Order matching method and system for bulk commodity transaction

Abstract

The invention provides an order matching method and system for bulk commodity transaction. Wherein order requests from a plurality of participants are received, and the order requests comprise commodity types, numbers and price conditions; automatically adjusting upper and lower limits of a price interval based on historical transaction data and market fluctuation conditions; screening the received order requests according to a dynamic price interval model, and only reserving effective orders of which the price conditions fall in a price interval; an intelligent matching algorithm is utilized to analyze the supply-demand relationship between the effective orders, and an optimal order pairing scheme is determined, and the optimal order pairing scheme is determined according to the number scale, the time priority and the participant credit rating of the effective orders; and executing the optimal order pairing scheme, generating corresponding transaction confirmation information, sending the transaction confirmation information to the corresponding participants, and updating the dynamic price interval model to reflect the latest transaction condition. According to the technical scheme provided by the invention, the transaction efficiency and the market mobility are improved, and the flexibility and the stability of the system are enhanced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to an order matching method and system for bulk commodity trading. Background Art

[0002] With the buying and selling of commodities such as metals, energy, and agricultural products, market participants need an efficient, fair, and transparent trading platform to execute order matching. Such a platform must be able to process large amounts of real-time data and provide accurate price range forecasts to facilitate the best match between buyers and sellers. In addition, due to the volatility of the market and the diversity of participants, the system should also have the ability to dynamically adjust according to market conditions to ensure that each transaction is completed based on the conditions closest to the current market price. Therefore, it is particularly important to develop a method for matching commodity trading orders that can automatically adapt to market changes, quickly screen valid orders, and achieve intelligent matching.

[0003] There are currently a variety of electronic platform solutions for commodity trading on the market, which usually use fixed rules or algorithms to determine the price range and match orders through simple supply and demand comparison. These systems may combine some basic data analysis techniques to assist the decision-making process, such as using historical average prices as a reference to set a fixed price range; or using a preset priority list (for example, in chronological order) to arrange the order of order processing. Some advanced platforms have begun to try to introduce more complex models, including machine learning algorithms to improve matching efficiency and service quality.

[0004] Most existing systems rely on static price range settings, which makes it difficult to respond quickly to sudden changes in the market, resulting in the inability to accurately reflect the actual value of goods in extreme cases. Traditional order processing methods often lack sufficient intelligent means to support, especially in high-concurrency environments, which are prone to delays and affect user experience. As cyber attacks become increasingly sophisticated, some existing platforms are not doing enough to protect user information security, especially when it comes to the exchange of a large amount of sensitive financial information. In addition, the issue of how to reasonably evaluate and consider the credit rating of the participants has not been well resolved, which may lead to unfair competition. Summary of the invention

[0005] The embodiments of the present application provide a method and system for matching orders for bulk commodity transactions, so as to solve the problems of insufficient flexibility and stability in the prior art.

[0006] In a first aspect, an embodiment of the present application provides an order matching method for commodity trading, including:

[0007] Receiving order requests from multiple parties, wherein the order requests at least include commodity types, quantities, and price conditions;

[0008] Build a dynamic price range model to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations;

[0009] Filtering received order requests according to the dynamic price range model, and retaining only valid orders whose price conditions fall within the price range;

[0010] Analyze the supply and demand relationship between the valid orders using an intelligent matching algorithm to determine the optimal order matching solution, wherein the optimal order matching solution is determined based on the quantity, time priority and credit rating of the valid orders;

[0011] The optimal order matching solution is executed, and corresponding transaction confirmation information is generated and sent to the corresponding participants, and the dynamic price range model is updated to reflect the latest transaction situation.

[0012] Optionally, the dynamic price range model is constructed to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations, including:

[0013] Collect and analyze historical transaction records within a specific time period to obtain price distribution information of different commodity types at different time points;

[0014] Calculate the historical average price, highest price and lowest price of each commodity according to the price distribution information, and determine the initial price range;

[0015] Analyze market fluctuation trends using statistical methods or machine learning techniques, predict the range of future market price changes, and obtain prediction results, which include predicted values ​​of future market prices;

[0016] Combine the current market conditions and forecast results to set a variable dynamic price range adjustment factor;

[0017] The initial price range is adjusted by the adjustment factor of the dynamic price range to obtain the upper and lower limits of the price range.

[0018] Optionally, screening the received order requests according to the dynamic price range model to retain only valid orders whose price conditions fall within the price range includes:

[0019] Parsing the commodity types and corresponding price conditions in the order request;

[0020] Compare the price condition in the order request with the price range corresponding to the commodity category;

[0021] If the price condition is within the price range, the order is marked as a valid order and stored in the valid order list;

[0022] If the price condition is not within the range of the price interval, the order is marked as an invalid order.

[0023] Optionally, the valid orders include valid buyer orders and valid seller orders;

[0024] The intelligent matching algorithm is used to analyze the supply and demand relationship between the valid orders to determine the optimal order matching solution, including:

[0025] Calculate the quantity weight, time priority weight and participant credit rating weight of each valid order;

[0026] For each of the valid buyer orders, searching for orders with the same or compatible commodity categories from the valid seller orders to form an order matching solution;

[0027] Evaluate the order matching scheme, and generate a comprehensive matching score corresponding to the order matching scheme based on the quantity scale weight, time priority weight and participant credit rating weight;

[0028] The order matching scheme with the highest comprehensive matching score is selected as the optimal order matching scheme.

[0029] Optionally, executing the optimal order matching solution, generating corresponding transaction confirmation information and sending it to corresponding parties, and updating the dynamic price range model to reflect the latest transaction situation, include:

[0030] According to the determined optimal order matching plan, the corresponding valid buyer orders and valid seller orders are determined;

[0031] Generate transaction confirmation information according to the determined valid buyer's order and valid seller's order, and send the transaction confirmation information to the corresponding participants, the transaction confirmation information including the specific terms of the transaction, and the participants include the buyer and the seller;

[0032] When the parties complete the transaction based on the transaction confirmation information, the model parameters of the dynamic price range model are adjusted based on the transaction situation, and the dynamic price range model is updated based on the adjusted model parameters. The updated dynamic price range model is used to process subsequent order requests to ensure the accuracy of the price range.

[0033] Optionally, the current market status includes at least the historical average price of the commodity, the current price trend value, the number of transaction records, the transaction price, the current market transaction volume, and the time point of the most recent significant market event;

[0034] The adjustment factor of a variable dynamic price range is set in combination with the current market conditions and the forecast results, including:

[0035] Use the following calculation formula to determine the adjustment factor A for the dynamic price range:

[0036]

[0037] Among them, P avg is the historical average price of a commodity in a specific period of time, P std is the standard deviation of the price of a commodity in a specific period of time, α·(P avg -P std ) is the difference between the historical average price and the price standard deviation, P trend is the current price trend value obtained through time series analysis, β·(P trend -P avg ) is the difference between the current price trend and the historical average price, n is the number of transaction records, P i is the transaction price in the i-th transaction record, is a measure of price volatility, V current is the current market volume, V avg and V std are the mean and standard deviation of historical trading volume, is the difference between the current volume and the historical volume average, t is the time from the base point, T is the cycle length, It is a cyclical factor. is the impact of the most recent significant market event, t 0 is the time point when the most recent significant market event occurred, σ is the time decay coefficient of the impact of the market event, and P forecast represents the predicted value of future market price, θ·(P forecast -P avg ) represents the difference between the predicted value of future market price and the historical average price, θ is the weight coefficient, which is used to measure the importance of the prediction result to the future price range adjustment, α is the weight coefficient, which is used to measure the impact of the difference between the historical average price and the standard deviation on the price range, β is the weight coefficient, which is used to measure the impact of the difference between the current price trend and the historical average price on the price range, γ is the weight coefficient, which is used to measure the impact of the price volatility on the price range, δ is the weight coefficient, which is used to measure the impact of the difference between the current trading volume and the historical average trading volume on the price range, η is the weight coefficient, which is used to measure the impact of cyclical factors (such as time) on the price range, and ζ is the weight coefficient, which is used to measure the impact of the most recent significant market event on the price range.

[0038] Optionally, the evaluating the order matching scheme and generating a comprehensive matching score corresponding to the order matching scheme based on the quantity scale weight, the time priority weight and the participant credit rating weight include:

[0039] The comprehensive matching score S is calculated according to the following formula:

[0040] S=w q Q+w t ·T+w c ·C+w d ·D+w r ·R+w s ·S similarity +w p ·P predictive

[0041] Among them, Q is the quantity scale score, T is the time priority score, C is the participant credit rating score, D is the geographical distance score, R is the risk score, S similarity is the order similarity score, P predictive is the predicted score, w q ,w t ,w c ,w d ,w r ,w s ,w p are the weight coefficients of quantity scale, time priority, credit rating, geographical distance, risk score, order similarity, and prediction success.

[0042] In a second aspect, the embodiment of the present application provides an order matching system for commodity trading, including:

[0043] A receiving module, used to receive order requests from multiple parties, wherein the order requests at least include commodity types, quantities and price conditions;

[0044] A construction module is used to build a dynamic price range model, which automatically adjusts the upper and lower limits of the price range based on historical transaction data and market fluctuations;

[0045] A screening module, used to screen received order requests according to the dynamic price range model, and only retain valid orders whose price conditions fall within the price range;

[0046] An intelligent matching module, used to analyze the supply and demand relationship between the valid orders using an intelligent matching algorithm, and determine the optimal order matching solution, wherein the optimal order matching solution is determined according to the quantity scale, time priority and credit rating of the participating parties of the valid orders;

[0047] The execution module is used to execute the optimal order matching solution, generate corresponding transaction confirmation information and send it to the corresponding participants, and update the dynamic price range model to reflect the latest transaction situation.

[0048] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an order matching method for commodity trading as described in any one of the first aspects above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an order matching method for commodity trading as described in any one of the first aspects above.

[0050] In the embodiment of the present application, order requests from multiple participants are received, and the order requests include commodity types, quantities, and price conditions; the upper and lower limits of the price range are automatically adjusted based on historical transaction data and market fluctuations; the received order requests are screened according to the dynamic price range model, and only valid orders whose price conditions fall within the price range are retained; the supply and demand relationship between valid orders is analyzed using an intelligent matching algorithm to determine the optimal order matching solution, wherein the optimal order matching solution is determined based on the quantity scale, time priority, and credit rating of the valid orders; the optimal order matching solution is executed, and the corresponding transaction confirmation information is generated and sent to the corresponding participants, and the dynamic price range model is updated to reflect the latest transaction situation. The technical solution provided by the present application improves transaction efficiency and market liquidity, and enhances the flexibility and stability of the system.

[0051] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A flowchart of a method for matching orders for bulk commodity transactions provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of an order matching system for bulk commodity trading provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0059] Figure 1 A flowchart of a method for matching orders for bulk commodity transactions is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0060] 101. Receive order requests from multiple parties, where the order requests at least include commodity types, quantities, and price conditions;

[0061] In this step, the system receives order information submitted by the buyer or seller through a network interface or other means. This information includes but is not limited to the type of goods, order quantity and expected price conditions.

[0062] In the embodiment of the present application, an online trading platform can set an API (application programming interface) to allow third-party applications or users to directly submit orders. For example, user A wants to buy 500 units of wheat and is willing to trade at a price of RMB 2 per unit.

[0063] 102. Build a dynamic price range model to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations;

[0064] In this step, historical transaction data (such as the average price, highest price and lowest price over a period of time) is combined with current market fluctuations (such as changes in supply and demand, seasonal factors, etc.) to establish a price range model that can be automatically adjusted.

[0065] In the present embodiment, it is assumed that the system analyzes the transaction records of wheat in the past month and finds that the average price is 1.9 yuan / unit and the standard deviation is 0.1 yuan. Based on this information and the consideration of the recent weather forecast (drought may cause price increases), the system sets the current reasonable price range of wheat to 1.8 to 2.1 yuan / unit.

[0066] The present application takes into account that in traditional commodity pricing strategies, merchants usually set fixed prices based on factors such as cost, competitor prices, and market acceptance. However, this method lacks flexibility and cannot reflect market changes in a timely manner. Especially in a rapidly changing market environment, such as commodity transactions on e-commerce platforms, fixed prices may lead to missed sales opportunities or compressed profit margins. In order to solve the above problems, an embodiment of the present invention proposes a dynamic price range model that automatically adjusts the price range based on historical transaction data and market fluctuations. In this way, subtle changes in the market can be captured more accurately, and a quick response can be made accordingly, thereby optimizing pricing strategies and improving competitiveness.

[0067] The options are as follows:

[0068] Optionally, "the construction of a dynamic price range model to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations" in step 102 includes: collecting and analyzing historical transaction records within a specific time period to obtain price distribution information of different types of commodities at different time points; calculating the historical average price, highest price and lowest price of each commodity based on the price distribution information, and determining an initial price range; using statistical methods or machine learning techniques to analyze market fluctuation trends, predict the range of changes in future market prices, and obtain prediction results, which include predicted values ​​of future market prices; combining current market conditions and prediction results, setting a variable dynamic price range adjustment factor; adjusting the initial price range by the dynamic price range adjustment factor to obtain the upper and lower limits of the price range.

[0069] Historical transaction records: refers to the data set of all completed transactions within a specific period of time.

[0070] Price distribution information: Statistical characteristics of commodity prices obtained by analyzing historical transaction records.

[0071] Statistical methods / machine learning techniques: technical means used to discover patterns and trends from large amounts of data.

[0072] Market fluctuation trend: the pattern of changes in commodity prices in the market over time.

[0073] Adjustment factor for dynamic price range: A parameter determined based on current market conditions and forecast results, used to fine-tune the initially set price range.

[0074] First, obtain the buying and selling details of all relevant commodities in the past period of time, such as the transaction price. Secondly, use statistical principles to obtain the historical average selling price, the highest selling price, and the lowest selling price of each commodity. Then use statistical methods or machine learning algorithms to analyze the trends in historical data and estimate the price trend in the next period of time. Combined with the current market status (such as seasonal demand changes) and forecasts for future prices, define a factor that affects the width of the price range. Finally, apply the adjustment factor determined in the previous step to the previously established basic price range to form a new price range that can ultimately be used to guide actual operations.

[0075] In the embodiment of this application, suppose that an e-commerce platform wants to formulate a more flexible pricing strategy for its hot-selling product A. First, they collected all transaction records of product A in the past year; then, through data analysis, they learned that the average selling price of the product in the past year was ￥100 yuan, with the highest price reaching ￥150 yuan and the lowest price dropping to ￥70 yuan; then, a linear regression model was used to predict that its average price would increase to about ￥110 yuan in the next three months; considering that the upcoming shopping festival may bring additional demand, it was decided to use an adjustment factor of 1.2 to appropriately expand the price range; and finally determined that the new sales price should fluctuate between ￥84 yuan and ￥132 yuan.

[0076] By adopting the above dynamic adjustment mechanism, e-commerce companies can not only better cope with the challenges brought by market uncertainty, but also effectively improve customer satisfaction and corporate profitability. Specifically, this approach helps to get closer to the real supply and demand relationship, reduce the opportunity loss caused by improper pricing, and improve the decision-making efficiency of the entire supply chain management process.

[0077] 103. Screening received order requests according to the dynamic price range model, and retaining only valid orders whose price conditions fall within the price range;

[0078] In this step, all received orders are compared with the currently determined price range according to their proposed price conditions, and only those orders with quotes within the range are retained as valid orders.

[0079] In this embodiment of the present application, if the purchase price proposed by user A is 2 yuan / unit, and the valid price range at this time is 1.8 to 2.1 yuan, the order of user A is deemed valid; but if another user B bids only 1.7 yuan / unit, his order will be excluded.

[0080] This application takes into account that in the traditional order processing process, merchants usually receive all order requests, and then decide whether to accept these orders manually or through simple rules. This method wastes resources, and processing a large number of invalid orders that do not comply with price policies will take up valuable computing resources and human resources. It is also inefficient, and manual review of each order is not only time-consuming, but also prone to errors. And the customer experience is not good. If a customer's order is rejected because the price is not suitable, it may cause customer dissatisfaction. In order to solve these problems, an embodiment of the present invention proposes a method for automatically screening orders based on a dynamic price range model. This ensures that only orders that meet current market conditions and corporate pricing strategies will be further processed, thereby improving operational efficiency and optimizing user experience.

[0081] The options are as follows:

[0082] Optionally, the "screening the received order requests according to the dynamic price range model to retain only valid orders whose price conditions fall within the price range" in step 103 includes: parsing the commodity types and corresponding price conditions in the order request; comparing the price conditions in the order request with the price range corresponding to the commodity types; if the price conditions are within the price range, marking the order as a valid order and storing it in a valid order list; if the price conditions are not within the price range, marking the order as an invalid order.

[0083] Order request: A purchase intention submitted by a buyer to a seller, including information such as the selected product and the price expected to be paid.

[0084] Product Category: refers to the specific type or classification of a product.

[0085] Price Condition: The price you are willing to pay for the product as specified in the order request.

[0086] Valid order list: a collection of orders that meet the requirements and are retained after screening.

[0087] Invalid Order: An order that does not meet the price range criteria and is marked as infeasible.

[0088] First, key information is extracted from each order request received, namely the category of goods to be purchased and the corresponding quotation. Secondly, the price of each item in the order is compared with the current price range set for the item. Then, when the order price is within the preset price range, the system automatically marks it as valid and adds it to the list of valid orders for subsequent processing. Finally, for those orders whose prices are outside the allowed range, they will be marked as invalid and will not be processed further.

[0089] In the embodiment of this application, assume that an online retail platform is selling a smart watch. According to the latest market analysis results, its dynamic price range has been determined to be between ￥500 and ￥700. At this time, the platform receives three different order requests:

[0090] Order A wants to buy this watch for ￥650.

[0091] Order B proposes to purchase it for RMB 800.

[0092] Order C indicated that it was willing to pay RMB 450.

[0093] For order A, since ￥650 falls within the range of ￥500 to ￥700, it is marked as a valid order and stored.

[0094] Although Order B offers a higher bid (￥800), it is considered invalid because it exceeds the maximum price limit.

[0095] Similarly, the price offered by order C is lower than the minimum threshold (￥450 < ￥500) and is also classified as an invalid order.

[0096] By implementing such an automated order screening mechanism, unnecessary workload can be reduced, human intervention in checking each order can be avoided, and the customer service team can be freed up to focus on more important aspects of service. Not only can the transaction success rate be improved, only valid orders that meet business goals are processed, which helps to increase the transaction rate. It can also enhance user satisfaction, respond to customer needs quickly and accurately, reduce order returns caused by price mismatches, and improve the shopping experience. It can also optimize resource allocation, allowing the company to focus on serving truly potential customer groups and improve overall operational efficiency.

[0097] 104. Analyze the supply and demand relationship between the valid orders using an intelligent matching algorithm to determine an optimal order matching solution, wherein the optimal order matching solution is determined based on the quantity, time priority, and credit rating of the valid orders;

[0098] In this step, a specific algorithm is used to find the best matching combination by considering factors such as the number of valid orders, the order of submission time, and the credit rating of each participant.

[0099] In the example of this application, suppose there are three buyers who need 100, 300, and 500 units of wheat respectively, and two sellers provide a total of 900 units of wheat. The algorithm may give priority to buyers with higher credit ratings and try to meet their needs. At the same time, the first-come-first-served principle will also be taken into account to ensure that buyers who place orders earlier can get a faster response.

[0100] The present application takes into account that in the traditional order processing system, the matching of buyers and sellers is usually completed based on simple rules or manual decision-making. This method has low matching efficiency, the manual screening and matching process is time-consuming, and it is prone to human errors. Secondly, the resource utilization is insufficient, and the specific attributes of all orders cannot be fully considered, resulting in some potential best matches being ignored. And customer satisfaction is insufficient. Due to the single matching standard, buyers may not find suitable sellers, or sellers may miss the best sales opportunities. In order to solve these problems, the embodiment of the present invention proposes a solution based on an intelligent matching algorithm, which can comprehensively consider multiple factors such as the quantity scale, time priority and credit rating of the participants of the order, so as to determine the optimal order matching solution. This can not only improve transaction efficiency, but also ensure that resources are allocated in the most reasonable way, thereby improving the efficiency of the overall market operation and customer satisfaction.

[0101] The options are as follows:

[0102] Optionally, in step 104, "the valid orders include valid buyer orders and valid seller orders; the use of an intelligent matching algorithm to analyze the supply and demand relationship between the valid orders to determine the optimal order matching solution" includes: calculating the quantity scale weight, time priority weight and participant credit rating weight of each valid order; for each valid buyer order, searching for orders with the same or compatible product types from the valid seller orders to form an order matching solution; evaluating the order matching solution, and generating a comprehensive matching score corresponding to the order matching solution based on the quantity scale weight, time priority weight and participant credit rating weight; and selecting the order matching solution with the highest comprehensive matching score as the optimal order matching solution.

[0103] Valid buyer orders: purchase requests that meet the current market price strategy after price range screening.

[0104] Valid seller orders: sales requests that meet the current market price strategy after price range screening.

[0105] Quantity size weight: Different importance scores are assigned based on the quantity of goods in the order.

[0106] Time priority weight: The importance score assigned based on the time when the order is submitted.

[0107] Participant credit rating weight: a trust score given based on the buyer or seller's historical behavior record.

[0108] Comprehensive Match Score: A value calculated by combining quantity size, time priority and credit rating to measure the overall suitability of a specific matching solution.

[0109] First, each valid order (including buyers and sellers) is assigned a weight value for quantity scale, time priority, and credit rating. Secondly, for each valid buyer order, orders with the same or compatible product types are found from valid seller orders, and a pairing relationship is attempted. Then, based on the established pairing scheme, the comprehensive matching score of each pair of order combinations is calculated using a preset formula. Finally, the comprehensive scores of different pairing schemes are compared, and the highest score is selected as the optimal pairing scheme for final execution.

[0110] In the embodiment of the present application, it is assumed that a certain online trading platform has the following three valid buyer orders A, B, and C, and two valid seller orders X and Y. The relevant information is shown in the following Table 1:

[0111] Table 1

[0112]

[0113] The weight coefficients are defined as follows:

[0114] Quantity size weight w q =0.5

[0115] Time priority weight w t =0.3

[0116] Credit rating weight w c =0.2

[0117] For each pairing, the comprehensive matching score can be calculated using the following formula:

[0118]

[0119] Taking buyer order A and seller order X as an example, their comprehensive scores are:

[0120]

[0121] S AX =(0.83×0.5)+(0.42×0.3)+(0.16×0.2)≈0.5

[0122] Similarly, the comprehensive scores of other pairs can be calculated. For example, the comprehensive score of the buyer's order C and the seller's order X may be higher because their quantities are closer and their credit ratings are higher.

[0123] By comparing the comprehensive scores of all possible pairs, the pair with the highest score is finally selected as the optimal solution. In this example, it may be found that the combination of buyer order C and seller order X has the highest score and is therefore selected as the best pair.

[0124] By adopting this multi-dimensional intelligent matching algorithm, automated processing can be achieved, greatly reducing the time of manual operations and increasing the speed of matching. It also ensures that the needs of both buyers and sellers are met to the greatest extent and avoids waste of resources. Secondly, the transparent and fair matching mechanism increases users' trust in the platform. Finally, fast and accurate matching helps improve users' shopping experience and increase customer loyalty.

[0125] 105. Execute the optimal order matching solution, generate corresponding transaction confirmation information and send it to the corresponding participants, and update the dynamic price range model to reflect the latest transaction situation.

[0126] In this step, after the matching is completed, a transaction confirmation notification is sent to the relevant parties, and the price range model is adjusted according to the latest transaction results to more accurately reflect the market conditions.

[0127] In the embodiment of the present application, once a transaction agreement is reached, the system immediately sends an email or SMS notification to both the buyer and the seller. In addition, as more transactions occur, the system will continue to collect new data points and fine-tune the upper and lower limits of the price range accordingly to maintain the timeliness and accuracy of the model.

[0128] The present application takes into account that in the traditional order processing flow, once the optimal order matching solution is determined, the relevant parties are usually notified manually, and the price information in the system is manually updated. This method has low execution efficiency, and the manual confirmation and notification process takes a long time, which may lead to the loss of trading opportunities. In addition, the manual update of the price range model may not be fast enough, affecting the accuracy of subsequent order processing. Secondly, buyers and sellers need to wait for a long time to receive transaction confirmation, which affects their satisfaction. In order to solve these problems, an embodiment of the present invention proposes a method for automatically executing the optimal order matching solution, and updating the dynamic price range model in real time. This can ensure that the transaction process is more efficient and accurate, while improving the user experience.

[0129] The options are as follows:

[0130] Optionally, the "executing the optimal order matching scheme, generating corresponding transaction confirmation information and sending it to the corresponding participants, and updating the dynamic price range model to reflect the latest transaction situation" in step 105 includes: determining the corresponding valid buyer orders and valid seller orders based on the determined optimal order matching scheme; generating transaction confirmation information based on the determined valid buyer orders and valid seller orders, and sending the transaction confirmation information to the corresponding participants, the transaction confirmation information including the specific terms of the transaction, and the participants including the buyer and the seller; when the participants complete the transaction based on the transaction confirmation information, adjusting the model parameters of the dynamic price range model based on the transaction situation, and updating the dynamic price range model based on the adjusted model parameters, and the updated dynamic price range model is used for processing subsequent order requests to ensure the accuracy of the price range.

[0131] Transaction confirmation information: a notification containing the specific terms agreed upon by the buyer and seller, such as the quantity of goods, unit price, delivery time and other detailed information.

[0132] Model parameters: variables or coefficients used to calculate price ranges in a dynamic price range model.

[0133] Transaction status: actual transaction results, including transaction price, transaction volume and other information.

[0134] First, according to the optimal order matching scheme that has been determined, the valid buyer orders and valid seller orders participating in the transaction are clearly identified. Transaction confirmation information containing specific transaction terms is generated and automatically sent to the corresponding buyers and sellers. Secondly, the transaction confirmation information is sent to the corresponding buyers and sellers via email, SMS or other instant messaging tools so that they can view and confirm the transaction details. Then once the buyer and seller complete the transaction, the latest transaction data is collected. Use this new data to adjust the parameters in the dynamic price range model to reflect the actual situation of the current market. Finally, the adjusted model parameters are applied to the dynamic price range model, and the model is updated to ensure that it keeps pace with market conditions. The updated model will be used to process subsequent order requests to ensure the accuracy of the price range.

[0135] In the embodiment of the present application, it is assumed that an online trading platform has determined an optimal order pairing scheme through an intelligent matching algorithm, in which the buyer's order C (purchase of 200 items) and the seller's order X (sale of 120 items) are selected as the best pairing. The following operations are performed:

[0136] Generate transaction confirmation information:

[0137] The system automatically generates a transaction confirmation message, which includes: Buyer C agrees to purchase 120 items from Seller X at a price of RMB 600 per item, with a total price of RMB 72,000, and the delivery time is within one week.

[0138] This information is sent via email to Buyer C and Seller X.

[0139] Send transaction confirmation:

[0140] Buyer C and seller X received the emails respectively and replied to confirm after confirming that the transaction details were correct.

[0141] Adjust model parameters based on transaction conditions:

[0142] After the transaction is completed, the system records the price (￥600 / item) and quantity (120 items) of the transaction.

[0143] Assume that the original model parameter is the average price P avg , fluctuation range σ, adjust these parameters according to the new transaction data. avg =￥580, then the new P avg Adjustable to: P' avg =(P avg ×N+600×120) / (N+120) where N is the number of previous data points.

[0144] Update dynamic price range model:

[0145] Using adjusted P' avg And other relevant parameters update the model to form a new price range.

[0146] The new price range will take effect immediately and will be used to process future order requests.

[0147] By automatically executing the optimal order matching solution and updating the price range model in real time, the automated processing reduces the time of manual intervention and speeds up the transaction confirmation. Secondly, the real-time update of model parameters enables the price range to quickly reflect the latest market changes, improving the flexibility and accuracy of the pricing strategy. Furthermore, buyers and sellers can receive transaction confirmation information faster, improving the overall service quality. Finally, making decisions based on the latest data helps to better meet market demand and reduce inventory backlogs or out-of-stock situations.

[0148] The present application takes into account that in the traditional dynamic price range adjustment, simple statistical indicators or static rules are usually relied on to set the upper and lower limits of the price range. This method has fixed rules that may not adapt to the rapid changes in the market. Secondly, only some market data, such as historical average prices and standard deviations, are considered, while other important market factors are ignored. And the current market state is not fully combined with future forecasts, resulting in inaccurate settings of price ranges. In order to solve these problems, an embodiment of the present invention proposes a method for a dynamic price range adjustment factor based on a comprehensive calculation of multiple market factors. By introducing more comprehensive market state parameters (such as price trends, changes in trading volume, cyclical factors, and the impact of significant market events), and combining with forecasts of future market prices, the price range can be adjusted more accurately to cope with market volatility and uncertainty.

[0149] The options are as follows:

[0150] Optionally, the current market status includes at least the historical average price of the commodity, the current price trend value, the number of transaction records, the transaction price, the current market transaction volume, and the time point of the most recent significant market event;

[0151] The adjustment factor of a variable dynamic price range is set in combination with the current market conditions and the forecast results, including:

[0152] Use the following calculation formula to determine the adjustment factor A for the dynamic price range:

[0153]

[0154] Among them, P avg is the historical average price of a commodity in a specific period of time, P std is the standard deviation of the price of a commodity in a specific period of time, α·(P avg -P std ) is the difference between the historical average price and the price standard deviation, P trend is the current price trend value obtained through time series analysis, β·(P trend -P avg ) is the difference between the current price trend and the historical average price, n is the number of transaction records, P i is the transaction price in the i-th transaction record, is a measure of price volatility, V current is the current market volume, V avg and V std are the mean and standard deviation of historical trading volume, is the difference between the current volume and the historical volume average, t is the time from the base point, T is the cycle length, It is a cyclical factor. is the impact of the most recent significant market event, t 0 is the time point when the most recent significant market event occurred, σ is the time decay coefficient of the impact of the market event, and P forecast represents the predicted value of future market price, θ·(P forecast -P avg ) represents the difference between the predicted value of future market price and the historical average price, θ is the weight coefficient, which is used to measure the importance of the prediction result to the future price range adjustment, α is the weight coefficient, which is used to measure the impact of the difference between the historical average price and the standard deviation on the price range, β is the weight coefficient, which is used to measure the impact of the difference between the current price trend and the historical average price on the price range, γ is the weight coefficient, which is used to measure the impact of the price volatility on the price range, δ is the weight coefficient, which is used to measure the impact of the difference between the current trading volume and the historical average trading volume on the price range, η is the weight coefficient, which is used to measure the impact of cyclical factors (such as time) on the price range, and η is the weight coefficient, which is used to measure the impact of the most recent significant market event on the price range.

[0155] Historical average price (P avg ): The arithmetic mean of the transaction prices of goods within a specific time period.

[0156] Price standard deviation (P std ): A statistic that measures the degree of commodity price volatility over a specific time period.

[0157] Current price trend value (P trend ): The direction of commodity price changes obtained through time series analysis.

[0158] Number of transaction records (n): The total number of transactions in a specific time period.

[0159] Transaction price (P i ): The actual transaction price in the i-th transaction record.

[0160] Current market volume (V current ): Total market transaction volume during the current time period.

[0161] Historical volume average (V avg ): The average value of market trading volume over the past period of time.

[0162] Historical volume standard deviation (V std ): A statistic that measures the degree of volatility in market trading volume over a period of time.

[0163] Base point (t): The time from a certain starting point used to calculate the periodic factor.

[0164] Cycle length (T): The time span of a complete market cycle.

[0165] The time point when the most recent significant market event occurred (t 0 ): The time when the last event that had a significant impact on the market occurred.

[0166] Time decay coefficient of market event impact (σ): describes the speed at which the impact of market events gradually weakens over time.

[0167] Future market price forecast value (P forecast ): The price of a commodity at a certain point in the future predicted by the model.

[0168] First, determine the key market status parameters, collect and calculate basic market data such as the historical average price of the commodity, price standard deviation, current price trend value, etc. Secondly, calculate the impact weight of each factor, and measure the importance of different market factors to the price range adjustment according to the various weight coefficients in the formula (α, β, γ, δ, η, ζ, θ). Then apply the formula to calculate the adjustment factor, and use the provided formula to calculate the adjustment factor A of the dynamic price range, which reflects the comprehensive impact of the current market status and future forecast results on the price range. Finally, update the dynamic price range, apply the calculated adjustment factor to the existing price range, and form a new price range to better reflect the latest market conditions.

[0169] In the embodiment of the present application, it is assumed that the dynamic price range of an online sales platform for an electronic product is being adjusted. The following are specific numerical examples:

[0170] Historical average price avg =￥1000

[0171] Price standard deviation P std =￥50

[0172] Current price trend value P trend =￥1020

[0173] Number of transaction records n = 1000

[0174] The price of each transaction is P i Taken from historical data

[0175] Current market volume V current =2000

[0176] Historical volume average V avg =1800

[0177] Historical volume standard deviation V std =300

[0178] Time t = 6 months (assuming one month is one unit)

[0179] Cycle length T = 12 months

[0180] The time point t when the most recent significant market event occurred 0 =3 months

[0181] The time decay coefficient of the impact of market events σ = 1 month

[0182] Future market price forecast value P forecast =￥1050

[0183] α=0.2, β=0.1, Y=0.15, η=0.05, ζ=0.05, θ=0.3

[0184]

[0185] Assuming price volatility Then there are:

[0186]

[0187] A=190+2+375+0.0667+0+0.000335+15

[0188] A≈582.067

[0189] By adopting this dynamic price range adjustment method based on multiple factors, the price range can be quickly adjusted according to the current market status to better adapt to market changes. And by combining multiple market factors, the setting of price range is more scientific and reasonable. And by incorporating future market price forecasts into the adjustment factors, the forward-looking nature of the price range is improved. Secondly, a more flexible and accurate price strategy helps companies maintain their competitive advantage and optimize profits. Finally, a reasonable price range can increase users' willingness to buy, thereby improving customer satisfaction.

[0190] The present application takes into account that in the traditional order matching process, matching is usually based only on simple rules (such as price, quantity, etc.). This method only considers a few factors and ignores other important influencing factors, such as geographical distance, credit rating of the participants, etc. Secondly, fixed rules cannot adapt to different market conditions and changes in customer needs. And the lack of prediction of the possibility of successful transactions in the future may result in the matching result not being the optimal solution. In order to solve these problems, an embodiment of the present invention proposes a comprehensive matching algorithm based on multi-dimensional scoring. This method introduces more evaluation indicators and assigns corresponding weights to each indicator to more comprehensively evaluate the order matching scheme, thereby improving the quality and efficiency of matching.

[0191] The options are as follows:

[0192] Optionally, the evaluating the order matching scheme and generating a comprehensive matching score corresponding to the order matching scheme based on the quantity scale weight, the time priority weight and the participant credit rating weight include:

[0193] The comprehensive matching score S is calculated according to the following formula:

[0194] S=w q Q+w t ·T+w c ·C+w d ·D+w r ·R+w s ·S similarity +w p ·P predictive

[0195] Among them, Q is the quantity scale score, T is the time priority score, C is the participant credit rating score, D is the geographical distance score, R is the risk score, S similarity is the order similarity score, P predictive is the predicted score, w q ,w t ,w c ,w d ,w r ,w s ,w p are the weight coefficients of quantity scale, time priority, credit rating, geographical distance, risk score, order similarity, and prediction success.

[0196] Quantity score (Q): A score given based on the quantity of items in the order.

[0197] Time priority score (T): A score given based on the time when the order was submitted.

[0198] Participant Credit Rating (C): A trust rating given based on the buyer or seller's historical behavior records.

[0199] Geographic distance score (D): A score given based on the geographical distance between the buyer and the seller.

[0200] Risk Score (R): A score given based on potential risk factors such as default history.

[0201] Order similarity score (S similarity ): A score that measures the similarity between two orders.

[0202] Prediction score (P predictive): A score given based on the model’s prediction of the likelihood of future trades being successful.

[0203] Weight coefficient (w q ,w t ,w c ,w d ,w r ,w s ,w p ): A coefficient used to adjust the importance of each scoring item in the comprehensive matching score.

[0204] First, the quantity scale score, time priority score, credit rating score, geographical distance score, risk score, order similarity score and prediction score are calculated for each valid order. Secondly, the weight coefficients of each score are determined according to business needs and actual conditions. These coefficients reflect the relative importance of each score in the overall comprehensive matching score. Finally, the comprehensive scores of all matching schemes are compared, and the highest score is selected as the optimal matching scheme for final execution.

[0205] In the embodiment of the present application, it is assumed that an online trading platform needs to process the following three buyer orders A, B, and C, and two seller orders X and Y. The relevant information is shown in the following Table 1:

[0206] Table 1

[0207]

[0208]

[0209] Assume the weight coefficients are as follows:

[0210] Quantity size weight w q =0.2 time priority weight w t =0.1 credit rating weight w c =0.15 geographical distance weight w d =0.2 risk score weight w r =0.1 order similarity weight w s =0.15 prediction score weight w p =0.1

[0211] Taking buyer order A and seller order X as an example, their comprehensive scores are:

[0212] S AX =0.2×100+0.1×5+0.15×4+0.2×10+0.1×0.2+0.15×0.9+0.1×0.8

[0213] S AX =20+0.5+0.6+2+0.02+0.135+0.08

[0214] S AX =20+0.5+0.6+2+0.02+0.135+0.08

[0215] Similarly, the comprehensive scores of other pairs can be calculated. For example, the combination of buy order C and sell order X may get a higher comprehensive score because their quantities are closer and their credit rating is higher.

[0216] By comparing the comprehensive scores of all possible pairs, the pair with the highest score is finally selected as the optimal solution. In this example, it is assumed that the combination of buyer order C and seller order X has the highest score and is therefore selected as the best pair.

[0217] By adopting this comprehensive matching algorithm based on multi-dimensional scoring, multiple factors can be taken into account to ensure the selection of the most appropriate matching solution. The weight coefficient can be adjusted according to different situations to make the system more adaptable. Secondly, combined with predictive scoring, the ability to predict the success of future transactions is increased. Fast and accurate matching helps improve the user's shopping experience and increase customer satisfaction. Finally, by introducing risk scoring, the probability of bad transactions is reduced, protecting the interests of the platform and users.

[0218] Figure 2 A schematic diagram of the structure of an order matching system for bulk commodity trading is provided for the embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0219] The receiving module 21 is used to receive order requests from multiple parties, wherein the order request at least includes commodity type, quantity and price conditions;

[0220] A construction module 22 is used to construct a dynamic price range model, which automatically adjusts the upper and lower limits of the price range based on historical transaction data and market fluctuations;

[0221] A screening module 23, used to screen received order requests according to the dynamic price range model, and only retain valid orders whose price conditions fall within the price range;

[0222] Intelligent matching module 24, used to analyze the supply and demand relationship between the valid orders using intelligent matching algorithm, and determine the optimal order matching solution, wherein the optimal order matching solution is determined according to the quantity scale, time priority and credit rating of the participants of the valid orders;

[0223] The execution module 25 is used to execute the optimal order matching solution, generate corresponding transaction confirmation information and send it to the corresponding participants, and update the dynamic price range model to reflect the latest transaction situation.

[0224] Figure 2 The order matching system for bulk commodity trading can execute Figure 1 The implementation principle and technical effect of the order matching method for bulk commodity trading described in the embodiment are not described in detail. The specific way in which each module and unit performs operations in the order matching system for bulk commodity trading in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0225] In one possible design, Figure 2 The order matching system for commodity trading in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0226] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0227] The processing component 32 is used to: receive order requests from multiple participants, the order requests including commodity types, quantities and price conditions; automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations; filter the received order requests according to the dynamic price range model, and only retain valid orders whose price conditions fall within the price range; use the intelligent matching algorithm to analyze the supply and demand relationship between valid orders and determine the optimal order matching plan, wherein the optimal order matching plan is determined based on the quantity scale of valid orders, time priority and credit rating of the participants; execute the optimal order matching plan, generate corresponding transaction confirmation information and send it to the corresponding participants, and update the dynamic price range model to reflect the latest transaction situation.

[0228] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0229] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0230] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0231] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0232] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0233] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0234] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An order matching method for bulk commodity transactions in the illustrated embodiment.

[0235] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0236] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0237] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0238] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for matching orders for bulk commodity transactions, characterized in that: include: Receiving order requests from multiple parties, wherein the order requests at least include commodity types, quantities, and price conditions; Build a dynamic price range model to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations; Filtering received order requests according to the dynamic price range model, and retaining only valid orders whose price conditions fall within the price range; Analyze the supply and demand relationship between the valid orders using an intelligent matching algorithm to determine the optimal order matching solution, wherein the optimal order matching solution is determined based on the quantity, time priority and credit rating of the valid orders; The optimal order matching solution is executed, and corresponding transaction confirmation information is generated and sent to the corresponding participants, and the dynamic price range model is updated to reflect the latest transaction situation.

2. The method according to claim 1, characterized in that The dynamic price range model is constructed to automatically adjust the upper and lower limits of the price range based on historical transaction data and market fluctuations, including: Collect and analyze historical transaction records within a specific time period to obtain price distribution information of different commodity types at different time points; Calculate the historical average price, highest price and lowest price of each commodity according to the price distribution information, and determine the initial price range; Analyze market fluctuation trends using statistical methods or machine learning techniques, predict the range of future market price changes, and obtain prediction results, which include predicted values ​​of future market prices; Combine the current market conditions and forecast results to set a variable dynamic price range adjustment factor; The initial price range is adjusted by the adjustment factor of the dynamic price range to obtain the upper and lower limits of the price range.

3. The method according to claim 1, characterized in that The step of screening received order requests according to the dynamic price range model and retaining only valid orders whose price conditions fall within the price range includes: Parsing the commodity types and corresponding price conditions in the order request; Compare the price condition in the order request with the price range corresponding to the commodity category; If the price condition is within the price range, the order is marked as a valid order and stored in the valid order list; If the price condition is not within the range of the price interval, the order is marked as an invalid order.

4. The method according to claim 1, characterized in that: The valid orders include valid buyer orders and valid seller orders; The intelligent matching algorithm is used to analyze the supply and demand relationship between the valid orders to determine the optimal order matching solution, including: Calculate the quantity weight, time priority weight and participant credit rating weight of each valid order; For each of the valid buyer orders, searching for orders with the same or compatible commodity categories from the valid seller orders to form an order matching solution; Evaluate the order matching scheme, and generate a comprehensive matching score corresponding to the order matching scheme based on the quantity scale weight, time priority weight and participant credit rating weight; The order matching scheme with the highest comprehensive matching score is selected as the optimal order matching scheme.

5. The method according to claim 1, characterized in that The executing the optimal order matching scheme, generating corresponding transaction confirmation information and sending it to corresponding parties, and updating the dynamic price range model to reflect the latest transaction situation, includes: According to the determined optimal order matching plan, the corresponding valid buyer orders and valid seller orders are determined; Generate transaction confirmation information according to the determined valid buyer's order and valid seller's order, and send the transaction confirmation information to the corresponding participants, the transaction confirmation information including the specific terms of the transaction, and the participants include the buyer and the seller; When the parties complete the transaction based on the transaction confirmation information, the model parameters of the dynamic price range model are adjusted based on the transaction situation, and the dynamic price range model is updated based on the adjusted model parameters. The updated dynamic price range model is used to process subsequent order requests to ensure the accuracy of the price range.

6. The method according to claim 2, characterized in that The current market status includes at least the historical average price of the commodity, the current price trend value, the number of transaction records, the transaction price, the current market transaction volume, and the time point when the most recent significant market event occurred; The adjustment factor of a variable dynamic price range is set in combination with the current market conditions and the forecast results, including: Use the following calculation formula to determine the adjustment factor A for the dynamic price range: Among them, P avg is the historical average price of a commodity in a specific period of time, P std is the standard deviation of the price of a commodity in a specific period of time, α·(P avg -P std ) is the difference between the historical average price and the price standard deviation, P trend is the current price trend value obtained through time series analysis, β·(P trend -P avg ) is the difference between the current price trend and the historical average price, n is the number of transaction records, P i is the transaction price in the i-th transaction record, is a measure of price volatility, V current is the current market volume, V avg and V std are the mean and standard deviation of historical trading volume, is the difference between the current volume and the historical volume average, t is the time from the base point, T is the cycle length, It is a cyclical factor. is the impact of the most recent significant market event, t0 is the time point when the most recent significant market event occurred, σ is the time attenuation coefficient of the impact of the market event, and P forecast represents the predicted value of future market price, θ·(P forecast -P avg ) represents the difference between the predicted value of future market price and the historical average price, θ is the weight coefficient, which is used to measure the importance of the prediction result to the future price range adjustment, α is the weight coefficient, which is used to measure the impact of the difference between the historical average price and the standard deviation on the price range, β is the weight coefficient, which is used to measure the impact of the difference between the current price trend and the historical average price on the price range, γ is the weight coefficient, which is used to measure the impact of the price volatility on the price range, δ is the weight coefficient, which is used to measure the impact of the difference between the current trading volume and the historical average trading volume on the price range, η is the weight coefficient, which is used to measure the impact of cyclical factors (such as time) on the price range, and ζ is the weight coefficient, which is used to measure the impact of the most recent significant market event on the price range.

7. The method according to claim 4, characterized in that The evaluating the order matching scheme and generating a comprehensive matching score corresponding to the order matching scheme based on the quantity scale weight, time priority weight and participant credit rating weight include: The comprehensive matching score S is calculated according to the following formula: S=w q ·Q+w t ·T+w c ·C+w d ·D+w r ·R+w s ·S similarity +w p ·P predictive Among them, Q is the quantity scale score, T is the time priority score, C is the participant credit rating score, D is the geographical distance score, R is the risk score, S similarity is the order similarity score, P predictive is the predicted score, w q ,w t ,w c ,w d ,w r ,w s ,w p are the weight coefficients of quantity scale, time priority, credit rating, geographical distance, risk score, order similarity, and prediction success.

8. An order matching system for bulk commodity trading, characterized in that: include: A receiving module, used to receive order requests from multiple parties, wherein the order requests at least include commodity types, quantities and price conditions; A construction module is used to build a dynamic price range model, which automatically adjusts the upper and lower limits of the price range based on historical transaction data and market fluctuations; A screening module, used to screen received order requests according to the dynamic price range model, and only retain valid orders whose price conditions fall within the price range; An intelligent matching module, used to analyze the supply and demand relationship between the valid orders using an intelligent matching algorithm, and determine the optimal order matching solution, wherein the optimal order matching solution is determined according to the quantity scale, time priority and credit rating of the participating parties of the valid orders; The execution module is used to execute the optimal order matching solution, generate corresponding transaction confirmation information and send it to the corresponding participants, and update the dynamic price range model to reflect the latest transaction situation.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an order matching method for commodity trading as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an order matching method for commodity trading as described in any one of claims 1 to 7 is implemented.

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