Overseas Art Exhibition Curation, Packaging, and Transportation System Based on Cross-border Logistics

Abstract

This invention discloses an overseas art exhibition curatorial packaging and transportation system adapted to cross-border logistics, relating to the fields of cross-border logistics and art exhibition protection technology. The system includes: an art exhibition-specific database module that stores three types of datasets and supports retrieval and updates; an AI intelligent processing module that calls data and applies two formulas to perform four functions, including material identification; a blockchain evidence storage module that stores evidence using a consortium blockchain and provides a verification interface; a logistics adaptation module that collects data, coordinates formulas to adjust packaging, and synchronizes data; an insurance collaboration module that connects to the claims system to retrieve evidence and generate application documents; a digital twin modeling and traceability module that constructs a digital twin and performs traceability; and an iterative optimization module that receives traceability results, adjusts parameters, and verifies them. This invention integrates multiple modules to form a closed loop, combines three core formulas to achieve compliance, logistics, and insurance collaboration, and incorporates a digital twin traceability and iterative optimization mechanism, solving the problems of independent processes and insufficient adaptation in traditional systems.

Description

Technical Field

[0001] This invention relates to the field of cross-border logistics and art exhibit protection technology, specifically to an overseas art exhibit curatorial packaging and transportation system adapted to cross-border logistics. Background Technology

[0002] With increasingly frequent cultural exchanges, the demand for cross-border curatorial exhibitions of overseas art exhibits continues to grow. This demand has spurred the need for cross-border logistics and art exhibit protection technologies. Overseas art exhibits are made of diverse materials, and some contain endangered species. Cross-border transportation must comply with the regulations of different regions and address various risks associated with different transportation scenarios, such as high humidity at sea, air pressure, and rough handling during land transport. The cross-border transportation of exhibits involves interconnected processes, including material identification, compliance documentation, packaging adaptation, logistics control, and insurance claims. Each stage demands high precision and protection of the exhibits. The seamless integration of these stages directly impacts the overall transportation process, necessitating a comprehensive solution that enables data sharing and seamless workflow across all stages.

[0003] Among existing technologies related to cargo transportation and packaging design, Chinese invention patent application CN120688969A discloses a multimodal transport intelligent and reliable collaborative method based on artificial intelligence technology. This method integrates AI, blockchain, and digital twin technologies to achieve standardized data collaboration, intelligent scheduling, and route optimization for general cargo multimodal transport, solving the problems of poor connectivity and data silos across different modes of transport for ordinary goods. Another Chinese invention patent application CN116541911A discloses an AI-based packaging design system that uses AI language models and image generation models to achieve intelligent design and editing of general product packaging, reducing the communication costs and manpower consumption of traditional packaging design. However, none of these existing technologies are specifically designed for the cross-border curatorial transportation of overseas art exhibits, exhibiting three significant limitations that make them difficult to adapt to actual needs. On the one hand, compliance, logistics, and insurance are independent processes with no unified data sharing platform. Compliance documents rely on manual processing, which can lead to information oversights. The claims process also lacks traceable and unified evidence, making it difficult to quickly determine liability. On the other hand, packaging parameters are mostly static settings based on experience, which cannot be dynamically adjusted in conjunction with the material characteristics of the exhibits and the real-time logistics risks during transportation, resulting in insufficient packaging adaptability. At the same time, there is a lack of a fault tracing mechanism for the entire transportation process. Even if problems such as exhibits being delayed or damaged occur, it is impossible to accurately locate the core cause of the problem, let alone a targeted adjustment mechanism based on the problem. Similar problems are prone to recurrence, creating a gap in process optimization. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an overseas art exhibition packaging and transportation system based on cross-border logistics adaptation. This system integrates seven modules, including an art exhibition-specific database, AI intelligent processing, and blockchain evidence storage, and combines three core formulas to construct a closed-loop system, achieving full-process control of overseas art exhibition packaging and transportation. The database provides data support; the AI ​​module applies formulas for collaborative adaptation and liability determination to complete functions such as material identification and compliance document generation; the logistics adaptation module works in conjunction with the iterative optimization module to dynamically adjust packaging parameters through optimization formulas; blockchain ensures data traceability; the digital twin module enables fault tracing; and the iterative module drives system self-optimization. This system solves problems such as insufficient packaging adaptation and difficulty in liability determination, improves transportation accuracy and security, and adapts to various complex cross-border exhibition packaging and transportation needs.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: an overseas art exhibition curatorial packaging and transportation system adapted to cross-border logistics, the system comprising: The dedicated database module for art exhibits is used to store compliance and regulatory datasets, exhibit material characteristics datasets, and cross-border logistics scenario datasets. It supports real-time data retrieval and updates, providing data support for the implementation of functions in various modules of the system. AI intelligent processing module: used to call up the exclusive database of art exhibits, apply the art exhibit compliance-logistics-material coordination and adaptation formula and the insurance liability intelligent determination probability formula, and perform material identification, automatic generation of compliance documents, logistics plan optimization and insurance liability determination. Blockchain Evidence Preservation Module: Adopting a consortium blockchain architecture, it is used to receive end-to-end data uploaded by various modules and perform tamper-proof evidence preservation, providing real-time verification interfaces to relevant nodes to achieve data traceability; Logistics adaptation module: Used to collect real-time scenario data of cross-border logistics, and work with the iterative optimization module to apply the dynamic optimization formula of packaging parameters to calculate the optimal packaging parameters, switch the modular packaging function module, adjust packaging parameters in real time, and synchronize logistics node data to the blockchain storage module and the digital twin modeling and traceability module to adapt to cross-border multimodal transport. Insurance Collaboration Module: This module connects to the insurance company's claims system, retrieves blockchain-stored claims evidence, combines it with liability determination results, generates standardized claims application documents, and initiates claims applications. Digital Twin Modeling and Traceability Module: Based on the dedicated database of art exhibits and the evidence stored in the blockchain evidence storage module, a three-dimensional digital twin of exhibits, packaging, and logistics is constructed. By linking the data across the entire chain through the timeline, the entire process of fault traceability is performed to locate the core cause of the problem. Iterative optimization module: Used to receive traceability results, adjust AI algorithm models, logistics packaging parameters, database compliance judgment rules and logistics scenario datasets, verify the optimization effect through digital twin simulation, and incorporate the verified parameters into the system default configuration.

[0006] Furthermore, the formula for the compatibility of art exhibits with logistics and materials is as follows: ;in For the degree of coordination and adaptation, the value ranges from 0 to 1. When the value is ≥0.85, automatic generation of compliance documents is triggered. System optimization is triggered when the value is less than 0.85. This is a vector representing the material characteristics of the art exhibits, including hardness, humidity tolerance, percentage of endangered components, brittleness coefficient, and chemical stability. The weight vector of compliance rules in the destination country, and One-to-one correspondence between dimensions; For compliance-material matching coefficients, endangered materials have a higher compatibility coefficient than ordinary materials; This represents a real-time risk value for cross-border logistics scenarios, ranging from 0 to 1. For logistics risk adaptation coefficient, the adaptation coefficient of high-value exhibits is higher than that of ordinary-value exhibits. The correction factor for endangered materials is higher than that for ordinary materials. The logistics timeliness sensitivity coefficient is represented by T, where T is the actual estimated logistics timeliness. The threshold for the validity period of compliance documents in the destination country.

[0007] Furthermore, the dynamic optimization formula for the packaging parameters is as follows: ;in For optimal packaging parameters, including cushioning layer inflation parameters and moisture resistance threshold; To package the current parameters; The threshold for the material tolerance of art exhibits; This represents the real-time measured value of logistics risks. The material-risk compatibility index shows that brittle materials have a higher index than flexible materials. The coefficient is adjusted through iterative correction based on the source; the more historical damages, the larger the coefficient. The number of similar historical cases, ≥3; The optimal packaging parameters are for the i-th historical case; These are the initial parameters for the i-th historical case; The source weight of the i-th case is negatively correlated with the case damage rate, and the sum of the source weights of all historical cases is 1.

[0008] Furthermore, the probability formula for intelligently determining insurance liability is as follows: ;in This represents the probability of liability, with a value ranging from 0 to 1. When the value is greater than 0.6, the corresponding responsible party is deemed primarily liable. To assess the strength of evidence regarding the logistics provider's liability, the data on the duration of exceeding the standard for transportation parameters and the degree of route deviation, which are stored on the blockchain, are normalized. The strength of evidence regarding the packaging party's liability is determined by normalizing the deviation of packaging parameters from optimal values ​​based on blockchain-stored evidence and sensor fault records. The strength of evidence for force majeure liability is derived from normalized meteorological and policy-related data. , , As a weight of responsibility, and Equal to and higher than ; It serves as a correction factor for the credibility of evidence and is positively correlated with the completeness of blockchain-stored evidence data.

[0009] Furthermore, the AI ​​intelligent processing module includes a material identification unit, a compliance document generation unit, a logistics solution optimization unit, and an insurance liability determination unit. The material identification unit uses a convolutional neural network combined with a transfer learning algorithm to extract material features from exhibit images and spectral data, performing endangered material component screening and accurate material identification with an accuracy of ≥99.5%. The compliance document generation unit generates electronic compliance documents containing electronic signatures and on-chain timestamps, automatically connecting to the verification interface of the customs supervision system. The logistics solution optimization unit calculates the results based on the formula for the collaborative adaptability of art exhibit compliance, logistics, and materials, matching transportation methods, packaging schemes, and optimizing transportation routes. The insurance liability determination unit calculates the results based on the intelligent insurance liability determination probability formula, performing liability attribution determination.

[0010] Furthermore, the logistics adaptation module has built-in sensors to collect real-time data on packaging operation status and logistics scenario risks. After inputting the collected data into the packaging parameter dynamic optimization formula to calculate the optimal packaging parameters, it automatically controls the switching of modular packaging function modules and parameter adjustments, forming a closed-loop adaptation process of data collection, formula calculation, and parameter adjustment.

[0011] Furthermore, the consortium blockchain nodes of the blockchain evidence storage module include system administrators, customs supervisors, logistics service providers, and insurance companies; the stored data includes electronic signatures on compliance documents, material identification reports, logistics node data, packaging sensor data, and claims-related evidence; each node reads or writes corresponding data according to preset permissions, the customs supervisor can read compliance documents and logistics data, the insurance company can read claims-related evidence data, and the logistics service provider can write logistics data.

[0012] Furthermore, the three-dimensional digital twin constructed by the digital twin modeling and traceability module embeds exhibit material parameters, packaging structure models, logistics path data, and key information of compliance documents; during fault tracing, the entire chain data is traced back through the timeline to locate the core reasons for packaging parameter defects, excessive logistics risks, material identification deviations, or omissions in compliance documents.

[0013] Furthermore, the iterative optimization module performs optimization effect simulation verification through digital twins in dimensions including exhibit protection capabilities, compliance adaptability, and logistics timeliness matching. After the simulation verification is passed, the optimization parameters are incorporated into the system's default configuration, and the logistics scenario dataset and compliance judgment rules of the art exhibit's exclusive database are updated synchronously.

[0014] Furthermore, the insurance collaboration module matches customized insurance solutions based on the material characteristics of the exhibits and the risks of cross-border logistics scenarios. It simultaneously pushes the claim evidence stored by the blockchain evidence storage module and the liability determination results of the AI ​​intelligent processing module to the insurance company's claim system to automatically initiate claim applications.

[0015] Compared with existing technologies, this overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation has the following advantages: I. This invention integrates multiple modules, including a dedicated database of art exhibits, AI intelligent processing, and blockchain-based evidence storage, to form a closed-loop system. By precisely applying three core formulas, it achieves close collaboration across compliance, logistics, and insurance. The art exhibit compliance-logistics-material compatibility formula dynamically calculates compatibility based on exhibit materials and destination compliance rules, automatically generating compliance documents and avoiding human oversight. The packaging parameter dynamic optimization formula adjusts packaging parameters based on real-time logistics risks, adapting to different transportation scenarios. The insurance liability intelligent determination probability formula defines liability based on blockchain-based evidence data, simplifying the claims process. The linkage between these three components and other modules solves the problem of traditionally independent processes, improving the accuracy and efficiency of cross-border transportation and reducing the risk of exhibits being delayed or damaged at ports.

[0016] II. This invention utilizes digital twin modeling and a traceability module to link exhibit, packaging, and logistics data across the entire supply chain. Combined with a dynamic adjustment mechanism from an iterative optimization module, it achieves precise fault tracing and system self-optimization. The digital twin can trace back the entire transportation process data, accurately pinpointing the root causes of problems such as packaging parameter defects and excessive logistics risks. The iterative optimization module adjusts the algorithm model, packaging parameters, and compliance rules based on the traceability results. After verification through digital twin simulation, these adjustments are incorporated into the default configuration, allowing the system to continuously adapt to different exhibit materials and logistics scenarios. This solves the problem of traditional solutions being unable to provide targeted improvements, enhances the security of cross-border transportation of art exhibits, adapts to various complex cross-border curatorial scenarios, and reduces various potential risks during compliance and transportation.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 This is a module structure diagram of the system of the present invention; Figure 2 This is a flowchart illustrating the process of handling art exhibits according to the present invention. Figure 3 This is a flowchart of the digital twin modeling and optimization process of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example This embodiment uses the cross-border curatorial transportation of art exhibits containing endangered species as an application scenario, detailing the specific operation process of an overseas art exhibit curatorial packaging and transportation system adapted to cross-border logistics. The various modules of the system operate collaboratively according to preset logic, such as... Figure 1 The diagram shown is a module structure diagram of the system of the present invention. The specific operation process is as follows: The dedicated database module for art exhibits pre-stores three core datasets: compliance and regulatory datasets for different regions, material characteristic datasets for various art exhibits, and risk datasets for various cross-border logistics scenarios. The compliance and regulatory dataset contains compliance determination rules specific to each art exhibit; the material characteristic dataset contains core parameters such as hardness, humidity tolerance, and the proportion of endangered components for different materials; and the logistics scenario risk dataset contains risk parameters for different transportation methods such as sea, air, and land transport. This module supports real-time data retrieval and updates, providing fundamental data support for all functional modules of the entire system. Subsequent operation of each module requires retrieving corresponding data from this database to ensure data consistency and accuracy, preventing system malfunctions due to data gaps.

[0022] After the AI ​​intelligent processing module is activated, it first accesses the material characteristic dataset from the art exhibit's dedicated database. Simultaneously, it receives images and spectral data of the exhibits collected by operators. The material recognition unit uses a convolutional neural network combined with transfer learning algorithms to extract features from the collected images and spectral data, filtering out the core material characteristics of the exhibits. This then performs endangered material component screening and precise material identification, ensuring an accuracy rate of over 99.5%. After identification, the AI ​​intelligent processing module calls the art exhibit compliance-logistics-material co-adaptation formula, which is as follows: The values ​​of each parameter and their basis are as follows. The value ranges from 0 to 1 and is used to determine the conditions for generating compliance documents and triggering system optimization. The material feature vector of this exhibit includes normalized data from five dimensions: hardness, humidity tolerance, proportion of endangered components, brittleness coefficient, and chemical stability. All data are retrieved from the material characteristic dataset of the art exhibit's dedicated database. Let the destination compliance rule weight vector be, and... The five dimensions correspond one-to-one, and the data is retrieved from the compliance and regulatory dataset to match the endangered component characteristics of the exhibit. For compliance purposes, a material matching coefficient is used, with a value ranging from 0.6 to 0.8. This is because the exhibit contains endangered components. Take a higher value of 0.8 to ensure the accuracy of compliance determination; This is a real-time risk value for cross-border logistics scenarios, ranging from 0 to 1, calculated by weighting various risk parameters corresponding to the logistics scenario in the database. The logistics risk adaptability coefficient ranges from 0.2 to 0.4. Given the high value of this exhibit, Taking a higher value of 0.4 improves the targeting of logistics risk adaptation; The endangered material correction factor is set at 1.0 for ordinary materials and 1.2 for this endangered material, which is used to enhance the compliance and logistics adaptability of endangered exhibits. is the logistics timeliness sensitivity coefficient, with a fixed value of 0.05, used to fit the nonlinear relationship between timeliness and collaborative adaptability; T is the actual logistics estimated timeliness, calculated based on the selected transportation mode and route; The validity period threshold for destination compliance documents is determined by retrieving the validity period parameter of the corresponding rule from the compliance regulations dataset. This is then calculated using this formula. =0.89, which satisfies the condition. When the value is ≥0.85, the system triggers automatic generation of compliance documents. The compliance document generation unit calls the compliance law dataset in the database to generate electronic compliance documents containing electronic signatures and on-chain timestamps, automatically connects to relevant verification interfaces, and simultaneously uploads the material identification results and compliance documents to the blockchain evidence storage module. The logistics solution optimization unit, based on the calculation results of this formula, matches the corresponding transportation method and initial packaging plan, optimizes the transportation route, and avoids high-risk transportation nodes. The insurance liability determination unit reserves the algorithmic foundation required for subsequent liability determination, simultaneously receiving material identification results and logistics solution data, awaiting subsequent claims-related data support. Figure 2 The diagram shown is a flowchart of the art exhibit processing of this invention, which clearly illustrates the entire process from material identification to the generation of compliance documents and the matching of logistics solutions.

[0023] The blockchain evidence storage module adopts a consortium blockchain architecture. Consortium blockchain nodes include system administrators, regulators, logistics service providers, and insurance companies. Each node's permissions are pre-defined to ensure the security and specificity of data access. This module receives material identification reports and electronic compliance documents uploaded by the AI ​​intelligent processing module in real time. It subsequently receives logistics node data and packaging sensor data uploaded by the logistics adaptation module, as well as claims-related evidence uploaded by the insurance collaboration module. All received data is stored in an immutable manner, and each data entry generates a unique on-chain timestamp to ensure data traceability. Simultaneously, this module provides real-time verification interfaces for each node. Each node reads or writes corresponding data according to pre-defined permissions. Regulators can read compliance documents and logistics data for compliance verification and logistics supervision; insurance companies can read claims-related evidence data for claims review; and logistics service providers can write logistics node data to update transportation status in real time, ensuring that data across the entire chain is traceable and verifiable, preventing data tampering or loss.

[0024] The logistics adaptation module incorporates built-in sensors, including vibration, humidity, and pressure sensors. Upon startup, it collects real-time data on packaging operation status and logistics scenario risks. Packaging operation status data includes parameters such as cushioning layer inflation and packaging humidity, while logistics scenario risk data includes parameters such as vibration intensity and ambient humidity during transportation. Simultaneously, this module accesses a dataset of exhibit material characteristics and a dataset of logistics scenarios from a dedicated database of art exhibits. This data, in collaboration with the iterative optimization module, applies a dynamic optimization formula for packaging parameters. The specific formula is as follows: The values ​​of each parameter and their basis are as follows: The optimal packaging parameters, including the inflation parameters of the cushioning layer and the moisture resistance threshold, are the core basis for subsequent packaging adjustments. The initial packaging parameters are determined based on the initial packaging scheme output by the AI ​​intelligent processing module; The material tolerance threshold for this exhibit, namely the parameters such as the material's impact resistance and humidity tolerance range, is retrieved from the material property dataset. These are the real-time measured values ​​of logistics risks collected by sensors, namely, the actual vibration intensity, environmental humidity, and other data during transportation. The material-risk fit index ranges from 0.8 to 1.5, as the exhibit's material is relatively brittle. A higher value of 1.5 is chosen to enhance the targeted protection of the packaging; This is the source tracing iteration correction coefficient, with a value ranging from 0.1 to 0.3. Initially, the number of historical damages is 0. Take an initial low value of 0.1; n is the number of historical similar cases. Retrieve more than 3 historical case data with the same material and logistics scenario from the database to ensure that n≥3; Let be the optimal packaging parameters for the i-th historical case. The initial parameters for the i-th historical case are all retrieved from the historical case data; The traceability weight for the i-th case is negatively correlated with the case's damage rate; the lower the damage rate, the higher the weight. The sum of the traceability weights for all historical cases is 1, ensuring the rationality of historical case references. After calculating the optimal packaging parameters using this formula, the logistics adaptation module automatically controls the switching of functional modules and parameter adjustments for modular packaging, forming a closed-loop adaptation process of data collection, formula calculation, and parameter adjustment, ensuring that packaging parameters are always adapted to real-time logistics risks. Simultaneously, this module synchronously uploads real-time collected logistics node data and packaging parameter adjustment data to the blockchain evidence storage module and the digital twin modeling and traceability module, updating transportation and packaging status in real time to meet the needs of cross-border multimodal transport and ensure smooth data connection between various transportation nodes.

[0025] The digital twin modeling and traceability module constructs a 3D digital twin of the exhibit, packaging, and logistics based on the exhibit's material parameters, packaging structure model, logistics route data from a dedicated database, and end-to-end data stored in a blockchain-based notarization module. Material parameters, packaging structure model, logistics route data, and key information from compliance documents are embedded into the digital twin, and data uploaded from each module is synchronized in real time, forming a timeline correlation of end-to-end data to ensure complete synchronization between the digital twin and the actual transportation process. During transportation, if minor damage or parameter anomalies occur, the digital twin modeling and traceability module traces back the end-to-end data along the timeline to accurately pinpoint the core cause of the problem, such as packaging parameter defects, excessive logistics risks, material identification deviations, or missing compliance documents. Simultaneously, the traceability results are pushed to the iterative optimization module, providing precise evidence for system optimization. If no faults occur, the module continues to synchronize data, presenting the entire transportation process in real time for operators to monitor.

[0026] The iterative optimization module receives traceability results from the digital twin modeling and traceability module. If no faults occur, it maintains all system parameters unchanged and continuously monitors the operational status of each module. If a fault occurs, it adjusts the algorithm model of the AI ​​intelligent processing module, the packaging parameters of the logistics adaptation module, and the compliance judgment rules and logistics scenario dataset of the art exhibit-specific database based on the traceability results. After adjustment, the module performs optimization effect simulation verification through the digital twin. Verification dimensions include exhibit protection capabilities, compliance adaptation, and logistics timeliness matching. After successful simulation verification, the optimized parameters are incorporated into the system's default configuration, and the relevant datasets in the art exhibit-specific database are updated synchronously, achieving system self-optimization and preventing similar faults from recurring. Figure 3 The diagram shown is a flowchart of the digital twin modeling and optimization process of this invention, illustrating the complete closed-loop process from fault tracing to iterative optimization.

[0027] After the insurance collaboration module is activated, it retrieves relevant data from the database based on the exhibit's material characteristics and cross-border logistics risks, matching a customized insurance plan to ensure the plan is suitable for the exhibit's endangered status and transportation risks. Simultaneously, this module receives data stored in the blockchain evidence module and relevant results from the AI ​​intelligent processing module in real time, continuously monitoring the risk status during transportation. If exhibit damage occurs during transportation, the insurance collaboration module retrieves the stored claim evidence from the blockchain evidence module, including packaging parameter data, logistics risk data, and damage records, and combines this with the liability determination result derived by the AI ​​intelligent processing module based on the insurance liability intelligent determination probability formula. The specific formula is: The values ​​of each parameter and their basis are as follows: This represents the probability of liability, ranging from 0 to 1. When the value is greater than 1, the corresponding party is determined to be primarily liable. To assess the strength of evidence of liability for the logistics provider, the data on the duration of exceeding the standard for transportation parameters and the degree of route deviation stored on the blockchain are normalized and the values ​​range from 0 to 1. The strength of evidence of the packaging party's liability is derived from the deviation of packaging parameters from the optimal value based on blockchain-stored evidence and sensor fault records after normalization, with a value range of 0-1. The strength of evidence for force majeure liability is derived from meteorological and policy-related data after normalization, and the value ranges from 0 to 1. , , As a weight of responsibility, and They are equal and both are 0.4. The value is 0.2, which is suitable for the high-value characteristics of art exhibits and strengthens the determination of responsibility between logistics providers and packaging providers; As an evidence credibility correction factor, because the integrity of blockchain-stored evidence data reaches over 95%, A value of 1.0 is used to ensure the credibility of the liability determination. The probability of liability is calculated using this formula. After clarifying the attribution of liability, the insurance collaboration module generates standardized claim application documents and simultaneously pushes the claim evidence and liability determination results to the insurance company's claim system, automatically initiating the claim application without manual intervention and simplifying the claims process.

[0028] This embodiment fully describes the entire operation process of the system of this invention through a cross-border curatorial transportation scenario of art exhibits containing endangered materials. Each module works collaboratively according to closed-loop logic, with data interconnection and smooth process flow. The three core formulas are precisely applied to their respective stages, clarifying the basis for the values ​​of each formula parameter, the calculation process, and the application effects, clearly presenting the working principles and data interaction logic of each module. The entire implementation process achieves full-process control over exhibit material identification, compliance document generation, packaging parameter optimization, logistics management, fault tracing, system optimization, and insurance claims, solving the problems of independent and insufficiently adaptable traditional processes.

[0029] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A curatorial packaging and transportation system for overseas art exhibitions based on cross-border logistics adaptation, characterized in that: The system includes: The dedicated database module for art exhibits is used to store compliance and regulatory datasets, exhibit material characteristics datasets, and cross-border logistics scenario datasets. It supports real-time data retrieval and updates, providing data support for the implementation of functions in various modules of the system. AI intelligent processing module: used to call up the exclusive database of art exhibits, apply the art exhibit compliance-logistics-material coordination and adaptation formula and the insurance liability intelligent determination probability formula, and perform material identification, automatic generation of compliance documents, logistics plan optimization and insurance liability determination. Blockchain Evidence Preservation Module: Adopting a consortium blockchain architecture, it is used to receive end-to-end data uploaded by various modules and perform tamper-proof evidence preservation, providing real-time verification interfaces to relevant nodes to achieve data traceability; Logistics adaptation module: Used to collect real-time scenario data of cross-border logistics, and work with the iterative optimization module to apply the dynamic optimization formula of packaging parameters to calculate the optimal packaging parameters, switch the modular packaging function module, adjust packaging parameters in real time, and synchronize logistics node data to the blockchain storage module and the digital twin modeling and traceability module to adapt to cross-border multimodal transport. Insurance Collaboration Module: This module connects to the insurance company's claims system, retrieves blockchain-stored claims evidence, combines it with liability determination results, generates standardized claims application documents, and initiates claims applications. Digital Twin Modeling and Traceability Module: Based on the dedicated database of art exhibits and the evidence stored in the blockchain evidence storage module, a three-dimensional digital twin of exhibits, packaging, and logistics is constructed. By linking the data across the entire chain through the timeline, the entire process of fault traceability is performed to locate the core cause of the problem. Iterative optimization module: Used to receive traceability results, adjust AI algorithm models, logistics packaging parameters, database compliance judgment rules and logistics scenario datasets, verify the optimization effect through digital twin simulation, and incorporate the verified parameters into the system default configuration.

2. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The formula for the compatibility of art exhibits with logistics and materials is as follows: ;in For the degree of coordination and adaptation, the value ranges from 0 to 1. When the value is ≥0.85, automatic generation of compliance documents is triggered. System optimization is triggered when the value is less than 0.

85. This is a vector representing the material characteristics of the art exhibits, including hardness, humidity tolerance, percentage of endangered components, brittleness coefficient, and chemical stability. The weight vector of compliance rules in the destination country, and One-to-one correspondence between dimensions; For compliance-material matching coefficients, endangered materials have a higher compatibility coefficient than ordinary materials; This represents a real-time risk value for cross-border logistics scenarios, ranging from 0 to 1. For logistics risk adaptation coefficient, the adaptation coefficient of high-value exhibits is higher than that of ordinary-value exhibits. The correction factor for endangered materials is higher than that for ordinary materials. This is the logistics timeliness sensitivity coefficient; T represents the actual estimated delivery time. The threshold for the validity period of compliance documents in the destination country.

3. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The dynamic optimization formula for the packaging parameters is as follows: ;in For optimal packaging parameters, including cushioning layer inflation parameters and moisture resistance threshold; To package the current parameters; The threshold for the material tolerance of art exhibits; This represents the real-time measured value of logistics risks. The material-risk compatibility index shows that brittle materials have a higher index than flexible materials. The coefficient is adjusted through iterative correction based on the source; the more historical damages, the larger the coefficient. The number of similar historical cases, ≥3; The optimal packaging parameters are for the i-th historical case; These are the initial parameters for the i-th historical case; The source weight of the i-th case is negatively correlated with the case damage rate, and the sum of the source weights of all historical cases is 1.

4. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The probability formula for intelligent determination of insurance liability is as follows: ;in This represents the probability of liability, with a value ranging from 0 to 1. When the value is greater than 0.6, the corresponding responsible party is deemed primarily liable. To assess the strength of evidence regarding the logistics provider's liability, the data on the duration of exceeding the standard for transportation parameters and the degree of route deviation, which are stored on the blockchain, are normalized. The strength of evidence regarding the packaging party's liability is determined by normalizing the deviation of packaging parameters from optimal values ​​based on blockchain-stored evidence and sensor fault records. The strength of evidence for force majeure liability is derived from normalized meteorological and policy-related data. , , As a weight of responsibility, and Equal to and higher than ; It serves as a correction factor for the credibility of evidence and is positively correlated with the completeness of blockchain-stored evidence data.

5. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The AI ​​intelligent processing module includes a material identification unit, a compliance document generation unit, a logistics solution optimization unit, and an insurance liability determination unit. The material identification unit uses a convolutional neural network combined with a transfer learning algorithm to extract material features from exhibit images and spectral data, performing endangered material component screening and accurate material identification with an accuracy of ≥99.5%. The compliance document generation unit generates electronic compliance documents containing electronic signatures and on-chain timestamps, automatically connecting to the verification interface of the customs supervision system. The logistics solution optimization unit calculates the results based on the formula for the collaborative adaptability of art exhibit compliance, logistics, and materials, matching transportation methods, packaging solutions, and optimizing transportation routes. The insurance liability determination unit calculates the liability attribution based on the results of the intelligent insurance liability determination probability formula.

6. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The logistics adaptation module has built-in sensors to collect real-time data on packaging operation status and logistics scenario risks. After inputting the collected data into the packaging parameter dynamic optimization formula to calculate the optimal packaging parameters, it automatically controls the switching of modular packaging function modules and parameter adjustments, forming a closed-loop adaptation process of data collection, formula calculation, and parameter adjustment.

7. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The blockchain evidence storage module's consortium blockchain nodes include system administrators, customs supervisors, logistics service providers, and insurance companies; the stored data includes electronic signatures on compliance documents, material identification reports, logistics node data, packaging sensor data, and claims-related evidence; each node reads or writes corresponding data according to preset permissions, the customs supervisor can read compliance documents and logistics data, the insurance company can read claims-related evidence data, and the logistics service provider can write logistics data.

8. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The three-dimensional digital twin constructed by the digital twin modeling and traceability module embeds exhibit material parameters, packaging structure models, logistics path data, and key information of compliance documents. When tracing faults, the entire chain data is traced back through the timeline to locate the core reasons for packaging parameter defects, excessive logistics risks, material identification deviations, or omissions in compliance documents.

9. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The iterative optimization module performs optimization effect simulation verification through digital twins in dimensions including exhibit protection capabilities, compliance adaptability, and logistics timeliness matching. After the simulation verification is passed, the optimization parameters are incorporated into the system default configuration, and the logistics scenario dataset and compliance judgment rules of the art exhibit exclusive database are updated synchronously.

10. The overseas art exhibition curatorial packaging and transportation system based on cross-border logistics adaptation as described in claim 1, characterized in that, The insurance collaboration module matches customized insurance solutions based on the material characteristics of the exhibits and the risks of cross-border logistics scenarios. It simultaneously pushes the claim evidence stored by the blockchain evidence storage module and the liability determination results of the AI ​​intelligent processing module to the insurance company's claim system to automatically initiate claim applications.

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