A method and system for sample pretreatment and detection linkage of consumer product detection

Abstract

The present application relates to a kind of consumer product detection sample pretreatment and detection linkage method and system, belong to consumer product safety detection technical field, including the input module for receiving sample information and detection data information;Standard knowledge base with multiple structured detection standard documents is stored;Matching engine module for matching target standard and extracting key elements according to sample information;Guidance operation process is generated based on key elements, and subsequent detection parameters can be dynamically adjusted according to the process generation module of pretreatment process data;And data analysis module for automatically performing quality control judgment and result calculation;The present application realizes the standardization, automation and intelligent guidance of whole process from sample input to report generation by intelligently integrating multiple national detection standards, significantly improves detection efficiency, accuracy and reliability, solves the problem of complicated operation, low efficiency and too high dependence on personnel experience under multiple standards.

Description

Technical Field

[0001] This invention relates to the field of consumer product safety testing technology, and in particular discloses a method and system for linking sample pretreatment and testing of consumer products. Background Technology

[0002] As people pay increasing attention to the safety of consumer products, especially children's products, regulations and standards in various countries are becoming increasingly stringent and complex regarding the types and limits of restricted chemicals in consumer products. These harmful substances include, but are not limited to: alkylphenols and alkylphenol polyoxyethylene ethers, and azo dyes in textiles; bisphenol A, phthalates, and N-nitrosamines in toys; formaldehyde in wood products; and heavy metals and volatile organic compounds, which are of widespread concern.

[0003] Currently, the detection of these hazardous substances mainly relies on scattered and independent national or industry standards (such as the GB / T series standards). Each standard typically targets only one or a few types of substances, specifying particular sample pretreatment methods, instrument analysis conditions, and result calculation formulas. In actual testing laboratories, operators need to be familiar with and frequently switch between dozens of standard procedures, resulting in low work efficiency, easy errors in method application, complex allocation of instrument resources, and difficulty in achieving comprehensive screening of multiple items and high throughput.

[0004] Therefore, there is an urgent need for a comprehensive testing solution that can integrate existing mainstream testing standards, achieve process standardization, intelligent operation, and data integration, in order to improve the efficiency, accuracy, and reliability of consumer product safety testing. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the existing technology, the purpose of this invention is to provide a system and method for the integrated pretreatment and detection of consumer product test samples.

[0006] To achieve the above objectives, the present invention provides a consumer product testing sample pretreatment and testing linkage system, comprising: Information entry module: Used to receive the first type of information and the second type of information. The first type of information includes the type of sample to be tested and the test items, and the second type of information includes the pretreatment process data and the test process data.

[0007] Standard Knowledge Base: Used to store multiple structured testing standard documents. Matching Engine Module: Connected to the Information Input Module and the Standard Knowledge Base, it is used to match the target detection standard document from the standard knowledge base based on the first type of information, and extract the key elements in the target detection standard document. The key elements include the description of the preprocessing steps, the description of the detection steps, the instrument working parameters, and the result calculation formula. Process generation module: Connected to the information entry module and the matching engine module, and configured as follows: a. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, a detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. b. When the key elements extracted determine that the output data of the preprocessing step needs to be used to determine the parameters of the detection step, the first sub-process sheet is generated first. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. The user enters the preprocessing process data obtained by executing the first sub-operation instruction into the information input module. The process generation module generates the second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. Data Analysis Module: Used to analyze and calculate the second type of information based on the calculation formula in the key elements, and output the detection results.

[0008] Furthermore, it also includes an instrument gateway and monitoring module, which is used to connect external testing equipment and external pre-processing equipment, and automatically return the second type of information generated by the external testing equipment and external pre-processing equipment to the information entry module.

[0009] Furthermore, it also includes a report generation module, which is used to generate a test report from the test results output by the data analysis module.

[0010] Furthermore, the testing standard documents stored in the standard knowledge base are structured data, and also include the standard number, scope of application, and lower limit of determination.

[0011] Furthermore, the process generation module automatically calculates the adjustment parameters required for the detection steps based on the received preprocessing data, and integrates these adjustment parameters into the corresponding operation instructions in the second sub-process sheet; the adjustment parameters include the dilution factor of the sample solution, the instrument injection volume, and the detection mode.

[0012] Furthermore, the process generation module is also configured to: when the first type of information contains multiple testing items, compare the key elements of each testing item, merge and optimize common preprocessing steps, generate parallel or integrated operation instructions, and schedule testing items that need to use the same external testing equipment.

[0013] Furthermore, the data analysis module is configured to compare the preprocessing data and / or testing data with the quality control indicators and / or preset reasonable ranges specified in the key elements. If they do not meet the requirements, an early warning message is output; if they do meet the requirements, the module performs analysis and calculation according to the calculation formula in the key elements and outputs the test results.

[0014] Furthermore, it also includes a data traceability and storage module, which is used to store the test task cycle data, including first type of information, second type of information, test process sheet / first sub-process sheet and second sub-process sheet, calculation formula and test results.

[0015] Another objective of this invention is to provide a method for linking sample pretreatment and detection in consumer product testing, comprising the following steps: S1. Sample Information Input: Users input the first type of information through the information input module, including the type of sample to be tested and the test items. S2, Standard Matching and Parsing: Based on the first type of information input in step S1, the matching engine module matches and parses the target detection standard document and its key elements from the standard knowledge base; S3. Generate the detection process sheet / first sub-process sheet and second sub-process sheet. S31. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, the detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. The user executes the general operation instructions to obtain the preprocessing process data and the detection process data. S32. When the output data of the extracted key elements determination preprocessing step needs to be used to determine the parameters of the detection step, a first sub-process sheet is first generated. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. After the first sub-operation instruction is executed to obtain the preprocessing process data, the process generation module generates a second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. The user executes the second sub-operation instruction to obtain the detection process data. S4. Calculation of test results: The user and / or instrument gateway and monitoring module input the preprocessing data and the test process data through the information input module; the data analysis module analyzes and calculates the second type of information according to the calculation formula in the key elements, and outputs the test results.

[0016] Furthermore, in step S4, the data analysis module compares the preprocessing data and / or the detection data with the quality control indicators and / or preset reasonable ranges specified in the key elements. If they do not meet the requirements, an early warning message is output; if they do meet the requirements, the module performs analysis and calculation according to the calculation formula in the key elements and outputs the detection results.

[0017] The beneficial effects of this invention are: (1) Comprehensiveness and systematicness: For the first time, dozens of national standard core methods involving various consumer products such as textiles, toys, and furniture, covering organic chemical hazards (such as APEO, BPA, plasticizers, nitrosamines, azo dyes, and VOCs) and inorganic hazards (such as heavy metals) are integrated into a unified platform.

[0018] (2) Standardization and traceability: The built-in process strictly follows national standards to ensure the compliance of the testing methods and the authority of the results. Every step of the operation is recorded, achieving full traceability.

[0019] (3) High efficiency and intelligence: By intelligently matching sample type and target object, the optimal detection path is recommended, reducing manual query and decision-making time. Automated or guided operation reduces over-reliance on the personal experience of operators.

[0020] (4) Flexibility and scalability: The modular design allows new testing standards and methods to be added to the system as independent modules, facilitating future updates and expansions.

[0021] (5) Data fusion and in-depth analysis: It can perform correlation analysis on multiple test results of the same sample, providing more comprehensive data support for consumer product safety risk assessment. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving the intended purpose, preferred embodiments of the present invention are given below, and the specific implementation methods, features and effects of the present invention are described in detail below.

[0023] The present invention provides a sample pretreatment and detection linkage system for consumer products, comprising: Information entry module: Used to receive the first type of information and the second type of information. The first type of information includes the type of sample to be tested, the material of the sample to be tested, and the test items. The second type of information includes pretreatment process data and test process data.

[0024] Standard Knowledge Base: Used to store multiple structured testing standard documents. Matching Engine Module: Connected to the Information Input Module and the Standard Knowledge Base, it is used to match the target detection standard document from the standard knowledge base based on the first type of information, and extract the key elements in the target detection standard document. The key elements include the description of the preprocessing steps, the description of the detection steps, the instrument working parameters, and the result calculation formula. Process generation module: Connected to the information entry module and the matching engine module, and configured as follows: a. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, a detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. b. When the key elements extracted determine that the output data of the preprocessing step needs to be used to determine the parameters of the detection step, the first sub-process sheet is generated first. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. The user enters the preprocessing process data obtained by executing the first sub-operation instruction into the information input module. The process generation module generates the second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. Data Analysis Module: Used to analyze and calculate the second type of information based on the calculation formula in the key elements, and output the detection results.

[0025] In actual use, the first type of information is manually selected by the user, and the second type of information can be manually entered by the user, or it can be automatically returned to the information entry module after being associated with external testing equipment and external pre-processing equipment through the instrument gateway and monitoring module.

[0026] In actual use, the types of samples to be tested and the test items are selected from a drop-down menu. First, the user needs to manually select the type of sample to be tested, including textiles, toys, and furniture. After selecting textiles as the type of sample to be tested, the test item options are automatically limited to heavy metals, volatile organic compounds, nickel release, alkylphenols, phenol, and bisphenol A. The user should select at least one of these. When the type of sample to be tested is toys, the test item options are automatically limited to bisphenol A, phthalates, nitrosamines, and formaldehyde. The user should select at least one of these.

[0027] In practice, the standard knowledge base includes: 001: GB / T 17593.1-2006 "Determination of Heavy Metals in Textiles – Part 1: Atomic Absorption Spectrophotometry" 002: GB / T 24281-2009 "Determination of Volatile Organic Compounds in Textiles – Gas Chromatography-Mass Spectrometry" 003: GB / T30158-2013 Determination of Nickel Emission from Textile Accessories 004: GB / T23322-2018 "Determination of Surfactants in Textiles - Alkylphenols and Alkylphenol Polyoxyethylene" 005: GB / T38420-2019 "Determination of Bisphenol A Migration in Polycarbonate and Polysulfone Materials for Toys - High Performance Liquid Chromatography-Tandem Mass Spectrometry" 006: GB / T 41531-2022 "Determination of Phenol and Bisphenol A in Textiles" 007: GB / T41649-2022 "Determination of Formaldehyde Emission from Wooden Toys - Flask Method" 008: GB / T41413-2022 Determination of the migration of N-nitrosamines and their precursors in toys by high performance liquid chromatography-tandem mass spectrometry.

[0028] Specifically, it also includes an instrument gateway and monitoring module, which is used to connect external testing equipment and external pre-processing equipment, and automatically return the second type of information generated by the external testing equipment and external pre-processing equipment to the information entry module.

[0029] In practical use, the instrument gateway and monitoring module act as a bridge between the system and physical devices, responsible for automated data acquisition and command issuance. For pretreatment equipment, such as electronic balances, precision pipettes, nitrogen blowing apparatus, and solid-phase extraction devices, connections are made via RS-232, USB, or Ethernet interfaces using standard or manufacturer-provided communication protocols. For analytical instruments, such as HPLC-MS, GC-MS, and AAS, connections are made via the application programming interface (API) provided by the instrument workstation software, standard communication protocols, or by directly reading the data files generated by the instrument. The instrument gateway and monitoring module automatically captures equipment status, process data, and raw detection data, and returns the structured data to the system's information entry module, eliminating the need for manual transcription and ensuring accurate and traceable data sources.

[0030] Specifically, it also includes a report generation module, which is used to generate a test report from the test results output by the data analysis module.

[0031] In practical use, the report generation module integrates the quantitative results from the data analysis module with the task metadata to automatically generate standardized testing reports that meet laboratory accreditation criteria and client requirements. This includes: Report cover: Report number, unique identifier, laboratory name and logo, client name.

[0032] Sample information: Sample name, model, color, submission date, and receipt status.

[0033] Testing basis: Clearly list the national standard number and name to be implemented.

[0034] Instruments used: List the names and models of the main instruments and equipment used in the test.

[0035] Summary of testing conditions: Briefly describe the key instrument parameters.

[0036] Test results: Each test item, test result, method limit of quantitation / lower limit of determination and limit requirements are clearly listed in tabular form.

[0037] Conclusion: Determine whether the sample meets the relevant standards or regulations.

[0038] Electronic signature area for those who prepare, review, and approve the report, along with the report date.

[0039] Declarations and Remarks: Includes explanations of testing uncertainty, a statement that the report is only responsible for the sample submitted, and explanations of any deviations from the standard.

[0040] Specifically, the testing standard documents stored in the standard knowledge base are structured data, and also include the standard number, scope of application, and lower limit of determination.

[0041] In practical use, standard knowledge bases are not only used to store PDF documents that can be accessed at any time, but also to deconstruct standard text into structured data that can be retrieved and accessed by computers. For example, take 004 as an example: Standard Number: GB / T 23322 Standard Name: Determination of Surfactants in Textiles - Alkylphenols and Alkylphenol Polyoxyethylene Ethers Scope of application: All kinds of textile products Target compound: [{"Name": "Octophenol", "CAS": "140-66-9"}, {"Name": "Nonylphenol", "CAS": "25154-52-3"}] Limit of Detection: [{"Compound": "Octylphenol", "LC-MS": "0.25 mg / kg"}, {"Compound": "Nonylphenol", "LC-MS": "0.5 mg / kg"}] Key pretreatment steps: {"Extraction solvent": "Methanol", "Temperature": "70±2℃", "Time": "60±5min"} Key instrument methods: {“Instrument type”:“LC-MS”, “Column”:“C18”, “Ion source”:“ESI”} Calculation formula: "X = (c - c0) × V / m" Quality control indicators: {"Recovery rate range": 80%~110%; "Tolerance for repeatability": ≤10%} Specifically, the process generation module automatically calculates the adjustment parameters required for the detection steps based on the received preprocessing data, and integrates these adjustment parameters into the corresponding operation instructions in the second sub-process sheet; the adjustment parameters include the dilution factor of the sample solution, the instrument injection volume, and the detection mode.

[0042] In practical use, the process generation module also includes an automatic parameter adjustment unit, which dynamically optimizes subsequent detection instructions based on pretreatment data. For example, after performing heavy metal extraction from textiles (GB / T 17593.1), if the pre-screening value of heavy metal concentration in the extract returned by the instrument gateway is too high and exceeds the linear range of the standard curve, the module will automatically calculate the required dilution factor (e.g., 10-fold dilution) and write the instruction "Take 1 mL of extract and dilute to 10 mL with acidic sweat" and the updated sample bottle position information into the second sub-process sheet. At the same time, it will automatically adjust the instrument injection volume or select a more suitable detection mode (e.g., the ashing and atomization temperature program of the graphite furnace method).

[0043] Specifically, the process generation module is also configured to: when the first type of information contains multiple testing items, compare the key elements of each testing item, merge and optimize common preprocessing steps, generate parallel or integrated operation instructions, and schedule testing items that need to use the same external testing equipment.

[0044] In practical use, when the same sample needs to be tested for "phenol in textiles" (GB / T 41531) and "alkylphenol in textiles" (GB / T 23322), both are extracted with methanol using ultrasonic extraction. The process generation module will recognize this commonality and generate a combined extraction step, such as "weigh 1g of sample, add 30mL of methanol, and extract with ultrasonic extraction at 70℃ for 60min". Subsequently, the sample can be separated and purified according to different standards or directly analyzed by different instruments.

[0045] If some samples in the same batch require GC-MS to determine plasticizers and others require HPLC-MS / MS to determine bisphenol A, the module will intelligently queue and generate an injection sequence based on the current instrument status and the estimated analysis time, maximizing instrument utilization.

[0046] Specifically, the data analysis module is configured to compare the preprocessing data and / or testing data with the quality control indicators and / or preset reasonable ranges specified in the key elements. If they do not meet the requirements, an early warning message is output. If they do meet the requirements, the module performs analysis and calculation according to the calculation formula in the key elements and outputs the test results.

[0047] In practical use, the data analysis module is used to compare process data with standard requirements in real time. For example, in a spiked recovery test, if the system calculates that the recovery rate of a certain phthalate is 65%, which is lower than the lower limit (68%) or higher than the upper limit (136%) specified in the standard (GB / T 39183), the module will immediately display a prominent warning on the operation interface: "Abnormal recovery rate (65%), exceeding the standard control range (68%-136%), it is recommended to check the pretreatment process or repeat the experiment." In qualitative analysis, if the system finds that the characteristic ion abundance ratio of a certain substance in the sample deviates from the standard by ±35%, exceeding the ±30% allowable range specified in the standard (such as Table 4 of GB / T 23322), it will issue a warning: "Qualitative ion abundance ratio deviation exceeds the standard, qualitative results are questionable, further confirmation is recommended."

[0048] After passing the quality control check, the data analysis module automatically calls the corresponding calculation formula in the structured knowledge base, substitutes the detection process data, such as peak area and concentration, and sample information, such as mass and volume, to accurately calculate the final content or migration amount.

[0049] Specifically, it also includes a data traceability and storage module, which is used to store the test task cycle data, including first type of information, second type of information, test process sheet / first sub-process sheet and second sub-process sheet, calculation formula and test results.

[0050] In practical use, the data traceability and storage module is used to establish a complete electronic experimental record that spans the entire lifecycle of the testing task, including: Task Information: First category of information (customer, sample, testing items).

[0051] Process data: Second type of information (automatically collected weighing, temperature, volume, and raw instrument data files).

[0052] Process log: The generated detection process sheet, the first / second sub-process sheet and their execution log.

[0053] Calculation process: calculation formulas used, intermediate calculation results.

[0054] Final results and report: Test results and generated test report PDF.

[0055] Traceability Function: Through the unique task ID, every step of the sample testing process, every data point, and every instrument spectrum can be traced back, meeting the requirements of the laboratory quality management system for data integrity and traceability.

[0056] Another objective of this invention is to provide a method for linking sample pretreatment and detection in consumer product testing, comprising the following steps: S1. Sample Information Input: Users input the first type of information through the information input module, including the type of sample to be tested, the material of the sample to be tested, and the test items. S2, Standard Matching and Parsing: Based on the first type of information input in step S1, the matching engine module matches and parses the target detection standard document and its key elements from the standard knowledge base; S3. Generate the detection process sheet / first sub-process sheet and second sub-process sheet. S31. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, the detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. The user executes the general operation instructions to obtain the preprocessing process data and the detection process data. S32. When the output data of the extracted key elements determination preprocessing step needs to be used to determine the parameters of the detection step, a first sub-process sheet is first generated. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. After the first sub-operation instruction is executed to obtain the preprocessing process data, the process generation module generates a second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. The user executes the second sub-operation instruction to obtain the detection process data. S4. Calculation of test results: The user and / or instrument gateway and monitoring module input the preprocessing data and the test process data through the information input module; the data analysis module analyzes and calculates the second type of information according to the calculation formula in the key elements, and outputs the test results.

[0057] Specifically, in step S4, the data analysis module compares the preprocessing data and / or the detection data with the quality control indicators and / or preset reasonable ranges specified in the key elements. If they do not meet the requirements, an early warning message is output; if they do meet the requirements, the module performs analysis and calculation according to the calculation formula in the key elements and outputs the detection results.

[0058] The method and system are further described below with reference to specific embodiments.

[0059] Example: Comprehensive Detection of Bisphenol A and Volatile Organic Compounds in Textiles (1) Sample information input and intelligent analysis: When a user logs into the system, they select "Textiles" from the "Sample Type" dropdown menu in the information entry interface. The "Test Items" dropdown menu then updates dynamically, and the user selects "Bisphenol A" and "Volatile Organic Compounds (VOCs)".

[0060] (2) Standard matching and intelligent planning: The matching engine module receives the first type of structured information.

[0061] For “Textiles - Bisphenol A”, the standard 006 is matched: GB / T 41531-2022 “Determination of Phenol and Bisphenol A in Textiles”.

[0062] For “Textiles - Volatile Organic Compounds”, the standard is 002: GB / T 24281-2009 “Determination of Volatile Organic Compounds in Textiles by Gas Chromatography-Mass Spectrometry”.

[0063] The matching engine module analyzes the key elements of two criteria: GB / T 41531 (Bisphenol A): Pretreatment is "methanol ultrasonic extraction" (35±5℃, 40±2 min).

[0064] GB / T 24281 (VOC): Pretreatment is "headspace solid-phase microextraction" (sample placed in a headspace vial at 120℃).

[0065] Reasoning and Optimization Suggestions: The spectral analysis indicates that the initial pretreatment steps for both projects (cutting and mincing a representative sample from the original sample) can be combined. Although the extraction methods are completely different, the process generation module is configured to identify deeper commonalities: GB / T 41531 requires a methanol solution for the final sample, while GB / T 24281 requires a dry sample. The system provides an intelligent solution: "Detection process optimization suggestion: Sample preparation steps can be combined, but sample splitting is required during the extraction stage. Should the optimization solution be adopted?" The user selects "Yes".

[0066] (3) Process generation and execution: The process generation module generates an integrated workflow based on the optimization plan confirmed by the user.

[0067] Generate the first sub-process sheet (combining sample preparation and sample splitting): 3.1 Take a representative sample, cut it into pieces smaller than 5mm × 5mm, and mix it thoroughly.

[0068] 3.2 Accurately weigh 4.0g of the shredded sample (data collected automatically by the balance).

[0069] 3.3 Sample splitting: Branch A (for bisphenol A detection): Weigh 1.0g of sample (from 4.0g) and place it in a 40mL glass extraction bottle.

[0070] Branch B (for VOC detection): Place the remaining approximately 3.0g of sample in a 120℃ oven and dry for 20 minutes (refer to GB / T24281 blank sample preparation principle to remove potential interference). After cooling, accurately weigh two 1.0g portions for later use. Once the operator has completed the task, the balance data (total weight 4.02g, branch A weight 1.00g, branch B weight after drying 0.98g / part) is automatically entered through the instrument gateway.

[0071] Generate the second sub-process sheet (parallel preprocessing and detection): After receiving the weight data of the sample split, the process generation module dynamically generates a list of operation instructions containing two parallel tasks: Task A1 (Bisphenol A Extraction): "Add 30.0 mL of methanol to the sample vial of branch A, tighten the screw, and place it in an ultrasonic cleaner at 35°C for extraction for 40 min." Task B1 (VOC sampling preparation): "Place one dried sample (0.98g) from branch B into a 22mL headspace vial and seal it." The operator executes tasks A1 and B1 in parallel.

[0072] After task A1 is completed, the system automatically generates task A2: "Filter the bisphenol A extract through a 0.45μm PTFE membrane, and put the filtrate into the HPLC autosampler sample vial and place it at position H3." Once Task B1 is ready, the system automatically generates Task B2: "Place the headspace vial containing the sample into a 120°C headspace sampler, insert the activated 75μm Carboxen / PDMS SPME extraction head, equilibrate for 60 min, adsorb for 20 min, and then automatically inject the sample into the GC-MS for analysis." (4) Detection, analysis and early warning: The instrument gateway and monitoring module synchronously control the operation of HPLC (configured with a fluorescence detector) and GC-MS, and acquire data.

[0073] Real-time processing by the data analysis module: For bisphenol A data: Quantification was performed using the external standard method, and the calculated content was "0.85 mg / kg". The system automatically searched the knowledge base for the limit of determination of this standard (2.0 mg / kg by HPLC / FLD method) and judged the result as "not detected (below the limit of quantitation)", but gave the note "Results are for reference only, and it is recommended to use LC-MS / MS method for confirmation".

[0074] For VOC data: Spectral library search and internal standard method quantification were performed. Quality control early warning was triggered: The system calculated that the abundance ratio of characteristic ions (m / z 91, 92) of toluene deviated from the standard by ±28%, which is within the standard allowable range (±30%), and the qualitative analysis passed. However, when calculating total volatile organic compounds (TVOC), the system found that the peak area of ​​naphthalene was abnormally high and deviated significantly from historical data of similar textiles. The adaptive optimization and data feedback module triggered a level-two early warning based on the historical data model: "Note: The naphthalene content in the sample is significantly higher than the historical average level of similar matrices, which may indicate a specific source of pollution. It is recommended to make a note in the report or initiate further investigation." (5) Report generation and data traceability: After all analyses were completed and the first-level quality control was passed, the data analysis module output the final results: Bisphenol A "< 2.0 mg / kg", Total Volatile Organic Compounds (TVOC) "12.4 mg / kg". 2 "Naphthalene 8.7 mg / m 2 ".

[0075] (6) The report generation module automatically generates a report. Below the test results table, the system prompts the following notes: "Note 1: The bisphenol A result was determined by HPLC / FLD method in GB / T 41531 and is below the limit of quantitation of this method. Note 2: The naphthalene content in TVOC is significant and the data is for reference only." (7) The data traceability and storage module archives the complete data chain of this test, including: the initial dialogue record, the raw weighing data of the combined sample preparation, the experimental records of the two parallel processing branches, the raw chromatographic files of HPLC and GC-MS, all intermediate calculated values ​​in the data analysis process, triggered early warning information and processing records. All digital footprints of the entire test process can be traced back with one click through the report number.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A sample pretreatment and testing linkage system for consumer products, characterized in that, include: Information entry module: used to receive the first type of information and the second type of information. The first type of information includes the type of sample to be tested and the test items. The second type of information includes the pre-processing process data and the test process data. Standard Knowledge Base: Used to store multiple structured testing standard documents. Matching Engine Module: Connected to the Information Input Module and the Standard Knowledge Base, it is used to match the target detection standard document from the standard knowledge base based on the first type of information, and extract the key elements in the target detection standard document. The key elements include the description of the preprocessing steps, the description of the detection steps, the instrument working parameters, and the result calculation formula. Process generation module: Connected to the information entry module and the matching engine module, and configured as follows: a. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, a detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. b. When the key elements extracted determine that the output data of the preprocessing step needs to be used to determine the parameters of the detection step, the first sub-process sheet is generated first. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. The user enters the preprocessing process data obtained by executing the first sub-operation instruction into the information input module. The process generation module generates the second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. Data Analysis Module: Used to analyze and calculate the second type of information based on the calculation formula in the key elements, and output the detection results.

2. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: It also includes an instrument gateway and monitoring module, which is used to connect external testing equipment and external pre-processing equipment, and automatically return the second type of information generated by the external testing equipment and external pre-processing equipment to the information entry module.

3. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: It also includes a report generation module, which is used to generate test reports from the test results output by the data analysis module.

4. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: The testing standard documents stored in the standard knowledge base are structured data, and also include the standard number, scope of application, and lower limit of determination.

5. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: Based on the received preprocessing data, the process generation module automatically calculates the adjustment parameters required for the detection steps and integrates these adjustment parameters into the corresponding operation instructions in the second sub-process sheet. The adjustment parameters include the dilution factor of the sample solution, the instrument injection volume, and the detection mode.

6. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: The process generation module is also configured to: when the first type of information contains multiple testing items, compare the key elements of each testing item, merge and optimize common preprocessing steps, generate parallel or integrated operation instructions, and schedule testing items that need to use the same external testing equipment.

7. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: The data analysis module is configured to compare the preprocessing data and / or testing data with the quality control indicators and / or preset reasonable ranges specified in the key elements, and output early warning information if they do not meet the requirements. If the criteria are met, the analysis and calculation will be performed according to the calculation formula in the key elements, and the detection results will be output.

8. The consumer product testing sample pretreatment and testing linkage system according to claim 1, characterized in that: It also includes a data traceability and storage module, which is used to store the test task cycle data, including the first type of information, the second type of information, the test process sheet / first sub-process sheet and second sub-process sheet, calculation formulas and test results.

9. A method for linking sample pretreatment and detection in consumer product testing, characterized in that, Includes the following steps: S1. Sample Information Input: Users input the first type of information through the information input module, including the type of sample to be tested and the test items. S2, Standard Matching and Parsing: Based on the first type of information input in step S1, the matching engine module matches and parses the target detection standard document and its key elements from the standard knowledge base; S3. Generate the detection process sheet / first sub-process sheet and second sub-process sheet. S31. When the output data of the preprocessing step is not used to adjust the parameters of the detection step, the detection process sheet is generated. The detection process sheet includes the general operation instructions for the preprocessing step and the detection step. The user executes the general operation instructions to obtain the preprocessing process data and the detection process data. S32. When the output data of the extracted key elements determination preprocessing step needs to be used to determine the parameters of the detection step, a first sub-process sheet is first generated. The first sub-process sheet includes the first sub-operation instruction of the preprocessing step. After the first sub-operation instruction is executed to obtain the preprocessing process data, the process generation module generates a second sub-process sheet through the preprocessing process data. The second sub-process sheet includes the second sub-operation instruction of the detection step. The user executes the second sub-operation instruction to obtain the detection process data. S4. Calculation of test results: The user and / or instrument gateway and monitoring module input the preprocessing data and the test process data through the information input module; The data analysis module analyzes and calculates the second type of information based on the calculation formula in the key elements, and outputs the detection results.

10. The method for linking sample pretreatment and detection in consumer product testing according to claim 9, characterized in that: In step S4, the data analysis module compares the preprocessing data and / or the detection data with the quality control indicators and / or preset reasonable ranges specified in the key elements. If they do not meet the requirements, an early warning message is output. If the criteria are met, the analysis and calculation will be performed according to the calculation formula in the key elements, and the detection results will be output.