Transaction data optimization method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a transaction data optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly, obtaining transaction data of a plurality of transaction objects, and selecting target transaction objects one by one according to the transaction data from small to large; then, selecting at least one piece of transaction data adjacent to the target transaction data according to the sequence to calculate a profit index parameter corresponding to the target transaction object, calculating a profit index value of the target transaction object according to the profit index parameter, and updating the profit index parameter based on the profit index value until the difference absolute value is smaller than or equal to a preset difference value, obtaining optimized transaction data of the target transaction object according to the final profit index value; and finally, adjusting the profit index parameter of the next target transaction object based on the optimized transaction data, and obtaining the optimized transaction data corresponding to each target transaction object, thereby effectively improving the calculation efficiency of the optimized transaction data on the premise of improving the profit.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a transaction data optimization method, device, electronic device and storage medium. Background Art

[0002] In an Internet-based trading system, the transaction price of goods often changes as the transaction progresses during the process of buying, selling, transferring and circulating. Especially for a series of complex and interrelated commodity sequences, it is even more necessary to make appropriate transaction data decisions to improve the benefits of transaction resources.

[0003] In the related art, when formulating transaction data for a series of interrelated complex commodity sequences, mathematical modeling is usually performed for these complex commodity sequences, and then the optimization problem formed by the mathematical modeling is solved using the operations research optimization method to obtain appropriate transaction data decisions. However, as the scale of complex commodity sequences increases, the optimization problems they constitute will gradually become more complicated. If the operations research optimization method is still used to solve the problem, the computational complexity will be too high, resulting in low efficiency in obtaining transaction data decisions. Summary of the invention

[0004] The embodiments of the present application provide a transaction data optimization method, device, electronic device and storage medium, which can improve the calculation efficiency of transaction data when formulating transaction data for a series of interrelated complex commodity sequences.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a transaction data optimization method, the method comprising:

[0006] Acquire transaction data of multiple transaction objects, and select target transaction objects one by one in the order of the transaction data from small to large, the transaction data of the target transaction objects being the target transaction data;

[0007] Obtaining a profit index parameter corresponding to the target transaction object, and calculating a profit index value of the target transaction object according to at least one of the transaction data adjacent to the target transaction data and the profit index parameter, updating the profit index parameter based on the profit index value, and recalculating the profit index value, obtaining an absolute value of a difference between the profit index value and the profit index parameter, and if the absolute value of the difference is greater than or equal to a preset difference, iteratively updating the profit index value until the absolute value of the difference is less than or equal to the preset difference, and obtaining optimized transaction data of the target transaction object according to the final profit index value;

[0008] The profit indicator parameter of the next target transaction object is adjusted based on the optimized transaction data, and the optimized transaction data corresponding to each target transaction object is obtained.

[0009] In some embodiments, the calculating the profit index value of the target transaction object according to at least one of the transaction data adjacent to the target transaction data and the profit index parameter comprises:

[0010] Obtaining a transaction object index parameter of the target transaction object;

[0011] Obtaining a transaction data model of the target transaction object based on the target transaction data and at least one of the transaction data adjacent to the target transaction data;

[0012] Obtaining a profit indicator function of the target transaction object based on the transaction data model;

[0013] Substitute the transaction object index parameter and the profit index parameter into the profit index function to calculate the profit index value.

[0014] In some embodiments, obtaining the transaction data model of the target transaction object based on the target transaction data and at least one of the transaction data adjacent to the target transaction data includes:

[0015] Acquire the preceding price change data between the target transaction object and the previous adjacent transaction object, and the following price change data between the target transaction object and the next adjacent transaction object;

[0016] Acquire an indication function, and obtain a change price effect function based on the preceding price change data, the succeeding price change data, and the indication function;

[0017] The transaction data model is generated based on the change price effect function.

[0018] In some embodiments, generating the transaction data model based on the change price effect function includes:

[0019] Obtaining resource fixed parameters, sensitivity coefficients and random factors, and generating a transaction object utility function based on the change price effect function, the resource fixed parameters, the sensitivity coefficients and the random factors;

[0020] generating a transaction object selection probability function based on an exponential function of the transaction object utility function, and generating an expected profit index function based on the transaction object selection probability function;

[0021] The transaction data model is generated based on the change price effect function, the transaction object utility function, the transaction object selection probability function and the expected profit indicator function.

[0022] In some embodiments, obtaining the profit indicator function of the target transaction object based on the transaction data model includes:

[0023] Dividing the plurality of transaction objects into a first transaction object subsequence and a second transaction object subsequence according to the order;

[0024] generating an initial profit indicator function based on the transaction data model, the first transaction object subsequence, and the second transaction object subsequence;

[0025] The initial profit indicator function is transformed into the profit indicator function based on the profit indicator parameter.

[0026] In some embodiments, converting the initial profit indicator function into the profit indicator function based on the profit indicator parameter includes:

[0027] Converting the initial profit indicator function into an equivalent profit indicator function based on the profit indicator parameter;

[0028] Acquire a transaction object parameter constraint, where the transaction object parameter constraint is generated based on the first transaction object subsequence, the second transaction object subsequence, and the transaction object index parameter;

[0029] The equivalent profit index function is converted into the profit index function based on the transaction object parameter constraint.

[0030] In some embodiments, substituting the transaction object index parameter and the profit indicator parameter into the profit indicator function to calculate the profit indicator value includes:

[0031] Based on the first-order derivative of the profit indicator function, a profit indicator derivative function is obtained;

[0032] Acquire transaction data constraints of the transaction data, and obtain a transaction data parsing function of the transaction data based on the profit indicator derivative function and the transaction data constraints;

[0033] The optimized transaction data of the profit indicator parameter is obtained based on the transaction data analysis function, and the profit indicator value is obtained by substituting the optimized transaction data, the transaction object index parameter and the profit indicator parameter into the profit indicator function.

[0034] In some embodiments, updating the profit indicator parameter based on the profit indicator value includes:

[0035] Obtaining a gap value corresponding to the profit indicator value and the profit indicator parameter;

[0036] When the gap value is greater than the preset difference value, updating the parameter iteration lower bound based on the profit indicator parameter, and updating the profit indicator value based on the average of the updated parameter iteration lower bound and the parameter iteration upper bound;

[0037] When the gap value is less than or equal to the preset difference value, the parameter iteration upper bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration upper bound and parameter iteration lower bound.

[0038] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a transaction data optimization device, the device comprising:

[0039] A transaction data acquisition module is used to acquire transaction data of multiple transaction objects, and select target transaction objects one by one in the order of the transaction data from small to large, and the transaction data of the target transaction objects are target transaction data;

[0040] an optimized transaction data calculation module, used to obtain the profit index parameter corresponding to the target transaction object, and calculate the profit index value of the target transaction object according to at least one of the transaction data adjacent to the target transaction data and the profit index parameter, update the profit index parameter based on the profit index value, and recalculate the profit index value, obtain the absolute value of the difference between the profit index value and the profit index parameter, if the absolute value of the difference is greater than or equal to the preset difference, iteratively update the profit index value until the absolute value of the difference is less than or equal to the preset difference, and obtain the optimized transaction data of the target transaction object according to the final profit index value;

[0041] The optimized data adjustment module is used to adjust the profit indicator parameters of the next target transaction object based on the optimized transaction data, and obtain the optimized transaction data corresponding to each target transaction object.

[0042] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the transaction data optimization method described in the first aspect when executing the computer program.

[0043] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the transaction data optimization method described in the first aspect is implemented.

[0044] The transaction data optimization method, device, electronic device and storage medium proposed in the embodiments of the present application include: first, obtaining transaction data of multiple transaction objects, and selecting target transaction objects one by one in the order of transaction data from small to large, and the transaction data of the target transaction objects are the target transaction data; then, obtaining the profit indicator parameter corresponding to the target transaction object, and calculating the profit indicator value of the target transaction object according to at least one transaction data adjacent to the target transaction data and the profit indicator parameter, updating the profit indicator parameter based on the profit indicator value, and recalculating the profit indicator value, obtaining the absolute value of the difference between the profit indicator value and the profit indicator parameter, if the absolute value of the difference is greater than or equal to the preset difference, iteratively updating the profit indicator value until the absolute value of the difference is less than or equal to the preset difference, and obtaining the optimized transaction data of the target transaction object according to the last profit indicator value; finally, adjusting the profit indicator parameter of the next target transaction object based on the optimized transaction data, and obtaining the optimized transaction data corresponding to each target transaction object. When formulating transaction data for a series of interrelated complex commodity sequences, the embodiment of the present application is based on the idea of ​​binary iteration, and uses the relationship between profit indicator parameters and profit indicator values ​​for each target transaction object one by one, so as to increase profits. The profit indicator values ​​are calculated using transaction data adjacent to the target transaction object to iteratively update the profit indicator parameters, so as to obtain the optimal optimized transaction data for the target transaction object after convergence, and avoid complex operational optimization steps, so as to effectively improve the calculation efficiency of the optimized transaction data while increasing profit returns.

[0045] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of a transaction data optimization method provided in one embodiment of the present application.

[0047] Figure 2 yes Figure 1 Flow chart of step 102 in FIG.

[0048] Figure 3 yes Figure 2 Flow chart of step 202 in FIG.

[0049] Figure 4 yes Figure 3 Flow chart of step 303 in FIG.

[0050] Figure 5 yes Figure 2 The watershed diagram of step 203 in FIG.

[0051] Figure 6 yes Figure 5 Flow chart of step 503 in FIG.

[0052] Figure 7 yes Figure 2 Flow chart of step 204 in FIG.

[0053] Figure 8 yes Figure 1 Another flow chart of step 102 in FIG.

[0054] Fig. 9 It is a schematic diagram of an algorithm flow of a transaction data optimization method provided in an embodiment of the present application.

[0055] Fig.10 It is a structural diagram of a transaction data optimization device provided in one embodiment of the present application.

[0056] Fig.11 It is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] In an Internet-based trading system, the transaction price of goods often changes as the transaction progresses during the process of buying, selling, transferring and circulating. Especially for a series of complex and interrelated commodity sequences, it is even more necessary to make appropriate transaction data decisions to improve the benefits of transaction resources.

[0061] The pricing problem based on the discrete choice model is one of the problems faced by enterprises in actual operation scenarios. Prospect Theory is an important theory in behavioral economics. It believes that individuals evaluate goods based on the location of the reference point rather than the absolute size of each attribute of the goods. When consumers make a purchase, the relevant information of other goods (such as price) will affect their perception of the price of a particular product, and then affect their purchasing behavior. This price influence effect is called the reference price in the literature. In actual application scenarios, consumers tend to pay more attention to the price difference between two adjacent products. This special reference price is called the price distance effect. Studies have shown that if the price distance effect is ignored, it will affect the pricing decision of the enterprise, resulting in a large loss of revenue. However, in actual operation scenarios, how to efficiently solve the pricing problem considering the price distance effect is a difficult problem that needs to be solved urgently. The pricing problem is a multi-dimensional optimization problem, and the introduction of the price distance effect makes the objective function more complicated, which makes the optimization problem have no good structure and properties (such as convexity, unimodality, etc.), which brings great challenges to the accurate solution of the pricing problem.

[0062] In the related art, when formulating transaction data for a series of interrelated complex commodity sequences, mathematical modeling is usually performed for these complex commodity sequences, and then the optimization problem formed by the mathematical modeling is solved using the operations research optimization method to obtain appropriate transaction data decisions. However, as the scale of complex commodity sequences increases, the optimization problems they constitute will gradually become more complicated. If the operations research optimization method is still used to solve the problem, the computational complexity will be too high, resulting in low efficiency in obtaining transaction data decisions.

[0063] In order to improve the computational efficiency of transaction data when formulating transaction data for a series of interrelated complex commodity sequences, the embodiment of the present application formulates transaction data for a series of interrelated complex commodity sequences. Based on the idea of ​​binary iteration, the relationship between profit indicator parameters and profit indicator values ​​is used for each target transaction object one by one, so as to increase profits. The profit indicator values ​​are calculated using transaction data adjacent to the target transaction object to iteratively update the profit indicator parameters, so as to obtain the optimal optimized transaction data for the target transaction object after convergence, and avoid complex operational optimization steps, so as to effectively improve the computational efficiency of the optimized transaction data while increasing profits.

[0064] The transaction data optimization method, device, electronic device and storage medium provided in the embodiments of the present application will be further described below. The transaction data optimization method provided in the embodiments of the present application can be applied to any server, processing terminal and the like.

[0065] The transaction data optimization method in the embodiment of the present application will be described in detail below. Figure 1 , which is an optional flow chart of the transaction data optimization method provided in the embodiment of the present application, Figure 1 The method in the embodiment may include but is not limited to steps 101 to 103. Figure 1 The order of step 101 to step 103 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0066] Step 101: Acquire transaction data of multiple transaction objects, and select target transaction objects one by one in order of transaction data from small to large.

[0067] The following is a detailed description of step 101.

[0068] In some embodiments, when making transaction data decisions for a series of interrelated complex commodity sequences, since transaction users usually consider the transaction data of neighboring transaction objects when selecting transaction objects, it is necessary to obtain the transaction data corresponding to this series of interrelated transactions. The sequence of transaction objects is represented by J:=1,2,…,I, and the transaction object set of the complex commodity sequence is The transaction data of each transaction object is represented as In addition, the arrangement of the transaction objects in the commodity sequence is sorted from small to large according to the transaction data of the transaction objects. Therefore, when making transaction data decisions, the target transaction objects will be selected one by one from multiple transaction objects in the order of the transaction data from small to large, and the transaction data of the target transaction objects will be the target transaction data.

[0069] Step 102: Obtain the profit indicator parameter corresponding to the target transaction object, and calculate the profit indicator value of the target transaction object based on at least one transaction data adjacent to the target transaction data and the profit indicator parameter, update the profit indicator parameter based on the profit indicator value, and recalculate the profit indicator value to obtain the absolute value of the difference between the profit indicator value and the profit indicator parameter. If the absolute value of the difference is greater than or equal to the preset difference, iteratively update the profit indicator value until the absolute value of the difference is less than or equal to the preset difference, and obtain the optimized transaction data of the target transaction object according to the final profit indicator value.

[0070] The following is a detailed description of step 102.

[0071] In some embodiments, after determining the target transaction object, at least one transaction data adjacent to the target transaction data is selected in order to calculate the profit index parameter corresponding to the target transaction object, and then the idea of ​​dichotomy is used to maximize the profit index value of the target transaction object, so as to gradually update the iterative profit index parameter to effectively improve the profit index value. This will be further described below.

[0072] First, for the target transaction object, the lowest transaction data is p1 and the highest transaction data is p I , further generate the initial parameter iteration lower bound x based on the lowest transaction data p1 L = p1, and based on the highest transaction data p I Generate the initial parameter iteration upper bound x U =p I , and then iterate the lower bound x based on the initial parameters L and the initial parameter iteration upper bound x U The mean of generates the initial profit indicator parameter x of the target transaction object M =(x U +x L ) / 2.

[0073] Next, based on at least one transaction data adjacent to the target transaction data and the profit indicator parameter x M Calculate the profit index value of the target transaction object, as described below.

[0074] Reference Figure 2 , calculating the profit index value of the target transaction object according to at least one transaction data adjacent to the target transaction data and the profit index parameter, including the following steps 201 to 204.

[0075] Step 201: Obtain the transaction object index parameter of the target transaction object.

[0076] Step 202: Obtain a transaction data model of the target transaction object based on the target transaction data and at least one transaction data adjacent to the target transaction data.

[0077] Steps 201 to 202 are described in detail below.

[0078] In some embodiments, the transaction object index parameter of the target transaction object, ie, i∈J, is first determined, and then a transaction data model of the target transaction object is obtained based on the target transaction data and at least one transaction data adjacent to the target transaction data, as described in detail below.

[0079] Reference Figure 3, based on the target transaction data and at least one transaction data adjacent to the target transaction data, obtaining a transaction data model of the target transaction object includes the following steps 301 to 303.

[0080] Step 301: Acquire the preceding price change data between the target transaction object and the adjacent previous transaction object, and the succeeding price change data between the target transaction object and the adjacent subsequent transaction object.

[0081] Step 302: Obtain an indicator function, and obtain a change price effect function based on the preceding price change data, the succeeding price change data, and the indicator function.

[0082] Step 303: Generate a transaction data model based on the changing price effect function.

[0083] Steps 301 to 303 are described in detail below.

[0084] In some embodiments, when making transaction data decisions for a series of interrelated complex commodity sequences, it is assumed that there is I transaction object, using Indicates that its corresponding product set is And the transaction data is When the user considers whether to select a transaction object i, he or she will not only i When evaluating, the transaction data of its adjacent transaction objects will also be considered, that is, p i-1 and p i+1 .

[0085] Therefore, it is also necessary to obtain the previous price change data p of the target transaction object and the adjacent previous transaction object. i -p i-1 , and the subsequent price change data p of the target transaction object and the adjacent next transaction object i+1 -p i Then, based on the previous price change data, the subsequent price change data and the indicator function, the change price effect function is obtained as shown in the following formula (1).

[0086] Next, considering the special price distance effect of linear price distance effect, the change price effect function can be further transformed into the form shown in the following formula (2) based on the indicator function.

[0087]

[0088] Among them, λ≥0 and γ≥0. is the indicator function, λ and γ measure the size of the positive transaction data distance effect and the negative transaction data distance effect respectively. In this formula, G represents the gain-seeking user, L represents the loss-averse user, and satisfies Since the utility of the target transaction object is related to the price distance between it and the two transaction objects before and after it, the transaction object at the endpoint is a special case. For a given product set, since users tend to sort transaction data in ascending order of price, transaction object 1 and transaction object I are the cheapest and most expensive transaction objects respectively. In this case, in order to simplify the analysis process and complexity, it is assumed that there is no price distance between the transaction objects at the endpoint.

[0089] Next, a transaction data model is generated based on the change price effect function, as described in detail below.

[0090] Reference Figure 4 , generating a transaction data model based on the changing price effect function, including the following steps 401 to 403.

[0091] Step 401: Obtain resource fixed parameters, sensitivity coefficients and random factors, and generate a transaction object utility function based on the variable price effect function, resource fixed parameters, sensitivity coefficients and random factors.

[0092] Step 402: Generate a transaction object selection probability function based on the exponential function of the transaction object utility function, and generate an expected profit index function based on the transaction object selection probability function.

[0093] Step 403: Generate a transaction data model based on the change price effect function, the transaction object utility function, the transaction object selection probability function and the expected profit index function.

[0094] Steps 401 to 403 are described in detail below.

[0095] In some embodiments, first determine the resource fixed parameter α corresponding to the target transaction object i i >0, sensitivity coefficient β>0 and random factor ξ i The resource has a fixed parameter α i It is used to represent the determined part of the inherent utility of the target transaction object i, and the sensitivity coefficient β is used to represent the sensitivity of the transaction data. Next, based on the change price effect function (2), the resource fixed parameter α i , sensitivity coefficient β and random factor ξ i The utility function of the generated transaction object is shown in the following formula (3).

[0096]

[0097] According to the random utility maximization theory and the Multinomial logit model (MNL), given the product set, the initial transaction data of the target transaction object and the change price effect function (2), the transaction object selection probability function corresponding to the probability of the target transaction object being selected is generated based on the indicator function of the transaction object utility function (3) as shown in the following formula (4).

[0098]

[0099] Assume that the market size is normalized to 1. Let c = (c1,…,c I ) represents the unit cost vector, where c i represents the unit cost of the target transaction object i. Therefore, under the MNL model considering the price distance effect, when determining the product set When the transaction data p of the transaction object is obtained, the expected profit index function can be obtained based on the transaction object selection probability function (4) as shown in the following formula (5).

[0100]

[0101] Among them, p i -c i Represents the unit profit of target transaction object i.

[0102] The MNL model considering the price distance effect is scalable. Although the MNL model is simple, it is limited by the independence of irrelevant alternatives (IIA), that is, the selection probability ratio of two different transaction objects is only related to the utility of these two transaction objects, and has nothing to do with the utility function and provision of other transaction objects. When the substitution effect between transaction objects is strong, the user's selection behavior no longer satisfies the independence of irrelevant alternatives. However, the MNL model considering the price distance effect does not satisfy the independence of irrelevant alternatives, which means that the selection model is more flexible and can be used in more application scenarios.

[0103] Next, we consider the pure pricing optimization problem when the product set is given. Without loss of generality, let the given product set be J: = 1, 2, ..., I, and the goal is to solve the optimal transaction data p of each transaction object so as to maximize the total expected profit, that is, based on the change price effect function (2), the transaction object utility function (3), the transaction object selection probability function (4) and the expected profit indicator function (5), the transaction data model is generated as shown in the following formula (6).

[0104]

[0105] The price of each transaction object i∈J is limited to the range [p min ,p max ], where p min >0 and p max <∞, and the upper and lower limits of these transaction data can be obtained. Assume that there is ξ>0, so that the lower bound of the maximum total expected profit (i.e., the profit index value) is ξ. This condition is mild and easy to meet in real application scenarios, so negative expected profits mean an unfavorable business scenario from the beginning.

[0106] In this optimization problem (6), some parameters have a certain order. First, the deterministic part of the inherent utility of the transaction object is increasing, that is, α1≤α23≤…≤α I Similarly, the cost of the transaction object also follows the same order, that is, c1≤c2≤…≤c I It is worth noting that c i and α i All fall in the interval (0,∞), which means they are finite positive numbers, which is consistent with the reality. In addition, this order of α and c actually represents the order of the quality of the trading objects. In other words, trading object 1 represents the most common trading object, while trading object I represents the trading object with the best quality.

[0107] Through the above steps 301 to 303 and 401 to 403, the transaction data of the transaction objects adjacent to the target transaction object are used to generate a changing price effect function to reflect that the user also considers the characteristics of the adjacent transaction objects when selecting the transaction object, and further generates a transaction data model based on the changing price effect function to characterize the selection probability of the user when selecting the transaction object under different circumstances, and takes the maximum profit index value as the target, thereby improving the subsequent generation efficiency of the transaction data model to generate optimized transaction data suitable for the target transaction object.

[0108] Step 203: Obtaining a profit indicator function of the target transaction object based on the transaction data model.

[0109] Step 203 is described in detail below.

[0110] In some embodiments, after obtaining the transaction data model (6) of the entire commodity sequence, the profit indicator function of the target transaction object i will be further converted based on the transaction data model (6), which will be further described in detail below.

[0111] Reference Figure 5 , obtaining the profit indicator function of the target transaction object based on the transaction data model, including the following steps 501 to 503.

[0112] Step 501: sort multiple transaction objects into a first transaction object subsequence and a second transaction object subsequence.

[0113] Step 502: Generate an initial profit indicator function based on the transaction data model, the first transaction object subsequence, and the second transaction object subsequence.

[0114] Step 503: Converting the initial profit indicator function into a profit indicator function based on the profit indicator parameter.

[0115] Steps 501 to 503 are described in detail below.

[0116] In some embodiments, in order to solve the transaction data model (6), it is critical to understand the structure of the optimal transaction data. First, note that the cost of the transaction object is in ascending order, and divide all transaction objects into low-cost transaction objects and high-cost transaction objects. That is, for 1≤i≤I, products 1, 2, ..., i are low-cost transaction objects (i.e., the first transaction object subsequence), and products i+1, i+2, ..., I are high-cost transaction objects (i.e., the second transaction object subsequence). When the linear price distance is p i+1 -p i When , by deriving the unimodal expression and solving the key points, the optimal pricing strategy can be obtained: the prices of all low-cost products are the same, and the prices of all high-cost products are the same.

[0117] More formally, we can reorder the indices of all products using an index mapping that satisfies the following properties: σ(1) represents the product with the lowest price and there are p σ(1) ≤p σ(2) ≤…≤p σ(I) In addition, there exists a certain index i such that the set J can be divided into the following first transaction object subsequences: and the second transaction object subsequence

[0118] Next, the above structure is very useful for designing an estimation algorithm. If a profit of m is charged for product 1, 2, ..., i, and a price of p is charged for product i+1, i+2, ..., I, then the transaction data model (6) can be simplified to a two-dimensional optimization problem, that is, based on the first transaction object subsequence and the second transaction object subsequence, the transaction data model (6) is converted into an initial profit indicator function as shown in the following formula (7).

[0119]

[0120] Among them, the constraint m+c i≤p ensures that price p is charged to the high-cost counterparty.

[0121] Then, further, using the profit indicator parameter x in the current iteration M =x transforms the initial profit index function (7) into a profit index function, as described below.

[0122] Reference Figure 6 , based on the profit indicator parameter, the initial profit indicator function is converted into a profit indicator function, including the following steps 601 to 603.

[0123] Step 601: Converting an initial profit indicator function into an equivalent profit indicator function based on a profit indicator parameter.

[0124] Step 602: Obtain transaction object parameter constraints, where the transaction object parameter constraints are generated based on the first transaction object subsequence, the second transaction object subsequence, and the transaction object index parameter.

[0125] Step 603: Convert the equivalent profit index function into a profit index function based on the transaction object parameter constraints.

[0126] Steps 601 to 603 are described in detail below.

[0127] In some embodiments, according to the above description, the optimal transaction data of the transaction object can be obtained by solving a two-dimensional optimization problem, namely, the initial profit index function (7). However, the objective function of the optimization problem does not have an ideal structure (such as joint concavity, etc.), and since the transaction data grid of the transaction object satisfies p1≤p2≤…≤p I ,Based on the previous analysis, an efficient algorithm is needed to solve it.

[0128] Based on this, first use the profit indicator parameter x M =x Combined with the transaction data model (6), let R(J,p J )=x * , that is, the initial profit index function (7) can be transformed into an equivalent profit index function as shown in the following formula (8).

[0129]

[0130] Note that for any given λ, x * =Λ(x;λ) is strictly decreasing with respect to x, but this still requires solving a multidimensional optimization problem. Next, first design a solution for x given the profit indicator parameter x. * =Λ(x;λ); then based on the bisection method, find the equation x * = the only solution x of Λ(x;λ) * .

[0131] The expression on the right side of formula (8) can be further written as shown in the following formula (9).

[0132]

[0133] Next, for the target transaction object i=σ(1),σ(2),…,σ(I), a transaction object parameter constraint is generated based on the first transaction object subsequence, the second transaction object subsequence and the transaction object index parameter.

[0134] Then, based on the transaction object parameter constraints, the equivalent profit index function (9) is further transformed into a profit index function as shown in the following formula (10).

[0135]

[0136] Through the above steps 501 to 503 and steps 601 to 603, the cost parameters of the transaction objects are used to divide multiple transaction objects into two transaction object subsequences, and the transaction data model is further converted into an initial profit indicator function of the two-dimensional optimization problem. Then, the profit indicator parameters and the transaction object parameter constraints generated by the two transaction object subsequences are combined to further convert the initial profit indicator function into a solvable profit indicator function, so as to facilitate the subsequent use of the transaction object index function and the profit indicator function given in the iteration to substitute into the profit indicator function to solve and obtain the profit indicator value, without the need for complex gradient iterations such as in the operations research optimization method, so as to ensure the reliability of the optimized transaction data obtained by the solution while improving the computational efficiency of solving the optimized transaction data.

[0137] Step 204: Substitute the transaction object index parameter and the profit index parameter into the profit index function to calculate the profit index value.

[0138] Step 204 is described in detail below.

[0139] In some embodiments, after obtaining the profit indicator function (10) that can be directly solved, the transaction object index parameter i corresponding to the target transaction object and the profit indicator parameter x in the current iteration process are M Substitute the profit indicator function (10) to obtain the optimal transaction data and the corresponding profit indicator value corresponding to the profit indicator function, as described below.

[0140] Reference Figure 7 , substituting the transaction object index parameter and the profit indicator parameter into the profit indicator function, and calculating the profit indicator value, including the following steps 701 to 703.

[0141] Step 701: Based on the first-order derivative of the profit indicator function, a profit indicator derivative function is obtained.

[0142] Step 702: Acquire transaction data constraints of the transaction data, and obtain a transaction data analysis function of the transaction data based on the profit indicator derivative function and the transaction data constraints.

[0143] Step 703: Obtain optimized transaction data of profit indicator parameters based on the transaction data analysis function, and substitute the optimized transaction data, transaction object index parameters and profit indicator parameters into the profit indicator function to obtain a profit indicator value.

[0144] Steps 701 to 703 are described in detail below.

[0145] In some embodiments, in order to solve the profit indicator function (10), since the profit indicator parameter x≥0, the inverse parameters A>0, B≥0, C>0. Therefore, based on the first-order derivative H of the profit indicator function (10) (σ ,m ,n) The first-order derivative of (x,z-Δ,z), that is, the derivative function of the profit indicator is shown in the following formula (11).

[0146]

[0147] So we can solve 1+c i +x-βp=0 to get the solution of profit index derivative function (11) being 0. If profit index derivative function (11) is 0, there is no solution or the only solution, then profit index derivative function (11) ≥ 0, H (σ,i) (x,p) increases with respect to the transaction data p. If the profit indicator derivative function (11) is 0, there are two different solutions: and (Without loss of generality, assume ) then the profit index derivative function (11) is Increasing upward, The solution of profit indicator derivative function (11) being 0 can be calculated by Lambert W function. After setting the separation point, i.e., i, there is also the following constraint to make the highest price p, i.e., the transaction data constraint of transaction data p is shown in the following formula (12).

[0148]

[0149] That is, the transaction data constraint (12) is equivalent to the following formula (13).

[0150]

[0151] Therefore, the transaction data analysis function can be obtained as shown in the following formula (14).

[0152]

[0153] Then, the profit indicator parameter x in the current iteration process is M Substituting x into the transaction data analysis function (14), we can get the profit indicator parameter x in the current iteration process. M Corresponding optimized transaction data And further optimize transaction data Trading object index parameter i and profit indicator parameter x M Substituting into the profit indicator function (10), we can get the profit indicator value corresponding to the profit indicator parameter in the current iteration process.

[0154] Through the above steps 701 to 703, the profit indicator derivative function corresponding to the first-order derivative of the profit indicator function is analyzed to obtain the transaction data analysis function corresponding to the analytical solution of the transaction data, so that the optimized transaction data and profit indicator value of the profit indicator parameter teammates in the current iteration process can be obtained quickly and accurately, thereby ensuring the reliability and effectiveness of the optimized transaction data of the target transaction object and improving the calculation rate.

[0155] In getting the profit indicator parameter x of the current iteration process M Corresponding profit indicator value Then, using the dichotomy idea, based on the profit index value H σ,i (x,p) Update profit indicator parameter x M , as described below.

[0156] Reference Figure 8 , updating the profit indicator parameter based on the profit indicator value, including the following steps 801 to 803.

[0157] Step 801: Obtain the gap value corresponding to the profit indicator value and the profit indicator parameter.

[0158] Step 802: When the gap value is greater than a preset difference value, the parameter iteration lower bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration lower bound and the parameter iteration upper bound.

[0159] Step 803: When the gap value is less than or equal to the preset difference value, the parameter iteration upper bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration upper bound and the parameter iteration lower bound.

[0160] Steps 801 to 803 are described in detail below.

[0161] In some embodiments, when obtaining the profit indicator parameter x in the current iteration process, M The corresponding profit indicator value is After that, the difference value corresponding to the profit index value and the profit index parameter is determined. When the difference value is greater than the preset difference value, when the preset difference value is 0, that is, At this time, the parameter iteration lower bound will be updated based on the profit indicator parameter, that is, x L =x M , then based on the average of the updated parameter iteration lower bound and parameter iteration upper bound, update the profit indicator value x M =(x U +x L ) / 2.

[0162] Correspondingly, when the difference value is less than or equal to the preset difference value, when the preset difference value is 0, that is, Update the upper bound of parameter iteration based on the profit indicator parameter, that is, x U =x M , and based on the mean of the updated parameter iteration upper bound and parameter iteration lower bound, update the profit indicator value x M =(x U +x L ) / 2.

[0163] Then, the profit index value is recalculated to obtain the absolute value of the difference between the profit index value and the profit index parameter. If the absolute value of the difference is greater than or equal to the preset difference, the profit index value is iteratively updated using the binary search method until the absolute value of the difference is less than or equal to the preset difference. When the preset difference is 0, that is, The optimized transaction data of the target transaction object is obtained according to the optimized transaction data corresponding to the final profit indicator value.

[0164] Step 103: Adjust the profit indicator parameters of the next target transaction object based on the optimized transaction data, and obtain the optimized transaction data corresponding to each target transaction object.

[0165] The following is a detailed description of step 103.

[0166] In some embodiments, after obtaining the optimized transaction data of the target transaction data i, the optimized transaction data is used to update the optimized transaction data corresponding to the target transaction data in a series of interrelated complex commodity sequences (i.e., a set of transaction objects). It can be understood that since this solution considers the transaction price impact between adjacent transaction objects (i.e., formula (1)), the updated optimized transaction data of the target transaction object will affect the optimized transaction data decision of the next target transaction object. Then, based on this, the optimized transaction data decisions corresponding to subsequent transactions are made one by one to obtain the optimized transaction data corresponding to all transaction objects.

[0167] Reference Fig. 9 , is a schematic diagram of the algorithm flow of a transaction optimization method provided by this application. Fig. 9 As shown in , first, for a fixed parameter combination (including the transaction object index and the initial transaction data), the profit indicator function (10) is strictly decreasing with respect to the profit indicator parameter, that is, for any given transaction object index (σ, i) and transaction data p, the equation x = H (σ，i) (x, p) has a unique solution x * Therefore, in this embodiment, a binary search algorithm is used to quickly find a solution for each transaction object.

[0168] like Fig. 9 As shown in , the binary search process includes:

[0169] 1) Initialize the upper and lower bounds of the profit indicator parameter x, which are x U and x L , corresponding to the first transaction object and transaction data and the transaction data of the last transaction object.

[0170] 2) Based on the midpoint of the upper and lower bounds, we get the profit indicator parameter x during the iteration process. M , and perform iterative calculations. During the iterative calculation process, check whether

[0171] 3) If The maximum value is greater than the profit indicator parameter x M , then update the lower bound; if The maximum value is less than or equal to the profit indicator parameter x M , then update the upper bound; and perform iterative calculations to gradually narrow the interval and converge to the optimal profit indicator parameter x * .

[0172] 4) Finally, output the optimal parameters corresponding to all transaction objects The profit indicator values ​​of all trading objects have been optimized.

[0173] Additionally, to improve computational efficiency, prices are rounded to the power of (1+e), where e is a small positive number. This reduces the number of possible price values ​​that need to be evaluated, speeding up the optimization process.

[0174] The transaction data optimization method, device, electronic device and storage medium proposed in the embodiments of the present application include: first, obtaining the transaction data of multiple transaction objects, and selecting the target transaction objects one by one in the order of the transaction data from small to large, and the transaction data of the target transaction objects are the target transaction data; then, obtaining the profit indicator parameter corresponding to the target transaction object, and obtaining the transaction object index parameter of the target transaction object, obtaining the preceding price change data of the target transaction object and the adjacent previous transaction object, and the subsequent price change data of the target transaction object and the adjacent subsequent transaction object, obtaining the indicator function, and obtaining the change price effect function based on the preceding price change data, the subsequent price change data and the indicator function, and obtaining the resource fixed The method comprises the steps of: generating a transaction object utility function based on a variable price effect function, a resource fixed parameter, a sensitivity coefficient and a random factor, generating a transaction object utility function based on a variable price effect function, a resource fixed parameter, a sensitivity coefficient and a random factor, generating a transaction object selection probability function based on an exponential function of the transaction object utility function, and generating an expected profit index function based on the transaction object selection probability function, generating a transaction data model based on the variable price effect function, the transaction object utility function, the transaction object selection probability function and the expected profit index function, dividing a plurality of transaction objects into a first transaction object subsequence and a second transaction object subsequence according to the order, generating an initial profit index function based on the transaction data model, the first transaction object subsequence and the second transaction object subsequence, and converting the initial profit into the expected profit index function based on the profit index parameter. The indicator function is converted into an equivalent profit indicator function, and the transaction object parameter constraint is obtained. The transaction object parameter constraint is generated based on the first transaction object subsequence, the second transaction object subsequence and the transaction object index parameter. The equivalent profit indicator function is converted into a profit indicator function based on the transaction object parameter constraint. Based on the first-order derivative of the profit indicator function, a profit indicator derivative function is obtained to obtain the transaction data constraint of the transaction data, and based on the profit indicator derivative function and the transaction data constraint, a transaction data parsing function of the transaction data is obtained. Based on the transaction data parsing function, optimized transaction data of the profit indicator parameter is obtained, and based on the optimized transaction data, the transaction object index parameter and the profit indicator parameter, the profit indicator value is obtained by substituting the profit indicator parameter into the profit indicator function to obtain the profit. The gap value corresponding to the indicator value and the profit indicator parameter, when the gap value is greater than the preset difference, the parameter iteration lower bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration lower bound and the parameter iteration upper bound, when the gap value is less than or equal to the preset difference, the parameter iteration upper bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration upper bound and the parameter iteration lower bound, and the profit indicator value is recalculated to obtain the absolute value of the gap between the profit indicator value and the profit indicator parameter, if the absolute value of the gap is greater than or equal to the preset difference, the profit indicator value is iteratively updated until the absolute value of the gap is less than or equal to the preset difference, and the optimized transaction data of the target transaction object is obtained according to the final profit indicator value;Finally, adjust the profit indicator parameters of the next target transaction object based on the optimized transaction data, and obtain the optimized transaction data corresponding to each target transaction object. ;

[0175] When formulating transaction data for a series of interrelated complex commodity sequences, the embodiment of the present application uses the binary iterative idea to target each target transaction object one by one, and uses the relationship between the profit indicator parameter and the profit indicator value, so as to improve the profit as the goal, and uses the transaction data adjacent to the target transaction object to calculate the profit indicator value to iteratively update the profit indicator parameter, so as to obtain the optimal optimized transaction data for the target transaction object after convergence, and avoid complex operational optimization steps, so as to effectively improve the calculation efficiency of the optimized transaction data while improving the profit return; in addition, the transaction data of the transaction objects adjacent to the target transaction object are used to generate a change price effect function, so as to reflect the characteristics of the user considering the adjacent transaction objects when selecting the transaction object, and further based on the change price effect function, a function is generated to characterize the selection probability of the user when selecting the transaction object under different circumstances, and the maximum profit indicator value is used as the goal, so as to generate a transaction data model corresponding to the target transaction object in the transaction scenario, thereby improving the subsequent use of the transaction data model to generate a transaction data model suitable for the target transaction object. The efficiency of generating optimized transaction data is improved; and the cost parameters of the transaction objects are used to divide multiple transaction objects into two transaction object subsequences, and the transaction data model is further converted into the initial profit indicator function of the two-dimensional optimization problem, and then the profit indicator parameters and the transaction object parameter constraints generated by the two transaction object subsequences are combined to further convert the initial profit indicator function into a solvable profit indicator function, so as to facilitate the subsequent use of the transaction object index function and the profit indicator function given in the iteration to substitute into the profit indicator function for solving to obtain the profit indicator value, and there is no need to perform complex gradient iterations such as in the operations research optimization method, so as to ensure the reliability of the solved optimized transaction data while improving the computational efficiency of solving the optimized transaction data; in addition, the profit indicator derivative function corresponding to the first-order derivative of the profit indicator function is analyzed to obtain the transaction data analytical function corresponding to the analytical solution of the transaction data, so that the optimized transaction data and profit indicator value of the profit indicator parameter teammates in the current iteration process can be obtained quickly and accurately, thereby ensuring the reliability and effectiveness of the optimized transaction data of the target transaction object and also improving the calculation rate.

[0176] The present application also provides a transaction data optimization device, which can implement the above transaction data optimization method. Fig.10 , the device 1000 comprises:

[0177] The transaction data acquisition module 1010 is used to acquire the transaction data of multiple transaction objects, and select the target transaction objects one by one in the order of the transaction data from small to large, and the transaction data of the target transaction objects are the target transaction data;

[0178] The optimized transaction data calculation module 1020 is used for 02: obtaining a profit index parameter corresponding to a target transaction object, and calculating a profit index value of the target transaction object according to at least one transaction data adjacent to the target transaction data and the profit index parameter, updating the profit index parameter based on the profit index value, and recalculating the profit index value, obtaining an absolute value of a difference between the profit index value and the profit index parameter, and if the absolute value of the difference is greater than or equal to a preset difference, iteratively updating the profit index value until the absolute value of the difference is less than or equal to the preset difference, and obtaining optimized transaction data of the target transaction object according to the final profit index value;

[0179] The optimized data adjustment module 1030 is used to adjust the profit indicator parameters of the next target transaction object based on the optimized transaction data, and obtain the optimized transaction data corresponding to each target transaction object.

[0180] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0181] Get the transaction object index parameter of the target transaction object;

[0182] Obtaining a transaction data model of a target transaction object based on the target transaction data and at least one transaction data adjacent to the target transaction data;

[0183] Obtaining the profit indicator function of the target transaction object based on the transaction data model;

[0184] Substitute the transaction object index parameter and the profit indicator parameter into the profit indicator function to calculate the profit indicator value.

[0185] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0186] Obtaining the preceding price change data between the target transaction object and the adjacent previous transaction object, and the following price change data between the target transaction object and the adjacent subsequent transaction object;

[0187] Obtaining an indicator function, and obtaining a change price effect function based on the preceding price change data, the succeeding price change data, and the indicator function;

[0188] Generate a transaction data model based on the changing price effect function.

[0189] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0190] Obtain resource fixed parameters, sensitivity coefficients and random factors, and generate a transaction object utility function based on the variable price effect function, resource fixed parameters, sensitivity coefficients and random factors;

[0191] generating a transaction object selection probability function based on an exponential function of the transaction object utility function, and generating an expected profit index function based on the transaction object selection probability function;

[0192] A transaction data model is generated based on the changing price effect function, the transaction object utility function, the transaction object selection probability function and the expected profit indicator function.

[0193] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0194] Dividing the plurality of transaction objects into a first transaction object subsequence and a second transaction object subsequence according to the order;

[0195] Generate an initial profit indicator function based on the transaction data model, the first transaction object subsequence, and the second transaction object subsequence;

[0196] The initial profit indicator function is transformed into a profit indicator function based on the profit indicator parameter.

[0197] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0198] The initial profit indicator function is transformed into an equivalent profit indicator function based on the profit indicator parameter;

[0199] Obtaining a transaction object parameter constraint, where the transaction object parameter constraint is generated based on the first transaction object subsequence, the second transaction object subsequence, and the transaction object index parameter;

[0200] The equivalent profit indicator function is transformed into a profit indicator function based on the transaction object parameter constraints.

[0201] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0202] Based on the first-order derivative of the profit indicator function, the profit indicator derivative function is obtained;

[0203] Acquire transaction data constraints of the transaction data, and obtain a transaction data parsing function of the transaction data based on the profit indicator derivative function and the transaction data constraints;

[0204] Based on the transaction data parsing function, the optimized transaction data of the profit indicator parameters are obtained, and based on the optimized transaction data, the transaction object index parameters and the profit indicator parameters, the profit indicator function is substituted to obtain the profit indicator value.

[0205] In some embodiments, the optimized transaction data calculation module 1020 is further used to:

[0206] Get the gap value corresponding to the profit indicator value and the profit indicator parameter;

[0207] When the gap value is greater than the preset difference value, the parameter iteration upper bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration upper bound and the parameter iteration lower bound;

[0208] When the gap value is less than or equal to the preset difference, the parameter iteration lower bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration lower bound and the parameter iteration upper bound.

[0209] In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, the specific implementation method of the transaction data optimization device is basically the same as the specific implementation method of the above transaction data optimization method, and will not be repeated here.

[0210] In the embodiment of the present application, when the transaction data optimization device formulates transaction data for a series of interrelated complex commodity sequences, based on the binary iterative idea, for each target transaction object one by one, the relationship between the profit indicator parameter and the profit indicator value is used, so as to improve the profit as the goal, and the profit indicator value is calculated by using the transaction data adjacent to the target transaction object to iteratively update the profit indicator parameter, so as to obtain the optimal optimized transaction data for the target transaction object after convergence, and avoid complex operational optimization steps, so as to effectively improve the calculation efficiency of the optimized transaction data under the premise of improving profit returns; in addition, the transaction data of the transaction objects adjacent to the target transaction object are used to generate a change price effect function, so as to reflect that the user also considers the characteristics of the adjacent transaction objects when selecting the transaction object, and further based on the change price effect function, a selection probability for representing the user when selecting the transaction object under different situations is generated, and the maximum profit indicator value is used as the goal, so as to generate a transaction data model corresponding to the target transaction object in the transaction scenario, thereby improving the subsequent use of the transaction data model to generate a transaction data model suitable for the target The efficiency of generating optimized transaction data of the target transaction object is improved; and the cost parameters of the transaction object are used to divide multiple transaction objects into two transaction object sub-sequences, and the transaction data model is further converted into the initial profit indicator function of the two-dimensional optimization problem, and then the profit indicator parameters and the transaction object parameter constraints generated by the two transaction object sub-sequences are combined to further convert the initial profit indicator function into a solvable profit indicator function, so as to facilitate the subsequent use of the transaction object index function and the profit indicator function given in the iteration to substitute into the profit indicator function for solving to obtain the profit indicator value, and there is no need to perform complex gradient iterations such as in the operations research optimization method, so as to ensure the reliability of the solved optimized transaction data while improving the computational efficiency of solving the optimized transaction data; in addition, the profit indicator derivative function corresponding to the first-order derivative of the profit indicator function is analyzed to obtain the transaction data analytical function corresponding to the analytical solution of the transaction data, so that the optimized transaction data and profit indicator value of the profit indicator parameter teammates in the current iteration process can be obtained quickly and accurately, thereby ensuring the reliability and effectiveness of the optimized transaction data of the target transaction object and also improving the calculation rate.

[0211] The present application also provides an electronic device, including:

[0212] at least one memory;

[0213] at least one processor;

[0214] at least one program;

[0215] The program is stored in the memory, and the processor executes the at least one program to implement the transaction data optimization method implemented in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0216] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0217] The processor 1101 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0218] The memory 1102 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 1102 can store operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1102, and the processor 1101 calls and executes the transaction data optimization method of the embodiments of this application;

[0219] Input / output interface 1103, used to implement information input and output;

[0220] The communication interface 1104 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0221] A bus 1105 that transmits information between various components of the device (e.g., the processor 1101, the memory 1102, the input / output interface 1103, and the communication interface 1104);

[0222] The processor 1101 , the memory 1102 , the input / output interface 1103 and the communication interface 1104 are connected to each other in communication within the device via the bus 1105 .

[0223] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned transaction data optimization method is implemented.

[0224] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0225] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0226] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0227] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0228] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0229] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0230] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0231] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0232] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0233] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0234] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0235] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A transaction data optimization method, characterized in that: The method comprises: Acquire transaction data of multiple transaction objects, and select target transaction objects one by one in the order of the transaction data from small to large, the transaction data of the target transaction objects being the target transaction data; Obtaining a profit index parameter corresponding to the target transaction object, and calculating a profit index value of the target transaction object according to at least one of the transaction data adjacent to the target transaction data and the profit index parameter, updating the profit index parameter based on the profit index value, and recalculating the profit index value, obtaining an absolute value of a difference between the profit index value and the profit index parameter, and if the absolute value of the difference is greater than or equal to a preset difference, iteratively updating the profit index value until the absolute value of the difference is less than or equal to the preset difference, and obtaining optimized transaction data of the target transaction object according to the final profit index value; The profit indicator parameter of the next target transaction object is adjusted based on the optimized transaction data, and the optimized transaction data corresponding to each target transaction object is obtained.

2. The transaction data optimization method according to claim 1, characterized in that: The calculating the profit index value of the target transaction object according to at least one transaction data adjacent to the target transaction data and the profit index parameter comprises: Obtaining a transaction object index parameter of the target transaction object; Obtaining a transaction data model of the target transaction object based on the target transaction data and at least one of the transaction data adjacent to the target transaction data; Obtaining a profit indicator function of the target transaction object based on the transaction data model; Substitute the transaction object index parameter and the profit index parameter into the profit index function to calculate the profit index value.

3. The transaction data optimization method according to claim 2, characterized in that: The obtaining, based on the target transaction data and at least one of the transaction data adjacent to the target transaction data, a transaction data model of the target transaction object comprises: Acquire the preceding price change data between the target transaction object and the previous adjacent transaction object, and the following price change data between the target transaction object and the next adjacent transaction object; Acquire an indication function, and obtain a change price effect function based on the preceding price change data, the succeeding price change data, and the indication function; The transaction data model is generated based on the change price effect function.

4. The transaction data optimization method according to claim 3, characterized in that: The generating the transaction data model based on the change price effect function comprises: Obtaining resource fixed parameters, sensitivity coefficients and random factors, and generating a transaction object utility function based on the change price effect function, the resource fixed parameters, the sensitivity coefficients and the random factors; generating a transaction object selection probability function based on an exponential function of the transaction object utility function, and generating an expected profit index function based on the transaction object selection probability function; The transaction data model is generated based on the change price effect function, the transaction object utility function, the transaction object selection probability function and the expected profit indicator function.

5. The transaction data optimization method according to claim 4, characterized in that: The step of obtaining the profit indicator function of the target transaction object based on the transaction data model includes: Dividing the plurality of transaction objects into a first transaction object subsequence and a second transaction object subsequence according to the order; generating an initial profit indicator function based on the transaction data model, the first transaction object subsequence, and the second transaction object subsequence; The initial profit indicator function is transformed into the profit indicator function based on the profit indicator parameter.

6. The transaction data optimization method according to claim 5, characterized in that: The converting the initial profit index function into the profit index function based on the profit index parameter comprises: Converting the initial profit indicator function into an equivalent profit indicator function based on the profit indicator parameter; Acquire a transaction object parameter constraint, where the transaction object parameter constraint is generated based on the first transaction object subsequence, the second transaction object subsequence, and the transaction object index parameter; The equivalent profit index function is converted into the profit index function based on the transaction object parameter constraint.

7. The transaction data optimization method according to claim 2, characterized in that: Substituting the transaction object index parameter and the profit index parameter into the profit index function to calculate the profit index value includes: Based on the first-order derivative of the profit indicator function, a profit indicator derivative function is obtained; Acquire transaction data constraints of the transaction data, and obtain a transaction data parsing function of the transaction data based on the profit indicator derivative function and the transaction data constraints; The optimized transaction data of the profit indicator parameter is obtained based on the transaction data analysis function, and the profit indicator value is obtained by substituting the optimized transaction data, the transaction object index parameter and the profit indicator parameter into the profit indicator function.

8. The transaction data optimization method according to claim 1, characterized in that: The updating of the profit indicator parameter based on the profit indicator value comprises: Obtaining a gap value corresponding to the profit indicator value and the profit indicator parameter; When the gap value is greater than the preset difference value, updating the parameter iteration lower bound based on the profit indicator parameter, and updating the profit indicator value based on the average of the updated parameter iteration lower bound and the parameter iteration upper bound; When the gap value is less than or equal to the preset difference value, the parameter iteration upper bound is updated based on the profit indicator parameter, and the profit indicator value is updated based on the average of the updated parameter iteration upper bound and parameter iteration lower bound.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the transaction data optimization method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the transaction data optimization method according to any one of claims 1 to 8 is implemented.

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