An authorization scene risk assessment system and method based on an internet of things

Abstract

The application discloses an authorized scene risk assessment system and method based on Internet of Things, and relates to the technical field of Internet of Things.The application generates product initial inspection quality benchmark data after initial inspection of products produced in a factory; real-time transportation risk data is collected when the initial inspection qualified products are transported; the products arrive at a storage center and are subjected to a first inspection according to the initial inspection standard; if unqualified, transportation risk tracing is triggered, the risk source is located, the transportation strategy is optimized, unqualified products are returned to the factory for rework, and the strategy is followed in subsequent transportation; the first inspection qualified products are put into storage, and storage parameters of the storage area are monitored; the products are subjected to a second inspection before being sent to a delivery center, unqualified product storage parameters are analyzed, storage risk sources are located, storage strategies are optimized, the storage strategy is followed in subsequent storage, and unqualified products are returned to the factory for rework; the second inspection qualified products are sent to the delivery center according to the determined transportation strategy, and product full-link risk data reports are generated.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based system and method for risk assessment in authorized scenarios. Background Technology

[0002] With the widespread adoption of IoT technology in the product supply chain, the market's demand for end-to-end risk management from production to delivery is becoming increasingly urgent, especially for high-precision, high-value products, which require effective risk identification, traceability, and prevention. However, existing technologies have significant shortcomings in practical applications and are insufficient to meet these needs.

[0003] In the product transportation stage, existing technologies mostly collect basic information, lacking comprehensive risk data monitoring of product status, transportation environment, and operational behavior. Furthermore, the collected transportation data is disconnected from the quality inspection results in the subsequent storage stage, making it impossible to locate risk sources during transportation through data correlation. In the product storage stage, existing technologies monitor the storage environment primarily based on single parameters, failing to cover key factors affecting product quality. Simultaneously, the quality inspection before product shipment and the parameters during storage lack causal correlation analysis, making it impossible to trace specific risks in the storage stage when product defects are discovered. At the product end-to-end management level, existing technologies have not constructed a complete closed-loop process, and data from each stage cannot be linked. Moreover, the process design lacks embedded mechanisms for risk assessment and dynamic optimization; each stage completes its task in isolation, unable to predict risks based on data from preceding stages or iteratively improve based on results from subsequent stages. This results in risks throughout the product's entire lifecycle not being systematically identified, traced, and controlled, making it difficult to meet the quality assurance requirements of high-quality products. Summary of the Invention

[0004] The purpose of this invention is to provide an authorization scenario risk assessment system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for risk assessment of authorized scenarios based on the Internet of Things, comprising:

[0007] After the factory completes product production, it conducts an initial inspection of the products and generates initial inspection quality benchmark data. Products that pass the initial inspection are then transported, and transportation risk data is collected in real time through the Internet of Things (IoT) devices on the transport vehicles.

[0008] When a product arrives at the storage center, it undergoes an initial inspection according to the same inspection standards as the initial product inspection. If the product fails the inspection, the transportation risk tracing is triggered, transportation risk data is retrieved, and the transportation risk source is located by combining the initial inspection results. For products that have problems during transportation, the transportation strategy is optimized based on the transportation risk tracing. The non-conforming products are returned to the factory for rework. The optimized transportation strategy is used for the secondary transportation of reworked products and subsequent transportation of similar products.

[0009] Products that pass the first inspection are stored in the warehouse, and storage parameters are monitored in real time by IoT devices in the storage area. Before the products are sent to the delivery center, a second inspection is carried out to analyze the storage parameters of unqualified products, locate the storage risk sources, optimize the storage strategy, and use the same optimized storage strategy for subsequent storage of similar products. Unqualified products are sent back to the factory for rework.

[0010] Products that pass the second inspection are sent to the delivery center according to the determined transportation strategy, and a full-chain risk data report for the product is generated.

[0011] In conjunction with the first aspect, in the first implementation of the first aspect of this application, after the factory completes product production, it conducts an initial inspection of the product and generates initial inspection quality benchmark data, including:

[0012] The product undergoes a full inspection using authorized intelligent testing equipment, collecting visible indicators of the product's appearance, detecting hidden indicators of the product's internal structure, and recording the product's core functional parameters. The inspection data is then integrated into the product's initial inspection quality benchmark data, including the inspection time, testing equipment number, measured values ​​of various indicators, and pass / fail results. A unique identification code is generated for each product and linked to the product's initial inspection quality benchmark data.

[0013] When a product is initially deemed unqualified during inspection, it is directly transferred to the factory for rework without generating a unique identification code, thus preventing unqualified products from entering subsequent transportation stages. When a product is initially deemed qualified during inspection, transportation protection requirements are added to the unique identification code before it enters the product transportation stage.

[0014] In conjunction with the first aspect, in the second implementation of the first aspect of this application, the transportation of products that have passed the initial inspection includes, through the Internet of Things (IoT) device of the transport vehicle, real-time collection of transportation risk data during the transportation process, including:

[0015] Based on the transportation protection requirements attached to the unique identification code of the initially qualified products, the transport vehicles are equipped with authorized Internet of Things monitoring equipment, including a triaxial accelerometer, cargo pressure sensor, GPS positioning module, temperature and humidity sensor and driving behavior recorder.

[0016] During transportation, the Internet of Things device collects transportation risk data in real time according to the preset data collection frequency, including the peak value of jolt acceleration, the cargo stacking pressure value, the real-time transportation section information, the temperature and humidity values inside the carriage, and the number of hard accelerations and hard brakes per hour of the driver; the collected data is uploaded and bound to the product unique identification code to form the corresponding relationship between the product and the transportation risk data; after the transportation is completed, all the collected transportation risk data is sorted by timestamp to generate the transportation risk data log of the product.

[0017] Combined with the first aspect, in the third implementation manner of the first aspect of this application, when the product arrives at the storage center, a primary inspection is carried out according to the same inspection standard as the factory initial inspection, including:

[0018] The storage center retrieves the factory initial inspection quality benchmark data of the product through the product unique identification code, and calibrates the inspection equipment in the storage center according to the calibration parameters during the initial inspection; referring to the initial inspection index system, a primary inspection is carried out on the product;

[0019] After the inspection is completed, the primary inspection data is uploaded and compared item by item with the factory initial inspection quality benchmark data to generate an inspection difference report; for the indicators with differences, the difference type and specific values are marked;

[0020] When the deviation of all indicators in this inspection from the initial inspection benchmark data is within the allowable range, it is determined that the primary inspection is qualified, and the product enters the storage and warehousing process; when there are indicators exceeding the deviation range of the initial inspection benchmark or there are defects not recorded during the initial inspection, it is determined that the primary inspection is unqualified, and a transportation risk traceability start instruction is triggered to suspend the subsequent process of the product.

[0021] Combined with the first aspect, in the fourth implementation manner of the first aspect of this application, when the product inspection is unqualified, transportation risk traceability is triggered, the transportation risk data is retrieved, and combined with the primary inspection result, the transportation risk source is located, including:

[0022] After the transportation risk traceability is triggered, the transportation risk data bound to the product unique identification code and the inspection difference report generated by the primary inspection are retrieved to determine the unqualified item type and specific location; for the unqualified item type, the associated transportation risk data is screened and compared directionally;

[0023] When the unqualified item is an obvious physical damage, analyze the time period when the peak value of jolt acceleration during transportation exceeds the preset jolt acceleration threshold, the duration when the cargo stacking pressure is greater than the preset cargo stacking pressure threshold, and the time points of hard brakes and hard accelerations in the driving behavior, and match the flatness of the corresponding section through GPS positioning to determine whether the damage is caused by impacts or squeezes beyond the protection ability;

[0024] When the non-conformance is a latent physical defect, retrieve the vibration frequency and amplitude data recorded by the triaxial accelerometer during transportation, compare it with the vibration resistance threshold of the component recorded during the initial inspection of the product, and analyze whether there is structural damage caused by resonance or high-frequency vibration.

[0025] When the non-compliance item is an environmental-related defect, check the time period when the temperature and humidity sensor in the compartment is out of range to determine whether the product quality change is due to the transportation environment not meeting the standards.

[0026] By aligning transportation risk data and defect characteristics with timestamps, specific risk source types can be identified. When data shows excessive bump acceleration and the corresponding road segment is a bumpy road segment, the risk source is improper transportation route selection. When data shows excessive stacking pressure and cargo securing methods not performed according to initial inspection requirements, the risk source is a violation of cargo loading procedures. When data shows the number of emergency brakings exceeds the threshold and driver driving behavior records are abnormal, the risk source is non-standard driving operations. When data shows temperature and humidity exceeding the range and malfunction of the compartment temperature control equipment, the risk source is failure of transportation environment control.

[0027] The identified risk sources are linked to a single non-conforming item in an inspection for causal verification. Once confirmed, a transportation risk source location report is generated, clearly defining the risk source type, the time period of occurrence, related data, and causal relationship, and is then bound to the product's unique identification code.

[0028] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, the method of optimizing the transportation strategy based on transportation risk tracing for products that encounter problems during transportation, returning unqualified products to the factory for rework, and using the optimized transportation strategy for secondary transportation of reworked products and subsequent transportation of similar products, includes:

[0029] Based on the transportation risk source location report, the storage center marks the product's unique identification code, non-conforming items, and associated transportation risk sources, and returns the product to the factory. After rework, the factory's initial inspection must be performed again to generate new initial inspection quality benchmark data and unique identification codes.

[0030] Based on the risk source types in the transportation risk source location report, develop transportation strategy optimization plans; when the risk source is an inappropriate transportation route selection, optimize route planning, select low-bump and high-smooth road sections, and set priority transportation routes for similar products; when the risk source is a violation of the cargo loading plan, set the maximum number of stacking layers and stacking pressure threshold, and generate a cargo fixing diagram for reference and guidance; when the risk source is non-standard driving operation, inform the transportation party to train the drivers; when the risk source is the failure of transportation environmental control, require the transportation party to regularly inspect and maintain the temperature control and dehumidification equipment in the carriage.

[0031] By linking the optimized transportation strategy with similar products, when reworked products are transported a second time, the transportation provider will use the corresponding optimized transportation strategy.

[0032] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, the step of storing the products that have passed the first inspection into a warehouse, and monitoring the storage parameters in real time according to the IoT devices in the storage area, includes:

[0033] The storage center retrieves product characteristic information based on the unique identification code of a product that has passed inspection, and assigns a suitable storage location to the product by combining the functional zoning of the storage area. Upon receipt, the unique identification code of the product is read, and the corresponding shelf number and storage location coordinates are associated to generate a product-storage location binding record. Within the assigned storage area, temperature and relative humidity sensors monitor the storage environment temperature and relative humidity; shelf pressure sensors monitor the total weight of stacked products; infrared cameras monitor the product stacking status; and real-time temperature and humidity values, real-time shelf load-bearing capacity, and images of product stacking status are collected.

[0034] The system compares the collected parameters with preset thresholds in real time. When a parameter exceeds the limit or an abnormal status occurs, the responsible personnel are notified to handle the situation and a parameter log is generated and stored.

[0035] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, the product undergoes a secondary inspection before being sent to the delivery center. This involves analyzing the storage parameters of non-conforming products, identifying storage risk sources, optimizing the storage strategy, and subsequently using this optimized storage strategy for storing similar products. Non-conforming products are then returned to the factory for rework. This includes:

[0036] Before products are shipped to the delivery center, the storage center initiates a second inspection, compares the results of the second inspection with the pass standards of the first inspection, marks unqualified products and specific defects, and retrieves the storage parameter logs of unqualified products during storage, and performs targeted correlation analysis according to defect type and parameter fluctuations.

[0037] By aligning defect characteristics with abnormal parameter periods using timestamps, the types of risk sources are identified: when data shows excessive temperature and humidity and abnormal equipment operation logs, the risk source is a storage environment control equipment failure; when data shows excessive load capacity and stacking schemes that do not match product characteristics, the risk source is a storage planning violation; when data shows storage duration exceeding the upper limit and material performance degradation curves match, the risk source is uncontrolled storage cycle management. Based on the location of the risk source, storage strategy optimization plans are developed. For equipment failures, equipment health inspection reminders are added, and warnings are triggered when parameters exceed limits. For uncontrolled storage planning, product parameters, including storage environment, physical load capacity, and special adaptation requirements, are retrieved upon warehousing. Shelf parameters, including shelf hardware parameters and storage location environment parameters, are also retrieved to verify the product and shelf matching. The comparison dimensions are environmental adaptability, load capacity adaptability, and hardware type. If any comparison fails, warehousing is rejected. For uncontrolled cycle management, a maximum storage countdown is added to the product's unique identification code, and a warning is pushed before the expiration date, prioritizing shipment.

[0038] Products that fail the second inspection are returned to the storage center for rework. They must pass the initial inspection at the factory and the first inspection at the storage center before being put back into the warehouse. The optimized storage strategy is bound to products of the same type, and this strategy will be used for storage when similar products are put into the warehouse in the future.

[0039] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, the step of sending the product that has passed the second inspection to the delivery center according to the determined transportation strategy and generating a full-chain risk data report for the product includes:

[0040] The storage center retrieves the corresponding transportation strategy using the product's unique identification code and transports the products that have passed the second inspection to the delivery center according to the determined transportation strategy. After the product arrives at the delivery center and completes acceptance, it triggers end-to-end data integration and generates an end-to-end risk data report identified by the product's unique identification code, including data from the factory, initial transportation, storage, and delivery transportation stages.

[0041] Secondly, the present invention provides an authorization scenario risk assessment system based on the Internet of Things, comprising:

[0042] The factory-side initial inspection and benchmark management module includes an intelligent inspection unit, a benchmark data generation unit, and a unique identification code management unit. The intelligent inspection unit collects product appearance, internal, and core functional parameters. The benchmark data generation unit integrates inspection data to generate benchmark data and associates it with the product's unique identification code. The unique identification code management unit generates a unique identification code for qualified products and triggers rework for unqualified products.

[0043] The transportation risk monitoring and data association module includes an IoT device configuration unit, a real-time data acquisition unit, and a data transmission and binding unit. The IoT device configuration unit configures authorized devices according to transportation protection requirements and matches product characteristics. The real-time data acquisition unit controls the devices to collect transportation risk data at a preset frequency. The data transmission and binding unit encrypts and uploads data, binds identification codes, and generates a timestamped transportation risk data log.

[0044] The storage center inspection and environmental monitoring module includes a primary inspection unit, a secondary inspection unit, a storage environment monitoring unit, and a storage location management unit. The primary inspection unit retrieves benchmark data, calibrates equipment to perform a primary inspection, generates a discrepancy report, and determines compliance. The secondary inspection unit performs a secondary inspection before shipment, comparing the results to the primary inspection standards and marking any non-compliance or defects. The storage environment monitoring unit collects storage parameters in real time through equipment and generates storage parameter logs. The storage location management unit allocates suitable storage locations, associates shelves and storage locations, and generates product-storage location binding records.

[0045] The risk tracing and strategy optimization module includes a transportation risk tracing unit, a storage risk tracing unit, a transportation strategy optimization unit, and a storage strategy optimization unit. Specifically, the transportation risk tracing unit retrieves data, aligns defect characteristics, locates the transportation risk source, and generates a report when a first inspection fails. The storage risk tracing unit analyzes storage parameters and defect correlations to locate the storage risk source when a second inspection fails. The transportation strategy optimization unit formulates optimization plans based on the transportation risk source, binds them to similar products, and pushes them to the relevant departments. The storage strategy optimization unit formulates optimization plans based on the storage risk source, binds them to similar products, and applies them to the relevant departments.

[0046] The end-to-end data management and authorization module includes a data integration unit, a report generation unit, and an authorization and permission control unit. The data integration unit aggregates data from each stage to form an end-to-end dataset with an identification code at its core. After delivery, the report generation unit integrates the data to generate an end-to-end risk report, covering risks and optimization results. The authorization and permission control unit allocates data access permissions to ensure that each entity can only query data within its authorized scope.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. This invention collects transportation risk data during vehicle transportation, combines it with a single inspection to locate the source of transportation risk, optimizes the transportation strategy based on the traceability results, and uses the same strategy for subsequent similar transportation.

[0049] 2. This invention analyzes the impact of storage on products. Through secondary inspection, it analyzes the storage parameters of non-conforming products, locates the storage risk sources, and optimizes the storage strategy accordingly. Subsequent similar storage will adopt this strategy.

[0050] 3. This invention constructs a detailed process for product production, transportation to the storage center, first product inspection, product storage, second product inspection, and transportation to the delivery center, forming a closed-loop circulation system with data linkage at each stage, thereby achieving precise risk management throughout the product lifecycle. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the steps of an IoT-based risk assessment method for authorized scenarios according to the present invention.

[0052] Figure 2 This invention presents a closed-loop flowchart of risk assessment and strategy optimization for product transportation in a licensing scenario risk assessment method based on the Internet of Things.

[0053] Figure 3 This is a system architecture diagram of an IoT-based authorization scenario risk assessment system according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example: Figures 1-3 As shown, the present invention provides a technical solution.

[0056] like Figure 1 A schematic diagram illustrating the steps of an IoT-based authorization scenario risk assessment method is provided. The present invention provides an IoT-based authorization scenario risk assessment method, comprising:

[0057] Step S100: After the factory completes product production, it conducts an initial inspection of the product and generates initial inspection quality benchmark data; products that pass the initial inspection are transported, and transportation risk data during the transportation process is collected in real time through the Internet of Things devices on the transport vehicles.

[0058] Specifically, the product undergoes a full inspection using authorized intelligent testing equipment, collecting visible indicators of the product's appearance, detecting hidden indicators of the product's internal structure, and recording the product's core functional parameters. The inspection data is then integrated into the product's initial inspection quality benchmark data, including the inspection time, testing equipment number, measured values ​​of various indicators, and pass / fail results. A unique identification code is generated for the product and bound to the product's initial inspection quality benchmark data.

[0059] When a product is initially deemed unqualified during inspection, it is directly transferred to the factory for rework without generating a unique identification code, thus preventing unqualified products from entering subsequent transportation stages. When a product is initially deemed qualified during inspection, transportation protection requirements are added to the unique identification code before it enters the product transportation stage.

[0060] Based on the transportation protection requirements attached to the unique identification code of the initially qualified products, the transport vehicles are equipped with authorized Internet of Things monitoring equipment, including a triaxial accelerometer, cargo pressure sensor, GPS positioning module, temperature and humidity sensor and driving behavior recorder.

[0061] During transportation, IoT devices collect transportation risk data in real time at a preset data collection frequency, including peak turbulence acceleration, cargo stacking pressure, real-time transportation route information, temperature and humidity inside the vehicle, and the number of times the driver accelerates and brakes suddenly per hour. The collected data is uploaded and linked to the product's unique identification code to form a correspondence between the product and the transportation risk data. After transportation is completed, all collected transportation risk data is sorted by timestamp to generate a transportation risk data log for the product.

[0062] In a specific embodiment, after a factory produces a batch of products, the appearance of the products is inspected by an authorized high-definition vision detector, and scratches with a length of ≤0.3 mm and a color difference of ≤1.5 are collected. The internal hidden indicators are detected by an ultrasonic flaw detector, and it is confirmed that the depth of the internal crack is 0 mm. The core function parameters are recorded by a function tester as an operating temperature of 35°C and a response time of 0.8 s. On September 29, 2025, at 08:30, these data are integrated into the initial inspection quality benchmark data of the product. The qualified judgment result is qualified, and a unique identification code PROD20250929001 is generated and bound to the benchmark data. At the same time, the transportation protection requirements of temperature and humidity of 30±2°C / 50±5%RH, stacking pressure of ≤500 N, and bump acceleration of ≤2g are added to the identification code. Subsequently, an authorized three-axis acceleration sensor, a cargo pressure sensor, a GPS positioning module, a temperature and humidity sensor, and a driving behavior recorder are configured for the transport vehicle. During the transportation process, data is collected in real time at a frequency of once every 10 minutes, including a peak bump acceleration of 1.2g, a cargo stacking pressure of 380 N, the real-time transportation section is from K120 to K180 on the G15 highway, the temperature and humidity inside the carriage are 29°C / 52%RH, the driver accelerates suddenly 2 times per hour and brakes suddenly 1 time per hour. The collected data is uploaded in real time and bound to PROD20250929001. After the transportation is completed, a transportation risk data log from 09:00:00 to 14:30:00 on September 29, 2025 is generated according to the time stamp sorting.

[0063] Step S200: When the product arrives at the storage center, a first inspection is carried out according to the same inspection standards as the initial inspection of the product. When the product inspection is unqualified, the transportation risk traceability is triggered, the transportation risk data is retrieved, and combined with the first inspection result, the transportation risk source is located. For the products with problems during the transportation process, the transportation strategy is optimized according to the transportation risk traceability, the unqualified products are sent back to the factory for rework, and the optimized transportation strategy is adopted for the second transportation of the reworked products and the subsequent transportation of the same type of products.

[0064] Specifically, the storage center retrieves the initial inspection quality benchmark data of the product through the unique product identification code, and calibrates the inspection equipment in the storage center according to the calibration parameters during the initial inspection. Referring to the initial inspection index system, a first inspection is carried out on the product.

[0065] After the inspection is completed, the first inspection data is uploaded and compared item by item with the initial inspection quality benchmark data of the factory to generate an inspection difference report. For the indicators with differences, the difference type and specific values are marked.

[0066] When the deviation of all indicators in this inspection from the initial inspection benchmark data is within the allowable range, it is determined that the first inspection is qualified, and the product enters the storage and warehousing process. When there are indicators exceeding the deviation range of the initial inspection benchmark or there are defects not recorded during the initial inspection, it is determined that the first inspection is unqualified, and the transportation risk traceability start instruction is triggered to suspend the subsequent process of the product.

[0067] Once the transportation risk tracing is triggered, the transportation risk data bound to the product's unique identification code and the inspection difference report generated from a single inspection are retrieved to determine the type and specific location of non-conformities. For each type of non-conformity, the associated transportation risk data is selectively compared.

[0068] When the non-conformity is obvious physical damage, analyze the time period during which the peak of the bump acceleration exceeds the preset bump acceleration threshold, the duration of the cargo stacking pressure exceeding the preset cargo stacking pressure threshold, and the time points of sudden braking and acceleration during driving behavior. By matching the smoothness of the corresponding road section with GPS positioning, determine whether the damage is caused by impact or compression that exceeds the protection capacity.

[0069] When the non-conformance is a latent physical defect, retrieve the vibration frequency and amplitude data recorded by the triaxial accelerometer during transportation, compare it with the vibration resistance threshold of the component recorded during the initial inspection of the product, and analyze whether there is structural damage caused by resonance or high-frequency vibration.

[0070] When the non-compliance item is an environmental-related defect, check the time period when the temperature and humidity sensor in the compartment is out of range to determine whether the product quality change is due to the transportation environment not meeting the standards.

[0071] By aligning transportation risk data and defect characteristics with timestamps, specific risk source types can be identified. When data shows excessive bump acceleration and the corresponding road segment is a bumpy road segment, the risk source is improper transportation route selection. When data shows excessive stacking pressure and cargo securing methods not performed according to initial inspection requirements, the risk source is a violation of cargo loading procedures. When data shows the number of emergency brakings exceeds the threshold and driver driving behavior records are abnormal, the risk source is non-standard driving operations. When data shows temperature and humidity exceeding the range and malfunction of the compartment temperature control equipment, the risk source is failure of transportation environment control.

[0072] The identified risk sources are linked to a single non-conforming item in an inspection for causal verification. Once confirmed, a transportation risk source location report is generated, clearly defining the risk source type, the time period of occurrence, related data, and causal relationship, and is then bound to the product's unique identification code.

[0073] Based on the transportation risk source location report, the storage center marks the product's unique identification code, non-conforming items, and associated transportation risk sources, and returns the product to the factory. After rework, the factory's initial inspection must be performed again to generate new initial inspection quality benchmark data and unique identification codes.

[0074] Based on the risk source types in the transportation risk source location report, develop transportation strategy optimization plans; when the risk source is an inappropriate transportation route selection, optimize route planning, select low-bump and high-smooth road sections, and set priority transportation routes for similar products; when the risk source is a violation of the cargo loading plan, set the maximum number of stacking layers and stacking pressure threshold, and generate a cargo fixing diagram for reference and guidance; when the risk source is non-standard driving operation, inform the transportation party to train the drivers; when the risk source is the failure of transportation environmental control, require the transportation party to regularly inspect and maintain the temperature control and dehumidification equipment in the carriage.

[0075] By linking the optimized transportation strategy with similar products, when reworked products are transported a second time, the transportation provider will use the corresponding optimized transportation strategy.

[0076] In one specific embodiment, after receiving a product with the unique identification code PROD20250929001, the storage center retrieves its initial factory inspection quality benchmark data using this code. The storage center's inspection equipment is then calibrated as it was during the initial inspection. Subsequently, an inspection is performed on the batch of products, detecting a scratch length of 0.8mm, a response time of 1.5s, and an operating temperature of 38℃. These data are compared with the initial inspection benchmark data to generate an inspection difference report, noting deviations of +0.5mm for scratch length, +0.5s for response time, and +0.5s for operating temperature. A temperature difference of +3℃ was detected, indicating a failed inspection and triggering a transportation risk tracing process. During the tracing, the transportation risk data log linked to PROD20250929001 was retrieved. The log revealed that during transportation, the peak turbulence acceleration reached 2.8g between 10:15 and 10:30, exceeding the preset 2g threshold; between 11:00 and 11:30, the driver braked suddenly 4 times per hour, exceeding the preset threshold of 1 time per hour; and the temperature and humidity inside the vehicle reached 35℃ / 62%RH between 13:00 and 13:20, exceeding protection requirements. Timestamp alignment confirmed that the 10:20... New scratches appeared on the product exterior after the peak period of bumpy conditions; response time began to deviate after the emergency braking period at 11:15; and operating temperature increased after the period of excessive temperature and humidity at 13:10. Causal verification determined the risk sources to be excessive bumpiness on the K150-K160 transportation route, improper emergency braking by the driver, and malfunctioning temperature control in the vehicle compartment. A transportation risk source location report was generated and bound to an identification code. The batch of products was then returned to the factory for rework. After rework, a new initial inspection was conducted, generating a new unique identification code PROD20250930001. The storage center then developed a system based on the risk sources. Transportation strategy optimization plan: Adjust the transportation route of similar products to G15 Expressway K120-K140, require drivers to brake suddenly ≤1 time / hour and conduct special training, and the temperature control equipment in the compartment must be calibrated daily. The secondary transportation of the reworked PROD20250930001 product and subsequent transportation of similar products will follow this optimized transportation strategy. During the secondary transportation, the peak of the bump acceleration is controlled at 1.5g, the sudden braking is 1 time / hour, and the temperature and humidity are stabilized at 29℃ / 53%RH. During the first inspection, all indicators meet the deviation range of the initial inspection benchmark.

[0077] Step S300: Store the products that pass the first inspection into the warehouse, and monitor the storage parameters in real time according to the IoT devices in the storage area;

[0078] Specifically, the storage center retrieves product characteristic information based on the unique identification code of a product that has passed inspection, and assigns a suitable storage location to the product in conjunction with the functional zoning of the storage area. Upon receipt, the unique identification code of the product is read, and the corresponding shelf number and storage location coordinates are associated to generate a product-storage location binding record. Within the assigned storage area, temperature and relative humidity sensors monitor the storage environment temperature and relative humidity; shelf pressure sensors monitor the total weight of stacked products; infrared cameras monitor the product stacking status; and real-time temperature and humidity values, real-time shelf load-bearing capacity, and images of product stacking status are collected.

[0079] The system compares the collected parameters with preset thresholds in real time. When a parameter exceeds the limit or an abnormal status occurs, the responsible personnel are notified to handle the situation and a parameter log is generated and stored.

[0080] In one specific embodiment, for products that pass inspection on the first attempt, the storage center uses the unique identification code PROD20250930002 to retrieve their product characteristic information. Storage requirements are: temperature 25±3℃, relative humidity 45±5% RH, total stacked weight ≤800N, and stacking height ≤3 layers. Based on the storage center's functional zoning, this product is assigned a storage location on shelf S-B12 in zone B, on the second floor. Upon entry, a barcode scanner reads PROD20250930002, automatically associating shelf number S-B12 with storage location coordinates X12-Y09-Z02 to generate a product-storage location binding record. Simultaneously, storage parameters are collected in real-time every 5 minutes, with temperature and humidity sensors recording a real-time temperature of 24℃ and a relative humidity of 47% RH. The shelf pressure sensor detected a total product stack weight of 620N, and the infrared camera captured that the products were stacked in three layers without tilting or shifting. At 14:20 that day, the temperature and humidity sensor detected that the temperature had risen to 28.5℃, exceeding the 25±3℃ threshold, immediately triggering an alarm and notifying the storage administrator. After inspection, the administrator found that the air conditioning filter in that area was clogged. After cleaning, the temperature returned to 25℃ at 14:35. All collected parameters were recorded with timestamps, generating a storage parameter log for the product from 14:00 to 18:00.

[0081] Step S400: Before the product is sent to the delivery center, a second inspection is carried out to analyze the storage parameters of the non-conforming products, locate the storage risk source, optimize the storage strategy, and use the optimized storage strategy for subsequent storage of similar products. Non-conforming products are sent back to the factory for rework.

[0082] Specifically, before a product is scheduled to be sent to the delivery center, the storage center initiates a second inspection, compares the results of the second inspection with the first inspection pass standard, marks unqualified products and specific defects, retrieves the storage parameter logs of unqualified products during storage, and performs targeted correlation analysis based on defect type and parameter fluctuations.

[0083] By aligning defect characteristics with abnormal parameter periods using timestamps, the types of risk sources are identified: when data shows excessive temperature and humidity and abnormal equipment operation logs, the risk source is a storage environment control equipment failure; when data shows excessive load capacity and stacking schemes that do not match product characteristics, the risk source is a storage planning violation; when data shows storage duration exceeding the upper limit and material performance degradation curves match, the risk source is uncontrolled storage cycle management. Based on the location of the risk source, storage strategy optimization plans are developed. For equipment failures, equipment health inspection reminders are added, and warnings are triggered when parameters exceed limits. For uncontrolled storage planning, product parameters, including storage environment, physical load capacity, and special adaptation requirements, are retrieved upon warehousing. Shelf parameters, including shelf hardware parameters and storage location environment parameters, are also retrieved to verify the product and shelf matching. The comparison dimensions are environmental adaptability, load capacity adaptability, and hardware type. If any comparison fails, warehousing is rejected. For uncontrolled cycle management, a maximum storage countdown is added to the product's unique identification code, and a warning is pushed before the expiration date, prioritizing shipment.

[0084] Products that fail the second inspection are returned to the storage center for rework. They must pass the initial inspection at the factory and the first inspection at the storage center before being put back into the warehouse. The optimized storage strategy is bound to products of the same type, and this strategy will be used for storage when similar products are put into the warehouse in the future.

[0085] In one specific embodiment, the storage center plans to send a batch of products that passed the first inspection to the delivery center. Upon initiating a second inspection, referring to the first inspection pass standards, it was found that five products exhibited rust marks of 0.5-0.7mm on their appearance and a response time of 1.3-1.5s. These defects were marked as appearance rust and exceeding the response time standard. Subsequently, the storage parameter logs for these five products were retrieved. The logs showed that from 09:00 to 16:00 on October 3rd, the relative humidity collected by the temperature and humidity sensors reached 65%RH, exceeding the preset threshold of 45±5%RH. The stacking weight monitored by the shelf pressure sensor was 720N, within the 800N threshold. The infrared camera recorded no abnormalities in the stacking status, and the equipment operation log showed that the dehumidifier's operating current was only 0.3A during this period, with a normal operating current of 0.8-1.2A. This was confirmed through timestamp alignment. After 12:00 on October 3rd, the products began to show signs of corrosion, and the response time began to deviate after 14:00. It was determined that the storage risk source was a malfunction of the temperature and humidity control equipment, resulting in excessive humidity. Based on this, an optimized storage strategy was developed: health check reminders were added to dehumidifiers, air conditioners, and other equipment in the storage area every 3 days; a level 3 warning was set in the storage parameter monitoring system when the humidity exceeds 50%RH; and the operating status of the temperature and humidity control equipment in the corresponding storage location must be verified through the equipment management module before storage, and storage location allocation was refused if the standard was not met. Subsequently, these 5 unqualified products were sent back to the factory for rework. After rework, they passed the initial factory inspection again, and the optimized storage strategy was used when they were put into storage again. During the storage period, the humidity was stable at 46-48%RH, and all indicators met the standards during the second inspection. The same temperature and humidity equipment inspection and warning strategy was used for subsequent storage of similar products.

[0086] Step S500: Send the products that pass the second inspection to the delivery center according to the determined transportation strategy, and generate a full-chain risk data report for the product.

[0087] Specifically, the storage center retrieves the corresponding transportation strategy through the product's unique identification code and transports the products that have passed the second inspection to the delivery center according to the determined transportation strategy. After the product arrives at the delivery center and completes acceptance, it triggers end-to-end data integration and generates an end-to-end risk data report identified by the product's unique identification code, including data from the factory, initial transportation, storage, and delivery transportation stages.

[0088] In one specific embodiment, for products that pass the second inspection, the storage center retrieves the bound transportation strategy through the identification code, and then arranges transportation vehicles, starting transportation at 09:00 on October 8, 2025. During transportation, the equipment collects data every 10 minutes, recording the peak turbulence acceleration of 1.2g, the temperature and humidity of the compartment of 24℃ / 46%RH, and the driver's emergency braking once per hour, all of which meet the strategy requirements. At 11:30, the product successfully arrives at the delivery center. The delivery center reads PROD20251005001 with a barcode scanner and accepts it according to the factory's initial inspection standards. The inspection found an appearance scratch of 0.2mm and a response time of 0.9s, and the product was deemed to have passed the acceptance. After the acceptance is completed, the system automatically triggers full-link data integration and generates a full-link risk data report identified by the identification code, fully presenting the product's full-link risk management status.

[0089] like Figure 2 A closed-loop flowchart of risk assessment and strategy optimization for product transportation in an IoT-based authorization scenario risk assessment method is shown. This invention provides an IoT-based authorization scenario risk assessment method, including:

[0090] Starting with products that have passed the initial factory inspection, and based on the transportation protection requirements attached to the product's unique identification code, authorized IoT devices are configured for transport vehicles, including a three-axis accelerometer, a cargo pressure sensor, a GPS positioning module, a temperature and humidity sensor, and a driver behavior recorder. During transportation, the IoT devices collect transportation risk data at a preset frequency, including peak bump acceleration, cargo stacking pressure, real-time transportation route information, temperature and humidity inside the vehicle, and the number of times the driver accelerates and brakes suddenly per hour. The collected data is uploaded in real time and bound to the product's unique identification code. After transportation is completed, a transportation risk data log is generated by sorting the data according to the timestamp.

[0091] When a product arrives at the storage center, the factory's initial inspection quality benchmark data is retrieved, and the storage center's inspection equipment is calibrated according to the calibration parameters used during the initial inspection. An inspection is then completed in accordance with the initial inspection index system. After the inspection, the data is uploaded and compared item by item with the initial inspection benchmark to generate an inspection difference report that indicates the type of difference and the specific value. Based on this, the result of the first inspection is determined: qualified products enter the storage and warehousing process; unqualified products trigger the traceability of transportation risks.

[0092] During the transportation risk tracing stage, transportation risk data and inspection difference reports bound to the product's unique identification code are retrieved. After determining the type and location of non-conformities, targeted analysis is conducted. The risk source type is located by aligning the data with the defect characteristics through timestamps. After causal verification, a transportation risk source location report is generated and bound to the identification code.

[0093] Based on the type of risk source, an optimized transportation strategy is developed. Non-conforming products are marked with relevant information and returned to the factory for rework. After rework, a new initial inspection is carried out and new baseline data and identification codes are generated. The optimized transportation strategy is bound to similar products. The secondary transportation of reworked products and subsequent transportation of similar products all follow the optimized transportation strategy, thus completing the closed loop of risk control in the transportation process.

[0094] like Figure 3 The system architecture diagram of an IoT-based authorization scenario risk assessment system is shown. This invention provides an IoT-based authorization scenario risk assessment system, comprising:

[0095] The factory-side initial inspection and benchmark management module includes an intelligent inspection unit, a benchmark data generation unit, and a unique identification code management unit. The intelligent inspection unit collects product appearance, internal, and core functional parameters. The benchmark data generation unit integrates inspection data to generate benchmark data and associates it with the product's unique identification code. The unique identification code management unit generates a unique identification code for qualified products and triggers rework for unqualified products.

[0096] The transportation risk monitoring and data association module includes an IoT device configuration unit, a real-time data acquisition unit, and a data transmission and binding unit. The IoT device configuration unit configures authorized devices according to transportation protection requirements and matches product characteristics. The real-time data acquisition unit controls the devices to collect transportation risk data at a preset frequency. The data transmission and binding unit encrypts and uploads data, binds identification codes, and generates a timestamped transportation risk data log.

[0097] The storage center inspection and environmental monitoring module includes a primary inspection unit, a secondary inspection unit, a storage environment monitoring unit, and a storage location management unit. The primary inspection unit retrieves benchmark data, calibrates equipment to perform a primary inspection, generates a discrepancy report, and determines compliance. The secondary inspection unit performs a secondary inspection before shipment, comparing the results to the primary inspection standards and marking any non-compliance or defects. The storage environment monitoring unit collects storage parameters in real time through equipment and generates storage parameter logs. The storage location management unit allocates suitable storage locations, associates shelves and storage locations, and generates product-storage location binding records.

[0098] The risk tracing and strategy optimization module includes a transportation risk tracing unit, a storage risk tracing unit, a transportation strategy optimization unit, and a storage strategy optimization unit. Specifically, the transportation risk tracing unit retrieves data, aligns defect characteristics, locates the transportation risk source, and generates a report when a first inspection fails. The storage risk tracing unit analyzes storage parameters and defect correlations to locate the storage risk source when a second inspection fails. The transportation strategy optimization unit formulates optimization plans based on the transportation risk source, binds them to similar products, and pushes them to the relevant departments. The storage strategy optimization unit formulates optimization plans based on the storage risk source, binds them to similar products, and applies them to the relevant departments.

[0099] The end-to-end data management and authorization module includes a data integration unit, a report generation unit, and an authorization and permission control unit. The data integration unit aggregates data from each stage to form an end-to-end dataset with an identification code at its core. After delivery, the report generation unit integrates the data to generate an end-to-end risk report, covering risks and optimization results. The authorization and permission control unit allocates data access permissions to ensure that each entity can only query data within its authorized scope.

[0100] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for assessing the risk of an authorization scenario based on the Internet of Things, characterized in that, The application relates to a product quality risk tracing method and system. After a factory completes product production, the products are subjected to preliminary inspection, and product preliminary inspection quality benchmark data is generated; The products that pass the preliminary inspection are transported, and transportation risk data in the transportation process is collected in real time through Internet of Things equipment of a transportation vehicle; When the products arrive at a storage center, one-time inspection is performed according to the same inspection standard of the product preliminary inspection; when the products do not pass the one-time inspection, transportation risk tracing is triggered, transportation risk data is called, and the transportation risk source is located in combination with the one-time inspection result; for the products that have problems in the transportation process, the transportation strategy is optimized according to the transportation risk tracing, and the unqualified products are sent back to the factory for rework; the unqualified products are sent back to the factory for rework; the unqualified products are sent back to the factory for rework; The products that pass the one-time inspection are stored in a warehouse, and storage parameters are monitored in real time according to Internet of Things equipment in the storage area; before the products are sent to a delivery center, secondary inspection is performed, the storage parameters of unqualified products are analyzed, the storage risk source is located, the storage strategy is optimized, and the optimized storage strategy is used for subsequent storage of the same type of products; the unqualified products are sent back to the factory for rework; The products that pass the secondary inspection are sent to the delivery center according to the determined transportation strategy, and a whole-link risk data report of the products is generated. 2.The method of claim 1, wherein, After the factory completes product production, the products are subjected to preliminary inspection, and product preliminary inspection quality benchmark data is generated, including: The products are subjected to whole-item inspection through authorized intelligent detection equipment, the product appearance explicit indicators are collected, the product internal implicit indicators are detected, and the product core function parameters are recorded; the inspection data is integrated into the product preliminary inspection quality benchmark data, including the inspection time, the detection equipment number, the measured values of various indicators and the qualified judgment result; a unique identification code is generated for the product, and the identification code is bound with the product preliminary inspection quality benchmark data; When the preliminary inspection determines that the product is unqualified, the product is directly sent to the factory rework process, and no unique identification code is generated, so as to avoid the unqualified product from flowing into the subsequent transportation link; when the preliminary inspection determines that the product is qualified, the transportation protection requirement is added to the unique identification code, and the product enters the product transportation stage. 3.The method of claim 1, wherein, The products that pass the preliminary inspection are transported, and transportation risk data in the transportation process is collected in real time through Internet of Things equipment of a transportation vehicle, including: According to the transportation protection requirement added to the unique identification code of the product that passes the preliminary inspection, authorized Internet of Things monitoring equipment is configured for the transportation vehicle, including a three-axis acceleration sensor, a cargo pressure sensor, a GPS positioning module, a temperature and humidity sensor and a driving behavior recorder; In the transportation process, the Internet of Things equipment collects transportation risk data in real time at a preset data collection frequency, including the peak value of the jolt acceleration, the cargo stacking pressure value, the real-time transportation route information, the temperature and humidity value in the vehicle compartment, the number of times of sudden acceleration and sudden braking of the driver per hour; the collected data is uploaded, bound with the unique identification code of the product, and the correspondence between the product and the transportation risk data is formed; after the transportation is completed, all the collected transportation risk data is sorted according to the time stamp, and the transportation risk data log of the product is generated. 4.The method of claim 1, wherein, When the products arrive at a storage center, one-time inspection is performed according to the same inspection standard of the product preliminary inspection, including: The storage center retrieves the factory initial inspection quality benchmark data of the product through the product unique identification code, calibrates the storage center inspection equipment according to the calibration parameters at the initial inspection, and performs a first inspection on the product according to the initial inspection index system; After the inspection is completed, the first inspection data is uploaded and compared with the factory initial inspection quality benchmark data item by item to generate an inspection difference report; for the indexes with differences, the difference type and specific value are marked; When the deviation of all indexes in this inspection from the initial inspection benchmark data is within the allowable range, it is determined that the first inspection is qualified, and the product enters the storage warehouse process; when there is an index that exceeds the deviation range of the initial inspection benchmark or a defect that is not recorded in the initial inspection, it is determined that the first inspection is unqualified, a transportation risk traceability start instruction is triggered, and the subsequent process of the product is suspended. 5.The method of claim 2, wherein, The transportation risk traceability is triggered when the product inspection is unqualified, the transportation risk data is retrieved, and the transportation risk source is located combined with the first inspection result, including: After the transportation risk traceability is triggered, the transportation risk data bound to the product unique identification code and the inspection difference report generated by the first inspection are retrieved to determine the unqualified item type and specific location; for the unqualified item type, the associated transportation risk data is selectively screened and compared; When the unqualified item is a visible physical damage, the time period when the jolt acceleration peak value in the transportation process exceeds the preset jolt acceleration threshold value, the duration when the cargo stacking pressure is greater than the preset cargo stacking pressure threshold value, and the time point when the sudden braking and sudden acceleration occur in the driving behavior are analyzed, the flatness of the corresponding section is matched through GPS positioning to determine whether the damage is caused by impact or extrusion that exceeds the protection capability; When the unqualified item is an invisible physical defect, the vibration frequency and amplitude data recorded by the three-axis acceleration sensor in the transportation process are retrieved, the component anti-vibration threshold value recorded during the initial inspection of the product is compared, and whether there is a structure damage caused by resonance or high-frequency vibration is analyzed; When the unqualified item is an environment-related defect, the out-of-range period recorded by the temperature and humidity sensor in the vehicle compartment is checked to determine whether the product quality change is caused by the substandard transportation environment; The specific risk source type is determined by aligning the transportation risk data and the defect characteristics through the time stamp; when the data shows that the jolt acceleration exceeds the standard and the corresponding section is a jolt section, the risk source is improper transportation route selection; when the data shows that the stacking pressure exceeds the standard and the cargo fixing method does not meet the initial inspection requirements, the risk source is cargo loading scheme violation; when the data shows that the number of sudden braking exceeds the threshold value and the driver's driving behavior record is abnormal, the risk source is non-standard driving operation; when the data shows that the temperature and humidity exceed the range and the vehicle compartment temperature control equipment is faulty, the risk source is transportation environment control failure; The located risk source and the unqualified item in the first inspection are causally verified, and a transportation risk source positioning report is generated after the verification is confirmed to be correct, which clearly shows the risk source type, occurrence period, associated data and causal relationship, and is bound to the product unique identification code. 6.The method of claim 1, wherein, The transportation strategy is optimized according to the transportation risk traceability for the products with problems in the transportation process, the unqualified products are returned to the factory for rework, the reworked products are transported again, and the optimized transportation strategy is used for the transportation of subsequent products of the same type. The storage center returns the product to the factory according to the transport risk source positioning report, marks the product unique identification code, unqualified items and associated transport risk sources, and returns the product to the factory; after the rework is completed, the factory preliminary inspection needs to be performed again to generate new preliminary inspection quality benchmark data and a unique identification code; Based on the risk source type in the transport risk source positioning report, a transport strategy optimization scheme is developed; when the risk source is improper transport route selection, the route planning is optimized, low jolt and high flatness sections are selected, and a preferred transport route is set for the same type of product; when the risk source is a cargo loading scheme violation, the maximum stacking layer number and stacking pressure threshold are set, and a cargo fixing diagram is generated as a reference guide; when the risk source is non-standard driving operation, the transport party is informed to train the driver; when the risk source is transport environment control failure, the transport party is required to regularly maintain the carriage temperature control and dehumidification equipment; The transport strategy optimization scheme is bound to the same type of product, and when the reworked product is transported for the second time, the transport party transports according to the corresponding optimized transport strategy.

7. The method of claim 1, wherein, The product that passes the first inspection is stored in the warehouse, and the storage parameters are monitored in real time according to the Internet of Things equipment in the storage area, including: The storage center retrieves product characteristic information according to the unique identification code of the product that passes the first inspection, allocates an adaptive storage location for the product in combination with the functional partition of the storage area; when warehousing, the product unique identification code is read to associate the corresponding shelf number and storage location coordinates, and a product-storage location binding record is generated; in the allocated storage area, the storage environment temperature and relative humidity are monitored through a temperature and humidity sensor; the total weight of the product stack is monitored through a shelf pressure sensor; the product stacking state is monitored through an infrared camera; real-time temperature and humidity values, shelf real-time load and product stacking state images are collected; The collected parameters are compared with the preset threshold in real time, and when the parameters exceed the threshold or the state is abnormal, the responsible personnel are notified to handle it, and a storage parameter log is generated. 8.The method of claim 1, wherein, The product is sent to the delivery center for secondary inspection before delivery, the storage parameters of unqualified products are analyzed, the storage risk source is located, the storage strategy is optimized, and the same type of product is stored following the optimized storage strategy, and the unqualified products are returned to the factory for rework, including: Before the product is planned to be sent to the delivery center, the storage center starts secondary inspection, compares the secondary inspection result with the first inspection qualified standard, marks the unqualified product and specific defect items; the storage parameter log of the unqualified product during storage is retrieved, and directional correlation analysis is performed according to the defect type and parameter fluctuation; Determine the risk source type by aligning the defect features with the parameter abnormal period through the timestamp: when the data shows that the temperature and humidity exceed the standard and the equipment operation log is abnormal, the risk source is the storage environment control equipment failure; when the data shows that the load exceeds the limit and the stacking scheme does not match the product characteristics, the risk source is the storage planning violation; when the data shows that the storage time exceeds the upper limit and the material performance attenuation curve is consistent, the risk source is the storage cycle management out of control; based on the positioning of the risk source, develop a storage strategy optimization scheme, for equipment failure, add equipment health inspection reminders, and trigger warnings when parameters exceed the standard; for storage planning violations, retrieve product parameters when warehousing, including storage environment, physical load, and special adaptation requirements, retrieve shelf parameters, including shelf hardware parameters and warehouse location environment parameters, verify the matching degree of products and shelves, compare the dimensions of environmental adaptability, load adaptability, and hardware type, and reject warehousing if any comparison fails; for cycle management out of control, add a maximum storage countdown to the product unique identification code, and send a warning before expiration, and prioritize shipping; The storage center returns unqualified products from the second inspection, and after rework, they need to pass the factory initial inspection and the storage center first inspection again before being warehoused again. The optimized storage strategy is bound to the same type of product, and the same type of product is stored using this strategy when warehoused in the future. 9.The method of claim 1, wherein, The products that pass the second inspection are sent to the delivery center according to the determined transportation strategy, and a full-link risk data report for the product is generated, including: The storage center retrieves the corresponding transportation strategy through the product unique identification code and transports the products that pass the second inspection to the delivery center according to the determined transportation strategy; after the products arrive at the delivery center and complete acceptance, full-link data integration is triggered, and a full-link risk data report is generated with the product unique identification code as the identifier, including data from the factory, first transportation, storage, and delivery transportation links.

10. An Internet of Things based authorized scenario risk assessment system using the Internet of Things based authorized scenario risk assessment method of any one of claims 1-9. Including: Factory initial inspection and benchmark management module: including intelligent detection unit, benchmark data generation unit, and unique identification code management unit; wherein the intelligent detection unit collects product appearance, internal and core function parameters; the benchmark data generation unit integrates inspection data to generate benchmark data, and associates the product unique identification code; the unique identification code management unit generates a unique identification code for qualified products, and triggers rework for unqualified products; Transportation risk monitoring and data association module: including Internet of Things device configuration unit, real-time data acquisition unit, and data transmission and binding unit; wherein the Internet of Things device configuration unit configures authorized devices according to transportation protection requirements and matches product characteristics; the real-time data acquisition unit controls the device to collect transportation risk data at a preset frequency; the data transmission and binding unit uploads encrypted data, binds the identification code, and generates a timestamped transportation risk data log; The storage center inspection and environment monitoring module comprises a primary inspection unit, a secondary inspection unit, a storage environment monitoring unit and a storage location management unit; wherein the primary inspection unit calls reference data, calibrates equipment to perform primary inspection, generates a difference report and determines eligibility; the secondary inspection unit performs secondary inspection before shipment by comparing the primary inspection standard, marks unqualified and defective items; the storage environment monitoring unit collects storage parameters in real time through equipment to generate a storage parameter log; the storage location management unit allocates adaptive storage locations, associates shelves and storage locations and generates product-storage location binding records; The risk traceability and strategy optimization module comprises a transportation risk traceability unit, a storage risk traceability unit, a transportation strategy optimization unit and a storage strategy optimization unit; wherein the transportation risk traceability unit calls data when the primary inspection is unqualified, aligns defect features, locates a transportation risk source and generates a report; the storage risk traceability unit analyzes storage parameters and defect correlations when the secondary inspection is unqualified to locate a storage risk source; the transportation strategy optimization unit formulates an optimization scheme based on the transportation risk source, binds the same type of products and performs pushing; the storage strategy optimization unit formulates an optimization scheme based on the storage risk source, binds the same type of products and performs application; The full-link data management and authorization module comprises a data integration unit, a report generation unit and an authorization and permission control unit; wherein the data integration unit aggregates data at each link to form a full-link data set with an identification code as the core; the report generation unit integrates data after delivery to generate a full-link risk report covering risk and optimization results; the authorization and permission control unit allocates data access permissions to ensure that each subject only queries data within the authorized range.

Images