Currency product pricing and sales prediction method and device, equipment and storage medium

Abstract

The invention discloses a currency product pricing and sales prediction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a basic price, basic cost, specification information, a pricing mode and a historical sales model of a currency product; according to the pricing mode, the basic cost and the specification information, obtaining a cost additional price; obtaining a current sales volume model according to the historical sales volume model; and predicting the pricing and sales volume of currency products according to the basic price, the cost addition price and at least two of the current sales volume model. The problem that a traditional currency product has limitation in pricing and sales volume prediction is solved, and the accuracy and flexibility of the currency product in pricing and sales volume prediction are improved.

Description

Technical Field

[0001] The present application relates to the technical field of artificial intelligence, and in particular to a method, device, equipment and storage medium for pricing and sales forecasting of currency products. Background Art

[0002] In the monetary product market, especially in the field of gold products, traditional pricing methods often rely on historical experience and simple mathematical calculations, such as setting prices through tools such as Excel. Although this method can reflect the cost and market conditions of the product to a certain extent, it has obvious limitations. The pricing results are too formulaic and lack in-depth consideration of market dynamics and consumer demand, resulting in pricing strategies that may not be flexible and accurate enough. More importantly, traditional pricing methods cannot intuitively show the impact of different pricing strategies on sales. Decision makers can often only rely on experience and intuition to set prices, lacking scientific data support and decision-making basis. This not only increases the risk of pricing, but may also miss the best market opportunities. However, there are still some challenges in the application of existing neural network technology in the field of monetary product pricing. For example, some solutions fail to fully consider the particularity of gold products, such as large price fluctuations and being affected by multiple factors. In addition, some solutions also have limitations in visual decision support, and cannot intuitively show the impact of different pricing strategies on sales, thereby limiting the judgment of decision makers.

[0003] Therefore, how to overcome the limitations of traditional currency products in pricing and sales forecasting is an issue that needs to be addressed urgently. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for pricing and sales forecasting of a monetary product, aiming to solve the technical problems of limitations in pricing and sales forecasting of traditional monetary products.

[0005] To achieve the above objectives, the present application proposes a method for pricing and sales volume forecasting of currency products, the method comprising:

[0006] Obtain the basic price, basic fee, specification information, pricing method and historical sales model of currency products;

[0007] Obtaining a cost markup based on the pricing method, the basic cost, and the specification information;

[0008] Obtaining a current sales model according to the historical sales model;

[0009] The pricing and sales volume of the monetary product are predicted based on at least two of the base price, the cost markup, and the current sales volume model.

[0010] In one embodiment, the step of obtaining a cost markup based on the pricing method, the basic fee, and the specification information includes:

[0011] When the pricing method is floating pricing, the specification information is symbolically deconstructed and continuity checked to obtain a weight range corresponding to the current specification;

[0012] A cost markup is obtained based on the basic charge and the weight range.

[0013] In one embodiment, the step of obtaining a cost markup based on the basic fee and the weight interval includes:

[0014] Obtaining a cost price according to the basic fee and the weight range;

[0015] A cost markup is obtained based on the cost price.

[0016] In one embodiment, the step of obtaining the current sales model according to the historical sales model includes:

[0017] Get the current retail markup and markup step;

[0018] A current sales model is obtained according to the current retail markup, the markup step, and the historical sales model.

[0019] In one embodiment, the step of predicting the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup, and the current sales volume model comprises:

[0020] forecasting the pricing of the monetary product based on the base price and the cost markup;

[0021] The monetary product sales volume is predicted based on the monetary product pricing and the current sales volume model.

[0022] In one embodiment, the step of obtaining the historical sales model includes:

[0023] Get historical data from historical trading orders;

[0024] Filter the historical data according to preset filtering rules to obtain historical prices;

[0025] A historical sales model is constructed based on the preset strategy and the historical prices.

[0026] In one embodiment, the step of constructing a historical sales model according to a preset strategy and the historical price includes:

[0027] Get adjustment parameters;

[0028] Constructing corresponding initial weight coefficients according to the historical data;

[0029] Optimizing the initial weight coefficient according to the preset strategy and the historical price to obtain a target weight coefficient;

[0030] A historical sales model is obtained according to the adjustment parameters and the target weight coefficient.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for pricing and sales volume forecasting of currency products, the device comprising:

[0032] Data acquisition module, used to obtain the basic price, basic fee, specification information, pricing method and historical sales model of currency products;

[0033] A cost calculation module, used for obtaining a cost markup according to the pricing method, the basic fee and the specification information;

[0034] A model building module, used to obtain a current sales model based on the historical sales model;

[0035] The data prediction module is used to predict the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup and the current sales volume model.

[0036] In addition, to achieve the above-mentioned objectives, the present application also proposes a pricing and sales forecasting device for a monetary product, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the pricing and sales forecasting method for a monetary product as described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the pricing and sales forecasting method of currency products as described above are implemented.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the pricing and sales forecasting method of currency products as described above.

[0039] The present application provides a method for pricing and sales forecasting of a currency product, and the method of the present application includes: obtaining the base price, base fee, specification information, pricing method and historical sales model of the currency product; obtaining the cost markup according to the pricing method, the base fee and the specification information; obtaining the current sales model according to the historical sales model; and forecasting the pricing and sales of the currency product according to at least two of the base price, the cost markup and the current sales model. In summary, the present application realizes the accurate forecasting of the pricing and sales of gold products by constructing a pricing calculation model and a past sales model, solves the problem of the limitations of traditional currency products in pricing and sales forecasting, and improves the accuracy and flexibility of currency products in pricing and sales forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flowchart diagram of the first embodiment of the method for pricing and sales volume forecasting of currency products of the present application;

[0043] Figure 2 A flowchart diagram of the second embodiment of the method for pricing and sales volume forecasting of currency products of the present application;

[0044] Figure 3 This is a schematic diagram of a transaction data interface scenario in an embodiment of a method for pricing and sales volume forecasting of currency products of this application;

[0045] Figure 4 A gradient descent loss graph in an embodiment of a method for pricing and sales volume prediction of a currency product of the present application;

[0046] Figure 5 A three-dimensional schematic diagram of gradient descent in an embodiment of a method for pricing and sales volume prediction of a currency product of the present application;

[0047] Figure 6 A flowchart diagram of the third embodiment of the method for pricing and sales volume forecasting of currency products of the present application;

[0048] Figure 7 This is a schematic diagram of the module structure of the device for pricing and sales volume prediction of currency products according to an embodiment of the present application;

[0049] Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for pricing and sales volume prediction of currency products in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solutions of the embodiments of the present application are: obtaining the base price, basic fee, specification information, pricing method and historical sales model of the monetary product; obtaining the cost markup based on the pricing method, the basic fee and the specification information; obtaining the current sales model based on the historical sales model; and predicting the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup and the current sales model.

[0054] In the monetary product market, especially in the field of gold products, traditional pricing methods often rely on historical experience and simple mathematical calculations, such as setting prices through tools such as Excel. Although this method can reflect the cost and market conditions of the product to a certain extent, it has obvious limitations. The pricing results are too formulaic and lack in-depth consideration of market dynamics and consumer demand, resulting in pricing strategies that may not be flexible and accurate enough. More importantly, traditional pricing methods cannot intuitively show the impact of different pricing strategies on sales. Decision makers can often only rely on experience and intuition to set prices, lacking scientific data support and decision-making basis. This not only increases the risk of pricing, but may also miss the best market opportunities. However, there are still some challenges in the application of existing neural network technology in the field of monetary product pricing. For example, some solutions fail to fully consider the particularity of gold products, such as large price fluctuations and being affected by multiple factors. In addition, some solutions also have limitations in visual decision support, and cannot intuitively show the impact of different pricing strategies on sales, thereby limiting the judgment of decision makers. Therefore, how to overcome the limitations of traditional monetary products in pricing and sales forecasting is a problem that needs to be solved urgently.

[0055] This application achieves accurate prediction of gold product pricing and sales volume by constructing a pricing calculation model and a past sales volume model, solves the limitations of traditional monetary products in pricing and sales volume prediction, and improves the accuracy and flexibility of monetary products in pricing and sales volume prediction.

[0056] It should be noted that the execution subject of this embodiment can be a pricing and sales forecasting system for currency products, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the pricing and sales forecasting functions of the above-mentioned currency products, etc., and this embodiment does not specifically limit this. The pricing and sales forecasting system for currency products is taken as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the present application embodiment provides a method for pricing and sales volume forecasting of a currency product, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for pricing and sales volume forecasting of currency products of the present application.

[0058] In this embodiment, the pricing and sales volume forecasting method of the currency product includes steps S10 to S40:

[0059] Step S10: Obtain the basic price, basic fee, specification information, pricing method and historical sales model of the currency product.

[0060] It should be noted that in this step, the system will obtain the basic price of the currency product entered by the user or obtained through automatic collection. The basic fees include processing fees, packaging fees, direct mail insurance fees, warehousing insurance fees, etc. These fees are determined according to the specific specifications and process requirements of the product. Specification information refers to the detailed information of the price range corresponding to the different weight ranges of the currency product. This information is used to determine the cost structure of the product under different weights. Pricing methods are divided into fixed pricing and floating pricing. Fixed pricing means that the retail price is fixed and does not fluctuate with the basic price; while floating pricing means that the markup is fixed and the retail price fluctuates with the basic price. The historical sales model refers to a forecasting model built based on past sales data, which is used to analyze the sales performance of currency products under different pricing strategies.

[0061] In addition, it should be noted that, assuming that the currency commodity is gold, the base price refers to the raw material cost of the gold product, that is, the gold price, which changes with market fluctuations. The base cost refers to other costs that need to be paid for the production and sale of gold products in addition to the gold price. Specification information is used to distinguish different products and ensure the accuracy of pricing. The pricing method determines how the retail price of the product is calculated, and the historical sales model provides reference data for sales under different pricing strategies.

[0062] Step S20: Obtain a cost markup based on the pricing method, the basic fee, and the specification information.

[0063] It should be noted that in this step, the system will calculate the cost markup based on the pricing method (fixed pricing or floating pricing), base costs, and product specification information. Cost markup refers to the additional costs added to the base costs to cover production costs and obtain reasonable profits (that is, the cost markup is the base cost plus the additional costs). For floating-priced products, the cost markup is fixed, but the retail price is not fixed and will change with the fluctuations of the base price. For fixed-priced products, the retail price of the product is fixed, but the cost markup will change based on historical purchase data and the base price. In addition, it should be noted that during the pricing process, the system will analyze the historical sales data and the base price, and automatically adjust the cost markup to ensure the market competitiveness and profitability of the product.

[0064] Step S30: Obtaining a current sales model according to the historical sales model.

[0065] It should be noted that in this step, the system will use the historical sales model, combined with the specification information, pricing method and basic costs of the current product, to build the current sales model. The current sales model is used to predict the sales performance of products under different pricing strategies. The system uses neural network technology to analyze and train historical sales data to obtain the correlation between various influencing factors (such as processing fees, process fees, handling fees, retail markups, retail prices, basic prices, etc.) and sales. Then, according to the specific circumstances of the current product, these influencing factors are substituted into the current sales model to obtain the current sales model. For example, for a 10-gram gold pendant, the system can predict the expected sales under different cost markups and retail price strategies based on historical sales data and neural network models. By comparing the expected sales and profit levels under different pricing strategies, the system can help users choose the optimal pricing strategy.

[0066] In addition, it should be noted that the current sales model is a prediction model built based on historical sales data and neural network technology. It takes into account the impact of multiple factors on sales and can be dynamically adjusted according to the specific situation of the product. By using the current sales model, the system can more intuitively display the sales performance under different pricing strategies, thereby helping users choose the optimal pricing strategy.

[0067] In a feasible implementation manner, the step S30 specifically includes:

[0068] Step S301: Obtain the current retail markup and markup step.

[0069] It should be noted that the retail markup refers to the portion of the gold product's selling price that exceeds the base gold price, including the cost markup. The markup step is the range of each price adjustment preset by the system, which can be modified according to demand. It can be understood that the retail markup refers to the price added to the cost markup after the purchase of monetary commodities (such as gold products) in order to cover their operating costs (such as store rent, employee wages, water and electricity bills, etc.), marketing expenses, taxes and expected profits.

[0070] Step S302: obtaining a current sales model according to the current retail markup, the markup step, and the historical sales model.

[0071] It should be noted that in this step, the system will build the current sales model based on the historical sales model and the current retail markup and markup step. Among them, the historical sales model is a trained model that can predict future sales based on past sales data. It can be understood that the historical sales model is constructed using neural network technology, which can handle complex nonlinear relationships and has high prediction accuracy. Specifically, the system will calculate the expected sales under different retail markups based on the current retail markup and markup step, as well as the historical sales model. This calculation process is implemented through the forward propagation algorithm of the neural network, which can calculate the output value (i.e., expected sales) based on the input value and model parameters, as shown in Formula 1:

[0072]

[0073] Among them, p1 is the initial retail markup, p n is the retail markup from p1 to the nth markup step m, S(p) is the total sales volume, and m(x) is the historical sales model.

[0074] In addition, it should be noted that the current sales model is a dynamic model that changes with the current retail markup and the markup step. Therefore, the system will build different current sales models based on different pricing strategies to better understand the sales performance of products at different prices.

[0075] Step S40: predicting the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup, and the current sales volume model.

[0076] It should be noted that in this step, the system will calculate the retail price and expected sales volume of the product based on the base price (such as gold price), cost markup, and current sales model. For fixed-price products, the system's pre-set retail price is the final retail price. For floating-price products, the system calculates the retail price based on the base price and cost markup as the product's pricing.

[0077] In addition, it should be noted that the retail price is the actual selling price of the product in the market, which determines the market competitiveness and profitability of the product. The expected sales volume is the future sales performance predicted based on the current sales model, which provides users with an important basis for pricing decisions.

[0078] In a feasible implementation manner, the step S40 specifically includes:

[0079] Step S401: predicting the pricing of a currency product based on the base price and the cost markup.

[0080] It should be noted that in this step, assuming that gold is the currency product predicted by the system, the system will calculate the gold price (i.e. currency product pricing) based on the current market gold price (i.e. base price), the basic cost obtained according to the specifications, process and other requirements of the gold product, and the cost markup obtained by adding the cost price to be earned on this basis. In addition, it should be noted that the final currency product pricing can also be calculated based on the base price of the product and the retail markup of the product. The retail markup includes the cost markup part, but is not limited to the cost markup. It also includes all additional costs and expected profits incurred by retailers during the sales process.

[0081] Step S402: predicting the monetary product sales volume based on the monetary product pricing and the current sales volume model.

[0082] It should be noted that in this step, the current sales model will use the neural network technology to predict the product sales based on the pricing of the currency product and the data of the historical sales model. For example, assume that the current sales model has learned the correlation between the pricing and sales of gold products through historical sales data. When the price of a certain gold product is input as 850 yuan per gram, the model can predict the sales volume of the product in the next period of time.

[0083] This embodiment provides a method for predicting the pricing and sales volume of a currency product. The method of this embodiment includes: obtaining the base price, base fee, specification information, pricing method and historical sales model of the currency product; obtaining the cost markup according to the pricing method, the base fee and the specification information; obtaining the current sales model according to the historical sales model; predicting the pricing and sales volume of the currency product according to at least two of the base price, the cost markup and the current sales model. In summary, this embodiment achieves accurate prediction of the pricing and sales volume of gold products by constructing a pricing calculation model and a past sales volume model, solves the problem of the limitations of traditional currency products in pricing and sales volume prediction, and improves the accuracy and flexibility of currency products in pricing and sales volume prediction.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the method for pricing and sales volume forecasting of currency products of the present application. The steps of obtaining the historical sales volume model specifically include steps A10 to A30:

[0085] Step A10: Obtain historical data from historical transaction orders.

[0086] It should be noted that in this step, Figure 3 As shown in Figure 1, obtaining historical data from historical transaction orders is the basis for building a historical sales model. The system will extract all relevant historical transaction data from the database over a period of time (such as the past year, half a year, three months, or one month). These data contain detailed transaction information of the goods, such as transaction time, transaction quantity, transaction price, product specifications, processing fees, process fees, handling fees, retail markup, retail price, base price, etc. It is understandable that these data provide a rich sample for the subsequent analysis of the relationship between product sales and prices.

[0087] In addition, it should be noted that the quality and integrity of historical data are crucial to building an accurate historical sales model. Therefore, when acquiring historical data, the system needs to clean and preprocess the data to eliminate noise and outliers in the data to ensure the accuracy and reliability of the data.

[0088] Step A20: Filter the historical data according to preset filtering rules to obtain historical prices.

[0089] It should be noted that in this step, the system will further filter the historical data according to the preset filtering rules. These filtering rules include time range, product specifications, process cost range, etc. By executing these filtering rules, data that does not meet the conditions can be excluded, thereby obtaining more accurate and useful historical price data. For example, the system will only select transaction records that have occurred in the last six months according to the preset filtering rules, and the product specifications and process costs are within a certain range. This ensures that the selected data is the latest and relevant to the products and processes of the demand analysis. It is understandable that the preset filtering rules are preset according to specific needs and products, and different needs and products may require different filtering rules. Therefore, before executing this step, it is necessary to clarify the specific needs and products, and set appropriate filtering rules based on these needs and products.

[0090] In addition, it should be noted that before this step, the system will also use T-test to analyze historical data to verify the significant correlation between processing fees, process costs, handling fees, and retail markups and retail prices and sales volumes.

[0091] Step A30: construct a historical sales model based on the preset strategy and the historical prices.

[0092] It should be noted that the preset strategies include weighted operations and gradient algorithms. The gradient algorithm is an optimization algorithm that can find the parameter value that minimizes the loss function in an iterative manner. In this process, the weights reflect the different degrees of their impact on sales. Specifically, in this step, the system will weight the various price factors in the historical price data set (such as processing fees, process fees, handling fees, retail markups, retail prices, base prices, etc.), and build a historical sales model based on these weighted price factors, and iteratively optimize the weights through the gradient algorithm.

[0093] In addition, it should be noted that the gradient algorithm and the method of adding weights are only one feasible solution for building a historical sales model. In actual applications, the system can also select other suitable algorithms and models to build a historical sales model according to specific needs and data characteristics. For example, machine learning algorithms such as support vector machines and random forests can be used to build a more complex sales forecasting model.

[0094] In a feasible implementation manner, the step A30 specifically includes:

[0095] Step A301: Obtain adjustment parameters.

[0096] It should be noted that in this step, obtaining the adjustment parameter is the first step in building the historical sales model. The adjustment parameter is mainly used to adjust the fluctuation range of sales. It can be understood that in the process of linear regression, the role of the adjustment parameter is to adjust the benchmark level of the overall sales forecast value. Therefore, the adjustment parameter can be regarded as a benchmark point or starting point, which affects the upper and lower floating range of the entire forecast value.

[0097] Step A302: Construct corresponding initial weight coefficients according to the historical data.

[0098] It should be noted that in this step, Figure 4 As shown in the figure, the system will construct the corresponding initial weight coefficients based on historical data. These weight coefficients reflect the impact of various expenses on sales. Specifically, the system will use the gradient descent algorithm to initialize these weight coefficients.

[0099] Step A303: Optimize the initial weight coefficient according to the preset strategy and the historical price to obtain the target weight coefficient.

[0100] It should be noted that in this step, Figure 5 As shown in the figure, the system determines the impact of each cost item on sales based on historical price data and adds corresponding weights (i.e., initial weight coefficients) to them. The system then uses a gradient algorithm to optimize the initial weight coefficients to find the weight value that minimizes the loss function.

[0101] Step A304: Obtain a historical sales model according to the adjustment parameters and the target weight coefficient.

[0102] It should be noted that in this step, the system will build a historical sales model based on the optimized weight value (i.e., the target weight coefficient). The historical sales model can be expressed as m(x), where b represents the adjustment parameter, x represents different cost items, m(x) represents the sales forecast value under a given cost item, and w represents the weight coefficient corresponding to different cost items, as shown in Formula 2:

[0103] m(x)=w1x1+w2x2+...w6x6+b(Formula 2)

[0104] For example, the system will use Python's machine learning library (such as scikit-learn) to implement gradient algorithms and model construction. The specific implementation process includes steps such as data preprocessing, model training, and model evaluation. It is understandable that an accurate and reliable historical sales model can be constructed through this step. The model can predict the impact of different cost items (such as processing fees, process fees, handling fees, retail markups, retail prices, base prices, etc.) on the sales of currency products, providing users with accurate data support.

[0105] In this embodiment, the historical data is processed by the gradient algorithm and the method of adding weights to obtain a historical sales model that can accurately predict the sales of currency products. This model can provide users with accurate data support and help users formulate more reasonable pricing strategies and sales plans. At the same time, the system can also visualize and analyze these data information based on this model.

[0106] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to the above description, and will not be described in detail later. Figure 6 , Figure 6 This is a flow chart of the third embodiment of the method for pricing and sales volume forecasting of currency products of the present application, wherein step S20 specifically includes steps S201 to S202:

[0107] Step S201: When the pricing method is floating pricing, the specification information is subjected to symbol deconstruction and continuity check to obtain a weight range corresponding to the current specification.

[0108] It should be noted that in this step, the system will first determine the product's specification information, especially its weight, so as to determine the corresponding cost markup based on the weight range. The specification information is usually expressed in the form of a string, for example, ">=5&<10", which means that the weight of the product is between 5 grams (inclusive) and 10 grams (exclusive). In order to parse this specification information, the system needs to perform symbolic deconstruction and convert it into the maximum value, minimum value of the weight range, and whether the weight range contains these values. Specifically, the system will deconstruct the specification information, such as ">=5&<10", into maxValue=10, includeMax=false, minValue=5, includeMin=true, where maxValue is the maximum value, minValue is the minimum value, includeMax is a Boolean type indicating whether the maximum value is included, true is included, false is not included, and includeMin indicates whether the minimum value is included. In addition, it should be noted that in order to ensure that the parsed weight ranges are continuous and non-overlapping, the system will also perform a continuity check. For example, for a set of weight ranges, the system will check whether these ranges cover the entire range from 0 to positive infinity and there is no cross-overlap.

[0109] It can be understood that the continuity check ensures that all possible weight values ​​are correctly classified into a certain interval to avoid omissions or duplications.

[0110] Step S202: Obtain a cost markup based on the basic fee and the weight range.

[0111] It should be noted that in this step, after the system determines the weight range, it will calculate the cost markup based on the basic fees (such as processing fees, packaging fees, direct mail insurance fees, warehousing insurance fees, etc.) and the weight range. Taking gold as a monetary product as an example, the cost markup is usually expressed as the price per gram, with the unit of yuan per gram.

[0112] In a feasible implementation manner, the step S202 specifically includes:

[0113] Step C10: Obtaining a cost price according to the basic fee and the weight range.

[0114] It should be noted that the purpose of this step is to determine the cost price corresponding to each weight interval based on the basic fees (including processing fees, packaging fees, direct mail freight insurance fees, warehousing freight insurance fees, etc.) and the weight interval of the currency product. Specifically, the system will obtain the data of the basic fees, which have been classified or set according to different weight intervals. For example, the processing fee has different price standards for different weight intervals such as 5g-10g, 10g-20g, etc. Then, the system will determine the weight interval to which it belongs based on the actual weight of the currency product. For example, if the weight of a currency product is 7g, it belongs to the weight interval of 5g-10g. Then, the system will find the corresponding basic fees based on the determined weight interval, and add these fees to obtain the cost price of the weight interval. For example, if the processing fee of a 7g currency product in the 5g-10g interval is X yuan / g, the packaging fee is Y yuan / g, and other fees are negligible, then the cost price of the product is (X+Y)*7 yuan.

[0115] Step C20: Obtain a cost markup based on the cost price.

[0116] It should be noted that in this step, the system will add up the cost price corresponding to each basic fee of the currency product to obtain the cost markup corresponding to the currency product, specifically, as shown in Formula 3:

[0117]

[0118] Among them, p represents the cost markup, p i Indicates the cost markup corresponding to each basic fee within the corresponding weight range.

[0119] In this embodiment, the weight range of gold products is accurately determined by symbol deconstruction and continuity check of specification information; and the cost markup is calculated based on the weight range and basic cost data, providing a key basis for subsequent pricing. This process ensures the accuracy and rationality of pricing, and improves the efficiency and accuracy of gold product pricing and sales volume measurement.

[0120] This application also provides a pricing and sales forecasting device for currency products, please refer to Figure 7 , the pricing and sales volume forecasting device of the currency product comprises:

[0121] The data acquisition module 10 is used to obtain the basic price, basic fee, specification information, pricing method and historical sales model of the currency product;

[0122] A cost calculation module 20, for obtaining a cost markup according to the pricing method, the basic fee and the specification information;

[0123] A model building module 30, used to obtain a current sales model according to the historical sales model;

[0124] The data prediction module 40 is used to predict the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup and the current sales volume model.

[0125] The device for pricing and sales forecasting of currency products provided in this application adopts the method for pricing and sales forecasting of currency products in the above-mentioned embodiment, and can solve the technical problem that traditional currency products have limitations in pricing and sales forecasting. Compared with the prior art, the beneficial effects of the device for pricing and sales forecasting of currency products provided in this application are the same as the beneficial effects of the method for pricing and sales forecasting of currency products provided in the above-mentioned embodiment, and other technical features in the device for pricing and sales forecasting of currency products are the same as the features disclosed in the above-mentioned embodiment method, which will not be described in detail here.

[0126] In one embodiment, the data acquisition module 10 is further used to acquire historical data in historical transaction orders; filter the historical data according to preset filtering rules to obtain historical prices; and construct a historical sales model according to preset strategies and the historical prices.

[0127] In one embodiment, the data acquisition module 10 is also used to obtain adjustment parameters; construct a corresponding initial weight coefficient based on the historical data; optimize the initial weight coefficient according to a preset strategy and the historical price to obtain a target weight coefficient; and obtain a historical sales model based on the adjustment parameters and the target weight coefficient.

[0128] In one embodiment, the cost calculation module 20 is further used to perform symbol deconstruction and continuity verification on the specification information to obtain the weight range corresponding to the current specification when the pricing method is floating pricing; and to obtain the cost markup based on the basic fee and the weight range.

[0129] In one embodiment, the cost calculation module 20 is further used to obtain a cost price according to the basic fee and the weight range; and obtain a cost markup according to the cost price.

[0130] In one embodiment, the model building module 30 is further used to obtain the current retail markup and the markup step; and obtain the current sales model according to the current retail markup, the markup step and the historical sales model.

[0131] In one embodiment, the data prediction module 40 is further used to predict the monetary product pricing based on the base price and the cost markup; and predict the monetary product sales volume based on the monetary product pricing and the current sales volume model.

[0132] The present application provides a device for pricing and sales forecasting of currency products, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for pricing and sales forecasting of currency products in the above-mentioned embodiment one.

[0133] Reference below Figure 8 , which shows a schematic diagram of the structure of a device for pricing and sales volume prediction of a currency product suitable for implementing the embodiment of the present application. The device for pricing and sales volume prediction of a currency product in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The pricing and sales volume forecasting device for currency products shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0134] like Figure 8As shown, the pricing and sales forecasting device for currency products may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the pricing and sales forecasting device for currency products are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the pricing and sales forecasting device for currency products to communicate wirelessly or wired with other devices to exchange data. Although the figure shows the pricing and sales forecasting device for currency products with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0135] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0136] The device for pricing and sales forecasting of currency products provided in this application adopts the method for pricing and sales forecasting of currency products in the above-mentioned embodiment, and can solve the technical problem that traditional currency products have limitations in pricing and sales forecasting. Compared with the prior art, the beneficial effects of the device for pricing and sales forecasting of currency products provided in this application are the same as the beneficial effects of the method for pricing and sales forecasting of currency products provided in the above-mentioned embodiment, and other technical features in the device for pricing and sales forecasting of currency products are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0137] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0139] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the pricing and sales volume forecasting method for currency products in the above-mentioned embodiment.

[0140] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0141] The computer-readable storage medium may be included in the device for predicting the pricing and sales volume of a monetary product; or it may exist independently without being assembled into the device for predicting the pricing and sales volume of a monetary product.

[0142] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the pricing and sales forecasting device of the monetary product, the pricing and sales forecasting device of the monetary product: obtains the base price, basic fee, specification information, pricing method and historical sales model of the monetary product; obtains the cost markup according to the pricing method, the basic fee and the specification information; obtains the current sales model according to the historical sales model; and predicts the pricing and sales of the monetary product according to at least two of the base price, the cost markup and the current sales model.

[0143] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0145] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0146] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for pricing and sales forecasting of currency products, and can solve the technical problem that traditional currency products have limitations in pricing and sales forecasting. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the method for pricing and sales forecasting of currency products provided in the above-mentioned embodiments, and will not be elaborated here.

[0147] The present application also provides a computer program product, including a computer program, which implements the steps of the pricing and sales forecasting method of currency products as described above when executed by a processor.

[0148] The computer program product provided by this application can solve the technical problem that traditional currency products have limitations in pricing and sales forecasting. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the pricing and sales forecasting method of currency products provided by the above embodiments, and will not be repeated here.

[0149] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for pricing and sales volume forecasting of a currency product, characterized in that: The method comprises: Obtain the basic price, basic fee, specification information, pricing method and historical sales model of currency products; Obtaining a cost markup based on the pricing method, the basic fee, and the specification information; Obtaining a current sales model according to the historical sales model; The pricing and sales volume of the monetary product are predicted based on at least two of the base price, the cost markup, and the current sales volume model.

2. The method according to claim 1, characterized in that The step of obtaining a cost markup based on the pricing method, the basic fee and the specification information comprises: When the pricing method is floating pricing, the specification information is symbolically deconstructed and continuity checked to obtain a weight range corresponding to the current specification; A cost markup is obtained based on the basic charge and the weight range.

3. The method according to claim 2, characterized in that The step of obtaining a cost markup based on the basic fee and the weight interval comprises: Obtaining a cost price according to the basic fee and the weight range; A cost markup is derived from the cost price.

4. The method according to claim 1, characterized in that The step of obtaining the current sales model according to the historical sales model comprises: Get the current retail markup and markup step; A current sales model is obtained according to the current retail markup, the markup step, and the historical sales model.

5. The method according to claim 1, characterized in that The step of predicting the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup, and the current sales volume model: forecasting the pricing of the monetary product based on the base price and the cost markup; The monetary product sales volume is predicted based on the monetary product pricing and the current sales volume model.

6. The method according to claim 1, characterized in that The steps to obtain the historical sales model include: Get historical data from historical trading orders; Filter the historical data according to preset filtering rules to obtain historical prices; A historical sales model is constructed based on the preset strategy and the historical prices.

7. The method according to claim 6, characterized in that The step of constructing a historical sales model according to the preset strategy and the historical price includes: Get adjustment parameters; Constructing corresponding initial weight coefficients according to the historical data; Optimizing the initial weight coefficient according to the preset strategy and the historical price to obtain a target weight coefficient; A historical sales model is obtained according to the adjustment parameters and the target weight coefficient.

8. A device for pricing and sales volume forecasting of a monetary product, characterized in that: The device comprises: Data acquisition module, used to obtain the basic price, basic fee, specification information, pricing method and historical sales model of currency products; A cost calculation module, used for obtaining a cost markup according to the pricing method, the basic fee and the specification information; A model building module, used to obtain a current sales model based on the historical sales model; The data prediction module is used to predict the pricing and sales volume of the monetary product based on at least two of the base price, the cost markup and the current sales volume model.

9. A device for pricing and sales volume forecasting of a monetary product, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for pricing and sales volume forecasting of a currency product according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for pricing and sales volume forecasting of a currency product as described in any one of claims 1 to 7 are implemented.

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