CN110880082B - Service evaluation method, device, system, electronic device and readable storage medium - Google Patents

Service evaluation method, device, system, electronic device and readable storage medium Download PDF

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CN110880082B
CN110880082B CN201911216924.4A CN201911216924A CN110880082B CN 110880082 B CN110880082 B CN 110880082B CN 201911216924 A CN201911216924 A CN 201911216924A CN 110880082 B CN110880082 B CN 110880082B
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service
client
data
clustering algorithm
sample
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CN110880082A (en
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王玥
张烨
张奇峰
辛丽娟
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services

Abstract

The present disclosure provides a service evaluation method, including: acquiring characteristic information and transaction information of a client transacting business currently; classifying the customers according to the characteristic information and the transaction information of the customers; and pushing the corresponding evaluation items to the client according to the type of the client so that the client evaluates the service of the service provider, wherein different types of clients respectively correspond to different evaluation items for the same type of service. The present disclosure also provides a service evaluation apparatus, a service evaluation system, an electronic device, and a computer-readable storage medium.

Description

Service evaluation method, device, system, electronic equipment and readable storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a service evaluation method, a service evaluation apparatus, a service evaluation system, an electronic device, and a computer-readable storage medium.
Background
Currently, various industries need to provide customer service, for example, banks need to provide counter service and online banking service. In order to make services more client-oriented, feedback from the client is typically collected after the service to rate different types of services and make corresponding improvements. However, most customers do not want to spend more time evaluating, and in order to encourage customers to evaluate, service providers typically employ some means to conduct guided evaluations on all customers.
However, the inventors found that although a large amount of evaluation content is acquired, no effective feedback can be formed, and the reason for this is as follows: on one hand, the data is rough, a large amount of invalid data exists, the evaluation of the client group really facing the service cannot be accurately identified, and effective evaluation cannot be formed. For example, a customer who is not familiar with the business field may have a problem of incomprehensible understanding to a certain extent, even an evaluation result is completely wrong, specifically, for example, if an old uses an online bank to handle business, if an operation flow is relatively tedious compared with the old, the old will generally have a relatively low evaluation on the online bank, and a truly oriented customer group of the online bank is a young person or a middle-aged person; on the other hand, the conventional evaluation data is relatively one-sided and inaccurate, and even the evaluation data is wrong. For example, simple statistics on the evaluation data may not accurately reflect the degree of satisfaction of the service.
In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art: the service is evaluated by adopting the related technology, and the obtained service satisfaction degree cannot reflect the actual situation.
Disclosure of Invention
In view of the above, the present disclosure provides a service evaluation method, a service evaluation apparatus, a service evaluation system, an electronic device, and a computer-readable storage medium.
One aspect of the present disclosure provides a service evaluation method, including: acquiring characteristic information and transaction information of a client handling business currently; classifying the customers according to the characteristic information and the transaction information of the customers; and pushing corresponding evaluation items to the client according to the type of the client so that the client can evaluate the service of the service provider, wherein different types of clients respectively correspond to different evaluation items for the same type of service.
According to an embodiment of the present disclosure, wherein: classifying the customer according to the characteristic information and the transaction information of the customer includes: classifying the customers by using a clustering algorithm model according to the characteristic information and the transaction information of the customers; the service evaluation method further comprises the step of obtaining the clustering algorithm model through pre-training, and the training process comprises the following steps: acquiring sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and training a clustering algorithm model based on the sample data so that the clustering algorithm model can classify newly input customer data.
According to the embodiment of the disclosure, the clustering algorithm model comprises a K-Means clustering algorithm, and training the clustering algorithm model based on the sample data comprises the following steps: determining a K value, and randomly selecting K central samples from the sample data as the central point of each group in K groups; calculating the distance between each sample and each of the K central samples, wherein each sample obtains corresponding K distances; for each sample, dividing the sample into a group of central samples with the shortest distance; exchanging positions of target samples close to the central samples in the central samples of the groups in space with samples in other groups, and taking the target samples as new central points; and repeatedly executing the step of training the clustering algorithm model for T times after the K value is fixed.
According to an embodiment of the present disclosure, the method further comprises: acquiring newly acquired sample data in a data platform server according to a preset time interval; and training the clustering algorithm model based on the newly acquired sample data.
Another aspect of the present disclosure provides a service evaluation apparatus including: the acquisition module is used for acquiring characteristic information and transaction information of a client transacting business currently; the classification module is used for classifying the customers according to the characteristic information and the transaction information of the customers; and the pushing module is used for pushing corresponding evaluation items to the client according to the type of the client so that the client can evaluate the service of the service provider, wherein different types of clients respectively correspond to different evaluation items for the same type of service.
According to an embodiment of the present disclosure, wherein: the classification module is used for classifying the customers by using a clustering algorithm model according to the characteristic information and the transaction information of the customers;
the classification module is further configured to pre-train to obtain the clustering algorithm model, and the training process includes: acquiring sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and training a clustering algorithm model based on the sample data so that the clustering algorithm model can classify newly input customer data.
According to the embodiment of the disclosure, the clustering algorithm model comprises a K-Means clustering algorithm, and training the clustering algorithm model based on the sample data comprises the following steps: determining a K value, and randomly selecting K central samples from the sample data as the central point of each group in K groups; calculating the distance between each sample and each of the K central samples, wherein each sample obtains the corresponding K distances; for each sample, dividing the sample into a group of central samples with the shortest distance; exchanging positions of target samples close to the central samples in the central samples of the groups in space with samples in other groups, and taking the target samples as new central points; and repeatedly executing the step of training the clustering algorithm model for T times after the K value is fixed.
According to an embodiment of the present disclosure, wherein: the acquisition module is also used for acquiring the latest acquired sample data in the data platform server according to a preset time interval; and the classification module is also used for training the clustering algorithm model based on the newly acquired sample data.
Another aspect of the present disclosure provides a service evaluation system including: the front-end access server is used for acquiring the characteristic information and the transaction information of a client currently transacting business; the main control server is used for receiving the characteristic information and the transaction information of the client acquired by the front-end access server; the artificial intelligence platform server is used for receiving the characteristic information and the transaction information of the client from the main control server and classifying the client according to the characteristic information and the transaction information of the client; and the master control server is also used for receiving the types of the clients generated by the artificial intelligence platform server and pushing corresponding evaluation items to the clients through the front-end access server according to the types of the clients so that the clients evaluate the services of the service provider, wherein for the same type of services, different types of clients respectively correspond to different evaluation items.
According to an embodiment of the present disclosure, the system further comprises: the big data platform server is used for storing sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; the artificial intelligence platform server can access sample data in the big data platform server so as to train a clustering algorithm model in the artificial intelligence platform server based on the sample data.
Another aspect of the present disclosure provides an electronic device including: one or more processors; memory to store one or more instructions, wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to implement a method as described above.
Another aspect of the disclosure provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method as described above.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the method and the device, the clients are classified according to the characteristic information and the transaction information of the clients, different types of clients correspond to different evaluation items respectively for the same type of service, and the corresponding evaluation items are pushed to the clients according to the types of the clients, so that the clients evaluate the service of a service provider. Therefore, the technical problem that the service satisfaction degree cannot reflect the actual situation when the service is evaluated in the related technology is at least partially solved, and the technical effect of acquiring more effective evaluation data to reflect the real evaluation of the service is further achieved.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically illustrates an exemplary system architecture to which the service evaluation method and apparatus may be applied, according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a service evaluation method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow diagram for training a clustering algorithm model based on sample data according to an embodiment of the present disclosure;
FIG. 4 schematically shows a schematic diagram of a service evaluation system according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a service evaluation system according to another embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of an artificial intelligence platform server in accordance with an embodiment of the disclosure;
FIG. 7 schematically illustrates a block diagram of a big data platform server, according to an embodiment of the disclosure;
FIG. 8 schematically illustrates a flow chart of a method of machine learning based service valuation in accordance with another embodiment of the present disclosure;
FIG. 9 schematically shows a block diagram of a service evaluation apparatus according to an embodiment of the present disclosure; and
FIG. 10 schematically illustrates a block diagram of a computer system suitable for implementing a service evaluation method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). Where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).
An embodiment of the present disclosure provides a service evaluation method, including: acquiring characteristic information and transaction information of a client handling business currently; classifying the customers according to the characteristic information and the transaction information of the customers; and pushing corresponding evaluation items to the client according to the type of the client so that the client evaluates the service of the service provider, wherein different types of clients respectively correspond to different evaluation items for the same type of service.
Fig. 1 schematically illustrates an exemplary system architecture to which the service evaluation method and apparatus may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include a terminal device 101, a network 102, and a server 103. Network 102 is the medium used to provide communication links between terminal equipment 101 and server 103. Network 102 may include various connection types, such as wired and/or wireless communication links, and so forth.
A user may use terminal device 101 to interact with server 103 over network 102 to receive or send messages or the like. Various messaging client applications, such as cell phone banking, shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, and/or social platform software, etc. (by way of example only) may be installed on terminal device 101. In one application scenario, the terminal 101 may also be an electronic device on a counter of a financial institution, and the electronic device may be a device facing a customer to handle business.
The terminal device 101 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 103 may be a server that provides various services, such as a background management server (for example only) that provides support for pages browsed by a user using the terminal device 101. The backend management server may analyze and process the received data such as the user request, and feed back a processing result (for example, a web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the service evaluation method provided by the embodiment of the present disclosure may be generally executed by the server 103. Accordingly, the service evaluation device provided by the embodiment of the present disclosure may be generally disposed in the server 103. The service evaluation method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 103 and is capable of communicating with the terminal device 101 and/or the server 103. Accordingly, the service evaluation device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster that is different from the server 103 and is capable of communicating with the terminal device 101 and/or the server 103. Alternatively, the service evaluation method provided by the embodiment of the present disclosure may also be executed by the terminal device 101, or may also be executed by another terminal device different from the terminal device 101. Accordingly, the service evaluation apparatus provided in the embodiment of the present disclosure may also be disposed in the terminal device 101, or disposed in another terminal device different from the terminal device 101.
For example, the characteristic information of the customer and the transaction information may be acquired by the terminal device 101, or acquired by another device and may be transmitted to the terminal device 101. Then, the terminal device 101 may locally execute the service evaluation method provided by the embodiment of the present disclosure, or send the characteristic information and the transaction information of the client to another terminal device, a server, or a server cluster, and execute the service evaluation method provided by the embodiment of the present disclosure by another terminal device, a server, or a server cluster that receives the characteristic information and the transaction information of the client.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.
Fig. 2 schematically shows a flow chart of a service evaluation method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S230.
In operation S210, characteristic information and transaction information of a client currently transacting a service are acquired.
According to an embodiment of the present disclosure, the characteristic information of the client may include, for example, age, sex, education level, and the like. The transaction information may include, for example, a transaction manner such as an internet banking transaction, an over-the-counter transaction, an ATM machine, and the like, and a transaction type such as a deposit, withdrawal, and borrowing, and the like. The transaction information may be other information such as a transaction location.
In operation S220, the customers are classified according to their characteristic information and transaction information.
According to the embodiment of the present disclosure, the customers can be classified using a clustering algorithm model according to the characteristic information and the transaction information of the customers.
According to the embodiment of the disclosure, a clustering algorithm model can be obtained by pre-training, and the training process comprises the following steps: acquiring sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and training the clustering algorithm model based on the sample data so that the clustering algorithm model can classify the newly input client data.
According to an embodiment of the present disclosure, for example, the characteristic information of the client currently transacting business is age 65, gender male, level of schoolwork; the transaction information is the account transfer business handled by the online bank. The client may be classified as an elderly type. For another example, the characteristic information of the client currently transacting business is age 30, sex male, university level; the transaction information is the account transfer business handled by the online bank. The customers may be classified into young types.
In operation S230, corresponding evaluation items are pushed to the customer according to the type of the customer so that the customer evaluates the service of the service provider, wherein different types of customers respectively correspond to different evaluation items for the same type of service.
Since different types of business have different customer groups, for example, internet banking is generally directed to services for non-elderly people, while over-the-counter business is directed to elderly people or certain business scenarios, etc.
According to the embodiment of the disclosure, for example, for the online banking transfer service, a first type of evaluation item can be pushed for young people, and a second type of evaluation item can be pushed for old people. The first evaluation item and the second evaluation item can be designed by the service provider according to the user group. For example, for the internet banking transfer service, the first evaluation item may include transaction fluency, whether the interface is beautiful, whether the system is stable, whether the function is diverse, and the like. The second evaluation item may include whether the page is brief, whether the operation flow is complex, whether the page font is appropriate, and the like.
According to the service evaluation method, the customers are classified, different types of users are allocated with different evaluation items, and compared with the related technology that all the customers use the same evaluation item to evaluate the service of the service aiming at the same service, such as online banking service, the service evaluation method disclosed by the invention has the advantage that the evaluation is more targeted.
According to the method and the device, the clients are classified according to the characteristic information and the transaction information of the clients, different types of clients correspond to different evaluation items respectively for the same type of service, and the corresponding evaluation items are pushed to the clients according to the types of the clients, so that the clients evaluate the service of a service provider. Therefore, the technical problem that the service satisfaction degree cannot reflect the actual situation when the service is evaluated in the related technology is at least partially solved, and the technical effect of acquiring more effective evaluation data to reflect the real evaluation of the service is further achieved.
When the evaluation content of the customer is analyzed, the evaluation items of the customer group facing the service are intelligently screened out through the labels of the customer and the service, and the intention of the customer is intelligently analyzed through the mutual relation between the evaluation contents, so that the accuracy and the effectiveness of evaluation feedback are effectively improved.
The method shown in fig. 2 is further described with reference to fig. 3-8 in conjunction with specific embodiments.
Fig. 3 schematically shows a flowchart for training a clustering algorithm model based on sample data according to an embodiment of the present disclosure.
The clustering algorithm model includes a K-Means clustering algorithm, as shown in FIG. 3, the method includes operations S310 to S350.
In operation S310, a K value is determined, and K center samples are randomly selected from the sample data as center points of respective groups of the K groups.
According to an embodiment of the present disclosure, the value of K may be determined according to the number of service types. For example, if the number of service types is 4, the value K may take 8. The central sample U of K groups can be randomly selected for all samples 1 ，U 2 ，......，U k As the center point of each packet.
In operation S320, a distance between each sample and each of K center samples is calculated, wherein each sample gets the corresponding K distances.
According to an embodiment of the present disclosure, for each sample X i Can be calculated by the distance formula Y i ＝argmin||X i -U j Calculating sample X by 1 < = j < = k i To each central point U j Will calculate the sample X i Is divided into a distance Y i Minimum center point U j In the packet(s).
In operation S330, for each sample, the samples are divided into groups of center samples having the shortest distance.
According to an embodiment of the present disclosure, the mean of all samples in the group may be calculated as a new center point U j . This step is performed in a loop until the center point U of each packet j There was no change after the new calculation.
In operation S340, the target samples spatially close to the center samples among the center samples of the respective groups are swapped with the samples in the other groups in position, and the target samples are taken as new center points.
According to embodiments of the present disclosure, to avoid the algorithm from falling into a locally optimal solution, the center point U of each packet may be assigned j A sample in the vicinity randomly swaps its position with any sample in other packets as a new center point.
In operation S350, the step of training the clustering algorithm model is repeatedly performed T times after the K value is fixed.
According to the embodiment of the disclosure, the target samples close to the central samples in the central samples of each group in the spatial position are exchanged with the samples in other groups, and the target samples are used as new central points, so that the algorithm can be prevented from falling into a local optimal solution, the existing clustering algorithm is optimized, and a better classification effect is achieved.
Through the embodiment of the disclosure, different services can be more closely associated with the client group facing the service, and the evaluation feedback content of the client group is collected, so that the problem of poor effectiveness of the evaluation feedback content caused by unfamiliarity with the service or other factors of other client groups is effectively avoided.
According to the embodiment of the disclosure, a service evaluation system is further provided, and compared with the prior art, the service evaluation system is more decoupled and is an improved service evaluation system.
Fig. 4 schematically shows a schematic diagram of a service evaluation system according to an embodiment of the present disclosure.
As shown in fig. 4, the service evaluation system 400 includes a front-end access server 410, a master server 420, and an artificial intelligence platform server 430.
The front-end access server 410 is used for acquiring characteristic information and transaction information of a client currently transacting business.
The master server 420 is used to receive the customer's characteristic information and transaction information acquired by the front-end access server 410.
The artificial intelligence platform server 430 is used for receiving the characteristic information and the transaction information of the customer from the master control server 420 and classifying the customer according to the characteristic information and the transaction information of the customer.
According to the embodiment of the present disclosure, the master server 420 is further configured to receive the type of the client generated by the artificial intelligence platform server 430, and push a corresponding evaluation item to the client through the front-end access server 410 according to the type of the client, so that the client evaluates the service of the service provider, where for the same type of service, different types of clients respectively correspond to different evaluation items.
According to an embodiment of the present disclosure, the service evaluation system 400 further includes a big data platform server 440 for storing sample data, wherein the sample data includes a plurality of samples, and each sample includes characteristic information and transaction information of one customer.
The artificial intelligence platform server 430 can ask for sample data in the big data platform server 440, so that the clustering algorithm model in the artificial intelligence platform server 430 is trained based on the sample data.
According to an embodiment of the present disclosure, the artificial intelligence platform server 430 may obtain, according to a preset time interval, the most recently acquired sample data in the big data platform server 440, and train the clustering algorithm model based on the most recently acquired sample data. The clustering algorithm model is automatically updated according to a certain time interval, and the robustness of the clustering algorithm model is effectively improved.
According to the method and the device, the clients are classified according to the characteristic information and the transaction information of the clients, different types of clients correspond to different evaluation items respectively for the same type of service, and the corresponding evaluation items are pushed to the clients according to the types of the clients, so that the clients evaluate the service of a service provider. Therefore, the technical problem that the service satisfaction cannot reflect the actual situation in the related technology of evaluating the service is at least partially solved, and the technical effect of acquiring more effective evaluation data to reflect the real evaluation of the service is further achieved.
Fig. 5 schematically illustrates a block diagram of a service evaluation system according to another embodiment of the present disclosure.
As shown in fig. 5, the service evaluation system 500 may further include a database server 450 and a data collection server 460 in addition to the front-end access server 410, the master server 420, the artificial intelligence platform server 430, and the big data platform server 440. It should be noted that, in this embodiment, the front-end access server 410, the master server 420, the artificial intelligence platform server 430 and the big data platform server 440 may refer to the description in fig. 4, but further, in fig. 5, the front-end access server 410, the master server 420, the artificial intelligence platform server 430 and the big data platform server 440 may have further functions.
For example, the front-end access server 410 may provide a front-end interactive interface for customer and business access, sending commands and data to the master server 420 and receiving data back.
The main control server 420 may control the main flow of the service evaluation system 500, and control the interactive transmission of commands and data. The method mainly comprises the steps of receiving a command initiated by a front-end access server 410, then sending the command to a database server 450 and a big data platform server 440 to access data, and finally returning the data to the front-end access server 410; or the main control server 420 periodically initiates a command, sends the command to access the data of the database server 450, sends the command and the data to the artificial intelligence platform server 430, receives the data returned by the artificial intelligence platform server 430, and finally stores the data in the database server 450.
The artificial intelligence platform server 430 may perform data processing, model training, and model prediction. After receiving the commands and data of the main control server 420, sending the commands to the big data platform server 440 to access the data, then carrying out processing pretreatment on the data, establishing or updating a model for model training, and returning the relevant data of the model to the main control server 420 after the model training is finished; or after receiving the command and the data, classifying the user by using the trained model, and returning classification result data after completing the classification.
The big data platform server 440 may store the data with larger magnitude required by the service evaluation system 500, the data in the big data platform server 440 may be maintained by the main control server 420 and the data collection server 460, and the artificial intelligence platform server 430 may access the data in the big data platform server 440. Embodiments of the present disclosure relate to customer information data, transaction information data of a business channel, and evaluation information data of a customer, wherein the evaluation information data of the customer may be maintained by the main control server 420, and the remaining data is maintained by the data collection server 460.
Alternatively, the data processing method and the model algorithm that may be used may be maintained in advance in the artificial intelligence platform server 430, which facilitates the actual modeling.
The database server 450 may store data with a smaller magnitude required by the service evaluation system 500, and mainly receives a command from the main control server 420 to perform a read-write operation on the data. Embodiments of the present disclosure relate to evaluating project data, model training result data.
The data collection server 460 can collect the customer information registered by the product user and the service detail information generated when the service is provided, and maintain the information on the big data platform server 440 in real time.
The data acquisition server 460 maintains data in the big data platform server 440 in real time, and the present disclosure uses different servers for different functions, which can reduce the coupling degree and improve the fault handling capability. The data collection server 460 may be a lightweight server, and is mainly used for collecting data, and the big data platform server 440 is mainly used for storing massive data, so that IO performance optimization of big data is better.
FIG. 6 schematically illustrates a block diagram of an artificial intelligence platform server, according to an embodiment of the disclosure.
As shown in fig. 6, the artificial intelligence platform server 430 includes: an artificial intelligence platform processor module 431, a data storage module 432, a data processing module 433, a model training module 434, a model prediction module 435, and a memory module 436.
The artificial intelligence platform processor module 431 is used for processing received commands and data, calling the model training module 434 or the model prediction module 435 to execute tasks, and returning data after the tasks are finished.
The data storage module 432 is used for storing models, data related to the models, data processing methods and algorithms used by the models, and is convenient for model training or prediction.
The data processing module 433 is used for calling in model training or prediction, and preprocessing data by using a data processing method in the data storage module 432.
The model training module 434 is configured to train the data in the memory module 436 using the model-related data in the data storage module 432 and the algorithm used by the model, and return the model and the training result after the training is successful.
The model prediction module 435 is configured to predict data in the memory module 436 by using the model in the data storage module 432, and return a prediction result after the prediction is successful.
The memory module 436 is used to store the received data for use in model training and model prediction.
FIG. 7 schematically illustrates a block diagram of a big data platform server, according to an embodiment of the disclosure.
As shown in fig. 7, the big data platform server 440 includes a big data platform processor module 441, a data warehouse module 442, a resolution optimizer module 443, and a cache database module 444.
The big data platform processor module 441 is used for processing received commands and data, and performs data interaction with the data storage module 442 and the cache database 444.
The data warehouse module 442 is used for Hbase or other databases supporting large data volumes and read-write security, and persistently stores the received data. In the example, the transaction information data of the business channel and the evaluation information data of the customer are related to the data with higher security requirement.
The analysis optimizer module 443 is used for adding an analysis optimizer to optimize a reading mode and improve the reading speed due to the problem that the database such as Hbase is slow in reading speed.
The cache database module 444 is used for Redis or other databases that utilize memory for reading and writing, and some data with lower security improve the speed of writing and reading by caching the database. The example relates to customer information data, the amount of data is relatively small and the updates are infrequent.
Fig. 8 schematically shows a flowchart of a method of machine learning-based service evaluation according to another embodiment of the present disclosure.
As shown in fig. 8, the method includes operations S801 to S808.
In operation S801, the cache database module 444 and the data warehouse module 442 of the big data platform server 440 are maintained in real time by collecting the customer information data and the transaction information data of the business channel through the data collecting server 460. According to the embodiment of the disclosure, the service channels can comprise two service channels of ATM deposit and withdrawal and credit card application of the mobile phone APP. The service types may include ATM location, a depositing and withdrawing transaction process, a credit card application process, and credit card mailing.
In operation S802, a portion of the data is extracted for data analysis, and a data processing method and a model using algorithm, i.e., a clustering algorithm, of the artificial intelligence platform server 430 are maintained in advance according to the analysis result.
In operation S803, the main control server 420 periodically initiates a model training command and sends the model training command to the artificial intelligence platform server 430, the artificial intelligence platform server 430 sends a request to the big data platform server 440 to access the customer information data and the transaction information data of the business channel, the big data platform server 440 reads the customer information data from the cache database module 444, and reads the transaction information data of each channel from the data warehouse 442 after being processed by the parsing optimizer module 443, and the returned data is stored in the memory module 436 of the artificial intelligence platform server 430. The data processing module 433 preprocesses the customer information data and the transaction information data of the service channel in the memory module 436 by the data processing method in the data storage module 432. The model training module 434 accesses the customer information data and the transaction information data of the business channel in the clustering algorithm and memory module, which are the model use algorithms in the data storage module 432, performs model training, stores the relevant data of the models and the models in the data storage module 432 after the training is completed, and returns the model training result, i.e., the relationship result between the customer and the service, to the main control server 420 and stores the model training result in the database server 450.
In operation S804, the service person inquires the result of the relationship between the client and the service through the front-end access server 410, designs an evaluation item according to the result of the relationship between the client and the service, and stores the evaluation item in the database server 450. According to the embodiment of the present disclosure, evaluation items may be designed according to service types, for example, including whether an ATM location is convenient, whether a deposit and withdrawal process is convenient, whether a credit card application process is convenient, whether a credit card posting is delivered on time, and the like.
In operation S805, after the customer service is finished, the data collecting server 460 collects the transaction information data of the corresponding service channel, stores the transaction information data in the big data platform server 440, triggers model prediction, and sends the customer information data and the transaction information data to the artificial intelligence platform server 430 along with a command, and stores the customer information data and the transaction information data in the memory module 436.
In operation S806, the model prediction module 435 of the artificial intelligence platform server 430 predicts the customer information data and the transaction information data in the memory module 436 using the model in the data storage module 432, and returns the prediction result to the main control server 420. The main control server 420 receives the prediction result data, accesses the evaluation items of the database server 450, and pushes the corresponding evaluation items to the client.
In operation S807, after receiving the push of the service evaluation, the client accesses the page through the front-end access server 410, sends a command to the main control server 420 to access the evaluation item of the database server 450, and finally stores the evaluation data of the client to the big data platform server 440 after the client completes the filling.
In operation S808, the service personnel queries the customer 'S rating data through the front-end access server 410 and performs an analysis improvement on the service according to the customer' S rating data.
Fig. 9 schematically shows a block diagram of a service evaluation apparatus according to an embodiment of the present disclosure.
As shown in fig. 9, the service evaluation apparatus 900 includes an obtaining module 910, a classifying module 920, and a pushing module 930.
The obtaining module 910 is configured to obtain feature information and transaction information of a client currently transacting business.
The classification module 920 is used for classifying the customers according to the characteristic information and the transaction information of the customers.
The pushing module 930 is configured to push corresponding evaluation items to the customer according to the type of the customer so that the customer evaluates the service of the service provider, where, for the same type of service, different types of customers respectively correspond to different evaluation items.
According to an embodiment of the present disclosure, the classification module 920 is configured to classify the customer using a clustering algorithm model according to the characteristic information and the transaction information of the customer.
The classification module 920 is further configured to pre-train to obtain a clustering algorithm model, and the training process includes: acquiring sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and training the clustering algorithm model based on the sample data so that the clustering algorithm model can classify the newly input customer data.
According to the embodiment of the disclosure, the clustering algorithm model comprises a K-Means clustering algorithm, and training the clustering algorithm model based on sample data comprises the following steps: determining a K value, and randomly selecting K central samples from the sample data as the central point of each group in K groups; calculating the distance between each sample and each center sample in the K center samples, wherein each sample obtains the corresponding K distances; for each sample, dividing the sample into groups of central samples with the shortest distance; exchanging positions of target samples close to the central samples in the central samples of each group in spatial position with samples in other groups, and taking the target samples as new central points; and after the K value is fixed, repeatedly executing the step of training the clustering algorithm model for T times.
According to an embodiment of the present disclosure, the obtaining module 910 is further configured to obtain sample data newly collected in the data platform server according to a preset time interval. The classification module 920 is further configured to train the clustering algorithm model based on the latest acquired sample data.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the obtaining module 910, the classifying module 920 and the pushing module 930 may be combined into one module/unit/sub-unit to be implemented, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the present disclosure, at least one of the obtaining module 910, the classifying module 920 and the pushing module 930 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or may be implemented by any one of three implementations of software, hardware and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 910, the classifying module 920 and the pushing module 930 may be at least partly implemented as a computer program module, which when executed may perform a corresponding function.
It should be noted that the service evaluation device portion in the embodiment of the present disclosure corresponds to the service evaluation method portion in the embodiment of the present disclosure, and the description of the service evaluation device portion specifically refers to the service evaluation method portion, which is not described herein again.
The present disclosure also provides an electronic device comprising one or more processors; a memory for storing one or more instructions, wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to implement a service evaluation method provided by the present disclosure.
FIG. 10 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method, according to an embodiment of the present disclosure. The computer system illustrated in FIG. 10 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 10, a computer system 1000 according to an embodiment of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. Processor 1001 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the present disclosure.
In the RAM 1003, various programs and data necessary for the operation of the system 1000 are stored. The processor 1001, ROM 1002, and RAM 1003 are connected to each other by a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1002 and/or the RAM 1003. Note that the programs may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
System 1000 may also include an input/output (I/O) interface 1005, the input/output (I/O) interface 1005 also being connected to bus 1004, according to an embodiment of the present disclosure. The system 1000 may also include one or more of the following components connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1008 including a hard disk and the like; and a communication portion 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The driver 1010 is also connected to the I/O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1010 as necessary, so that a computer program read out therefrom is mounted into the storage section 1008 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication part 1009 and/or installed from the removable medium 1011. The computer program performs the above-described functions defined in the system of the embodiment of the present disclosure when executed by the processor 1001. The above described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be embodied in the device/apparatus/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 1002 and/or the RAM 1003 described above and/or one or more memories other than the ROM 1002 and the RAM 1003.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It will be appreciated by those skilled in the art that various combinations and/or combinations of the features recited in the various embodiments of the disclosure and/or the claims may be made even if such combinations or combinations are not explicitly recited in the disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (7)

1. A service evaluation method, comprising:
the method comprises the steps that characteristic information and transaction information of a client transacting business currently are obtained through a front-end access server, wherein the transaction information comprises a transaction mode, a transaction type and a transaction place;
classifying the clients according to the characteristic information and the transaction information of the clients through a master control server; and
pushing corresponding evaluation items to the client through the master control server according to the type of the client so that the client can evaluate the service of a service provider, wherein different types of clients respectively correspond to different evaluation items for the same type of service; the evaluation project is designed by a service provider according to a customer group; different types of services have different said customer populations, including the elderly population;
wherein:
classifying the customer according to the characteristic information and the transaction information of the customer comprises: classifying the customers by using a clustering algorithm model according to the characteristic information and the transaction information of the customers;
the service evaluation method further comprises the step of obtaining the clustering algorithm model through pre-training, and the training process comprises the following steps:
obtaining sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and
training a clustering algorithm model based on the sample data so that the clustering algorithm model can classify newly input customer data;
the clustering algorithm model comprises a K-Means clustering algorithm, and training the clustering algorithm model based on the sample data comprises the following steps:
determining a K value, and randomly selecting K central samples from the sample data as the central point of each group in K groups;
calculating the distance between each sample and each of the K central samples, wherein each sample obtains corresponding K distances;
for each sample, dividing the sample into a group of central samples with the shortest distance;
exchanging positions of target samples close to the central samples in the central samples of the groups in space with samples in other groups, and taking the target samples as new central points; and
and after the K value is fixed, repeatedly executing the step of training the clustering algorithm model for T times.
2. The method of claim 1, further comprising:
acquiring newly acquired sample data in a data platform server according to a preset time interval; and
and training the clustering algorithm model based on the newly acquired sample data.
3. A service evaluation apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring characteristic information and transaction information of a client transacting business currently through a front-end access server, and the transaction information comprises a transaction mode, a transaction type and a transaction place;
the classification module is used for classifying the clients according to the characteristic information and the transaction information of the clients through the main control server; and
the pushing module is used for pushing corresponding evaluation items to the client through the master control server according to the type of the client so that the client can evaluate the service of a service provider, wherein for the same type of service, different types of clients respectively correspond to different evaluation items; the evaluation item is designed by a service provider according to a customer group; different types of services have different said customer populations, including the elderly population;
wherein:
the classification module is used for classifying the customers by using a clustering algorithm model according to the characteristic information and the transaction information of the customers;
the classification module is further used for pre-training to obtain the clustering algorithm model, and the training process comprises the following steps:
acquiring sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client; and
training a clustering algorithm model based on the sample data so that the clustering algorithm model can classify newly input customer data;
the clustering algorithm model comprises a K-Means clustering algorithm, and training the clustering algorithm model based on the sample data comprises the following steps:
determining a K value, and randomly selecting K central samples from the sample data as the central point of each group in K groups;
calculating the distance between each sample and each of the K central samples, wherein each sample obtains corresponding K distances;
for each sample, dividing the sample into a group of central samples with the shortest distance;
exchanging positions of target samples close to the central samples in the central samples of the groups in space with samples in other groups, and taking the target samples as new central points; and
and after the K value is fixed, repeatedly executing the step of training the clustering algorithm model for T times.
4. The apparatus of claim 3, wherein:
the acquisition module is also used for acquiring the latest acquired sample data in the data platform server according to a preset time interval; and
the classification module is further used for training the clustering algorithm model based on the latest acquired sample data.
5. A service evaluation system comprising:
the front-end access server is used for acquiring characteristic information and transaction information of a client transacting business currently, wherein the transaction information comprises a transaction mode, a transaction type and a transaction place;
the main control server is used for receiving the characteristic information and the transaction information of the client acquired by the front-end access server;
the artificial intelligence platform server is used for receiving the characteristic information and the transaction information of the client from the main control server and classifying the client according to the characteristic information and the transaction information of the client; and
the main control server is also used for receiving the types of the clients generated by the artificial intelligence platform server and pushing corresponding evaluation items to the clients through the front-end access server according to the types of the clients so that the clients can evaluate the services of the service provider, wherein for the same type of services, different types of clients respectively correspond to different evaluation items; the evaluation project is designed by a service provider according to a customer group; different types of services have different said customer populations, including the elderly population;
further comprising:
the big data platform server is used for storing sample data, wherein the sample data comprises a plurality of samples, and each sample comprises characteristic information and transaction information of a client;
the artificial intelligence platform server can access sample data in the big data platform server so as to train a clustering algorithm model in the artificial intelligence platform server based on the sample data.
6. An electronic device, comprising:
one or more processors;
a memory to store one or more instructions that,
wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-2.
7. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 2.
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