After-sales service processing method, intelligent terminal and storage medium based on big data

Abstract

The present invention discloses an after-sales service processing method based on big data, an intelligent terminal and a storage medium, the method comprising: obtaining the original problem data sent by the user, and pre-processing the original problem data to generate target problem data; according to the target problem data, determining the associated problem data corresponding to the target problem data, and sending it to the corresponding after-sales platform; when the after-sales data fed back by the after-sales platform is detected, judging whether the equipment failure corresponding to the target problem data is not resolved; if the equipment failure in the target problem data is not resolved, sending the after-sales data to the research and development platform; when the diagnostic data fed back by the research and development platform is detected, sending the diagnostic data to the corresponding after-sales platform, and saving it as historical problem data. The present invention can update the solution to the equipment failure in real time, thereby speeding up the processing efficiency of the user's after-sales problems.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an after-sales service processing method based on big data, an intelligent terminal and a storage medium. Background Art

[0002] Due to the vast territory and large population in China, when a product has a problem and needs service from the manufacturer, the customer usually reports the problem first, and the local after-sales point or outsourced after-sales point sends someone to the customer's home or the user takes the product to the customer's home to determine the problem. If the after-sales point cannot solve the problem, the after-sales point will report the problem to the manufacturer, and the manufacturer will solve the problem based on the faulty product. In addition, due to the regionality and uncertainty of the distribution of after-sales points, there is no shared platform for information sharing, and some of the same common problems may take a lot of repeated time to solve.

[0003] Generally, several rounds of trial production and testing are carried out before mass production of new equipment. The testing methods are generally subjective testing by testers, testing according to test cases, and automatic stress testing. However, in the actual use process of users, there are differences between the use habits and the tester's methods or test cases, which leads to some problems that are only discovered during the user's use. And after after-sales problems occur, due to the uneven quality of the outsourced after-sales team, and the fact that roughly the same external problem phenomenon may be caused by different internal reasons, in many cases, after-sales technicians cannot solve the problem quickly and effectively when they visit the site, and they also need to spend a lot of resources to communicate with the manufacturer to locate the problem. Therefore, the current after-sales service has problems such as long time consumption, low efficiency and high cost in diagnosing problems and determining solutions. Summary of the invention

[0004] The main purpose of the present invention is to provide an after-sales service processing method, an intelligent terminal and a storage medium based on big data, aiming to solve the problem of low efficiency in handling product failures in the after-sales process in the prior art.

[0005] To achieve the above object, the present invention provides an after-sales service processing method based on big data, and the after-sales service processing method based on big data comprises the following steps:

[0006] Acquire original question data sent by the user, and perform keyword extraction processing on the original question data to generate target question data, wherein the target question data includes device information and address information;

[0007] Determine, according to the target question data, associated question data corresponding to the target question data;

[0008] According to the address information, the target problem data and the associated problem data are sent to a corresponding after-sales platform;

[0009] When after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, determining whether the equipment failure corresponding to the target problem data is unresolved;

[0010] If the equipment failure in the target problem data is not resolved, the after-sales data is sent to the R&D platform;

[0011] When the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data.

[0012] Optionally, in the after-sales service processing method based on big data, the types of the original question data include text type and voice type, and the obtaining of the original question data sent by the user and performing keyword extraction processing on the original question data to generate target question data specifically includes:

[0013] Get the original question data sent by the user;

[0014] When the type of the original question data is text type, extracting keywords from the original data according to a preset keyword extraction rule to generate target question data;

[0015] When the type of the original question data is voice type, the voice type original question data is converted into text type original question data, and according to the keyword extraction rule, keywords in the converted original data are extracted to generate target question data.

[0016] Optionally, the after-sales service processing method based on big data, wherein the obtaining of original question data sent by the user and performing keyword extraction processing on the original question data to generate target question data, further comprises:

[0017] Performing desensitization processing on the target problem data to generate desensitized data;

[0018] The desensitized data is encrypted, and the encrypted desensitized data is used as updated target question data.

[0019] Optionally, the after-sales service processing method based on big data, wherein the step of determining, according to the target problem data, the associated problem data corresponding to the target problem data, specifically includes:

[0020] Determine, according to the device information, a target question database corresponding to the target question data in a preset question database;

[0021] According to the fault words in the keywords, corresponding associated problem data in the target problem database is determined.

[0022] Optionally, the after-sales service processing method based on big data, wherein the step of determining the corresponding associated problem data in the target problem database according to the fault words in the keywords, specifically includes:

[0023] Calculating the matching degree between the fault words in the keywords and each historical question data in the target question database;

[0024] According to the matching degree, associated question data corresponding to the target question data in the historical question data is determined.

[0025] Optionally, the after-sales service processing method based on big data, wherein, when the after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, after determining whether the equipment failure corresponding to the target problem data is unresolved, further comprises:

[0026] If the equipment failure corresponding to the target problem data has been resolved, the after-sales data is saved as historical problem data.

[0027] Optionally, in the after-sales service processing method based on big data, wherein the diagnostic data includes hardware problem data, when the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, further comprising:

[0028] Determine corresponding material information according to the component information in the hardware problem data;

[0029] Generate a component report corresponding to the component name according to the material information.

[0030] Optionally, in the after-sales service processing method based on big data, wherein the diagnostic data includes software upgrade data, when the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, further comprising:

[0031] Determining software information corresponding to the software upgrade data according to the device information;

[0032] The software upgrade data is pushed to the candidate device corresponding to the software information, so that the candidate device can perform software upgrade according to the software upgrade data.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides an intelligent terminal, wherein the intelligent terminal includes: a memory, a processor, and an after-sales service processing program based on big data stored on the memory and executable on the processor, and when the after-sales service processing program based on big data is executed by the processor, the steps of the after-sales service processing method based on big data as described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, wherein the storage medium stores an after-sales service processing program based on big data, and when the after-sales service processing program based on big data is executed by the processor, the steps of the after-sales service processing method based on big data as described above are implemented.

[0035] The present invention provides an after-sales service processing method based on big data, in which the user first sends the original problem data containing the equipment information to the server, and the server determines the associated problem data associated with the original problem data in the pre-saved historical problem data, and then sends it to the after-sales platform. If the subsequent after-sales platform can solve the problem, the solved fault and solution are sent to the server, and the server updates the historical problem data. If it cannot be solved, the server sends it to the corresponding R&D platform. After the R&D platform determines the solution, the fault and solution are sent to the server as diagnostic data, so as to be sent to the after-sales platform through the server. The after-sales platform then solves the equipment fault according to the solution and feeds back to the server. Through the information cycle of the after-sales platform and the R&D platform mediated by the server, the amount of historical problem data stored in the server is continuously increased. If similar problems occur later, the server can help after-sales personnel quickly locate solutions, thereby improving the efficiency of solving after-sales problems.

[0036] In addition, based on the hardware problem data in the diagnostic data, the server can generate a component report sheet, which lists the information of the faulty components and the corresponding supplier information. Therefore, subsequent purchasing and R&D personnel can improve the quality of component procurement and subsequent R&D direction based on the component report sheet. And if there are high-frequency faulty components in the subsequent new model projects, multiple rounds of testing can be arranged before mass production to focus on testing and analyzing the faulty components. If the fault is a software problem, the diagnostic data also includes software upgrade information. The server can push the software upgrade data to devices installed with the same software for these devices to perform software upgrades, thereby avoiding subsequent after-sales. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of an embodiment of the after-sales service processing method based on big data provided by the present invention;

[0038] Figure 2It is a schematic diagram of the entire method execution process in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0039] Figure 3 It is a flow chart of interaction between different platforms in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0040] Figure 4 It is a flowchart of step S100 in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0041] Figure 5 It is a flow chart of processing different types of original problem data in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0042] Figure 6 It is a flowchart of the steps after step S100 in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0043] Figure 7 It is a flow chart of step S200 in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0044] Figure 8 It is a flowchart of step S220 in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0045] Fig. 9 It is a flowchart of hardware problem data processing in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0046] Fig.10 It is a flowchart of software upgrade data processing in a preferred embodiment of the after-sales service processing method based on big data of the present invention;

[0047] Fig.11 Schematic diagram of the operating environment of a preferred embodiment of the intelligent terminal of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] The after-sales service processing method based on big data described in the preferred embodiment of the present invention is described in detail in Figure 1 , Figure 2 and Figure 3 , the after-sales service processing method based on big data includes the following steps:

[0050] Step S100, obtaining original question data sent by the user, and performing keyword extraction processing on the original question data to generate target question data, wherein the target question data includes device information and address information.

[0051] In this embodiment, the execution subject is an after-sales service processing program based on big data installed on the cloud server. When a user encounters a problem when using a product, the user can provide feedback on the problem through a web page or a telephone. Taking a web page as an example, the user can select problem feedback on the official website, and then describe the fault in the problem feedback column in the form of text, pictures, videos, etc. The client corresponding to the web page packages this information and uploads it to the cloud server as an information data packet. In addition, users can also feedback problems by phone. When a user calls to feedback a problem, the voice recognition program first converts the user's voice into text, and then extracts keywords from the text, such as address, product model, problems, etc. Finally, these keywords are packaged and sent to the cloud server as an information data packet.

[0052] For further information, see Figure 4 and Figure 5 The types of the original question data include text type and voice type, and step S100 includes:

[0053] Step S111, obtaining original question data sent by the user.

[0054] Specifically, the user first sends original problem data to the cloud server via a web page or phone. The original problem data includes the model of the failed device, the purchase date, the serial number, the user's address, contact information, a description of the device failure, etc.

[0055] Step S112, when the type of the original question data is text type, extract keywords from the original data according to preset keyword extraction rules to generate target question data.

[0056] Specifically, since the information in the original question data is very complex, in addition to useful information, there may also be irrelevant information, so it is necessary to perform an extraction process on the original question to remove useless information. In this embodiment, a keyword extraction method is adopted, and a keyword extraction rule can be pre-set. It is worth noting that the keywords referred to in this embodiment do not only refer to single words, but also sentences containing keywords. The keywords extracted by the keyword extraction rule are the user's name, address, device model, and words or sentences describing the fault. The keyword extraction rule can be implemented by regular expressions, preset keyword text libraries, etc.

[0057] Since the types of the original question data include text type and voice type, most of the data extraction and processing are currently implemented on the basis of text. Therefore, when it is text type, the keywords in the original data can be extracted according to the preset keyword extraction rules to generate keyword data. In this embodiment, the keyword extraction rules are briefly described based on regular expressions. Regular expressions are a string matching pattern used to check whether a string contains a certain substring and replace or extract the matching substring. The original question data can be a string processed by a regular expression. A regular expression is set to extract the text between "Name:" and "," to obtain the user's name. All extracted keywords are summarized to obtain keyword data.

[0058] Step S113, when the type of the original question data is voice type, convert the voice type original question data into text type original question data, and extract keywords from the converted original data according to the keyword extraction rule to generate target question data.

[0059] Specifically, when the type of the original question data is voice type, the voice type original question data is first converted into text type through voice recognition technology, and then the above keyword extraction process is performed to generate keyword data.

[0060] Further, see Figure 6 , after step 100, including:

[0061] Step S121, desensitizing the target question data to generate desensitized data.

[0062] Specifically, desensitizing data means deforming the data through certain algorithmic rules to achieve reliable protection of sensitive or private data, such as randomly transforming a segment of characters into another segment of characters. Since some user information, such as telephone numbers and addresses, is personal privacy, in order to ensure that the user's information is not leaked, the after-sales service processing program based on big data needs to desensitize the keywords related to personal information in the keyword data to obtain desensitized data.

[0063] Step S122, encrypt the desensitized data, and use the encrypted desensitized data as updated target question data.

[0064] Specifically, during the data transmission and processing, there may be external intrusion and tampering, but the target problem data itself does not need to be modified. Therefore, in this solution, the after-sales service processing program based on big data encrypts the desensitized data and uses the encrypted desensitized data as the updated target problem data, thereby improving the security of the target problem data. Since data encryption itself is a common method, it will not be described in detail here.

[0065] Step S200: determining, according to the target question data, associated question data corresponding to the target question data.

[0066] The cloud server stores a problem database, which stores a plurality of historical problem data, including data on fault problems and solutions reported by the after-sales platform. According to the keywords in the target problem data, the historical problem data may contain historical problem data that contains solutions to the equipment faults corresponding to the target problem data, and the historical problem data may be used as the associated problem data corresponding to the target problem data.

[0067] For further information, see Figure 7 , step S200 includes:

[0068] Step S210: determining a target question database corresponding to the target question data in a preset question database according to the device information.

[0069] Specifically, there are multiple problem databases stored in the cloud server, and the problem databases are divided based on different device information, and the device information includes device model, device number, device type, etc. For example, the historical problem data stored in the target problem database A all come from the device with device number A. Therefore, according to the device information, the target problem database corresponding to the target problem data can be determined in the problem database.

[0070] Step S220: determining corresponding associated problem data in the target problem database according to the fault words in the keywords.

[0071] Specifically, in addition to the user's personal information, the keywords also include fault words describing the equipment fault. After determining the corresponding target problem database, the corresponding associated problem data is determined based on the fault words. For example, if a fault word is "cannot boot", all historical problem data in the target problem database are retrieved, and historical problem data containing the word "cannot boot" are selected and used as associated problem data.

[0072] For further information, see Figure 8 , step S220 includes:

[0073] Step S221, calculating the matching degree between the fault words in the keywords and each historical question data in the target question database.

[0074] Specifically, the fault words in the keywords are matched with the historical problem data in the corresponding target problem database, and the corresponding matching degree is calculated. The matching degree can be calculated by using the traditional single word matching method, the Knuth-Morris-Pratt algorithm, short text matching, etc., or based on deep learning calculations, etc.

[0075] Step S222: determining the associated question data corresponding to the target question data according to the matching degree.

[0076] Specifically, this embodiment can adopt two schemes to determine the associated problem data. One is to preset a matching degree threshold, such as 80%, and use the historical problem data exceeding the matching degree threshold as the associated problem data corresponding to the target problem data; the other is to pre-set the number of associated problem data, such as 5, sort the matching degrees, and use the historical problem data with the top five matching degrees as the associated problem data.

[0077] Step S300: sending the target problem data and the associated problem data to a corresponding after-sales platform according to the address information.

[0078] Specifically, after determining the associated problem data, the maintenance process is started. The target problem data contains the user's address information. Since the after-sales platforms are distributed in multiple cities, in order to save costs, based on the principle of proximity, if the user's address is City A, the target problem data and the associated problem data are sent to the after-sales platform in City A.

[0079] The subsequent after-sales team can receive user information, target problem data, and related problem data from a dedicated after-sales platform website, email, WeChat, SMS, and other channels. Then, according to the solution in the related problem data, go to the location of the address provided by the user to locate the problem. The after-sales team can first use the solution in the related problem data and their previous experience to try to solve the equipment failure. If it can be solved, the solution status, the actual solution, and the equipment failure description will be fed back to the cloud server as after-sales data. If it cannot be solved, according to the actual situation, the equipment failure can be detected and investigated in more detail, the description of the problem phenomenon can be improved, or the user can be consulted for more details, and then the detailed fault description and solution status will be fed back to the cloud server as after-sales data. If the after-sales staff judges based on experience that it may need to be returned to the factory for inspection and repair, the equipment can be sent back to the manufacturer later.

[0080] Step S400, when after-sales data fed back by the after-sales platform based on the target problem data and the associated problem data is detected, it is determined whether the equipment failure corresponding to the target problem data is unresolved.

[0081] Specifically, when after-sales data fed back by the after-sales platform is detected, it can be determined whether the equipment failure corresponding to the target problem data is unresolved based on the resolution status in the after-sales data. In addition, in addition to the resolution status, it can also be determined whether it is resolved based on keywords related to whether it is resolved in the after-sales data.

[0082] Furthermore, after step S400, the method further includes: if the equipment failure corresponding to the target problem data has been resolved, saving the after-sales data as historical problem data.

[0083] Specifically, if the after-sales platform reports that the equipment failure corresponding to the target problem data has been resolved, the after-sales data is used as historical problem data and saved in the corresponding problem database.

[0084] Step S500: If the equipment failure in the target problem data is not resolved, the after-sales data is sent to the corresponding R&D platform.

[0085] Specifically, the cloud server is connected to one or more R&D platforms, which are controlled by R&D engineers. If the after-sales platform feedback fails to solve the equipment failure, the after-sales data is sent to the R&D platform.

[0086] The R&D manager determines the possible causes of the equipment failure based on the after-sales data. If it is a software problem, the R&D manager forwards the after-sales problem data to the software engineer. The software engineer will turn on the print function of the log file hidden in the device software, such as the log file, and then, based on the details of the failure, additionally print and analyze a specific part of the code to confirm the problem in the software, and send the cause and solution of the problem as diagnostic data to the cloud server. If it is a hardware problem, the R&D manager forwards the after-sales problem data to the hardware engineer, who first determines whether the power supply of each module and system of the product is normal. If not, each module is checked until the problematic component is located, and then the original component with the problem is repaired, and the product is checked after the repair. If it is normal, it means that the product failure is caused by the component, and the cause and solution of the problem are uploaded to the cloud server as problem solving data. If the power supply is normal, check whether the components of the accessory type such as the screen are faulty. If it is faulty, replace the component and check whether it is normal. If it is normal after replacement, the solution is sent to the cloud server through the R&D platform as diagnostic data.

[0087] Step S600, when the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data.

[0088] Specifically, when the diagnostic data fed back by the R&D platform is detected, the diagnostic data is sent to the corresponding after-sales platform for the after-sales team to perform further repairs based on the diagnostic data, and the diagnostic data is saved as historical problem data. Therefore, regardless of whether the fault can be solved later, the more fault types and possible solutions are stored in the problem database, the more solutions are available if such faults occur later.

[0089] Therefore, this embodiment can continuously accumulate possible faults and solutions that may occur in the actual process, and can update the after-sales platform in a timely manner to ensure that when providing after-sales services to users, previously existing faults can be quickly resolved, thereby improving the efficiency of equipment fault repair.

[0090] For further information, see Fig. 9 , the diagnostic data includes hardware problem data, and after step S600, it also includes:

[0091] Step S611, determining corresponding material information according to the component information in the hardware problem data.

[0092] Specifically, the hardware problem data refers to the information of the components with problems and the corresponding solutions. The information of the components with problems includes the name, model and batch of the components. After receiving the hardware problem data, the cloud server can determine the corresponding bill of materials from the procurement system, such as the SAP R / 3 system issued by SAP, based on the component information. The bill of materials includes the supplier corresponding to the component of this model, name or batch, and the equipment that already contains the component.

[0093] Step S612: Generate a component report corresponding to the component information according to the material information.

[0094] Specifically, the material information and the components are matched to generate the component report. When the bill of materials contains the supplier corresponding to the component information, the component report can be used by subsequent purchasing personnel to evaluate and screen the supplier to improve the quality of subsequent products. When the bill of materials contains the equipment corresponding to the component information, the component report can be used to take these equipment as candidate faulty equipment, and track the fault conditions of the candidate faulty equipment, determine the probability of the same fault, or facilitate subsequent R&D personnel to improve the hardware on this basis, such as replacing components that often fail with better quality components. And if there are high-frequency faulty components in the subsequent new model project, multiple rounds of testing can be arranged before mass production, and key test analysis can be performed on the faulty components. In addition, in order to facilitate procurement personnel and R&D personnel to analyze according to the component report, the component report also includes the frequency of failure of components of the same model and the frequency within a certain period.

[0095] For further information, see Fig.10 , the diagnostic data includes software upgrade data, after step S600, further comprising:

[0096] Step S621: Determine the software information corresponding to the software upgrade data according to the device information.

[0097] Specifically, the diagnostic data includes software upgrade data, which refers to the patch or updated software data package that the software developer improves the software based on the determined software fault. The software installed on different devices may be different, so the name, number, etc. of the software installed on the device, i.e., the software information, is first determined based on the device information.

[0098] Step S622: Push the software upgrade data to the candidate device corresponding to the software information, so that the candidate device can perform software upgrade according to the software upgrade data.

[0099] Specifically, for example, the same software is installed on device A and device B. If device A fails, device A and device B are corresponding candidate devices. After the R&D personnel upload the software upgrade data according to the failure of device A, the cloud server pushes the software upgrade data to device A and device B at the same time through Over The Air (OTA) technology, so that device A and device B can perform software upgrades, thereby resolving the failure of device A.

[0100] In addition, the software upgrade data is pushed to the candidate device and also pushed to the after-sales platform. If the fault of device A is serious, such as being unable to boot or connect to the Internet, the after-sales team can obtain the software upgrade data from the cloud server through the after-sales platform, and then manually flash device A to repair the fault of device A.

[0101] Furthermore, if Fig.11 As shown, based on the above-mentioned after-sales service processing method based on big data, the present invention also provides a smart terminal accordingly, and the smart terminal includes a processor 10, a memory 20 and a display 30. Fig.11 Only some components of the smart terminal are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0102] The memory 20 may be an internal storage unit of the smart terminal in some embodiments, such as a hard disk or memory of the smart terminal. The memory 20 may also be an external storage device of the smart terminal in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the smart terminal. Further, the memory 20 may also include both an internal storage unit of the smart terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the smart terminal, such as the program code of the smart terminal installed. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an after-sales service processing program 40 based on big data, and the after-sales service processing program 40 based on big data can be executed by the processor 10, thereby realizing the after-sales service processing method based on big data in the present application.

[0103] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the after-sales service processing method based on big data.

[0104] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the smart terminal and to display a visual user interface. The components 10-30 of the smart terminal communicate with each other via a system bus.

[0105] In one embodiment, when the processor 10 executes the big data-based after-sales service processing program 40 in the memory 20, the following steps are implemented:

[0106] Acquire original question data sent by the user, and perform keyword extraction processing on the original question data to generate target question data, wherein the target question data includes device information and address information;

[0107] Determine, according to the target question data, associated question data corresponding to the target question data;

[0108] According to the address information, the target problem data and the associated problem data are sent to a corresponding after-sales platform;

[0109] When after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, determining whether the equipment failure corresponding to the target problem data is unresolved;

[0110] If the equipment failure in the target problem data is not resolved, the after-sales data is sent to the R&D platform;

[0111] When the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data.

[0112] The types of the original question data include text type and voice type. The obtaining of the original question data sent by the user and performing keyword extraction processing on the original question data to generate target question data specifically includes:

[0113] Get the original question data sent by the user;

[0114] When the type of the original question data is text type, extracting keywords from the original data according to a preset keyword extraction rule to generate target question data;

[0115] When the type of the original question data is voice type, the voice type original question data is converted into text type original question data, and according to the keyword extraction rule, keywords in the converted original data are extracted to generate target question data.

[0116] The method further comprises: obtaining the original question data sent by the user, performing keyword extraction processing on the original question data, and generating the target question data;

[0117] Performing desensitization processing on the target problem data to generate desensitized data;

[0118] The desensitized data is encrypted, and the encrypted desensitized data is used as updated target question data.

[0119] The step of determining, based on the target question data, the associated question data corresponding to the target question data specifically includes:

[0120] Determine, according to the device information, a target question database corresponding to the target question data in a preset question database;

[0121] According to the fault words in the keywords, corresponding associated problem data in the target problem database is determined.

[0122] The step of determining the corresponding associated problem data in the target problem database according to the fault words in the keywords specifically includes:

[0123] Calculating the matching degree between the fault words in the keywords and each historical question data in the target question database;

[0124] According to the matching degree, associated question data corresponding to the target question data in the historical question data is determined.

[0125] Wherein, when after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, after determining whether the equipment failure corresponding to the target problem data is unresolved, the method further includes:

[0126] If the equipment failure corresponding to the target problem data has been resolved, the after-sales data is saved as historical problem data.

[0127] Wherein, the diagnostic data includes hardware problem data, and when the diagnostic data fed back by the R&D platform according to the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, and further includes:

[0128] Determine corresponding material information according to the component information in the hardware problem data;

[0129] Generate a component report corresponding to the component name according to the material information.

[0130] Wherein, the diagnostic data includes software upgrade data, and when the diagnostic data fed back by the R&D platform according to the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, and further includes:

[0131] Determining software information corresponding to the software upgrade data according to the device information;

[0132] The software upgrade data is pushed to the candidate device corresponding to the software information, so that the candidate device can perform software upgrade according to the software upgrade data.

[0133] The present invention also provides a storage medium, wherein the storage medium stores an after-sales service processing program based on big data, and when the after-sales service processing program based on big data is executed by a processor, the steps of the after-sales service processing method based on big data as described above are implemented.

[0134] In summary, the present invention provides an after-sales service processing method based on big data, an intelligent terminal and a storage medium, the method comprising: obtaining the original problem data sent by the user, and preprocessing the original problem data to generate target problem data; according to the target problem data, determining the associated problem data corresponding to the target problem data, and sending it to the corresponding after-sales platform; when the after-sales data fed back by the after-sales platform is detected, judging whether the equipment failure corresponding to the target problem data is not resolved; if the equipment failure in the target problem data is not resolved, sending the after-sales data to the research and development platform; when the diagnostic data fed back by the research and development platform is detected, sending the diagnostic data to the corresponding after-sales platform, and saving it as historical problem data. The present invention can update the solution to the equipment failure in real time, thereby speeding up the processing efficiency of the user's after-sales problems.

[0135] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, an optical disk, etc.

[0136] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for after-sales service processing based on big data, characterized in that: The after-sales service processing method based on big data includes: Acquire original question data sent by the user, and perform keyword extraction processing on the original question data to generate target question data, wherein the target question data includes device information and address information; Determine, according to the device information, a target question database corresponding to the target question data in a preset question database; Calculating the matching degree between the fault words in the keywords and each historical question data in the target question database; According to the matching degree, determining the associated question data corresponding to the target question data in the historical question data; the determination of the associated question data may be by presetting a matching degree threshold or a number of associated question data; According to the address information, the target problem data and the associated problem data are sent to a corresponding after-sales platform; When after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, determining whether the equipment failure corresponding to the target problem data is unresolved; If the equipment failure in the target problem data is not resolved, the after-sales data is sent to the R&D platform; When the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data.

2. The after-sales service processing method based on big data according to claim 1 is characterized in that: The types of the original question data include text type and voice type. The obtaining of the original question data sent by the user and performing keyword extraction processing on the original question data to generate target question data specifically includes: Get the original question data sent by the user; When the type of the original question data is text type, extract the keywords in the original data according to the preset keyword extraction rules to generate target question data; When the type of the original question data is voice type, the voice type original question data is converted into text type original question data, and according to the keyword extraction rule, keywords in the converted original data are extracted to generate target question data.

3. The after-sales service processing method based on big data according to claim 2 is characterized in that: The method further includes: obtaining the original question data sent by the user, performing keyword extraction processing on the original question data, and generating target question data; Performing desensitization processing on the target problem data to generate desensitized data; The desensitized data is encrypted, and the encrypted desensitized data is used as updated target question data.

4. The after-sales service processing method based on big data according to claim 1, characterized in that: When after-sales data fed back by the after-sales platform according to the target problem data and the associated problem data is detected, after determining whether the equipment failure corresponding to the target problem data is unresolved, the method further includes: If the equipment failure corresponding to the target problem data has been resolved, the after-sales data is saved as historical problem data.

5. The after-sales service processing method based on big data according to any one of claims 1 to 4, characterized in that: The diagnostic data includes hardware problem data. When the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, and further includes: Determine corresponding material information according to the component information in the hardware problem data; Generate a component report corresponding to the component name according to the material information.

6. The after-sales service processing method based on big data according to any one of claims 1 to 4, characterized in that: The diagnostic data includes software upgrade data. When the diagnostic data fed back by the R&D platform based on the after-sales data is detected, the diagnostic data is sent to the corresponding after-sales platform and saved as historical problem data, and further includes: Determining software information corresponding to the software upgrade data according to the device information; The software upgrade data is pushed to the candidate device corresponding to the software information, so that the candidate device can perform software upgrade according to the software upgrade data.

7. An intelligent terminal, characterized in that: The intelligent terminal includes: a memory, a processor, and an after-sales service processing program based on big data stored in the memory and executable on the processor. When the after-sales service processing program based on big data is executed by the processor, the steps of the after-sales service processing method based on big data are implemented.

8. A storage medium, characterized in that: The storage medium stores an after-sales service processing program based on big data, and when the after-sales service processing program based on big data is executed by the processor, the steps of the after-sales service processing method based on big data are implemented.

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