A blockchain-based commodity trade business management system

Abstract

The application 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 acquires 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 ledger; a trade risk quantification analysis module generates a real-time risk coefficient set of a trade link based on ledger real-time data, in combination with a trade compliance rule library, historical order fulfillment data and current logistics state data; when the risk coefficient exceeds a preset threshold, a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes, calculates a collaborative execution cost factor when the qualification state of an enterprise is changed in combination with the logistics node information, and obtains a set of interference influence values of each scheme on a trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to an optimal risk control strategy scheme, thereby assisting in 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.

[0003] 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 documents online, 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 incompatible 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 rapid resolution of disputes and restricts the efficient and smooth development of commodity trade business as a whole. SUMMARY

[0004] 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.

[0005] To achieve the above-mentioned purpose, the present application provides a commodity trade business management system based on a block chain, which comprises:

[0006] 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;

[0007] 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;

[0008] 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;

[0009] 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.

[0010] Preferably, the commodity basic information includes a commodity production traceability code, a quality detection certificate hash value and a customs classification code.

[0011] The logistics node information includes warehouse temperature and humidity records, transportation tool positioning trajectories and customs declaration timestamp sequences.

[0012] The real-time risk coefficient set includes a qualification compliance risk value, a logistics timeliness risk value and an order fulfillment deviation risk value.

[0013] Preferably, when the trade risk quantification analysis module generates the qualification compliance risk value, the following operations are performed:

[0014] The validity period identifier of the trade enterprise qualification information is extracted and matched and verified with qualification requirement clauses of the trade compliance rule library;

[0015] The deviation duration of the customs declaration timestamp sequence in the current logistics state data from a scheduled declaration plan is obtained;

[0016] The weighted sum of the matching and verifying result and the deviation duration is taken as the qualification compliance risk value.

[0017] Preferably, the risk control strategy collaborative analysis module includes:

[0018] The risk control policy scheme generation unit is configured to generate a plurality of candidate risk control policy schemes including logistics path adjustment, enterprise qualification re-inspection, and order clause revision based on the clause constraint conditions of the trade compliance rule base;

[0019] The coordination cost calculation unit is configured to calculate a coordination execution cost factor when the trade enterprise qualification state changes, including qualification verification delay duration, associated enterprise re-authentication quantity, and blockchain smart contract modification times;

[0020] The interference impact analysis unit is configured to calculate the coordination execution cost factor and the implementation complexity of each candidate risk control policy scheme to output the set of interference impact values.

[0021] Preferably, the risk control policy coordination analysis module further includes a risk policy index library configured to store historical risk control policy schemes and their corresponding logistics feature vectors, qualification state feature vectors, and order risk feature vectors.

[0022] The risk policy index library performs the following operations:

[0023] A three-dimensional policy mapping space is constructed, 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.

[0024] The logistics feature vectors, qualification state feature vectors, and order risk feature vectors of historical risk control policy schemes are mapped to the three-dimensional policy mapping space to form policy index nodes.

[0025] Preferably, when the risk control policy scheme generation unit calls the risk policy index library, it performs the following operations:

[0026] The logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link are obtained.

[0027] The logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link are obtained.

[0028] The logistics path anomaly coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient of the current commodity trade link are obtained.

[0029] Preferably, the system further includes a trade data fluctuation monitoring module configured to perform the following operations:

[0030] The commodity price sequence, logistics time sequence, and order fulfillment rate sequence in the blockchain distributed ledger are continuously collected.

[0031] The data stability measure value of each sequence within a preset time window is calculated.

[0032] trigger a real-time risk coefficient updating instruction of the trade risk quantification analysis module when the data stability metric value exceeds a preset fluctuation threshold.

[0033] Preferably, when calculating the data stability metric value, the trade data fluctuation monitoring module performs the following operations:

[0034] identifies the number of consecutive fluctuation direction changes of the commodity price sequence and the amplitude difference between adjacent extreme points;

[0035] counts the distribution density of abnormal events exceeding the promised time limit for an effect in the logistics time limit for an effect sequence;

[0036] normalizes and weights the number of consecutive fluctuation direction changes of the commodity price sequence, the amplitude difference between adjacent extreme points, and the distribution density of abnormal events exceeding the promised time limit for an effect in the logistics time limit for an effect sequence.

[0037] 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.

[0038] The risk control strategy knowledge base performs the following operations:

[0039] constructs a multi-dimensional feature vector group including trade region type, commodity risk level, enterprise credit score, and logistics complexity coefficient;

[0040] records the association relationship between the multi-dimensional feature vector group and the optimal risk control strategy in the historical scene and the implementation accuracy index;

[0041] When a new trade scene is added, a similar historical scene of the multi-dimensional feature vector group is matched and an associated risk control strategy is recommended.

[0042] Preferably, the risk control strategy execution feedback module comprises:

[0043] a smart contract updating unit for converting the optimal risk control strategy scheme into a blockchain smart contract execution parameter;

[0044] 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;

[0045] The audit log construction unit synchronously writes the structured audit log into the tamper-proof storage layer of the blockchain distributed ledger.

[0046] Compared with the prior art, the present application has the following advantages:

[0047] 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 preset data interface by means of a blockchain data acquisition module, and synchronizes the information to a blockchain distributed ledger. The characteristics of the blockchain distributed ledger make all the collected information recorded and cannot be tampered with at will, and all trade participants can access the information in the ledger through legal authority, breaking the information barrier caused by the dispersed storage of information in the 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.

[0048] The trade risk quantitative analysis module generates a real-time risk coefficient set based on real-time data in the blockchain distributed ledger, in combination with a pre-stored trade compliance rule library, historical order fulfillment data and current logistics state data. Compared with the traditional risk assessment method that relies on only a single data source, this module integrates multi-dimensional dynamic data to 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 late risk identification.

[0049] When there is a risk coefficient exceeding a preset threshold in the real-time risk coefficient set, the risk control strategy collaborative analysis module generates multiple candidate risk control strategy schemes based on the trade compliance rule library, and calculates the collaborative execution cost factor when the trade enterprise qualification state changes in combination with the logistics node information, and then obtains a set of interference impact values of each candidate scheme on the trade process. This approach changes the problem of ignoring execution cost and process interference in the traditional risk control strategy development process, allowing the selection of a scheme that is more suitable for the current trade scenario and has less interference with the trade process, effectively controlling risks while reducing the impact of improper risk control measures on trade efficiency and ensuring the smooth progress of the trade process.

[0050] The risk control strategy execution feedback module updates the blockchain smart contract execution instructions according to the selected best risk control strategy scheme, and synchronizes the trade risk control audit log to the blockchain distributed ledger. The automatic execution feature of the smart contract reduces errors and delays caused by manual operations, ensuring that the risk control strategy can be quickly implemented. The audit log synchronized to the blockchain provides a complete and reliable record for the trade risk control process, thanks to the tamper-proof and traceable nature of the blockchain. When it is necessary to check the trade risk behavior or handle trade disputes, the original log can be directly retrieved from the ledger without worrying about tampering or loss, improving the transparency and credibility of the trade risk control process and helping the commodity trade business to operate efficiently in a standardized environment. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 a timing diagram of the blockchain-based commodity trade business management system described in the present application;

[0052] Figure 2 A flowchart for commodity basic information, logistics node information and real-time risk coefficient set;

[0053] Figure 3 A flowchart for generating qualification compliance risk value;

[0054] Figure 4 A flowchart for risk policy index library operation. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0056] Please refer to Figure 1 The present application provides a commodity trade business management system based on a blockchain, which comprises:

[0057] The system acquires commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface of a blockchain data acquisition module, and synchronizes these information to a blockchain distributed ledger. A trade risk quantification analysis module generates a real-time risk coefficient set of a commodity trade link based on real-time data recorded by the blockchain distributed ledger, in combination with a pre-stored trade compliance rule library, historical order fulfillment data and current logistics state data. When there is a risk coefficient exceeding a preset threshold in the real-time risk coefficient set, a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes based on the trade compliance rule library, and calculates 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 updates a blockchain smart contract execution instruction according to a screened best risk control strategy scheme, and generates a trade risk control audit log to be synchronized to the blockchain distributed ledger. The system adopts a distributed architecture, and each module performs data interaction through a blockchain network, to ensure data consistency and non-tamperability.

[0058] Embodiment 1: refer to Figure 2The product production traceability code in the product basic information is generated by the manufacturer at the end of the production line. After the assembly of each smart phone is completed, its unique serial number is bound with the motherboard code, processor number and screen code, and a globally unique traceability code is synthesized through a standardized algorithm. The code uses the ISO / IEC15459 standard format, including vendor 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 collection module. The generation of the quality detection certificate hash value begins with the comprehensive detection of the finished smart 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 summary, 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 entered into the system by the customs declaration personnel of the trading enterprise when the goods are stored, and is transmitted to the blockchain network through the data interface.

[0059] The warehouse temperature and humidity records in the logistics node information are collected by deploying Internet of Things sensors in each area of the warehouse. The sensor records 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 smart phones enters the warehousing link, the system automatically creates an environmental data log corresponding to the batch, and all sensor readings are marked with a geographic location identifier and a timestamp. The transportation tool positioning track data is derived from the GPS tracker installed on the transportation vehicle. The device collects position information every 30 seconds, with longitude and latitude coordinates using the WGS84 standard, and altitude data is also recorded. During transportation, the system calculates the deviation of the vehicle 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 uses the ISO8601 standard format, accurate to the millisecond level, and is associated with the corresponding customs declaration number.

[0060] The qualification compliance risk value in the real-time risk coefficient set is calculated based on the import and export business license, electronic product sales license, and tax registration status of the trading enterprise. The system regularly obtains the enterprise qualification status from the data interface of government departments such as industry and commerce, taxation, etc., 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 obtained 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, and transfer times, to calculate the delay probability and impact of each link. The order performance 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, shipping time, etc. key indicators, and uses a sliding window algorithm to calculate the performance deviation trend.

[0061] In this smart phone trade example, when a batch of new model phones are shipped from a Chinese factory to a European distribution center, the system records all the basic information and logistics information. The production traceability code helps to trace the production batch of each phone, the quality detection 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 confirm 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 indicates that the customs procedures are completed within the specified time limit. Based on these data, the real-time risk coefficient set generated by the system provides a comprehensive risk assessment basis for all parties involved in trade. The qualification compliance risk value reflects that the enterprise qualification status is good, the logistics time efficiency risk value shows that the transportation progress is normal, and the order performance deviation risk value indicates that the order execution meets the expectations. All data is stored in a distributed manner through the blockchain network, ensuring the non-tamperability and traceability of the information, providing reliable data support for international trade.

[0062] Example 2: see Figure 3, the operation mode of the qualification compliance risk value generation mechanism and the risk control strategy collaborative analysis module, which is fully presented through the following specific examples. 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 electronic certificates such as the registration certificate of the import and export consignee, the customs senior certification enterprise qualification, and the import plant and animal quarantine permit. These certificates are accompanied by digital signatures and timestamps, and the system verifies the authenticity of 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, the State Administration of Quality Supervision, and other departments. The system automatically matches and verifies the validity 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 categories must be completely consistent with the actual imported frozen shrimp products. The matching and verification process is executed by the rule engine, and the structured verification results are output, including "valid period meets" and "permit range matches" status codes.

[0063] The system obtains the customs declaration timestamp sequence in the current logistics state data, and the batch of goods needs to complete the pre-declaration within 24 hours before arrival. The deviation of the actual declaration timestamp from the scheduled plan is accurately recorded. The system calculates the deviation duration of each link, such as the customs declaration submission time being delayed by 3 hours and 17 minutes from the plan, and the inspection and quarantine declaration being 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 time efficiency, and 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 the enterprise's qualification compliance status and customs time efficiency.

[0064] 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 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 that the goods be transferred from the current port to a special supervision site with cold chain inspection facilities; enterprise qualification re-inspection scheme, requiring the import 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 standards.

[0065] The synergy cost calculation unit begins 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 fees, and the cost of redeploying 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 smart contract on the blockchain. These cost factors are calculated through a multi-dimensional quantitative model, and the final output is a numerical synergy execution cost indicator.

[0066] The interference impact analysis unit receives these cost factor data and performs coupled analysis with the implementation complexity of each candidate risk control strategy scheme. Implementation complexity assessment includes technical difficulty, required human resource allocation, system modification requirements, and other aspects of scheme execution. Through the established multivariate analysis model, the system calculates the interference impact value of each scheme on the current trade process. For example, although the logistics path adjustment scheme can reduce the risk of customs clearance, its interference impact value is high because it requires coordination among multiple logistics service providers to adjust transportation plans. The qualification re-inspection scheme, although relatively simple to implement, may affect the preservation period of the goods due to its time cost.

[0067] Finally, the system outputs a set of interference impact values for all candidate risk control strategy schemes, providing a basis for decision-making in 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 in a manner that cannot be tampered with, ensuring the transparency and traceability of the risk assessment and strategy 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.

[0068] Embodiment 3: Referring to Figure 4 , the construction of the risk strategy index library and the risk control strategy generation mechanism are fully presented through the following specific examples. This example takes a large-scale agricultural product trade from South America to Asia as the background, and details how the system uses historical data to construct the strategy mapping space and generate effective risk control strategy schemes. The construction of the risk strategy index library begins with the systematic organization of historical risk control strategy schemes. The system collects the execution records of risk control strategies 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 includes transportation distance, number of transfers, temperature control requirements, etc.; 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 period, and quality clauses.

[0069] The process of constructing the three-dimensional strategy mapping space adopts a spatial geometry modeling method. The calculation of the first dimension, the logistics path abnormality coefficient, is based on the comprehensive evaluation of the deviation and delay rate of the transportation path, which reflects the deviation degree of actual logistics execution from the predetermined plan. 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 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 make their value range between 0 and 1.

[0070] In the three-dimensional strategy mapping space, each historical risk control strategy scheme 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 the interruption of cold-chain logistics, its logistics path abnormality 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 location 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.

[0071] When the system processes a new agricultural product trade case, the risk control strategy scheme 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 abnormality coefficient, enterprise qualification fluctuation coefficient, and order fulfillment deviation coefficient are calculated. The calculation of the logistics path abnormality 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 residence time of the transit station during transportation. 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 review, and historical trade dispute records. The calculation of the order fulfillment deviation coefficient refers to the payment timeliness rate of the current order, the pre-inspection pass rate of the goods, and the compliance of the estimated delivery time.

[0072] The system uses spatial distance calculation to locate the most matched strategy index node, and uses the following formula for similarity evaluation:

[0073]

[0074] Where: represents the spatial distance between the current scenario and the historical strategy node, represents the current logistics path abnormality coefficient, represents the logistics path abnormality coefficient of the historical node, representing the current enterprise qualification fluctuation coefficient, representing the historical node enterprise qualification fluctuation coefficient, representing the current order fulfillment deviation coefficient, representing the historical node order fulfillment deviation coefficient. The system traverses all historical strategy nodes and selects the top K nodes with the smallest spatial distance as the matching results.

[0075] After extracting the historical risk control strategy solutions associated with these matching nodes, the system performs adaptive adjustment of the solutions, taking into account the specific differences of different trade cases. The system will optimize and calibrate the parameters of historical solutions. For example, according to the characteristics of the current case's transportation conditions, the system adjusts the backup route selection in the logistics path solution; according to the current supplier's qualification status, the system modifies the specific inspection items in the qualification re-inspection solution; combined with the special terms of the current order, the system revises the default responsibility provisions in the order terms solution.

[0076] The final candidate risk control strategy solution not only retains the core elements of historical successful experience, but also is customized for the particularity of the current case. All 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.

[0077] Example 4: The trade data fluctuation monitoring module 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 blockchain distributed ledger through a data acquisition channel. The system is configured with a special data listening service that scans the ledger for data updates related to the target trade every 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 navigation data recording system, including departure time, arrival time, and transit time; the order fulfillment rate sequence is calculated based on the execution data of both parties, including delivery quantity accuracy, payment timeliness, and other indicators.

[0078] The system sets a 30-day rolling time window as the monitoring period, within which all data sequences are stored in chronological order and updated dynamically. The commodity price sequence records the daily spot price changes of cobalt ore, including opening price, highest price, lowest price, and closing price, among other detailed data. The logistics time sequence records the time stamps of each link from ore unloading to unloading at the destination port, including customs clearance time, port operation time, sea travel time, and other specific values. The order fulfillment rate sequence calculates the daily fulfillment completion, including the difference between planned delivery and actual delivery, payment delay days, and other parameters.

[0079] To evaluate data stability, the system analyzes each sequence separately. For the commodity price sequence, the system identifies the number of consecutive changes in fluctuation direction. This process is achieved by analyzing the price change direction at adjacent time points. When the price changes from rising to falling or from falling to rising, a direction change is recorded. Meanwhile, the system calculates the amplitude difference between adjacent extreme points, i.e., the absolute value of the price difference between the price peak and the price valley. These calculations help identify the frequency and intensity characteristics of price fluctuations.

[0080] The analysis of the logistics time sequence focuses on the detection of abnormal events. The system identifies logistics links that exceed the promised time limit based on the time limit specified in the trade contract. For example, if the sea transportation time specified in the contract is 25 days, the system will mark all transportation records that exceed this time limit. The calculation of abnormal event distribution density is based on the number of delayed events per unit time, taking into account the severity and impact of the delay. The monitoring of the order fulfillment rate sequence uses a sliding window statistical method. The system calculates the moving average of the fulfillment rate in units of days, while tracking the variability of the fulfillment rate. When there is a sharp decrease or abnormal fluctuation in the fulfillment rate, the system records these abnormal points and analyzes their duration and fluctuation amplitude. Representative data sequences collected in a certain monitoring period are shown in Table 1.

[0081] Table 1: Representative data sequence table collected in a certain monitoring period.

[0082] 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

[0083] In the calculation of data stability metrics, the system normalizes the analysis results of the three sequences. The fluctuation 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 considers the impact of different sequences on trade risk. The weight of the commodity price sequence is usually higher because price fluctuations directly affect the economic benefits of trade; the weight of the logistics time sequence is second, and the weight of the order fulfillment rate sequence is relatively low but cannot be ignored.

[0084] When the integrated stability measure exceeds a pre-set volatility threshold, the system automatically triggers an update instruction for the trade risk quantification module. The volatility threshold is a dynamic value derived from statistical analysis of historical trade data, with different threshold standards for different commodity categories and trade routes. 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.

[0085] After the update instruction is triggered, the trade risk quantification module immediately starts 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. Throughout the monitoring process, the system maintains continuous observation and analysis of data sequences. Whenever a new data point is generated, the system recalculates the stability measure value to ensure that it can timely capture the changing trend of data volatility. This continuous monitoring mechanism enables the system to discover and respond to early signs of trade risk, 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.

[0086] Embodiment 5: The construction of the risk control strategy knowledge base and the feedback mechanism of the risk control strategy implementation 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, and the construction of the risk control strategy knowledge base starts from 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 and importing countries, and the global trade region is divided 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 logistics complexity coefficient integrates transportation distance, transit times, temperature control requirements, special handling needs, and other parameters, and obtains a quantitative indicator through a weighted algorithm.

[0087] These multi-dimensional feature data are organized into structured feature vector groups, and each historical trade scenario is transformed into a data object containing dozens of feature dimensions. The system records the optimal risk control strategies adopted in each historical scenario and their 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 data's non-tamperability and traceability.

[0088] When a new electronic product trade case starts, the system initiates the scenario matching process, first extracting the feature data of the current trade case, including the export country being a technology-developed region in East Asia, the import country being a developed country in Western Europe, the commodity being a high-value smartphone, the enterprise credit score being in the excellent level, and the logistics complexity coefficient being relatively high. The system uses a feature similarity algorithm to calculate the similarity between the current feature vector group and the historical feature vector group, identifying the historical trade scenarios with high similarity.

[0089] During the matching process, the system uses a multi-dimensional weighted similarity calculation method, giving different weight coefficients to different feature dimensions. The trade region type has a higher matching weight because the trade policies and regulatory environments of different regions differ significantly; the commodity risk level has a lower weight, and the enterprise credit score and logistics complexity coefficient have relatively balanced but not negligible weights. The system selects several historical scenarios with the highest similarity and extracts the optimal risk control strategies used in these scenarios and their related parameters.

[0090] 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 copying the strategy, but rather adapting it to the specificities 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 declaration audit strategy are adjusted; and based on the specific cooperation history of the current trade partner, the trigger conditions of the credit risk management strategy are improved.

[0091] In the risk control strategy execution phase, the smart contract update unit starts to operate, 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, which automatically monitors the execution of the trade process and triggers the corresponding risk control measures when the preset conditions are met.

[0092] The audit log construction unit synchronously carries out work, and the unit encapsulates complete data records of the entire risk control decision-making process. It includes the calculation process data of the real-time risk coefficient set, the generation and screening records of the candidate risk control strategy scheme, 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, ensuring machine readability and human interpretability of the log content.

[0093] The generated audit log is written into the tamper-proof storage layer of the distributed ledger through a blockchain transaction. The writing process uses 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 structure. Any tampering with historical logs will cause the hash values to mismatch and be detected by the system. The storage of audit logs uses a multi-copy distributed architecture, with complete copies saved on multiple nodes of the blockchain network, ensuring high availability and anti-destroyability of the data.

[0094] 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.

[0095] 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 a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article or device.

[0096] 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 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 blockchain-based commodity trade business management system, characterized by, The system comprises: A blockchain data collection module for acquiring commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizing the commodity basic information, trade enterprise qualification information and logistics node information to a blockchain distributed ledger; A trade risk quantitative analysis module for generating a real-time risk coefficient set of a commodity trade link based on real-time data recorded in the blockchain distributed ledger, combining a pre-stored trade compliance rule library, historical order fulfillment data and current logistics state data; A risk control strategy collaborative analysis module for generating 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, calculating a collaborative execution cost factor when a trade enterprise qualification state changes 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 for updating a blockchain smart contract execution instruction according to a screened best risk control strategy scheme, and generating a trade risk control audit log to be synchronized to the blockchain distributed ledger; The risk control strategy collaborative analysis module comprises: A risk control strategy scheme generation unit for generating a plurality of candidate risk control strategy schemes including logistics path adjustment, enterprise qualification re-inspection and order clause revision based on the clause constraint conditions of the trade compliance rule library; A collaborative cost calculation unit for calculating a collaborative execution cost factor when a trade enterprise qualification state changes, including a qualification verification delay length, a number of associated enterprise re-authentications and a number of blockchain smart contract modifications; An interference influence analysis unit for coupling calculation of the collaborative execution cost factor and the implementation complexity of each candidate risk control strategy scheme to output the set of interference influence values. 2.The blockchain-based commodity trade business management system of claim 1, wherein, The commodity basic information includes a commodity production traceability code, a quality inspection 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 time limit risk value and an order performance deviation risk value. 3.The blockchain-based commodity trade business management system of claim 2, wherein, When the trade risk quantitative analysis module generates the qualification compliance risk value, the following operations are performed: Matching verification of the expiration date identifier of the trade enterprise qualification information and the qualification requirement clauses of the trade compliance rule library is performed; The deviation length of the customs declaration timestamp sequence in the current logistics state data from the 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. 4.The blockchain-based commodity trade business management system of claim 1, wherein, The risk control strategy collaborative analysis module further comprises a risk strategy index library for storing 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 performs the following operations: A three-dimensional strategy mapping space is constructed, wherein the first dimension is a logistics path anomaly coefficient, the second dimension is an enterprise qualification fluctuation coefficient and the third dimension is an order fulfillment deviation coefficient; Map the logistics feature vector, the qualification state feature vector and the order risk feature vector of the historical risk control policy scheme to the three-dimensional policy mapping space to form a policy index node.

5. The blockchain-based commodity trading business management system according to claim 4, characterized in that, When the risk control policy scheme generation unit calls the risk policy index library, the following operations are performed: Obtain the logistics path abnormality coefficient, the enterprise qualification fluctuation coefficient and the order fulfillment deviation coefficient of the current commodity trade link; Locate the policy index node matching the logistics path abnormality coefficient, the enterprise qualification fluctuation coefficient and the order fulfillment deviation coefficient of the current commodity trade link in the three-dimensional policy mapping space; Extract the historical risk control policy scheme associated with the policy index node as a candidate scheme generation benchmark.

6. The blockchain-based commodity trade business management system according to claim 1, characterized in that, The system also includes a trade data fluctuation monitoring module for performing the following operations: Continuously collect commodity price sequences, logistics time limit sequences and order fulfillment rate sequences in the blockchain distributed ledger; Calculate the data stability measure value of each sequence within a preset time window; When the data stability measure value exceeds a preset fluctuation threshold, trigger the real-time risk coefficient update instruction of the trade risk quantification analysis module.

7. The blockchain-based commodity trading business management system according to claim 6, wherein When the trade data fluctuation monitoring module calculates the data stability measure value, the following operations are performed: Identify the number of consecutive fluctuation direction changes and the amplitude difference between adjacent extreme points of the commodity price sequence; Statistical abnormal event distribution density of the logistics time limit sequence exceeding the promised time limit; Normalize and weight 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 limit sequence exceeding the promised time limit. 8.The blockchain-based commodity trade business management system of claim 1, wherein, The system also includes a risk control policy knowledge base for storing the mapping relationship between historical trade scenario features and optimal risk control policies; The risk control policy knowledge base performs the following operations: Construct a multi-dimensional feature vector group, including trade area type, commodity risk level, enterprise credit score and logistics complexity coefficient; Record the association relationship and implementation accuracy index of the multi-dimensional feature vector group and the optimal risk control policy in the historical scenario; When a new trade scenario is added, match similar historical scenarios of the multi-dimensional feature vector group and recommend associated risk control policies. 9.The blockchain-based commodity trade business management system of claim 1, wherein, The risk control policy execution feedback module includes: An intelligent contract update unit for converting the best risk control policy scheme into a blockchain smart contract execution parameter; An audit log construction unit for packaging the real-time risk coefficient set, the screening process data of the candidate risk control policy scheme and the policy 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.

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