Quality tracing and after-sales service system for online sales products

Abstract

The invention discloses a quality traceability and after-sales service system for online sales products, and relates to the technical field of product traceability, and the system comprises a data collection module which is used for obtaining target whole-process data of products from production, transportation to use; the data processing module is used for generating a verification two-dimensional code and sending the verification two-dimensional code to a user The quality health assessment module is used for acquiring a product model and generating a quality health report; the after-sales scheme generation module is used for generating a dynamic after-sales service scheme according to the quality health report; the quality tracing module is used for tracing the product quality to obtain a quality tracing result; an after-sales scheme adjustment module used for obtaining a final after-sales scheme; and the online product processing module is used for generating a product quality manual and binding the product quality manual with the online sales product. The method has the effects of improving the quality tracing efficiency, the after-sales service quality and the integrity and credibility in the data processing process.

Description

Technical Field

[0001] This application relates to the technical field of product traceability, and in particular to a quality traceability and after-sales service system for online sales products. Background Art

[0002] With the continuous improvement of people's living standards, consumers have higher and higher requirements for product quality. In order to further improve the production quality of products, realize the whole-process control of quality management, and conduct all-round monitoring from the entire life cycle of products, so as to trace product quality and provide after-sales service in a timely manner.

[0003] In the prior art, traditional product quality traceability and after-sales service usually rely on manual spot checks, or wait for consumers to discover quality problems, collect and process problems in the production process, and then deal with after-sales. During the production and transportation of products, a large amount of data will be generated, and these data are fragmented and of low credibility during the collection process. Only relying on manual methods requires a large amount of time and manpower, and the efficiency is very low. At the same time, the after-sales service of products cannot be completed in a timely and efficient manner, bringing a very poor experience to consumers. Summary of the Invention

[0004] The purpose of the present invention is to provide a quality traceability and after-sales service system for online sales products to solve the problems raised in the above background art.

[0005] This application provides a quality traceability and after-sales service system for online sales products, and the system includes: Data acquisition module: used to obtain the whole-process data of products from production, transportation to use, and clean the whole-process data to obtain target whole-process data; Data processing module: used to pack the target whole-process data into data blocks in chronological order, and write the data blocks into a preset private chain, and the private chain generates a verification QR code for the data blocks and sends it to the user; Quality health assessment module: used to obtain the product model, obtain the product performance according to the product model, obtain the user's usage environment data and usage behavior data, and generate a quality health report according to the product performance, the usage environment data and the usage behavior data; After-sales solution generation module: used to obtain the failure probability data of the product and the user's geographical location according to the quality health report, and generate a dynamic after-sales service solution based on the failure probability data and the user's geographical location; Quality traceability module: used to obtain product quality problem data, obtain the cause of the quality problem according to the quality problem data, and trace the product quality based on the cause of the quality problem and the full-process data to obtain a quality traceability result; After-sales solution adjustment module: used to generate a target after-sales solution according to the dynamic after-sales service solution and the quality traceability result, send it to the user, obtain the user satisfaction, and adjust the target after-sales solution according to the user satisfaction to obtain the final after-sales solution; Online product processing module: used to obtain the current online sales product information according to the product model, generate a product quality manual based on the online sales product information and the quality traceability result, and bind the product quality manual to the online sales product.

[0006] Preferably, the steps of data-packing the target full-process data into data blocks in chronological order, writing the data blocks into a preset private chain, and the private chain generating a verification QR code for the data blocks and sending it to the user are specifically as follows: Data-cut the full-process data in chronological order to obtain multiple data segments, and data-pack the multiple data segments to generate multiple groups of data blocks; Establish a private chain, connect the private chain to the production end, transportation end, and usage end, and write multiple groups of the data blocks into the private chain; The private chain evaluates the value of the data in each data block to obtain the data value of the data in each data block, and extracts the target data with the highest data value; Generate a target data set according to the target data in each data block, and generate a verification QR code based on the target data set and send it to the user.

[0007] Preferably, the steps of generating a quality health report according to the product performance, the usage environment data, and the usage behavior data are specifically as follows: Establish a quality health model, and input the product performance, the usage environment data, and the usage behavior data into the quality health model; The quality health model obtains the stable parameters of the product running under preset standard external conditions according to the product performance, and obtains the initial quality health of the product according to the stable parameters; Generate static external conditions based on the usage environment data, and obtain the environmental impact parameters of the product during operation according to the static external conditions; Generate dynamic external conditions based on the usage behavior data, and obtain the behavior impact parameters of the product during operation according to the dynamic external conditions; Modify the initial quality and health according to the environmental impact parameters and the behavior impact parameters to obtain the target quality and health, and generate a quality and health report based on the target quality and health.

[0008] Preferably, the steps of generating a dynamic after-sales service plan based on the failure probability data and the user's geographical location are specifically as follows: Based on the failure probability data, perform data analysis on the failure probability data to obtain the failure rate of each product during use; Generate a geographical center based on the user's geographical location, scan around the geographical center with a preset radius value as the scan radius to obtain the number of maintenance points and the locations of the maintenance points; Based on the number of maintenance points and the locations of the maintenance points, select the maintenance point closest to the user's geographical location as the target point; Obtain the daily after-sales service plan of the target point, and make dynamic adjustments based on the daily maintenance plan to generate a dynamic after-sales service plan.

[0009] Preferably, before the step of performing data analysis on the failure probability data based on the failure probability data to obtain the failure rate of each product during use, it further includes: Based on the quality and health report, extract the failure data of the product during use; According to the failure data, extract the failure types of the product, identify the failure types, and compare them with the preset software failures, hardware minor failures, and major defects respectively; If the failure type is the software failure, communicate with the user and provide remote guidance; If the failure type is the major defect, return the product to the factory for processing; If the failure type is the hardware minor failure, arrange maintenance personnel to repair the product for the user.

[0010] Preferably, the steps of obtaining product quality problem data, obtaining the reasons for quality problems according to the quality problem data, and tracing the product quality based on the reasons for quality problems and the whole-process data to obtain the quality tracing result are specifically as follows: Obtain the quality problem data of the product, locate the failure points of the product according to the quality problem data to obtain quality failure data; According to the quality failure data, trace the failure sources of the failure points of the product to obtain the failure reasons, and generate the reasons for quality problems according to the failure reasons; Trace according to the reasons for quality problems in the whole-process data to obtain the target process that causes quality problems; Generate quality problem points based on the causes of the quality problems, compare them in the target process according to the quality problem points to obtain quality problem steps, and generate quality traceability results according to the quality problem steps.

[0011] Preferably, the steps of generating a target after-sales plan according to the dynamic after-sales service plan and the quality traceability result, sending it to the user to obtain user satisfaction, and adjusting the target after-sales plan according to the user satisfaction to obtain the final after-sales plan are specifically as follows: Modify the dynamic after-sales plan according to the quality traceability result to generate a target after-sales plan; Send the target after-sales plan to the user, and the user gives user satisfaction regarding the target after-sales plan; Extract the content to be adjusted that the user deems dissatisfied according to the user satisfaction, communicate with the user according to the content to be adjusted, and finalize the final content; Modify the target after-sales plan according to the final content to obtain the final after-sales plan.

[0012] Preferably, the steps of obtaining current online sales product information according to the product model, generating a product quality manual based on the online sales product information and the quality traceability result, and binding the product quality manual to the online sales product are specifically as follows: Obtain the product batches with quality problems according to the product model and the target process, and collect the online sales product information of the products currently on sale according to the product batches; Generate precautions during the use of the product and / or quality information of the product itself according to the quality traceability result; Add the precautions and / or the quality information to the online sales product information to generate a product quality manual, and bind the product quality manual to the online sales product.

[0013] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining the full-process data of product production, transportation, and use, then segmenting and packaging the full-process data in chronological order to generate data blocks, writing them into a preset private blockchain, generating a verification QR code and sending it to the user. Then obtain the product model, get the product performance of each product according to the product model, as well as the usage environment data and usage behavior data of each user using the product, generate a quality health report, and obtain the failure probability data and user geographical location of the product according to the quality health report to generate a dynamic after-sales service plan. Obtain the quality problem data of the product, conduct quality traceability in the full-process data to obtain the quality traceability result, and adjust the dynamic after-sales service plan according to the quality traceability result to obtain the final after-sales plan for the user. Specifically, according to the product model and the quality traceability result, generate a quality manual for each product being sold, and then bind the quality manual to the product. This improves the efficiency of quality traceability and the quality of after-sales service, as well as the integrity and credibility in the data processing process. Brief Description of the Drawings

[0014] Figure 1 It is a block diagram of a quality traceability and after-sales service system for online sales products provided by an embodiment of the present application.

[0015] Description of the reference numerals: 1. Data acquisition module; 2. Data processing module; 3. Quality health assessment module; 4. After-sales service plan generation module; 5. Quality traceability module; 6. After-sales service plan adjustment module; 7. Online product processing module. Detailed Embodiment

[0016] The following is a further detailed description of the present application in conjunction with Figure 1 This application, but the implementation manner of the present invention is not limited thereto.

[0017] An embodiment of the present application discloses a quality traceability and after-sales service system for online sales products.

[0018] In this embodiment, a quality traceability and after-sales service system for online sales products includes: Data acquisition module 1: used to obtain the full-process data of the product from production, transportation to use, and perform data cleaning on the full-process data to obtain the target full-process data; Data processing module 2: used to package the target full-process data into data blocks in chronological order, and write the data blocks into a preset private blockchain. The private blockchain generates a verification QR code from the data blocks and sends it to the user; Quality health assessment module 3: used to obtain the product model, obtain the product performance according to the product model, obtain the usage environment data and usage behavior data of the user, and generate a quality health report according to the product performance, usage environment data, and usage behavior data; After-sales solution generation module 4: It is used to obtain the failure probability data of the product and the user's geographical location according to the quality health report, and generate a dynamic after-sales service solution based on the failure probability data and the user's geographical location; Quality traceability module 5: It is used to obtain product quality problem data, obtain the reasons for quality problems according to the quality problem data, and trace the product quality according to the reasons for quality problems and the whole-process data to obtain the quality traceability result; After-sales solution adjustment module 6: It is used to generate a target after-sales solution according to the dynamic after-sales service solution and the quality traceability result and send it to the user to obtain the user satisfaction, and adjust the target after-sales solution according to the user satisfaction to obtain the final after-sales solution; Online product processing module 7: It is used to obtain the current online sales product information according to the product model, generate a product quality manual based on the online sales product information and the quality traceability result, and bind the product quality manual to the online sales product.

[0019] It should be noted that the above modules are only the basic modules of this embodiment. In the specific implementation process, without affecting the overall implementation effect, some modules can be appropriately added, reduced or modified.

[0020] The steps of data-packing the target whole-process data into data blocks in chronological order and writing the data blocks into a preset private chain, and the private chain generating a verification QR code for the data blocks and sending it to the user are specifically as follows: Cut the whole-process data in chronological order to obtain multiple data segments, and data-pack the multiple data segments to generate multiple groups of data blocks; Establish a private chain, connect the private chain with the production end, the transportation end and the usage end, and write the multiple groups of data blocks into the private chain; The private chain evaluates the value of the data in each data block to obtain the data value of the data in each data block, and extracts the target data with the highest data value; Generate a target data set according to the target data in each data block, and generate a verification QR code based on the target data set and send it to the user.

[0021] In operation, taking a certain model of intelligent air purifier (model KJ-800F) as an example, its full-process data includes component inspection records in the production stage (such as the passing rate of HEPA filter seal test is 99.8%), temperature and humidity monitoring data in the transportation stage (the internal temperature of the logistics box is stable at 15-25°C, and the humidity ≤ 60%RH), and user operation data in the use stage (the average daily purification duration is 6 hours). The data processing module cuts the full-process data into production batches (such as batch number #2403 in 2024Q1), transportation batches (logistics order number YZ-20240315), and usage cycles (data of user D's cumulative usage of 90 days) in chronological order, and packs them into 3 groups of data blocks. The private chain nodes are respectively connected to the production end (the factory MES system records the motor assembly torque value of 12.5 N·m ± 0.2), the transportation end (cold chain logistics GPS positioning data), and the use end (the remaining filter life recorded by the APP is 23%). The private chain uses a data value evaluation algorithm to identify that the motor performance test data in the production link (value score 98 points) is higher than the transportation vibration data (value score 75 points), extracts high-value data to generate a target data set. Finally, the data set is encoded into a verification QR code (such as QR-KJ800F-2403). After scanning by the user, they can view the filter production batch inspection report, the alarm record of unpacking during transportation, and the cumulative purification amount (1.2×10 6 μg).

[0022] The steps to generate a quality and health report based on product performance, usage environment data, and usage behavior data are specifically as follows: Establish a quality and health model, and input product performance, usage environment data, and usage behavior data into the quality and health model; Based on the product performance, the quality and health model obtains the stable parameters of the product running under preset standard external conditions, and obtains the initial quality and health of the product according to the stable parameters; Based on the usage environment data, generate static external conditions, and obtain the environmental impact parameters of the product during operation according to the static external conditions; Based on the usage behavior data, generate dynamic external conditions, and obtain the behavior impact parameters of the product during operation according to the dynamic external conditions; Modify the initial quality and health according to the environmental impact parameters and behavior impact parameters to obtain the target quality and health, and generate a quality and health report according to the target quality and health.

[0023] In operation, taking a certain model of smart air purifier (model KJ-800F) as an example, for the smart air purifier of user D, the quality and health model inputs product performance parameters (rated CADR value of 800 m³ / h), usage environment data (bedroom area of 25 ㎡, PM2.5 baseline value of 75 μg / m³), and usage behavior data (the sleep mode is turned on for 8 hours every night). The model calculates that the filter life under standard conditions (PM2.5 = 35 μg / m³) is 12 months (initial health value). Static environment analysis shows that a high PM2.5 environment increases the filter load by 40%, and dynamic behavior analysis finds that frequent start-stop leads to an 18% increase in the wear rate of the motor bearings. After comprehensive correction, the target health value drops to 8.5 months, and a report is generated to prompt "It is recommended to clean the pre-filter every two weeks and start the Turbo mode when PM2.5 > 100". The report is synchronously pushed to the user APP, along with a link to a filter replacement guidance video and the contact information of local service providers.

[0024] Steps to generate a dynamic after-sales service plan based on failure probability data and user location are as follows: Based on the failure probability data, perform data analysis on the failure probability data to obtain the failure rate of each product during use; Generate a geographical center based on the user location, scan around the geographical center with a preset radius value as the scan radius, and obtain the number of repair outlets and the locations of repair outlets; Based on the number of repair outlets and their locations, select the repair outlet closest to the user location as the target outlet; Obtain the daily after-sales plan of the target outlet, and make dynamic adjustments based on the daily repair plan to generate a dynamic after-sales service plan.

[0025] In operation, taking a certain model of smart air purifier (model KJ-800F) as an example, the system detects that the failure probability in the area where user D is located (Pudong New Area, Shanghai) is 15%. Taking the user's address (longitude 121.5°E, latitude 31.2°N) as the center, 8 authorized outlets within a scan radius of 30 kilometers are scanned. The Lujiazui Service Center, which is the closest (straight-line distance of 2.3 kilometers), is preferentially selected to obtain its daily plan (48-hour response + free detection). Combining with the motor abnormal noise warning in the quality and health report (failure probability score of 82 / 100), it is dynamically adjusted to "priority on-site visit within 12 hours + provision of a standby machine". After the user APP receives the plan, the user can select a reservation time slot from 9 to 11 am the next day, and the system automatically generates an electronic work order with a service code (SV-20240517-0032) and synchronizes it to the repair station.

[0026] Before the step of performing data analysis on the failure probability data based on the failure probability data to obtain the failure rate of each product during use, it also includes: Extract the fault data of the product during use based on the quality health report; According to the fault data, extract the fault types of the product, identify the fault types, and compare them with the preset software faults, minor hardware faults, and major defects respectively; If the fault type is a software fault, communicate with the user and provide remote guidance; If the fault type is a major defect, return the product to the factory for processing; If the fault type is a minor hardware fault, arrange maintenance personnel to repair the product for the user.

[0027] During operation, take a certain model of intelligent air purifier (model KJ-800F) as an example. User D reported that the device had an error of "filter reset failed". The system extracted the fault data as the RFID recognition failure rate of 100% (the standard should be ≤5%). The fault type recognition module compared the preset classification: the software fault (there is a communication protocol conflict in the firmware version V2.3.1) triggered the remote repair process. The engineer pushed a hot fix patch (V2.3.1-patch01), and the fault was resolved after the user restarted according to the instructions. If it is a hardware fault (such as abnormal motor current fluctuation of ±15%), arrange an engineer to replace the motor of the same model (model M-800-24V) on site. For major defects (such as the circuit board burning and smoking), the system automatically generates a return factory form (return factory code RT-20240517-005), and SF Express picks up the product on site and sends it to the Shenzhen factory for X-ray flaw detection.

[0028] The steps to obtain the product quality problem data, obtain the quality problem causes based on the quality problem data, and trace the product quality according to the quality problem causes and the whole process data to obtain the quality traceability result are as follows: Obtain the quality problem data of the product, locate the fault points of the product according to the quality problem data, and obtain the quality fault data; According to the quality fault data, trace the fault sources of the product's fault points to obtain the fault causes, and generate quality problem causes according to the fault causes; Trace according to the quality problem causes in the whole process data to obtain the target process where the quality problem occurs; Generate quality problem points according to the quality problem causes, compare them in the target process according to the quality problem points to obtain the quality problem steps, and generate the quality traceability result according to the quality problem steps.

[0029] In operation, taking a certain model of smart air purifier (model KJ-800F) as an example, trace the "sensor drift" problem of the smart air purifier in batch #2403, and locate the fault point as the calibration deviation of the PM2.5 sensor (measured error +20%). The full-process data shows that the sensor calibration record in the production link is "single calibration" (the standard requires three cross-calibrations), and the peak vibration acceleration in the transportation link reaches 5.2G (exceeding the safety threshold of 4G). The system marks the "sensor quality inspection workstation" in the production process as the problem step and generates a traceability report "missing calibration process leads to cumulative error". Accordingly, update the production line to a three-calibration process and push an OTA calibration instruction to 200 devices of the same batch.

[0030] According to the dynamic after-sales service plan and the quality traceability results, generate a target after-sales plan and send it to the user to obtain the user satisfaction. Adjust the target after-sales plan according to the user satisfaction to obtain the final after-sales plan. The specific steps are as follows: Modify the dynamic after-sales service plan according to the quality traceability results to generate a target after-sales plan; Send the target after-sales plan to the user, and the user gives the user satisfaction for the target after-sales plan; According to the user satisfaction, extract the content to be adjusted that the user is dissatisfied with, communicate with the user according to the content to be adjusted, and finalize the final content; Modify the target after-sales plan according to the final content to obtain the final after-sales plan.

[0031] In operation, taking a certain model of smart air purifier (model KJ-800F) as an example, user D complains about "excessive night noise", and the initial plan is "in-store inspection". The quality traceability shows that the noise reduction coefficient of the sound insulation cotton supplier (batch #SN-202312) does not meet the standard (measured NRC = 0.75, requirement ≥0.85), and it is dynamically adjusted to "free upgrade to double-layer sound insulation cotton + compensate for a silent filter". The user feedback hopes to keep the original appearance color scheme, and the customer service adds "optional silver / white sound insulation cover" and coordinates for a 2-hour express delivery from the local warehouse. The final plan is integrated into "personalized sound insulation components + lifetime noise warranty", and the silencing test report (night mode noise drops from 45dB to 38dB) is uploaded synchronously when the work order is closed.

[0032] According to the product model, obtain the current online sales product information. Based on the online sales product information and the quality traceability results, generate a product quality manual and bind the product quality manual to the online sales product. The specific steps are as follows: According to the product model and the target process, obtain the product batches with quality problems, and collect the online sales product information of the products currently on sale according to the product batches; Generate precautions for the product during use and / or quality information of the product itself based on the quality traceability results; Precautions and / or quality information are added to the product information sold online, a product quality manual is generated, and the product quality manual is bound to the product sold online.

[0033] In use, taking a certain model of smart air purifier (model KJ-800F) as an example, in response to the WiFi disconnection problem of batch #2403, the online product processing module captured 83 products of the same batch on sale on the e-commerce platform (JD warehouse inventory numbers #JDL-2403-01 to 83). According to the traceability results (antenna welding defect rate 8.7%), the manual added a new chapter "Check the antenna contacts before the first networking" and bound it to the "Expert Guide" column on the product details page. When consumers scan the code to view, the page simultaneously displays the "Passed 72-hour stress test (updated standard)" certification mark. The warehouse retested the antenna impedance of unsold equipment (qualified value ≤0.5Ω), and reworked the three devices that exceeded the standard and put them back on the shelves.

[0034] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An online product sales quality traceability and after-sales service system, characterized in that, Including: Data acquisition module: used to obtain the whole-process data of the product from production, transportation to use, clean the whole-process data, and obtain the target whole-process data; Data processing module: used to pack the target whole-process data into data blocks in chronological order, and write the data blocks into a preset private chain. The private chain generates a verification QR code for the data blocks and sends it to the user; Quality and health assessment module: used to obtain the product model, obtain the product performance according to the product model, obtain the user's usage environment data and usage behavior data, and generate a quality and health report according to the product performance, the usage environment data and the usage behavior data; After-sales solution generation module: used to obtain the product failure probability data and the user's geographical location according to the quality and health report, and generate a dynamic after-sales service solution based on the failure probability data and the user's geographical location; Quality traceability module: used to obtain the product quality problem data, obtain the cause of the quality problem according to the quality problem data, and trace the product quality according to the cause of the quality problem and the whole-process data to obtain the quality traceability result; After-sales solution adjustment module: used to generate a target after-sales solution according to the dynamic after-sales service solution and the quality traceability result and send it to the user, obtain the user satisfaction, and adjust the target after-sales solution according to the user satisfaction to obtain the final after-sales solution; Online product processing module: used to obtain the current online sales product information according to the product model, generate a product quality manual based on the online sales product information and the quality traceability result, and bind the product quality manual to the online sales product.

2. The quality traceability and after-sales service system for online sales products according to claim 1, wherein The step of packing the target whole-process data into data blocks in chronological order and writing the data blocks into a preset private chain. The private chain generates a verification QR code for the data blocks and sends it to the user is specifically as follows: Cut the whole-process data in chronological order to obtain multiple data segments, and pack the multiple data segments to generate multiple groups of data blocks; Establish a private chain, connect the private chain to the production end, transportation end and use end, and write multiple groups of data blocks into the private chain; The private chain evaluates the value of the data in each data block to obtain the data value of the data in each data block, and extracts the target data with the highest data value; Generate a target data set according to the target data in each data block, and generate a verification QR code based on the target data set and send it to the user.

3. An online sales product quality traceability and after-sales service system according to claim 1, characterized in that, The step of generating a quality and health report according to the product performance, the usage environment data and the usage behavior data is specifically as follows: Establish a quality and health model, and input the product performance, the usage environment data and the usage behavior data into the quality and health model; The quality and health model obtains the stable parameters of the product running under preset standard external conditions according to the product performance, and obtains the initial quality and health of the product according to the stable parameters. Generate static external conditions based on the usage environment data, and obtain the environmental impact parameters of the product during operation according to the static external conditions; Generate dynamic external conditions based on the usage behavior data, and obtain the behavior impact parameters of the product during operation according to the dynamic external conditions; Correct the initial quality and health based on the environmental impact parameters and the behavior impact parameters to obtain the target quality and health, and generate a quality and health report according to the target quality and health.

4. An online product sales quality traceability and after-sales service system according to claim 3, characterized in that, The steps of generating a dynamic after-sales service plan based on the failure probability data and the user's geographical location are specifically as follows: Based on the failure probability data, perform data analysis on the failure probability data to obtain the failure rate of each product during use; Generate a geographical center based on the user's geographical location, scan around according to the geographical center, and the scanning radius is a preset radius value to obtain the number of maintenance points and the locations of the maintenance points; Based on the number of maintenance points and the locations of the maintenance points, select the maintenance point closest to the user's geographical location as the target point; Obtain the daily after-sales plan of the target point, and make dynamic adjustments based on the daily maintenance plan to generate a dynamic after-sales service plan.

5. An online sales product quality traceability and after-sales service system according to claim 4, characterized in that, Before the step of performing data analysis on the failure probability data based on the failure probability data to obtain the failure rate of each product during use, it further includes: Extract the failure data of the product during use based on the quality and health report; According to the failure data, extract the failure types of the product, and identify the failure types, and compare them with the preset software failures, hardware minor failures, and major defects respectively; If the failure type is the software failure, communicate with the user and provide remote guidance; If the failure type is the major defect, return the product to the factory for processing; If the failure type is the hardware minor failure, arrange maintenance personnel to repair the user.

6. An online sales product quality traceability and after-sales service system according to claim 5, characterized in that, The steps of obtaining the product quality problem data, obtaining the cause of the quality problem according to the quality problem data, and tracing the product quality according to the cause of the quality problem and the full-process data to obtain the quality tracing result are specifically as follows: Obtain the quality problem data of the product, and locate the failure point of the product according to the quality problem data to obtain the quality failure data; According to the quality failure data, trace the failure source of the failure point of the product to obtain the cause of the failure, and generate the cause of the quality problem according to the cause of the failure; Trace according to the cause of the quality problem in the full-process data to obtain the target process where the quality problem occurs; Generate a quality problem point according to the cause of the quality problem, compare it in the target process according to the quality problem point to obtain the quality problem step, and generate a quality tracing result according to the quality problem step.

7. An online sales product quality traceability and after-sales service system according to claim 6, characterized in that, The steps of generating a target after-sales plan according to the dynamic after-sales service plan and the quality tracing result, sending it to the user to obtain the user satisfaction, and adjusting the target after-sales plan according to the user satisfaction to obtain the final after-sales plan are specifically as follows: Modify the dynamic after-sales plan according to the quality traceability result to generate a target after-sales plan; Send the target after-sales plan to the user, and the user gives the user satisfaction with respect to the target after-sales plan; Extract the content to be adjusted that the user is dissatisfied with according to the user satisfaction, communicate with the user according to the content to be adjusted, and finalize the final content; Modify the target after-sales plan according to the final content to obtain the final after-sales plan.

8. The quality traceability and after-sales service system for online sales products according to claim 6, characterized in that, The steps of obtaining the current online sales product information according to the product model, generating a product quality manual based on the online sales product information and the quality traceability result, and binding the product quality manual to the online sales product are specifically as follows: Obtain the product batches with quality problems according to the product model and the target process, and collect the online sales product information of the currently sold products according to the product batches; Generate the precautions during the use of the product and / or the quality information of the product itself according to the quality traceability result; Add the precautions and / or the quality information to the online sales product information to generate a product quality manual, and bind the product quality manual to the online sales product.

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