Supply chain profit sharing settlement method and system based on block chain

Abstract

The invention relates to a supply chain profit sharing settlement method and system based on a block chain, and the method comprises the steps: receiving user order data, and carrying out the analysis to generate a standardized order object; performing dimension analysis on the standardized order object to generate structured data; matching a corresponding profit sharing identity proportion set according to the discount rate; generating profit sharing integral data in combination with the geocoding space dimension and the profit sharing identity proportion set; when the order state is changed to be payment success, marking the profit sharing point data as a frozen state; when the change is completed, constructing a block chain transaction request and submitting the block chain transaction request to a block chain node; automatically analyzing the block chain transaction request, executing point transfer operation and generating an on-chain settlement voucher; carrying out transaction validity verification on the on-chain settlement voucher, and generating a fund transfer instruction; and updating the point account of the user and pushing a settlement result. The real-time performance, the accuracy and the credibility of profit sharing decision making are realized while the high concurrent processing performance is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain digital management, in particular to a supply chain profit distribution settlement method and system based on a block chain. BACKGROUND

[0002] With the rapid development of digital economy and the deep penetration of Internet technology, the traditional supply chain system is undergoing a profound structural change. In the past industrial economy era, commodity circulation mainly relies on the linear chain of "factory-wholesaler-retailer-consumer", each link has clear responsibilities and clear levels, and the profit distribution mechanism is relatively fixed and concentrated in the hands of the intermediate channel merchants. However, this traditional mode centered on centralized control has exposed many inherent drawbacks in the face of modern consumer demand for increasing personalization, fragmentation, and high frequency: information transmission lag leads to imbalance between supply and demand, data island phenomenon seriously hinders collaborative efficiency, and separation of inventory and logistics systems causes resource waste. More importantly, the massive data value generated by the purchasing behavior of end consumers has long been monopolized by platforms, and their core contribution as market drivers has not been reasonably rewarded.

[0003] In recent years, with the continuous evolution of Internet business models, especially the rise of social fission marketing and decentralized promotion models, the composition of stakeholders in the supply chain has become increasingly diversified, including not only manufacturers, distributors, and retailers, but also a large number of individualized promoters and end consumers themselves. This new business paradigm of "everyone can distribute" has significantly improved market penetration efficiency, but it has also brought unprecedented complexity in profit distribution. Since the profit distribution rules often have strong conditionalities, they depend on the combination of multiple dimensions such as product category, sales price discount, regional policy, and time node, the traditional settlement mode based on fixed scripts or manual review is slow to respond and has poor fault tolerance, which can easily lead to distribution disputes. Moreover, the existing systems generally lack a dynamic tracking mechanism for the entire life cycle of transaction status, making it difficult to effectively distinguish between intermediate states where payment is successful but the contract has not been fulfilled, leading to difficulties in recovering funds after premature distribution, and even causing repeated settlement or malicious arbitrage behavior.

[0004] Therefore, how to ensure high concurrent processing performance while achieving real-time, accuracy, and credibility of profit distribution decisions is a technical problem that needs to be solved at present. SUMMARY

[0005] To solve the above technical problems, the present application provides a supply chain profit distribution settlement method and system based on a block chain.

[0006] In a first aspect, the present application provides a supply chain profit distribution settlement method based on a block chain, which adopts the following technical solution: A blockchain-based supply chain profit sharing settlement method, the profit sharing settlement method comprising: receiving user order data, and parsing to generate a standardized order object containing a product identifier, a discount rate, a geographic location, and a timestamp; dimensionally analyzing the standardized order object to generate structured data containing a time dimension slice and a geocoded spatial dimension; calling a preset proportion parameter table according to the discount rate to match a corresponding profit sharing identity proportion set; obtaining a product profit base value based on the product identifier, and generating profit sharing point data in combination with the geocoded spatial dimension of the structured data and the profit sharing identity proportion set; listening to order state change events, and when the order state changes to payment success, marking the profit sharing point data as a frozen state and storing it in an off-chain database; when the order state changes to completion, constructing a blockchain transaction request containing the profit sharing point data in the frozen state, and submitting it to a blockchain node; automatically analyzing the blockchain transaction request through a smart contract, performing a point transfer operation in a distributed ledger, and generating an on-chain settlement voucher; sending the on-chain settlement voucher to a third-party settlement platform for transaction validity verification, and generating a fund transfer instruction; updating the user point account according to the fund transfer instruction and pushing the settlement result.

[0007] By adopting the above technical solution, an end-to-end automated, full-process traceable, and full-node verifiable supply chain profit sharing settlement system is constructed. Compared with the traditional backward mode of relying on manual reconciliation and periodic payment, the technical solution of the present application improves the profit sharing efficiency and accuracy, reduces the operating cost and credit risk, and at the same time, gives consumers more sense of participation and gain, and promotes the transformation of the supply chain ecosystem from "centralized control" to "distributed co-governance". The technical effect not only lies in the optimization of single function, but also lies in the innovation of the overall business model. By establishing a trust infrastructure through blockchain, the benefit distribution mechanism is remodeled through data algorithm, and finally a sustainable development pattern of win-win for all parties in the supply chain is realized.

[0008] In a second aspect, the present application provides a blockchain-based supply chain profit sharing settlement system, which adopts the following technical solution: A blockchain-based supply chain profit sharing settlement system, the profit sharing settlement system comprising: an order data analysis module for receiving user order data, and parsing to generate a standardized order object containing a product identifier, a discount rate, a geographic location, and a timestamp; A dimension analysis module is configured to perform dimension analysis on the standardized order object to generate structured data including a time dimension slice and a geocoded spatial dimension. A proportion matching module is configured to call a preset proportion parameter table according to the discount rate to match a corresponding set of sharing identity proportions. A sharing point generation module is configured to obtain a product profit base value based on the product identifier, and generate sharing point data by combining the geocoded spatial dimension of the structured data and the set of sharing identity proportions. A freezing module is configured to listen to order state change events, and when the order state is changed to payment success, mark the sharing point data as a frozen state and store it in an off-chain database. A request module is configured to, when the order state is changed to complete, construct a blockchain transaction request containing the sharing point data in the frozen state, and submit it to a blockchain node. A point transfer operation module is configured to automatically parse the blockchain transaction request through a smart contract, execute a point transfer operation in a distributed ledger, and generate an on-chain settlement voucher. An effectiveness verification module is configured to send the on-chain settlement voucher to a third-party settlement platform for transaction effectiveness verification, and generate a fund transfer instruction. An account update module is configured to update a user point account according to the fund transfer instruction and push a settlement result.

[0009] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to the first aspect.

[0010] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical solution: A computer readable storage medium stores a computer program that can be loaded and executed by a processor to implement any one of the methods according to the first aspect.

[0011] In summary, the present application includes at least one of the following beneficial technical effects: by fusing blockchain technology and supply chain management, a complete, safe and efficient distribution settlement system is constructed. The scheme realizes full-process automatic processing, from order receiving to final settlement without manual intervention, greatly improving the processing efficiency; through multi-dimensional data analysis (time, geography, commodity, etc. dimensions), the accuracy and scientificity of the distribution calculation are ensured; using blockchain smart contract technology, the automatic execution and non-tamperability of the settlement process are realized, enhancing the credibility and transparency of the system; a hybrid architecture combining off-chain storage and on-chain verification is adopted, which ensures the data processing efficiency and the security of the key information; through the order state monitoring mechanism and the distribution data freezing function, the transaction risk and the fund safety problem are effectively prevented. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is a first flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0013] Figure 2 is a second flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0014] Figure 3 is a third flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0015] Figure 4 is a fourth flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0016] Figure 5 is a fifth flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0017] Figure 6 is a sixth flowchart of a supply chain distribution settlement method according to an embodiment of the present application.

[0018] Figure 7 is a seventh flowchart of a supply chain distribution settlement method according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the present application more clear, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Figures 1-7

[0020] ​At present, the common distribution system still mostly follows the traditional financial processing flow, relies on manual reconciliation, periodic accounting and manual payment, and usually performs batch settlement in units of months. Such a way can still be maintained in a low-frequency transaction scenario, but when facing massive orders, multi-level recommendation relationships and dynamic incentive rules, it is easy to have problems such as accounting delay, data omission, unfair distribution, etc. More prominent is that due to the lack of unified data standards and reliable technical carriers, it is difficult for each participant to establish a real trust foundation, the platform has all the data rights, other roles cannot independently verify the authenticity of the distribution results, and disputes and disputes are often caused by information asymmetry.

[0021] In addition, the existing system generally lacks the ability to track the status of the entire life cycle of a transaction, and cannot effectively identify abnormal situations such as "paid but not performed", "order cancellation", "after-sales refund", etc., making it difficult to recover the income paid in advance, and there is a risk of being maliciously arbitrated. At the same time, in terms of data analysis, most systems can only provide statistical reports based on sales, amount, etc. macro dimension, lack of fine modeling capability for multi-dimensional cross factors such as time, region, commodity category, identity role, etc., and it is difficult to support scientific and reasonable incentive policy formulation and regional differentiated operation decision.

[0022] In this background, the embodiments of the present application disclose a supply chain distribution settlement method based on a block chain.

[0023] Reference Figure 1 A supply chain distribution settlement method based on a block chain, the distribution settlement method comprising: Step S101, receiving user order data, and parsing to generate a standardized order object containing a commodity identifier, a discount rate, a geographic location and a timestamp; Specifically, in actual business scenarios, order data from different channels (such as e-commerce platforms, applets, APPs, etc.) have different formats, field names are not uniform, and there are missing or redundant fields, which will lead to confusion in subsequent processing logic if used directly. Therefore, the system first cleans, completes and semantically maps the received heterogeneous order data, and converts it into a standardized order object in a unified format. This object not only contains a unique identifier (such as SKU code) for product identification, but also explicitly extracts key business parameters: discount rate (reflecting the ratio of actual sales price to benchmark price), geographic location (usually the delivery address or user registration location latitude / longitude / administrative division code), and timestamp (records the specific time of order creation or payment).

[0024] It can be understood that the selection of these fields is not arbitrary, but serves the subsequent complex analysis and decision-making process. The product identifier is the basic unit of profit attribution, the discount rate determines the strength of the incentive, the geographic location supports the regional differentiation strategy, and the timestamp provides an anchor point for time series analysis. Through the standardized packaging of raw data, the system ensures that all downstream modules can operate in a consistent data context, avoiding logical deviations caused by input differences. This step essentially constitutes the "data entry governance layer" of the entire profit distribution system, providing a reliable premise for subsequent high-precision operations.

[0025] Step S102, dimension analysis of standardized order objects, generating structured data containing time dimension slices and geocoded spatial dimensions; For the time dimension, the system uses time series analysis techniques to perform multi-level slicing on the original timestamp, automatically categorizing time points accurate to the second into different granularity time windows such as "day", "month", "quarter", and even "year". This processing method makes it possible to perform report statistics, trend prediction, and periodic profit distribution accounting, such as supporting monthly aggregation of total profit for a certain brand of goods in a certain region, or detecting the impact of seasonal fluctuations on distribution revenue.

[0026] For geographic location information, the unstructured address description is converted into structured provincial-city-district three-level administrative codes through a geocoding service, forming a so-called "geocoded spatial dimension". This coding system not only serves as the basis for visualization, but more importantly, as a key variable for regional strategy control, allowing the system to set differentiated rate coefficients based on market maturity, competition situation, or policy environment in different regions, thereby achieving fine-grained operations. The joint modeling of these two dimensions forms the rudiment of a multi-dimensional data cube, allowing each order to have a clear position in the spatio-temporal coordinate system, greatly enhancing the granularity of data analysis and business insight.

[0027] Step S103, calling the pre-configured proportion parameter table according to the discount rate to match the corresponding profit identity proportion set; The discount rate is not only a promotional tool, but also a design lever for incentive mechanisms. The system maintains a dynamically configured parameter table, divided into multiple intervals (such as 0~5 times, 5~6 times, etc.), each interval corresponding to a fixed set of profit proportion combinations, respectively specifying the profit share percentages for retailers (i.e. consumers themselves), wholesalers (referrers), and agents (second-level referrers). When an order's discount rate falls into a certain interval, the system immediately retrieves and loads the proportion set within that interval as the weight basis for this time's profit distribution calculation.

[0028] It can be understood that this design embodies the marginal incentive idea in economics: when selling at a low price, although the profit per unit is low, it needs to be promoted through high rebates; when selling at a high price, the profit space is large, but the basic incentive still needs to be maintained to keep the distribution network active. The proportion parameter table can be flexibly adjusted through the background to adapt to the changes in demand of different commodity categories or marketing stages, and embodies good configurability and extensibility. It should be noted that this step realizes the transition from static rules to dynamic response, so that the distribution mechanism can be self-adapted to market conditions, rather than a fixed and rigid distribution scheme.

[0029] In step S104, the profit base value of the commodity is obtained based on the commodity identifier, and the distribution point data is generated by combining the geographic coding space dimension of the structured data and the distribution identity proportion set; The profit base value is usually the pre-approved unit gross profit amount of the commodity, which comes from the cost accounting system or the procurement contract data, and represents the total distributable profit released for each sold commodity. On this basis, the system introduces a regional rate coefficient, which is obtained by looking up the geographic coding space dimension table, and is used to reflect the differences in operating costs, tax policies or strategic priorities in different regional markets. For example, first-tier cities may set a lower rate coefficient value due to higher logistics costs, while emerging markets may give higher incentive multiples to encourage penetration. The final distribution point value is not the result of simple multiplication, but a comprehensive output that integrates the profitability of the commodity, the characteristics of the sales channel, the spatial distribution strategy and the identity role weight. In essence, it is a quantitative assessment of the contributions of multiple parties. The points exist in the form of virtual assets and have not yet entered the circulation link, representing only the value amount to be redeemed. Its generation process fully embodies the fairness and scientific nature of data-driven, avoiding subjective bias caused by manual estimation.

[0030] In step S105, the order state change event is listened to, and when the order state is changed to payment success, the distribution point data is marked as frozen and stored in the off-chain database; In order to ensure the safety of funds and the integrity of transactions, the system continuously listens to the state change events in the order lifecycle. When the order state is updated to "payment success" for the first time, it means that the user has completed the payment action, and the transaction has preliminary fulfillment conditions. At this time, the system marks the just-generated distribution point data as "frozen state" and stores it in the high-performance off-chain database.

[0031] Understandably, the core purpose of the freeze mechanism is to mitigate the risks of transaction uncertainty. In cases such as subsequent refunds, order cancellations, or logistical anomalies, the system can directly invalidate or deduct relevant points without affecting the user's account, avoiding complex on-chain rollback operations. The choice of off-chain storage is also based on performance considerations: blockchain itself is not suitable for high-frequency writing to temporary intermediate states, while traditional relational or NoSQL databases can efficiently support read and write access to such short-lifecycle data. This step essentially constructs a "buffer isolation layer," separating irreversible on-chain settlements from volatile off-chain business states, ensuring both smooth main processes and sufficient fault tolerance.

[0032] Step S106: When the order status changes to completed, construct a blockchain transaction request containing the frozen profit-sharing points data and submit it to the blockchain node. Specifically, the system constructs a blockchain transaction request containing previously frozen profit-sharing points data and submits it to a pre-defined blockchain node cluster. This request is essentially an operation instruction with a digital signature, declaring the set of user addresses to be executed, the function name to be called, the input parameters (such as the beneficiary's wallet address, points amount, etc.), and the order's unique identifier. Once submitted, the request enters the blockchain network's pending queue, awaiting packaging and on-chain processing.

[0033] Understandably, this step signifies that value transfer has officially entered a decentralized and trustworthy environment. Any subsequent operations will be subject to the consensus mechanism and cannot be unilaterally altered or revoked. By strictly binding settlement trigger conditions to the order completion status, the system ensures that only genuine and valid transactions can earn profit-sharing rights, fundamentally eliminating the possibility of arbitrage through fraudulent transactions and enhancing the overall integrity of the ecosystem.

[0034] Step S107: Automatically parse the blockchain transaction request through the smart contract, execute the points transfer operation in the distributed ledger and generate on-chain settlement vouchers; A smart contract is a self-executing program that runs in a blockchain virtual machine. Its code logic is open and transparent and cannot be changed once deployed. Upon receiving a legitimate transaction request, the contract first verifies the initiator's permissions and signature validity, then decodes the user address and points amount, and calls the built-in `transfer()` function to send the corresponding number of points tokens to the target address. Because all operations are recorded on the blockchain, each transfer generates a unique transaction hash (Tx Hash), which, together with the block height and timestamp, constitutes an unforgeable historical record.

[0035] More importantly, the system uses the Merkle Tree structure to aggregate the summary of batch transactions, generates the root hash and anchors to the main chain, so as to realize efficient verification and tamper-proof protection across batch transactions. The final on-chain settlement voucher not only includes transaction details, but also includes encryption proof materials, which can be used for audit traceability or third-party verification, and truly realizes the trust paradigm of "code as law".

[0036] Step S108, sending the on-chain settlement voucher to the third-party settlement platform for transaction validity verification, and generating a fund transfer instruction; Among them, the verification process includes two key links: one is to query whether the number of confirmations of the transaction in the public network reaches the preset threshold (such as 12 block confirmations) through the block chain browser API, to ensure that the transaction has been accepted by enough nodes and there is no risk of reorganization or double spending; the second is to use the elliptic curve digital signature algorithm (ECDSA) to check whether the digital signature in the original transaction request is signed by a legal private key, to prevent impersonation or man-in-the-middle attacks. Only when both verifications pass, the settlement request is considered valid and reliable. This double-factor verification mechanism greatly improves the anti-attack ability of the system, especially suitable for scenarios involving real fund flow. The third-party settlement platform plays the role of a "on-chain-off-chain" bridge, which is both an observer of on-chain data and a connector of real-world bank systems, and is responsible for converting digital rights into legal currency.

[0037] Step S109, updating the user's points account according to the fund transfer instruction and pushing the settlement result.

[0038] Specifically, the points account update operation is not a simple numerical addition or subtraction, but a cross-system coordination process: on the one hand, the on-chain points balance needs to be synchronized to the local distributed database for real-time display by the front-end application; on the other hand, all changes must trigger audit log writing operations to record operation time, performer, pre-post balance and associated order number, to meet the requirements of compliance review.

[0039] At the same time, the event will also activate the real-time statistical engine of the data analysis system, refreshing the sales ranking list, regional contribution map, user growth level and other visual reports, providing immediate decision support for management. The entire update process emphasizes consistency and observability, ensuring that every small change can be tracked, interpreted and reproduced. Finally, the user will receive a settlement reminder on the mobile end, including the points credited amount, source order and cashable time, completing the complete experience closed loop from consumption to benefit.

[0040] In the above embodiments, a set of end-to-end automated, full-process traceable, and full-node verifiable supply chain distribution settlement system is constructed. Compared with the traditional backward mode relying on manual reconciliation and periodic payment, the technical scheme of the present application improves the distribution efficiency and accuracy, reduces the operating cost and credit risk, and at the same time, gives consumers more sense of participation and satisfaction, and promotes the transformation of the supply chain ecology from "centralized control" to "distributed co-governance". The technical effect not only lies in the optimization of single function, but also lies in the innovation of the overall business model. Through the establishment of a trust infrastructure based on blockchain, the interest distribution mechanism is remodeled by data algorithm, and finally a sustainable development pattern of win-win for all parties in the supply chain is realized.

[0041] With reference to Figure 2 As an embodiment of step S102, the step of performing dimensional analysis on the standardized order object to generate structured data containing time dimension slices and geocoded spatial dimensions includes: Step S201, receiving a standardized order object, extracting a timestamp field and a geographic location field; Specifically, in the actual operation of the supply chain system, each order is accompanied by a time stamp accurate to the millisecond, recording the time when key events such as user ordering, payment or delivery occur; at the same time, the geographic location field usually exists in the form of latitude and longitude coordinates or structured address, reflecting the physical space location where the transaction behavior occurs. Although these two fields seem independent, they together constitute the basic framework for understanding the spatiotemporal laws of consumer behavior. By separating them from the standardized order object and processing them separately, the system can start deep semantic analysis of order data, i.e., no longer treating orders as isolated transaction records, but as nodes of socio-economic activities occurring at specific times and places, thereby laying the foundation for building high-dimensional data analysis models.

[0042] Step S202, calling a time series analysis engine to discretize the timestamp field and generate period identifiers divided by a preset time granularity; Specifically, the system analyzes the year, month and day components in the timestamp according to the Gregorian calendar rules (such as leap year judgment, month day difference, etc.), and classifies and divides them according to the preset time granularity (such as "day", "month", "quarter", "year"). For example, a timestamp occurring on August 15, 2024 at 14:32:18 will be classified into "202408" when processed by "month" granularity, and into "2024Q3" when processed by "quarter". Each time unit is assigned a unique numerical code as its identifier. This coding method not only facilitates database indexing and fast retrieval, but also effectively supports cross-period comparison analysis.

[0043] More importantly, this discretization is not simply a truncation, but follows strict semantic consistency principles to ensure the comparability of data from different sources at the same time scale, avoiding statistical bias caused by ambiguous time boundaries. After this step, the originally chaotic time information is organized into an ordered time dimension slice, becoming the basic unit for subsequent multi-dimensional cross-analysis.

[0044] Step S203, input the geographic location field into the geocoding service interface, and output the hierarchical administrative region code chain; It can be understood that in reality, the geographic location of the user can exist in various forms: some only provide provincial and municipal fuzzy information, some are detailed addresses accurate to the house number, and some directly use the latitude and longitude coordinates obtained by GPS. In order to unify the expression and support regional aggregation analysis, the system must convert these heterogeneous spatial descriptions into a standard administrative division code system.

[0045] Among them, the geocoding service plays a key role in this process, which receives the input coordinates or address string, matches the closest administrative division attribution using the built-in map database, and outputs a three-level code path composed of provincial, municipal, and county-level codes. For example, an order located at Wen San Road, Xihu District, Hangzhou, after processing, can generate a chain code such as "330000_330100_330106" (where the prefix represents Zhejiang, Hangzhou, and Xihu). This code chain not only preserves the accurate hierarchical relationship of the spatial location, but also naturally supports top-down drilling analysis, which can be used for provincial sales summary or in-depth micro-insight into a certain county. In addition, for the case where part of the hierarchical information is missing (such as only the province without the city and district), the system will automatically mark and leave the corresponding node empty, reserving space for subsequent filling.

[0046] Step S204, construct a multi-way tree data structure, with time period identifiers as root nodes and administrative region code chains as child node branches; Among them, the tree structure takes the time period identifier after discretization as the root node, forming the first layer of time axis; each specific administrative region code chain is taken as its child node branch extending downward, constituting the second layer of space axis. This tree organization method simulates the causal order of "first time period, then regional activity" in the real world, and also conforms to human cognitive habits.

[0047] Exemplarily, when multiple orders fall within the same time period but are distributed in different regions, the system will create multiple parallel spatial branches under the corresponding root node, independent of each other but belonging to the same time container; conversely, if multiple orders come from the same region but in different time periods, they will be distributed under different root nodes. The advantage of this structure is that it naturally supports recursive traversal and dynamic expansion, and can efficiently accommodate massive order data without losing structure.

[0048] More importantly, the form of multi-ary tree enables fast positioning through path traversal for any dimensional combination, greatly improving query efficiency.

[0049] Step S205, traverse the multi-ary tree data structure, merge duplicate nodes and fill in missing levels to generate structured data containing time dimension slices and geocoded spatial dimensions.

[0050] Among them, merging duplicate nodes means that order items that occur multiple times under the same time and space conditions should be aggregated rather than listed repeatedly. For example, when there are multiple sales in the same month in the same county, the system will merge them into one summary record to avoid redundant storage. "Fill in missing levels" is aimed at the problem of incomplete order geographic information, such as knowing only the city but not the county. The system will automatically complete the intermediate level as null or default placeholder to ensure the consistency of the entire coding chain length, thus maintaining the regularity of the data structure.

[0051] After the above optimization is completed, the system outputs a two-dimensional structured data matrix, whose row index is various time period identifiers (such as daily, monthly), and column index is the complete administrative region coding chain, and the matrix is filled with order statistics (such as quantity, amount, profit, etc.) under each dimensional combination. This matrix is not only the direct input source for report generation, but also the basic carrier for advanced analysis tasks such as machine learning model training, anomaly detection, and trend prediction.

[0052] In the above implementation, the time-space dual-track coordinate system with analysis value is extracted from the original business data, providing a foundation for subsequent data aggregation, trend identification, and intelligent decision-making. Compared with the traditional single-dimensional statistical method, this technical solution realizes the deep integration of time and space dimensions, enhances the granularity and flexibility of data analysis, and enables enterprises to accurately capture regional seasonal fluctuations, market penetration rhythm, and channel efficiency differences from the complex order flow, thereby driving more scientific inventory allocation, marketing investment, and profit distribution strategy formulation, and achieving the digital operation goal of "data-driven decision-making".

[0053] Referring to Figure 3 As an embodiment of step S104, based on the commodity identifier, the commodity profit base value is obtained, and the step of generating the profit distribution points data in combination with the geocoded spatial dimensions of the structured data and the profit distribution identity proportion set includes: Step S301, query the commodity database based on the commodity identifier to obtain the corresponding commodity profit base value; Specifically, in the digital supply chain system, each on-shelf commodity is assigned a unique commodity identifier (such as SKU code), which is not only the basic unit of inventory management and order tracking, but also the key index connecting the economic attributes of the commodity. When an order is confirmed, the system immediately uses the commodity identifier as the key value to query the commodity database maintained in the background, and reads the "commodity profit base value" associated with it. This value usually refers to the remaining distributable gross profit amount of the unit commodity after deducting necessary expenses such as procurement cost, logistics cost, platform operation cost, etc., and its determination basis may come from the supply contract, historical cost accounting model or dynamic pricing algorithm output result.

[0054] For example, a commodity priced at 100 yuan, if the comprehensive cost is 80 yuan, its profit base value is 20 yuan. This value serves as the starting point for all subsequent distribution calculations, and has high sensitivity and accuracy requirements, and must ensure that the source is authoritative, timely and tamper-proof. By strongly binding the profit base value with the commodity identifier, the system realizes the differentiation of the profitability of different commodities, avoiding the incentive imbalance problem caused by "one-size-fits-all" distribution.

[0055] Step S302, extracting the geocoding spatial dimension from the structured data, and parsing the administrative region level code; Among them, each order has been assigned a chain code composed of provincial, municipal and district administrative division codes (such as "330000_330100_330106" represents Hangzhou Xihu District, Zhejiang Province). This code not only records the physical location where the transaction takes place, but also carries important semantic information of regional market characteristics.

[0056] Step S303, matching the preset regional rate coefficient table according to the administrative region level code to obtain the regional adjustment factor; Specifically, the system uses the administrative region level code as an index to match and find in the preset "regional rate coefficient table", thereby obtaining the corresponding "regional adjustment factor". This coefficient table is a strategic configuration tool that sets different adjustment weights according to factors such as economic development level, market competition degree, logistics accessibility, policy support strength, etc. in different regions.

[0057] For example, for the western remote areas newly entering the market, a positive incentive coefficient higher than 1.0 (such as 1.2) can be set to encourage penetration; while for the first-tier cities in the east with high saturation, a conservative coefficient slightly lower than 1.0 (such as 0.9) can be used to prevent excessive subsidies. This regional adjustment mechanism makes the distribution amount of the same commodity in different regions produce reasonable differences, which not only conforms to the market law, but also serves the overall strategic layout of the enterprise.

[0058] Step S304, call the commission identity proportion set, read the identity commission proportion associated with the current order; Wherein, each identity includes but is not limited to retailers, wholesalers and agents; In the embodiment of the application, the three identities are not independent commercial entities in the traditional sense, but are role labels dynamically generated based on user social relationship chains: the retailer is the ordering consumer himself, the wholesaler is his direct recommender, and the agent is the recommender's superior recommender. The three constitute a three-level distribution incentive network, and the proportion obtained by each is automatically determined by the system according to the sales discount interval - high incentive to promote at low price, and low return to protect profit at high price. These proportion parameters are not fixed, but are stored in a configurable rule base, supporting flexible adjustment according to product categories, activity periods or channel types. The system reads the set to determine the contribution weight of each participant in this transaction, providing a basis for subsequent personalized point issuance.

[0059] Step S305, combining the commodity profit base value, the regional adjustment factor and the identity commission proportion, the commission point data is calculated and generated.

[0060] Wherein, the commission point data includes: retailer points = commodity profit base value x regional adjustment factor x retailer commission proportion; wholesaler points = commodity profit base value x regional adjustment factor x wholesaler commission proportion; agent points = commodity profit base value x regional adjustment factor x agent commission proportion.

[0061] It can be understood that this step is essentially a comprehensive evaluation of the value contribution of multiple parties: the commodity profit base value reflects the profit potential of the commodity itself, the regional adjustment factor reflects the market adjustment effect brought by the geographical position, and the identity commission proportion describes the promotion contribution degree of each node in the social chain. The result of the multiplication of the three forms the amount of points that each identity should obtain. Although the points exist in a virtual form, they correspond to real and redeemable economic rights behind them.

[0062] In the above embodiment, this process embodies the core logic of the transformation from original transaction information to quantifiable rights and interests, and is a key link for the entire supply chain commission system to realize "precise incentive, fair distribution and intelligent execution". By introducing the regional adjustment factor, the flexibility and strategic adaptability of the business model are enhanced, enabling enterprises to have stronger control capability in expanding markets, optimizing channels and motivating users. The final commission point data is not only a value certificate for consumers to participate in the digital economy, but also a trust cornerstone for the platform to build a decentralized collaborative ecosystem, truly realizing a new supply chain governance paradigm of "consumers have returns, promotion has value, and data can confirm rights".

[0063] Reference Figure 4As an embodiment of step S303, the step of obtaining the regional adjustment factor according to the administrative region level code matching the preset regional rate coefficient table further comprises: Step S401, obtaining the administrative region level code parsed from the structured data; Specifically, in the actual order processing process, the user's delivery address usually exists in the form of natural language, and such unstructured text needs to be converted into a spatial identifier with clear administrative attribution through reverse geocoding technology. The system maps the latitude and longitude coordinates corresponding to the address to the coding tree defined by the national standard through the geographic information system (GIS) platform, and extracts the provincial, municipal and district level codes level by level.

[0064] Step S402, generating an initial regional adjustment factor according to the administrative region level code matching the preset regional rate coefficient table; The regional rate coefficient table is the output result of a quantitative model constructed by regression analysis on historical big data. Behind it reflects the comprehensive differences in economic development level, density of logistics infrastructure, and fluctuations in market demand in different regions. For example, for high-tech industrial clusters, the per capita GDP is much higher than the national average, and there are dense warehouse nodes and efficient distribution networks, so the initial adjustment factor of this region will be given a higher value (such as 1.25), indicating that this region has stronger cost bearing capacity and higher business activity; Remote areas such as Yushu County in Qinghai are limited by poor transportation, sparse storage and few orders, and the marginal cost of logistics rises significantly, although the market potential is limited, but in order to maintain basic service coverage, more resources need to be invested, so the adjustment factor is lower (such as 0.82), reflecting the compensation mechanism for high operating costs.

[0065] It can be understood that these initial regional adjustment factors are essentially a function mapping of regional economic and geographic characteristics, combining the influence weights of multiple dimensions such as the proportion of per capita GDP relative to the benchmark value, the number distribution of logistics hubs per unit area, and the intensity of seasonal order fluctuations, etc. Fitted by ridge regression and other statistical methods, effectively avoiding parameter distortion caused by multicollinearity, ensuring model stability and explanatory power.

[0066] Step S403, constructing a three-dimensional spatiotemporal data container; Wherein, the first dimension is a time slice sequence, the second dimension is a geohash spatial partition index, and the third dimension layer stores order quantity fluctuation rate and logistics time efficiency deviation; Specifically, the container adopts a tensor structure design, including three orthogonal dimensions: the first dimension is a time slice sequence, which divides the continuous time flow into fixed-granularity time slices (such as one slice every 15 minutes) through a sliding time window algorithm, and each slice records the key business indicators in the period, such as the sudden growth pulse of fresh food orders in the breakfast peak period; the second dimension is a geohash spatial partition index, which uses the Geohash algorithm to encode two-dimensional latitude and longitude coordinates into a one-dimensional string (such as “ws101”), and uses Hilbert space-filling curves at the bottom layer for sorting, so that spatially adjacent regions also maintain proximity in memory storage, greatly improving the access efficiency of graph neural networks when performing neighborhood aggregation; the third dimension layer is specially used to store two types of dynamic features, order quantity volatility and logistics timeliness deviation, order quantity volatility measures the deviation of the order quantity in the current time slice relative to the historical same period mean, normalized by standard deviation, reflecting the sudden changes in the demand side; the logistics timeliness deviation is the ratio of the actual delivery time to the promised timeliness, which is used to quantify the stability of the fulfillment link.

[0067] It can be understood that the three dimensions together constitute a high-dimensional spatiotemporal feature cube, not only preserving the time continuity and spatial locality of the original data, but also adapting to the input requirements of modern deep learning models through structured packaging, solving the problem of fragmentation and difficulty in modeling of spatiotemporal data in traditional systems.

[0068] Step S404, inputting the three-dimensional spatiotemporal data container into a pre-trained spatiotemporal graph neural network model; Among them, the model regards each administrative region as a node in the graph, establishes edge connection relationship according to physical transportation paths such as highway network and railway network, and forms a weighted undirected graph reflecting the real supply chain topology. On this graph structure, the model first realizes local feature aggregation through the graph convolution layer (GCN), that is, when processing a city (such as Dongguan), it automatically integrates relevant feature information from its geographically adjacent cities (such as Guangzhou, Shenzhen, and Huizhou), simulating the radiation effect of commodity flow in reality.

[0069] Step S405, calculating the region correlation weight matrix through the spatial attention layer of the spatiotemporal graph neural network model; Among them, the region correlation weight value is generated based on the historical commodity circulation frequency between regions and the supply chain topological distance; Specifically, the spatiotemporal graph neural network model introduces a spatial attention mechanism, moving beyond mere geometric proximity to dynamically calculate the strength of inter-regional connections based on historical commodity circulation frequency and supply chain response speed. For example, if two regions have a long history of high-frequency commodity exchange and rapid logistics response (i.e., short transportation cycles), their attention weight will significantly increase. This mechanism reduces the weight of long-distance or slow-moving relationships by introducing a "time decay function" (e.g., 1 / (1+transport days)), allowing the model to focus more on regions that truly constitute efficient collaborative networks, such as the frequently interacting electronic component supply chain network within the Yangtze River Delta urban agglomeration.

[0070] Meanwhile, the gated recurrent unit (GRU) is responsible for capturing long-term dependencies in the time dimension and learning periodic patterns such as "weekend consumption spills over to surrounding cities" and "hoarding behavior before holidays starts a week in advance," so that the model can not only understand the current state, but also predict future trends.

[0071] Step S406: Dynamically correct the initial regional adjustment factor based on the regional correlation weight matrix to generate the optimized regional adjustment factor.

[0072] This correction process is essentially an information fusion mechanism based on Bayesian thinking: the final adjustment factor for each region consists of two parts, one part is the "self-confidence" derived from the assessment of the integrity and quality of local historical data, and the other part is the collaborative influence from its high-weight neighboring nodes.

[0073] For example, if the data collection coverage rate of a certain area is as high as 98%, it means that its own information is reliable enough, and its self-confidence is high (such as 0.95), which plays a dominant role in the correction process. However, when the external environment changes suddenly (such as a typhoon causing logistics disruptions and severe delays in delivery to adjacent areas), although they are still geographically close, the model will automatically reduce its contribution value in the weight matrix due to the sharp increase in "logistics delivery time deviation" to prevent the data of the failed area from being used for inference incorrectly.

[0074] Furthermore, the system introduces a network coordination gain coefficient, which assesses the connectivity strength of the entire supply chain network based on the maximum flow minimum cut theorem. When a key node is disconnected, the overall coordination capability decreases, and the corresponding adjustment factor will also be adjusted accordingly. This dynamic correction mechanism endows the regional rate system with the ability to self-evolve, enabling it to maintain accuracy and fairness under uncertain conditions such as emergencies, policy adjustments, or market migrations.

[0075] The above implementation achieves a leap from static rules to dynamic perception, and deeply integrates the principles of geoeconomics with complex network theory, enabling regional adjustment factors to possess intelligent characteristics of spatiotemporal perception, correlation reasoning, and anti-interference adjustment.

[0076] Referring to Figure 5 As an embodiment of step S406, the step of dynamically correcting the initial region adjustment factor according to the region correlation weight matrix comprises: Step S501, determining a list of adjacent administrative regions according to the administrative region level code, and matching the adjacent region adjustment factor based on the preset region rate coefficient table; Wherein, this adjacent region identification process not only relies on the traditional geographical boundary sharing principle, that is, two administrative divisions are considered adjacent if they have a common boundary line (for example, Shenzhen Nanshan District and Baoan District are considered adjacent because they share a coastline), but further introduces the actual interaction intensity of economic activities and logistics behavior as a supplementary basis for judgment. For example, although Dongguan and Shenzhen belong to different prefecture-level cities, but due to the huge number of cross-regional orders between the two places every day, the high degree of upstream and downstream industry collaboration (such as Shenzhen design and Dongguan manufacturing), and the frequent highway traffic interconnection, the system analyzes historical logistics path data and concludes that the "economic correlation" between them is very strong, so even if there is no direct geographical adjacency, they can still be included in the category of functionally adjacent regions. These multi-dimensional criteria collectively constitute a functional adjacent network that transcends the limitations of physical space, so that the selection of adjacent regions is no longer limited to geometric proximity, but truly reflects the real structure of commodity flow, capital flow, and information exchange in the real world.

[0077] Subsequently, the system calls the preset region rate coefficient table to map each identified adjacent region code to its corresponding adjacent region adjustment factor. These factors are the result of weighted calculation of multiple dimensions such as logistics cost index, consumption capacity level, and industrial agglomeration degree, and have clear economic semantic interpretation. For example, Dongguan has transportation hubs such as Humen Port, which reduces unit transportation costs, and a high degree of electronic manufacturing industry agglomeration, which brings stable order flow, so its initial adjustment factor is set to 1.18, making it a high-influence reference source.

[0078] Step S502, based on the region correlation weight matrix, calculating the weighted average of the initial region adjustment factor and the adjacent region factor; Wherein, the region correlation weight matrix reflects the implicit collaboration patterns and dependency relationships formed between regions in long-term operation. Specifically, the model automatically identifies which regions have closer interactions by jointly modeling historical order flow frequency, transportation time efficiency stability, and industrial complementarity strength, and gives higher connection weights. For example, in the Yangtze River Delta region, the commodity circulation density between Shanghai and Suzhou is much higher than that between Shanghai and other cities, so the attention weight between them is significantly improved; similarly, due to the deep coupling of the industrial chain between Shenzhen and Dongguan, even though they are administratively independent, they will still get a higher correlation score.

[0079] Further, the weighted average process is not a simple arithmetic average, but a Bayesian inference mechanism with prior confidence: the initial factor of the current area itself is given a certain proportion of "self-weight" (w0), which depends on the quality and integrity of the local data and the coverage of historical records (for example, Nanshan District has full data collection, timely updates, and data integrity score of 98%, so its self-weight is set to 0.4), and the rest is filled by the neighborhood factor according to the standardized weight distribution. This design ensures that when the local data is sufficient and reliable, it will not be excessively affected by external fluctuations; when the local data is sparse or interrupted, it can rely on the information of mature nodes in the surrounding area for reasonable extrapolation, achieving a balance between local autonomy and global coordination.

[0080] Step S503, judge whether the deviation of the weighted average value and the historical reference value exceeds the preset deviation threshold; if yes, jump to step S504; if no, jump to step S505; Step S504, activate the adaptive smoothing algorithm based on the connectivity of the supply chain network, and output the smoothed optimized regional adjustment factor.

[0081] Step S505, output the weighted average value as the optimized regional adjustment factor.

[0082] Wherein, only completing the weighted average is not enough to deal with the systemic disturbance caused by extreme events, therefore the system further introduces a deviation detection mechanism to judge whether the newly generated weighted average value deviates significantly from the normal operation interval.

[0083] Specifically, the historical reference value is not a fixed historical mean, but a spatio-temporal baseline calculated by a dynamic rolling window, usually a moving average of the past 12 periods (such as weeks or months), combined with the development trend of the higher-level economic circle. The deviation threshold is set by the adaptive standard deviation method, that is, the median is taken as the center, combined with the standard deviation multiple of historical fluctuations to determine the abnormal boundary. The key is that the standard deviation multiple "k" is not constant, but is dynamically adjusted according to the external environment: a lower value (such as k=1.5) is taken during normal operation, allowing small fluctuations; and automatically increases to a higher level (such as k=3) during major emergencies (such as typhoon disasters), enhancing the sensitivity and response ability of the system.

[0084] Once it is detected that the currently calculated weighted average value exceeds the dynamic threshold range, the adaptive smoothing algorithm is triggered to prevent profit distribution imbalance caused by short-term dramatic fluctuations. If the deviation value is within the threshold, it means that the current regional fluctuation is within the historical normal range, and no additional smoothing is needed.

[0085] In the embodiments of the present application, the core idea of the adaptive smoothing algorithm is to select the filtering strategy differently according to the structural position of the target region in the entire supply chain network, that is, the connectivity of the supply chain network. The connectivity is quantified by the "betweenness centrality" in graph theory: it represents how many shortest logistics paths must pass through the node, and the higher the value, the more important the region plays in the network as a transfer or hub. For high-connectivity regions (such as Shanghai and Guangzhou, which are national logistics hubs), their state changes often truly reflect macro trends and should not be easily removed, so the system uses advanced state estimation techniques such as Kalman filtering to retain the mutation characteristics while suppressing noise interference; for low-connectivity edge regions (such as Lhasa and Yushu, remote areas), their data are easily affected by accidental factors to produce false peaks, so the system uses conservative strategies such as mean filtering or exponential smoothing to effectively eliminate random disturbances.

[0086] More importantly, the system has built-in anomaly isolation mechanism: when a neighboring region experiences a dramatic mutation in its adjustment factor due to natural disasters, policy restrictions, etc. (such as the paralysis of Dongguan logistics caused by a typhoon, resulting in a serious delay in time), the system will immediately identify this abnormal signal and temporarily attenuate its contribution in the weight matrix to 30% of the original value, or even completely shield it, blocking the transmission of false information to the core region and ensuring the robustness of the overall system.

[0087] In the above embodiments, an intelligent regional adjustment system that can perceive spatial correlation, understand economic pulsation, and resist external shocks is constructed, which breaks through the rigid drawbacks brought by relying on static rates in traditional supply chain systems, deeply integrates the spatial interaction theory in geographical economics and the resilience modeling of complex networks, so that the regional adjustment factor is no longer an isolated parameter, but a dynamic variable embedded in the entire supply chain ecosystem.

[0088] Referring to Figure 6 As an embodiment of step S107, the step of automatically parsing the blockchain transaction request by the smart contract, performing the point transfer operation in the distributed ledger, and generating the on-chain settlement voucher includes: Step S601, receiving a blockchain transaction request, decoding the frozen state of the distribution points data, the user address set, and the digital signature contained therein; The blockchain transaction request is triggered by the off-chain business platform (such as the order management system) of the supply chain system after confirming that a certain order has been completed and meets the distribution conditions. Its essence is a structured binary data packet that encapsulates the core information required for this settlement: including the distribution points data in the frozen state, the user address set participating in the distribution, and the digital signature for identity authentication.

[0089] Specifically, the "frozen state sharing points" refers to the virtual equity amount that has been calculated according to the commodity profit base value, regional adjustment factor and identity proportion, but has not been actually issued, which represents the economic value to be redeemed. The "user address set" corresponds to the wallet public key addresses of retailers, wholesalers, agents and other different roles, which are the target accounts of the points credit. The "digital signature" is a piece of encrypted digest generated by the request initiator using the private key, which is used to prove the authenticity and integrity of the request.

[0090] After receiving the transaction request, the system first decodes it to restore it from the serialized format (such as ABI encoding) to readable structured fields for subsequent processing. This decoding process must strictly follow the data specifications defined by the underlying blockchain platform to ensure accurate field offset and type matching, otherwise the subsequent logic execution will fail.

[0091] Step S602, perform security verification condition check on the user address set to confirm address validity and signature authority; Specifically, the verification content mainly includes two levels: one is the digital signature validity check based on the elliptic curve cryptography (ECC), that is, using the standard ECDSA algorithm to verify the received signature in reverse, confirming whether it is indeed signed by the private key of the trusted node, so as to exclude the possibility of fake request; the second is the authority review, checking whether the blockchain node initiating this transaction request has the authority to call the points transfer function in the specific smart contract, which is usually realized through access control list (ACL) or role permission contract. For example, only entities authorized as "settlement service nodes" can trigger the sharing distribution process, and ordinary users or external attackers cannot successfully submit transactions even if they obtain the request template.

[0092] In addition, the system also performs format compliance detection on the user address itself to avoid permanent loss of points due to address errors. If any verification step fails, the entire process is immediately terminated and enters the exception handling path to ensure that any potential risks are blocked before execution.

[0093] Step S603, call the points transfer function of the smart contract to distribute the points amount in the sharing points data to the user address set according to the pre-set identity proportion; Among them, the integral transfer function as the execution engine of the whole mechanism, embedded with the preset identity role and distribution ratio mapping relationship, can automatically split and send to the corresponding user wallet address according to the input integral amount. For example, if the total frozen points are 1000, according to the proportion of 60% for retailers, 30% for wholesalers and 10% for agents, 600, 300 and 100 points of integral will be transferred to the three addresses respectively. These points usually exist in the form of self-defined tokens (such as ERC-20 standard Token), and the transfer process is essentially an operation of modifying the internal account balance mapping table of the smart contract. Since the process runs completely in the blockchain virtual machine (such as EVM), it is protected by the consensus mechanism, so once the execution is successful, it cannot be reversed, eliminating the risk of human tampering or repeated payment.

[0094] More importantly, the entire transfer behavior is recorded in the block, and all participants can query the account situation through the public interface, greatly improving the transparency and trust of the distribution process.

[0095] Step S604, based on the transfer result, a chain settlement voucher containing transaction hash, timestamp, digital signature and user address allocation details is generated; Among them, the chain settlement voucher is a carefully designed structured data body with multiple anti-fake features. Its core elements include: a unique transaction hash (Transaction Hash) as the unique identifier of this operation worldwide; an accurate timestamp, usually taken from the generation time of the latest block header, to ensure that the time information cannot be forged; original user address and integral amount allocation details to form a clear responsibility chain; and key digital signature information for tracing the request source.

[0096] In the embodiments of the present application, in order to improve storage efficiency and enhance data integrity, the system also adopts Merkle Tree (Merkle Tree) structure to compress multiple address-integral mapping relationships, and only the root hash needs to be saved to verify the authenticity of any child node. The finally generated settlement voucher is embedded in the transaction receipt in the form of event log, becoming part of the distributed ledger, which can be checked by third-party auditors, regulatory authorities or users.

[0097] Step S605, write the chain settlement voucher into the transaction receipt of the distributed ledger, and return the transaction execution result to the off-chain system.

[0098] Among them, the write action itself is the result of the blockchain consensus process, which needs to go through multiple steps such as packaging, broadcasting and confirmation, and usually reaches final consensus within a few seconds to a few minutes. Once successfully chained, the voucher has the evidential effect in a legal sense, because its content cannot be tampered with, the time cannot be reversed, and the source can be traced.

[0099] At the same time, the system returns the transaction execution result (such as success / failure status, transaction hash, error code, etc.) to the original initiator (i.e. the off-chain supply chain system), enabling it to update the credit status in the local database (such as from "frozen" to "issued") and notify the relevant user of the account information. This two-way linkage mechanism bridges the gap between on-chain trusted execution and off-chain efficient management, realizing seamless connection between business flow and value flow.

[0100] In the above implementation, a full-link automated settlement system is constructed from request decoding, security verification, smart contract execution to on-chain certificate generation, completely changing the traditional inefficient mode of relying on manual checking, bank payment, and paper certificate archiving. This technical solution not only greatly reduces the financial operation cost and operational risk, but also realizes the high transparency, real-time traceability and anti-repudiation of the distribution process by virtue of the technical characteristics of the blockchain, providing a solid technical support for building a fair, trustworthy and sustainable digital supply chain ecosystem.

[0101] Reference Figure 7 As an embodiment of step S108, the step of sending the on-chain settlement certificate to the third-party settlement platform for transaction validity verification and generating the fund transfer instruction includes: Step S701, transmitting the on-chain settlement certificate to the API gateway of the third-party settlement platform through an encrypted channel; Specifically, after the smart contract completes the credit distribution, it will generate an on-chain settlement certificate containing the transaction hash value, timestamp, user address distribution details and digital signature, and output it as the basis for settlement. To ensure that this sensitive data is not stolen or tampered with during transmission, the system uses a high-strength encrypted channel (such as TLS 1.3 or a secure tunnel based on the national cryptographic algorithm SM2 / SM4) for encapsulated transmission. This encrypted channel not only guarantees the confidentiality of the data, but also has identity authentication capabilities to prevent man-in-the-middle attacks or fake requests from accessing. The target recipient (i.e. a third-party settlement platform with legal qualifications) receives the certificate through its publicly exposed standard API gateway interface. This gateway is usually deployed in a high-availability, high-protection-level service cluster, supporting HTTPS protocol, OAuth2.0 authentication and flow limiting and fusing mechanisms to cope with large-scale concurrent call scenarios. Once the certificate is successfully delivered and parsed, it marks the official start of the off-chain settlement process and provides the original input basis for subsequent legality verification.

[0102] Step S702, calling the blockchain browser interface of the third-party settlement platform to obtain the real-time transaction confirmation number based on the transaction hash value in the on-chain settlement certificate; Specifically, the transaction hash value carried in the on-chain settlement voucher is used as a unique index to actively query the actual transaction status in the underlying blockchain network, focusing on obtaining information on how many subsequent blocks the current transaction has been confirmed, i.e., the "real-time transaction confirmation number". The core purpose of this step is to determine whether the transaction has reached an irreversible final consistent state. In the mainstream public chain (such as Ethereum) or consortium chain environment, there is still a small probability of rollback due to forking when the transaction is just packaged into a block, so it must wait for enough subsequent block confirmations to be considered safe. For example, in the Ethereum ecosystem, 6 confirmations are generally considered stable; while in high-performance consortium chains, only 3 confirmations may be required. The default threshold is set by the platform based on the type of blockchain relied upon, the strength of the consensus mechanism, and the business risk tolerance.

[0103] In addition, if the query result shows that the current confirmation number is lower than the threshold, it means that the points transfer has not been completely solidified, and there may be a double-spending or cancellation risk, so the real fund transfer should not be triggered. This state verification mechanism based on on-chain facts enables the settlement platform to break away from the reliance on single system statements and instead builds on objective and verifiable data, greatly enhancing the anti-risk ability of settlement behavior.

[0104] Step S703, check whether the transaction confirmation number reaches the preset threshold, and check the validity of the digital signature in the on-chain settlement voucher; The digital signature is usually generated by the supply chain platform initiating the settlement request using its private key to encrypt the hash digest of the original message (including transaction hash, user address list, points amount, etc. Key fields). The verification process first separates the signature data from the original message, and then uses the corresponding public key pre-configured in the trust library of the third-party platform to decrypt the received signature and restore the original digest value.

[0105] At the same time, the system uses the same hash algorithm (such as SHA-256) to recalculate the digest value of the locally received original message. Only when the two digest values are exactly the same, can it be determined that the signature is valid, proving that the voucher indeed comes from a trusted subject and has not been modified during transmission. This process is essentially an identity authentication mechanism based on asymmetric encryption, which constitutes the technical cornerstone of anti-fraud and anti-counterfeiting. If the signature verification fails, whether the transaction confirmation number meets the threshold or not, the entire voucher should be determined invalid to prevent false settlement information maliciously constructed from inducing incorrect fund flow.

[0106] Step S704, if the verification is passed, extract the user address and points amount mapping relationship in the on-chain settlement voucher, and generate the fund transfer instruction; wherein the fund transfer instruction includes the target bank account, the transfer amount, and the settlement serial number.

[0107] When the two core verifications (transaction confirmation number meets the standard and digital signature is valid) are successfully passed, the system enters the fund mapping stage, starts to extract the mapping relationship between the user address and the integral amount recorded in the on-chain settlement voucher, and generates fund transfer instructions that can be used for bank system execution. Each blockchain wallet address has been bound to the corresponding bank account information (such as account name, card number, and opening bank) through the real-name authentication process before this. These information are stored in the user data database protected by regulatory compliance and are subject to strict access control policies. The system finds the matching bank account according to the address as the target entity of the fund receipt. Then, according to the unified integral redemption exchange rate set by the platform (such as 100 points = 1 yuan RMB), the virtual integral amount is converted into specific fiat currency amount. The exchange rate can be adjusted periodically but needs to be publicly announced to ensure fairness.

[0108] Finally, the system packages the target account, the transfer amount, and a globally unique settlement serial number derived from the order unique identifier into a standardized fund transfer instruction, and submits it to the cooperative bank or payment clearing agency for payment action. The serial number is not only a key voucher for financial reconciliation, but also realizes the bidirectional traceability association between on-chain transactions and off-chain fund flow, facilitating audit and dispute handling.

[0109] In the above implementation, the closed-loop path between the blockchain virtual economy and the real financial system is opened up. With the verifiability of the blockchain and the security of the cryptography, the ability to complete cross-domain value transfer without intermediary guarantee is realized. Especially in scenarios involving a large number of small, high-frequency, and distributed consumer rebates and distribution incentives, this method exhibits high automation and operational reliability, realizing a new settlement paradigm of "on-chain identification and off-chain redemption", and providing solid technical support for a trusted profit-sharing ecosystem in the digital economy era.

[0110] The application also discloses a supply chain profit-sharing settlement system based on a blockchain.

[0111] A supply chain profit-sharing settlement system based on a blockchain, the profit-sharing settlement system comprising: An order data analysis module configured to receive user order data and analyze and generate a standardized order object containing a product identifier, a discount rate, a geographic location, and a timestamp; A dimension analysis module configured to analyze the standardized order object in dimensions and generate structured data containing a time dimension slice and a geographic coding space dimension; A proportion matching module configured to call a preset proportion parameter table according to the discount rate and match a corresponding profit-sharing identity proportion set; A profit-sharing integral generation module configured to obtain a product profit base value based on the product identifier and generate profit-sharing integral data in combination with the geographic coding space dimension of the structured data and the profit-sharing identity proportion set. a freezing module configured to listen to an order state change event, and mark the commission points data as a frozen state and store the commission points data to an off-chain database when the order state is changed to a payment success; a request module configured to construct a blockchain transaction request of the commission points data in the frozen state when the order state is changed to a completion, and submit the blockchain transaction request to a blockchain node; a points transfer operation module configured to automatically parse the blockchain transaction request through a smart contract, execute a points transfer operation in a distributed ledger, and generate an on-chain settlement voucher; an effectiveness verification module configured to send the on-chain settlement voucher to a third-party settlement platform for transaction effectiveness verification, and generate a fund transfer instruction; an account updating module configured to update a user points account according to the fund transfer instruction and push a settlement result.

[0112] The supply chain commission settlement system based on the blockchain provided in the embodiments of the present application can implement any of the above methods, and the specific working processes of the modules in the system can refer to the corresponding processes in the above method embodiments.

[0113] In the several embodiments provided in the present application, it should be understood that the provided methods and systems can be implemented in other manners. For example, the system embodiments described above are merely schematic; for example, the division of a certain module is merely a logical function division, and there can be another division manner in actual implementation; for example, a plurality of modules or features can be combined or integrated into another system, or some features can be ignored or not executed.

[0114] The embodiments of the present application also disclose a computer device.

[0115] The computer device comprises a memory, a processor, and a computer program stored in the memory and capable of being run on the processor, and the processor implements the supply chain commission settlement method based on the blockchain as described above when running the computer program.

[0116] The embodiments of the present application also disclose a computer readable storage medium.

[0117] The computer readable storage medium stores a computer program capable of being loaded and executed by a processor to implement any of the supply chain commission settlement methods based on the blockchain as described above.

[0118] The computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or apparatus; the program code contained in the computer readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any appropriate combination of the above.

[0119] In this application, the terms "first", "second", etc. are used only to describe specific instances and do not imply or suggest relative importance or a specific number of the indicated technical characteristics. Thus, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the application, the meaning of "a plurality" is two or more, unless specifically limited otherwise.

[0120] Although the present application has been described in connection with various embodiments thereof, it will be understood that other modifications and variations will be apparent to those skilled in the art in view of the foregoing disclosure, the drawings, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. A single processor or other unit can fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to an advantage.

[0121] The above are only preferred embodiments of the present application and are not intended to limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and drawings) can be replaced by other equivalent or similar features unless specifically stated otherwise. That is, each feature is only an example of a series of equivalent or similar features unless specifically stated.

Claims

1. A blockchain-based supply chain profit-sharing settlement method, characterized in that, The profit-sharing settlement method includes: Receive user order data, parse and generate standardized order objects containing product identifiers, discount rates, geographical locations, and timestamps; The standardized order objects are parsed to generate structured data containing time dimension slices and geocoded spatial dimensions; The preset ratio parameter table is called according to the discount rate to match the corresponding profit-sharing identity ratio set. Based on the product identifier, the basic profit value of the product is obtained, and the profit sharing points data are generated by combining the geocoding spatial dimension of the structured data with the profit sharing identity ratio set. Listen for order status change events. When the order status changes to successful payment, mark the profit-sharing points data as frozen and store it in the off-chain database. When the order status changes to completed, a blockchain transaction request containing the profit-sharing points data of the frozen status is constructed and submitted to the blockchain node; The blockchain transaction request is automatically parsed through smart contracts, and the points transfer operation is executed in the distributed ledger to generate on-chain settlement vouchers. The on-chain settlement certificate is sent to a third-party settlement platform for transaction validity verification, and a fund transfer instruction is generated. Update the user's points account according to the fund transfer instruction and push the settlement result.

2. The supply chain profit-sharing settlement method based on blockchain according to claim 1, characterized in that, The steps for performing dimensional parsing on the standardized order objects to generate structured data containing time dimension slices and geocoded spatial dimensions include: Receive standardized order objects and extract the timestamp and geolocation fields; The time series analysis engine is invoked to discretize the timestamp field, generating time period identifiers divided according to a preset time granularity; Input the geographic location field into the geocoding service interface, and output a hierarchical administrative region coding chain; Construct a multi-branch tree data structure, with the time period identifier as the root node and the administrative region code chain as the child node branches; Traverse the multi-branch tree data structure, merge duplicate nodes and fill missing levels to generate structured data containing time-dimension slices and geocoded spatial dimensions.

3. The supply chain profit-sharing settlement method based on blockchain according to claim 2, characterized in that, The steps of obtaining the basic profit value of the product based on the product identifier, and generating profit-sharing points data by combining the geocoding spatial dimension of the structured data with the profit-sharing identity ratio set, include: Based on the product identifier, query the product database to obtain the corresponding basic product profit value; The geocoding spatial dimension is extracted from the structured data, and the administrative region hierarchical code is parsed out. The regional adjustment factor is obtained by matching the pre-set regional fee rate coefficient table with the administrative region hierarchical code. Call the aforementioned profit-sharing identity ratio set and read the profit-sharing ratio of each identity associated with the current order; Based on the commodity profit base value, regional adjustment factor, and profit sharing ratio of each identity, profit sharing score data is calculated and generated.

4. The supply chain profit-sharing settlement method based on blockchain according to claim 3, characterized in that, The step of obtaining the regional adjustment factor by matching the administrative region hierarchical code with a pre-set regional rate coefficient table further includes: Obtain the administrative region hierarchy codes parsed from structured data; Based on the administrative region hierarchical code, a pre-set regional fee rate coefficient table is matched to generate an initial regional adjustment factor; Construct a three-dimensional spatiotemporal data container; where the first dimension is the time slice sequence, the second dimension is the geohash spatial partition index, and the third dimension layer stores the order volume volatility and logistics timeliness deviation. The three-dimensional spatiotemporal data container is input into a pre-trained spatiotemporal graph neural network model; The regional correlation weight matrix is ​​calculated through the spatial attention layer of the spatiotemporal graph neural network model; wherein, the regional correlation weight values ​​are generated based on the historical commodity circulation frequency and supply chain topological distance between regions; The initial regional adjustment factor is dynamically corrected based on the regional correlation weight matrix to generate an optimized regional adjustment factor.

5. The supply chain profit-sharing settlement method based on blockchain according to claim 4, characterized in that, The step of dynamically correcting the initial regional adjustment factor based on the regional correlation weight matrix includes: The list of adjacent administrative regions is determined based on the administrative region hierarchical code, and the adjustment factor of adjacent regions is obtained by matching based on the preset regional fee rate coefficient table. Based on the regional correlation weight matrix, calculate the weighted average of the initial regional adjustment factor and the adjacent regional factors; Determine whether the deviation of the weighted average from the historical benchmark exceeds a preset deviation threshold; if so, activate the adaptive smoothing algorithm based on supply chain network connectivity and output the smoothed optimized regional adjustment factor; if not, output the weighted average as the optimized regional adjustment factor.

6. The supply chain profit-sharing settlement method based on blockchain according to claim 1, characterized in that, The steps of automatically parsing the blockchain transaction request through a smart contract, executing the points transfer operation in the distributed ledger, and generating on-chain settlement vouchers include: Receive blockchain transaction requests and decode the frozen profit-sharing points data, user address sets, and digital signatures contained therein; Perform security verification condition checks on the user address set to confirm address validity and signature permissions; Call the points transfer function of the smart contract to allocate the points amount in the profit-sharing points data to the user address set according to the preset identity ratio; Generate on-chain settlement credentials based on the transfer results, including transaction hash value, timestamp, digital signature, and user address allocation details; The on-chain settlement voucher is written into the transaction receipt of the distributed ledger, and the transaction execution result is returned to the off-chain system.

7. A blockchain-based supply chain profit-sharing settlement method according to any one of claims 1 to 6, characterized in that, The steps of sending the on-chain settlement certificate to a third-party settlement platform for transaction validity verification and generating a fund transfer instruction include: The on-chain settlement certificate is transmitted to the API gateway of the third-party settlement platform through an encrypted channel; The blockchain explorer interface of the third-party settlement platform is invoked to obtain the real-time transaction confirmation count based on the transaction hash value in the on-chain settlement certificate; Verify whether the number of transaction confirmations has reached a preset threshold, and verify the validity of the digital signature in the on-chain settlement certificate; If the verification passes, the mapping relationship between the user address and the points amount in the on-chain settlement certificate is extracted, and a fund transfer instruction is generated; wherein, the fund transfer instruction includes the target bank account, the transfer amount and the settlement serial number.

8. A blockchain-based supply chain profit-sharing settlement system, characterized in that, The profit-sharing settlement system includes: The order data parsing module is used to receive user order data and parse it to generate standardized order objects containing product identifiers, discount rates, geographical locations, and timestamps. The dimension parsing module is used to perform dimension parsing on the standardized order object to generate structured data containing time dimension slices and geocoded spatial dimensions; The ratio matching module is used to call a preset ratio parameter table according to the discount rate and match the corresponding profit-sharing identity ratio set. The profit sharing points generation module is used to obtain the basic profit value of the product based on the product identifier, and generate profit sharing points data by combining the geocoding spatial dimension of the structured data with the profit sharing identity ratio set. The freeze module is used to listen for order status change events. When the order status changes to payment success, the commission points data is marked as frozen and stored in the off-chain database. The request module is used to construct a blockchain transaction request containing the profit-sharing points data of the frozen state when the order status changes to completed, and submit it to the blockchain node. The points transfer operation module is used to automatically parse the blockchain transaction request through a smart contract, execute the points transfer operation in the distributed ledger, and generate on-chain settlement vouchers. The validity verification module is used to send the on-chain settlement certificate to a third-party settlement platform for transaction validity verification and generate a fund transfer instruction; The account update module is used to update the user's points account and push the settlement result according to the fund transfer instruction.

9. A computer device, characterized in that: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 7.

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