Second-hand commodity digital receiving and selling operation system and method

Abstract

The invention discloses a second-hand commodity digital receiving and selling operation system, which comprises an information acquisition module, a data analysis module and a commodity transaction module, and is characterized in that the information acquisition module comprises a client information acquisition module and a client classification module; the data analysis module comprises a commodity determination module, a gray processing module, a defect identification module, a commodity pricing module and a management module, and the commodity transaction module comprises an information interaction module, a recovery application module, an aggregation pricing module, a commodity sales module and a display module. The invention further discloses an operation method of the second-hand commodity digital receiving and selling operation system. According to the system and the method for improving second-hand commodity recycling and selling, accurate pricing of second-hand commodities is realized, the management efficiency, the visualization degree and the customer experience of recycling and selling are improved, and the operation cost of an enterprise is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of second-hand commodity recycling, and in particular to the digital purchase and sale operation of second-hand commodities. Background Art

[0002] With the rapid development of the Internet and the continuous improvement of people's living standards, the speed of commodity renewal is also increasing. As a result, people's demand for selling second-hand goods is also expanding. In the process of selling second-hand goods, people can use online applications to appraise the second-hand goods. If the valuation is appropriate, they can create a recycling order for the second-hand goods through the online application to complete the sale of the second-hand goods.

[0003] The Chinese invention patent with publication number CN117952702A discloses a commodity recycling method, system, device, electronic device and storage medium, wherein the commodity recycling method includes: displaying a commodity recycling interface for customers to fill in commodity information of multiple commodities to be recycled; in response to the confirmation operation of the commodity information of multiple commodities to be recycled filled in the commodity recycling interface, displaying commodity valuation information of multiple commodities to be recycled sent by the client server; in response to the customer's confirmation operation of the commodity valuation information of at least one commodity to be recycled, obtaining commodity recycling order information corresponding to at least one commodity to be recycled. The commodity recycling method realizes the simultaneous packaging of multiple commodities to be recycled, and no longer needs to create orders for multiple commodities in sequence, which improves the actual experience of customers.

[0004] The problem with the above patented technology is that it lacks corresponding management during actual use, which can easily lead to serious private orders and the second-hand goods recycling market being encroached upon by scalpers. At the same time, when customers recycle second-hand goods, they cannot understand the quotations of different recyclers at the same time, resulting in a poor customer experience. Summary of the invention

[0005] One of the technical problems to be solved by the present invention is to provide a digital purchase and sale operation system for second-hand goods, which can accurately price second-hand goods, improve the management efficiency of the purchase and sale of second-hand goods, and reduce operating costs.

[0006] In order to solve the above technical problems, the second-hand goods digital purchase and sale operation system of the present invention includes:

[0007] The information collection module is used to obtain the basic information and contact information of customers, and to establish customer group portraits based on the basic information obtained, and to classify customers according to their customer group portraits;

[0008] The data analysis module is used to identify and analyze the images of second-hand goods, determine whether the second-hand goods have defects and the degree of defects, and determine the pricing area of ​​the second-hand goods to be priced based on the type of second-hand goods, the degree of defects and historical transaction analysis data of related goods;

[0009] The commodity trading module is used to receive the customer's input information to verify the customer's identity, establish a second-hand commodity price evaluation, recycling and sales platform, sell the recycled second-hand commodities, and sell the corresponding accessories and integrated products, service products and operator businesses, and display the analysis report of second-hand commodities, the recycling business of recyclers and the recycling price of second-hand commodities evaluated by recyclers.

[0010] The information collection module preferably includes:

[0011] The customer information acquisition module is used to obtain the basic information and contact information of customers, and to establish customer profiles based on the basic information obtained;

[0012] The customer classification module is used to classify customers into old customers who do not come to the store, potential customers who come to the store but do not make a deal, and new customers who come to the store to purchase a device based on their customer profile.

[0013] The data analysis module preferably includes:

[0014] A commodity determination module is used to capture images of second-hand commodities, determine identification information of second-hand commodities to be recycled based on the captured images of second-hand commodities, and match corresponding commodity types and original prices of second-hand commodities based on the identification information;

[0015] The defect recognition module is used to analyze the photographed second-hand goods image to determine whether the second-hand goods have defects and the extent of the defects;

[0016] The commodity pricing module is used to query the grid pricing information of the second-hand commodity corresponding to the SKU of the second-hand commodity to be priced based on the type and degree of defect of the second-hand commodity, based on the original price and flow rate, and determine the pricing area of ​​the second-hand commodity to be priced;

[0017] The management module is used to set reward rankings based on employees' actual recycling and sales, and distribute corresponding rewards based on the reward rankings, establish an online sales knowledge base, provide sales training for employees, and understand the information of second-hand goods and conduct market analysis on second-hand goods.

[0018] The commodity pricing module may further include:

[0019] The commodity historical quotation acquisition module is used to query and retrieve the historical second-hand commodity transaction data of the same SKU model or the same series of competing products based on the basic pricing parameters of the second-hand commodity defect degree, using the SKU with the same defect degree as the current second-hand commodity;

[0020] The market wave point factor value acquisition module is used to set up a quotation amount set for each time period as a parameter for calculating the market wave point factor after the historical transaction data of the second-hand goods of the same SKU model that meet the conditions in the historical quotation acquisition module is queried in a continuous time period, and calculate the market wave point factor value for fine-tuning the pricing area of ​​the second-hand goods;

[0021] The module for obtaining the fine-tuning coefficient value of the pricing area is used to calculate the fine-tuning coefficient value of the pricing area by using multiple market wave point factor values.

[0022] The management module may further include:

[0023] The employee reward module is used to set reward rankings based on the actual recycling and sales of employees, and to issue corresponding rewards based on the reward rankings;

[0024] Mobile learning module, used to build an online sales knowledge base, provide sales training to employees, and learn about second-hand goods;

[0025] The market analysis module is used to analyze the recycling and sales data of second-hand goods and generate market analysis reports.

[0026] Preferably, the data analysis module may further include a grayscale processing module for extracting second-hand commodity images and performing grayscale processing on the second-hand commodity images to obtain a grayscale image corresponding to the second-hand commodity images. The grayscale processing module preferably includes:

[0027] A first image region acquisition module is used to extract an image region corresponding to the second-hand commodity from a grayscale image corresponding to the second-hand commodity image as a first image region;

[0028] A second image region acquisition module is used to extract an image region corresponding to the background from the grayscale image corresponding to the second-hand commodity image as the second image region;

[0029] The first grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the first image area, and obtain the first grayscale index parameter using the grayscale values ​​of the pixels contained in the first image area; the first grayscale index parameter is obtained by the following formula:

[0030]

[0031] Among them, G 01represents the first grayscale index parameter; n represents the number of pixels contained in the first image area; I 01i represents the gray value of the i-th pixel contained in the first image area; I p01 represents the grayscale average value corresponding to the n pixels contained in the first image area; z01 represents the intermediate value corresponding to the n pixels contained in the first image area; I f01 represents the variance corresponding to the n pixels contained in the first image area;

[0032] The second grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the second image area, and obtain the second grayscale index parameter using the grayscale values ​​of the pixels contained in the second image area; the second grayscale index parameter is obtained by the following formula:

[0033]

[0034] Among them, G 02 represents the second grayscale index parameter; m represents the number of pixels contained in the second image area; I 02i represents the gray value of the i-th pixel contained in the second image area; p02 I represents the grayscale average value corresponding to the m pixels contained in the second image area; z02 represents the middle value corresponding to the m pixels contained in the second image area; I f02 represents the variance corresponding to the m pixels contained in the second image area;

[0035] The grayscale value adjustment module is used to compare the first grayscale index parameter with the second grayscale index parameter, and when the difference between the first grayscale index parameter and the second grayscale index parameter is lower than a preset difference threshold, adjust the grayscale value of the first image area.

[0036] The gray value adjustment module may further include:

[0037] A difference extraction module, used for extracting the difference between the first grayscale index parameter and the second grayscale index parameter;

[0038] A gray value data extraction module, used to extract the gray values ​​of the pixels contained in the first image area;

[0039] The target grayscale value acquisition module is used to obtain the target grayscale value corresponding to each pixel by using the difference between the first grayscale index parameter and the second grayscale index parameter combined with the grayscale value of the pixel contained in the first image area; the target grayscale value is obtained by the following formula:

[0040]

[0041] Among them, I m Represents the target gray value; I y represents the gray value of the pixels contained in the first image area; G 01 Represents the first gray index parameter; G 02 represents the second gray index parameter;

[0042] The grayscale adjustment execution module is used to adjust the grayscale of each pixel according to the target grayscale value corresponding to each pixel, so that the grayscale value of each pixel reaches the target grayscale value.

[0043] The commodity trading module preferably includes:

[0044] The information interaction module is used to receive the customer's input information to verify the customer's identity and establish a second-hand commodity price evaluation, recycling and sales platform;

[0045] The recycling application module is used to provide customers with a second-hand goods information filling interface, obtain product-related information, and submit second-hand goods recycling application information;

[0046] Aggregate bidding module, used to aggregate multiple recyclers to bid on the recycling price of second-hand goods, and determine the final recycling price based on the highest bid;

[0047] The product sales module is used to sell recycled second-hand products, as well as corresponding accessories and integrated products, service products and operator businesses;

[0048] The display module is used to display the analysis report of the second-hand goods, the recycling business of the recycler and the recycling price of the second-hand goods evaluated by the recycler.

[0049] The recycling application module may further include:

[0050] The information recording module is used to provide customers with a second-hand goods information filling interface, obtain second-hand goods related information, and submit second-hand goods recycling application information;

[0051] The information recommendation module is used to match the second-hand goods recycling application information according to the recycling intention configuration of the recycler in real time, and distribute and recommend it to the matching recyclers;

[0052] The information alliance module is used for product placement, finding and managing partner merchants, and recommending and promoting popular products.

[0053] The commodity sales module may further include:

[0054] The recycling confirmation module is used to display the recycling price of second-hand goods given by the recycler. The store selects the required recycler based on the recycling price given by the recycler, and selects the recycling method and payment account;

[0055] The recycling payment module is used to confirm the transaction. After that, the recycler will deposit the recycling amount into the payment account reserved by the customer through the platform and push the order confirmation information to the store. The store will recycle the second-hand goods and send them to the recycler.

[0056] The second technical problem to be solved by the present invention is to provide an operating method of the above-mentioned second-hand goods digital purchase and sale operation system, which comprises the following steps:

[0057] 1) Store staff obtains the customer's contact information based on the customer's recycling intention. If the customer is willing to sell the second-hand goods, the store staff will contact the customer to come to the store for evaluation of the second-hand goods;

[0058] 2) The customer accepts the invitation and brings the second-hand goods to the store. The employee takes photos of the customer's second-hand goods and uploads the photos and videos of the second-hand goods to the platform. The platform performs feature recognition on the photos and videos of the second-hand goods to obtain the recognition results. If the recognition results are inconsistent with the original image features, it is determined that the second-hand goods have defects and the defect location is marked;

[0059] 3) Obtaining the marked defect information, including the image, video shooting angle, and defect coordinates in the product image and video screen, and reviewing the marked defect information to obtain a classification result of the degree of defect;

[0060] 4) querying the associated parameter data of the grid pricing information of the second-hand goods corresponding to the SKU of the second-hand goods to be priced, and determining the base price parameter corresponding to the pricing area according to the degree of defect of the second-hand goods;

[0061] 5) After the valuation is completed, the second-hand goods recycling application information is matched with the recycling intention configuration of the recycler in real time. After the matching is completed, the second-hand goods valuation information is sent to multiple matching recyclers. Multiple recyclers bid for the second-hand goods, and the bids of the recyclers are displayed. The highest bid price is used as the transaction price of the second-hand goods;

[0062] 6) The customer confirms the transaction, and the recycler deposits the recycling amount into the payment account reserved by the seller of the second-hand goods through the platform, and pushes the order confirmation information to the store. The store recycles the second-hand goods from the seller and ships the recycled second-hand goods to the recycler.

[0063] In the above step 2), before performing feature recognition on the second-hand commodity photos and videos, it is preferred to first perform grayscale processing on the second-hand commodity photos and videos.

[0064] In the above step 4), preferably, after determining the base price parameters, the market wave point factor value for fine-tuning the second-hand commodity pricing area can be calculated based on the historical transaction analysis data of related commodities, and the current second-hand commodity pricing area can be fine-tuned. The fine-tuning method is:

[0065] 41) Calculate the average of the quotation collection amount A1, A2, A3, ... A in each historical trading period 10 , where A i is the collective mean of the quotation amount in the i-th time period, and the market wave point factor value parameter of each time period is obtained;

[0066] 42) For the mean of each two adjacent time periods, the month-on-month value is calculated as the market wave point factor value. The calculation method is to subtract 1 from the ratio of the mean of the latter time period to the mean of the previous time period, and then multiply by 100 to get the percentage; the calculation formula is as follows:

[0067] where i=1,2,3,...,9

[0068] 43) Calculate the average of all month-on-month values ​​to obtain the overall price change trend percentage coefficient for the next cycle, which is used as the pricing area fine-tuning coefficient value. The calculation formula is as follows:

[0069]

[0070] 44) The upper or lower limit of the final pricing range is obtained by multiplying the basic price of the second-hand goods' degree of defect by the pricing area fine-tuning coefficient.

[0071] In the above step 5), the matching method is preferably:

[0072] 51) Initialize bitmap: create a bitmap for each recycler’s recycling intention dimension point and each recycled product’s attribute dimension point, where each bit in the bitmap corresponds to a dimension point;

[0073] 52) According to the recycling intention configuration of the recycler and the attribute information of the recycled goods, the corresponding dimension point is set to 1, and the matching weight of each recycling intention point is set;

[0074] 53) Multiplying the bitmap of each dimension point by its corresponding matching weight to obtain a weighted bitmap;

[0075] 54) Performing bitwise operations on the weighted bitmaps of all recyclers to obtain a combined bitmap, i.e., a matching degree bitmap;

[0076] 55) Calculate the matching degree: count the number of bits with a value of 1 in the matching degree bitmap to obtain the matching degree value.

[0077] Compared with the existing second-hand commodity recycling technology, the second-hand commodity digital purchase and sale operation system and method of the present invention has the following advantages and beneficial effects:

[0078] 1. The present invention makes the recycling of second-hand goods more standardized by unified management and scheduling of transactions between customers, stores and recyclers, avoids the serious problems of private orders and the erosion of the commodity recycling market by scalpers, and ensures the success rate of second-hand commodity recycling;

[0079] 2. The present invention can more accurately estimate the price of second-hand goods by identifying the types and defects of second-hand goods of customers. Through online bidding and the highest bidder wins, customers can choose appropriate recyclers to recycle second-hand goods according to the prices given by different recyclers, which not only improves the efficiency and visualization of recycling and management, as well as the speed of response, but also improves the customer experience;

[0080] 3. The present invention can realize the integration of price evaluation, recycling and sales of second-hand goods, as well as full-scene and full-process digital management and operation services, so as to help stores intensively and efficiently manage different recycling scenarios and links, and reduce operation, manpower and risk costs;

[0081] 4. The present invention can simplify image data, improve image quality, enhance image contrast, make details of commodity areas clearer and background cleaner by grayscale processing of second-hand commodity images, extracting image areas, calculating grayscale index parameters (the parameters comprehensively consider grayscale average value, median value and variance, and can provide comprehensive grayscale information) and adjusting grayscale values, thereby improving the visual effect of the image and further improving the convenience and accuracy of subsequent image analysis (such as commodity type identification, defect identification, etc.); in addition, the grayscale adjustment process of the present invention is based on the difference of grayscale index parameters, which means that it can adaptively adjust the difference threshold according to different images and different grayscale differences, so it has certain flexibility and adaptability;

[0082] 5. The present invention can obtain an estimated value of the price trend in the next time period by analyzing the quotation amount data of second-hand goods or competing products of the same series in the previous half year through big data. This estimated value has a certain positive correlation with the actual market conditions, which can better fit the market valuation, help recyclers formulate more accurate quotation strategies, and effectively improve the quotation efficiency of the recycling link. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 This is a module diagram of a second-hand goods digital purchase and sale operation system according to an embodiment of the present invention;

[0084] Figure 2 1. is a schematic diagram of the purchase and sale process of the second-hand goods digital purchase and sale operation system according to an embodiment of the present invention;

[0085] Figure 3 It is a schematic diagram of the purchase and sale operation link of the second-hand commodity digital purchase and sale operation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to have a more specific understanding of the technical content, characteristics and effects of the present invention, the technical solution of the present invention is further described in detail in conjunction with the accompanying drawings and specific embodiments.

[0087] Example 1

[0088] like Figure 1 As shown, the second-hand goods digital purchase and sale operation system of this embodiment includes:

[0089] The information collection module is used to obtain the basic information and contact information of customers, and to establish customer profiles based on the basic information obtained. Customers are classified according to their customer profiles, and their tendency to sell second-hand goods is collected. Customers who are interested in selling second-hand goods are paid special attention to, so as to ensure the success rate of second-hand goods recycling;

[0090] The data analysis module is used to identify and analyze the photographed images of second-hand goods, analyze the quality of second-hand goods, determine whether there are defects in second-hand goods and the degree of defects, and determine the pricing area of ​​second-hand goods to be priced based on the type of second-hand goods and the degree of defects and historical transaction analysis data of related goods; the data analysis module can identify the product type and determine the degree of defects of customers' second-hand goods, so as to accurately price the second-hand goods and ensure the rationality of second-hand goods recycling;

[0091] The commodity transaction module is used to receive the customer's input information to verify the customer's identity, establish a second-hand commodity price evaluation, recycling and sales platform, sell the recycled second-hand commodities, and sell the corresponding accessories and integrated products, service products and operator business, and display the analysis report of second-hand commodities, the recycling business of recyclers and the recycling price of second-hand commodities evaluated by recyclers; by establishing a second-hand commodity price evaluation, recycling and sales platform, customers and recyclers can evaluate the price and recycle second-hand commodities on the platform, which is more convenient to use. At the same time, customers can also sell second-hand commodities through the platform, meeting customer needs.

[0092] Wherein, the information collection module includes:

[0093] The customer information acquisition module is used to obtain the basic information and contact information of customers, and to establish customer profiles based on the basic information obtained;

[0094] The customer classification module is used to classify customers into old customers who do not come to the store, potential customers who come to the store but do not make a deal, and new customers who come to the store to purchase a device based on their customer profile.

[0095] The data analysis module further comprises:

[0096] A commodity determination module is used to capture images of second-hand commodities, determine identification information of second-hand commodities to be recycled based on the captured images of second-hand commodities, and match corresponding commodity types and original prices of second-hand commodities based on the identification information;

[0097] A grayscale processing module is used to extract a second-hand commodity image and perform grayscale processing on the second-hand commodity image to obtain a grayscale image corresponding to the second-hand commodity image;

[0098] The defect recognition module is used to analyze the photographed second-hand goods image to determine whether the second-hand goods have defects and the extent of the defects;

[0099] The commodity pricing module is used to query the grid pricing information of the second-hand commodity corresponding to the SKU of the second-hand commodity to be priced based on the type and degree of defect of the second-hand commodity, based on the original price and flow rate, and determine the pricing area of ​​the second-hand commodity to be priced;

[0100] The management module is used to set reward rankings based on employees' actual recycling and sales, issue corresponding rewards based on reward rankings, establish an online sales knowledge base, conduct sales training for employees, and understand information about second-hand goods and conduct market analysis on second-hand goods. Through sales training for employees, employees can understand the market conditions and knowledge of second-hand goods when recycling second-hand goods, and thus better serve customers and improve customer experience.

[0101] The commodity trading module includes:

[0102] The information interaction module is used to receive the customer's input information to verify the customer's identity and establish a second-hand commodity price evaluation, recycling and sales platform;

[0103] The recycling application module is used to provide customers with a second-hand goods information filling interface, obtain product-related information, and submit second-hand goods recycling application information. Second-hand goods related information includes product images, product specifications, product usage information, and product defect levels;

[0104] Aggregate bidding module, used to aggregate multiple recyclers to bid on the recycling price of second-hand goods, and determine the final recycling price based on the highest bid;

[0105] The product sales module is used to sell recycled second-hand products, as well as corresponding accessories and integrated products, service products and operator businesses;

[0106] The display module is used to display the analysis report of the second-hand goods, the recycling business of the recycler and the recycling price of the second-hand goods evaluated by the recycler.

[0107] The grayscale processing module in the data analysis module includes:

[0108] A first image region acquisition module is used to extract an image region corresponding to the second-hand commodity from a grayscale image corresponding to the second-hand commodity image as a first image region;

[0109] A second image region acquisition module is used to extract an image region corresponding to the background from the grayscale image corresponding to the second-hand commodity image as the second image region;

[0110] The first grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the first image area, and obtain the first grayscale index parameter using the grayscale values ​​of the pixels contained in the first image area; wherein the first grayscale index parameter is obtained by the following formula:

[0111]

[0112] Among them, G 01 represents the first grayscale index parameter; n represents the number of pixels contained in the first image area; I 01i represents the gray value of the i-th pixel contained in the first image area; I p01 represents the grayscale average value corresponding to the n pixels contained in the first image area; z01 represents the intermediate value corresponding to the n pixels contained in the first image area; I f01 represents the variance corresponding to the n pixels contained in the first image area;

[0113] The second grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the second image area, and obtain the second grayscale index parameter using the grayscale values ​​of the pixels contained in the second image area; wherein the second grayscale index parameter is obtained by the following formula:

[0114]

[0115] Among them, G 02 represents the second grayscale index parameter; m represents the number of pixels contained in the second image area; I 02i represents the gray value of the i-th pixel contained in the second image area; p02 I represents the grayscale average value corresponding to the m pixels contained in the second image area; z02 represents the middle value corresponding to the m pixels contained in the second image area; If02 represents the variance corresponding to the m pixels contained in the second image area;

[0116] The grayscale value adjustment module is used to compare the first grayscale index parameter with the second grayscale index parameter, and when the difference between the first grayscale index parameter (product area) and the second grayscale index parameter (background area) is lower than a preset difference threshold, adjust the grayscale value of the first image area.

[0117] The grayscale value adjustment module includes a difference extraction module, a grayscale value data extraction module, a target grayscale value acquisition module and a grayscale adjustment execution module. Among them, the difference extraction module is used to extract the difference between the first grayscale index parameter and the second grayscale index parameter; the grayscale value data extraction module is used to extract the grayscale value of the pixels contained in the first image area; the target grayscale value acquisition module is used to use the difference between the first grayscale index parameter and the second grayscale index parameter combined with the grayscale value of the pixels contained in the first image area to obtain the target grayscale value corresponding to each pixel (this calculation method can ensure the accuracy of grayscale adjustment); the grayscale adjustment execution module is used to adjust the grayscale of each pixel in the commodity area according to the target grayscale value corresponding to each pixel, so that the grayscale value of each pixel reaches the target grayscale value (when the grayscale difference between the commodity area and the background area is small, this adjustment can effectively enhance the contrast of the image and make the commodity more prominent in the image). The target grayscale value is obtained by the following formula:

[0118]

[0119] Among them, I m Represents the target gray value; I y represents the gray value of the pixels contained in the first image area; G 01 Represents the first gray index parameter; G 02 Represents the second grayscale index parameter.

[0120] The commodity pricing module in the data analysis module includes:

[0121] The commodity historical quotation acquisition module is used to query the historical transaction data of second-hand commodities with the same SKU model based on the basic pricing parameters of the second-hand commodity defect degree, using the SKU with the same defect degree as the current second-hand commodity, and sequentially retrieve all quotation amounts from the previous 6th to 1st month, the first 1st to 3rd of a month, and this week, a total of 10 historical transaction time periods. If there are less than 3 and discontinuous cases, all query results are discarded, and the historical transaction data of the same series of competing SKUs is queried instead;

[0122] The module for obtaining the market wave point factor value is used to set up the quotation amount set of each time period as the parameter for calculating the market wave point factor after the commodity historical quotation acquisition module has queried more than three data results with continuous time periods, and calculate the market wave point factor value for fine-tuning the second-hand commodity pricing area;

[0123] The module for obtaining the fine-tuning coefficient value of the pricing area is used to calculate the fine-tuning coefficient value of the pricing area by using multiple market wave point factor values.

[0124] The management module in the data analysis module includes:

[0125] The employee reward module is used to set reward rankings based on the actual recycling and sales of employees, and to issue corresponding rewards based on the reward rankings;

[0126] Mobile learning module, used to build an online sales knowledge base, provide sales training to employees, and learn about second-hand goods;

[0127] The market analysis module is used to analyze the recycling and sales data of second-hand goods and generate market analysis reports.

[0128] The recycling application module in the commodity trading module includes:

[0129] The information recording module is used to provide customers with a second-hand goods information filling interface, obtain second-hand goods related information, and submit second-hand goods recycling application information;

[0130] The information recommendation module is used to match the second-hand goods recycling application information in real time according to the recycling intention configuration of the recycler (for example, the intended recycling channel, intended goods category, intended goods brand, intended goods series, intended goods model, intended goods quality, intended goods valuation range, intended cooperative merchants, etc.), and distribute and recommend it to the matching recyclers;

[0131] The information alliance module is used for product placement, finding and managing cooperative merchants, and recommending and promoting popular products, helping merchants to promote products and manage cooperation.

[0132] The commodity sales module in the commodity transaction module includes:

[0133] The recycling confirmation module is used to display the recycling price of second-hand goods given by the recycler. The store selects the required recycler based on the recycling price given by the recycler, and selects the recycling method (door-to-door or in-store recycling) and the payment account;

[0134] The recycling payment module is used to confirm the transaction. The recycler will deposit the recycling amount into the payment account reserved by the customer through the platform, and push the order confirmation information to the store. The store recycles the second-hand goods and sends them to the recycler.

[0135] The operating method of the second-hand goods digital purchase and sale operation system of this embodiment is shown in Figures 2-3 As shown, the following steps are included:

[0136] Step 1: Store staff obtain the customer's contact information based on the customer's recycling intention. If the customer is willing to sell the second-hand goods, the store staff will contact the customer to come to the store for evaluation of the second-hand goods. During the contact process, the interested customers will be marked and paid special attention;

[0137] Step 2: The customer accepts the invitation and brings the second-hand goods to the store. The employee takes photos of the customer's second-hand goods and uploads the photos and / or videos of the second-hand goods to the platform for grayscale processing. The platform performs feature recognition on the grayscale processed photos of the second-hand goods to obtain recognition results. If the recognition results are inconsistent with the original image features, it is determined that the second-hand goods have defects and the defect location is marked;

[0138] Step 3: Obtain the marked defect information, including the shooting angle of the picture and / or video and the coordinates of the defect in the product picture and / or video, and review the marked defect information to obtain the classification result of the degree of defect (including no defect, minor defect, medium defect and serious defect);

[0139] Step 4: Query the associated parameter data of the grid pricing information of the second-hand goods corresponding to the SKU of the second-hand goods to be priced, and determine the base price parameter corresponding to the pricing area according to the degree of defect of the second-hand goods (when the second-hand goods have defects, the base price parameter is the depreciation rate. The greater the defect, the greater the depreciation rate. The recycling base pricing is obtained based on the depreciation rate and the original price of the second-hand goods to be priced; when the second-hand goods do not have defects, the base price parameter is a fixed value, which is used as the recycling base pricing). Then, based on the transaction analysis data of historical related goods, calculate the market wave point factor value for fine-tuning the second-hand goods pricing area, and fine-tune the current second-hand goods pricing area. The specific method of fine-tuning is as follows:

[0140] First, calculate the average of the quotation collection amount in each historical trading period (A1, A2, A3...A 10 , where A i is the aggregate mean of the quotation amount in the i-th time period) to obtain the market wave point factor value parameters for each time period;

[0141] Then, for the mean of each two adjacent time periods, calculate the month-on-month value as the market wave point factor value, that is, the ratio of the mean of the latter time period to the mean of the previous time period minus 1, and then multiply by 100 to get the percentage. The calculation formula is as follows:

[0142] where i=1,2,3,...,9

[0143] Next, calculate the average of all month-on-month values ​​to obtain the overall price change trend percentage coefficient for the next cycle (i.e., the pricing area fine-tuning coefficient value), and the calculation formula is as follows:

[0144]

[0145] Finally, the upper or lower limit of the final pricing range is obtained by multiplying the basic price of the second-hand product's degree of defect by the pricing area fine-tuning coefficient.

[0146] Step 5: After the valuation is completed, the second-hand goods recycling application information is matched with the recycling intention configuration of the recycler in real time. The matching method is as follows:

[0147] Initialize the bitmap: Create a bitmap for each recycler's recycling intention dimension point and each recycled product's attribute dimension point. Each bit in the bitmap corresponds to a dimension point.

[0148] Set matching parameters: according to the recycling intention configuration of the recycler and the attribute information of the recycled goods, set the corresponding dimension point to 1, and set the matching weight of each recycling intention point;

[0149] Multiply bitmap by weight: multiply the bitmap of each dimension point by its corresponding matching weight to obtain a weighted bitmap;

[0150] Bitmap addition: Perform bitwise operations (OR operations) on the weighted bitmaps of all recyclers to obtain a combined bitmap representing the matching degree;

[0151] Calculate the matching degree: count the number of bits with a value of 1 in the matching degree bitmap, which is the matching degree value.

[0152] Convert the above matching method into a mathematical function:

[0153] Assuming there are (n) recycling intention dimension points of recyclers and (m) attribute dimension points of recycled goods, the bitmap length of each dimension point is (k), and the matching degree (M) is calculated as follows:

[0154] Assume that the recycling intention point map of recycler (i) is (R_i) and the matching weight is (W_i);

[0155] Let the attribute dimension point map of recycled product (j) be (P_j);

[0156] The weighted bitmap is (RW_i=R_i\times W_i);

[0157] The bitmap of the matching degree is (M=RW_1:|:RW_2:|:...:|:RW_n:|:P_1:|:P_2:|:...:|:P_m);

[0158] Among them, (\times) represents the bitwise AND operation, (|) represents the bitwise OR operation, (M) represents the matching degree, (RW_1, RW_2, ..., RW_n) represents the weights to be matched, and (P_1, P_2, ..., P_m) represents the related attributes, and (:|:) means taking the previous item as the numerator and the following item as the denominator, and dividing them in sequence;

[0159] By counting the number of bits with a value of 1 in the matching degree bitmap, the specific value of the matching degree can be obtained. That is, the overall matching degree between a recycler and a second-hand product can be obtained through the following mathematical formula:

[0160] M=\sum_{i=1}^{n}\left(RW_i\right):|:\sum_{j=1}^{m}\left(P_j\right)

[0161] This matching method takes into account the matching weight, which can better reflect the importance of each recycler's recycling intention point, and more accurately measure and flexibly adjust the matching degree during the matching process, providing more accurate matching results for recyclers and recycled goods;

[0162] After the matching is completed, the second-hand goods valuation information is sent to multiple matching recyclers, and multiple recyclers bid on the second-hand goods. The bids of the recyclers are displayed, and the highest bid price is used as the transaction price of the second-hand goods;

[0163] Step 6: After the customer confirms the transaction, the recycler deposits the recycling amount into the payment account reserved by the seller of the second-hand goods through the platform, and pushes the order confirmation information to the store. The store recycles the second-hand goods from the seller and ships the recycled second-hand goods to the recycler. After the recycling is completed, the customer is added to the employee's private sales domain, which can fully grasp the customer relationship, realize online and offline linkage, establish a long-term trust relationship with the customer, and subtly influence the customer through continuous content and services, making them fans or even followers of the brand.

[0164] Example 2

[0165] When a customer needs to recycle a used mobile phone, he or she will send the phone to the store. The store staff will take detailed photos and videos of the customer's phone and upload them to the platform to identify defects on the phone.

[0166] When it is identified that the customer's second-hand mobile phone has defects, the depreciation of the second-hand mobile phone is estimated based on the degree of defects and the price of the new second-hand mobile phone. For example, if the price of a new mobile phone is 3,000 yuan, the degree of defects is minor, and the mobile phone has been used for 1 year, the newness of the mobile phone is determined based on the one-year usage of the mobile phone, and then the depreciation of the minor defects is deducted from the basic price of the new mobile phone of 3,000 yuan, and then the depreciation of the mobile phone after one year of use is deducted to get the final estimated price of the second-hand mobile phone;

[0167] After the valuation is completed, the valuation information of the second-hand mobile phone will be sent to all recyclers on the platform, and the recyclers will bid for the second-hand mobile phone. For example, the platform estimates the second-hand mobile phone at 1,800 yuan, the price given by recycler A is 1,900, the price given by recycler B is 1,950, the price given by recycler C is 2,000, and so on. When all recyclers have finished bidding, the platform will display the bids of all recyclers from high to low;

[0168] When the customer determines that the bid offered by recycler C is the highest, he / she chooses to trade with recycler C and provides the payment account. After recycler C deposits the recycling amount into the customer's account through the platform, it sends the order confirmation information to the store;

[0169] The store recycles customers' used mobile phones and sends them to recyclers by express delivery.

[0170] The above embodiments are merely feasible or preferred embodiments of the present invention and are used to illustrate the present invention rather than to limit the scope of the patent application of the present invention. Therefore, all equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope covered by the patent of the present invention.

Claims

1. A digital purchase and sale operation system for second-hand goods, characterized in that: include: The information collection module is used to obtain the basic information and contact information of customers, and to establish customer group portraits based on the basic information obtained, and to classify customers according to their customer group portraits; The data analysis module is used to identify and analyze the images of second-hand goods, determine whether the second-hand goods have defects and the degree of defects, and determine the pricing area of ​​the second-hand goods to be priced based on the type of second-hand goods, the degree of defects and historical transaction analysis data of related goods; The commodity trading module is used to receive the customer's input information to verify the customer's identity, establish a second-hand commodity price evaluation, recycling and sales platform, sell the recycled second-hand commodities, and sell the corresponding accessories and integrated products, service products and operator businesses, and display the analysis report of second-hand commodities, the recycling business of recyclers and the recycling price of second-hand commodities evaluated by recyclers.

2. The second-hand goods digital purchase and sale operation system according to claim 1 is characterized in that: The information collection module comprises: The customer information acquisition module is used to obtain the basic information and contact information of customers, and to establish customer profiles based on the basic information obtained; The customer classification module is used to classify customers into old customers who do not come to the store, potential customers who come to the store but do not make a deal, and new customers who come to the store to purchase a device based on their customer profile.

3. The second-hand goods digital purchase and sale operation system according to claim 1 is characterized in that: The data analysis module comprises: A commodity determination module is used to capture images of second-hand commodities, determine identification information of second-hand commodities to be recycled based on the captured images of second-hand commodities, and match corresponding commodity types and original prices of second-hand commodities based on the identification information; The defect recognition module is used to analyze the photographed second-hand goods image to determine whether the second-hand goods have defects and the extent of the defects; The commodity pricing module is used to query the grid pricing information of the second-hand commodity corresponding to the SKU of the second-hand commodity to be priced based on the type and degree of defect of the second-hand commodity, based on the original price and flow rate, and determine the pricing area of ​​the second-hand commodity to be priced; The management module is used to set reward rankings based on employees' actual recycling and sales, and distribute corresponding rewards based on the reward rankings, establish an online sales knowledge base, provide sales training for employees, and understand the information of second-hand goods and conduct market analysis on second-hand goods.

4. The second-hand goods digital purchase and sale operation system according to claim 3 is characterized in that: The data analysis module also includes a grayscale processing module, which is used to extract the second-hand commodity image and perform grayscale processing on the second-hand commodity image to obtain a grayscale image corresponding to the second-hand commodity image.

5. The second-hand goods digital purchase and sale operation system according to claim 4 is characterized in that: The grayscale processing module comprises: A first image region acquisition module is used to extract an image region corresponding to the second-hand commodity from a grayscale image corresponding to the second-hand commodity image as a first image region; A second image region acquisition module is used to extract an image region corresponding to the background from the grayscale image corresponding to the second-hand commodity image as the second image region; The first grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the first image area, and obtain the first grayscale index parameter using the grayscale values ​​of the pixels contained in the first image area; the first grayscale index parameter is obtained by the following formula: Among them, G 01 represents the first grayscale index parameter; n represents the number of pixels contained in the first image area; I 01i represents the gray value of the i-th pixel contained in the first image area; I p01 represents the grayscale average value corresponding to the n pixels contained in the first image area; z01 represents the intermediate value corresponding to the n pixels contained in the first image area; I f01 represents the variance corresponding to the n pixels contained in the first image area; The second grayscale index parameter acquisition module is used to extract the grayscale values ​​of the pixels contained in the second image area, and obtain the second grayscale index parameter using the grayscale values ​​of the pixels contained in the second image area; the second grayscale index parameter is obtained by the following formula: Among them, G 02 represents the second grayscale index parameter; m represents the number of pixels contained in the second image area; I 02i represents the gray value of the i-th pixel contained in the second image area; p02 I represents the grayscale average value corresponding to the m pixels contained in the second image area; z02 represents the middle value corresponding to the m pixels contained in the second image area; I f02 represents the variance corresponding to the m pixels contained in the second image area; The grayscale value adjustment module is used to compare the first grayscale index parameter with the second grayscale index parameter, and when the difference between the first grayscale index parameter and the second grayscale index parameter is lower than a preset difference threshold, adjust the grayscale value of the first image area.

6. The second-hand goods digital purchase and sale operation system according to claim 5 is characterized in that: The gray value adjustment module comprises: A difference extraction module, used for extracting the difference between the first grayscale index parameter and the second grayscale index parameter; A gray value data extraction module, used to extract the gray values ​​of the pixels contained in the first image area; The target grayscale value acquisition module is used to obtain the target grayscale value corresponding to each pixel by using the difference between the first grayscale index parameter and the second grayscale index parameter combined with the grayscale value of the pixel contained in the first image area; the target grayscale value is obtained by the following formula: Among them, I m Represents the target gray value; I y represents the grayscale value of the pixels contained in the first image area; G 01 Represents the first gray index parameter; G 02 represents the second gray index parameter; The grayscale adjustment execution module is used to adjust the grayscale of each pixel according to the target grayscale value corresponding to each pixel, so that the grayscale value of each pixel reaches the target grayscale value.

7. The second-hand goods digital purchase and sale operation system according to claim 3 is characterized in that: The commodity pricing module includes: The commodity historical quotation acquisition module is used to query and retrieve the historical second-hand commodity transaction data of the same SKU model or the same series of competing products based on the basic pricing parameters of the second-hand commodity defect degree, using the SKU with the same defect degree as the current second-hand commodity; The market wave point factor value acquisition module is used to set up a quotation amount set for each time period as a parameter for calculating the market wave point factor after the historical transaction data of the second-hand goods of the same SKU model that meet the conditions in the historical quotation acquisition module is queried in a continuous time period, and calculate the market wave point factor value for fine-tuning the pricing area of ​​the second-hand goods; The module for obtaining the fine-tuning coefficient value of the pricing area is used to calculate the fine-tuning coefficient value of the pricing area by using multiple market wave point factor values.

8. The second-hand goods digital purchase and sale operation system according to claim 3 is characterized in that: The management module comprises: The employee reward module is used to set reward rankings based on the actual recycling and sales of employees, and to issue corresponding rewards based on the reward rankings; Mobile learning module, used to build an online sales knowledge base, provide sales training to employees, and learn about second-hand goods; The market analysis module is used to analyze the recycling and sales data of second-hand goods and generate market analysis reports.

9. The second-hand goods digital purchase and sale operation system according to claim 1 is characterized in that: The commodity trading module includes: The information interaction module is used to receive the customer's input information to verify the customer's identity and establish a second-hand commodity price evaluation, recycling and sales platform; The recycling application module is used to provide customers with a second-hand goods information filling interface, obtain product-related information, and submit second-hand goods recycling application information; Aggregate bidding module, used to aggregate multiple recyclers to bid on the recycling price of second-hand goods, and determine the final recycling price based on the highest bid; The product sales module is used to sell recycled second-hand products, as well as corresponding accessories and integrated products, service products and operator businesses; The display module is used to display the analysis report of the second-hand goods, the recycling business of the recycler and the recycling price of the second-hand goods evaluated by the recycler.

10. The second-hand goods digital purchase and sale operation system according to claim 9 is characterized in that: The recycling application module includes: The information recording module is used to provide customers with a second-hand goods information filling interface, obtain second-hand goods related information, and submit second-hand goods recycling application information; The information recommendation module is used to match the second-hand goods recycling application information according to the recycling intention configuration of the recycler in real time, and distribute and recommend it to the matching recyclers; The information alliance module is used for product placement, finding and managing partner merchants, and recommending and promoting popular products.

11. The second-hand goods digital purchase and sale operation system according to claim 9 is characterized in that: The commodity sales module includes: The recycling confirmation module is used to display the recycling price of second-hand goods given by the recycler. The store selects the required recycler based on the recycling price given by the recycler, and selects the recycling method and payment account; The recycling payment module is used to confirm the transaction. After that, the recycler will deposit the recycling amount into the payment account reserved by the customer through the platform and push the order confirmation information to the store. The store will recycle the second-hand goods and send them to the recycler.

12. The method for operating the second-hand goods digital purchase and sale operation system according to any one of claims 1 to 11, characterized in that: The following steps are involved: 1) Store staff obtains the customer's contact information based on the customer's recycling intention. If the customer is willing to sell the second-hand goods, the store staff will contact the customer to come to the store for evaluation of the second-hand goods; 2) The customer accepts the invitation and brings the second-hand goods to the store. The employee takes photos of the customer's second-hand goods and uploads the photos and videos of the second-hand goods to the platform. The platform performs feature recognition on the photos and videos of the second-hand goods to obtain the recognition results. If the recognition results are inconsistent with the original image features, it is determined that the second-hand goods have defects and the defect location is marked; 3) Obtaining the marked defect information, including the image, video shooting angle, and defect coordinates in the product image and video screen, and reviewing the marked defect information to obtain a classification result of the degree of defect; 4) querying the associated parameter data of the grid pricing information of the second-hand goods corresponding to the SKU of the second-hand goods to be priced, and determining the base price parameter corresponding to the pricing area according to the degree of defect of the second-hand goods; 5) After the valuation is completed, the second-hand goods recycling application information is matched with the recycling intention configuration of the recycler in real time. After the matching is completed, the second-hand goods valuation information is sent to multiple matching recyclers. Multiple recyclers bid for the second-hand goods, and the bids of the recyclers are displayed. The highest bid price is used as the transaction price of the second-hand goods; 6) The customer confirms the transaction, and the recycler deposits the recycling amount into the payment account reserved by the seller of the second-hand goods through the platform, and pushes the order confirmation information to the store. The store recycles the second-hand goods from the seller and ships the recycled second-hand goods to the recycler.

13. The operating method according to claim 12, characterized in that: Step 2), before performing feature recognition on the second-hand commodity photos and videos, grayscale processing is first performed on the second-hand commodity photos and videos.

14. The operating method according to claim 12, characterized in that: Step 4), after determining the base price parameters, calculate the market volatility point factor value for fine-tuning the second-hand commodity pricing area based on the historical transaction analysis data of related commodities, and fine-tune the current second-hand commodity pricing area. The fine-tuning method is: 41) Calculate the average of the quotation collection amount A1, A2, A3, ... A in each historical trading period 10 , where A i is the collective mean of the quotation amount in the i-th time period, and the market wave point factor value parameter of each time period is obtained; 42) For the mean of each two adjacent time periods, the month-on-month value is calculated as the market wave point factor value. The calculation method is to subtract 1 from the ratio of the mean of the latter time period to the mean of the previous time period, and then multiply by 100 to get the percentage; the calculation formula is as follows: where i=1,2,3,...,9 43) Calculate the average of all month-on-month values ​​to obtain the overall price change trend percentage coefficient for the next cycle, which is used as the pricing area fine-tuning coefficient value. The calculation formula is as follows: 44) The upper or lower limit of the final pricing range is obtained by multiplying the basic price of the second-hand goods' degree of defect by the pricing area fine-tuning coefficient.

15. The operating method according to claim 12, characterized in that: Step 5), the matching method is: 51) Initialize bitmap: create a bitmap for each recycler’s recycling intention dimension point and each recycled product’s attribute dimension point, where each bit in the bitmap corresponds to a dimension point; 52) According to the recycling intention configuration of the recycler and the attribute information of the recycled goods, the corresponding dimension point is set to 1, and the matching weight of each recycling intention point is set; 53) Multiplying the bitmap of each dimension point by its corresponding matching weight to obtain a weighted bitmap; 54) Performing bitwise operations on the weighted bitmaps of all recyclers to obtain a combined bitmap, i.e., a matching degree bitmap; 55) Calculate the matching degree: count the number of bits with a value of 1 in the matching degree bitmap to obtain the matching degree value.

Images