Commodity trade business management system based on block chain

Abstract

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of commodity trade management, in particular to a commodity trade business management system based on a block chain. BACKGROUND

[0002] In the current commodity trade business development process, the trade chain involves multiple links such as commodity production, circulation, and transaction, and the participating subjects include production enterprises, trade companies, logistics service providers, etc. The information generated at each link is scattered in the internal systems of different subjects, and it is difficult to achieve efficient sharing. Under the traditional commodity trade business management mode, commodity basic information is recorded by each participant, information updating is delayed, and there is a lack of unified verification mechanism, resulting in frequent information asymmetry between trade participants. For example, it is difficult for a purchasing enterprise to quickly verify the authenticity of the basic information such as commodity specifications and origin provided by a supplier, and it is also difficult to keep abreast of the logistics node dynamics of the commodity during transportation, increasing the uncertainty of trade decision-making. The management of trade enterprise qualification information also has shortcomings. Under the traditional management mode, the audit of enterprise qualifications relies on the submission of paper materials or non-encrypted online documents, and problems such as failure to detect expired qualifications and difficulty in identifying false qualifications may occur during the audit process. Moreover, changes in qualification information cannot be timely synchronized to all trade-related parties, which may result in the interruption of subsequent trade links due to non-compliance of qualifications. In terms of trade risk control, existing management systems often rely only on historical order fulfillment data for risk assessment, without combining real-time logistics status data and dynamically updated trade compliance rules. The risk analysis dimension is single, and it is difficult to accurately capture real-time risks in the trade chain. When risks occur, the process of developing risk control strategies lacks overall consideration of the trade process, and the generation of candidate strategies is based on fixed rules without calculating the execution cost when the trade enterprise qualification status changes or assessing the degree of interference of the strategy on the current trade process, resulting in the selected risk control strategy may cause problems such as trade link stagnation and cost increase. In addition, the storage of trade risk control audit logs often uses centralized databases, which have the risk of being tampered with or lost. Once a trade dispute occurs, it is difficult to provide reliable traceability evidence, which is not conducive to the quick resolution of disputes, and overall restricts the efficient and smooth development of commodity trade business. SUMMARY

[0003] The present application aims to provide a commodity trade business management system based on a block chain to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides a commodity trade business management system based on a block chain, which comprises: a blockchain data collection module, configured to acquire commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronize the commodity basic information, the trade enterprise qualification information and the logistics node information to a blockchain distributed ledger; a trade risk quantification analysis module, configured to generate a real-time risk coefficient set of a commodity trade link based on real-time data recorded in the blockchain distributed ledger, in combination with a pre-stored trade compliance rule library, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module, configured to generate a plurality of candidate risk control strategy schemes based on the trade compliance rule library when there is a risk coefficient exceeding a preset threshold in the real-time risk coefficient set, and calculate a collaborative execution cost factor when a trade enterprise qualification state is changed in combination with the logistics node information to obtain a set of interference influence values of each candidate risk control strategy scheme on a trade process; a risk control strategy execution feedback module, configured to update a blockchain smart contract execution instruction according to a screened best risk control strategy scheme, and generate a trade risk control audit log synchronized to the blockchain distributed ledger.

[0005] Preferably, the commodity basic information includes a commodity production traceability code, a quality detection certificate hash value and a customs classification code. The logistics node information includes warehouse temperature and humidity records, transportation tool positioning trajectories and customs declaration timestamp sequences. The real-time risk coefficient set includes a qualification compliance risk value, a logistics timeliness risk value and an order performance deviation risk value.

[0006] Preferably, when the trade risk quantification analysis module generates the qualification compliance risk value, the following operations are performed: The validity period identifier of the trade enterprise qualification information is matched and verified with qualification requirement clauses of the trade compliance rule library; The deviation length of the customs declaration timestamp sequence in the current logistics state data from a scheduled declaration plan is obtained; The weighted sum of the matching verification result and the deviation length is taken as the qualification compliance risk value.

[0007] Preferably, the risk control strategy collaborative analysis module includes: a risk control strategy scheme generation unit, configured to generate a plurality of candidate risk control strategy schemes including logistics path adjustment, enterprise qualification re-inspection and order clause revision based on clause constraint conditions of the trade compliance rule library; a collaborative cost calculation unit, configured to calculate a collaborative execution cost factor when a trade enterprise qualification state is changed, including a qualification verification delay length, a number of associated enterprise re-authentications and a number of blockchain smart contract modifications; An interference impact analysis unit is configured to couple the collaborative execution cost factor with the implementation complexity of each candidate risk control strategy scheme, and output the set of interference impact values.

[0008] Preferably, the risk control strategy collaborative analysis module further comprises a risk strategy index library configured to store historical risk control strategy schemes and their corresponding logistics feature vectors, qualification state feature vectors, and order risk feature vectors. The risk strategy index library is configured to perform the following operations: A three-dimensional strategy mapping space is constructed, with the first dimension being a logistics path anomaly coefficient, the second dimension being an enterprise qualification fluctuation coefficient, and the third dimension being an order fulfillment deviation coefficient. The logistics feature vectors, qualification state feature vectors, and order risk feature vectors of historical risk control strategy schemes are mapped to the three-dimensional strategy mapping space to form strategy index nodes.

[0009] Preferably, when the risk control strategy scheme generation unit calls the risk strategy index library, it performs the following operations: The logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link are obtained. The strategy index node that matches the logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link is located in the three-dimensional strategy mapping space. The historical risk control strategy scheme associated with the strategy index node is extracted as a candidate scheme generation reference.

[0010] Preferably, the system further comprises a trade data fluctuation monitoring module configured to perform the following operations: The commodity price sequence, logistics time efficiency sequence, and order fulfillment rate sequence in the blockchain distributed ledger are continuously collected. The data stability measure value of each sequence within a preset time window is calculated. When the data stability measure value exceeds a preset fluctuation threshold, a real-time risk coefficient update instruction of the trade risk quantification analysis module is triggered.

[0011] Preferably, when the trade data fluctuation monitoring module calculates the data stability measure value, it performs the following operations: The number of consecutive fluctuation direction changes and the amplitude difference between adjacent extreme points of the commodity price sequence are identified. The abnormal event distribution density of the logistics time efficiency sequence that exceeds the promised time efficiency is counted. The number of consecutive fluctuation direction changes, the amplitude difference between adjacent extreme points of the commodity price sequence, and the abnormal event distribution density of the logistics time efficiency sequence that exceeds the promised time efficiency are normalized and weighted.

[0012] Preferably, the system further comprises a risk control strategy knowledge base for storing the mapping relationship between historical trade scene features and optimal risk control strategies. The risk control strategy knowledge base performs the following operations: A multi-dimensional feature vector group is constructed, including trade region type, commodity risk level, enterprise credit score, and logistics complexity coefficient. The association between the multi-dimensional feature vector group and the optimal risk control strategy in the historical scene and the implementation accuracy index are recorded. When a new trade scene is added, similar historical scenes of the multi-dimensional feature vector group are matched and associated risk control strategies are recommended.

[0013] Preferably, the risk control strategy execution feedback module comprises: An intelligent contract updating unit for converting the optimal risk control strategy scheme into a blockchain intelligent contract execution parameter; An audit log construction unit for encapsulating the real-time risk coefficient set, the screening process data of the candidate risk control strategy scheme, and the strategy execution timestamp to generate a structured audit log; The audit log construction unit synchronously writes the structured audit log into the tamper-proof storage layer of the blockchain distributed ledger.

[0014] Compared with the prior art, the present application has the following advantages: The blockchain-based commodity trade business management system acquires commodity basic information, trade enterprise qualification information, and logistics node information in real time through a blockchain data acquisition module via a preset data interface, and synchronizes these information to a blockchain distributed ledger. The characteristics of the blockchain distributed ledger make all collected information once recorded cannot be tampered with at will, and trade participants can access the information in the ledger through legal access, breaking the information barriers caused by dispersed storage in traditional management mode, allowing all participants to obtain real and consistent trade-related data in a timely manner, reducing trade misunderstandings and disputes caused by information asymmetry. The trade risk quantification analysis module generates a real-time risk coefficient set based on real-time data in the blockchain distributed ledger, combined with a pre-stored trade compliance rule library, historical order fulfillment data, and current logistics state data. Compared with the traditional risk assessment method relying on a single data source, this module integrates multi-dimensional dynamic data, which can more comprehensively and timely reflect the risk status in the commodity trade link, helping trade participants to discover potential risks earlier and avoid losses caused by risk identification lag. When a risk factor exceeds a preset threshold in the real-time risk factor set, the risk control strategy collaborative analysis module generates multiple candidate risk control strategy solutions based on the trade compliance rule base. It also combines logistics node information to calculate the collaborative execution cost factor when the trading enterprise's qualification status changes, thereby obtaining a set of interference impact values ​​for each candidate solution on the trade process. This approach overcomes the problem of neglecting execution costs and process interference in the traditional risk control strategy formulation process. It can select solutions from multiple candidate solutions that are more suitable for the current trade scenario and have less interference with the trade process. While effectively managing risks, it reduces the impact of improper risk control measures on trade efficiency and ensures the smooth progress of the trade process. The risk control strategy execution feedback module updates the blockchain smart contract execution instructions based on the optimal risk control strategy solution selected and synchronizes the trade risk control audit log to the blockchain distributed ledger. The automated execution of smart contracts reduces errors and delays caused by manual operations, ensuring the rapid implementation of risk control strategies. The audit log synchronized to the blockchain, leveraging the blockchain's immutability and traceability, provides a complete and reliable record of the trade risk control process. When verifying trade risk control actions or resolving trade disputes, the original log can be retrieved directly from the ledger without worrying about log tampering or loss. This improves the transparency and credibility of the trade risk control process and helps commodity trade operations operate efficiently in a standardized environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a timing diagram of the blockchain-based commodity trade business management system described in the present invention; Figure 2 A flow chart consisting of basic product information, logistics node information, and real-time risk coefficients; Figure 3 Flowchart for generating qualification compliance risk value; Figure 4 Flowchart for risk strategy indexing library operations. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a commodity trade business management system based on blockchain, the system comprising: The system acquires commodity basic information, trade enterprise qualification information and logistics node information in real time through the preset data interface of the blockchain data acquisition module, and synchronizes these information to the blockchain distributed ledger. The trade risk quantification analysis module generates a set of real-time risk coefficients of the commodity trade link based on the real-time data recorded in the blockchain distributed ledger, in combination with the pre-stored trade compliance rule library, historical order fulfillment data and current logistics state data. When there is a risk coefficient exceeding the preset threshold in the set of real-time risk coefficients, the risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes based on the trade compliance rule library, and calculates the collaborative execution cost factor when the qualification state of the trade enterprise is changed in combination with the logistics node information, to obtain a set of interference influence values of each candidate risk control strategy scheme on the trade process. The risk control strategy execution feedback module updates the blockchain smart contract execution instruction according to the best risk control strategy scheme screened, and generates a trade risk control audit log synchronized to the blockchain distributed ledger. The system adopts a distributed architecture, and each module interacts with data through the blockchain network to ensure data consistency and non-tamperability.

[0018] Embodiment 1: refer to Figure 2 The commodity production traceability code in the commodity basic information is generated by the manufacturer at the end of the production line. After each smart phone is assembled, its unique serial number is bound with the mainboard code, processor number and screen code, and a globally unique traceability code is synthesized through a standardized algorithm. The code adopts the ISO / IEC15459 standard format, including fields such as manufacturer code, production date, batch number and single product serial number. The code is immediately written into the read-only memory of the device after generation, and is synchronized to the preset data interface of the blockchain data acquisition module. The generation of the quality detection certificate hash value begins with the comprehensive detection of the finished mobile phone by the third-party detection agency. The detection items include battery safety, radio frequency performance, electromagnetic compatibility and a total of 127 indicators. After the detection is completed, the agency issues a digital certificate, which includes a detection result abstract, an agency digital signature and a timestamp. The system uses the SHA-256 algorithm to hash the certificate content, and the obtained 256-bit hash value is bound and stored with the traceability code of the smart phone. The customs classification code is determined according to the Harmonized Commodity Description and Coding System commonly used in international trade. For smart phones, the code is usually classified as 8517.12.00 category, indicating "portable wireless telephone". The code is input into the system by the customs declaration personnel of the trade enterprise when the commodity is warehoused, and is transmitted to the blockchain network through the data interface.

[0019] The warehouse temperature and humidity records in the logistics node information are collected by Internet of Things sensors deployed in each area of the warehouse. The sensors record environmental data every five minutes, with a temperature measurement range of -20°C to 50°C and an accuracy of ±0.5°C; a humidity measurement range of 0% to 100% RH and an accuracy of ±3% RH. When a batch of smartphones enters the warehouse link, the system automatically creates an environmental data log corresponding to the batch, and all sensor readings are labeled with geographic location and timestamp. The transportation tool positioning trajectory data is derived from GPS trackers installed on transport vehicles. The device collects position information every 30 seconds, with longitude and latitude coordinates using the WGS84 standard, and altitude data recorded simultaneously. During transportation, the system calculates the deviation of the vehicle's driving route from the predetermined path in real time, and generates an abnormal event record when the deviation exceeds the allowed range. The customs declaration timestamp sequence records all key time points of the goods in the customs clearance process. Including declaration submission time, customs acceptance time, inspection start time, inspection end time and release time. Each timestamp is in ISO8601 standard format, accurate to millisecond level, and associated with the corresponding customs declaration number.

[0020] The qualification compliance risk value in the real-time risk coefficient set is calculated based on the import and export business rights of the trade enterprise, the sales license of electronic products and the tax registration status. The system regularly obtains the enterprise qualification status from the data interface of government departments such as industry and commerce and taxation, compares the qualification validity period with the current time, and checks whether the enterprise has administrative penalty records or credit downgrade. The logistics time efficiency risk value is derived by analyzing the difference between the planned time and the actual completion time of each logistics node. The system establishes a multi-dimensional time efficiency evaluation model, considering factors such as transportation distance, transportation mode, number of transfers, etc., to calculate the delay probability and impact of each link. The order fulfillment deviation risk value is calculated by comparing the difference between the delivery quantity, delivery time agreed in the trade contract and the actual execution. The system continuously monitors the order status changes, including production progress, inventory quantity, shipment time and other key indicators, and uses a sliding window algorithm to calculate the fulfillment deviation trend.

[0021] In this example of a smartphone trade, when a batch of new model smartphones is shipped from a factory in China to a distribution center in Europe, the system records all the basic information and logistics information. The production traceability code helps to trace the production batch of each smartphone, the quality inspection hash value verifies that the product meets the EU CE certification standard, and the customs classification code ensures the accuracy of the customs clearance process. The warehouse temperature and humidity data confirms that the product has been in a suitable environment during transportation, the GPS trajectory shows that the transportation route meets the predetermined plan, and the customs time stamp sequence shows that the customs procedures are completed within the specified time limit. Based on these data, the system generates a real-time risk coefficient set that provides a comprehensive risk assessment basis for all parties involved in the trade. The qualification compliance risk value reflects the good status of enterprise qualification, the logistics timeliness risk value shows that the transportation progress is normal, and the order performance deviation risk value indicates that the order execution meets the expected value. All data is stored in a distributed manner through the blockchain network, ensuring the non-tamperability and traceability of the information, and providing reliable data support for international trade.

[0022] Example 2: Referring to Figure 3 , the qualification compliance risk value generation mechanism and the operation mode of the risk control strategy collaborative analysis module are fully presented through the following specific example. This example revolves around a batch of frozen aquatic products exported from a certain country in Southeast Asia to China. When a batch of frozen shrimp products enters the Chinese port by sea, the trade risk quantification analysis module of the system begins to execute the generation process of the qualification compliance risk value. First, the system extracts the qualification information provided by the import enterprise, including the import and export cargo receiver registration certificate, the customs high-level certification enterprise qualification, and the import plant and animal quarantine permit. These certificates are accompanied by digital signatures and timestamps, and the system verifies their authenticity by verifying the digital signatures. Subsequently, the system accesses the pre-stored trade compliance rule library, which is continuously synchronized with the latest regulations and provisions of the General Administration of Customs of China, the State Administration of Quality Supervision, and other departments. The system automatically matches and verifies the effective period of the enterprise's qualification information with the requirements in the rule library. For example, the import plant and animal quarantine permit must be within the valid period, and the permitted product category must be completely consistent with the actual imported frozen shrimp products. The matching and verification process is performed by the rule engine, which outputs structured verification results, including "valid period meets" and "permission range matches" status codes.

[0023] The system obtains a sequence of customs declaration timestamps in the current logistics state data. The batch of goods needs to complete the pre-declaration within 24 hours before arrival. The deviation between the actual declaration timestamp and the scheduled plan is accurately recorded. The system calculates the deviation duration of each link, such as the customs declaration submission time is delayed by 3 hours and 17 minutes compared to the plan, and the inspection and quarantine declaration is 1 hour and 45 minutes ahead of the scheduled time. These deviation data are weighted and integrated with the matching verification results. The weighting coefficients are configured according to the special nature of aquatic product trade, considering the sensitivity of fresh products to customs clearance time, the time deviation weight is appropriately increased. The final generated qualification compliance risk value is a comprehensive numerical value, reflecting the overall risk level of enterprise qualification compliance status and customs clearance timeliness.

[0024] In the case where the risk coefficient exceeds the preset threshold, the risk control strategy collaborative analysis module starts running. The risk control strategy scheme generation unit generates multiple candidate risk control strategy schemes based on the clause constraint conditions of the trade compliance rule library. For this batch of frozen shrimp products, the generated schemes include: adjusting the logistics path scheme, suggesting transferring the goods from the current port to a special supervision site with cold chain inspection facilities; enterprise qualification re-inspection scheme, requiring the importing enterprise to provide updated quarantine permits and health certificates; order clause revision scheme, suggesting that the buyer and seller negotiate to adjust the delivery time clause and quality acceptance standard.

[0025] The collaborative cost calculation unit starts to calculate the execution cost factors of each scheme. For the logistics path adjustment scheme, the system calculates the additional time cost required for transfer, the increase in transportation cost, and the redeployment cost of temperature monitoring equipment. For the enterprise qualification re-inspection scheme, the system estimates the average length of time required for re-issuing certificates, the number of associated enterprises (including foreign manufacturers and domestic distributors) that need to be re-certified, and the number of modifications required for the blockchain smart contract. These cost factors are calculated through a multi-dimensional quantitative model, and finally a numerical collaborative execution cost index is output.

[0026] The interference impact analysis unit receives these cost factor data and couples with the implementation complexity of each candidate risk control strategy scheme. Implementation complexity evaluation includes technical difficulty of scheme execution, required human resource allocation, system modification requirements, etc. Through the established multivariate analysis model, the system calculates the interference impact value of each scheme on the current trade process. For example, the logistics path adjustment scheme can reduce the customs risk, but its interference impact value is high because it needs to coordinate multiple logistics service providers to adjust the transportation plan; while the qualification re-inspection scheme is relatively simple to implement, but its time cost may affect the preservation period of the goods.

[0027] Finally, the system outputs a set of interference impact values for all candidate risk control policy schemes, providing a decision basis for subsequent scheme selection. The entire processing process is based entirely on real-time data stored on the blockchain, and all operation records are encrypted and stored and cannot be tampered with, ensuring the transparency and traceability of the risk assessment and policy generation process. Through this mechanism, the system can quickly generate multiple response schemes when risks are discovered and accurately assess the impact of each scheme on the trade process, providing effective technical support for risk management in complex international trade environments.

[0028] Embodiment 3: Refer to Figure 4 , the construction of the risk policy index library and the risk control policy generation mechanism are fully presented through the following specific examples. This example takes a bulk agricultural product trade from South America to Asia as the background, and details how the system uses historical data to construct a policy mapping space and generate effective risk control policy schemes. The construction of the risk policy index library begins with the systematic organization of historical risk control policy schemes. The system collects the implementation records of risk control policies in all agricultural product trade cases over the past three years, including logistics path adjustment schemes, enterprise qualification re-inspection schemes, and order clause revision schemes, among others. Each historical scheme is converted into a feature vector form for storage, where the logistics feature vector contains transportation distance, transit times, temperature control requirements, and other elements; the qualification status feature vector covers enterprise credit rating, number of certification certificates, and historical violation records; and the order risk feature vector includes payment conditions, delivery time limits, quality clauses, and other parameters.

[0029] The system constructs a three-dimensional policy mapping space using spatial geometric modeling methods. The calculation of the first dimension, the logistics path anomaly coefficient, is based on the comprehensive evaluation of the deviation and delay rate of the transportation path, which reflects the degree of deviation between actual logistics execution and the planned schedule. The calculation of the second dimension, the enterprise qualification fluctuation coefficient, considers factors such as the remaining proportion of the validity period of the qualification certificate, the annual review pass rate, and the frequency of violation records, representing the stability of the enterprise's qualification status. The calculation of the third dimension, the order fulfillment deviation coefficient, covers indicators such as delivery time accuracy, product quality compliance, and quantity difference rate, measuring the reliability of order execution. The values of each dimension are normalized to have a range of 0 to 1.

[0030] In the three-dimensional strategy mapping space, each historical risk control strategy solution is mapped to a specific coordinate point. The mapping process is achieved through dimension reduction conversion of feature vectors, projecting multi-dimensional feature data into a three-dimensional space. For example, in a historical case involving disruption of cold chain logistics, the logistics path anomaly coefficient is 0.87, the enterprise qualification fluctuation coefficient is 0.45, and the order fulfillment deviation coefficient is 0.92. These three values together determine the specific position of the case in the strategy mapping space. The distribution pattern of the strategy index nodes formed by all historical cases in the space, the areas with high node density represent common risk scenarios, while isolated nodes correspond to special or rare trade scenarios.

[0031] When the system processes a new agricultural product trade case, the risk control strategy solution generation unit calls the risk strategy index library for strategy matching. First, the real-time monitoring data of the current trade link is obtained, and the current logistics path anomaly coefficient, enterprise qualification fluctuation coefficient and order fulfillment deviation coefficient are calculated. The calculation of the logistics path anomaly coefficient considers the ratio of the actual driving distance to the planned distance of the current transportation route, the number of abnormal alarms of the temperature monitoring equipment and the deviation of the stay time during transit, etc. The evaluation of the enterprise qualification fluctuation coefficient is based on the remaining days of the validity period of the supplier's qualification certificate, the result of the latest annual inspection and the historical trade dispute records, etc. The calculation of the order fulfillment deviation coefficient refers to the current order payment timeliness rate, goods pre-inspection pass rate and predicted delivery time compliance degree, etc.

[0032] The system uses spatial distance calculation to locate the most matched strategy index node, and uses the following formula for similarity evaluation:

[0033] Wherein: represents the spatial distance between the current scenario and the historical strategy node, represents the current logistics path anomaly coefficient, represents the logistics path anomaly coefficient of the historical node, represents the current enterprise qualification fluctuation coefficient, represents the enterprise qualification fluctuation coefficient of the historical node, represents the current order fulfillment deviation coefficient, represents the order fulfillment deviation coefficient of the historical node. The system traverses all historical strategy nodes and selects the top K nodes with the smallest spatial distance as the matching result.

[0034] After extracting the historical risk control strategy solutions associated with these matching nodes, the system performs adaptive adjustment of the solutions. Considering the specific differences of different trade cases, the system optimizes and calibrates the parameters of the historical solutions. For example, according to the characteristics of the transportation conditions of the current case, the backup route selection in the logistics path solution is adjusted; according to the qualification status of the current supplier, the specific inspection items of the qualification re-inspection solution are modified; combined with the special terms of the current order, the default responsibility agreement in the order terms solution is revised.

[0035] The finally generated candidate risk control strategy solutions not only retain the core elements of historical successful experience, but also are customized for the particularity of the current case. All the solution generation processes are recorded on the blockchain, including the basis for selecting historical nodes, the calculation results of spatial distance, and the specific parameters of solution adjustment, ensuring the transparency and traceability of the entire decision-making process. Through this strategy generation mechanism based on historical data mining and spatial mapping, the system can effectively utilize past experience to deal with new trade risk scenarios, improving the pertinence and effectiveness of risk control strategies.

[0036] Embodiment 4: The trade data fluctuation monitoring module involved is fully presented through the following specific example. This example takes a batch of rare metal ore trade from Africa to Europe as the background, and details how the system continuously monitors the fluctuation of multiple data sequences and triggers the corresponding risk analysis mechanism. When the trade data fluctuation monitoring module starts running, it first establishes a connection with the distributed ledger of the blockchain. The system configures a special data listening service to scan the data updates related to the target trade in the ledger at a frequency of once per second. For this batch of cobalt ore trade, the system continuously collects three core data sequences: the commodity price sequence comes from the real-time quotation data of the London Metal Exchange, with the latest price obtained every 5 minutes through an API interface; the logistics time sequence is derived from the ship's voyage data recording system, including departure time, arrival time, transit time, etc.; the order fulfillment rate sequence is calculated based on the execution data of both parties, including delivery quantity accuracy, payment timeliness, etc.

[0037] The system sets a 30-day rolling time window as the monitoring period, and within this time window, all data sequences are stored and dynamically updated in chronological order. The commodity price sequence records the daily spot price changes of cobalt ore, including opening price, highest price, lowest price, and closing price, etc. The logistics time sequence records the time stamp of each link from the ore out of the warehouse to the unloading at the destination port, including customs clearance time, port operation time, sea voyage time, etc. The order fulfillment rate sequence calculates the daily fulfillment completion, including the difference between planned delivery volume and actual delivery volume, payment delay days, etc.

[0038] To assess data stability, the system performs separate computational analysis for each sequence. For commodity price sequences, the system identifies the number of consecutive changes in volatility direction. This process is achieved by analyzing the price change direction between adjacent time points, and a change in direction is recorded when the price transitions from an upward trend to a downward trend or vice versa. Simultaneously, the system calculates the amplitude difference between adjacent extreme points, which is the absolute value of the price difference between the price peak and the price trough. These calculations help identify the frequency and intensity characteristics of price volatility.

[0039] The analysis of logistics timeliness sequences focuses on the detection of abnormal events, with the system identifying logistics stages that exceed the promised time limits according to the timeliness standards specified in trade contracts. For example, if the contract specifies a 25-day sea transportation time, the system will flag all transportation records that exceed this time limit. The calculation of abnormal event distribution density is based on the number of delayed events occurring per unit of time, taking into account the severity and impact range of the delays. The monitoring of order fulfillment rate sequences uses a sliding window statistical method. The system calculates the moving average of the fulfillment rate on a daily basis, while tracking the variability of the fulfillment rate. When there is a sharp decline or abnormal fluctuation in the fulfillment rate, the system records these abnormal points and analyzes their duration and fluctuation amplitude. Representative data sequences collected within a certain monitoring period are shown in Table 1.

[0040] Table 1: Representative data sequence table collected within a certain monitoring period. Timestamp Commodity Price (USD / ton) Logistics Time (days) Order Fulfillment Rate (%) Price Movement Direction Logistics Anomaly Flag Fulfillment Anomaly Flag 2023-05-01 35,450 24 98.2 Up No No 2023-05-02 35,620 26 97.8 Up No No 2023-05-03 35,210 25 96.5 Down No Yes 2023-05-04 34,980 28 95.2 Down Yes Yes 2023-05-05 35,350 27 97.1 Up Yes No 2023-05-06 35,720 26 98.5 Up No No 2023-05-07 36,050 29 96.8 Up Yes Yes 2023-05-08 35,810 28 95.7 Down Yes Yes 2023-05-09 35,950 25 97.3 Up No No 2023-05-10 36,210 24 98.9 Up No No In the calculation of data stability metrics, the system normalizes the analysis results of the three sequences. The volatility indicators of each sequence are converted to relative values between 0 and 1, and then weighted and integrated according to pre-set weight coefficients. The allocation of weight coefficients takes into account the influence of different sequences on trade risk, with commodity price sequences generally having higher weights because price fluctuations directly affect the economic benefits of trade; logistics timeliness sequences have lower weights, and order fulfillment rate sequences have relatively low but non-negligible weights.

[0041] When the integrated stability metric value exceeds the pre-set volatility threshold, the system automatically triggers the update instruction of the trade risk quantification analysis module. The volatility threshold is a dynamic value obtained from statistical analysis of historical trade data, and different commodity categories and trade routes have corresponding threshold standards. For this batch of cobalt ore trade, the system sets the threshold level based on the volatility characteristic data of similar trades in the past 12 months.

[0042] Upon the update instruction trigger, the trade risk quantification analysis module immediately initiates the real-time risk coefficient recalculation process. The module accesses the latest blockchain ledger data, combines the latest price information, logistics status, and performance, and recalculates the qualification compliance risk value, logistics timeliness risk value, and order performance deviation risk value. All these calculation processes are carried out in the blockchain network, and all operation records are permanently saved and cannot be tampered with. During the entire monitoring process, the system maintains continuous observation and analysis of the data sequence. Whenever a new data point is generated, the system recalculates the stability metric value to ensure that it can timely capture the changing trend of data fluctuations. This continuous monitoring mechanism enables the system to discover and respond to early signs of trade risks, providing timely data support for risk management decisions. All monitoring results and trigger records are automatically executed through the blockchain smart contract, ensuring the transparency and reliability of the entire process.

[0043] Embodiment 5: The construction of the risk control strategy knowledge base and the feedback mechanism of the risk control strategy execution are fully presented through the following specific example. This example takes the international trade case of a batch of high-value electronic products from an East Asian production base to a European distribution center as the background. The construction of the risk control strategy knowledge base starts with the systematic induction and arrangement of the characteristics of historical trade scenarios. The system collects the scenario characteristic data of all electronic product international trade cases in the past five years, including trade region type, commodity risk level, enterprise credit score, and logistics complexity coefficient, and other key dimensions. The classification of trade region type is based on the economic development level, trade policy stability, and customs supervision characteristics of the exporting country and the importing country, and divides the global trade region into multiple type labels. The assessment of commodity risk level considers product value, vulnerability, technical sensitivity, and regulatory requirements, and adopts a multi-level classification system for labeling. The enterprise credit score integrates bank credit rating, business partner evaluation, and historical transaction performance records, and forms a standardized scoring model. The calculation of the logistics complexity coefficient integrates transportation distance, transit times, temperature control requirements, and special handling needs, and obtains a quantitative indicator through a weighted algorithm.

[0044] These multi-dimensional characteristic data are organized into structured feature vector groups, and each historical trade scenario is converted into a data object containing dozens of feature dimensions. The system records the optimal risk control strategy adopted in each historical scenario and its execution effect data, including strategy type, specific parameter settings, execution time node, and risk change after implementation. All these data are stored in the distributed storage layer of the blockchain network, ensuring the data's non-tamperability and traceability.

[0045] When a new electronic product trade case is initiated, the system starts the scenario matching process. First, it extracts the characteristic data of the current trade case, including the fact that the exporting country is a technology-rich region in East Asia, the importing country is a developed country in Western Europe, the commodity is a high-value smartphone, the enterprise credit score is at an excellent level, and the logistics complexity coefficient is relatively high. The system uses a feature similarity algorithm to match and calculate between the current feature vector group and the historical feature vector group, identifying historical trade scenarios with high similarity.

[0046] During the matching process, the system uses a multi-dimensional weighted similarity calculation method to assign different weight coefficients to different feature dimensions. The matching weight of the trade region type is higher because the trade policies and regulatory environments of different regions differ significantly. The weight of the commodity risk level is second, and the weights of the enterprise credit score and the logistics complexity coefficient are relatively balanced but cannot be ignored. The system selects several historical scenarios with the highest similarity and extracts the optimal risk control strategies and related parameters used in these scenarios.

[0047] Based on these historical optimal strategies, the system generates a recommended risk control strategy for the current trade case. The recommendation process is not simply a copy of the strategy, but an adaptive adjustment considering the particularity of the current case. For example, considering the climate characteristics of the current transportation season, the parameters of the logistics monitoring strategy are optimized; according to the latest changes in customs supervision policies, the intensity settings of the customs audit strategy are adjusted; based on the specific cooperation history of the current trade partner, the trigger conditions of the credit risk management strategy are improved.

[0048] In the risk control strategy execution phase, the smart contract update unit starts to work, which converts the determined optimal risk control strategy into executable parameters of the blockchain smart contract. The conversion process includes strategy instruction coding, execution condition setting, trigger mechanism configuration, and other steps. All parameter settings are subjected to multiple verifications to ensure compatibility with the blockchain network and execution reliability. The updated smart contract is deployed to the blockchain network, automatically monitoring the execution of the trade process and triggering the corresponding risk control measures when the preset conditions are met.

[0049] The audit log construction unit also works synchronously, which encapsulates the complete data records of the entire risk control decision-making process. This includes the calculation process data of the real-time risk coefficient set, the generation and screening records of the candidate risk control strategy, the timestamp sequence of the strategy decision, and the details of the finally adopted strategy parameters. These data are organized into a structured audit log format, encoded using international audit data standards to ensure machine readability and human interpretability of the log content.

[0050] The generated audit logs are written into the tamper-proof storage layer of the distributed ledger through blockchain transactions, and the writing process adopts block encryption and distributed storage technology to ensure the integrity and security of the log data. Each audit log block contains the hash value of the previous block, forming a continuous blockchain-like structure, and any tampering with historical logs will cause the hash value to mismatch and be detected by the system. The storage of audit logs adopts a multi-copy distributed architecture, saving complete copies on multiple nodes of the blockchain network to ensure high availability and invulnerability of data.

[0051] The entire risk control strategy forms a complete closed loop from knowledge base matching to execution feedback, and all operation records are permanently saved on the blockchain. The system realizes continuous optimization iteration of risk control strategies through this mechanism, and the processing results of each new case are fed back to the knowledge base, enriching historical data accumulation and providing stronger data support for subsequent decision-making. This blockchain-based technical architecture ensures the transparency and auditability of the risk control decision-making process, and all participants can verify the rationality of the decision and the effectiveness of the execution, thereby establishing a reliable trade risk management mechanism.

[0052] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0053] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A commodity trade business management system based on blockchain, characterized in that: The system comprises: The blockchain data collection module is used to obtain basic commodity information, trading enterprise qualification information and logistics node information in real time through a preset data interface, and synchronize the basic commodity information, trading enterprise qualification information and logistics node information to the blockchain distributed ledger; A trade risk quantification analysis module for generating a real-time risk coefficient set for the commodity trade chain based on real-time data recorded in the blockchain distributed ledger, combined with a pre-stored trade compliance rule library, historical order fulfillment data, and current logistics status data; A risk control strategy collaborative analysis module is configured to generate multiple candidate risk control strategy solutions based on the trade compliance rule base when a risk factor exceeding a preset threshold exists in the real-time risk factor set; calculate collaborative execution cost factors when the trading enterprise's qualification status changes in combination with the logistics node information, and obtain a set of interference impact values ​​of each candidate risk control strategy solution on the trade process; The risk control strategy execution feedback module is used to update the blockchain smart contract execution instructions based on the selected optimal risk control strategy solution, and generate trade risk control audit logs and synchronize them to the blockchain distributed ledger.

2. A commodity trade business management system based on blockchain according to claim 1, characterized in that: The basic commodity information includes the commodity production traceability code, quality inspection certificate hash value and tariff classification code; The logistics node information includes storage temperature and humidity records, transportation tool positioning trajectory and customs declaration timestamp sequence; The real-time risk factor set includes qualification compliance risk value, logistics timeliness risk value and order fulfillment deviation risk value.

3. A commodity trade business management system based on blockchain according to claim 2, characterized in that: When the trade risk quantitative analysis module generates the qualification compliance risk value, it performs the following operations: Extracting the validity period identifier of the trading enterprise qualification information and matching and verifying it with the qualification requirement clauses in the trade compliance rule base; Obtain the deviation time between the customs declaration timestamp sequence in the current logistics status data and the scheduled declaration plan; The weighted sum of the matching verification result and the deviation duration is used as the qualification compliance risk value.

4. A commodity trade business management system based on blockchain according to claim 1, characterized in that: The risk control strategy collaborative analysis module includes: a risk control strategy scheme generating unit, configured to generate, based on the terms and constraints of the trade compliance rule base, a plurality of candidate risk control strategy schemes including logistics route adjustment, enterprise qualification re-verification, and order terms revision; Collaboration cost calculation unit, used to calculate the collaborative execution cost factors when the trading enterprise's qualification status changes, including the qualification verification delay, the number of related enterprises re-certification and the number of blockchain smart contract modifications; The interference impact analysis unit is used to couple the collaborative execution cost factor with the implementation complexity of each candidate risk control strategy solution and output the interference impact value set.

5. A commodity trade business management system based on blockchain according to claim 4, characterized in that: The risk control strategy collaborative analysis module also includes a risk strategy index library for storing historical risk control strategy solutions and their corresponding logistics feature vectors, qualification status feature vectors, and order risk feature vectors; The risk strategy index library performs the following operations: Construct a three-dimensional strategy mapping space, where the first dimension is the logistics path anomaly coefficient, the second dimension is the enterprise qualification fluctuation coefficient, and the third dimension is the order fulfillment deviation coefficient; The logistics feature vector, qualification status feature vector and order risk feature vector of the historical risk control strategy plan are mapped to the three-dimensional strategy mapping space to form a strategy index node.

6. A commodity trade business management system based on blockchain according to claim 5, characterized in that: When the risk control strategy scheme generation unit calls the risk strategy index library, the following operations are performed: Obtain the logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link; Locating a strategy index node in the three-dimensional strategy mapping space that matches the logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link; The historical risk control strategy solutions associated with the strategy index node are extracted as a benchmark for generating candidate solutions.

7. The blockchain-based commodity trade business management system according to claim 1, characterized in that: The system further includes a trade data fluctuation monitoring module for performing the following operations: Continuously collect commodity price sequences, logistics time sequences, and order fulfillment rate sequences from the blockchain distributed ledger; Calculate the data stability metric of each series within the preset time window; When the data stability metric exceeds a preset fluctuation threshold, a real-time risk coefficient update instruction of the trade risk quantitative analysis module is triggered.

8. A commodity trade business management system based on blockchain according to claim 7, characterized in that: When the trade data fluctuation monitoring module calculates the data stability metric value, the following operations are performed: Identify the number of consecutive fluctuation direction changes in commodity price series and the amplitude difference between adjacent extreme points; Statistical distribution density of abnormal events exceeding the promised time in the logistics time sequence; The number of consecutive changes in the fluctuation direction of the commodity price series, the amplitude difference between adjacent extreme points, and the distribution density of abnormal events exceeding the promised time in the logistics time series are normalized and weighted.

9. The blockchain-based commodity trade business management system according to claim 1, characterized in that: The system also includes a risk control strategy knowledge base for storing the mapping relationship between historical trade scenario characteristics and optimal risk control strategies; The risk control strategy knowledge base performs the following operations: Construct a multidimensional feature vector group, including trade area type, commodity risk level, enterprise credit score and logistics complexity coefficient; Record the correlation between the multi-dimensional feature vector group and the optimal risk control strategy in the historical scenario and the implementation accuracy index; When a new trade scenario is added, similar historical scenarios of the multi-dimensional feature vector group are matched and associated risk control strategies are recommended.

10. A commodity trade business management system based on blockchain according to claim 1, characterized in that: The risk control strategy execution feedback module includes: A smart contract updating unit, configured to convert the optimal risk control strategy into blockchain smart contract execution parameters; An audit log construction unit, configured to encapsulate the real-time risk coefficient set, the screening process data of the candidate risk control strategy schemes, and the strategy execution timestamp, and generate a structured audit log; The audit log construction unit synchronously writes the structured audit log into the tamper-proof storage layer of the blockchain distributed ledger.

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