Consultation and complaint information processing method and related device based on big data

Abstract

The present application relates to intelligent recommendation in the field of big data technology, and specifically to a consultation complaint information processing method based on big data and related devices, the method comprising: obtaining work order information of a target customer, the work order information comprising a target customer identifier, target work order content, and target business classification; determining a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier; determining a second customer classification label corresponding to the target customer according to the target work order content; determining a target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label, and the target business classification, and displaying the target user portrait and the target consultation complaint handling suggestion. The use of the embodiments of the present application helps to improve the efficiency and accuracy of responses to consultation complaints.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and specifically to a consultation and complaint information processing method and related devices based on big data. Background Art

[0002] At present, the response process for consultation complaints is as follows: when a consultation complaint is detected, the type of the consultation complaint is manually determined, the best consultation complaint case is manually found from the consultation complaint case library corresponding to the type of consultation complaint, consultation complaint suggestions are determined based on the best consultation complaint case, and the consultation complaint suggestions are displayed. This method results in low efficiency and accuracy in responding to consultation complaints. Summary of the invention

[0003] The embodiments of the present application provide a consultation and complaint information processing method and related devices based on big data, which are used to improve the low efficiency and accuracy of consultation and complaint responses.

[0004] In a first aspect, the present application embodiment provides a method for processing consultation and complaint information based on big data, including:

[0005] Obtaining work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification;

[0006] Determine a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier;

[0007] Determining a second customer classification label corresponding to the target customer according to the target work order content;

[0008] The target consultation and complaint handling suggestions corresponding to the target customers are determined according to the first customer classification label, the second customer classification label and the target business classification, and the target user portrait and the target consultation and complaint handling suggestions are displayed.

[0009] In a second aspect, the embodiment of the present application provides a consultation and complaint information processing device based on big data, including:

[0010] An acquisition unit, used to acquire work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification;

[0011] A first determining unit, configured to determine a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier;

[0012] A second determining unit, configured to determine a second customer classification label corresponding to the target customer according to the target work order content;

[0013] A third determining unit is used to determine a target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification;

[0014] A display unit is used to display the target user portrait and the target consultation and complaint handling suggestions.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing some or all of the steps in the method of the first aspect of the embodiment of the present application.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program. The computer program is executed by a processor to implement some or all of the steps described in the method described in the first aspect of the embodiment of the present application.

[0017] It can be seen that compared to manually searching for the best consulting complaint case from the consulting complaint case library corresponding to the type of consulting complaint, and determining and displaying the consulting complaint suggestion based on the best consulting complaint case, in the embodiment of the present application, the consulting complaint processing device determines the target consulting complaint processing suggestion corresponding to the target customer based on the first customer classification label, the second customer classification label identifier and the target business classification, and displays the target user portrait and the target consulting complaint processing suggestion. Since the entire consulting complaint response process does not require human participation, it helps to improve the efficiency and accuracy of the response to consulting complaints.

[0018] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0020] Figure 1A It is a flowchart of a method for processing consultation and complaint information based on big data provided in an embodiment of the present application;

[0021] Figure 1B It is a schematic diagram of determining target consultation complaint handling suggestions corresponding to target customers provided by an embodiment of the present application;

[0022] Figure 2It is a flowchart of another method for processing consultation and complaint information based on big data provided in an embodiment of the present application;

[0023] Figure 3 This is a block diagram of the functional units of a consultation and complaint information processing device based on big data provided in an embodiment of the present application;

[0024] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0025] Specific implementation method

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] The following are detailed descriptions of each.

[0028] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] Electronic devices may include various handheld devices, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem with wireless communication functions, as well as various forms of user equipment (UE), mobile stations (MS), terminal devices, etc.

[0031] The following is a detailed description of the embodiments of the present application.

[0032] See also Figure 1A , Figure 1A 1 is a flowchart of a method for processing consultation and complaint information based on big data provided by an embodiment of the present application. The method for processing consultation and complaint information based on big data includes steps 101-104, which are as follows:

[0033] 101: A consulting complaint handling device obtains work order information of a target customer, where the work order information includes a target customer identifier, target work order content, and a target business classification.

[0034] The customer ID is identification information that uniquely identifies the customer. For example, the customer ID is a unique identification number generated when the customer leaves the vehicle.

[0035] The content of the work order includes the customer's inquiry or complaint; the inquiry or complaint content can be in text or voice form.

[0036] Business classification is configured by business personnel. For example, when the business classification is a four-level classification, the business classification is: loan → other consumer loan → new loan / excellent + loan → overdue loan.

[0037] 102: The consultation complaint processing device determines a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier.

[0038] Customer classification labels may include sensitivity, rationality, interest, and venting. The above is merely an exemplary description of customer classification labels and should not be construed as a limitation on customer classification labels.

[0039] The user portrait may include the customer’s regulatory complaints in the past two years, complaint history, zodiac sign, resident city, birthday, age, education level, gender, occupation, etc.

[0040] The consulting complaint handling device may first determine the first customer classification label corresponding to the target customer based on the target customer identifier, and then determine the target user portrait corresponding to the target customer based on the target customer identifier; the consulting complaint handling device may first determine the target user portrait corresponding to the target customer based on the target customer identifier, and then determine the first customer classification label corresponding to the target customer based on the target customer identifier; in the process of the consulting complaint handling device determining the first customer classification label corresponding to the target customer based on the target customer identifier, the consulting complaint handling device determines the target user portrait corresponding to the target customer based on the target customer identifier.

[0041] In a possible example, the consultation complaint handling device determines the first customer classification label and the target user portrait corresponding to the target customer according to the target customer identifier, including:

[0042] The consultation complaint processing device obtains the target user information and the target historical consultation complaint record of the target customer according to the target customer identifier;

[0043] The consultation complaint processing device determines a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation complaint record;

[0044] The consultation complaint processing device determines the first user portrait corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the user portrait;

[0045] The consultation complaint processing device determines the first user portrait as the target user portrait corresponding to the target customer.

[0046] The consultation complaint processing device can determine the first user information corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the user information, and determine the first user information as the target user information of the target customer.

[0047] The mapping relationship between the customer identification and the user information is pre-stored in the consultation complaint processing device. The mapping relationship between the customer identification and the user information is shown in Table 1 below:

[0048] Table 1

[0049] Customer Logo User Information First customer logo First user information Second customer ID Second user information Third customer identification Third-party user information …… ……

[0050] The consultation and complaint device can determine the first historical consultation and complaint record corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the historical consultation and complaint record, and determine the first historical consultation and complaint record as the target historical consultation and complaint record of the target customer.

[0051] The mapping relationship between the customer identification and the historical consultation and complaint records is pre-stored in the consultation and complaint device. The customer identification and the historical consultation and complaint records are shown in Table 2 below:

[0052] Table 2

[0053] Customer Logo Historical consultation and complaint records First customer logo First historical consultation and complaint record Second customer ID Second historical consultation and complaint records Third customer identification Third, historical consultation and complaint records …… ……

[0054] The implementation method of the consultation complaint processing device determining the first customer classification label corresponding to the target customer according to the target user information and the target historical consultation record may be:

[0055] The consultation complaint processing device analyzes the target user information to obtain the personality type of the target customer included in the target user information;

[0056] The consultation complaint processing device determines a first target customer classification label corresponding to the target customer's personality type according to a pre-stored mapping relationship between personality type and customer classification label;

[0057] The consultation complaint processing device parses the target historical consultation complaint record to obtain the consultation complaint type of the most recent consultation complaint record in the target historical consultation complaint record;

[0058] The consultation complaint processing device determines the second target customer classification label corresponding to the consultation complaint type of the most recent consultation complaint record according to the pre-stored mapping relationship between the consultation complaint type and the customer classification label;

[0059] If the first target customer classification label is the same as the second target customer classification label, the consultation complaint processing device determines the first target customer classification label as the first customer classification label corresponding to the target customer;

[0060] If the first target customer classification label is different from the second target customer classification label, the consultation complaint processing device determines the second target customer classification label as the first customer classification label corresponding to the target customer.

[0061] The mapping relationship between personality type and customer classification label is pre-stored in the consultation complaint processing device. The mapping relationship between personality type and customer classification label is shown in Table 3 below:

[0062] Table 3

[0063] Personality Type Customer classification label First personality type Customer classification label 11 Second personality type Customer classification label 12 The third personality type Customer classification label 13 …… ……

[0064] As shown in Table 3, when the personality type is the first personality type, the customer classification label is customer classification label 11.

[0065] The mapping relationship between the consulting complaint type and the customer classification label is pre-stored in the consulting complaint processing device. The mapping relationship between the consulting complaint type and the customer classification label is shown in Table 4 below:

[0066] Table 4

[0067] Consulting complaint type Customer classification label First consultation complaint type Customer classification label 21 Second consultation complaint type Customer classification label 22 Third consultation complaint type Customer classification label 23 …… ……

[0068] The mapping relationship between the customer identification and the user portrait is pre-stored in the consultation complaint processing device. The mapping relationship between the customer identification and the user portrait is shown in Table 5 below:

[0069] Table 5

[0070] Customer Logo User Profile First customer logo First user portrait Second customer ID Second user portrait Third customer identification The third user portrait …… ……

[0071] 103: The consultation complaint processing device determines a second customer classification label corresponding to the target customer according to the target work order content.

[0072] In a possible example, the consultation complaint handling device determines the second customer classification label corresponding to the target customer according to the target work order content, including:

[0073] The consultation complaint processing device extracts keywords from the target work order content to obtain at least one keyword included in the target work order content;

[0074] The consultation complaint handling device determines at least one target customer classification label corresponding to at least one keyword according to the pre-stored mapping relationship between the keywords and the customer classification labels, and the at least one target customer classification label corresponds to the at least one keyword in a one-to-one manner;

[0075] The consultation complaint handling device determines the at least one target customer classification label as a second customer classification label corresponding to the target customer.

[0076] The consultation complaint processing device calls a pre-stored keyword extraction algorithm, uses the keyword extraction algorithm to extract keywords from the target work order content, and obtains at least one keyword included in the target work order content; wherein the keyword extraction algorithm is pre-stored in the consultation complaint processing device.

[0077] The mapping relationship between keywords and customer classification labels is pre-stored in the consultation complaint processing device. The mapping relationship between keywords and customer classification labels is shown in Table 6 below:

[0078] Table 6

[0079] Keywords Customer classification label First keyword Customer classification label 31 Second keyword Customer classification label 32 The third keyword Customer classification label 33 …… ……

[0080] 104: The consultation complaint processing device determines the target consultation complaint processing suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification, and displays the target user portrait and the target consultation complaint processing suggestion.

[0081] The consultation complaint handling device includes a display screen, and the consultation complaint handling device displays the target user portrait and the target consultation complaint handling suggestions through the display screen.

[0082] It can be seen that compared to manually searching for the best consulting complaint case from the consulting complaint case library corresponding to the type of consulting complaint, and determining and displaying the consulting complaint suggestion based on the best consulting complaint case, in the embodiment of the present application, the consulting complaint processing device determines the target consulting complaint processing suggestion corresponding to the target customer based on the first customer classification label, the second customer classification label identifier and the target business classification, and displays the target user portrait and the target consulting complaint processing suggestion. Since the entire consulting complaint response process does not require human participation, it helps to improve the efficiency and accuracy of the response to consulting complaints.

[0083] In a possible example, the consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification, including:

[0084] The consultation complaint processing device determines a third customer classification label according to the first customer classification label and the second customer classification label;

[0085] The consultation complaint handling device selects a first consultation complaint handling case set corresponding to the third customer classification label from the consultation complaint handling case library;

[0086] The consultation complaint handling device selects a second consultation complaint handling case set corresponding to the target business classification from the consultation complaint handling case library;

[0087] The consultation complaint handling device determines a target consultation complaint handling suggestion corresponding to the target customer based on the first consultation complaint handling case set and the second consultation complaint handling case set.

[0088] The third customer classification label is a union of the first customer classification label and the second customer classification label.

[0089] The consultation complaint handling device selects a first consultation complaint handling case set corresponding to the third customer classification label from the consultation complaint case library according to the pre-stored mapping relationship between the customer classification label and the consultation complaint handling case.

[0090] The mapping relationship between the customer classification labels and the consultation complaint handling cases is pre-stored in the consultation complaint handling device. The mapping relationship between the customer classification labels and the consultation complaint handling cases is shown in Table 7 below:

[0091] Table 7

[0092]

[0093]

[0094] The consultation complaint handling device selects a second consultation complaint handling case set corresponding to the target business classification from the consultation complaint handling case library according to the pre-stored mapping relationship between the business classification and the consultation complaint handling cases.

[0095] The mapping relationship between the business classification and the consultation complaint handling case is pre-stored in the consultation complaint handling device. The mapping relationship between the business classification and the consultation complaint handling case is shown in Table 8 below:

[0096] Table 8

[0097] Business Classification Consulting complaint handling cases First business category Consultation complaint handling case 21 Second business category Consultation complaint handling case 22 The third business category Consulting complaint handling case 23 …… ……

[0098] In a possible example, the consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second consultation complaint handling case set, including:

[0099] The consultation complaint handling device determines whether the first consultation complaint handling case set is an empty set;

[0100] If so, the consultation complaint handling device selects a target consultation complaint handling case corresponding to the preset customer classification label from the second consultation complaint handling case set;

[0101] The consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer based on the target consultation complaint handling case.

[0102] The preset customer classification label is configured by the customer, for example, the preset customer classification label is rational.

[0103] In a possible example, the consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second complaint consultation handling case set, including:

[0104] The consultation complaint handling device determines whether the first consultation complaint handling case set is an empty set;

[0105] If not, the consultation complaint handling device determines the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case;

[0106] The consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer based on the target consultation complaint handling case.

[0107] For example, if Figure 1B As shown, Figure 1B This is a schematic diagram of determining a target consultation complaint handling suggestion corresponding to a target customer provided by an embodiment of the present application. The step of determining a target consultation complaint handling suggestion corresponding to a target customer includes steps A1-A4, which are as follows:

[0108] A1: The consultation complaint handling device determines whether the first consultation complaint handling case set is an empty set;

[0109] If yes, then execute steps A2 and A4.

[0110] If not, execute steps A3 and A4.

[0111] A2: The consultation complaint handling device selects a target consultation complaint handling case corresponding to a preset customer classification label from the second consultation complaint handling case set.

[0112] A3: The consultation complaint handling device determines the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case.

[0113] A4: The consultation complaint handling device determines the target consultation complaint handling suggestions corresponding to the target customers based on the target consultation complaint handling cases.

[0114] In a possible example, the consultation complaint handling device determines the target consultation complaint handling suggestion corresponding to the target customer according to the target consultation complaint handling case, including:

[0115] The consultation complaint processing device calls a pre-stored processing suggestion extraction algorithm;

[0116] The consultation complaint handling device uses the processing suggestion extraction algorithm to extract the consultation complaint processing suggestion from the target consultation complaint handling case, and obtains the first consultation complaint handling suggestion included in the target consultation complaint handling case;

[0117] The consultation complaint handling device determines the first consultation complaint handling suggestion as the target consultation complaint handling suggestion corresponding to the target customer.

[0118] The processing suggestion extraction algorithm is pre-stored in the consultation complaint processing device.

[0119] With the above Figure 1A For details on the embodiments shown in the drawings, see Figure 2 , Figure 2 2 is a flow chart of another method for processing consultation and complaint information based on big data provided by an embodiment of the present application. The method for processing consultation and complaint information based on big data includes steps 201-212, which are as follows:

[0120] 201: The consultation complaint processing device obtains work order information of a target customer, where the work order information includes a target customer identifier, target work order content, and target business classification.

[0121] 202: The consultation complaint processing device obtains the target user information and the target historical consultation complaint record of the target customer according to the target customer identifier.

[0122] 203: The consultation complaint processing device determines a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation complaint record.

[0123] 204: The consultation complaint processing device determines the target user portrait of the target customer corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the user portrait.

[0124] 205: The consultation complaint processing device extracts keywords from the target work order content to obtain at least one keyword included in the target work order content.

[0125] 206: The consultation complaint processing device determines at least one target customer classification label corresponding to at least one keyword according to the pre-stored mapping relationship between the keywords and the customer classification labels, and the at least one target customer classification label corresponds to the at least one keyword in a one-to-one manner.

[0126] 207: The consultation complaint processing apparatus determines the at least one target customer classification label as a second customer classification label corresponding to the target customer.

[0127] 208: The consultation complaint processing device determines a third customer classification label according to the first customer classification label and the second customer classification label.

[0128] 209: The consultation complaint handling device selects a first consultation complaint handling case set corresponding to the third customer classification label from the consultation complaint handling case library.

[0129] 210: The consultation complaint handling device selects a second consultation complaint handling case set corresponding to the target business classification from the consultation complaint handling case library.

[0130] 211: The consultation complaint handling device determines a target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second consultation complaint handling case set.

[0131] 212: The consultation complaint handling device displays the target user portrait and the target consultation complaint handling suggestion.

[0132] It should be noted that Figure 2 The specific implementation process of each step of the method shown can refer to the specific implementation process described in the above method, which will not be described here.

[0133] The above embodiments mainly introduce the scheme of the embodiments of the present application from the perspective of the execution process on the method side. It can be understood that in order to realize the above functions, the consultation and complaint information processing device based on big data includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0134] The embodiment of the present application can divide the functional units of the consultation and complaint information processing device based on big data according to the method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0135] See also Figure 3 , Figure 3 1 is a functional unit block diagram of a consultation and complaint information processing device based on big data provided in an embodiment of the present application. The consultation and complaint information processing device based on big data 300 includes:

[0136] An acquisition unit 301 is used to acquire work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification;

[0137] A first determining unit 302 is used to determine a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier;

[0138] A second determining unit 303, configured to determine a second customer classification label corresponding to the target customer according to the target work order content;

[0139] A third determining unit 304 is used to determine a target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification;

[0140] The display unit 305 is used to display the target user portrait and the target consultation complaint handling suggestion.

[0141] It can be seen that compared to manually searching for the best consulting complaint case from the consulting complaint case library corresponding to the type of consulting complaint, and determining and displaying the consulting complaint suggestion based on the best consulting complaint case, in the embodiment of the present application, the consulting complaint processing device determines the target consulting complaint processing suggestion corresponding to the target customer based on the first customer classification label, the second customer classification label identifier and the target business classification, and displays the target user portrait and the target consulting complaint processing suggestion. Since the entire consulting complaint response process does not require human participation, it helps to improve the efficiency and accuracy of the response to consulting complaints.

[0142] In a possible example, in determining the first customer classification label and the target user portrait corresponding to the target customer according to the target customer identifier, the first determining unit 302 is specifically configured to:

[0143] Obtaining target user information and target historical consultation and complaint records of the target customer according to the target customer identifier;

[0144] Determine a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation and complaint record;

[0145] Determine a first user portrait corresponding to the target customer identifier according to a pre-stored mapping relationship between the customer identifier and the user portrait;

[0146] The first user portrait is determined as the target user portrait corresponding to the target customer.

[0147] In a possible example, in determining the second customer classification label corresponding to the target customer according to the target work order content, the second determining unit 303 is specifically configured to:

[0148] Perform keyword extraction on the target work order content to obtain at least one keyword included in the target work order content;

[0149] Determine at least one target customer classification label corresponding to at least one keyword according to a pre-stored mapping relationship between keywords and customer classification labels, wherein the at least one target customer classification label corresponds to the at least one keyword in a one-to-one manner;

[0150] The at least one target customer classification label is determined as a second customer classification label corresponding to the target customer.

[0151] In a possible example, in determining the target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification, the third determining unit 304 is specifically used to:

[0152] determining a third customer classification label according to the first customer classification label and the second customer classification label;

[0153] Selecting a first consulting complaint handling case set corresponding to the third customer classification label from a consulting complaint handling case library;

[0154] Selecting a second consulting complaint handling case set corresponding to the target business classification from the consulting complaint handling case library;

[0155] The target consulting complaint handling suggestions corresponding to the target customers are determined according to the first consulting complaint handling case set and the second consulting complaint handling case set.

[0156] In a possible example, in determining the target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second consultation complaint handling case set, the third determining unit 304 is specifically used to:

[0157] Determining whether the first consultation complaint handling case set is an empty set;

[0158] If so, selecting a target consulting complaint handling case corresponding to the preset customer classification label from the second consulting complaint handling case set;

[0159] Determine the target consultation complaint handling suggestions corresponding to the target customers based on the target consultation complaint handling cases.

[0160] In a possible example, in determining the target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second complaint consultation handling case set, the third determining unit 304 is specifically used to:

[0161] Determining whether the first consultation complaint handling case set is an empty set;

[0162] If not, determining the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case;

[0163] Determine the target consultation complaint handling suggestions corresponding to the target customers based on the target consultation complaint handling cases.

[0164] In a possible example, in determining the target consultation complaint handling suggestion corresponding to the target customer according to the target consultation complaint handling case, the third determining unit 304 is specifically used to:

[0165] Calling a pre-stored processing suggestion extraction algorithm;

[0166] Extract consultation complaint handling suggestions from the target consultation complaint handling case using the handling suggestion extraction algorithm to obtain a first consultation complaint handling suggestion included in the target consultation complaint handling case;

[0167] The first consultation complaint handling suggestion is determined as the target consultation complaint handling suggestion corresponding to the target customer.

[0168] With the above Figure 1A and Figure 2 For details on the embodiments shown in the drawings, see Figure 4 , Figure 4 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 400 includes a display screen, and the electronic device 400 also includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. The program includes instructions for executing the following steps:

[0169] Obtaining work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification;

[0170] Determine a first customer classification label and a target user portrait corresponding to the target customer according to the target customer identifier;

[0171] Determining a second customer classification label corresponding to the target customer according to the target work order content;

[0172] The target consultation and complaint handling suggestions corresponding to the target customers are determined according to the first customer classification label, the second customer classification label and the target business classification, and the target user portrait and the target consultation and complaint handling suggestions are displayed.

[0173] It can be seen that compared to manually searching for the best consulting complaint case from the consulting complaint case library corresponding to the type of consulting complaint, and determining the consulting complaint suggestion based on the best consulting complaint case and displaying the consulting complaint suggestion, in the embodiment of the present application, the electronic device determines the target consulting complaint handling suggestion corresponding to the target customer based on the first customer classification label, the second customer classification label identifier and the target business classification, and displays the target user portrait and the target consulting complaint handling suggestion. Since the entire consulting complaint response process does not require human participation, it helps to improve the efficiency and accuracy of the response to consulting complaints.

[0174] In a possible example, in terms of determining the first customer classification label and the target user portrait corresponding to the target customer according to the target customer identifier, the above program includes instructions specifically for executing the following steps:

[0175] Obtaining target user information and target historical consultation and complaint records of the target customer according to the target customer identifier;

[0176] Determine a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation and complaint record;

[0177] Determine a first user portrait corresponding to the target customer identifier according to a pre-stored mapping relationship between the customer identifier and the user portrait;

[0178] The first user portrait is determined as the target user portrait corresponding to the target customer.

[0179] In a possible example, in terms of determining the second customer classification label corresponding to the target customer according to the target work order content, the above program includes instructions specifically for executing the following steps:

[0180] Perform keyword extraction on the target work order content to obtain at least one keyword included in the target work order content;

[0181] Determine at least one target customer classification label corresponding to at least one keyword according to a pre-stored mapping relationship between keywords and customer classification labels, wherein the at least one target customer classification label corresponds to the at least one keyword in a one-to-one manner;

[0182] The at least one target customer classification label is determined as a second customer classification label corresponding to the target customer.

[0183] In a possible example, in terms of determining the target consultation complaint handling suggestion corresponding to the target customer according to the first customer classification label, the second customer classification label and the target business classification, the above program includes instructions for performing the following steps:

[0184] determining a third customer classification label according to the first customer classification label and the second customer classification label;

[0185] Selecting a first consulting complaint handling case set corresponding to the third customer classification label from a consulting complaint handling case library;

[0186] Selecting a second consulting complaint handling case set corresponding to the target business classification from the consulting complaint handling case library;

[0187] The target consulting complaint handling suggestions corresponding to the target customers are determined according to the first consulting complaint handling case set and the second consulting complaint handling case set.

[0188] In one possible example, in determining the target consulting complaint handling suggestion corresponding to the target customer according to the first consulting complaint handling case set and the second consulting complaint handling case set, the above program includes instructions for performing the following steps:

[0189] Determining whether the first consultation complaint handling case set is an empty set;

[0190] If so, selecting a target consulting complaint handling case corresponding to the preset customer classification label from the second consulting complaint handling case set;

[0191] Determine the target consultation complaint handling suggestions corresponding to the target customers based on the target consultation complaint handling cases.

[0192] In one possible example, in determining the target consultation complaint handling suggestion corresponding to the target customer according to the first consultation complaint handling case set and the second complaint consultation handling case set, the above program includes instructions for performing the following steps:

[0193] Determining whether the first consultation complaint handling case set is an empty set;

[0194] If not, determining the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case;

[0195] Determine the target consultation complaint handling suggestions corresponding to the target customers based on the target consultation complaint handling cases.

[0196] In a possible example, in terms of determining the target consulting complaint handling suggestion corresponding to the target customer according to the target consulting complaint handling case, the above program includes instructions for performing the following steps:

[0197] Calling a pre-stored processing suggestion extraction algorithm;

[0198] Extract consultation complaint handling suggestions from the target consultation complaint handling case using the handling suggestion extraction algorithm to obtain a first consultation complaint handling suggestion included in the target consultation complaint handling case;

[0199] The first consultation complaint handling suggestion is determined as the target consultation complaint handling suggestion corresponding to the target customer.

[0200] An embodiment of the present application also provides a computer storage medium, which is used to store a computer program. The computer program is executed by a processor to implement part or all of the steps of any method recorded in the above method embodiment. The computer includes an electronic device.

[0201] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0202] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0203] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0204] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0205] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0207] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0208] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0209] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for processing consultation and complaint information based on big data, characterized in that: include: Obtaining work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification; The target business classification is determined based on the classification level of the business corresponding to the work order information, the classification level of the business corresponding to the work order information is multi-level, and the target business classification includes a corresponding number of classification information of multiple different granularities; Obtain target user information and target historical consultation and complaint records of the target customer according to the target customer identifier, and determine a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation and complaint records; Determine the first user portrait corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the user portrait, and determine the first user portrait as the target user portrait corresponding to the target customer; the target user portrait includes the target customer's regulatory complaints, complaint history, zodiac sign, and resident city; the first customer classification label is determined by: obtaining the target historical consultation and complaint record corresponding to the target customer identifier and parsing it to obtain the consultation and complaint type of the most recent consultation and complaint record in the target historical consultation and complaint record; Determine the second target customer classification label corresponding to the consultation complaint type of the most recent consultation complaint record according to the pre-stored mapping relationship between the consultation complaint type and the customer classification label; The second target customer classification label is determined as the first customer classification label corresponding to the target customer; the consultation complaint type is different from the personality type of the target customer; Determining a second customer classification label corresponding to the target customer according to the target work order content; Determine a third customer classification label according to the first customer classification label and the second customer classification label, wherein the third customer classification label is a union of the first customer classification label and the second customer classification label; Selecting a first consulting complaint handling case set corresponding to the third customer classification label from a consulting complaint handling case library; Selecting a second consulting complaint handling case set corresponding to the target business classification from the consulting complaint handling case library; Determining whether the first consultation complaint handling case set is an empty set; If so, selecting a target consulting complaint handling case corresponding to the preset customer classification label from the second consulting complaint handling case set; If not, determining the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case; The target consultation complaint handling suggestions corresponding to the target customers are determined based on the target consultation complaint handling cases, and the target user portrait and the target consultation complaint handling suggestions are displayed.

2. The method according to claim 1, characterized in that The determining, according to the target work order content, a second customer classification label corresponding to the target customer includes: Perform keyword extraction on the target work order content to obtain at least one keyword included in the target work order content; Determine at least one target customer classification label corresponding to at least one keyword according to a pre-stored mapping relationship between keywords and customer classification labels, wherein the at least one target customer classification label corresponds to the at least one keyword in a one-to-one manner; The at least one target customer classification label is determined as a second customer classification label corresponding to the target customer.

3. The method according to claim 1, characterized in that Determining the target consultation complaint handling suggestions corresponding to the target customers according to the target consultation complaint handling cases includes: Calling a pre-stored processing suggestion extraction algorithm; Extract consultation complaint handling suggestions from the target consultation complaint handling case using the handling suggestion extraction algorithm to obtain a first consultation complaint handling suggestion included in the target consultation complaint handling case; The first consultation complaint handling suggestion is determined as the target consultation complaint handling suggestion corresponding to the target customer.

4. A consultation and complaint information processing device based on big data, characterized in that: include: An acquisition unit, used to acquire work order information of a target customer, wherein the work order information includes a target customer identifier, target work order content, and target business classification; The target business classification is determined based on the classification level of the business corresponding to the work order information, the classification level of the business corresponding to the work order information is multi-level, and the target business classification includes a corresponding number of classification information of multiple different granularities; A first determination unit is used to obtain target user information and target historical consultation and complaint records of the target customer according to the target customer identifier, and determine a first customer classification label corresponding to the target customer according to the target user information and the target historical consultation and complaint records; Determine the first user portrait corresponding to the target customer identifier according to the pre-stored mapping relationship between the customer identifier and the user portrait, and determine the first user portrait as the target user portrait corresponding to the target customer; the target user portrait includes the target customer's regulatory complaints, complaint history, zodiac sign, and resident city; the first customer classification label is determined by: obtaining the target historical consultation and complaint record corresponding to the target customer identifier and parsing it to obtain the consultation and complaint type of the most recent consultation and complaint record in the target historical consultation and complaint record; Determine the second target customer classification label corresponding to the consultation complaint type of the most recent consultation complaint record according to the pre-stored mapping relationship between the consultation complaint type and the customer classification label; The second target customer classification label is determined as the first customer classification label corresponding to the target customer; the consultation complaint type is different from the personality type of the target customer; A second determining unit, configured to determine a second customer classification label corresponding to the target customer according to the target work order content; a third determining unit, configured to determine a third customer classification label according to the first customer classification label and the second customer classification label, wherein the third customer classification label is a union of the first customer classification label and the second customer classification label; Selecting a first consulting complaint handling case set corresponding to the third customer classification label from the consulting complaint handling case library; selecting a second consulting complaint handling case set corresponding to the target business classification from the consulting complaint handling case library; determining whether the first consulting complaint handling case set is an empty set; if so, selecting a target consulting complaint handling case corresponding to the preset customer classification label from the second consulting complaint handling case set; If not, determining the intersection of the first consultation complaint handling case set and the second consultation complaint handling case set as the target consultation complaint handling case; Determine the target consulting complaint handling suggestions corresponding to the target customers based on the target consulting complaint handling cases; A display unit is used to display the target user portrait and the target consultation and complaint handling suggestions.

5. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing part or all of the steps in the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 3.

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