CN115438976A - User demand processing method and device based on intelligent counter - Google Patents
User demand processing method and device based on intelligent counter Download PDFInfo
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Abstract
The invention provides a user demand processing method and device based on an intelligent counter, which can be used in the financial field or other fields. The method comprises the following steps: acquiring user behavior data sent by an intelligent counter, and inputting the user behavior data into a pre-established user demand model to obtain a user demand prediction result; determining operation guide data and user preference data according to a user demand prediction result, and sending the operation guide data and the user preference data to the intelligent counter; and receiving actual operation data fed back by the intelligent counter, and correcting the user demand model by using the actual operation data. The invention utilizes the user demand model established by the artificial intelligence algorithm to analyze the service demand of the user, accurately positions the user demand, accurately provides high-quality, high-quality and high-intelligence intelligent counter guidance service point to point, improves the human-computer interaction experience, relieves the queuing phenomenon in a network, improves the customer satisfaction, shortens the operation time of the user on the intelligent counter and improves the working efficiency.
Description
Technical Field
The invention relates to the field of intelligent counters, in particular to a user demand processing method and device based on an intelligent counter.
Background
At present, the intelligent counter provides convenient and fast common counter service for customers, and users can operate the intelligent counter by themselves or complete business operation under the support of staff at branch points. However, for middle-aged and old customers and customers handling part of more complex services, the customers often need to queue up at a website to receive the guiding operation of workers, and the customers wait for a long time, and do not want the workers to accompany the operation for privacy reasons.
Therefore, the existing service function of the intelligent counter cannot well guide the user to the business with complicated procedures or flows. The cooperative operation of branch service personnel is needed, but under the environment of artificial intelligence popularization, the user experience needs to be improved urgently.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiments of the present invention mainly aim to provide a user demand processing method and device based on an intelligent counter, so as to improve the service processing efficiency of the intelligent counter and improve the user experience.
In order to achieve the above object, an embodiment of the present invention provides a user demand processing method based on an intelligent counter, where the method includes:
acquiring user behavior data sent by an intelligent counter, and inputting the user behavior data into a pre-established user demand model to obtain a user demand prediction result;
determining operation guide data and user preference data according to a user demand prediction result, and sending the operation guide data and the user preference data to the intelligent counter;
and receiving actual operation data fed back by the intelligent counter, and correcting the user demand model by using the actual operation data.
Optionally, in an embodiment of the present invention, the user requirement model is established in the following manner:
obtaining historical behavior data authorized by a user, and dividing the historical behavior data into a training sample set and a test sample set;
training a preset initial prediction model by utilizing a supervised learning algorithm and a training sample set;
and updating the trained initial prediction model by using the test sample set to obtain a user demand model.
Optionally, in an embodiment of the present invention, the inputting the user behavior data into a pre-established user demand model, and obtaining the user demand prediction result includes:
and inputting the user behavior data into a pre-established user demand model to obtain a user operation prediction result corresponding to the user behavior data, and taking the user operation prediction result as a user demand prediction result.
Optionally, in an embodiment of the present invention, determining the operation guidance data and the user preference data according to the user demand prediction result includes:
determining service information and user information corresponding to the subsequent operation of the user according to the user demand prediction result;
and determining operation guide data and user preference data according to the service information and the user information.
The embodiment of the invention also provides a user demand processing device based on the intelligent counter, which comprises:
the demand forecasting module is used for acquiring user behavior data sent by the intelligent counter and inputting the user behavior data into a pre-established user demand model to obtain a user demand forecasting result;
the operation guide module is used for determining operation guide data and user preference data according to a user demand prediction result and sending the operation guide data and the user preference data to the intelligent counter;
and the model correction module is used for receiving the actual operation data fed back by the intelligent counter and correcting the user demand model by using the actual operation data.
Optionally, in an embodiment of the present invention, the apparatus further includes:
the data acquisition module is used for acquiring historical behavior data authorized by a user and dividing the historical behavior data into a training sample set and a test sample set;
the model training module is used for training a preset initial prediction model by utilizing a supervised learning algorithm and a training sample set;
and the prediction model module is used for updating the trained initial prediction model by utilizing the test sample set to obtain a user demand model.
Optionally, in an embodiment of the present invention, the demand prediction module is further configured to input the user behavior data into a pre-established user demand model, obtain a user operation prediction result corresponding to the user behavior data, and use the user operation prediction result as the user demand prediction result.
Optionally, in an embodiment of the present invention, the operation guidance module includes:
the service information unit is used for determining service information and user information corresponding to the subsequent operation of the user according to the user demand prediction result;
and the operation guide unit is used for determining operation guide data and user preference data according to the service information and the user information.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for executing the above method.
The invention also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above method.
The invention utilizes the user demand model established by the artificial intelligence algorithm to analyze the service demand of the user, accurately positions the user demand, accurately provides high-quality, high-quality and high-intelligence intelligent counter guidance service point to point, improves the human-computer interaction experience, relieves the queuing phenomenon in a network, improves the customer satisfaction, shortens the operation time of the user on the intelligent counter and improves the working efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of a user demand processing method based on an intelligent counter according to an embodiment of the present invention;
FIG. 2 is a flow chart of establishing a user demand model in an embodiment of the present invention;
FIG. 3 is a flow chart of determining operation guidance data and user preference data in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system applying a user demand processing method based on an intelligent counter according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a user demand processing device based on an intelligent counter according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a user demand processing device based on an intelligent counter according to another embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an operation guidance module according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a user demand processing method and device based on an intelligent counter, which can be used in the financial field and other fields.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating a user requirement processing method based on an intelligent counter according to an embodiment of the present invention, and an execution subject of the user requirement processing method based on the intelligent counter according to the embodiment of the present invention includes, but is not limited to, a computer. The invention utilizes the user demand model established by the artificial intelligence algorithm to analyze the service demand of the user, accurately positions the user demand, accurately provides high-quality, high-quality and high-intelligence intelligent counter guidance service point to point, improves the human-computer interaction experience, relieves the queuing phenomenon in a network, improves the customer satisfaction, shortens the operation time of the user on the intelligent counter and improves the working efficiency. The method shown in the figure comprises the following steps:
the method comprises the following steps of S1, obtaining user behavior data sent by an intelligent counter, and inputting the user behavior data into a user demand model established in advance to obtain a user demand prediction result;
s2, determining operation guide data and user preference data according to a user demand prediction result, and sending the operation guide data and the user preference data to the intelligent counter;
and S3, receiving actual operation data fed back by the intelligent counter, and correcting the user demand model by using the actual operation data.
The user behavior data is operation data of a user at the intelligent counter, and specifically, the user behavior data is related information of a fund product searched. In addition, the user behavior data acquired by the background server is acquired after the user authorization, and specifically, the background server may be a bank background server.
Furthermore, the pre-established user demand model is obtained by utilizing an artificial intelligence algorithm to supervise and learn the collected operation habits and operation behaviors of the user and analyze the user behaviors, so that the optimal auxiliary guidance is obtained by practicing and learning.
Further, the background server inputs the user behavior data acquired from the intelligent counter into the demand model to obtain a user demand prediction result. Specifically, for example, the user demand prediction result may be that the user wants to purchase a certain fund product.
The background server determines operation guide data and preference data for guiding the user to perform subsequent operation according to the user demand prediction result. Specifically, the operation guidance data may be a purchase description and a purchase page link for a fund product, the user preference data may be a risk type (e.g., a low risk type) of investment by the user, and a preferred product type for purchase by the user, such as a fund financing product. Thus, user preference data may be particularly useful for including product recommendation information, such as advertisements for certain types of financial products, and the like.
Further, the user preference data may be determined based on both the user historical purchase information and the current user behavior data. The user historical purchase information is used for expressing the historical preference of the user, for example, a certain fund product is browsed according to the current user behavior data, and the same type of products as the fund financial product purchased by the user can be used as the user preference data according to the fund financial product purchased in the user historical purchase information. In addition, the operation guidance data is determined according to the current user behavior data, and specifically, for example, the user currently browses the relevant information of the fund product a, the operation guidance data may be a detailed introduction, a purchase description, a purchase page link, and the like of the fund product a.
Further, after the user preference data and the operation guide data are determined, the background server sends the user preference data and the operation guide data to the intelligent counter so that the user can browse and refer conveniently.
Wherein the user continues to perform the relevant operations at the intelligent counter, such as purchasing fund product a, based on the operation guidance data and the user preference data. The background server receives the actual operation data fed back by the intelligent counter, and specifically, the actual operation data can be the purchase of the fund product B. And updating and optimizing the user demand model by using the actual operation data, specifically, for example, if the user demand prediction result is that the user wants to purchase a fund product A, but the user actual operation data shows that the user purchases a fund product B, updating and correcting the user demand model by using the user behavior data and the actual operation data, so that model optimization is realized, and the prediction accuracy is improved.
As an embodiment of the present invention, as shown in fig. 2, a user demand model is established as follows:
step S21, obtaining historical behavior data authorized by a user, and dividing the historical behavior data into a training sample set and a test sample set;
s22, training a preset initial prediction model by using a training sample set by using a supervised learning algorithm;
and S23, updating the trained initial prediction model by using the test sample set to obtain a user demand model.
The historical behavior data of the user, such as information related to a certain transaction performed by the user on the intelligent counter, is obtained, and the obtaining of the historical behavior data is authorized by the user.
Furthermore, historical behavior data of the user is divided into a training sample set and a testing sample set, and then the initial prediction model is trained by the aid of the training sample set through a supervised learning algorithm. Specifically, user behavior data in the historical behavior data is used as input data of the initial prediction model, and actual operation data in the historical behavior data is used as output data of the initial prediction model. Specifically, the initial prediction model may adopt a conventional model, such as a neural network model, and the process of performing model training by using a supervised learning algorithm is a conventional process, which is not described herein again.
As an embodiment of the present invention, inputting user behavior data into a pre-established user demand model, and obtaining a user demand prediction result includes: and inputting the user behavior data into a pre-established user demand model to obtain a user operation prediction result corresponding to the user behavior data, and taking the user operation prediction result as a user demand prediction result.
The background server inputs user behavior data acquired from the intelligent counter into the demand model to obtain a user demand prediction result, wherein the user demand prediction result is a user operation prediction result. Specifically, for example, the user demand prediction result may be that the user wants to purchase a certain fund product.
As an embodiment of the present invention, as shown in fig. 3, the determining the operation guidance data and the user preference data according to the user demand prediction result includes:
step S31, determining service information and user information corresponding to the subsequent operation of the user according to the user demand prediction result;
and step S32, determining operation guide data and user preference data according to the service information and the user information.
The background server determines operation guide data and preference data for guiding the user to perform subsequent operation according to the user demand prediction result. Specifically, the operation guide data may be a purchase specification and a purchase page link for a certain fund product, the user preference data may be a risk type (such as a low risk type) of investment of the user, and a product type preferred by the user for purchase, such as a fund financing product. Thus, user preference data may be particularly useful for including product recommendation information, such as advertisements for certain types of financial products.
Further, the user preference data may be determined based on both the user historical purchase information and the current user behavior data. The user historical purchase information is used for expressing user historical preference, for example, a certain fund product is browsed according to the current user behavior data, and products of the same type as the fund financial product purchased by the user can be used as user preference data according to the fund financial product purchased in the user historical purchase information. In addition, the operation guidance data is determined according to the current user behavior data, specifically, for example, the user currently browses the relevant information of the fund product a, the operation guidance data may be a detailed introduction, a purchase instruction, a purchase page link, and the like of the fund product a.
Further, after the user preference data and the operation guide data are determined, the background server sends the user preference data and the operation guide data to the intelligent counter so that the user can browse and refer conveniently.
In an embodiment of the present invention, as shown in fig. 4, a system diagram of a user demand processing method based on an intelligent counter in the embodiment of the present invention is applied. The system shown in the figure is based on cross-platform application of Flutter framework design, native development can be quickly converted into application capable of being embedded into an intelligent counter or mobile phone app for popularization and online, multi-channel customer information and user data sharing are provided, and diverse data samples are provided for supervised learning of artificial intelligence technology. Furthermore, the system analyzes and processes the client operation by applying an artificial intelligence technology, quickly selects an optimal operation scheme for the client and visually pushes the optimal operation scheme to the user. Specifically, the system shown in fig. 4 includes:
a client data acquisition module: the method mainly comprises the steps of obtaining user operation data through technologies such as big data or embedded points at an intelligent counter and a mobile phone bank, analyzing the data to predict the operation habits of customers, and selecting the most frequently used operation of the customers as supervised learning data. The pain point of an authorized client when the client transacts business at the intelligent counter is obtained through big data technology.
A user center module: the system comprises user data, a bank card list, a user name and a password; the collection function comprises website service transaction records, service calls and the like collected by the user, and is used for reference when the user transacts services next time; personal setting, account information modification, notification push setting, security and privacy setting, general setting, version updating, customer service center, log-out.
The operation behavior analysis module: and through an artificial intelligence algorithm, the collected user operation habits and operation behaviors are supervised and learned, the user behaviors are analyzed, and an optimal auxiliary guidance pushing scheme is obtained through practicing learning.
Visual auxiliary operation module: the compatible front-end platform built based on the Flutter frame supports multiple channels such as an intelligent counter and a mobile banking, realizes visual operation guide by using voice recognition and AI man-machine interaction, and enables a user to perform intelligent counter business handling without manual participation. And displaying map network points, navigating paths and the like.
In this embodiment, artificial Intelligence (AI) is a technical science that uses a computer technology for simulating human cognitive ability to research and develop theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. The research fields of artificial intelligence include language identification, image identification, natural language processing, expert systems, and the like.
In addition, the AioT (artificial intelligence Internet of things) technology applied at present generates and collects mass data through the Internet of things and stores the mass data in a cloud end and an edge end, and then realizes all-material datamation and all-material intelligent connection through big data analysis and artificial intelligence in a higher form, and the Internet of things technology is applied to the artificial intelligence.
Further, flutter is a cross-platform mobile app development framework developed based on the Dart language. Its performance is more powerful than the RN, ionic frames. Meanwhile, the Dart language is object-oriented, the grammar of the Dart language is clean and strong, and similar to other popular languages (such as JavaScript, java and C #), developers with development experience of other languages are easy to master, and the maintenance is convenient. Flutter provides rich components and interfaces, and developers can quickly add Native extensions to Flutter. And meanwhile, the Flutter can also use a Native engine to render the view, which undoubtedly can provide a good experience for the user.
The specific working process of the system shown in fig. 4 includes:
1. the user behavior data is collected in multiple channels, for example, mobile terminal equipment embedded point collection, intelligent counter collection and the like.
2. Based on an SVM supervised learning algorithm, classification and regression are carried out on small samples and nonlinear data, and a global optimal solution of a learning period is obtained through an optimal hyperplane based on a feature space. And establishing and correcting a supervised learning model for analyzing the acquired user data.
3. And storing the user behavior model and the user information obtained by analysis in a user center module, and controlling the whole system by the module.
4. The user center module provides user data and user operation behavior guide data analyzed based on an artificial intelligence algorithm for the front end of the intelligent counter, such as user operation habits, business emphasis, financial management preference and the like, so that the front end can visually display corresponding recommendation functions.
5. And real operation data of the front end is collected and fed back to the user center, internal training data is provided for the data collection module, and multiple iterative learning is completed.
Furthermore, the system calculates and analyzes similar operation information stored in the system through a big data technology according to the operation requirements of the intelligent counter business of the customer, pushes operation guides required by the customer, encrypts the customer information and returns the encrypted customer information to the user center module.
The invention is beneficial to improving the working efficiency of inline customers at the network points and optimizing the customer experience. The application system developed in a cross-platform mode can be divided into multiple channels, the operation flow of the intelligent counter is optimized through an artificial intelligence technology, and the analysis and the solution of common problems are facilitated.
The invention combines an artificial intelligence algorithm with a front-end stream adaptation framework, adopts an artificial intelligence technology to ensure that simple and easy-to-use visual operation guide is provided for customers, utilizes a Flutter framework with strong compatibility to develop, can embed an operation guide system into channels such as an intelligent counter, a mobile phone bank and the like, can reduce the waiting time of the customers and improve the office efficiency.
The method analyzes the service requirements of the user by using the user requirement model established by the artificial intelligence algorithm, accurately positions the user requirements, accurately puts the private and public service operation guidance of the website, accurately provides high-quality, high-quality and high-intelligence intelligent counter guidance service point to point, improves the human-computer interaction experience, relieves the queuing phenomenon in the website, improves the customer satisfaction, shortens the operation time of the user on the intelligent counter, and improves the working efficiency.
Fig. 5 is a schematic structural diagram of a user demand processing device based on an intelligent counter according to an embodiment of the present invention, where the device includes:
the demand forecasting module 10 is used for acquiring user behavior data sent by the intelligent counter and inputting the user behavior data into a pre-established user demand model to obtain a user demand forecasting result;
the operation guidance module 20 is used for determining operation guidance data and user preference data according to the user demand prediction result and sending the operation guidance data and the user preference data to the intelligent counter;
and the model correction module 30 is used for receiving the actual operation data fed back by the intelligent counter and correcting the user demand model by using the actual operation data.
As an embodiment of the present invention, as shown in fig. 6, the apparatus further includes:
the data acquisition module 40 is configured to acquire historical behavior data authorized by a user, and divide the historical behavior data into a training sample set and a test sample set;
the model training module 50 is used for training a preset initial prediction model by utilizing a training sample set by utilizing a supervised learning algorithm;
and the prediction model module 60 is configured to update the trained initial prediction model by using the test sample set to obtain a user demand model.
As an embodiment of the present invention, the demand forecasting module 10 is further configured to input the user behavior data into a pre-established user demand model, obtain a user operation forecasting result corresponding to the user behavior data, and use the user operation forecasting result as the user demand forecasting result.
As an embodiment of the present invention, as shown in fig. 7, the operation guidance module 20 includes:
the service information unit 21 is configured to determine service information and user information corresponding to subsequent operations of the user according to the user demand prediction result;
and an operation guidance unit 22 for determining operation guidance data and user preference data according to the service information and the user information.
Based on the same application concept as the user demand processing method based on the intelligent counter, the invention also provides the user demand processing device based on the intelligent counter. Because the principle of solving the problems of the user demand processing device based on the intelligent counter is similar to that of the user demand processing method based on the intelligent counter, the implementation of the user demand processing device based on the intelligent counter can refer to the implementation of the user demand processing method based on the intelligent counter, and repeated parts are not repeated.
The invention combines an artificial intelligence algorithm with a front-end stream adaptation framework, adopts an artificial intelligence technology to ensure that simple and easy-to-use visual operation guide is provided for customers, utilizes a Flutter framework with strong compatibility to develop, can embed an operation guide system into channels such as an intelligent counter, a mobile phone bank and the like, can reduce the waiting time of the customers and improve the office efficiency.
The invention utilizes the user demand model established by the artificial intelligence algorithm to analyze the service demand of the user, accurately positions the user demand, accurately puts the operation guidance of the network point to the private and public services, accurately provides high-quality, high-quality and high-intelligence intelligent counter guidance service in a point-to-point manner, improves the human-computer interaction experience, relieves the queuing phenomenon in the network point, improves the customer satisfaction, shortens the operation time of the user on the intelligent counter and improves the working efficiency.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.
The invention also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above method.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for executing the above method.
As shown in fig. 8, the electronic device 600 may further include: communication module 110, input unit 120, audio processor 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 8; furthermore, the electronic device 600 may also comprise components not shown in fig. 8, which may be referred to in the prior art.
As shown in fig. 8, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the cpu 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142 for storing application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A user demand processing method based on an intelligent counter is characterized by comprising the following steps:
the method comprises the steps of obtaining user behavior data sent by an intelligent counter, inputting the user behavior data into a pre-established user demand model, and obtaining a user demand prediction result;
determining operation guide data and user preference data according to the user demand prediction result, and sending the operation guide data and the user preference data to the intelligent counter;
and receiving actual operation data fed back by the intelligent counter, and correcting the user demand model by using the actual operation data.
2. The method of claim 1, wherein the user demand model is created by:
obtaining historical behavior data authorized by a user, and dividing the historical behavior data into a training sample set and a testing sample set;
training a preset initial prediction model by utilizing a supervised learning algorithm and a training sample set;
and updating the trained initial prediction model by using the test sample set to obtain the user demand model.
3. The method of claim 1, wherein inputting the user behavior data into a pre-established user demand model to obtain a user demand prediction result comprises:
inputting the user behavior data into a pre-established user demand model to obtain a user operation prediction result corresponding to the user behavior data, and taking the user operation prediction result as a user demand prediction result.
4. The method of claim 1, wherein determining operation guidance data and user preference data based on the user demand prediction comprises:
determining service information and user information corresponding to the subsequent operation of the user according to the user demand prediction result;
and determining operation guide data and user preference data according to the service information and the user information.
5. A user demand handling apparatus based on an intelligent counter, the apparatus comprising:
the demand forecasting module is used for acquiring user behavior data sent by the intelligent counter and inputting the user behavior data into a pre-established user demand model to obtain a user demand forecasting result;
the operation guide module is used for determining operation guide data and user preference data according to the user demand prediction result and sending the operation guide data and the user preference data to the intelligent counter;
and the model correction module is used for receiving the actual operation data fed back by the intelligent counter and correcting the user demand model by using the actual operation data.
6. The apparatus of claim 5, further comprising:
the data acquisition module is used for acquiring historical behavior data authorized by a user and dividing the historical behavior data into a training sample set and a test sample set;
the model training module is used for training a preset initial prediction model by utilizing a supervised learning algorithm and a training sample set;
and the prediction model module is used for updating the trained initial prediction model by utilizing the test sample set to obtain the user demand model.
7. The apparatus of claim 5, wherein the demand forecasting module is further configured to input the user behavior data into a pre-established user demand model, obtain a user operation forecasting result corresponding to the user behavior data, and use the user operation forecasting result as the user demand forecasting result.
8. The apparatus of claim 5, wherein the operation guidance module comprises:
the service information unit is used for determining service information and user information corresponding to the subsequent operation of the user according to the user demand prediction result;
and the operation guide unit is used for determining operation guide data and user preference data according to the service information and the user information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that it stores a computer program for performing the method of any of claims 1 to 4.
11. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 4.
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