A profit and benefit pre-distribution system and method based on a dynamic value algorithm

Abstract

The application discloses a profit and income pre-distribution system and method based on a dynamic value algorithm, which distributes future profit income in the form of digital equity certificates to key contributors in advance, thereby locking supply and creating demand, the digital equity certificates can be used as a mortgage for "production loans" or "advance payment" paid to suppliers to lock production capacity. The equity certificates are given or sold, so that the consumption behavior is changed into "investment consumption", that is, the future success bonus of the purchased product is enjoyed, the method converts future market demand into a "demand option" that can be priced and traded through an algorithm, provides deterministic guidance and front-end capital for production, before production starts, a part of future profit is pre-distributed to production factor input parties and early demand parties according to simulated value flow, the after-distribution mode is changed, and capital, talents, technology and other factors can be guided to flow to the "new supply" field that can best create new demand through dynamic transaction conditions.

Description

Technical Field

[0001] This invention relates to the technical field of commodity digital asset trading mechanisms, specifically to a profit and revenue pre-distribution system and method based on a dynamic value algorithm. Background Technology

[0002] Existing transaction systems primarily address supply and demand issues, largely channeling goods to consumers through a manufacturer-agent-retailer hierarchy. This multi-level distribution model in traditional supply chains leads to inefficient data flow, a lack of dynamic pricing mechanisms based on real-time transaction data, and an inability to quickly respond to market changes. Furthermore, these systems fail to effectively analyze and utilize consumer behavior data, resulting in resource misallocation. Current e-commerce platforms lack effective integration of data across the entire transaction chain. The systems cannot quantify consumer participation into distributable revenue data, and traditional transaction models cannot pre-allocate revenue before goods are sold. Traditional inventory management systems lack a linkage mechanism with real-time consumer behavior data, making it impossible to predict the speed of goods circulation and market acceptance through algorithmic models. The separation of inventory and sales data leads to slow system responses when supply and demand imbalances occur. Traditional pricing systems cannot dynamically adjust based on multi-dimensional transaction data (number of participants, transaction duration, transaction frequency) and lack algorithmic models to convert consumer participation into price parameters. Price adjustments lag behind market changes, resulting in system inefficiency.

[0003] To overcome these limitations, this invention designs a unique trading method that can reflect market supply and demand while ensuring transaction liquidity and economic benefits, thereby solving the aforementioned objective problems. Summary of the Invention

[0004] The technical problem this invention aims to solve is to overcome existing deficiencies and provide a profit and revenue pre-distribution system and method based on a dynamic value algorithm. Compared with existing technologies, this invention can not only respond to market fluctuations in real time, but also achieve efficient resource allocation and configuration through blockchain automated settlement and intelligent order generation mechanisms. It distributes a portion of the profits before commodity transactions, greatly improving the enthusiasm of participants. In addition, the innovation of this invention lies in its consideration of the balance and distribution of interests among all parties involved in the transaction, which can effectively solve the problems in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a supply and demand coordination and profit pre-allocation system based on dynamic value stream simulation, comprising: The data sensing module is used to collect market demand signals and supply chain capability data in real time; The dynamic value stream simulator, built on a multi-agent reinforcement learning model, is used to simulate the value flow and profit distribution that may arise from new supply schemes in future market environments. The equity certificate issuance module is used to generate digital equity certificates representing future profit dividend rights based on the expected value stream and value contribution report output by the simulator.

[0006] A revenue pre-distribution transaction method based on a power-law-based increasing sorting algorithm is used in the aforementioned revenue pre-distribution system. This method utilizes a commodity digital asset trading platform as a carrier, and the transaction content is the underlying asset or the corresponding digitized asset. Data is used as a production factor in the sales process for investment or trading. The specific steps are as follows: Step 1: Preset the ex-factory price of the product manufacturer and the terminal price of the retailer; Step 2: Set the number of trading rounds n=1, 2, 3, ..., N, and set the number of targets Q(n) in each round, where Q(n) increases in the form of a power function and Q(n)>Q(n-1); Step 3: Set the target unit price B(n) for each round, where B(1) is the initial unit price and B(n) ≥ B(n-1). Step 4, for the nth round of transactions: (1) Receive consumer orders participating in the nth round of transactions, each order corresponding to a purchase request for a target; (2) Calculate the total transaction amount T(n) = P(n) for this round. B(n), where P(n) is the number of participants in the nth round; (3) Deduct the commission rebate from the previous round of traders from the total transaction amount: Rcommission(n) = Q(n-1) (B(n) - B(n-1)); (4) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 5: Determine if the settlement condition is met: R(n) ≥ Q(N) S; Step 6: When the settlement conditions are met and B(N) ≤ M, proceed with transaction settlement. Step 7: In the Nth round, the trader acquires the underlying asset at a unit price B(N); Step 8: Traders in the first N-1 rounds receive corresponding trading rebates; Step 9: Deduct the cost of the underlying asset Q(N) from the remaining amount. The amount after "S" is used as a platform service fee.

[0007] As a preferred embodiment of the present invention, in step two, the quantity Q(n) of the target is expressed in the form of a power function as follows: Q(n) = k n^p Where k is the base coefficient, p is the power exponent, and 0.5 ≤ p ≤ 3.

[0008] As a preferred embodiment of the present invention, in step three, the unit price B(n) is calculated using the following formula: B(n) = B(n-1) (1+α) (tref / tactual) Where α is the price adjustment coefficient, 0 < α ≤ 0.5; tref is the reference trading time; and tactual is the actual completion time of the previous round of trading.

[0009] As a preferred embodiment of the present invention, in step five, when the settlement condition R(n) ≥ Q(N) cannot be satisfied simultaneously... When S and price condition B(N) ≤ M, the transaction terminates and all participants' funds are returned to their original source.

[0010] A method for profit redistribution in transactions of low-profit products, used on commodity digital asset trading platforms as the medium for trading commodities with a profit margin of less than 5%, where the profit margin = (final price - ex-factory price) / ex-factory price, includes the following steps: Step 1: Preset the ex-factory price S and the end-user price M of the target asset; Step 2: Set the number of trading rounds n=1,2,3,...,N. In each round, set the number of participants P(n) and the unit price B(n), where B(1) is the initial unit price and B(n)=B(n-1). (1+β), 0<β≤0.05; Step 3, for the nth round of transactions: (1) Calculate the total transaction amount T(n) = P(n) for this round. B(n); (2) Deduct the commission rebate from the previous round of traders from the total transaction amount. Rcommission(n) = R(n-1) γ, where 0 < γ ≤ 0.3; (3) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 4: Determine if the settlement condition is met: R(n) ≥ Q_total S, where Q_total is the total number of items; Step 5: When the settlement conditions are met and B(N)≤M, the transaction is settled. The Nth round trader obtains the underlying asset at a unit price of B(N), and the first N-1 round traders receive the corresponding transaction rebates.

[0011] A method for redistributing the profits of a lottery-type product, which uses a commodity digital asset trading platform as the trading vehicle, involves the following steps after the final round of transactions: Some participants receive the goods, while others split the remaining amount R(n) after deducting all fees. Step 1: Preset the ex-factory price S of the target asset and set the initial unit price B (1). Step 2: Set the number of trading rounds n=1,2,3,...,N, and set the number of participants P(n) and the unit price B(n) for each round, where B(n)≥B(n-1); Step 3: When B(n) > S, the system starts shipping, and the shipping quantity Q(n) is determined according to a preset function; Step 4, for the nth round of transactions: (1) Calculate the total transaction amount T(n) = P(n) for this round. B(n); (2) Calculate the commission amount for this round, Rcommission(n) = Q(n). (B(n)-S) δ, where 0 < δ ≤ 0.5; (3) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 5: Determine if the settlement condition is met: R(n) ≥ Q(n) S; Step Six: When the settlement conditions are met, proceed with transaction settlement. (1) According to the preset ratio, some of the Nth round traders will obtain the underlying assets; (2) The remaining traders in the Nth round share the lottery amount L = R(n) - Q(n) with the traders in other rounds. S.

[0012] The formula for calculating the transaction fee C(n) is as follows: C(n) = T(n) η Where η is the commission rate, and 0.001≤η≤0.05.

[0013] As a preferred technical solution of the present invention, the digital asset trading platform uses blockchain technology to achieve the immutability of transaction records and the automated execution of transaction settlement.

[0014] As a preferred technical solution of the present invention, before the transaction begins, the initial values ​​of the transaction round N, the number of participants P(n) in each round, and the unit price B(n) of the target are predicted through big data analysis.

[0015] As a preferred embodiment of the present invention, the underlying asset is a physical commodity, a digital commodity, a service right, or a financial product.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. By using a power-law-based incremental algorithm, exponentially efficient processing of transaction data is achieved. A dynamic pricing model based on multi-dimensional parameters such as time and number of participants is established, which improves the system's response speed to market changes. The traditional linear transaction process is reconstructed into a cyclical redistribution model, which significantly improves the processing efficiency and utilization rate of transaction data.

[0017] 2. An algorithm-based advance profit distribution technology was developed, enabling the system to accurately calculate the profits of all parties before the transaction is completed. Through an automated settlement mechanism, manual intervention was reduced, the transaction dispute rate was lowered, and a data correlation model between consumer participation and profit distribution was constructed, which improved user stickiness and system activity.

[0018] 3. An inventory demand forecasting algorithm based on transaction rounds and the number of participants was established, improving the accuracy by more than 40%. By locking in transaction volume in advance, the manufacturer's production plan is better matched with market demand data, reducing information asymmetry problems in the traditional supply chain and improving inventory turnover.

[0019] 4. A dynamic pricing algorithm based on parameters such as trading time and number of participants was developed, making prices closer to actual market demand. The congestion problem of the trading system during peak periods was solved by using a power function growth model, which improved the system's processing capacity. A non-linear relationship model between price and trading volume was established, enabling the system to more accurately balance supply and demand.

[0020] 5. By reducing intermediate steps, the transaction system directly connects the production and consumption ends, reducing system resource consumption. Through algorithm optimization, the efficiency of transaction matching is improved, reducing server resource usage. The automated settlement mechanism reduces manual review steps and lowers system operation and maintenance costs. Attached Figure Description

[0021] Figure 1 This is an example diagram of the transaction method of the present invention; Figure 2 This is a graph showing the relationship between the unit price of the commodity in the current round and the time relationship of the previous round in this invention; Figure 3 Example diagram of a transaction method for redistributing profits from low-profit products; Figure 4Example diagram of a transaction method for redistributing the profits of lottery-type products; Figure 5 This is a schematic diagram of the interaction process in this invention. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention essentially solves the technical problems of inefficiency, uneven profit distribution, and supply-demand imbalance in traditional transaction systems through specific data processing algorithms and transaction mechanism design. Its core value lies in providing a complete and quantifiable method for transaction data processing, rather than simply a business method. Compared with traditional e-commerce platforms, this technical solution transforms consumers from mere buyers into value co-creators, converting user participation behavior into quantifiable data assets through algorithms, thereby optimizing the resource allocation efficiency and operational performance of the entire transaction system.

[0024] Please see Figure 1-4 This invention provides a technical solution: a profit and revenue pre-allocation system and method based on a dynamic value algorithm. Based on a multi-agent reinforcement learning economic system simulation environment, it incorporates real-time demand data, supply chain data, and macroeconomic policy data to simulate future value creation and flow under different supply strategies. According to the "value stream prediction" output by the simulator, future profits are pre-allocated to key contributors (such as R&D personnel, producers, and early consumers) in the form of digital equity certificates, thereby locking in supply and creating demand. Digital equity certificates are issued based on the "expected total value stream" and "value contribution" output by the simulator. These certificates can be used as collateral for "production loans" or as "prepayments" to suppliers, locking in production capacity. During pre-sales or early purchases, these equity certificates can be gifted or sold, transforming consumption behavior into "investment consumption," meaning enjoying the future success dividends of the purchased products.

[0025] Specifically, this method includes three different trading approaches for different scenarios, as shown below: Method 1, a profit pre-distribution trading method based on a power-law-based increasing sorting algorithm, uses a commodity digital asset trading platform as its platform. The specific steps are as follows: 1. Preset the ex-factory price of the product manufacturer and the terminal price of the retailer; 2. The transaction price of the platform is calculated as follows: the cumulative remaining amount in each round during the current transaction process accumulates to meet the total amount of orders generated by the number of traders in the final round, and the transaction price is obtained by dividing said total amount by the number of traders in the final round. The transaction price meets the following conditions: Ex-factory price ≤ transaction price ≤ terminal price; 3. The quantity of commodities in each round is set, and the unit price of commodities in each round is calculated for the bid price of the next round by taking the length of consumption time of consumers in the previous round as a variable, or is set in advance, or is bid by consumers themselves. The number of transaction rounds and the unit price of commodities in each round can also be calculated through big data; The calculation formula for the unit price of commodities in each round is as follows: P(t)=ac / t^b+c wherein, P(t) is the unit price of commodities in the current round, t is the transaction time of the previous round, calculated in minutes; a is the number of traders in the previous round, which is a proportional coefficient that varies with time and controls the speed of price fluctuation, and a is a positive number; b is obtained by dividing the number of traders in the previous round by the cumulative number of traders in all previous rounds, and represents the speed of price increase, 0 < b < 1. The smaller the value of b, the smoother the price fluctuation curve; c is the base of the initial price, that is, the unit price of the previous round; The unit price of commodities set by the platform or given by consumers themselves shall not be lower than the highest unit price of commodities in the previous round; 4. When the cumulative remaining amount of each round in the transaction process meets the total amount of orders generated by the number of traders in the final round, the platform automatically settles and the transaction is completed. At this time, the remaining amount meets the following conditions: Number of orders in the final round × ex-factory price ≤ remaining amount of the final round; The gross profit of this transaction is obtained by subtracting the ex-factory price of all commodities from the transaction price of all commodities. After the gross profit deducts the platform service fee, all remaining proceeds are returned to all consumers who participated in the transaction before the final round according to rules. The platform extracts 3% to 7% from the rebate as its profit. When the two conditions that the commodities in the final round are sold out within the set time and the unit price of the commodities in the final round is between the ex-factory price and the terminal price are not satisfied at the same time, the commodities are unsold, and the funds of traders are returned to the original accounts; wherein, in step 3, the quantity of commodities in each round of transaction increases in a power function form, and the quantity of commodities in each round of transaction is a power function, wherein the increasing integer is taken as the base of the power. After calculation, the platform uses a constant or variable integer or decimal as the exponent of the power. The quantity of commodities in the next round includes all the quantity of commodities in the previous round. After traders in the next round make a bid, a part of the bid is used to purchase the commodities traded by traders in the previous round, and the price difference obtained by traders in the previous round is the rebate.

[0026] The specific transaction process of the method is demonstrated below with an example: Taking a mobile phone with a factory price of 1,000 yuan and a market price of 1,700 yuan as an example, the base of the power function is the cycle, and the exponent of the power function is 2. In the first round, the quantity of goods was 1² units, and the unit price was 0.01 yuan. After these 1² units were sold, the goods entered the second round, and the balance of the funds pool in the first round was 0.01 yuan. In the second round, the quantity of goods was 2² units, with a unit price of 0.1 yuan. After these 2² units were sold, they entered the third round. One² unit of these units was purchased from a participant in the first round, thus the first round participant profited 0.1 yuan. 1²-0.01 1² yuan, or 0.09 yuan, the second round of fund pool balance is 0.1 yuan. (2²-1²)+0.01, which equals 0.31 yuan; In the third round, 3² units of goods were sold at a price of 1 yuan each. These 3² units were then auctioned off and entered into the fourth round. Two² units of these goods were purchased from the second round's buyers, thus the second round's buyers profited 1 yuan. 2²-0.1 2² yuan, or 3.6 yuan, the balance of the third round of funds pool is 1. (3²-2²)+0.31, which equals 5.31 yuan; In the fourth round, 4² units of goods were sold at a unit price of 10 yuan. These 4² units were then auctioned off and entered into the fifth round. Of these, 3² units were purchased from the third-round trader, thus the third-round trader profited 10 yuan. 3²-1 3² yuan, or 81 yuan, the fourth round of funding pool balance is 10. (4²-3²)+5.31, which equals 75.31 yuan; In the fifth round, 5² units of goods were sold at a unit price of 100 yuan. These 5² units were then auctioned off and entered into the sixth round. Four² units of these goods were purchased from the fourth round trader, thus the fourth round trader profited 100 yuan. 4²-10 4² yuan, or 1440 yuan, with a remaining balance of 100 yuan in the second round of funding. (5²-4²)+75.31, which equals 975.31 yuan; In the sixth round, 6² units of goods were auctioned off at a unit price of 1000 yuan. These 6² units then entered the seventh round, in which 5² units were purchased from the fifth round trader. Therefore, the fifth round trader profited 1000 yuan. 5²-100 5² yuan, or 22,500 yuan, with a remaining balance of 1,000 yuan in the sixth round of funding. (6²-5²)+975.31, which equals 11975.31 yuan; In the seventh round, 7² units of goods were sold at a unit price of 1100 yuan. These 7² units were then auctioned off and entered the eighth round. Six² units of these goods were purchased from the sixth round trader, thus the sixth round trader profited 1100 yuan. 6²-1000 6² yuan, or 3600 yuan, the seventh round of funding pool balance is 1100 yuan. (7²-6²)+11975.31, which equals 26275.31 yuan; In the eighth round, 8² units of goods were sold at a unit price of 1200 yuan. These 8² units were then auctioned off and entered the ninth round. Seven² units of these ninth units were purchased from the seventh round trader, thus the seventh round trader profited 1200 yuan. 7²-1100 7² yuan, or 4900 yuan, with a remaining balance of 1200 yuan in the eighth round of funding. (8²-7²)+26275.31, which equals 44275.31 yuan; In the ninth round, 9² units of goods were auctioned off at a unit price of 1300 yuan. These 9² units then entered the tenth round, with 8² units purchased from the eighth round trader. Therefore, the eighth round trader profited 1300 yuan. 8²-1200 8² yuan, or 6400 yuan, with a remaining balance of 1300 yuan in the ninth round of funding. (9²-8²)+44275.31, which equals 66375.31 yuan; In the tenth round, 10² units of goods were auctioned off at a unit price of 1400 yuan. These 10² units then entered the eleventh round, in which 9² units were purchased from the ninth round trader. Therefore, the ninth round trader profited 1400 yuan. 9²-1300 9² yuan, or 8100 yuan, with a remaining balance of 1400 yuan in the tenth round of funding. (10²-9²)+66375.31, which equals 92975.31 yuan; In the eleventh round, the quantity of goods was 11² units, with a unit price of 1500 yuan. Since the unit price met the platform's set price, these 11² units were sold and entered the settlement process. Of these, 10² units were purchased from the trader in the tenth round, thus the tenth round trader profited 1500 yuan. 11²-1400 11² yuan, or 12,100 yuan, with a remaining balance of 1,500 yuan in the eleventh round of funding. (11²-10²)+92975.31, which equals 124475.31 yuan; At this point, the final round of orders... If the ex-factory price is less than or equal to the balance amount of the final round, the transaction is completed. In this auction, 121 mobile phones are sold, the balance amount of the final round is 124,475.31 yuan, and the number of orders in the final round The ex-factory price is 121 ×1000, which is 121,000 yuan. The balance of 124,475.31-121,000 in the capital pool, which is 3,475.31 yuan, is used as the platform service fee.

[0027] In this transaction, the final round bidding price minus the final round balance, that is 11² ×1500-124475.31=57024.69, which means all consumers who participated in the transaction bidding benefit 57,024.69 yuan. The trading platform draws 3% to 7% from the 57,024.69 yuan of income as the platform profit, and the price of the mobile phone purchased by consumers in the final round is also lower than the market price of 1,700 yuan. The producer sells 121 mobile phones at one time, and the platform and the logistics provider also obtain profits, achieving five-party benefits, and playing the roles of stimulating consumption, giving rebates to consumers and destocking.

[0028] In this example, for the convenience of calculation and demonstration, simple numbers are used as the unit price of each round. However, in practical application, the unit price of commodities in each round will be refined to more than two decimal places, and the calculation method of the unit price of each round is core confidential information that will not be disclosed to users, which prevents speculators from manipulating the entry and exit timing through calculation, and ensures the fairness, openness and impartiality of the transaction process. Wherein, the unit price of commodities in each round can be calculated by the following calculation formula: P(t)=ac / t^b+c Wherein P (t) is the unit price of the commodity in the current round, and t is the transaction time of the previous round, calculated in minutes; a is the number of participants in the previous round, which is a proportional coefficient that changes with time, and controls the speed of price fluctuation. a is a positive number; b is the number of participants in the previous round divided by the cumulative number of participants in all previous rounds, which represents the speed of price increase, 0 < b < 1. The smaller the value of b, the smoother the price fluctuation curve; c is the initial price base, that is, the price of the previous round.

[0029] Assume that a=1 and b=0.5, then as time t increases, the price will increase at a slower and slower rate, that is, the shorter the time, the faster the price rises, and the larger the increment compared with the previous round. If 0 < b < 1, the price increases with time, which means the shorter the time, the more the price increases. Therefore, the price will have a relatively large increase, and the curve will become steeper as the value of b decreases.

[0030] In this function, as t increases, the price increment approaches 0. The shorter the time between the first and last person to purchase in the current round, the higher the unit price set by the system in the next round. The longer the time, the lower the unit price in the next round, until the purchase price infinitely approaches the purchase price of the current round.

[0031] This formula is only one embodiment in practical application, and the specific formula in practical application is kept as a trade secret.

[0032] In practical applications, increasing power function sequences include, but are not limited to: 1^2, 2^2, 3^2, 4^2, 5^2, 6^2,...N^2; 1^2, 3^2, 5^2, 7^2, 9^2, 11^2,...(2N-1)^2; 1^3, 2^3, 3^3, 4^3, 5^3, 6^3,...N^3.

[0033] This method allows for gradual increases in bids from a minimum to a maximum, and flexible adjustments to bids based on market conditions. Furthermore, by considering factors such as the number of bidders, bid duration, and bid frequency, transactions can be conducted at more reasonable prices. Therefore, this method has high practical value and promising application prospects.

[0034] In addition, this method also has a priority strategy that prioritizes the users who bid first when using the power function growth method to encourage early participation and maximize profits, while also taking into account the users who bid last to quickly trade and obtain profits or items.

[0035] This strategy effectively shortens market response time, reduces transaction costs, and increases the participation of both buyers and sellers, thereby increasing the success rate and trading volume. Simultaneously, by introducing market-based pricing, it effectively eliminates the negative impact of market volatility and improves the stability of the entire market ecosystem.

[0036] In the procurement process, this method reduces inventory pressure and improves inventory turnover by accurately predicting and allocating market demand. Simultaneously, it uses the power function growth method to calculate reasonable selling prices, enabling manufacturers to lock in profits in advance.

[0037] For businesses, this method not only reduces inventory buildup but also enables timely responses to market demands, achieving optimal operational results. For consumers, it provides more choices, generates transaction revenue, and enhances the shopping experience by allowing them to profit from transactions, gain investment opportunities, and enjoy the best prices.

[0038] Method 2, a trading method based on Method 1, please refer to [link / reference]. Figure 3We also provide a method for the redistribution of profits from low-profit products. This method addresses the issue of unsold low-profit goods and is applicable to commodities traded on digital asset trading platforms with a profit margin below 5%. The profit margin is calculated as (final price - ex-factory price) / ex-factory price. This method includes the following steps: 1. Determine the manufacturer's ex-factory price S and the retailer's final price M; 2. The platform's transaction price is calculated by dividing the total amount of orders generated from the remaining balance in each round of the current transaction process, which is sufficient to cover the number of participants in the final round, by the number of participants in the final round. The transaction price must meet the following conditions: Factory price S ≤ Transaction price ≤ Terminal price M; 3. Define a function to determine the number of participants P(n) in each round, and set the initial unit price of the product B(1), where P(n) = f(n, P(n-1)) and B(n) = B(n-1). (1+b), where b is the price increase coefficient and f is the population change function; Step 4: When the remaining amount R(n) in each round is not less than the total cost Ctotal, the platform automatically settles the transaction, and the transaction is completed. At this time, the remaining amount meets the following conditions: Ctotal ≤ R(n); Total cost paid: Ctotal = Number of orders shipped Factory price S; The remaining amount R(n) in each round = the remaining amount R(n-1) of the previous round + the total bid amount T(n) of this round - the rebate Rreuse(n) - the transaction fee C(n) of this round. During the transaction, the quantity of goods in the next round includes all the quantities of goods in the previous round. A portion of the bids placed by traders in the next round is used to purchase the goods traded by traders in the previous round. The difference earned by traders in the previous round is the rebate Rreuse(n). The transaction fee C(n) = c The total bid amount in each round is T(n), where c is the commission rate. The total bid amount in each round is T(n) = the number of participants in each round is P(n). The unit price of each commodity in each round is B(n).

[0039] Method 3, to increase participants' bidding enthusiasm, is based on the trading method of Method 1. Please refer to [link / reference]. Figure 4 We also provide a method for redistributing the proceeds of a lottery-style product. This method uses a commodity digital asset trading platform as the vehicle for transactions. After the final round of transactions, some participants receive the goods, while others split the remaining amount R(n) after deducting all fees. Specifically: First, determine the ex-factory price S of the commodity manufacturer. When the bid amount B(n) in each round is greater than the ex-factory price S, the shipment begins. The shipment quantity Q(n) is calculated by the system according to the function. T(n) - Transaction fee per round C(n) - Rebate R commission (n) = Remaining amount R(n) in each round; T(n) = ∑ i=1 P（n） B(i,n); During the transaction, the quantity of goods in the next round includes all the goods in the previous round. A portion of the bid placed by the trader in the next round is used to purchase the goods traded by the trader in the previous round. The difference earned by the trader in the previous round is the rebate R. commission (n), commission R commission (n) = r Shipments Q(n) The bid amount is B(n), and r is the rebate ratio coefficient. When R(n) ≥ S When Q(n) is reached, the system performs a final settlement. After settlement, (bid amount B(end) - ex-factory price S) The number of participants in each round P(end) - the handling fee in each round C(end) - the rebate Rcommission(end) + the remaining amount in the previous round R(end-1) = the lottery amount L, which is allocated by the system according to the set algorithm.

[0040] This invention relates to a profit and revenue pre-distribution system and method based on a dynamic value algorithm. Using a commodity digital asset trading platform as the carrier, the transaction content is the underlying asset or its corresponding digitized asset. Data is used as a production factor in the sales process for investment or trading. Traditionally, suppliers directly supply the goods themselves, and consumers directly consume the goods. When data is used as a production factor, suppliers can first supply the underlying commodity data asset, and consumers can first trade the commodity data asset. In the process, consumers can obtain asset returns or physical assets.

[0041] The three transaction methods provided by this invention are used to establish a participant incentive model based on user participation time and goods as the underlying assets. According to the data elements of the underlying assets, the data elements such as participants and time nodes are integrated. Through the evaluation of digital assets, digital quality of goods, and digital price of goods, a model for quickly screening and matching assets that meet the standards and determining the transaction price is formed. A transaction model for digital assets with a cyclical hierarchy is constructed to form a transaction process for digital assets. The value distribution model of data elements is established by using product information, number of participants, time nodes, and cyclical hierarchy as data element allocation factors.

[0042] In the three transaction methods provided by this invention, data is used through algorithms and models to provide scientific evidence for the supply, sale, transaction, and acquisition of target logistics. This mechanism enables data to play a greater role in the sales process, improving sales efficiency and sales volume.

[0043] The evolution of data value forms has progressed from the profit or acquisition of data assets during data transactions based on the order of acquisition or algorithmic mechanisms.

[0044] Of the three trading methods, trading is a superficial behavior; in essence, the entire process is an investment activity in which users contribute and receive returns.

[0045] Among the three transaction methods, the core concept is a decentralized model, which we call self-centered creation. Each user is their own production center, and all user participation in the transaction is a data contribution behavior, that is, a creation originating from self-centeredness. Users create value through contribution and ultimately obtain benefits. Therefore, users are the creators of value. In the process of participation, the profit of the goods is distributed in advance, which effectively improves the enthusiasm of participants and provides the underlying business logic for improving the dissemination efficiency of this business model.

[0046] The self-centered model is at the heart of all current changes, balancing individual rights, freedoms, and responsibilities. Self-centeredness is the origin of the next mode of production; it will transform our production methods. Therefore, on the internet and within the digital economy, every individual is a free agent and a creator of their own value. Value will be rewarded within the digital economy—this is the first core issue arising from the digital age.

[0047] This method allows for gradual increases in bids from a minimum to a maximum, and flexible adjustments to bids based on market conditions. Furthermore, by considering factors such as the number of bidders, bid duration, and bid frequency, transactions can be conducted at more reasonable prices. Therefore, this method has high practical value and promising application prospects.

[0048] In this scheme, digital assets are digital certificates of rights to specific goods within a user's warehouse, possessing a clear fair value. Their write-off is treated as a payment transaction.

[0049] Payment is a transaction in which "digital assets" are used as a payment instrument to fully or partially offset the amount payable.

[0050] Split-payment transactions are the technological cornerstone for achieving a hybrid payment system that combines "payment for goods" with cash. A single payment order can be split into multiple settlement parties and funding sources.

[0051] Escrow transactions are a secure model that protects the interests of both buyers and sellers in a "delivery" scenario.

[0052] To more intuitively demonstrate the core interactive flow of the entire solution, especially the flow of funds, goods, and digital assets between "payment" and "collection," please participate... Figure 5 .

[0053] The specific process is as follows: Step 1, Valuation: The system performs a fair value assessment on both used and new goods.

[0054] Step 2, Generate a new order: Create a new order with the amount being the difference between the new price and the old price.

[0055] Step 3, handle the difference: If a price difference is required, the user shall pay the difference.

[0056] If a refund is due, the platform will refund the price difference to the user's digital assets or balance.

[0057] Step 4, perform the replacement: Simultaneously cancel the user's old digital assets and generate new digital assets or ship them directly.

[0058] The foundation of this technical solution lies in risk control, reconciliation, and compliance assurance, including the following aspects: KYC / Real-name Authentication: All users and merchants must complete real-name authentication to prevent money laundering risks.

[0059] Transaction monitoring: Establish risk control rules to monitor "digital asset" transactions and prevent fraudulent transactions and arbitrage.

[0060] Fund reconciliation: Daily reconciliation of cash flow with payment channels such as WeChat and Alipay.

[0061] Business reconciliation: a core step. It is essential to verify each "delivery" order against digital asset write-off records, cash disbursement records, and goods ownership change records to ensure complete consistency and prevent asset discrepancies.

[0062] Furthermore, this method employs a priority strategy that prioritizes the earliest bidders during the power function growth process, encouraging early participation to maximize profits. Simultaneously, it considers the later bidders, enabling them to quickly trade and acquire rewards or items. This strategy effectively shortens market response time, reduces transaction costs, and increases the participation of both buyers and sellers, thereby increasing the success rate and trading volume. Simultaneously, by introducing market-based pricing, it effectively eliminates the negative impacts of market volatility, enhancing the stability of the entire market ecosystem.

[0063] In the procurement process, this method reduces inventory pressure and improves inventory turnover by accurately predicting and allocating market demand. At the same time, it uses the power function growth method to calculate a reasonable selling price, enabling manufacturers to lock in profits in advance.

[0064] For businesses, this method not only reduces inventory backlog but also enables timely response to market demand, achieving optimal operational results. By eliminating middlemen who profit from price differences, consumers benefit from the transaction process, gain investment opportunities, and enjoy the best prices, providing them with more choices, increased transaction revenue, and a enhanced shopping experience.

[0065] This method does not involve human-to-human transfer; instead, it distributes tangible product profit margins to participants. Merchants pre-budget their profits, and participants can potentially obtain goods regardless of their stage of participation. Its purpose is to solve sales problems. This system is based on the delivery of real goods and services, possesses robust risk control and exit mechanisms, and its profit distribution and fund flows are completely transparent and auditable.

[0066] The three trading methods provided in this invention distribute a portion of the profits before the commodity transaction, greatly increasing the enthusiasm of participants, which is reflected in the following aspects: 1. By using a power-law-based incremental algorithm, exponentially efficient processing of transaction data is achieved. A dynamic pricing model based on multi-dimensional parameters such as time and number of participants is established, which improves the system's response speed to market changes. The traditional linear transaction process is reconstructed into a cyclical redistribution model, which significantly improves the processing efficiency and utilization rate of transaction data.

[0067] 2. An algorithm-based advance profit distribution technology was developed, enabling the system to accurately calculate the profits of all parties before the transaction is completed. Through an automated settlement mechanism, manual intervention was reduced, the transaction dispute rate was lowered, and a data correlation model between consumer participation and profit distribution was constructed, which improved user stickiness and system activity.

[0068] 3. An inventory demand forecasting algorithm based on transaction rounds and the number of participants was established, improving the accuracy by more than 40%. By locking in transaction volume in advance, the manufacturer's production plan is better matched with market demand data, reducing information asymmetry problems in the traditional supply chain and improving inventory turnover.

[0069] 4. A dynamic pricing algorithm based on parameters such as trading time and number of participants was developed, making prices closer to actual market demand. The congestion problem of the trading system during peak periods was solved by using a power function growth model, which improved the system's processing capacity. A non-linear relationship model between price and trading volume was established, enabling the system to more accurately balance supply and demand.

[0070] 5. By reducing intermediate steps, the transaction system directly connects the production and consumption ends, reducing system resource consumption. Through algorithm optimization, the efficiency of transaction matching is improved, reducing server resource usage. The automated settlement mechanism reduces manual review steps and lowers system operation and maintenance costs.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A supply-demand coordination and profit pre-allocation system based on dynamic value stream simulation, characterized in that, include: The data sensing module is used to collect market demand signals and supply chain capability data in real time; The dynamic value stream simulator, built on a multi-agent reinforcement learning model, is used to simulate the value flow and profit distribution that may arise from new supply schemes in future market environments. The equity certificate issuance module is used to generate digital equity certificates representing future profit dividend rights based on the expected value stream and value contribution report output by the simulator.

2. A profit pre-distribution transaction method based on a power-law-based increasing sorting algorithm for the profit pre-distribution system of claim 1, characterized in that: This method uses a commodity digital asset trading platform as a carrier. The trading content is the underlying asset or the corresponding digitized asset. Data is used as a production factor in the sales process for investment or trading. The specific steps are as follows: Step 1: Preset the ex-factory price of the product manufacturer and the terminal price of the retailer; Step 2: Set the number of trading rounds n=1, 2, 3, ..., N, and set the number of targets Q(n) in each round, where Q(n) increases in the form of a power function and Q(n)>Q(n-1); Step 3: Set the target unit price B(n) for each round, where B(1) is the initial unit price and B(n) ≥ B(n-1). Step 4, for the nth round of transactions: (1) Receive consumer orders participating in the nth round of transactions, each order corresponding to a purchase request for a target; (2) Calculate the total transaction amount T(n) = P(n) for this round. B(n), where P(n) is the number of participants in the nth round; (3) Deduct the commission rebate from the previous round of traders from the total transaction amount: Rcommission(n) = Q(n-1) (B(n) - B(n-1)); (4) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 5: Determine if the settlement condition is met: R(n) ≥ Q(N) S; Step 6: When the settlement conditions are met and B(N) ≤ M, proceed with transaction settlement. Step 7: In the Nth round, the trader acquires the underlying asset at a unit price B(N); Step 8: Traders in the first N-1 rounds receive corresponding trading rebates; Step 9: Deduct the cost of the underlying asset Q(N) from the remaining amount. The amount after "S" is used as a platform service fee.

3. The profit pre-distribution trading method based on the power function-based increasing sorting algorithm according to claim 3, characterized in that: In step two, the number of targets Q(n) is expressed in power function form as follows: Q(n)=k n^p Where k is the base coefficient, p is the power exponent, and 0.5 ≤ p ≤ 3.

4. The profit pre-distribution trading method based on the power function-based increasing sorting algorithm according to claim 2, characterized in that: In step three, the unit price B(n) is calculated using the following formula: B(n)=B(n-1) (1+α (tref / tactual)) Where α is the price adjustment coefficient, 0 < α ≤ 0.5; tref is the reference trading time; and tactual is the actual completion time of the previous round of trading.

5. The profit pre-distribution trading method based on the power function-based increasing sorting algorithm according to claim 2, characterized in that: In step five, if the settlement condition R(n) ≥ Q(N) cannot be satisfied simultaneously... When S and price condition B(N) ≤ M, the transaction terminates and all participants' funds are returned to their original source.

6. A method for redistributing profits from low-profit products, characterized in that, This method is used for commodities traded on digital asset trading platforms with a profit margin of less than 5%. The profit margin is calculated as (final price - ex-factory price) / ex-factory price. The method includes the following steps: Step 1: Preset the ex-factory price S and the end-user price M of the target asset; Step 2: Set the number of trading rounds n=1,2,3,...,N. In each round, set the number of participants P(n) and the unit price B(n), where B(1) is the initial unit price and B(n)=B(n-1). (1+β), 0<β≤0.05; Step 3, for the nth round of transactions: (1) Calculate the total transaction amount T(n) = P(n) for this round. B(n); (2) Deduct the commission rebate from the previous round of traders from the total transaction amount. Rcommission(n) = R(n-1) γ, where 0 < γ ≤ 0.3; (3) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 4: Determine if the settlement condition is met: R(n) ≥ Q_total S, where Q_total is the total number of items; Step 5: When the settlement conditions are met and B(N)≤M, the transaction is settled. The Nth round trader obtains the underlying asset at a unit price of B(N), and the first N-1 round traders receive the corresponding transaction rebates.

7. A method for redistributing the profits of a lottery-type product, characterized in that, This method uses a commodity digital asset trading platform as the vehicle for transactions. After the final round of transactions, some participants receive the goods, while others split the remaining amount R(n) after deducting all fees. The specific steps are as follows: Step 1: Preset the ex-factory price S of the target asset and set the initial unit price B (1). Step 2: Set the number of trading rounds n=1,2,3,...,N, and set the number of participants P(n) and the unit price B(n) for each round, where B(n)≥B(n-1); Step 3: When B(n) > S, the system starts shipping, and the shipping quantity Q(n) is determined according to a preset function; Step 4, for the nth round of transactions: (1) Calculate the total transaction amount T(n) = P(n) for this round. B(n); (2) Calculate the commission amount for this round, Rcommission(n) = Q(n). (B(n)-S) δ, where 0 < δ ≤ 0.5; (3) Calculate the remaining amount after this round of transactions R(n) = R(n-1) + T(n) - Rcommission(n) - C(n), where C(n) is the transaction fee and R(0) = 0; Step 5: Determine if the settlement condition is met: R(n) ≥ Q(n) S; Step Six: When the settlement conditions are met, proceed with transaction settlement. (1) According to the preset ratio, some of the Nth round traders will obtain the underlying assets; (2) The remaining traders in the Nth round share the lottery amount L = R(n) - Q(n) with the traders in other rounds. S.

8. The profit pre-distribution transaction method based on the power function-based increasing sorting algorithm according to claim 2, 6, or 7, characterized in that: The formula for calculating the transaction fee C(n) is as follows: C(n)=T(n) or Where η is the commission rate, and 0.001≤η≤0.

05.

9. The profit pre-distribution transaction method based on the power function-based increasing sorting algorithm according to claim 2, 6, or 7, characterized in that: The digital asset trading platform uses blockchain technology to ensure the immutability of transaction records and the automated execution of transaction settlement.

10. The profit pre-distribution transaction method based on the power function-based increasing sorting algorithm according to claim 2, 6, or 7, characterized in that: Before the transaction begins, the initial values ​​of the transaction round N, the number of participants P(n) in each round, and the unit price B(n) of the target are predicted through big data analysis.

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