Mobile intelligent terminal commodity sales data processing method

Abstract

The invention discloses a mobile intelligent terminal commodity sales data processing method, and particularly relates to the technical field of commodity sales data processing. Comprising the steps of S01, construction of a thermodynamic diagram of a commodity sales area of the mobile intelligent terminal, S02, collection of user behavior data in the thermodynamic diagram area, S03, resource allocation processing of the mobile intelligent terminal, S04, commodity sales data management of the mobile intelligent terminal, S05, sales trend feedback of the mobile intelligent terminal, and S06, sales trend evaluation of the mobile intelligent terminal. The user purchase behavior data of the thermodynamic diagram is collected based on the time sequence, the intelligent mobile terminal resource scheduling index and the intelligent mobile terminal commodity sales characteristic index are calculated, the marketing strategy is analyzed, the sales trend feedback coefficient is adjusted and compared with the preset value, the sales hot spot area can be visually displayed, and therefore the sales hot spot area can be displayed according to market requirements. And valuable sales prediction and market trend analysis are provided for merchants.

Description

Technical Field

[0001] The present invention relates to the technical field of commodity sales data processing. More specifically, the present invention relates to a method for processing commodity sales data of a mobile intelligent terminal. Background Art

[0002] The commodity sales of mobile intelligent terminals generally refer to those terminal devices that integrate advanced information technologies such as the Internet of Things, cloud computing, big data, artificial intelligence, etc., and can realize intelligent and automated sales services. These devices usually have mobility, portability, and can perform real-time data exchange with the background server through a wireless network.

[0003] With the popularization of the mobile Internet, consumers increasingly access online retail platforms through mobile devices such as smart phones and tablets to browse, compare, and purchase commodities. By tracking the real-time monitoring data of the usage environment of mobile intelligent terminals and user behaviors, the commodity layout and recommendation algorithms can be optimized, thereby improving the conversion rate and customer satisfaction.

[0004] However, in actual use, there are still some drawbacks. For example, the existing management of commodity sales of mobile intelligent terminals lacks the analysis of user purchase behaviors and the real-time adjustment of mobile intelligent terminal resources and commodity sales strategies of mobile intelligent terminals to meet the market demand of commodity sales;

[0005] The existing early warning of commodity sales of mobile intelligent terminals lacks an intelligent data analysis function, and the frequency and depth of collecting commodity sales data are insufficient, unable to provide valuable sales forecasts and market trend analyses for merchants. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for processing commodity sales data of a mobile intelligent terminal to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for processing commodity sales data of a mobile intelligent terminal, including:

[0008] Step S01: Construction of a heat map of the commodity sales area of a mobile intelligent terminal: used to combine the demographic data and sales data of each commodity sales area of a mobile intelligent terminal, and use geographic information technology to construct a heat map of each commodity sales area of a mobile intelligent terminal, and number the heat maps of each commodity sales area of a mobile intelligent terminal.

[0009] Step S02: Collection of user behavior data in the heat map area: Based on time series, collect the user purchase behavior data of the heat map corresponding to the commodity sales area of the mobile intelligent terminal. The collection of user behavior data in the heat map area includes a mobile intelligent terminal resource allocation data collection unit and a mobile intelligent terminal commodity sales data collection unit.

[0010] Step S03: Mobile intelligent terminal resource allocation processing: Used to calculate the intelligent mobile terminal resource scheduling index of the commodities in each time series heat map according to the mobile intelligent terminal resource allocation data of the mobile intelligent terminal resource allocation data collection unit, compare it with the preset intelligent mobile terminal resource scheduling index, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal.

[0011] Step S04: Mobile intelligent terminal commodity sales data management: Used to calculate the intelligent mobile terminal commodity sales characteristic index of the commodities in each time series heat map according to the mobile intelligent terminal commodity sales data of the mobile intelligent terminal commodity sales data collection unit, compare it with the preset intelligent mobile terminal commodity sales characteristic index, and give an early warning of abnormal commodity sales strategies.

[0012] Step S05: Mobile intelligent terminal sales trend feedback: Used to obtain the intelligent mobile terminal resource scheduling index and the intelligent mobile terminal commodity sales characteristic index of the commodities in each time series heat map, and calculate the marketing strategy adjustment sales trend feedback coefficient of each time series heat map.

[0013] Step S06: Mobile intelligent terminal sales trend evaluation: Used to obtain the marketing strategy adjustment sales trend feedback coefficient of each time series heat map, compare it with the preset marketing strategy adjustment sales trend feedback coefficient, and process it.

[0014] Preferably, the construction of the mobile intelligent terminal commodity sales heat map is specifically as follows:

[0015] Step S21: The demographic data is the population density sk of the mobile intelligent terminal commodity sales area j , and the sales data is the sales volume sx of the mobile intelligent terminal commodity sales area j , the sales amount sm j , where j = 1, 2,... n, and j represents the number of the jth mobile intelligent terminal commodity sales area;

[0016] Step S22: Analyze the heat map distribution model:

[0017]

[0018] α j represents the heat map distribution model of the jth mobile intelligent terminal commodity sales area, sx jDenote the sales volume of the j-th mobile intelligent terminal product sales area as \(s_j\), \(\Delta s_x\) represents the average sales volume, \(\sigma_1\) represents the standard deviation of the sales volume, and \(s_m\). j Denote the sales amount of the j-th mobile intelligent terminal product sales area as \(m_j\), \(\Delta s_m\) represents the average sales amount, \(\sigma_2\) represents the standard deviation of the sales amount, and \(s_k\). j Denote the population density of the j-th mobile intelligent terminal product sales area as \(k_j\), \(\Delta s_k\) represents the average population density, \(e\) represents the natural constant, and \(n\) represents the total number of mobile intelligent terminal product sales areas.

[0019] Step S23: Rearrange the heat map distribution models of each mobile intelligent terminal product sales area in descending order, and use geographic information technology to construct the heat map corresponding to the mobile intelligent terminal product sales area. Color the mobile intelligent terminal product sales areas corresponding to each heat map distribution model from deep to light in turn, and sequentially number the heat maps corresponding to the mobile intelligent terminal product sales areas as 1, 2,... i,... n.

[0020] Preferably, the collection of user behavior data in the heat map area is specifically as follows:

[0021] Mobile intelligent terminal resource allocation data collection unit: Based on the time series, collect the sales volume of mobile intelligent terminal products, the inventory quantity of mobile intelligent terminal products, the order quantity of mobile intelligent terminal products, and the supply quantity of mobile intelligent terminal products corresponding to the heat map of the mobile intelligent terminal product sales area, and mark them as \(d_s\) iu , \(d_k\) iu , \(d_h\) iu , \(d_g\) i , where \(i = 1, 2,... n\), \(i\) represents the number of the i-th time series heat map, and \(u = 1, 2,... m\), \(u\) represents the number of the u-th product.

[0022] Mobile intelligent terminal product sales data collection unit: Based on the time series, collect the sales unit price of mobile intelligent terminal products, the sales volume corresponding to the lowest sales price of mobile intelligent terminal products, the sales volume corresponding to the highest sales price of mobile intelligent terminal products, and the return order rate of mobile intelligent terminal products corresponding to the heat map of the mobile intelligent terminal product sales area, and mark them as \(x_s\) iu , \(x_z\) iu , \(x_g\) iu , \(x_t\) iu .

[0023] Preferably, the processing of mobile intelligent terminal resource allocation is specifically as follows:

[0024] Step S41: Calculate the inventory safety monitoring indicators of the products in each time - series heat map based on the sales quantity of mobile intelligent - terminal products, the inventory quantity of mobile intelligent - terminal products, and the order quantity of mobile intelligent - terminal products:

[0025]

[0026] Among them, hk iu represents the inventory safety monitoring indicator of the u - th product in the i - th time - series heat map, ds iu represents the sales quantity of mobile intelligent - terminal products of the u - th product in the i - th time - series heat map, dk iu represents the inventory quantity of mobile intelligent - terminal products of the u - th product in the i - th time - series heat map, dh iu represents the order quantity of mobile intelligent - terminal products of the u - th product in the i - th time - series heat map;

[0027] Step S42: Calculate the mobile - intelligent - terminal demand warning indicators of the products in each time - series heat map based on the sales quantity of mobile intelligent - terminal products, the order quantity of mobile intelligent - terminal products, and the supply quantity of mobile intelligent - terminal products:

[0028]

[0029] Among them, hg iu represents the mobile - intelligent - terminal demand warning indicator of the u - th product in the i - th time - series heat map, dg i represents the supply quantity of mobile intelligent - terminal products of the i - th time - series heat map;

[0030] Step S43: The calculation formula of the intelligent mobile - terminal resource scheduling index is:

[0031]

[0032] Among them, β iu represents the intelligent mobile - terminal resource scheduling index of the u - th product in the i - th time - series heat map, hk iu represents the inventory safety monitoring indicator of the u - th product in the i - th time - series heat map, hk imax represents the maximum value of the inventory safety monitoring indicators of the i - th time - series heat map, Δhk i represents the average value of the inventory safety monitoring indicators of the i - th time - series heat map, hk imin represents the minimum value of the inventory safety monitoring indicators of the i - th time - series heat map, hg iu represents the mobile - intelligent - terminal demand warning indicator of the u - th product in the i - th time - series heat map, HG 预Denoted as the preset warning index for the demand of mobile intelligent terminals, λ1 and λ2 are respectively denoted as the influencing factors of the warning index for the demand of mobile intelligent terminals and the inventory safety monitoring index;

[0033] Step S44: Obtain the intelligent mobile terminal resource scheduling index of the products in each time-series heat map, and compare it with the preset intelligent mobile terminal resource scheduling index. If the intelligent mobile terminal resource scheduling index of the heat map corresponding to the sales area of the mobile intelligent terminal products in a certain time series is less than the preset intelligent mobile terminal resource scheduling index, it indicates that the user demand for the mobile intelligent terminal products is small, the inventory monitoring fluctuation is small, and the demand for mobile intelligent terminal resources is small. Then, count the number of the heat map corresponding to this time series, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal. On the contrary, it indicates that the user demand for the mobile intelligent terminal products is large, the inventory monitoring fluctuation is large, and the demand for mobile intelligent terminal resources is large. Then, count the number of the heat map corresponding to this time series, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal.

[0034] Preferably, the management of the mobile intelligent terminal product sales data is specifically as follows:

[0035] Step S51: Calculate the user purchase interest index of the products in each time-series heat map through the sales quantity of mobile intelligent terminal products, the sales volume corresponding to the lowest sales price of mobile intelligent terminal products, and the sales volume corresponding to the highest sales price of mobile intelligent terminal products:

[0036]

[0037] Among them, zg iu Denoted as the user purchase interest index of the u-th product in the i-th time-series heat map, ds iu Denoted as the sales quantity of the u-th mobile intelligent terminal product in the i-th time-series heat map, xz iu Denoted as the sales volume corresponding to the lowest sales price of the u-th mobile intelligent terminal product in the i-th time-series heat map, xg iu Denoted as the sales volume corresponding to the highest sales price of the u-th mobile intelligent terminal product in the i-th time-series heat map;

[0038] Step S52: Calculate the competitiveness index of the products in each time-series heat map through the sales unit price of mobile intelligent terminal products, the order cancellation rate of mobile intelligent terminal products, and the user purchase interest index:

[0039]

[0040] Among them, zt iu Denoted as the competitiveness index of the u-th product in the i-th time-series heat map, xt iuDenoted as the return rate of mobile intelligent terminal products for the \(u\)-th product in the \(i\)-th time series heat map, \(xs\). iu Denoted as the unit selling price of mobile intelligent terminal products for the \(u\)-th product in the \(i\)-th time series heat map.

[0041] Step S53: The calculation formula for the sales characteristic index of intelligent mobile terminal products is:

[0042]

[0043] where \(\chi\) iu Denoted as the sales characteristic index of intelligent mobile terminal products for the \(u\)-th product in the \(i\)-th time series heat map. Denoted as the minimum value of the user purchase interest index in the \(i\)-th time series heat map. Denoted as the minimum value of the competitiveness index in the \(i\)-th time series heat map. \(e\) denotes the natural constant, \(n\) denotes the total number of sales regions of mobile intelligent terminal products, and \(\varepsilon_1\) and \(\varepsilon_2\) respectively denote the influence factors of the user purchase interest index and the competitiveness index.

[0044] Step S54: Obtain the sales characteristic index of intelligent mobile terminal products for the products in each time series heat map, and compare it with the preset sales characteristic index of intelligent mobile terminal products. If the sales characteristic index of intelligent mobile terminal products for the products in a certain time series heat map is less than the preset sales characteristic index of intelligent mobile terminal products, it indicates that the user purchase interest index is smaller and the product competitiveness index is smaller, then the sales characteristic index of intelligent mobile terminal products is worse. The number of the heat map corresponding to this time series should be counted, and a sales strategy warning instruction should be sent to the management terminal. On the contrary, it indicates that the user purchase interest index is larger and the product competitiveness index is larger, then the sales characteristic index of intelligent mobile terminal products is better.

[0045] Preferably, the calculation formula for the sales trend feedback coefficient for adjusting the marketing strategy is:

[0046]

[0047] where \(\theta\) i Denoted as the sales trend feedback coefficient for adjusting the marketing strategy in the \(i\)-th time series heat map, \(\beta\) iu Denoted as the resource scheduling index of intelligent mobile terminal products for the \(u\)-th product in the \(i\)-th time series heat map, \(\chi\) iu Denoted as the sales characteristic index of intelligent mobile terminal products for the \(u\)-th product in the \(i\)-th time series heat map. Denoted as the resource scheduling index of intelligent mobile terminal products for the \(u\)-th product in the \((i - 1)\)-th time series heat map. denotes the intelligent mobile terminal product sales feature index of the u-th product in the (i - 1)-th time series heat map, and m denotes the number of products.

[0048] Preferably, the evaluation of the mobile intelligent terminal sales trend is specifically as follows:

[0049] Obtain the marketing strategy adjustment sales trend feedback coefficient of each time series heat map, and compare it with the preset marketing strategy adjustment sales trend feedback coefficient. If the marketing strategy adjustment sales trend feedback coefficient of a certain time series heat map is less than the preset marketing strategy adjustment sales trend feedback coefficient, it indicates that there is an abnormality in the mobile intelligent terminal product sales trend of this time series heat map, and the management personnel should be notified for handling. Otherwise, it indicates that there is no abnormal phenomenon in the mobile intelligent terminal product sales trend of this time series heat map.

[0050] The technical effects and advantages of the present invention:

[0051] 1. The present invention provides a method for processing mobile intelligent terminal product sales data. By using the steps of constructing the mobile intelligent terminal product sales area heat map, combining the demographic data and sales data of each mobile intelligent terminal product sales area, using geographic information technology to construct the heat map of each mobile intelligent terminal product sales area, and numbering the heat maps of each mobile intelligent terminal product sales area, and then collecting the user purchase behavior data of the heat map based on the time series. The heat map analysis can reflect the concentration degree and distribution of the terminal product sales data in space, so as to intuitively display the sales hot spots area. At the same time, by integrating the sales data of different heat map product sales areas, it provides comprehensive market insights for merchants;

[0052] 2. The present invention provides a method for processing mobile intelligent terminal commodity sales data. By allocating mobile intelligent terminal resources of the data acquisition unit according to the mobile intelligent terminal resource allocation data, calculating the intelligent mobile terminal resource scheduling index of the commodities in each time series heat map, comparing it with the preset intelligent mobile terminal resource scheduling index, and sending the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal. According to the mobile intelligent terminal commodity sales data of the mobile intelligent terminal commodity sales data acquisition unit, calculating the intelligent mobile terminal commodity sales characteristic index of the commodities in each time series heat map, comparing it with the preset intelligent mobile terminal commodity sales characteristic index, warning of abnormal commodity sales strategies, further analyzing to obtain the marketing strategy adjustment sales trend feedback coefficient of each time series heat map, and comparing it with the preset marketing strategy adjustment sales trend feedback coefficient. If the marketing strategy adjustment sales trend feedback coefficient of a certain time series heat map is less than the preset marketing strategy adjustment sales trend feedback coefficient, it indicates that there is an abnormality in the mobile intelligent terminal commodity sales trend of this time series heat map, and the management personnel should be notified for handling. Otherwise, it indicates that there is no abnormal phenomenon in the mobile intelligent terminal commodity sales trend of this time series heat map, so as to realize the timely allocation of mobile intelligent terminals according to market demand, meet market demand, realize the market demand of the commodity sales of visual mobile intelligent terminals, and the commodity sales change trend after the allocation of visual mobile intelligent terminals, providing valuable sales forecasts and market trend analyses for merchants. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic connection diagram of the method step process of the present invention.

[0054] Figure 2 It is a schematic diagram of the heat map area user behavior data acquisition structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Please refer to Figure 1 As shown, the present invention provides a method for processing mobile intelligent terminal commodity sales data, including step S01: constructing a heat map of the mobile intelligent terminal commodity sales area, step S02: collecting heat map area user behavior data, step S03: processing mobile intelligent terminal resource allocation, step S04: managing mobile intelligent terminal commodity sales data, step S05: feeding back the mobile intelligent terminal sales trend, and step S06: evaluating the mobile intelligent terminal sales trend.

[0057] Step S01: The construction of the heat map of the mobile intelligent terminal commodity sales area is connected to Step S02: The collection of user behavior data in the heat map area. Step S02: The collection of user behavior data in the heat map area is connected to Step S03: The mobile intelligent terminal resource allocation and processing. Step S03: The mobile intelligent terminal resource allocation and processing is connected to Step S04: The mobile intelligent terminal commodity sales data management. Step S04: The mobile intelligent terminal commodity sales data management is connected to Step S05: The mobile intelligent terminal sales trend feedback. Step S05: The mobile intelligent terminal sales trend feedback is connected to Step S06: The mobile intelligent terminal sales trend evaluation.

[0058] Step S01: The construction of the heat map of the mobile intelligent terminal commodity sales area is used to combine the demographic data and sales data of each mobile intelligent terminal commodity sales area, construct the heat map of each mobile intelligent terminal commodity sales area by using geographic information technology, and number the heat maps of each mobile intelligent terminal commodity sales area. The heat map analysis can reflect the concentration degree and distribution of the terminal commodity sales data in space, so as to intuitively display the sales hot spots.

[0059] In a possible design, the construction of the mobile intelligent terminal commodity sales heat map is specifically as follows:

[0060] Step S21: The demographic data is the population density sk of the mobile intelligent terminal commodity sales area j , and the sales data is the sales volume sx of the mobile intelligent terminal commodity sales area j , the sales amount sm j , where j = 1, 2,... n, and j represents the number of the jth mobile intelligent terminal commodity sales area;

[0061] Step S22: Analyze the heat map distribution model:

[0062]

[0063] α j represents the heat map distribution model of the jth mobile intelligent terminal commodity sales area, sx j represents the sales volume of the jth mobile intelligent terminal commodity sales area, Δsx represents the average sales volume, σ1 represents the standard deviation of the sales volume, sm j represents the sales amount of the jth mobile intelligent terminal commodity sales area, Δsm represents the average sales amount, σ2 represents the standard deviation of the sales amount, sk j represents the population density of the jth mobile intelligent terminal commodity sales area, Δsk represents the average population density, e represents the natural constant, and n represents the total number of mobile intelligent terminal commodity sales areas;

[0064] Step S23: Rearrange the heat map distribution models of the sales areas of each mobile intelligent terminal product in descending order, and use geographic information technology to construct the heat map corresponding to the sales area of the mobile intelligent terminal product. Then, color the sales areas of the mobile intelligent terminal products corresponding to each heat map distribution model from deep to light, and sequentially number the heat maps corresponding to the sales areas of the mobile intelligent terminal products as 1, 2,... i,... n.

[0065] In this embodiment, it should be specifically noted that the The The The

[0066] Please refer to Figure 2 As shown, in step S02: The heat map area user behavior data collection collects the user purchase behavior data of the heat map corresponding to the sales area of the mobile intelligent terminal product based on the time series. The heat map area user behavior data collection includes a mobile intelligent terminal resource allocation data collection unit and a mobile intelligent terminal product sales data collection unit. The user purchase behavior data includes mobile intelligent terminal resource allocation data and mobile intelligent terminal product sales data.

[0067] In a possible design, the heat map area user behavior data collection is specifically as follows:

[0068] Mobile intelligent terminal resource allocation data collection unit: Based on the time series, collect the sales quantity, inventory quantity, order quantity, and supply quantity of the mobile intelligent terminal product corresponding to the heat map of the sales area of the mobile intelligent terminal product, and mark them as ds iu 、dk iu 、dh iu 、dg i , where i = 1, 2,... n, i represents the number of the i-th time series heat map, and u = 1, 2,... m, u represents the number of the u-th product;

[0069] Mobile intelligent terminal product sales data collection unit: Based on the time series, collect the sales unit price, sales volume corresponding to the lowest sales price, sales volume corresponding to the highest sales price, and return order rate of the mobile intelligent terminal product corresponding to the heat map of the sales area of the mobile intelligent terminal product, and mark them as xs iu 、xz iu 、xg iu 、xt iu .

[0070] Step S03: The mobile intelligent terminal resource allocation process is used to calculate the intelligent mobile terminal resource scheduling index of the commodities in each time-series heat map according to the mobile intelligent terminal resource allocation data collected by the mobile intelligent terminal resource allocation data acquisition unit, compare it with the preset intelligent mobile terminal resource scheduling index, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal, so as to timely allocate the mobile intelligent terminal according to the market demand and meet the market demand.

[0071] In a possible design, the mobile intelligent terminal resource allocation process is specifically as follows:

[0072] Step S41: Calculate the inventory safety monitoring index of the commodities in each time-series heat map through the mobile intelligent terminal commodity sales quantity, mobile intelligent terminal commodity inventory quantity, and mobile intelligent terminal commodity order quantity:

[0073]

[0074] where hk iu represents the inventory safety monitoring index of the u-th commodity in the i-th time-series heat map, ds iu represents the mobile intelligent terminal commodity sales quantity of the u-th commodity in the i-th time-series heat map, dk iu represents the mobile intelligent terminal commodity inventory quantity of the u-th commodity in the i-th time-series heat map, dh iu represents the mobile intelligent terminal commodity order quantity of the u-th commodity in the i-th time-series heat map;

[0075] Step S42: Calculate the mobile intelligent terminal demand warning index of the commodities in each time-series heat map through the mobile intelligent terminal commodity sales quantity, mobile intelligent terminal commodity order quantity, and mobile intelligent terminal supply quantity:

[0076]

[0077] where hg iu represents the mobile intelligent terminal demand warning index of the u-th commodity in the i-th time-series heat map, dg i represents the mobile intelligent terminal supply quantity of the i-th time-series heat map;

[0078] Step S43: The calculation formula of the intelligent mobile terminal resource scheduling index is:

[0079]

[0080] where β iu represents the intelligent mobile terminal resource scheduling index of the u-th commodity in the i-th time-series heat map, hk iuDenoted as the inventory safety monitoring index of the \(u\)-th commodity in the \(i\)-th time series heat map, \(h_k\) imax Denoted as the maximum value of the inventory safety monitoring index in the \(i\)-th time series heat map, \(\Delta h_k\) i Denoted as the average value of the inventory safety monitoring index in the \(i\)-th time series heat map, \(h_k\) imin Denoted as the minimum value of the inventory safety monitoring index in the \(i\)-th time series heat map, \(h_g\) iu Denoted as the mobile intelligent terminal demand warning index of the \(u\)-th commodity in the \(i\)-th time series heat map, \(HG\) 预 Denoted as the preset mobile intelligent terminal demand warning index, where \(\lambda_1\) and \(\lambda_2\) respectively denote the influencing factors of the mobile intelligent terminal demand warning index and the inventory safety monitoring index;

[0081] Step S44: Obtain the intelligent mobile terminal resource scheduling index of the commodities in each time series heat map, and compare it with the preset intelligent mobile terminal resource scheduling index. If the intelligent mobile terminal resource scheduling index of the heat map corresponding to the sales area of the mobile intelligent terminal commodities in a certain time series is less than the preset intelligent mobile terminal resource scheduling index, it indicates that the user demand for mobile intelligent terminal commodities is small, the inventory monitoring fluctuation is small, and the demand for mobile intelligent terminal resources is small. Then, count the number of the heat map corresponding to this time series, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal. On the contrary, it indicates that the user demand for mobile intelligent terminal commodities is large, the inventory monitoring fluctuation is large, and the demand for mobile intelligent terminal resources is large. Then, count the number of the heat map corresponding to this time series, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal.

[0082] In this embodiment, it should be specifically noted that the management terminal includes but is not limited to the computers, mobile phones, and management servers of the management personnel;

[0083] \(m\) represents the number of commodities.

[0084] Step S04: Mobile intelligent terminal commodity sales data management is used to calculate the intelligent mobile terminal commodity sales characteristic index of the commodities in each time series heat map according to the mobile intelligent terminal commodity sales data of the mobile intelligent terminal commodity sales data acquisition unit, compare it with the preset intelligent mobile terminal commodity sales characteristic index, give an early warning of abnormal commodity sales strategies, and visualize the market demand for the mobile intelligent terminal commodities.

[0085] In a possible design, the mobile intelligent terminal commodity sales data management is specifically as follows:

[0086] Step S51: Calculate the user purchase interest index of the products in each time series heat map by using the sales volume of mobile intelligent terminal products, the sales volume corresponding to the lowest sales price of mobile intelligent terminal products, and the sales volume corresponding to the highest sales price of mobile intelligent terminal products:

[0087]

[0088] Among them, zg iu represents the user purchase interest index of the u-th product in the i-th time series heat map, ds iu represents the sales volume of the u-th product in the i-th time series heat map for mobile intelligent terminal products, xz iu represents the sales volume corresponding to the lowest sales price of the u-th product in the i-th time series heat map for mobile intelligent terminal products, xg iu represents the sales volume corresponding to the highest sales price of the u-th product in the i-th time series heat map for mobile intelligent terminal products;

[0089] Step S52: Calculate the competitiveness index of the products in each time series heat map by using the unit sales price of mobile intelligent terminal products, the order cancellation rate of mobile intelligent terminal products, and the user purchase interest index:

[0090]

[0091] Among them, zt iu represents the competitiveness index of the u-th product in the i-th time series heat map, xt iu represents the order cancellation rate of the u-th product in the i-th time series heat map for mobile intelligent terminal products, xs iu represents the unit sales price of the u-th product in the i-th time series heat map for mobile intelligent terminal products;

[0092] Step S53: The calculation formula for the sales feature index of the intelligent mobile terminal products is:

[0093]

[0094] Among them, χ iu represents the sales feature index of the u-th product in the i-th time series heat map for intelligent mobile terminal products, represents the minimum value of the user purchase interest index of the i-th time series heat map, represents the minimum value of the competitiveness index of the i-th time series heat map, e represents the natural constant, n represents the total number of sales regions of mobile intelligent terminal products, and ε1 and ε2 respectively represent the influence factors of the user purchase interest index and the competitiveness index;

[0095] Step S54: Obtain the intelligent mobile terminal product sales characteristic index of the products in each time-series heat map, and compare it with the preset intelligent mobile terminal product sales characteristic index. If the intelligent mobile terminal product sales characteristic index of the product in a certain time-series heat map is less than the preset intelligent mobile terminal product sales characteristic index, it indicates that the user purchase interest index is smaller and the product competitiveness index is smaller, then the intelligent mobile terminal product sales characteristic index is worse. The number of the heat map corresponding to this time series should be counted, and a product sales strategy warning instruction should be sent to the management terminal. On the contrary, it indicates that the user purchase interest index is larger and the product competitiveness index is larger, then the intelligent mobile terminal product sales characteristic index is better.

[0096] The said Step S05: Mobile intelligent terminal sales trend feedback is used to obtain the intelligent mobile terminal resource scheduling index and the intelligent mobile terminal product sales characteristic index of the products in each time-series heat map, calculate the marketing strategy adjustment sales trend feedback coefficient of each time-series heat map, and visualize the product sales change trend after the mobile intelligent terminal is allocated.

[0097] In a possible design, the calculation formula of the marketing strategy adjustment sales trend feedback coefficient is:

[0098]

[0099] where, θ i represents the marketing strategy adjustment sales trend feedback coefficient of the i-th time-series heat map, β iu represents the intelligent mobile terminal resource scheduling index of the u-th product in the i-th time-series heat map, χ iu represents the intelligent mobile terminal product sales characteristic index of the u-th product in the i-th time-series heat map, represents the intelligent mobile terminal resource scheduling index of the u-th product in the (i - 1)-th time-series heat map, represents the intelligent mobile terminal product sales characteristic index of the u-th product in the (i - 1)-th time-series heat map, and m represents the number of products.

[0100] The said Step S06: Mobile intelligent terminal sales trend evaluation is used to obtain the marketing strategy adjustment sales trend feedback coefficient of each time-series heat map, compare it with the preset marketing strategy adjustment sales trend feedback coefficient, and process it.

[0101] In a possible design, the mobile intelligent terminal sales trend evaluation is specifically:

[0102] Obtain the marketing strategy adjustment sales trend feedback coefficient of each time - series heat map, and compare it with the preset marketing strategy adjustment sales trend feedback coefficient. If the marketing strategy adjustment sales trend feedback coefficient of a certain time - series heat map is less than the preset marketing strategy adjustment sales trend feedback coefficient, it indicates that there is an abnormality in the sales trend of mobile intelligent terminal products in this time - series heat map, and the management personnel should be notified for handling. Otherwise, it indicates that there is no abnormal phenomenon in the sales trend of mobile intelligent terminal products in this time - series heat map.

[0103] In this embodiment, it should be specifically noted that the present invention uses the construction steps of the sales area heat map of mobile intelligent terminal products, combines the demographic data and sales data of each sales area of mobile intelligent terminal products, uses geographic information technology to construct the heat map of each sales area of mobile intelligent terminal products, numbers the heat maps of each sales area of mobile intelligent terminal products, and then collects the user purchase behavior data of the heat map based on the time series. The heat map analysis can reflect the concentration degree and distribution of terminal product sales data in space, thereby visually displaying the sales hot spots. At the same time, by integrating the sales data of different heat map product sales areas, it provides comprehensive market insights for merchants.

[0104] The present invention calculates the intelligent mobile terminal resource scheduling index of products in each time - series heat map according to the mobile intelligent terminal resource allocation data of the mobile intelligent terminal resource allocation data acquisition unit, compares it with the preset intelligent mobile terminal resource scheduling index, and sends the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal. According to the mobile intelligent terminal product sales data of the mobile intelligent terminal product sales data acquisition unit, it calculates the intelligent mobile terminal product sales characteristic index of products in each time - series heat map, compares it with the preset intelligent mobile terminal product sales characteristic index, gives an early warning of abnormal product sales strategies, and further analyzes to obtain the marketing strategy adjustment sales trend feedback coefficient of each time - series heat map, and compares it with the preset marketing strategy adjustment sales trend feedback coefficient. If the marketing strategy adjustment sales trend feedback coefficient of a certain time - series heat map is less than the preset marketing strategy adjustment sales trend feedback coefficient, it indicates that there is an abnormality in the sales trend of mobile intelligent terminal products in this time - series heat map, and the management personnel should be notified for handling. Otherwise, it indicates that there is no abnormal phenomenon in the sales trend of mobile intelligent terminal products in this time - series heat map, so as to realize the timely allocation of mobile intelligent terminals according to market demand, meet market demand, realize the market demand of the sales of visual mobile intelligent terminal products, the sales change trend of visual mobile intelligent terminal products after allocation, and provide valuable sales forecasts and market trend analyses for merchants.

[0105] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for processing commodity sales data of a mobile intelligent terminal, characterized in that: include: Step S01: constructing a heat map of mobile smart terminal commodity sales areas: combining demographic data and sales data of each mobile smart terminal commodity sales area, using geographic information technology to construct a heat map of each mobile smart terminal commodity sales area, and numbering the heat map of each mobile smart terminal commodity sales area; Step S02: Collecting user behavior data in the heat map area: collecting user purchase behavior data corresponding to the heat map of the mobile smart terminal commodity sales area based on time series, wherein the heat map area user behavior data collection includes a mobile smart terminal resource allocation data collection unit and a mobile smart terminal commodity sales data collection unit; Step S03: mobile intelligent terminal resource allocation processing: used to calculate the intelligent mobile terminal resource scheduling index of the commodity in each time series heat map according to the mobile intelligent terminal resource allocation data of the mobile intelligent terminal resource allocation data collection unit, compare it with the preset intelligent mobile terminal resource scheduling index, and send the corresponding mobile intelligent terminal resource scheduling instruction to the management terminal; Step S04: Mobile smart terminal commodity sales data management: used to calculate the smart mobile terminal commodity sales characteristic index of the commodity in each time series heat map according to the mobile smart terminal commodity sales data of the mobile smart terminal commodity sales data collection unit, compare it with the preset smart mobile terminal commodity sales characteristic index, and issue an early warning for abnormal commodity sales strategy; Step S05: Mobile smart terminal sales trend feedback: used to obtain the smart mobile terminal resource scheduling index and the smart mobile terminal commodity sales feature index of the commodities in each time series heat map, and calculate the marketing strategy adjustment sales trend feedback coefficient of each time series heat map; Step S06: Mobile smart terminal sales trend evaluation: used to obtain the marketing strategy adjustment sales trend feedback coefficient of each time series heat map, compare it with the preset marketing strategy adjustment sales trend feedback coefficient, and process it.

2. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The mobile smart terminal commodity sales heat map is constructed specifically as follows: Step S21: The demographic data is the population density sk of the mobile smart terminal commodity sales area j The sales data is the sales volume of mobile smart terminal products in the sales area sx j 、Sales amount sm j , where j = 1, 2, ... n, j represents the number of the j-th mobile smart terminal product sales area; Step S22: Analyze the heat map distribution model: α j It is represented as the heat map distribution model of the jth mobile smart terminal product sales area, sx j is the sales volume of the jth mobile smart terminal product sales area, Δsx is the mean sales volume, σ1 is the standard deviation of sales volume, sm j is the sales amount of the jth mobile smart terminal product sales area, Δsm is the mean sales amount, σ2 is the standard deviation of the sales amount, and sk j It is represented by the population density of the jth mobile smart terminal commodity sales area, Δsk is represented by the mean population density, e is represented by a natural constant, and n is represented by the total number of mobile smart terminal commodity sales areas; Step S23: rearrange the heat map distribution models of each mobile smart terminal commodity sales area in order from large to small, and use geographic information technology to construct a heat map corresponding to the mobile smart terminal commodity sales area, color the mobile smart terminal commodity sales area corresponding to each heat map distribution model from dark to light, and number the heat maps corresponding to the mobile smart terminal commodity sales area as 1, 2,...i,...n in sequence.

3. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The user behavior data collection in the heat map area is specifically as follows: Mobile smart terminal resource allocation data collection unit: collects the sales quantity of mobile smart terminal products, the inventory quantity of mobile smart terminal products, the order quantity of mobile smart terminal products, and the supply quantity of mobile smart terminals in the corresponding heat map of the mobile smart terminal product sales area based on time series, marked as ds iu 、dk iu dh iu 、dg i , where i=1,2,...n, i represents the number of the i-th time series heat map, u=1,2,...m, u represents the number of the u-th product; Mobile smart terminal product sales data collection unit: collects the mobile smart terminal product sales unit price, the sales volume corresponding to the lowest price of mobile smart terminal products, the sales volume corresponding to the highest price of mobile smart terminal products, and the mobile smart terminal product return rate of the heat map corresponding to the mobile smart terminal product sales area based on time series, marked as xs respectively iu 、xz iu ,xg iu , xt iu .

4. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The mobile intelligent terminal resource allocation process is specifically as follows: Step S41: Calculate the inventory safety monitoring index of the goods in each time series heat map through the sales quantity of mobile smart terminal goods, the inventory quantity of mobile smart terminal goods, and the order quantity of mobile smart terminal goods: Among them, hk iu It is represented as the inventory safety monitoring index of the uth commodity in the i-th time series heat map, ds iu It is represented by the sales quantity of the u-th mobile smart terminal product in the i-th time series heat map, dk iu It is represented by the inventory quantity of the u-th mobile smart terminal product in the i-th time series heat map, dh iu It is represented by the number of orders for the u-th product on the mobile smart terminal in the i-th time series heat map; Step S42: Calculate the mobile smart terminal demand warning index of the goods in each time series heat map through the sales quantity of mobile smart terminal goods, the order quantity of mobile smart terminal goods, and the supply quantity of mobile smart terminals: Among them, hg iu It is represented as the mobile smart terminal demand warning indicator of the u-th commodity in the i-th time series heat map, dg i The number of mobile smart terminal supplies represented by the heat map of the i-th time series; Step S43: The calculation formula of the smart mobile terminal resource scheduling index is: Among them, β iu It is represented as the smart mobile terminal resource scheduling index of the u-th commodity in the i-th time series heat map, hk iu It is represented as the inventory safety monitoring index of the u-th commodity in the i-th time series heat map, hk imax It is represented as the maximum value of the inventory safety monitoring index of the i-th time series heat map, Δhk i It is represented as the mean value of the inventory safety monitoring index of the i-th time series heat map, hk imin It is represented as the minimum value of the inventory safety monitoring index of the i-th time series heat map, hg iu It is represented as the mobile smart terminal demand warning indicator of the u-th commodity in the i-th time series heat map, HG 预 It represents the preset mobile intelligent terminal demand warning indicator, λ1 and λ2 represent the influencing factors of the mobile intelligent terminal demand warning indicator and the inventory safety monitoring indicator respectively; Step S44: Obtain the smart mobile terminal resource scheduling index of the goods in each time series heat map, and compare it with the preset smart mobile terminal resource scheduling index. If the smart mobile terminal resource scheduling index of the heat map corresponding to the sales area of ​​the mobile smart terminal goods in a certain time series is less than the preset smart mobile terminal resource scheduling index, it indicates that the user demand for the mobile smart terminal goods is small, the inventory monitoring fluctuation is small, and the demand for mobile smart terminal resources is small. Then, the number of the heat map corresponding to the time series is counted, and the corresponding mobile smart terminal resource scheduling instruction is sent to the management terminal. Otherwise, it indicates that the user demand for the mobile smart terminal goods is large, the inventory monitoring fluctuation is large, and the demand for mobile smart terminal resources is large. Then, the number of the heat map corresponding to the time series is counted, and the corresponding mobile smart terminal resource scheduling instruction is sent to the management terminal.

5. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The mobile smart terminal commodity sales data management is specifically as follows: Step S51: Calculate the user purchase interest index of the goods in each time series heat map through the sales quantity of mobile smart terminal goods, the sales volume corresponding to the lowest price of mobile smart terminal goods, and the sales volume corresponding to the highest price of mobile smart terminal goods: Among them, zg iu It is represented as the user purchase interest index of the u-th item in the i-th time series heat map, ds iu It is represented by the sales quantity of the u-th mobile smart terminal product in the i-th time series heat map, xz iu It is represented by the sales volume corresponding to the lowest price of the u-th mobile smart terminal product in the i-th time series heat map, xg iu It is represented by the sales volume corresponding to the highest sales price of the u-th product in the i-th time series heat map; Step S52: Calculate the competitiveness index of the products in each time series heat map through the sales unit price of mobile smart terminal products, the return rate of mobile smart terminal products, and the user purchase interest index: Among them, zt iu It is expressed as the competitiveness index of the u-th commodity in the i-th time series heat map, xt iu It is represented as the mobile smart terminal product return rate of the u-th product in the i-th time series heat map, xs iu It is represented by the sales unit price of the u-th product in the i-th time series heat map; Step S53: The calculation formula of the smart mobile terminal commodity sales characteristic index is: Among them, χ iu It is represented as the smart mobile terminal product sales characteristic index of the u-th product in the i-th time series heat map, It is represented as the minimum value of the user purchase interest index of the i-th time series heat map, It is represented as the minimum value of the competitiveness index of the i-th time series heat map, e is represented as a natural constant, n is represented as the total number of sales areas for mobile smart terminal products, ε1 and ε2 are the influencing factors of the user purchase interest index and the competitiveness index respectively; Step S54: Obtain the smart mobile terminal commodity sales characteristic index of the commodities in each time series heat map, and compare it with the preset smart mobile terminal commodity sales characteristic index. If the smart mobile terminal commodity sales characteristic index of the commodities in a certain time series heat map is less than the preset smart mobile terminal commodity sales characteristic index, it indicates that the smaller the user purchase interest index and the smaller the commodity competitiveness index are, the worse the smart mobile terminal commodity sales characteristic index is. The number of the heat map corresponding to the time series should be counted, and the commodity sales strategy early warning instruction should be sent to the management terminal. On the contrary, it indicates that the larger the user purchase interest index and the larger the commodity competitiveness index are, the better the smart mobile terminal commodity sales characteristic index is.

6. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The calculation formula of the marketing strategy adjustment sales trend feedback coefficient is: Among them, θ i It is represented as the marketing strategy adjustment sales trend feedback coefficient of the i-th time series heat map, β iu It is represented as the smart mobile terminal resource scheduling index of the u-th commodity in the i-th time series heat map, χ iu It is represented as the smart mobile terminal product sales characteristic index of the u-th product in the i-th time series heat map, It is represented as the smart mobile terminal resource scheduling index of the u-th commodity in the i-1-th time series heat map, It is represented as the smart mobile terminal commodity sales characteristic index of the u-th commodity in the i-1-th time series heat map, and m is represented as the number of commodities.

7. A method for processing commodity sales data of a mobile intelligent terminal according to claim 1, characterized in that: The mobile intelligent terminal sales trend assessment is specifically as follows: Obtain the marketing strategy adjustment sales trend feedback coefficient of each time series heat map, and compare it with the preset marketing strategy adjustment sales trend feedback coefficient. If the marketing strategy adjustment sales trend feedback coefficient of a time series heat map is less than the preset marketing strategy adjustment sales trend feedback coefficient, it indicates that the sales trend of mobile smart terminal products in the time series heat map is abnormal, and the management personnel should be notified to handle it. Otherwise, it indicates that there is no abnormality in the sales trend of mobile smart terminal products in the time series heat map.

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