Big data-based two-mobile phone receiving and selling operation life cycle management system

Abstract

The invention discloses a two-mobile-phone receiving and selling operation life cycle management system based on big data, which relates to the technical field of two-mobile-phone receiving and selling, and comprises a multi-source data acquisition module, a data processing and storage module, a core analysis and decision module and an application service module. According to the method, a unique life cycle file is established for each two mobile phones, the whole process of purchase, detection, maintenance, inventory, sales and after-sales of the two mobile phones can be run through, data of links such as IMEI, maintenance records and pricing history are associated in real time, the file can support dynamic updating and tracing, and a complete individual data link is formed; and secondly, information isolated island and decision lag are caused by data dispersion in each link in mobile phone receiving and selling, and the system realizes a data closed loop through a life cycle file so as to improve the risk early warning and operation efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of second-hand mobile phone acquisition and sale, and in particular to a second-hand mobile phone acquisition and sale operation lifecycle management system based on big data. Background Art

[0002] With the rapid iteration of electronic products and the increasing frequency of consumers replacing their phones, the second-hand phone market is growing. The traditional second-hand phone purchase and sale operation model has many pain points:

[0003] Inaccurate pricing: Relying on manual experience or simple market reference prices fails to consider complex factors such as the phone's actual condition, functional status, repair history, accessories, real-time market supply and demand, and regional differences. This can lead to purchase prices that are too high and result in losses, or too low and result in no goods being received, resulting in unreasonable sales prices.

[0004] Inefficient and inconsistent testing and evaluation: Manual testing is time-consuming and labor-intensive, standards are difficult to standardize, and potential issues are easily missed. Evaluation results are highly subjective, making it difficult to build consumer trust.

[0005] Data silos: Data from acquisition, testing, maintenance, and sales processes are scattered, making it impossible to trace and comprehensively analyze the entire life cycle of a single mobile phone.

[0006] Weak risk control: It is difficult to effectively identify and prevent risks such as counterfeit and inferior products, stolen products, and false quality reports;

[0007] Some existing second-hand machine acquisition and sales platforms or systems have partially applied big data or artificial intelligence technologies, such as pricing references based on historical transaction data, or using image recognition to assist in appearance inspection. However, these applications are often partial and fragmented, and fail to achieve integrated, intelligent, and data-driven management of the entire life cycle of second-hand machines from acquisition to after-sales. In particular, there is a lack of in-depth integration and analysis of multi-dimensional complex big data from different links and different modes to form a dynamic, complete, and detailed life cycle archive for a single device. Summary of the Invention

[0008] The purpose of the present invention is to provide a second-hand mobile phone purchase and sale operation life cycle management system based on big data to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a big data-based second-hand mobile phone purchase and sale operation lifecycle management system, the system comprising:

[0010] Multi-source data collection module, used to collect data on the acquisition, inspection and evaluation, repair and renovation, inventory and logistics, sales, market macro data, and external risk data of the second-hand machine operation life cycle;

[0011] The data processing and storage module is used to clean, convert, integrate and store the collected raw data, build core data archives, and includes:

[0012] The lifecycle profile generation and update module creates a unique digital profile for each used phone in the system, which covers the entire process from the first contact with the system to the final sale;

[0013] The multimodal feature extraction and fusion module extracts valuable features from different types of data, such as the second-hand mobile phone's structural data, text descriptions, images, and diagnostic logs, and fuses these features from different modalities to form multimodal feature data.

[0014] The core analysis and decision-making module conducts in-depth analysis and decision-making based on the data processed by the data processing and storage module and the life cycle archive of used devices, and includes:

[0015] The dynamic value assessment module uses the lifecycle archive and multimodal feature data of a single mobile phone's condition, functions, maintenance, and inventory, combined with real-time new market prices, competitive product prices, and market data on supply and demand, to assess the optimal reference price range for the phone in acquisition and sales scenarios;

[0016] The supply and demand forecasting module analyzes historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict the future demand and supply of used phones of different models and qualities. The forecast results and current inventory status are sent to staff to facilitate adjustments to inventory distribution, allocation, and used phone acquisitions.

[0017] The application service module converts the output results of the core analysis and decision-making module into specific operational instructions and information to realize business processing.

[0018] Furthermore, the multi-source data collection module includes an acquisition data collection module, a testing and evaluation data collection module, a repair and renovation data collection module, an inventory and logistics data collection module, a sales data collection module, a market macro data collection module, and an external risk data collection module;

[0019] The acquisition phase data collection module collects the mobile phone model, storage, color information, user-described quality, function self-test results, acquisition channel source, acquisition time, geographic location, and user portrait data submitted by the user;

[0020] The data acquisition module in the detection and evaluation phase collects the hardware functions, sensors, battery health diagnostic data, manually detected appearance defects, disassembly and repair traces, accessory status records, images and videos of the mobile phone's appearance, screen display, function demonstration data, maintenance history query data, and serial number query data of the second-hand mobile phone to determine whether it is a stolen phone and whether it is under warranty.

[0021] Furthermore, the maintenance and renovation phase data collection module collects the list of parts required for maintenance, parts cost, maintenance man-hours, maintenance personnel records, maintenance success rate, post-maintenance test results, and renovation cost data;

[0022] The inventory logistics data collection module collects inventory location, inbound and outbound time, logistics information, and storage cost data;

[0023] The sales link data collection module collects sales platform, sales time, sales price, buyer information, order status, browsing and purchasing behavior data, and user evaluation data;

[0024] The market macro data collection module collects data on the market price of new mobile phones, quotations of competitors' second-hand mobile phones, industry reports, holidays, promotional activities, regional economic data, and second-hand market supply and demand index data;

[0025] The external risk data collection module collects stolen machine databases, lists of untrustworthy persons, and third-party assessment reports.

[0026] Furthermore, the data processing and storage module includes a data cleaning and normalization module, a life cycle archive generation and update module, a multimodal feature extraction and fusion module, and a data storage module;

[0027] The data cleaning and normalization module processes noise, missing values, and format inconsistencies in the data, and standardizes the multi-source data collected by the multi-source data acquisition module;

[0028] The lifecycle archive generation and update module creates a unique digital archive for each used phone in the system. This archive runs through the entire process of the phone from its first contact with the system to its final sale, and aggregates and associates all data generated by the phone at each link in real time to form a dynamic, continuous and complete individual data chain (for example: the archive contains the phone's model, IMEI, first acquisition time, previous test results, maintenance records, inventory status, pricing history, and sales status).

[0029] Furthermore, the multimodal feature extraction and fusion module extracts valuable features from different types of data, such as the second-hand phone's own structural data, text descriptions, images, and diagnostic logs, and fuses these features from different modalities to form multimodal feature data (for example, extracting appearance defect features from images, hardware problem features from diagnostic logs, and user feedback features from text descriptions, and combining these features with the second-hand phone's structured model, configuration, and historical data to generate a high-dimensional, comprehensive feature vector).

[0030] The data storage module stores the original data and processed data of the second-hand mobile phone, as well as the life cycle archive of the second-hand mobile phone.

[0031] Furthermore, the core analysis and decision-making module includes a dynamic value assessment module, a risk prediction module, a supply and demand prediction module, a risk identification and early warning module, and an operational efficiency analysis module;

[0032] The dynamic value assessment module uses the lifecycle archive and multimodal feature data of a single mobile phone, including its condition, functions, maintenance, and inventory, combined with real-time market price, competitive price, and supply and demand data, to assess the optimal reference price range for the mobile phone in acquisition and sales scenarios.

[0033] The evaluation algorithm is as follows:

[0034] Vmin≤V=Vbase·α·β·γ≤Vmax

[0035] The base value (Vbase) is calculated based on the inherent attributes of the phone, such as model, configuration, condition, and functional status. The base value (Vbase) is calculated by weighting the inherent attributes of the phone. The formula is as follows:

[0036]

[0037] xi: the i-th attribute value (condition level xcondition, battery health xbattery, repair times xrepair);

[0038] fi(xi): attribute normalization function (color grade is mapped to percentage attenuation: fcondition = 1-0.1·(5-xcondition));

[0039] wi: attribute weight (obtained through historical transaction data or machine learning training, satisfying ∑wi = 1); maintenance and inventory decay factor (α): the negative impact of maintenance history and inventory time on value. The decay effect of maintenance and inventory time on value is modeled as an exponential function:

[0040] α=e

[0041] Nrepair: number of repairs, λ1 is the maintenance attenuation coefficient (the system sets λ1 = 0.05);

[0042] Tinventory: Inventory time (days), λ2 is the inventory time decay coefficient (the system sets λ2 = 0.001);

[0043] The algorithm is: if the mobile phone is repaired twice and has a 30-day inventory, then

[0044] α=e -0.05×2-0.001×30 =e -0.1-0.03 ≈0.886

[0045] Market Dynamic Adjustment Factor (β): This factor is influenced by real-time market supply and demand, competitive product prices, and brand-new device prices. The market factor is determined by the supply-demand ratio, competitive product prices, and brand-new device prices. The formula is as follows:

[0046]

[0047] Pnew: real-time price of a new machine of the same model;

[0048] Pcompetitor: average second-hand price of competing products;

[0049] Sdemand / Ssupply: supply-demand ratio (demand / supply), δ is the supply-demand sensitivity coefficient;

[0050] Risk correction factor (γ): A discount for external risks such as stolen devices and abnormal user behavior. The risk correction factor is dynamically adjusted based on external risk data. The formula is as follows:

[0051]

[0052] Rj: risk weight of the jth category (the system setting is Rstolen = 0.3 for stolen devices and Rcredit = 0.2 for user dishonesty records);

[0053] ηj: binary variable of risk event (1 if risk exists, 0 otherwise).

[0054] Furthermore, the risk prediction module predicts the actual quality grade of the mobile phone and the potential hardware and functional failure risks based on diagnostic data, manual inspection records, images, video data, maintenance history, and historical failure rate data of similar mobile phones;

[0055] The supply and demand forecasting module analyzes historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict the future demand and supply of used phones of different models and qualities. The forecast results and current inventory status are sent to staff to facilitate adjustments to inventory distribution, allocation, and used phone acquisition. The demand forecasting algorithm is as follows:

[0056]

[0057] μm,c: the average basic demand for model and color combination based on historical sales data;

[0058] γm,c: long-term market trend coefficient (such as annual growth rate);

[0059] ak, bk: seasonal Fourier series coefficients, T is the period (one year, half a year or one quarter).

[0060] βm,c: the promotion coefficient of promotion activities on demand, Ipromo,t is the promotion flag (0 / 1);

[0061] wm,c,r: The influence weight of region r on the demand for model and color combination, satisfying ∑ r wm,c,r=1;

[0062] The risk identification and early warning module detects abnormalities in the purchase process, such as the sale of a large number of high-value mobile phones in a short period of time, risky IMEI and serial number conditions, and abnormal maintenance records due to frequent replacement of high-value parts, and issues early warnings and automatic interception.

[0063] The operational efficiency analysis module analyzes the average time consumption, personnel efficiency, and cost structure of the inspection, maintenance, warehousing, and outbound processes, identifies abnormal conditions, and issues alerts to staff.

[0064] Furthermore, the application service modules include an automated detection and evaluation module, a repair and renovation management module, a precision sales and marketing module, and a customer service and after-sales module;

[0065] The automated detection and assessment module guides users and inspectors to perform standardized operations, automatically collects diagnostic data and images, and calls the risk prediction module to generate objective assessment reports and recommended maintenance items;

[0066] The repair and refurbishment management module generates a repair plan and a bill of materials based on the evaluation results, tracks the repair progress and cost, and updates the repair results to the life cycle file of the used mobile phone in real time.

[0067] Furthermore, the precision sales and marketing module generates detailed product descriptions based on the detailed condition and maintenance history data in the life cycle file of the mobile phone, and uses user portraits and behavior data to make personalized product recommendations and marketing push.

[0068] Furthermore, the customer service and after-sales module provides transparent warranty inquiry, maintenance record inquiry, after-sales service and returns and exchanges based on the life cycle archive of the mobile phone.

[0069] The present invention provides a second-hand mobile phone purchase and sale operation life cycle management system based on big data, which has the following beneficial effects:

[0070] 1. The present invention integrates multi-source heterogeneous data such as structured data such as the model and configuration of used mobile phones, unstructured text descriptions such as user feedback, image data such as appearance defects, diagnostic logs of hardware problems, etc. The system can extract and fuse multi-dimensional features to generate high-dimensional comprehensive feature vectors. Because traditional methods rely on a single data source such as manual inspection or simple historical data, they are prone to overlooking potential problems such as hidden faults or appearance details. Through multimodal fusion, the system can improve the objectivity and accuracy of inspection and evaluation, and reduce pricing deviations or risk omissions caused by incomplete information. In addition, the system can map features of different data types into a unified semantic space and perform weighted fusion to ensure that key features such as the number of repairs and market supply and demand have a priority impact on decision-making.

[0071] 2. The system of the present invention dynamically calculates the value range of used mobile phones based on mathematical modeling and real-time data. By introducing attenuation factors such as the number of repairs, inventory time, and risk events, and combining market dynamic adjustment factors and risk correction factors, it can achieve accurate adjustment of prices with market fluctuations and individual status. Because traditional pricing relies on static rules or experience and cannot reflect real-time market changes (such as promotional activities, regional supply and demand differences) or individual differences (such as implicit depreciation caused by multiple repairs), this application quantifies the negative impact of repairs and inventory by introducing an exponential decay model, which can avoid losses caused by high-priced purchases or missing out on high-quality sources at low prices, and adjusts market factors in real time through supply and demand sensitivity coefficients to ensure the scientific nature and flexibility of pricing.

[0072] 3. The present invention establishes a unique life cycle archive for each used mobile phone, which can run through the entire process of used mobile phone acquisition, inspection, maintenance, inventory, sales, and after-sales service. By real-time association of IMEI, maintenance records, pricing history and other link data, the archive can support dynamic update and traceability to form a complete individual data chain. In the traditional model, the data of each link in the acquisition and sale of used mobile phones are scattered (maintenance records are disconnected from sales data), resulting in information islands and delayed decision-making. The system realizes a data closed loop through the life cycle archive (quickly retrieving historical maintenance records to handle returns and exchanges in the after-sales stage, or tracing the legitimacy of the equipment source through archives in the acquisition stage) to improve the system's risk warning and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a schematic diagram of the dynamic value assessment and pricing process of a second-hand mobile phone purchase and sale operation life cycle management system based on big data of the present invention;

[0074] Figure 2 This is a schematic diagram of the full life cycle management process of a second-hand mobile phone purchase, sale and operation life cycle management system based on big data of the present invention;

[0075] Figure 3 This is a schematic diagram of the structure of a multi-source data acquisition module for a second-hand mobile phone purchase and sale operation lifecycle management system based on big data according to the present invention;

[0076] Figure 4 This is a schematic diagram of the data processing and storage module structure of a second-hand mobile phone purchase and sale operation life cycle management system based on big data of the present invention;

[0077] Figure 5 This is a schematic diagram of the core analysis and decision-making module structure of a second-hand mobile phone purchase and sale operation life cycle management system based on big data in the present invention;

[0078] Figure 6 This is a schematic diagram of the application service module structure of a second-hand mobile phone purchase, sale and operation life cycle management system based on big data in the present invention. DETAILED DESCRIPTION

[0079] See also Figures 1 to 6 The present invention provides a technical solution: a big data-based management system for the purchase and sale of used mobile phones. The system includes a multi-source data acquisition module for collecting data from the acquisition phase, inspection and evaluation phase, repair and renovation phase, inventory and logistics phase, sales phase, market macro data, and external risk data of the used mobile phone operation life cycle.

[0080] The data processing and storage module is used to clean, convert, integrate and store the collected raw data, build core data archives, and includes:

[0081] The lifecycle profile generation and update module creates a unique digital profile for each used phone in the system, which covers the entire process from the first contact with the system to the final sale;

[0082] The multimodal feature extraction and fusion module extracts valuable features from different types of data, such as the second-hand mobile phone's structural data, text descriptions, images, and diagnostic logs, and fuses these features from different modalities to form multimodal feature data.

[0083] The core analysis and decision-making module conducts in-depth analysis and decision-making based on the data processed by the data processing and storage module and the life cycle archive of used devices, and includes:

[0084] The dynamic value assessment module uses the lifecycle archive and multimodal feature data of a single mobile phone's condition, functions, maintenance, and inventory, combined with real-time new market prices, competitive product prices, and market data on supply and demand, to assess the optimal reference price range for the phone in acquisition and sales scenarios;

[0085] The supply and demand forecasting module analyzes historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict the future demand and supply of used phones of different models and qualities. The forecast results and current inventory status are sent to staff to facilitate adjustments to inventory distribution, allocation, and used phone acquisitions.

[0086] The application service module converts the output results of the core analysis and decision-making module into specific operational instructions and information to realize business processing.

[0087] The specific operation is as follows: during the acquisition stage, staff members provide on-site assistance or users go to stores to submit data information about the second-hand mobile phones being sold. The data collection module in the acquisition phase works to collect the mobile phone model, storage, color information, user-described condition, and function self-test result information submitted by the user, and at the same time generates acquisition channel source, acquisition time, geographic location, and user portrait data information. At the same time, staff members collect the hardware functions, sensors, and battery health diagnosis data of the second-hand mobile phone, manually detected appearance defects, disassembly and repair traces, accessory status records, images and videos of the mobile phone appearance, screen display, function demonstration data, repair history query data, and serial number query data to determine whether it is a stolen phone and whether it is in warranty status, and upload it to the data collection module in the inspection and evaluation phase. If the mobile phone has damage problems, automatic inspection and evaluation will be carried out. The module will collect diagnostic data and images, and call the risk prediction module to generate an objective assessment report and recommended maintenance items. After the data collection is completed, it will be uploaded to the data processing and storage module through the multi-source data collection module. After the data cleaning and normalization module standardizes the data, the life cycle archive generation and update module will generate the life cycle archive of the mobile phone based on the data. At the same time, the multimodal feature extraction and fusion module will perform preliminary feature extraction on the used mobile phone based on the data. After obtaining the preliminary extracted features, the core analysis and decision-making module calls the dynamic value assessment module, combines historical transactions, market conditions and mobile phone status, generates a purchase reference price, and uploads it to the application service module. The application service module activates the intelligent acquisition module to display the quotation to the user. The user accepts the quotation, and the staff purchases the used mobile phone.

[0088] After the physical mobile phone enters the warehouse, the inventory logistics data collection module starts to collect the mobile phone's inventory location, entry and exit time, logistics information, and storage cost data. After the data cleaning and normalization module processes this data, it is uploaded to the life cycle archive generation and update module to update the life cycle archive. At the same time, the risk prediction module starts to predict the actual quality grade of the mobile phone and the potential hardware and functional failure risks based on diagnostic data, manual inspection records, images, video data, maintenance history, and historical failure rate data of similar mobile phones.

[0089] In addition, if the mobile phone is damaged, the repair and refurbishment management module will generate a repair plan and a bill of materials based on the data evaluation results of the risk prediction module. At the same time, based on the evaluation report and cost-benefit analysis of the operation efficiency analysis module, it will decide whether to repair or refurbish the second-hand mobile phone. If repair is determined, the system will generate a repair task. The repair and refurbishment link data collection module will record and collect the required parts list, parts cost, repair hours, repair personnel records, repair success rate, post-repair inspection results, and refurbishment cost data. After the repair is completed, the repair and refurbishment management module will track and update the repair results to the life cycle file of the second-hand mobile phone in real time. The data will be uploaded to the life cycle file generation and update module to update the life cycle file of the second-hand mobile phone.

[0090] After the preparation is completed, the mobile phone is ready to be put on the shelves for sale. The precision sales and marketing module will generate a detailed product description based on the detailed condition and maintenance history data in the life cycle archive of the mobile phone, and use user portraits and behavioral data to make personalized product recommendations and marketing push. The market macro data collection module will collect the market price of new mobile phones, quotations of competitors' second-hand mobile phones, industry reports, holidays, promotional activities data, regional economic data, and second-hand market supply and demand index data. At the same time, the dynamic value assessment module will evaluate the optimal reference price of the mobile phone in the acquisition and sales scenarios based on the life cycle archive and multimodal feature data of the condition, function, maintenance, and inventory of a single mobile phone, combined with real-time new market price, competitor price and supply and demand market data. The system collects the sales platform, sales time, sales price, buyer information, order status, browsing and purchasing behavior data, and user evaluation data through the sales link data collection module after the phone is sold, and updates the life cycle file of the phone. After receiving the phone, if the buyer has any questions, he can query the life cycle file of the phone through the customer service and after-sales module, which provides transparent warranty query, repair record query, after-sales service and processing of returns and exchanges. If the phone is returned or repaired, its life cycle file will be updated with the return reason and repair record, and it will re-enter the inspection, repair, and inventory process.

[0091] Furthermore, during use, the system uses the supply and demand forecasting module to analyze historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict future demand and supply for used phones of different models and conditions. The forecast results and current inventory status are sent to staff, facilitating adjustments to inventory distribution, allocation, and used phone acquisitions. The risk prediction module predicts the actual condition of a phone and the potential risk of hardware and functional failure based on diagnostic data, manual inspection records, images, video data, repair history, and historical failure rate data for similar phones. If the risk is too high, the acquisition will be stopped. Furthermore, during the acquisition process, if the risk identification and warning module detects abnormalities such as the sale of a large number of high-value phones in a short period of time, risky IMEI and serial number status, or abnormal repair records involving frequent replacement of high-value parts, it will issue a warning and automatically intercept the acquisition, halting the acquisition. Furthermore, during system operation, the operational efficiency analysis module analyzes the average time, personnel efficiency, and cost structure of inspection, repair, warehousing, and outbound operations. Upon identifying abnormal conditions, the operational efficiency analysis module will alert staff to ensure normal system operation.

[0092] It should be noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements that are inherent to such process, method, article or apparatus.

[0093] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A second-hand mobile phone purchase and sale operation life cycle management system based on big data, characterized in that the system include: Multi-source data collection module, used to collect data on the acquisition, inspection and evaluation, repair and renovation, inventory and logistics, sales, market macro data, and external risk data of the second-hand machine operation life cycle; The data processing and storage module is used to clean, convert, integrate and store the collected raw data, build core data archives, and includes: The lifecycle profile generation and update module creates a unique digital profile for each used phone in the system, which covers the entire process from the first contact with the system to the final sale; The multimodal feature extraction and fusion module extracts valuable features from different types of data, including the second-hand mobile phone's structural data, text descriptions, images, and diagnostic logs, and fuses these features from different modalities to form multimodal feature data. The core analysis and decision-making module conducts in-depth analysis and decision-making based on the data processed by the data processing and storage module and the life cycle archive of used devices, and includes: The dynamic value assessment module uses the lifecycle archive and multimodal feature data of a single mobile phone's condition, functions, maintenance, and inventory, combined with real-time new market prices, competitive product prices, and market data on supply and demand, to assess the optimal reference price range for the phone in acquisition and sales scenarios; The supply and demand forecasting module analyzes historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict the future demand and supply of used phones of different models and qualities. The forecast results and current inventory status are sent to staff to facilitate adjustments to inventory distribution, allocation, and used phone acquisitions. The application service module converts the output results of the core analysis and decision-making module into specific operational instructions and information to realize business processing.

2. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 1 is characterized in that: The multi-source data collection module includes an acquisition data collection module, a testing and evaluation data collection module, a repair and renovation data collection module, an inventory and logistics data collection module, a sales data collection module, a market macro data collection module, and an external risk data collection module; The acquisition phase data collection module collects the mobile phone model, storage, color information, user-described quality, function self-test results, acquisition channel source, acquisition time, geographic location, and user portrait data submitted by the user; The data acquisition module in the detection and evaluation phase collects the hardware functions, sensors, battery health diagnostic data, manually detected appearance defects, disassembly and repair traces, accessory status records, images and videos of the mobile phone's appearance, screen display, function demonstration data, maintenance history query data, and serial number query data of the second-hand mobile phone to determine whether it is a stolen phone and whether it is under warranty.

3. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 2 is characterized in that: The repair and renovation data collection module collects the parts list required for repair, parts cost, repair man-hours, repair personnel records, repair success rate, post-repair inspection results, and renovation cost data; The inventory logistics data collection module collects inventory location, inbound and outbound time, logistics information, and storage cost data; The sales link data collection module collects sales platform, sales time, sales price, buyer information, order status, browsing and purchasing behavior data, and user evaluation data; The market macro data collection module collects data on the market price of new mobile phones, quotations of competitors' second-hand mobile phones, industry reports, holidays, promotional activities, regional economic data, and second-hand market supply and demand index data; The external risk data collection module collects stolen machine databases, lists of untrustworthy persons, and third-party assessment reports.

4. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 1 is characterized in that: The data processing and storage module includes a data cleaning and normalization module, a life cycle archive generation and update module, a multimodal feature extraction and fusion module, and a data storage module; The data cleaning and normalization module processes noise, missing values, and format inconsistencies in the data, and standardizes the multi-source data collected by the multi-source data acquisition module; The life cycle archive generation and update module creates a unique digital archive for each used mobile phone in the system. This archive runs through the entire process of the mobile phone from its first contact with the system to its final sale, and collects and associates all data generated by the mobile phone in various links in real time to form a dynamic, continuous and complete individual data chain.

5. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 4 is characterized in that: The multimodal feature extraction and fusion module extracts valuable features from different types of data such as the second-hand mobile phone's own structural data, text descriptions, images, and diagnostic logs, and fuses these features from different modalities to form multimodal feature data; The data storage module stores the original data and processed data of the second-hand mobile phone, as well as the life cycle archive of the second-hand mobile phone.

6. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 1 is characterized in that: The core analysis and decision-making module includes a dynamic value assessment module, a risk prediction module, a supply and demand prediction module, a risk identification and early warning module, and an operational efficiency analysis module; The dynamic value assessment module uses the lifecycle archive and multimodal feature data of a single mobile phone, including its condition, functions, maintenance, and inventory, combined with real-time market price, competitive price, and supply and demand data, to assess the optimal reference price range for the mobile phone in acquisition and sales scenarios. The evaluation algorithm is as follows: Vmin≤V=Vbase·α·β·γ≤Vmax The basic value is based on the inherent attributes of the mobile phone, including model, configuration, condition, and functional status. The basic value is calculated by weighting the inherent attributes of the mobile phone. The formula is as follows: xi: the i-th attribute value; fi(xi): attribute normalization function; wi: attribute weight; maintenance and inventory decay factor (α): the negative impact of maintenance history and inventory time on value. The decay effect of maintenance and inventory time on value is modeled as an exponential function: α＝e Nrepair: number of repairs, λ1 is the maintenance attenuation coefficient; Tinventory: inventory time, λ2 is the inventory time decay coefficient; The algorithm is: if the mobile phone is repaired twice and has a 30-day inventory, then α=e -0.05×2-0.001×30 =and -0.1-0.03 ≈0.886 Market Dynamic Adjustment Factor (β): This factor is influenced by real-time market supply and demand, competitive product prices, and brand-new device prices. The market factor is determined by the supply-demand ratio, competitive product prices, and brand-new device prices. The formula is as follows: Pnew: real-time price of a new machine of the same model; Pcompetitor: average second-hand price of competing products; Sdemand / Ssupply: supply-demand ratio, δ is the supply-demand sensitivity coefficient; Risk correction factor (γ): A discount for external risks such as stolen devices and abnormal user behavior. The risk correction factor is dynamically adjusted based on external risk data. The formula is as follows: Rj: risk weight of category j; ηj: binary variable of risk event.

7. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 6 is characterized in that: The risk prediction module predicts the actual quality grade of the mobile phone and the potential hardware and functional failure risks based on diagnostic data, manual inspection records, images, video data, maintenance history, and historical failure rate data of similar mobile phones; The supply and demand forecasting module analyzes historical sales and acquisition data, market trends, seasonal factors, regional differences, and promotional activities to predict the future demand and supply of used phones of different models and qualities. The forecast results and current inventory status are sent to staff to facilitate adjustments to inventory distribution, allocation, and used phone acquisition. The demand forecasting algorithm is as follows: μm,c: the average basic demand for model and color combination based on historical sales data; γm,c: market long-term trend coefficient; ak,bk: seasonal Fourier series coefficients, T is the period; βm,c: the promotion coefficient of promotion activities on demand, Ipromo,t is the promotion flag (0 / 1); wm,c,r: The influence weight of region r on the demand for model and color combination, satisfying ∑ r wm,c,r=1; The risk identification and early warning module detects abnormalities in the purchase process, such as the sale of a large number of high-value mobile phones in a short period of time, risky IMEI and serial number conditions, and abnormal maintenance records due to frequent replacement of high-value parts, and issues early warnings and automatic interception. The operational efficiency analysis module analyzes the average time consumption, personnel efficiency, and cost structure of the inspection, maintenance, warehousing, and outbound processes, identifies abnormal conditions, and issues alerts to staff.

8. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 1 is characterized in that: The application service modules include automated detection and evaluation module, maintenance and renovation management module, precision sales and marketing module, and customer service and after-sales module; The automated detection and assessment module guides users and inspectors to perform standardized operations, automatically collects diagnostic data and images, and calls the risk prediction module to generate objective assessment reports and recommended maintenance items; The repair and refurbishment management module generates a repair plan and a bill of materials based on the evaluation results, tracks the repair progress and cost, and updates the repair results to the life cycle file of the used mobile phone in real time.

9. The big data-based second-hand mobile phone purchase and sale operation lifecycle management system according to claim 8 is characterized in that: The precision sales and marketing module generates detailed product descriptions based on the detailed condition and maintenance history data in the mobile phone's life cycle archive, and uses user portraits and behavior data to make personalized product recommendations and marketing push.

10. The second-hand mobile phone purchase and sale operation life cycle management system based on big data according to claim 8 is characterized in that: The customer service and after-sales module provides transparent warranty inquiry, maintenance record inquiry, after-sales service and returns and exchanges based on the life cycle file of the mobile phone.

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