Intelligent cabinet grid pricing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of logistics data processing, and discloses an intelligent cabinet grid pricing method and device, computer equipment and a storage medium. The intelligent cabinet grid pricing method comprises the steps of obtaining community attribute data corresponding to an intelligent cabinet to be priced, and performing prediction processing on the community attribute data through a community part quantity prediction model to obtain a basic part quantity; obtaining bin price data and carrying out discretization processing to obtain bin candidate prices; performing conversion processing on the cell candidate prices through a community price elastic model to obtain a price elastic piece quantity; performing distribution fitting processing on the basic part quantity and the price elastic part quantity to obtain a community fitting part quantity; and determining a decision variable of the bin pricing optimization model according to the bin candidate price and the community fitting amount, solving the bin pricing optimization model, and determining a target bin price corresponding to the intelligent cabinet to be priced according to a solving result. According to the invention, dynamic pricing of the grids of the intelligent cabinet is realized, and the comprehensiveness and rationality of pricing are improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics data processing technology, and in particular to an intelligent cabinet pricing method, device, computer equipment and storage medium. Background Art

[0002] In urban life, population distribution and living patterns are highly organized, forming diverse living communities. Diversity is not only reflected in different geographical locations, but also in the different social functions and lifestyles they carry. Smart cabinets, as supporting facilities in living communities, can facilitate people's lives and work. The operation of smart cabinets is carried out based on living communities, and grid pricing is an important part of smart cabinet operations.

[0003] The existing grid pricing methods mainly adopt cost pricing, market pricing or competitive pricing. The cost pricing method is calculated as the sum of unit cost and realized profit. The cost of grid service is compensated through sales and a certain profit margin is achieved. The market pricing method determines the price based on user habits, price sensitivity and market supply and demand. The competitive pricing method uses the price of competitors as the main basis for product pricing, and sets prices higher, lower or equal to those of competitors. These existing pricing methods only consider one aspect of factors such as cost, supply and demand, and competitive products to set grid prices, and cannot comprehensively consider multiple factors. Moreover, the parameters of supply and demand and competitive products strongly rely on manual experience, and cannot achieve fast dynamic pricing. In addition, the factors affecting the use of smart cabinets in different living communities present different characteristics and regularities. The existing pricing methods cannot reflect the specificity and regularity of living communities, and are prone to unreasonable pricing, which in turn reduces the user experience of smart cabinets and affects the operation effect of smart cabinets. Summary of the invention

[0004] Based on this, it is necessary to provide an intelligent cabinet grid pricing method, device, computer equipment and storage medium to address the above technical problems, so as to solve the problems of incomplete pricing basis, low pricing efficiency and unreasonable pricing in the existing grid pricing method.

[0005] An intelligent cabinet pricing method, comprising: Acquire community attribute data corresponding to the smart cabinet to be priced, and perform prediction processing on the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain a basic piece quantity of the smart cabinet to be priced; Obtaining the price data of the smart cabinet to be priced, and performing discretization processing on the price data to obtain the candidate price of the smart cabinet to be priced; The candidate grid prices are converted by a community price elasticity model corresponding to the smart cabinet to be priced, to obtain a price elasticity quantity of the smart cabinet to be priced; Performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced; The decision variables of the grid pricing optimization model are determined according to the candidate grid prices and the community fitting quantity, the grid pricing optimization model is solved, and the target grid price corresponding to the smart cabinet to be priced is determined according to the decision variable results obtained after the solution.

[0006] An intelligent cabinet pricing device, comprising: A basic piece quantity determination module is used to obtain community attribute data corresponding to the smart cabinet to be priced, and to predict the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced; A price discretization processing module is used to obtain the grid price data of the smart cabinet to be priced, discretize the grid price data, and obtain the grid candidate price of the smart cabinet to be priced; A price elasticity quantity determination module, used to convert the candidate grid prices through a community price elasticity model corresponding to the smart cabinet to be priced, to obtain the price elasticity quantity of the smart cabinet to be priced; A distribution fitting processing module, used for performing distribution fitting processing on the basic piece quantity and the price elastic piece quantity to obtain the community fitting piece quantity of the smart cabinet to be priced; The optimization model solving module is used to determine the decision variables of the grid pricing optimization model according to the candidate grid prices and the community fitting quantity, solve the grid pricing optimization model, and determine the target grid price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process.

[0007] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the above-mentioned intelligent cabinet pricing method is implemented.

[0008] A computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the above-mentioned smart cabinet pricing method.

[0009] In the above-mentioned smart cabinet slot pricing method, device, computer equipment and storage medium, the smart cabinet slot pricing method obtains the community attribute data corresponding to the smart cabinet to be priced, and predicts the community attribute data through the community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced; obtains the slot price data of the smart cabinet to be priced, discretizes the slot price data, and obtains the slot candidate price of the smart cabinet to be priced; converts the slot candidate price through the community price elasticity model corresponding to the smart cabinet to be priced, and obtains the price elastic piece quantity of the smart cabinet to be priced; performs distribution fitting processing on the basic piece quantity and the price elastic piece quantity, and obtains the community fitting piece quantity of the smart cabinet to be priced; determines the decision variables of the slot pricing optimization model according to the slot candidate price and the community fitting piece quantity, solves the slot pricing optimization model, and determines the target slot price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process. The present invention adopts a combination of data mining and operations optimization technology to realize the dynamic pricing of smart cabinet slots, while improving the comprehensiveness of pricing and ensuring the rationality of pricing. In terms of data mining, the present invention determines the basic quantity based on the community quantity prediction model, and uses machine learning methods to consider the specificity of community influencing factors. At the same time, the price elastic quantity is determined based on the community price elasticity model, and the elastic relationship between community quantity and price under different conditions is considered. In addition, in terms of operational optimization, the present invention implements pricing under specific goals in the optimization model based on the quantity prediction and price elasticity calculation, based on distribution fitting and solving the optimization model, so that the pricing result will be more robust. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 It is a flow chart of a smart cabinet slot pricing method in one embodiment of the present invention; Figure 2 1 is a schematic diagram of a price elasticity curve of a smart cabinet slot pricing method in one embodiment of the present invention; Figure 3 It is a structural schematic diagram of an intelligent cabinet compartment pricing device in one embodiment of the present invention; Figure 4 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] In one embodiment, if Figure 1 As shown, a smart cabinet pricing method is provided, comprising the following steps S10-S50: S10. Obtain community attribute data corresponding to the smart cabinet to be priced, and perform prediction processing on the community attribute data through the community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced.

[0014] Understandably, the slot price of the smart cabinet can be adjusted continuously according to the operation needs, and the smart cabinet price adjustment cycle is from each slot price adjustment to the next slot price adjustment. The slot price adjustment can be automatically triggered at a fixed time (such as a fixed adjustment every week), or it can be manually triggered by the operator as needed, and after the triggering, the process of the smart cabinet slot pricing method for the smart cabinet to be priced is started.

[0015] Smart cabinets to be priced refer to smart cabinets that need to set prices for cabinet slots. The operation of smart cabinets is carried out based on living communities, and the cabinet slots of all smart cabinets belonging to the same living community are uniformly adjusted in price. Only one smart cabinet can be placed in a living community, in which case the corresponding smart cabinet to be priced is one smart cabinet; multiple smart cabinets can also be placed at the same time, in which case the corresponding smart cabinets to be priced are multiple smart cabinets. Similarly, the operation service end of the smart cabinet can adjust the price of only the smart cabinets in one living community, or it can adjust the prices of smart cabinets in multiple living communities at the same time.

[0016] The community attribute data corresponding to the smart cabinet to be priced refers to the community attribute data of the living community to which the smart cabinet to be priced belongs. The community attribute data is a data set used to describe the various characteristics and conditions of the living community. Community attribute data includes community type data, holiday activity data, cabinet operation data, etc. For example, community type data is used to characterize whether the living community to which the smart cabinet to be priced belongs is a school district community centered on a school or a business district community centered on a shopping mall. Holiday activity data is used to characterize whether the living community to which the smart cabinet to be priced belongs is on holidays or whether it participates in group buying activities. Cabinet operation data is used to characterize the complaint rate of the living community to which the smart cabinet to be priced belongs. These community attribute data may affect the number of items in the smart cabinet, that is, the total number of items received and delivered using the smart cabinet.

[0017] The basic quantity of the smart cabinet to be priced can be obtained by predicting the community attribute data through the community quantity prediction model corresponding to the smart cabinet to be priced. The community quantity prediction model corresponding to the smart cabinet to be priced refers to a pre-trained neural network model that uses the community attribute data of the living community to which the smart cabinet to be priced belongs at the current time to predict the quantity of received and delivered parcels in the living community at a specific time in the future (such as one week). Since the factors that affect the quantity of smart cabinets are different in different living communities, each living community corresponds to a different community quantity prediction model. The basic quantity of the smart cabinet to be priced refers to the demand for receiving and delivering parcels in the living community to which the smart cabinet to be priced belongs within a specific time in the future (such as one week) without considering price changes.

[0018] S20, obtaining the slot price data of the smart cabinet to be priced, discretizing the slot price data, and obtaining the slot candidate price of the smart cabinet to be priced.

[0019] Understandably, the grid price data of the smart cabinet to be priced is a set of data used to limit the pricing range of the cabinet grid, including the maximum price and the minimum price. In the actual pricing process, the price of the cabinet grid cannot be subdivided to any precision. For example, the maximum price in the grid price data is 2 yuan, and the minimum price is 1 yuan. The price of the cabinet grid cannot be subdivided to 1.0002 yuan. Therefore, it is necessary to discretize the grid price data. Discretization refers to dividing the pricing range according to specific rules (such as price interval division) to generate a set of grid candidate prices as the basis for subsequent price elasticity analysis and optimization model solution. The grid candidate price refers to all possible pricing of the cabinet grid of the smart cabinet to be priced. The smart cabinet grid pricing process is ultimately to determine a price from all grid candidate prices as the target grid price.

[0020] S30. Convert the candidate grid prices using a community price elasticity model corresponding to the smart cabinet to be priced, and obtain the price elasticity quantity of the smart cabinet to be priced.

[0021] Understandably, price elasticity is used to measure the impact of price on demand. For example, after the price of a commodity rises from 5 yuan to 5.5 yuan, consumers will completely stop buying the commodity, indicating that the demand for the commodity is price elastic. The community price elasticity model is a pre-built mathematical model used to characterize the influence relationship between the grid price and the quantity of smart cabinets in the living community to which the smart cabinet to be priced belongs. It can predict the possible changes in the quantity of smart cabinets at different grid price levels. Since the acceptance of prices that affect the quantity of smart cabinets by smart cabinet users in different living communities is different, each living community corresponds to a different community price elasticity model.

[0022] In the smart cabinet slot pricing process, the community price elasticity model refers to a mathematical curve of the ratio calculated with the price change rate as the denominator and the quantity change rate as the numerator when the slot price of the smart cabinet changes. By converting the candidate slot prices with the community price elasticity model corresponding to the smart cabinet to be priced, the price elasticity corresponding to the candidate slot prices and the price elasticity quantity of the smart cabinet to be priced can be obtained. The price elasticity quantity refers to the change in the quantity of the smart cabinet under different price elasticities, such as a reduction of 10 pieces. Different candidate slot prices have different price elasticities, and the corresponding price elasticity quantities are also different.

[0023] S40, performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced.

[0024] Understandably, after obtaining the basic piece quantity and the price elastic piece quantity, if these two piece quantity results are directly used as parameters and applied to the grid pricing optimization model, the optimization calculation results are prone to fluctuations and errors. In order to improve the robustness of the calculation results of the grid pricing optimization model, the basic piece quantity and the price elastic piece quantity are subjected to distribution fitting processing to obtain the community fitting piece quantity of the smart cabinet to be priced. The community fitting piece quantity refers to the piece quantity obtained after distribution fitting of the basic piece quantity and the price elastic piece quantity. The process of distribution fitting processing is to combine the basic piece quantity and the price elastic piece quantity, and use the distribution fitting function to calculate the distribution of the combined piece quantity and the probability corresponding to the piece quantity, so as to obtain a more comprehensive community fitting piece quantity.

[0025] S50. Determine the decision variables of the grid pricing optimization model according to the candidate grid prices and the number of community fitting parts, solve the grid pricing optimization model, and determine the target grid price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process.

[0026] Understandably, the grid pricing optimization model is a pre-established stochastic optimization model for optimizing the pricing of the cabinet grids of the smart cabinets, such as a linear programming model or an integer programming model. The grid pricing optimization model includes decision variables, constraints, and an objective function. The objective function reflects the pricing goal (such as maximizing revenue) and can be adjusted as needed. When the objective function is different, different decision variable results can be obtained by solving under the same constraints and decision variables, that is, the smart cabinets to be priced have different target grid prices under different pricing goals.

[0027] After the grid pricing optimization model is constructed, the solution algorithm for solving the grid pricing optimization model is also determined. For example, when the grid pricing optimization model is a linear programming model, the solution algorithm is a linear programming algorithm. The target grid price refers to the combination of the final pricing of each cabinet grid in the smart cabinet to be priced. The target grid price is the grid price after the smart cabinet grid pricing process adjusts the price of the smart cabinet to be priced, that is, the smart cabinet grid pricing process is that each cabinet grid in the smart cabinet to be priced selects a price from the grid candidate prices, and the set of prices selected by all cabinet grids constitutes the target grid price. Since the decision variables of the grid pricing optimization model are determined based on the grid candidate prices and the community fitting quantity, the decision variables include price variables and quantity variables. Therefore, the decision variable results can determine what price a specific cabinet grid in the smart cabinet to be priced adopts and how many pieces there will be, so as to calculate the revenue of the smart cabinet based on the price and quantity.

[0028] This embodiment obtains community attribute data corresponding to the smart cabinet to be priced, and predicts the community attribute data through the community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced; obtains the grid price data of the smart cabinet to be priced, discretizes the grid price data, and obtains the grid candidate price of the smart cabinet to be priced; converts the grid candidate price through the community price elasticity model corresponding to the smart cabinet to be priced, and obtains the price elastic piece quantity of the smart cabinet to be priced; performs distribution fitting processing on the basic piece quantity and the price elastic piece quantity, and obtains the community fitting piece quantity of the smart cabinet to be priced; determines the decision variables of the grid pricing optimization model according to the grid candidate price and the community fitting piece quantity, solves the grid pricing optimization model, and determines the target grid price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process. This embodiment adopts a combination of data mining and operations optimization technology to realize the dynamic pricing of the grid of the smart cabinet, while improving the comprehensiveness of pricing and ensuring the rationality of pricing. In terms of data mining, this embodiment determines the basic quantity based on the community quantity prediction model, uses machine learning methods to consider the specificity of community influencing factors, and at the same time, determines the price elastic quantity based on the community price elasticity model, considering the elastic relationship between community quantity and price under different conditions. In addition, in terms of operations optimization, based on the quantity prediction and the quantity of price elasticity calculation, based on distribution fitting and solving the optimization model, pricing under specific goals in the optimization model is achieved, so that the pricing results will be more robust.

[0029] In one embodiment, step S10, that is, before the community attribute data is predicted by the community item quantity prediction model corresponding to the smart cabinet to be priced, includes: S101, obtaining community sample data corresponding to the smart cabinet to be priced; S102, preprocessing the community sample data to obtain sample processing data; S103. Train the constructed gradient boosting model according to the sample processing data to obtain a community quantity prediction model corresponding to the smart cabinet to be priced.

[0030] Understandably, before the community attribute data is predicted and processed by the community quantity prediction model corresponding to the smart cabinet to be priced, it is necessary to first train the community quantity prediction model of the living community to which the smart cabinet to be priced belongs. The community sample data for model training is the historical smart cabinet operation data of the living community to which the smart cabinet to be priced belongs, including the historical community attribute data, the historical grid price data of the smart cabinet and the historical quantity data of the smart cabinet collected on a daily basis within a specific historical time period (such as the past 1 month). When training the community quantity prediction model, the community sample data cannot be used directly, but the sample processing data is used. The sample processing data refers to the data obtained after preprocessing the community sample data. Preprocessing can eliminate the influence of the historical grid price data of the smart cabinet in the community sample data on the historical quantity data of the smart cabinet, and retain the influence of the historical community attribute data on the historical quantity data of the smart cabinet, such as retaining the community type data, holiday activity data, cabinet operation data and historical quantity data of the smart cabinet in the historical community attribute data at the same price level. At the same time, the preprocessing also performs data processing such as missing value filling and standardization on the retained data.

[0031] The community volume prediction model is trained based on the gradient boosting model, which is also called the gradient boosting decision tree (GBDT). It is an iterative decision tree algorithm that uses weak classifiers (decision trees) to iteratively train the optimal model. It has the advantages of good training effect and low overfitting. The gradient boosting model can choose the lightweight gradient boosting model (Light Gradient Boosting Machine, Light GBM) or the extreme gradient boosting model (eXtreme Gradient Boosting, XGBoost) as needed.

[0032] In one embodiment, the loss function of the gradient boosting model is used to quantify the difference between the predicted output of the machine learning algorithm and the actual target value, that is, the loss function is based on the sample processing data as the training sample to evaluate the prediction performance of the neural network model. During the training process, the gradient of the loss function of the back propagation algorithm relative to the model parameters is used for iterative training. When the loss is minimized, the iteration is stopped to obtain the community quantity prediction model corresponding to the smart cabinet to be priced. Specifically, the loss function of the gradient boosting model training is: .in, Represents the average prediction error, that is, the mean absolute error (MAE); Indicates the actual number of smart cabinet history items in the training sample; Indicates the sequence number of the training sample; Represents the total number of training samples; Indicates the number of smart cabinets predicted by the model in one iteration.

[0033] This embodiment preprocesses the community sample data to eliminate the impact of the grid price on the quantity and ensure the availability of the sample data. The community quantity prediction model is trained using the sample processing data obtained by preprocessing, which can fully explore the impact of factors such as community attributes, holiday activities and cabinet operations on the quantity of smart cabinets, thereby improving the accuracy of the community quantity prediction model.

[0034] In one embodiment, in step S20, the grid price data is discretized to obtain the grid candidate price of the smart cabinet to be priced, including: S201, determining a price range of a grid according to the grid price data; S202, obtaining a preset pricing accuracy of the smart cabinet to be priced, and performing discretization processing on the grid price range according to the preset pricing accuracy to obtain at least two grid price sub-ranges; S203: Determine the candidate slot price of the smart cabinet to be priced according to all the slot price sub-intervals.

[0035] Understandably, the grid price interval is an interval obtained based on the maximum and minimum prices in the grid price data and is used to characterize the range of grid price values. The preset pricing precision is a pre-set minimum value unit for limiting the grid price value interval, such as 0.2. The grid price sub-interval is a price interval obtained by dividing the grid price interval. The grid price sub-interval is smaller than the grid price interval, so at least two grid price sub-intervals can be obtained after the discretization division process. When determining the candidate grid prices of the smart cabinet to be priced based on all grid price sub-intervals, the interval end value of the grid price sub-interval can be directly used as the candidate grid price.

[0036] In one embodiment, the maximum price in the grid price data is 2 yuan, the minimum price is 1 yuan, and the grid price range is [1, 2]. The preset pricing accuracy of the smart cabinet to be priced is 0.2. At this time, the grid price range is discretized and divided according to the preset pricing accuracy to obtain 5 grid price sub-ranges, namely [1, 1.2), [1.2, 1.4), [1.4, 1.6), [1.6, 1.8), [1.8, 2]. The grid candidate price of the smart cabinet to be priced is determined based on all the grid price sub-ranges. The grid candidate prices include 1, 1.2, 1.4, 1.6, 1.8 and 2 yuan. The smart cabinet operation service end can select any one of the grid candidate prices as the target grid price for each cabinet grid of the smart cabinet to be priced.

[0037] This embodiment discretizes the grid price data to obtain candidate grid prices for the smart cabinet to be priced, thereby ensuring the rationality of grid pricing and reducing the difficulty of solving the subsequent grid pricing optimization model.

[0038] In one embodiment, step S10, that is, before converting the candidate slot prices using the community price elasticity model corresponding to the smart cabinet to be priced, includes: S301, obtaining community sample data corresponding to the smart cabinet to be priced; S302, performing variance analysis and filtering on the community sample data to obtain price-sensitive sample data; S303. Determine the grid sample price and the sample price elasticity data corresponding to the grid sample price according to the price-sensitive sample data, perform curve fitting processing on the grid sample price and the sample price elasticity data, and determine the price elasticity curve obtained after the curve fitting processing as the community price elasticity model.

[0039] Understandably, before converting the candidate grid prices through the community price elasticity model corresponding to the smart cabinet to be priced, it is necessary to first construct a model of the community price elasticity model of the living community to which the smart cabinet to be priced belongs, that is, to fit the community price elasticity curve of the living community to which the smart cabinet to be priced belongs. Price elasticity describes the degree of change in the quantity of pieces as the grid price changes at the corresponding price level. In order to identify whether the change in quantity is caused by price, after obtaining the community sample data corresponding to the smart cabinet to be priced, and before fitting the community price elasticity curve, it is necessary to perform variance analysis and filtering on the community sample data to obtain price-sensitive sample data. Price-sensitive sample data refers to the data obtained after performing variance analysis and filtering on the community sample data. During the variance analysis, it is necessary to determine whether the p-value of the variance analysis is less than 0.05. If the p-value is less than 0.05, it means that the sample data has no significant impact; if the p-value is greater than or equal to 0.05, it means that the impact is not significant.

[0040] In one embodiment, the community sample data includes historical community attribute data, historical smart cabinet slot price data, and historical smart cabinet piece quantity data. First, a variance analysis is performed on the community sample data based on the historical community attribute data and the historical smart cabinet piece quantity data to obtain the result of the variance analysis. It is determined whether the p value in the result of the variance analysis is less than 0.05, and the community sample data less than 0.05 is filtered out to obtain the community sample data after the first filtering. The first filtering can exclude the community sample data whose historical smart cabinet piece quantity data is significantly affected by the community type data, holiday activity data, and cabinet operation data in the historical community attribute data. Then, a quadratic variance analysis is performed on the community sample data based on the historical smart cabinet slot price data to obtain the result of the quadratic variance analysis. It is determined whether the p value in the result of the quadratic variance analysis is less than 0.05, and the community sample data greater than or equal to 0.05 is filtered out to obtain the community sample data after the second filtering. The second filtering can exclude the community sample data whose historical smart cabinet piece quantity data is too little affected by the historical smart cabinet slot price data, that is, filter the community sample data that is not sensitive to price to obtain price sensitive sample data. For price-sensitive sample data, the price change rate is used as the denominator and the quantity change rate is used as the numerator to calculate the ratio, and the sample price elasticity data of the price-sensitive sample data under different sample prices is obtained. Specifically, a polynomial is used to fit the price elasticity data, and the curve coefficient is calculated by the least squares method, and finally the following is generated: Figure 2 The community price elasticity curve of the living community to which the smart cabinet to be priced belongs is shown. The horizontal axis is the sample price (price) and the vertical axis is the sample price elasticity data (elastic).

[0041] This embodiment performs variance analysis and filtering on the community sample data, and selects price-sensitive sample data from the community sample data. Function fitting is performed based on the price-sensitive sample data to obtain a price elasticity curve, fully explore the relationship between price elasticity and price, and determine the price elasticity curve as a community price elasticity model, so as to use the community price elasticity model to quickly determine the impact of price on the quantity of items when pricing smart cabinets, thereby improving the comprehensiveness of the pricing basis.

[0042] In one embodiment, in step S30, the conversion process of the candidate grid price is performed using the community price elasticity model corresponding to the smart cabinet to be priced to obtain the price elasticity quantity of the smart cabinet to be priced, including: S304, finding candidate price elasticity data corresponding to the slot candidate price through a community price elasticity model corresponding to the smart cabinet to be priced; S305: Obtain the initial price of the slot of the smart cabinet to be priced, perform conversion operations on the basic component quantity, the initial price of the slot and the candidate price elasticity data, and obtain the price elastic component quantity of the smart cabinet to be priced.

[0043] Understandably, the community price elasticity model corresponding to the smart cabinet to be priced is specifically the community price elasticity curve of the living community to which the smart cabinet to be priced belongs. When converting the candidate grid prices, obtain the community price elasticity curve of the living community to which the smart cabinet to be priced belongs, take the value of the candidate grid price as the horizontal coordinate, find the corresponding vertical coordinate in the community price elasticity curve, and obtain the candidate price elasticity data corresponding to the candidate grid price. The candidate price elasticity data is the price elasticity corresponding to the grid price when the grid price is the candidate grid price. After determining the candidate price elasticity data, obtain the initial grid price of the smart cabinet to be priced, and use the conversion relationship between price elasticity and price elasticity quantity to calculate the basic quantity, the initial grid price and the candidate price elasticity data to obtain the price elasticity quantity of the smart cabinet to be priced. The initial grid price refers to the price of the cabinet grid of the smart cabinet within the current price adjustment cycle, that is, the grid price before the smart cabinet grid pricing process is triggered.

[0044] This embodiment uses the community price elasticity model to find out the candidate price elasticity data corresponding to the candidate grid price, and determines the price elasticity quantity of the smart cabinet to be priced through conversion operation, thereby ensuring the accuracy of the price elasticity quantity.

[0045] In one embodiment, in step S40, that is, performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced, includes: S401, determining the number of parts to be fitted and the fitting distribution function according to the basic part quantity and the price elastic part quantity; S402. Perform distribution fitting processing on the quantity of items to be fitted according to the fitting distribution function to obtain the community fitting quantity of the smart cabinet to be priced.

[0046] Understandably, when the distribution fitting process is performed on the basic piece quantity and the price elastic piece quantity, the basic piece quantity and the price elastic piece quantity are summed to obtain the piece quantity to be fitted, and the fitting distribution function is further determined according to the relationship between the piece quantity to be fitted and the type of fitting distribution function. The piece quantity to be fitted refers to the piece quantity directly obtained from the basic piece quantity and the price elastic piece quantity of the smart cabinet to be priced.

[0047] In one embodiment, there is the following relationship between the number of parts to be fitted and the type of fitting distribution function: in, represents the fitted distribution function; Represents the number of parts to be fitted in the smart cabinet to be priced, that is, the sum of the basic number of parts and the price elastic number of parts. In this embodiment, when the number of parts to be fitted is less than 10, the number of parts to be fitted conforms to the Poisson distribution; when the number of parts to be fitted is greater than or equal to 10, the number of parts to be fitted conforms to the Gaussian distribution. Distribution fitting is performed on the number of parts to be fitted according to the fitting distribution function to obtain the community fitting number of parts in the smart cabinet to be priced. At this time, when the smart cabinet to be priced allocates the grid prices of each cabinet to generate a group of grid candidate prices, each group of grid candidate prices corresponds to a price elastic number of parts, and then corresponds to a number of parts to be fitted, and then distribution fitting is performed on the number of parts to be fitted to obtain the community fitting number of parts in the smart cabinet to be priced.

[0048] In addition, after the distribution fitting process is performed on the number of items to be fitted, in addition to the number of community fitting items of the smart cabinet to be priced, the distribution probability corresponding to the number of community fitting items can also be determined, so as to use the distribution probability to assist in solving the subsequent grid pricing optimization model. The formula for the distribution probability is as follows: in, The community fitting quantity is The probability of Indicates the cabinet slot type of the smart cabinet to be priced; Indicates the cabinet slot type is The number of parts to be fitted at the grid; Indicates the cabinet slot type is The upper limit of the number of pieces in the grid. Represents the cumulative probability. The types of cabinet slots of the smart cabinet to be priced include large slots, medium slots and small slots. When the smart cabinet is put into use, the number of large slots, medium slots and small slots is determined, such as Indicates a large mouth. Indicates the middle mouth. Indicates a small grid. When pricing the grid, all the grids in the smart cabinet to be priced can be numbered in sequence and priced, but this will require a lot of calculations. In order to simplify the pricing calculation, when pricing the grid, all the grids in the smart cabinet to be priced are divided into three types: large grid, medium grid and small grid, and the number of grids corresponding to each type of grid is counted, and pricing is performed based on the type of grid. At this time, grids of the same type have the same price, for example, all large grids of all smart cabinets in the same living community have the same target grid price.

[0049] This embodiment determines the quantity of pieces to be fitted based on the basic quantity and the price elasticity quantity, and uses the fitting distribution function to perform distribution fitting processing on the quantity of pieces to be fitted, which helps to improve the rationality of the distribution fitting processing and improve the efficiency of solving the subsequent grid pricing optimization model.

[0050] In one embodiment, the grid pricing optimization model further includes an objective function, which includes a maximum expected revenue target, a maximum expected quantity target, and a revenue quantity balance target; in step S50, the decision variables of the grid pricing optimization model are determined according to the grid candidate price and the community fitting quantity, the grid pricing optimization model is solved, and the target grid price corresponding to the smart cabinet to be priced is determined according to the decision variable results obtained after the solution, including: S501, obtaining a standard price for a slot, determining a discount variable according to the candidate slot price and the standard slot price, determining a quantity variable according to the community fitting quantity, and determining the discount variable and the quantity variable as decision variables; S502: When the objective function is to maximize the expected profit, the grid pricing optimization model is solved, and the target grid price that maximizes the profit of the smart cabinet to be priced is determined according to the decision variable results obtained after the solution; S503: When the objective function is to maximize the expected quantity, the grid pricing optimization model is solved, and the target grid price that enables the to-be-priced smart cabinet to achieve the maximum quantity is determined according to the decision variable results obtained after the solution process; S504. When the objective function is a revenue quantity equilibrium objective, the grid pricing optimization model is solved, and the target grid price that enables the to-be-priced smart cabinet to achieve revenue quantity equilibrium is determined based on the decision variable results obtained after the solution.

[0051] Understandably, the grid pricing optimization model includes decision variables, objective functions, and constraints. The objective functions include the objective of maximizing expected revenue, the objective of maximizing expected quantity, and the objective of balancing revenue and quantity. The objective of maximizing expected revenue is used to maximize the total revenue of the smart cabinet within a specific time period by adjusting the grid price. The objective of maximizing expected quantity is used to attract more users to use the smart cabinet by adjusting the grid price, thereby increasing the frequency of use of the smart cabinet and the number of items collected and delivered. The objective of balancing revenue and quantity is used to find the best balance between revenue and quantity by adjusting the grid price, which not only ensures a suitable level of revenue, but also maintains a high frequency of user use.

[0052] In one embodiment, in order to facilitate the operation of smart cabinets, the cabinet slots of the smart cabinets are provided with standard slot prices. Each price adjustment can change the discount on the basis of the standard slot price to complete the pricing, thus avoiding pricing errors. The standard slot price refers to the benchmark reference price of the cabinet slots of the smart cabinets. The standard slot prices of all smart cabinets are the same, which is convenient for unified pricing and batch price adjustment. Since the collection and delivery revenue of the smart cabinet to be priced is related to the discount and the number of pieces, the discount variable and the number of pieces variable are used as decision variables together, and the corresponding constraints include the discount range constraint and the upper limit constraint of the number of pieces. On the premise of obtaining the standard slot price, the discount variable can be determined according to the ratio between the candidate slot price and the standard slot price. That is, the change of the candidate slot price will cause the change of the discount variable, and then cause the change of the collection and delivery revenue. The number of pieces variable can be determined according to the community fitting number of pieces, that is, the change of the number of pieces in the smart cabinet will cause the change of the number of pieces variable, and then cause the change of the collection and delivery revenue.

[0053] In the smart locker grid pricing process, for the grid pricing optimization model, the decision variables and constraints are the same, and the objective function can be adjusted as needed. Specifically, the decision variables of the grid pricing optimization model are and ,in, Indicates the cabinet slot type of the smart cabinet to be priced. Indicates the cabinet slot type is Discounts on the price of goods; Indicates the cabinet slot type is The expected quantity of the slots after the price adjustment, such as the expected quantity of all large slots in the smart cabinet to be priced within one week after the price adjustment. The constraints are ,in, Indicates the cabinet slot type is The discount lower limit of the grid, Indicates the cabinet slot type is The discount limit of the grid, Represents the set of cabinet slot types. When the objective function is to maximize the expected return, the objective function of the slot pricing optimization model is ,in, Indicates the cabinet slot type is The upper limit of the number of pieces in the grid, Indicates the cabinet slot type is The expected quantity of the price-adjusted slot is The probability of Indicates the cabinet slot type is When the objective function is to maximize the expected quantity, the objective function of the grid pricing optimization model is When the objective function is the revenue quantity equilibrium objective, the objective function of the grid pricing optimization model is ,in, Represents the revenue quantity equilibrium coefficient.

[0054] This embodiment determines the discount variable based on the standard price of the grid, which is convenient for realizing unified pricing and batch price adjustment. At the same time, the grid pricing optimization model is solved with the discount variable and the quantity variable as decision variables, and different target grid prices are obtained under the pricing objectives of maximizing revenue, maximizing quantity, or balancing revenue and quantity, realizing dynamic grid pricing of smart cabinets and ensuring the rationality of pricing.

[0055] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0056] In one embodiment, a smart cabinet slot pricing device is provided, and the smart cabinet slot pricing device corresponds one-to-one with the smart cabinet slot pricing method in the above embodiment. Figure 3 As shown, the intelligent cabinet pricing device includes a basic piece quantity determination module 10, a price discretization processing module 20, a price elastic piece quantity determination module 30, a distribution fitting processing module 40 and an optimization model solving module 50. The functional modules are described in detail as follows: The basic piece quantity determination module 10 is used to obtain the community attribute data corresponding to the smart cabinet to be priced, and to predict the community attribute data through the community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced; The price discretization processing module 20 is used to obtain the grid price data of the smart cabinet to be priced, and discretize the grid price data to obtain the grid candidate price of the smart cabinet to be priced; A price elasticity component quantity determination module 30 is used to convert the candidate grid prices through a community price elasticity model corresponding to the smart cabinet to be priced, so as to obtain the price elasticity component quantity of the smart cabinet to be priced; A distribution fitting processing module 40 is used to perform distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced; The optimization model solving module 50 is used to determine the decision variables of the grid pricing optimization model according to the grid candidate prices and the community fitting quantity, solve the grid pricing optimization model, and determine the target grid price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process.

[0057] In one embodiment, the basic component quantity determination module 10 includes: A first sample data acquisition unit, used to acquire community sample data corresponding to the smart cabinet to be priced; A preprocessing unit, used for preprocessing the community sample data to obtain sample processing data; The prediction model training unit is used to train the constructed gradient boosting model according to the sample processing data to obtain a community piece quantity prediction model corresponding to the smart cabinet to be priced.

[0058] In one embodiment, the price discretization processing module 20 includes: A price range determination unit, used to determine a price range according to the price data; A price range division unit is used to obtain a preset pricing accuracy of the intelligent cabinet to be priced, and discretize the grid price range according to the preset pricing accuracy to obtain at least two grid price sub-ranges; The slot candidate price determination unit is used to determine the slot candidate price of the smart cabinet to be priced according to all the slot price sub-intervals.

[0059] In one embodiment, the price elastic component quantity determination module 30 includes: A second sample data acquisition unit is used to acquire community sample data corresponding to the smart cabinet to be priced; A filtering processing unit, used for performing variance analysis and filtering processing on the community sample data to obtain price-sensitive sample data; A curve fitting processing unit is used to determine the grid sample price and the sample price elasticity data corresponding to the grid sample price based on the price-sensitive sample data, and to perform curve fitting processing on the grid sample price and the sample price elasticity data, and to determine the price elasticity curve obtained after the curve fitting processing as the community price elasticity model.

[0060] In one embodiment, the price elastic component quantity determination module 30 further includes: A candidate price elasticity data determination unit, used to find out the candidate price elasticity data corresponding to the slot candidate price through the community price elasticity model corresponding to the smart cabinet to be priced; The conversion operation processing unit is used to obtain the initial price of the slot of the smart cabinet to be priced, and perform conversion operation processing on the basic component quantity, the initial price of the slot and the candidate price elasticity data to obtain the price elastic component quantity of the smart cabinet to be priced.

[0061] In one embodiment, the distribution fitting processing module 40 includes: A fitting distribution function determination unit, used to determine the number of parts to be fitted and the fitting distribution function according to the basic number of parts and the price elastic number of parts; The community fitting quantity determination unit is used to perform distribution fitting processing on the quantity of parts to be fitted according to the fitting distribution function to obtain the community fitting quantity of the smart cabinet to be priced.

[0062] In one embodiment, the optimization model solving module 50 includes: A decision variable determination unit, used for obtaining a standard price for a slot, determining a discount variable according to the candidate slot price and the standard price for the slot, determining a quantity variable according to the community fitting quantity, and determining the discount variable and the quantity variable as decision variables; The profit maximization solving unit is used to solve the grid pricing optimization model when the objective function is to maximize the expected profit, and determine the target grid price that maximizes the profit of the smart cabinet to be priced according to the decision variable results obtained after the solving process; A unit for maximizing the quantity of items is used to solve the grid pricing optimization model when the objective function is to maximize the expected quantity of items, and determine the target grid price that enables the intelligent cabinet to be priced to achieve the maximum quantity of items according to the decision variable results obtained after the solution process; The revenue quantity balance solving unit is used to solve the grid pricing optimization model when the objective function is the revenue quantity balance target, and determine the target grid price that makes the smart cabinet to be priced achieve revenue quantity balance based on the decision variable results obtained after the solution.

[0063] For the specific definition of the intelligent cabinet pricing device, please refer to the definition of the intelligent cabinet pricing method above, which will not be repeated here. Each module in the above-mentioned intelligent cabinet pricing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0064] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database of the computer device is used to store data involved in the intelligent cabinet pricing method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for pricing an intelligent cabinet is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0065] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to implement the following steps: Acquire community attribute data corresponding to the smart cabinet to be priced, and perform prediction processing on the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain a basic piece quantity of the smart cabinet to be priced; Obtaining the price data of the smart cabinet to be priced, and performing discretization processing on the price data to obtain the candidate price of the smart cabinet to be priced; The candidate grid prices are converted by a community price elasticity model corresponding to the smart cabinet to be priced, to obtain a price elasticity quantity of the smart cabinet to be priced; Performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced; The decision variables of the grid pricing optimization model are determined according to the candidate grid prices and the community fitting quantity, the grid pricing optimization model is solved, and the target grid price corresponding to the smart cabinet to be priced is determined according to the decision variable results obtained after the solution.

[0066] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. The readable storage media store computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the following steps are implemented: Acquire community attribute data corresponding to the smart cabinet to be priced, and perform prediction processing on the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain a basic piece quantity of the smart cabinet to be priced; Obtaining the price data of the smart cabinet to be priced, and performing discretization processing on the price data to obtain the candidate price of the smart cabinet to be priced; The candidate grid prices are converted by a community price elasticity model corresponding to the smart cabinet to be priced, to obtain a price elasticity quantity of the smart cabinet to be priced; Performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced; The decision variables of the grid pricing optimization model are determined according to the candidate grid prices and the community fitting quantity, the grid pricing optimization model is solved, and the target grid price corresponding to the smart cabinet to be priced is determined according to the decision variable results obtained after the solution.

[0067] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0068] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0069] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A smart cabinet pricing method, characterized in that: include: Acquire community attribute data corresponding to the smart cabinet to be priced, and perform prediction processing on the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain a basic piece quantity of the smart cabinet to be priced; Obtaining the price data of the smart cabinet to be priced, and performing discretization processing on the price data to obtain the candidate price of the smart cabinet to be priced; The candidate grid prices are converted by a community price elasticity model corresponding to the smart cabinet to be priced, to obtain a price elasticity quantity of the smart cabinet to be priced; Performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced; The decision variables of the grid pricing optimization model are determined according to the candidate grid prices and the community fitting quantity, the grid pricing optimization model is solved, and the target grid price corresponding to the smart cabinet to be priced is determined according to the decision variable results obtained after the solution.

2. The intelligent cabinet slot pricing method according to claim 1, characterized in that: Before the community attribute data is predicted by the community item quantity prediction model corresponding to the to-be-priced smart cabinet, the method includes: Obtain community sample data corresponding to the smart cabinet to be priced; Preprocessing the community sample data to obtain sample processed data; The constructed gradient boosting model is trained according to the sample processing data to obtain a community piece quantity prediction model corresponding to the smart cabinet to be priced.

3. The intelligent cabinet slot pricing method according to claim 1, characterized in that: The discretization processing of the grid price data to obtain the grid candidate price of the smart cabinet to be priced includes: Determine the price range of the grid according to the grid price data; Obtaining a preset pricing accuracy of the smart cabinet to be priced, and performing discretization processing on the grid price range according to the preset pricing accuracy to obtain at least two grid price sub-ranges; The candidate slot prices of the smart cabinet to be priced are determined according to all the slot price sub-intervals.

4. The intelligent cabinet slot pricing method according to claim 1, characterized in that: Before converting the candidate grid prices by using the community price elasticity model corresponding to the smart cabinet to be priced, the method includes: Obtain community sample data corresponding to the smart cabinet to be priced; Performing variance analysis and filtering on the community sample data to obtain price-sensitive sample data; The grid sample price and the sample price elasticity data corresponding to the grid sample price are determined according to the price-sensitive sample data, and curve fitting is performed on the grid sample price and the sample price elasticity data, and the price elasticity curve obtained after the curve fitting is determined as the community price elasticity model.

5. The intelligent cabinet slot pricing method according to claim 4, characterized in that: The price elasticity of the smart cabinet to be priced is obtained by converting the candidate grid price through the community price elasticity model corresponding to the smart cabinet to be priced, including Find out the candidate price elasticity data corresponding to the candidate price of the grid through the community price elasticity model corresponding to the smart cabinet to be priced; The initial price of the slot of the smart cabinet to be priced is obtained, and the basic piece quantity, the initial price of the slot and the candidate price elasticity data are converted and processed to obtain the price elastic piece quantity of the smart cabinet to be priced.

6. The intelligent cabinet slot pricing method according to claim 1, characterized in that: The performing distribution fitting processing on the basic quantity and the price elastic quantity to obtain the community fitting quantity of the smart cabinet to be priced includes: Determining the number of parts to be fitted and the fitting distribution function according to the basic part quantity and the price elastic part quantity; The number of parts to be fitted is subjected to distribution fitting processing according to the fitting distribution function to obtain the number of community fitting parts of the smart cabinet to be priced.

7. The intelligent cabinet slot pricing method according to claim 1, characterized in that: The grid pricing optimization model also includes an objective function, which includes a maximum expected revenue target, a maximum expected unit volume target, and a revenue-unit volume balance target; The step of determining the decision variables of the slot pricing optimization model according to the slot candidate price and the number of community fitting parts, solving the slot pricing optimization model, and determining the target slot price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process, includes: Obtaining a standard price for a grid, determining a discount variable according to the candidate grid price and the standard grid price, determining a quantity variable according to the community fitting quantity, and determining the discount variable and the quantity variable as decision variables; When the objective function is to maximize the expected profit, the grid pricing optimization model is solved, and the target grid price that maximizes the profit of the smart cabinet to be priced is determined according to the decision variable results obtained after the solution; When the objective function is to maximize the expected quantity, the grid pricing optimization model is solved, and the target grid price that enables the to-be-priced smart cabinet to achieve the maximum quantity is determined based on the decision variable results obtained after the solution; When the objective function is the revenue quantity equilibrium objective, the grid pricing optimization model is solved, and the target grid price that enables the to-be-priced smart cabinet to achieve revenue quantity equilibrium is determined based on the decision variable results obtained after the solution.

8. An intelligent cabinet slot pricing device, characterized in that: include: A basic piece quantity determination module is used to obtain community attribute data corresponding to the smart cabinet to be priced, and to predict the community attribute data through a community piece quantity prediction model corresponding to the smart cabinet to be priced, so as to obtain the basic piece quantity of the smart cabinet to be priced; A price discretization processing module is used to obtain the grid price data of the smart cabinet to be priced, discretize the grid price data, and obtain the grid candidate price of the smart cabinet to be priced; A price elasticity quantity determination module, used to convert the candidate grid prices through a community price elasticity model corresponding to the smart cabinet to be priced, to obtain the price elasticity quantity of the smart cabinet to be priced; A distribution fitting processing module, used for performing distribution fitting processing on the basic piece quantity and the price elastic piece quantity to obtain the community fitting piece quantity of the smart cabinet to be priced; The optimization model solving module is used to determine the decision variables of the grid pricing optimization model according to the candidate grid prices and the community fitting quantity, solve the grid pricing optimization model, and determine the target grid price corresponding to the smart cabinet to be priced according to the decision variable results obtained after the solving process.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the smart cabinet pricing method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by one or more processors, the one or more processors execute the smart cabinet pricing method as described in any one of claims 1 to 7.

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