A consumer product intelligent inspection code circulation whole-link anti-cheating credit investigation system based on human body toxicological evaluation

Abstract

The application discloses a kind of based on human toxicological evaluation's consumer goods intelligent inspection code flow circulation whole-link anti-cheating credit investigation system, it is related to consumer goods detection technical field.The system is bound to each consumer goods unique intelligent inspection code, goods enters warehouse and is automatically entered global random sampling pool, and human toxicological evaluation reinspection is carried out to on-sale commodity by AI irregular extraction;Detection equipment real-time self-checking calibration, toxicological detection data encryption chain storage evidence tamper-proof.The system is automatically marked risk to overdue refusal inspection, false sample mailing merchant, synchronously updates commodity credit investigation archives, locks operating subject and publicly shows risk information outside.The application solves the drawbacks of traditional consumer goods sampling human intervention, data tamperable, no evidence for accountability, realizes whole-process closed-loop supervision in circulation, reduces the risk of toxic and harmful consumer goods into market, and can be widely used in clothing, daily chemical and other consumer goods circulation control.

Description

Technical Field

[0001] This invention relates to the field of consumer product safety testing and distribution supervision technology, and in particular to a consumer product intelligent inspection code distribution full-chain anti-fraud credit system based on human toxicology assessment. Background Technology

[0002] Consumer products such as clothing and daily chemicals come into direct contact with the human body, and harmful chemicals in the raw materials can cause toxicological hazards to the human body. The existing circulation supervision has obvious defects: (1) The sampling inspection mode is arranged manually, and merchants can predict the sampling inspection cycle and replace qualified samples in advance to deal with the inspection, which has serious loopholes for cheating; (2) The test data is only stored in paper or local databases, which are easy to tamper with or lose, and there is a lack of complete traceability and accountability certificates after a safety accident; (3) There is a lack of a full-chain credit linkage mechanism, and there are no long-term constraints on merchants who submit false samples or refuse to cooperate with the sampling inspection; (4) The testing equipment lacks a real-time self-inspection process, and equipment failure will lead to the distortion of toxicological assessment results and misjudgment of product safety. The existing technology cannot realize the integrated closed-loop supervision of warehousing, blind inspection, toxicological testing, evidence storage and credit publicity, and the control of toxic and harmful consumer products in the market is difficult. Summary of the Invention

[0003] (1) Purpose of the invention Overcoming the shortcomings of existing sampling inspections, such as human intervention, easy data tampering, lack of long-term credit constraints, and lack of self-testing by testing equipment, this paper provides a consumer product intelligent inspection code anti-fraud credit system based on human toxicology assessment, which realizes anti-fraud throughout the entire circulation process, reliable data storage, and automatic credit disclosure of merchant risks.

[0004] (2) Technical solution A consumer product intelligent inspection code anti-fraud credit system based on human toxicology assessment is proposed, comprising: a distribution warehousing module, an AI random blind inspection scheduling module, a human toxicology detection module, an equipment self-testing and calibration module, a blockchain evidence storage module, and a credit risk marker disclosure module. The distribution warehousing module: after a consumer product enters a distributor / warehouse, it scans the unique intelligent inspection code of each product to complete real-name warehousing, updates the product ownership information, and automatically enters the product data into a global random sampling pool; AI... Random blind inspection scheduling module: Built-in random sampling algorithm selects on-sale items from the sampling pool periodically, automatically generates re-inspection tasks and sends them to the corresponding warehouse, requiring the extraction of actual on-sale inventory samples for testing; Human toxicology testing module: Receives submitted samples, conducts human toxicology safety assessments such as skin irritation and harmful precipitates, and generates standardized toxicology testing reports; Equipment self-calibration module: Real-time acquisition of instrument voltage, reagent baseline, and spectral parameters, compares them with standard baselines, identifies equipment deviations and malfunctions, and avoids distortion of toxicology testing results; Blockchain evidence storage module: Synchronously uploads warehousing records, sampling tasks, equipment self-calibration logs, and complete raw toxicology testing data to the consortium blockchain for permanent storage, ensuring data immutability and full traceability; Credit risk marking and disclosure module: Automatically identifies cheating behaviors such as overdue testing or sending false samples, marks the risk level of the business entity, synchronously updates the product credit file, publicly discloses the risk entity, and restricts the new product warehousing access of high-risk merchants. Furthermore, each consumer product is bound to a unique smart inspection code, storing production batch, manufacturer, and distributor's real-name information. Upon entering the warehouse, the code is scanned and automatically linked to the sampling pool, ensuring full traceability. Furthermore, the AI-powered random blind inspection algorithm operates without fixed timeframes or sampling ratios, preventing merchants from predicting inspection points and eliminating the possibility of premature sample replacement for cheating. Furthermore, the blockchain stores all original testing data; in the event of consumer product safety disputes, the on-chain data can serve as legal evidence for official accountability and credit rating. Furthermore, risk assessment is categorized into three levels: general risk, key risk, and severe risk. Merchants with severe risk are prohibited from adding new products to their warehouses and their information is publicly disclosed online.

[0005] (3) Beneficial effects 1) An AI-powered random blind inspection mechanism completely eliminates merchants' cheating behaviors such as predictive sampling and false submission of samples; 2) Real-time self-calibration of equipment ensures the authenticity and accuracy of human toxicology assessment data; 3) Full-process data is stored on a blockchain, which is tamper-proof and forms a complete traceability and accountability certificate; 4) Linked with the merchant credit system, it automatically marks and publicizes risk entities, effectively restraining non-compliant distributors in the long term; It realizes a complete closed-loop circulation supervision from goods warehousing, blind inspection, toxicology testing, data storage, and risk publicity, greatly reducing the risk of harmful consumer products flowing to the end market. Attached Figure Description

[0006] Figure 1 is a schematic diagram of the overall circulation process of this invention; 1. Production module: Mass-produced consumer products off the production line → Unique irreversible anti-tamper smart inspection code assignment → Cloud filing of batch / production line / raw material / factory information; 2. Testing module: Homogeneous sampling of the same batch → Physicochemical index testing + human toxicological risk assessment and grading → Direct transmission of raw data to the cloud; 3. Circulation module: Distributor scans code to enter the warehouse and confirm rights → Included in the random sampling pool → AI random blind inspection task issuance → Re-inspection of samples from the inventory on sale; 4. Quality control and evidence storage module: AI equipment fault self-inspection + accuracy calibration → Abnormal warning to suspend testing → Real-name record archiving → Cloud encrypted anti-tampering evidence storage; 5. Terminal display module: Consumer scans code to query → Three-status display of qualified / unqualified / not submitted for testing → Display of toxicology report + raw data + full-chain traceability record; 6. Responsibility isolation module: Automatic responsibility determination → Accountability for manufacturer / distributor violations → Platform and laboratory exemption from liability, forming a complete closed loop.

[0007] Figure 2 is a schematic diagram of the AI ​​random blind inspection logic triggered by the circulation and warehousing of this invention; 1. Goods enter the distributor / warehouse → 2. Smart inspection code scanning for real-name warehousing and updating ownership information → 3. Automatic entry into the global random sampling pool → 4. AI randomly selects single items for sale without any pattern → 5. Targeted issuance of official sampling tasks → 6. Sampling and re-inspection of real inventory for sale → 7. Re-inspection data is uploaded to the blockchain and the product credit is updated → 8. Risk marking for overdue refusal of inspection / false sample delivery → 9. Locking in the main body's rights and responsibilities and publicizing the risks.

[0008] Figure 3 is a schematic diagram of the self-inspection process of the detection equipment and the blockchain storage process of toxicological data of the present invention; 1. Fully automated robotic arm unmanned sampling and testing → 2. Real-time acquisition of equipment parameters and raw test data → 3. AI self-inspection model compares baseline parameters to determine status → 4. Normal data: cloud-encrypted archiving and locking, prohibiting manual modification → 5. Abnormal / fault: automatic error reporting, task suspension, and push of rectification plan → 6. Fault logs and handling records are uploaded to the blockchain with real names → 7. Full data is permanently stored → 8. Supports credit rating and traceability. Detailed Implementation Example 1

[0009] This system is deployed at consumer goods warehousing sites, third-party toxicology testing laboratories, and regulatory service cloud platforms. When clothing and consumer goods leave the factory, a unique smart inspection code is printed on them. After the goods are transported to the distributor's warehouse, the circulation warehousing module scans the code for real-name registration, and the product information is automatically entered into the global random sampling pool. The cloud-based AI random blind inspection scheduling module triggers random inspection tasks every day without any pattern. It randomly selects clothing items for sale and pushes the task to the warehouse. Warehouse staff must retrieve real samples of clothing from the shelves and send them to the laboratory. Before testing, the equipment self-test calibration module runs automatically to verify that the baseline of the chromatographic and spectroscopic instruments is normal. Then, the human toxicology assessment test is initiated to detect harmful indicators such as formaldehyde, heavy metals, and sensitizing dyes. All inbound logs, sampling instructions, equipment self-inspection records, and raw toxicological test data are uploaded to the consortium blockchain in real time; If a merchant fails to send samples within the specified period or sends counterfeit samples that do not match the batches currently on sale, the credit risk marking and publicity module will automatically mark it as a key risk, and the merchant's risk record will be publicized on the consumer goods supervision platform, restricting the subsequent entry of new products into the warehouse. Example 2

[0010] This system applies to daily chemical and personal care consumer products. Each bottle is bound to a smart inspection code and enters the sampling pool after being put into storage. AI randomly selects individual products for re-inspection of toxicological irritation indicators, and the test data is stored on the blockchain. Merchants who refuse inspection multiple times are marked as having serious risks, and their abnormal credit information is publicly disclosed on the entire network. Regulatory authorities can impose administrative penalties based on the evidence stored on the blockchain.

Claims

1. A consumer product intelligent inspection code circulation end-to-end anti-fraud credit system based on human toxicology assessment, characterized in that, include: The system includes a circulation and warehousing module, an AI-based random blind inspection scheduling module, a human toxicology testing module, an equipment self-testing and calibration module, a blockchain evidence storage module, and a credit risk labeling and disclosure module. The circulation warehousing module is used to receive consumer goods from distributors / warehouses, scan the smart inspection code bound to the consumer goods to complete real-name warehousing and update the ownership information, and automatically enter the goods information into the global random sampling pool. The AI ​​random blind inspection scheduling module uses an irregular random algorithm to select on-sale items from the inspection pool, issues official re-inspection tasks in a targeted manner, and instructs the warehouse to extract real on-sale samples and send them to the testing laboratory. The human toxicology detection module performs human toxicology safety assessments on submitted samples and outputs complete toxicology detection data. The device self-test calibration module collects the operating parameters of the testing instrument in real time and compares them with the standard baseline to complete the device fault self-test. The blockchain evidence storage module synchronously stores the re-inspection sample information, raw toxicology test data, and equipment self-inspection records on the blockchain. The credit risk marking and disclosure module identifies and marks risks related to overdue inspections and false sample delivery, identifies the corresponding business entities, and publicly discloses the credit risks of goods and merchants.

2. The system according to claim 1, characterized in that, The smart inspection code is a unique identifier for each consumer product, storing information such as product batch, manufacturer, and distributor's real name. When the code is scanned upon entry into the warehouse, it is automatically linked to the sampling pool database.

3. The system according to claim 1, characterized in that, The AI ​​random blind inspection scheduling module has no fixed period or fixed dealer weights in its sampling rules, thus preventing merchants from predicting the sampling time and falsifying data in advance.

4. The system according to claim 1, characterized in that, The blockchain evidence storage module uses a consortium blockchain for storage. The original toxicology data, equipment self-inspection records, and sampling task records cannot be deleted or tampered with, serving as evidence for merchant credit investigation and accountability.

5. The system according to claim 1, characterized in that, The credit risk labeling and disclosure module classifies risk levels based on the frequency of refusing inspection and the number of false inspection submissions. High-risk entities are restricted from new product entry and the information is made public.

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