Cold chain supply chain intelligent management system and method based on multi-terminal cooperation

Abstract

The invention relates to the technical field of data processing, and discloses a cold chain supply chain intelligent management system and method based on multi-terminal collaboration, and the method comprises the steps: carrying out the block chain Hash evidence storage of multi-modal basic data, and generating Hash evidence storage data; and determining risk information of the cold-chain supply chain through the financial management model and the hash evidence data, and generating target financial service information of the cold-chain supply chain according to the risk information. By means of the mode, the multi-modal basic data are collected through the terminal devices, block chain hash evidence storage is conducted on the data, and the trusted data chain which cannot be tampered is formed. The Hash evidence data is analyzed through the preset financial management model, the performance risk, the asset value and the fund flow health condition of the cold chain supply chain can be dynamically evaluated, an adaptive financial service scheme is automatically generated, and the financial risk coefficient of the cold chain supply chain is reduced.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an intelligent management system and method for cold chain supply chain based on multi-terminal collaboration. Background Technology

[0002] As a crucial link in ensuring food and drug safety, the management efficiency and capital turnover capacity of the cold chain supply chain directly impact the stability of the industry chain. Traditional cold chain supply chain management systems have achieved the informatization of basic business processes. For example, the Huixin system provided by Qingdao Huixin Internet Technology Co., Ltd. can complete the entire online operation from basic data maintenance, procurement contract management, storage and customs clearance tracking to financial payment settlement. Such systems use PC workstations for document entry and approval, support centralized management of basic data such as customers, suppliers, and commodity inventory, and integrate core business modules such as customs declaration and inspection, warehousing and outbound, and expense settlement, significantly improving the internal operational efficiency of enterprises. However, existing technologies still have the following problems that urgently need to be solved: First, traditional systems are primarily PC-based and lack data collaboration mechanisms with mobile devices and IoT (Internet of Things) cold chain equipment. Data from key nodes such as on-site inbound barcode scanning, in-transit temperature monitoring, and port inspection photography cannot be synchronized in real time, leading to business delays and impacting supply chain responsiveness.

[0003] Secondly, the existing financial management modules (such as import receipts, payments, and expense settlements) are disconnected from financial operations. Financing applications require manual preparation of contracts, customs declarations, invoices, and other materials for submission to banks, with approval cycles lasting 7-15 working days. Financial institutions, unable to verify the authenticity of trade backgrounds, ownership of goods, and cargo status in real time, generally face a "reluctance to lend" problem, resulting in financing costs for small and medium-sized suppliers reaching 8%-12%, with collateral ratios typically below 60%. This severe occupation of working capital significantly impacts the cold chain enterprises' ability to expand warehousing and stock up on inventory.

[0004] Third, risk control relies heavily on manual intervention and lacks intelligent early warning systems. Current systems primarily depend on human experience to assess market risks such as commodity price fluctuations, exchange rate risks, and losses from shelf-life futures, as well as operational risks like double pledging and fraudulent warehouse receipts. Post-loan supervision lacks automated monitoring of inventory dynamics, leading to delays in detecting risks such as expired or spoiled goods. This results in persistently high non-performing loan rates for financial institutions. Statistics show that the non-performing loan rate for cold chain cargo-backed financing is approximately 2.3%, significantly higher than that of ordinary supply chain finance. Therefore, improving the efficiency of multi-terminal collaboration in the cold chain supply chain, thereby reducing its financial risk coefficient, has become a pressing technical challenge. Summary of the Invention

[0005] This application provides a multi-terminal collaborative intelligent management system and method for cold chain supply chain to reduce the financial risk coefficient of cold chain supply chain.

[0006] Firstly, this application provides a cold chain supply chain intelligent management system based on multi-terminal collaboration, the system comprising: A cloud-based data platform is used for centralized storage and management of all business data; The basic data management module is used to maintain basic data related to the cold chain supply chain; The intelligent procurement management module, connected to the basic data management module, is used to manage the contracts and agreements of the cold chain supply chain; The intelligent warehousing and transportation module is connected to the intelligent procurement management module and is used to manage the customs declaration and inspection information, temperature monitoring information, location tracking information and abnormal alarm information of the cold chain supply chain. The supply chain finance management module is connected to the cloud data platform, the basic data management module, the intelligent procurement management module, and the intelligent warehousing and transportation module, and is used to provide financial services for the cold chain supply chain.

[0007] Secondly, this application also provides a method for intelligent management of cold chain supply chains based on multi-terminal collaboration, the method comprising: Collect and upload various types of multimodal basic data in the cold chain supply chain through various terminal devices; The multimodal basic data is stored using blockchain hash notarization to generate hash notarization data corresponding to each of the multimodal basic data. By using a pre-set financial management model and the hash-based evidence storage data, the risk information of the cold chain supply chain is determined, and the target financial service information of the cold chain supply chain is generated based on the risk information.

[0008] This application discloses a multi-terminal collaborative intelligent management system and method for cold chain supply chains. The method includes collecting and uploading various types of multimodal basic data in the cold chain supply chain through various terminal devices; performing blockchain hash storage on each of the multimodal basic data to generate hash storage data corresponding to each of the multimodal basic data; determining the risk information of the cold chain supply chain through a preset financial management model and each of the hash storage data, and generating target financial service information for the cold chain supply chain based on the risk information. Through the above method, this application collects multimodal basic data through various terminal devices and performs blockchain hash storage on this data to form an immutable and trustworthy data chain. By analyzing the hash storage data through a preset financial management model, the fulfillment risk, asset value, and cash flow health of the cold chain supply chain can be dynamically assessed, and suitable financial service solutions can be automatically generated, reducing the financial risk coefficient of the cold chain supply chain. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 is a schematic diagram of a module of a cold chain supply chain intelligent management system based on multi-terminal collaboration provided in an embodiment of this application; Figure 2 is a schematic flowchart of an intelligent management method for cold chain supply chain based on multi-terminal collaboration provided by an embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0013] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0014] This application provides an intelligent management system and method for cold chain supply chains based on multi-terminal collaboration. The method can be applied to such systems, collecting multimodal basic data from various terminal devices and storing this data using blockchain hashing to form an immutable and trustworthy data chain. By analyzing the hashed data using a pre-defined financial management model, the system can dynamically assess the fulfillment risk, asset value, and cash flow health of the cold chain supply chain, automatically generating suitable financial service solutions and reducing the financial risk coefficient of the cold chain supply chain.

[0015] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0016] Please see Figure 1 , Figure 1This is a schematic diagram of a module of a multi-terminal collaborative intelligent management system for cold chain supply chain provided in an embodiment of this application.

[0017] A multi-terminal collaborative intelligent management system for cold chain supply chains includes: A cloud-based data platform is used for centralized storage and management of all business data; The basic data management module is used to maintain basic data related to the cold chain supply chain; The intelligent procurement management module, connected to the basic data management module, is used to manage the contracts and agreements of the cold chain supply chain; The intelligent warehousing and transportation module is connected to the intelligent procurement management module and is used to manage the customs declaration and inspection information, temperature monitoring information, location tracking information and abnormal alarm information of the cold chain supply chain. The supply chain finance management module is connected to the cloud data platform, the basic data management module, the intelligent procurement management module, and the intelligent warehousing and transportation module, and is used to provide financial services for the cold chain supply chain.

[0018] Specifically, upon system startup, the cloud-based data platform first performs identity authentication and permission verification for each terminal device. The basic data management module synchronizes the latest basic information to each terminal, the intelligent procurement management module generates procurement suggestions based on the basic data, the intelligent warehousing and transportation module monitors the logistics status in real time, and the supply chain finance management module provides financial services based on data from the entire supply chain.

[0019] Furthermore, the supply chain finance management module includes: An accounts receivable financing unit is used to determine accounts receivable financing information based on the contracts and agreements of the cold chain supply chain. The inventory pledge financing unit is used to determine inventory pledge financing based on the customs declaration and inspection information, temperature monitoring information, location tracking information and abnormal alarm information of the cold chain supply chain; The prepayment financing unit is used to determine prepayment financing services based on purchase orders and supplier credit data.

[0020] Specifically, the accounts receivable financing unit is deeply integrated with the intelligent procurement management module. Once an import contract is approved, information such as the contract amount and payment terms is automatically extracted to generate an accounts receivable financing plan. The accounts receivable financing unit retrieves import contract data to verify the authenticity and validity of the contracts, analyzes customer credit ratings and historical payment records, generates financing interest rate suggestions based on the current capital market conditions, and stores the financing contracts through a blockchain-based notarization module.

[0021] The inventory-backed financing unit and the intelligent warehousing and transportation module work in real time. Inventory data collected through the IoT monitoring submodule is combined with market conditions to dynamically calculate the value of the pledged goods. For example, the inventory-backed financing unit obtains real-time temperature monitoring data of imported goods entering the warehouse, combines this data with the attributes of the goods in the warehouse and market prices, dynamically adjusts the pledge ratio based on warehousing conditions, sets early warning thresholds, and automatically issues an alert when the value of the pledged goods declines.

[0022] The prepayment financing unit provides financial support to buyers based on purchase orders and supplier credit data. The system determines the financing amount and risk control measures by analyzing the supplier's historical performance records and commodity market price trends.

[0023] Furthermore, the supply chain finance management module also includes: The risk assessment unit is used to predict financing risk information based on real-time temperature data, warehousing condition data, and transportation timeliness data of the cold chain supply chain. The automatic credit granting unit is used to generate credit limit information based on historical transaction data, credit rating data, and cold chain asset data. The smart contract execution unit is used to trigger financing, loan disbursement, repayment, and risk management operations based on blockchain technology when preset conditions are met.

[0024] Specifically, smart contract execution units can be used for financing and lending smart contracts and risk management smart contracts.

[0025] For example, loan disbursement is automatically triggered when the following conditions are met: The import contract has been approved and the blockchain evidence has been stored. The goods have completed import customs declaration and an inbound record has been generated. Risk assessment is conducted using preset thresholds; The digital signature verification by the relevant parties has been successful.

[0026] For example, risk management is automatically performed when the following conditions are met: The duration of the abnormal temperature exceeds a preset threshold; The transportation delay exceeds the time limit stipulated in the contract; The value of the pledged assets fell below the warning line; The triggering conditions are verified through blockchain-stored evidence data.

[0027] The blockchain evidence storage module is used to store the business data on the blockchain and generate blockchain evidence storage data.

[0028] Furthermore, the risk assessment unit includes: The temperature risk analysis subunit is used to analyze the temperature changes in the cold chain supply chain and generate a temperature compliance report. Specifically, the temperature risk analysis subunit receives temperature data uploaded by the IoT monitoring submodule in real time, analyzes temperature change patterns through machine learning algorithms, and generates a temperature compliance report. The risk assessment unit continuously monitors temperature data during cold chain transportation, compares it with preset temperature thresholds, identifies abnormal fluctuations, generates a temperature compliance score, and influences financing decisions. When temperature anomalies occur, the valuation of the pledged collateral is automatically adjusted.

[0029] The timeliness risk analysis subunit is used to monitor the timeliness performance of each link in the cold chain supply chain and determine delay risk information. Specifically, the timeliness risk analysis subunit monitors the timeliness performance of each stage based on shipping schedule and release tracking data. The system identifies potential delay risks by comparing planned and actual completion times. Through real-time collection of timestamps from each stage of customs declaration and inspection, the subunit establishes a timeliness benchmark by comparing historical data. When abnormal delays are detected, risk warnings are triggered, and financing plans are adjusted accordingly.

[0030] The value fluctuation analysis subunit is used to monitor market price fluctuations in the cold chain supply chain and determine risk information regarding changes in the value of pledged goods.

[0031] Specifically, the value fluctuation analysis subunit monitors the value fluctuation of pledged goods in real time by accessing external market price data sources and combining them with the characteristics of inventory goods, providing a basis for risk pricing for inventory pledge financing.

[0032] Furthermore, the intelligent warehousing and transportation module also includes: The Internet of Things (IoT) monitoring submodule is connected to temperature sensors, humidity sensors, and GPS positioning devices to collect environmental data from the cold chain supply chain. Specifically, the IoT monitoring submodule collects data through the following devices: Temperature sensor: Collects temperature data every 5 minutes; Humidity sensor: Real-time monitoring of warehouse humidity; GPS positioning devices: update location information every 30 seconds; Door magnetic sensor: monitors the entry and exit status of goods.

[0033] An anomaly warning submodule, connected to the IoT monitoring submodule, is used to send warning information to the cloud data platform when the environmental data exceeds a preset threshold. Specifically, the workflow of the anomaly warning submodule is as follows: It receives data streams from various sensors in real time, compares them with a preset threshold rule base, generates early warning events when an anomaly is detected, pushes early warning information through multiple terminals, and records the early warning processing process and results.

[0034] The quality traceability submodule is used to record the entire historical temperature information of the cold chain supply chain.

[0035] Specifically, the quality traceability submodule is based on blockchain technology and records historical temperature information throughout the entire cold chain supply chain. Each temperature data block contains metadata such as timestamp, location information, and device ID, forming an immutable quality traceability chain.

[0036] Based on the above embodiments, the specific implementation steps of this application can be as follows: The system achieves multi-terminal collaboration through the following methods: mobile terminal APP (supporting lightweight operations such as business approval and early warning viewing), web management terminal (providing complete business management functions), IoT terminal (automatically collecting and uploading monitoring data), and financial institution terminal (obtaining financing business data in real time).

[0037] Taking import business as an example, the multi-terminal collaborative process can enable purchasing staff to create import contracts through the web, managers to approve contracts through a mobile APP, IoT devices to automatically collect transportation process data, the system to generate financing plans based on complete business data, and financial institutions to participate in business collaboration through a dedicated interface.

[0038] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a multi-terminal collaborative intelligent management method for cold chain supply chains, provided in an embodiment of this application. This multi-terminal collaborative intelligent management method for cold chain supply chains can be applied to servers to reduce the financial risk factor in the cold chain supply chain.

[0039] like Figure 2 As shown, the intelligent management method for cold chain supply chain based on multi-terminal collaboration specifically includes steps S10 to S30.

[0040] Step S10: Collect and upload various types of multimodal basic data in the cold chain supply chain through each terminal device; Specifically, by deploying IoT sensors, mobile terminals, and business systems in warehouses, transport vehicles, and customs declaration sites, multimodal basic data in the cold chain supply chain are collected and uploaded in real time, including but not limited to real-time temperature and humidity sensing data of goods, electronic business document data, and image voucher data of key nodes.

[0041] Step S20: Perform blockchain hash storage on each of the multimodal basic data to generate hash storage data corresponding to each of the multimodal basic data; Specifically, the system uses blockchain technology to standardize and reliably store the aforementioned multimodal data. This involves adding timestamps and signatures to the perceived data to generate a first-level leaf node hash, converting business documents into a standard format to generate a second-level intermediate node hash, encoding and compressing the image data to generate a third-level leaf node hash, and finally synthesizing a unique hash evidence data using national cryptographic hash algorithms such as SM3 and storing it on the blockchain to ensure data integrity, tamper resistance, and traceability.

[0042] Step S30: Determine the risk information of the cold chain supply chain by using a preset financial management model and each of the hash-based evidence storage data, and generate the target financial service information of the cold chain supply chain based on the risk information.

[0043] Specifically, the pre-set financial management model (integrating pre-loan access, mid-loan monitoring, post-loan clearing, and risk circuit breaker mechanisms) will retrieve and analyze on-chain hash-based evidence data. By verifying the authenticity of the trade background, assessing the compliance of asset status, and calculating dynamic operational risk indices, it will generate accurate supply chain risk information and a comprehensive risk profile. Based on this, it will automatically match or generate optimal target financial service information, such as dynamic credit limits, accounts receivable financing solutions, or inventory pledge financing strategies, thereby achieving intelligent, real-time, and risk-controllable supply chain financial services.

[0044] In one embodiment, multiple types of terminal devices are deployed throughout the cold chain supply chain, including temperature sensors and GPS locators inside refrigerated containers, temperature and humidity probes and video surveillance in supervised warehouses, and mobile scanning terminals for business personnel. These devices collect multimodal basic data such as temperature, location, humidity, video images, and scanned documents in real time. After being formatted and preliminarily verified by edge computing nodes, the data is uploaded to the cloud data platform through an encrypted channel.

[0045] The blockchain evidence storage module of the cloud platform performs hash calculations on each batch of data packets received to generate a unique data fingerprint. It then combines the timestamp, device ID, and operator digital signature to construct an evidence storage transaction. By calling the smart contract, the hash value is written into the consortium blockchain to form immutable hash evidence storage data. Meanwhile, the original data is encrypted and distributed and stored in the cloud.

[0046] The financial services management module calls a pre-set financial management model to parse the hash-stored data in real time. Through the model's built-in temperature risk analysis sub-unit, it assesses temperature control compliance; the timeliness monitoring sub-unit calculates logistics delay risk; and the value fluctuation analysis sub-unit monitors market price changes. It then generates a risk report containing risk level and early warning information, and automatically matches financial service strategies accordingly. For example, it generates accounts receivable financing solutions for low-risk businesses, triggers dynamic reduction instructions for pledge ratios for businesses with abnormal temperature control, and automatically determines prepayment credit limits for high-quality customers. Finally, it forms target financial service information that includes financing amount, interest rate, term, and repayment method.

[0047] This embodiment discloses a multi-terminal collaborative intelligent management system and method for cold chain supply chains. The method includes collecting and uploading various types of multimodal basic data in the cold chain supply chain through various terminal devices; performing blockchain hash storage on each of the multimodal basic data to generate hash storage data corresponding to each of the multimodal basic data; determining the risk information of the cold chain supply chain through a preset financial management model and each of the hash storage data, and generating target financial service information for the cold chain supply chain based on the risk information. Through the above method, this application collects multimodal basic data through various terminal devices and performs blockchain hash storage on this data to form an immutable and trustworthy data chain. By analyzing the hash storage data through a preset financial management model, the fulfillment risk, asset value, and cash flow health of the cold chain supply chain can be dynamically assessed, and suitable financial service solutions can be automatically generated, reducing the financial risk coefficient of the cold chain supply chain.

[0048] based on Figure 2 In the illustrated embodiment, step S20 includes: The multimodal basic data is divided into IoT sensing data, business document data, and image voucher data; The multimodal basic data are preprocessed, and hash calculations are performed on the preprocessed multimodal basic data to generate hash fingerprints; The hash evidence data is generated based on the hash fingerprint, the IoT sensing data, the business document data, and the image certificate data.

[0049] Specifically, the collected multimodal basic data is automatically classified into three categories. Among them, IoT sensing data includes time-series data collected by temperature and humidity sensors, GPS positioning devices and warehouse location beacons at preset event intervals; business document data covers JSON structured messages of import contracts, customs declarations and warehouse entry orders; and image certificate data includes veterinary officer certificates, photos of container seals and inspection records.

[0050] Differentiated preprocessing is performed on three types of data: IoT sensing data is aggregated into time-slice data at the edge gateway and signed with the device's private key; sensitive fields of business document data are anonymized and sorted according to customs declaration logic; and image certificate data is base64 encoded, compressed in resolution, and embedded with digital watermarks. The SM3 national cryptographic algorithm is used to perform irreversible hash calculations on each type of preprocessed data to generate three independent hash fingerprints. These three types of hash fingerprints are then concatenated with the original multimodal basic data in the format of "hash fingerprint + IoT data + document data + image data" to form the final data string to be stored. The SM3 hash operation is then performed again to generate a unique top-level hash fingerprint.

[0051] The top-level hash fingerprint is bound to the timestamp, business order number, and device fingerprint, and uploaded to the consortium blockchain as hash evidence data. At the same time, the original data is encrypted and stored in the InterPlanetary File System, and an inverted index of on-chain hash and off-chain storage address is established. This enables the classification and preprocessing of multimodal data, hierarchical hash calculation, and trusted anchoring evidence storage, providing a precise data integrity verification foundation for subsequent financial risk control models.

[0052] In a specific embodiment, the multimodal basic data are preprocessed, and hash calculations are performed on the preprocessed multimodal basic data to generate hash fingerprints, including: Add timestamp information and private key signature information to the IoT sensing data at preset time intervals to generate the first-level leaf node hash value; The business document data is standardized according to the Extensible Markup Language (XML) format to generate a second-level intermediate node hash value; The image credential data is base64 encoded and compressed to generate a third-level leaf node hash value. The hash fingerprint is generated using the SM3 hash algorithm based on the hash values ​​of the first-level leaf nodes, the second-level intermediate nodes, and the third-level leaf nodes.

[0053] Specifically, hierarchical hash calculations are performed on three types of multimodal basic data. First, for IoT sensing data, the edge gateway aggregates temperature and humidity, GPS coordinates and vibration sensing values ​​at preset 15-minute intervals, and actively adds millisecond-accurate timestamp information and IoT device-specific private key signatures to ensure the spatiotemporal authenticity of the evidence.

[0054] The SM3 algorithm is used to hash the data packet to generate the first-level leaf node hash value. For business document data, it is imported into an Extensible Markup Language (XML) template for standardized conversion. Key fields (such as HS codes, commodity quantities, and amounts) in contracts, customs declarations, and warehouse entry documents are anonymized and sorted to ensure data structure consistency before generating the second-level intermediate node hash value. For image certificate data, including veterinarian certificates, box seal photos, and inspection records, base64 encoding is performed to convert binary images into text strings. Simultaneously, resolution compression to 1080P is performed to reduce storage load. After processing, the third-level leaf node hash value is generated.

[0055] The three types of hash values ​​are concatenated in the string format of "first-level leaf node hash value + second-level intermediate node hash value + third-level leaf node hash value", and the SM3 hash algorithm is called again to perform the final operation to generate a unique top-level hash fingerprint. This fingerprint serves as the overall identity identifier of multimodal data, possessing collision resistance and irreversibility, providing a trusted anchor for subsequent blockchain on-chain evidence storage, and realizing end-to-end cryptographic protection from data collection to hash generation.

[0056] In one embodiment, IoT sensing data (such as temperature and location) is collected at preset time intervals (such as every minute), and each data point is given a precise timestamp and a digital signature generated using the organization's private key. The hash value of the composite data is then calculated as the hash value of the first-level leaf node to ensure the timeliness and trustworthiness of the data source.

[0057] Various business documents (such as contracts and bills of lading) are converted into a unified Extensible Markup Language (XML) format for structuring and standardization. Hash values ​​are calculated for these documents to generate second-level intermediate node hash values, enabling standardized integration and efficient verification of business data. Simultaneously, image credential data (such as cargo photos and inspection reports) undergoes base64 encoding and lossy resolution compression to reduce data size while maintaining visual recognizability. The hash values ​​of these images are then calculated as third-level leaf node hash values, supporting lightweight storage of large files.

[0058] The SM3 hash algorithm is used to combine the hash values ​​of the above three levels in an orderly manner and perform a second hash calculation to generate a unique and irreversible final hash fingerprint. This fingerprint comprehensively represents the overall data status of IoT sensing, business documents and image vouchers, and is used to realize cross-modal data integrity verification and tamper-proof evidence storage.

[0059] In a specific embodiment, the hash fingerprint is generated using the SM3 hash algorithm based on the hash values ​​of the first-level leaf nodes, the second-level intermediate nodes, and the third-level leaf nodes, including: Get the hash index value; The SM3 hash algorithm is used to calculate a mixed business hash value between the hash value of the first-level leaf node and the hash value of the second-level intermediate node. The hash fingerprint is generated based on the hash index value, the mixed business hash value, and the third-level leaf node hash value.

[0060] Specifically, the business platform generates a hash index value based on the combination of cold chain container number, business order number, and timestamp. This index value serves as the anchor identifier of the root node of the Merkle tree and is used for global retrieval and association. The first-level leaf node hash value (IoT sensing data hash) and the second-level intermediate node hash value (standardized business document hash) are concatenated using the SM3 hash algorithm and then recalculated to generate a hybrid business hash value. This achieves deep binding and cross-validation between logistics sensing data and trade documents.

[0061] The hash index value, the mixed business hash value, and the third-level leaf node hash value (image certificate hash) are concatenated in sequence to form the string to be stored. The SM3 algorithm is then called to perform the final hash operation, generating a unique top-level hash fingerprint. The top-level hash fingerprint simultaneously carries three layers of information: container identity, business flow, and cargo status. It has strong anti-collision and traceability. After the on-chain storage is completed, financial institutions can quickly verify the authenticity and consistency of the entire chain of data through a single hash value, providing a reliable data integrity proof for the supply chain finance risk control model.

[0062] In one embodiment, a unique location identifier is generated for the cold chain business data of the current batch or unit, i.e., a hash index value is obtained. This value is usually obtained by truncating a fixed length from the business primary key (such as "contract number + batch number") after a basic hash operation, and is used for fast location and retrieval in the hash fingerprint database.

[0063] Based on the national cryptographic SM3 hash algorithm, the hash values ​​of the first-level leaf nodes (representing basic business metadata, such as product information hash) and the hash values ​​of the second-level intermediate nodes (representing aggregated information, such as the Merkle root hash of all temperature control records in this batch) are input respectively. The two are combined into a single input string according to preset rules (such as concatenation or XOR), and the SM3 algorithm is called to perform the operation, outputting an irreversible hybrid business hash value. This value comprehensively represents the association status between business attributes and process data.

[0064] The aforementioned hash index value, mixed business hash value, and third-level leaf node hash value (usually representing the latest or most critical auxiliary data hash, such as the current customs declaration image hash) are serialized according to a predetermined structure (such as JSON format, including field names and values). This serialized string is then input into the SM3 hash algorithm for final calculation. The final output hash value is the unique, tamper-proof hash fingerprint of this batch of business data. This fingerprint, along with the index values ​​of its components, will be stored and can be used for subsequent integrity verification and rapid comparison.

[0065] In a specific embodiment, step S30 includes: The hash-based evidence storage data is used as the input feature vector of the preset financial management model, wherein the preset financial management model includes a pre-loan access layer, a mid-loan monitoring layer, a post-loan clearing layer, and a risk circuit breaker layer. The operational risk index is calculated using the hash fingerprint of the perceived data; the trade authenticity score is verified using the hash fingerprint of the business documents; and the compliance level is assessed using the hash fingerprint of the image voucher. A dynamic risk profile is generated based on the operational risk index, the trade authenticity score, and the compliance level. The risk score level is calculated based on the dynamic risk profile, and the target financial service information is generated based on the risk score level.

[0066] Specifically, the hash-based evidence data stored on the blockchain is parsed and transformed into structured feature vectors, which serve as the unified input for a pre-defined financial management model. This model adopts a layered architecture, including a pre-loan access layer, a mid-loan monitoring layer, a post-loan clearing layer, and a risk circuit breaker layer throughout the entire process. The model first performs time-series analysis and abnormal pattern recognition on the hash fingerprints of the perceived data, and quantitatively calculates the operational risk index by combining indicators such as the frequency of temperature deviation and GPS trajectory offset. At the same time, it performs blockchain cross-validation and semantic consistency checks on the hash fingerprints of business documents. By comparing the amount, quantity, and HS code logical correlation between contracts, customs declarations, and settlement documents, it generates a trade authenticity score. It also performs image metadata verification and regulatory element matching on the hash fingerprints of image vouchers, and assesses the compliance level based on the validity of the veterinary officer's signature and the integrity of the inspection mark.

[0067] The three types of risk parameters mentioned above are input into the dynamic risk profiling engine, which integrates the company's historical performance data, industry prosperity index, and digital valuation of cold chain assets to generate a multi-dimensional risk profile that includes real-time risk rating, early warning threshold, and trend prediction. Based on this profile, a risk scoring card model is used to calculate a standardized risk score level (e.g., AAA to D). When the score is higher than the financing threshold, the automatic credit granting unit triggers the execution of a smart contract, outputting target financial service information including prepayment financing amount, inventory pledge ratio, accounts receivable period, and risk premium rate, thus achieving full automation from data verification and risk quantification to financial decision-making.

[0068] In one embodiment, the multimodal data collected in the cold chain supply chain and stored through blockchain hash notarization—including sensing data from IoT devices (such as temperature, humidity, and location), business document data (such as contracts, customs declarations, and warehouse entry slips), and image certificate data (such as cargo photos and quality inspection reports)—are each extracted to extract their corresponding hash notarization fingerprints, which are then structured and input as feature vectors into a preset financial management model.

[0069] The financial management model adopts a layered architecture: Pre-loan access layer: mainly verifies the authenticity of the trade background corresponding to the hash fingerprint of business documents, and outputs a trade authenticity score by combining historical data and third-party information; Loan monitoring layer: Real-time access to perceived data hash fingerprints to monitor the operational compliance and stability of transportation, warehousing and other links, and calculate operational risk index; Post-loan clearing layer and risk circuit breaker layer: Based on evidence data such as image certificates, assess the performance and compliance status, determine the compliance level, and trigger the circuit breaker mechanism when the risk indicators exceed the threshold, suspending or adjusting financial services.

[0070] The three outputs—operational risk index, trade authenticity score, and compliance level—are integrated and analyzed to construct a dynamic risk profile reflecting the real-time status of an enterprise or transaction. This profile is further used for weighted scoring and pattern recognition to calculate a risk score level (e.g., low risk, medium risk, high risk).

[0071] The system automatically matches or generates corresponding target financial service information based on the risk score level, for example: Offer preferential interest rates, increase credit lines, or extend payment terms to low-risk entities; For medium-risk entities, partial guarantees or dynamic monitoring will be used for loan disbursement. For high-risk entities, the system will warn of risks, require supplementary collateral, or reject financing applications, and will suspend ongoing financial transactions in real time through the risk circuit breaker mechanism.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cold chain supply chain intelligent management system based on multi-terminal collaboration, characterized in that, include: A cloud-based data platform is used for centralized storage and management of all business data; The basic data management module is used to maintain basic data related to the cold chain supply chain; The intelligent procurement management module, connected to the basic data management module, is used to manage the contracts and agreements of the cold chain supply chain; The intelligent warehousing and transportation module is connected to the intelligent procurement management module and is used to manage the customs declaration and inspection information, temperature monitoring information, location tracking information and abnormal alarm information of the cold chain supply chain. The supply chain finance management module is connected to the cloud data platform, the basic data management module, the intelligent procurement management module, and the intelligent warehousing and transportation module, and is used to provide financial services for the cold chain supply chain.

2. The intelligent cold chain supply chain management system based on multi-terminal collaboration according to claim 1, characterized in that, The supply chain finance management module includes: An accounts receivable financing unit is used to determine accounts receivable financing information based on the contracts and agreements of the cold chain supply chain. The inventory pledge financing unit is used to determine inventory pledge financing based on the customs declaration and inspection information, temperature monitoring information, location tracking information and abnormal alarm information of the cold chain supply chain; The prepayment financing unit is used to determine prepayment financing services based on purchase orders and supplier credit data.

3. The intelligent cold chain supply chain management system based on multi-terminal collaboration according to claim 1, characterized in that, The supply chain finance management module also includes: The risk assessment unit is used to predict financing risk information based on real-time temperature data, warehousing condition data, and transportation timeliness data of the cold chain supply chain. The automatic credit granting unit is used to generate credit limit information based on historical transaction data, credit rating data, and cold chain asset data. The smart contract execution unit is used to trigger financing, loan disbursement, repayment, and risk management operations based on blockchain technology when preset conditions are met. The blockchain evidence storage module is used to store the business data on the blockchain and generate blockchain evidence data.

4. The intelligent cold chain supply chain management system based on multi-terminal collaboration according to claim 3, characterized in that, The risk assessment unit includes: The temperature risk analysis subunit is used to analyze the temperature changes in the cold chain supply chain and generate a temperature compliance report. The timeliness risk analysis subunit is used to monitor the timeliness performance of each link in the cold chain supply chain and determine delay risk information. The value fluctuation analysis subunit is used to monitor market price fluctuations in the cold chain supply chain and determine risk information regarding changes in the value of pledged goods.

5. The intelligent cold chain supply chain management system based on multi-terminal collaboration according to claim 1, characterized in that, The intelligent warehousing and transportation module also includes: The Internet of Things (IoT) monitoring submodule is connected to temperature sensors, humidity sensors, and GPS positioning devices to collect environmental data from the cold chain supply chain. An anomaly warning submodule, connected to the IoT monitoring submodule, is used to send warning information to the cloud data platform when the environmental data exceeds a preset threshold. The quality traceability submodule is used to record the entire historical temperature information of the cold chain supply chain.

6. A method for intelligent management of cold chain supply chain based on multi-terminal collaboration, characterized in that, include: Collect and upload various types of multimodal basic data in the cold chain supply chain through various terminal devices; The multimodal basic data is stored using blockchain hash notarization to generate hash notarization data corresponding to each of the multimodal basic data. By using a pre-set financial management model and the hash-based evidence storage data, the risk information of the cold chain supply chain is determined, and the target financial service information of the cold chain supply chain is generated based on the risk information.

7. The intelligent management method for cold chain supply chain based on multi-terminal collaboration according to claim 6, characterized in that, The step of performing blockchain hash storage on each of the multimodal basic data to generate hash storage data corresponding to each of the multimodal basic data includes: The multimodal basic data is divided into IoT sensing data, business document data, and image voucher data; The multimodal basic data are preprocessed, and hash calculations are performed on the preprocessed multimodal basic data to generate hash fingerprints; The hash evidence data is generated based on the hash fingerprint, the IoT sensing data, the business document data, and the image certificate data.

8. The intelligent management method for cold chain supply chain based on multi-terminal collaboration according to claim 1, characterized in that, The step of preprocessing each of the multimodal basic data and performing hash calculations on the preprocessed multimodal basic data to generate hash fingerprints includes: Add timestamp information and private key signature information to the IoT sensing data at preset time intervals to generate the first-level leaf node hash value; The business document data is standardized according to the Extensible Markup Language (XML) format to generate a second-level intermediate node hash value; The image credential data is base64 encoded and compressed to generate a third-level leaf node hash value. The hash fingerprint is generated using the SM3 hash algorithm based on the hash values ​​of the first-level leaf nodes, the second-level intermediate nodes, and the third-level leaf nodes.

9. The intelligent management method for cold chain supply chain based on multi-terminal collaboration according to claim 1, characterized in that, The step of generating the hash fingerprint using the SM3 hash algorithm based on the hash values ​​of the first-level leaf nodes, the second-level intermediate nodes, and the third-level leaf nodes includes: Obtain the hash index value; calculate the mixed business hash value between the hash value of the first-level leaf node and the hash value of the second-level intermediate node using the SM3 hash algorithm; generate the hash fingerprint based on the hash index value, the mixed business hash value, and the hash value of the third-level leaf node.

10. The intelligent management method for cold chain supply chain based on multi-terminal collaboration according to claim 6, characterized in that, The hash-based evidence storage data includes perceptual data hash fingerprints, business document hash fingerprints, and image certificate hash fingerprints. The process involves determining the risk information of the cold chain supply chain using a pre-set financial management model and the aforementioned hash-based evidence storage data, and generating target financial service information for the cold chain supply chain based on the risk information, including: The hash-based evidence storage data is used as the input feature vector of the preset financial management model, wherein the preset financial management model includes a pre-loan access layer, a mid-loan monitoring layer, a post-loan clearing layer, and a risk circuit breaker layer. The operational risk index is calculated using the hash fingerprint of the perceived data; the trade authenticity score is verified using the hash fingerprint of the business documents; and the compliance level is assessed using the hash fingerprint of the image voucher. A dynamic risk profile is generated based on the operational risk index, the trade authenticity score, and the compliance level. The risk score level is calculated based on the dynamic risk profile, and the target financial service information is generated based on the risk score level.

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