盟实验室、联盟实验室检测结果数据库和四个基础数据子库、数据采集系统、数据智能分析系统；
    所述联盟实验室是指在全国建立的若干个标准实验室，按照五统一规范操作，封闭运行，对全国市售果蔬农药残留进行一年四季循环侦测；
    所述联盟实验室检测结果数据库中包含农药名称、农产品名称、采样时间、采样地点、检测方法、检测单位；所述四个基础数据子库为若干国的MRL标准数据库、农产品分类数据库、农药信息数据库和地理信息数据库；所述MRL表示农药残留限量标准；
    所述数据采集系统实现检测结果的自动上传、数据预处理和污染等级判定，建立国家农药残留侦测结果数据库；
    所述数据采集系统包括数据获取模块、数据预处理模块、污染等级判断模块、数据存储模块；所述数据获取模块负责获取各联盟实验室上报的农药残留检测结果；所述数据预处理模块负责对上报的检测数据进行处理，包括对上报数据的判定，对农药、地域和农产品分类信息的补充、分类、合并；所述污染等级判断模块负责根据各国或地区组织的MRL进行污染等级判定；所述数据存储模块负责最后形成结果记录存入数据库；
    所述数据智能分析系统实现检测结果数据库和四个基础数据子库的相互关联以及互联互通，并根据统计分析模型实现采样点、农药、农产品、污染等级多维度的交叉分析，根据用户的条件设定、读取数据、然后逐一根据统计分析模型，进行各项统计分析，生成图表，得出综合结论、检测报告，将分析结果返回给联盟实验室的客户端，供查看和下载；
    所述数据智能分析系统包括参数设置模块、单项分析模块、综合分析模块、报告生成模块、附表生成模块、预警报告模块；所述参数设置模块负责为用户提供参数设置的界面和通道；所述单项分析模块负责完成若干项单项统计功能；所述综合分析模块负责在单项分析结果的基础上完成若干项综合分析；所述报告生成模块负责将分析结果形成图文并茂的检测报告；所述附表生成模块负责生成各类统计报表；所述预警报告模块根据分析结果给出预警提示；
    所述数据智能分析系统的具体实现包括展现层、业务层、访问层和数据层；所述数据层由侦测结果数据库和四个基础数据子库以及相关文件组成，提供数据库和文件服务； 所述访问层通过数据库访问组件访问数据库中的数据并提供给业务层；所述业务层则根据统计分析模型实现采样点、农药、污染等级多维度的统计分析；展现层则实现按客户若干个条件要求的各类文图并茂的智能分析报告。
  2. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述若干国的MRL标准数据库包括中国MRL、香港MRL、美国MRL、欧盟MRL、日本MRL、CAC MRL，共计241527条MRL标准，所针对的农药、农产品、允许最大残留量、标准制定国家。
  3. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，农产品分类数据库包括中国分类、香港分类、美国分类、欧盟分类、日本分类、CAC分类标准。
  4. 根据权利要求3所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述农产品分类数据库具体包括农产品的名称、一级分类、二级分类信息、三级分类信息。
  5. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述农药信息数据库包含基本信息、毒性信息、功能信息、化学成份、禁用信息、衍生物信息。
  6. 根据权利要求5所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述农药信息数据库具体包括所有检出农药的名称、CAS码、毒性强度、是否代谢产物及其代谢前身、是否为标准禁用。
  7. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述地理信息数据库覆盖所需的地域范围，包括所有采样点所属的省级行政区划、地级行政区划、县级行政区划详细地址。
  8. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，所述数据采集系统的实现是基于浏览器、Web服务器、数据库服务器的三层架构，所述浏览器位于各地联盟实验室的客户端中，是用户访问系统的界面；所述Web服务器位于数据中心，负责访问数据库和执行预处理逻辑；所述数据库服务器位于数据中心，负责存储和管理农药残留各类数据。
  9. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数 据平台，其特征在于，所述数据智能分析系统的实现是基于浏览器、Web服务器、数据库服务器的三层架构；所述浏览器位于各地联盟实验室的客户端中，是用户访问系统的界面，设置统计参数、下载统计结果；所述Web服务器也位于数据中心，负责访问数据库和执行各类统计分析逻辑；所述数据库服务器位于数据中心，负责存储和管理农药残留各类数据。
  10. 根据权利要求1所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台，其特征在于，五统一规范包括统一采样方法、统一制样方法、统一检测方法、统一格式数据上传、统一格式统计分析报告。
  11. 依据权利要求1-10任一项所述的基于高分辨质谱、互联网和数据科学的农药残留侦测数据平台的农药残留的侦测报告自动生成方法，其特征在于，分布在全国各地的联盟实验室，在其客户端将农药残留检测原始结果通过Internet上报至数据采集系统；数据采集系统通过对数据获取、信息补充、衍生物合并、毒性分析、参照多国MRL标准进行污染等级判定、形成结果记录，存入检测结果数据库；智能分析系统根据用户的条件设定、读取数据、然后逐一根据统计分析模型，进行各项统计分析，生成图表，得出综合结论、检测报告，将分析结果返回给联盟实验室的客户端。
  12. 根据权利要求11所述的农药残留的侦测报告自动生成方法，其特征在于，所述联盟实验室利用液相色谱-四极杆-飞行时间质谱和气相色谱-四极杆-飞行时间质谱技术实现农药残留检测，上报一年四季循环侦测的农药残留检测数据。
  13. 根据权利要求11所述的农药残留的侦测报告自动生成方法，其特征在于，还包括：在智能分析系统中建立在线定制模式，支持用户自主选择和过滤统计数据以凸显兴趣数据或关键数据；支持用户定制报告类型和范围。
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