CN112804353A - Customer information management method, device and system based on deep data mining - Google Patents
Customer information management method, device and system based on deep data mining Download PDFInfo
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Abstract
The application discloses a customer information management method, a device and a system based on deep data mining, wherein the method comprises the following steps: receiving a user real-time control signal of a user terminal; acquiring reciprocating motion data of a user side based on a user real-time control signal; analyzing and predicting a user real-time control type through a motion data analysis model based on reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type; if the user real-time control type is predicted to be the target control type, the affiliated user information of the user terminal is recorded, and a feedback instruction corresponding to the user real-time control signal is pushed to the user terminal or a target app client corresponding to the affiliated user information. Therefore, the feedback can be carried out based on the real-time control signal of the user, the real-time dynamic characteristics of the user are mined, and the data mining efficiency is improved. And because the feedback instruction is pushed based on the real-time control signal of the user, the feedback accuracy is higher, and the feedback timeliness is better.
Description
Technical Field
The application relates to the technical field of big data, in particular to a customer information management method, a customer information management device and a customer information management system based on deep data mining.
Background
Data Mining (Data Mining) is a technique for searching its regularity from a large amount of Data by analyzing each Data, and mainly has 3 steps of Data preparation, regularity searching and regularity representation. The data mining task comprises association analysis, cluster analysis, classification analysis, anomaly analysis, specific group analysis, evolution analysis and the like. In the field of artificial intelligence, also known as Knowledge Discovery in Database (KDD), data mining is also considered as a basic step in the Knowledge Discovery process in Database. In the existing e-commerce or off-line service, the problems of low data mining efficiency, low service pushing accuracy, untimely service and the like are caused by the fact that the mining of inherent characteristic information of a user is mainly concerned and the mining of real-time dynamic characteristic or demand information of the user is lacked.
Disclosure of Invention
The invention provides a customer information management method, a customer information management device and a customer information management system based on deep data mining, and aims to solve the technical problems of low data mining efficiency, low service pushing accuracy and untimely service pushing in the prior art.
In a first aspect, the present invention provides a customer information management method based on deep data mining, including:
receiving a user real-time control signal of a user terminal;
acquiring reciprocating motion data of a user side based on the user real-time control signal;
analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type;
if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
Optionally, the method further includes:
acquiring action data analysis sample data comprising action data and a user actual operation purpose;
training a neural network model through the motion data analysis sample data to obtain the motion data analysis model.
Optionally, the method further includes:
comparing the reciprocating motion data of the user side with preset motion data, wherein the reciprocating motion data of the user side comprises a reciprocating motion amplitude, a frequency and a duration;
if the reciprocating motion data of the user side is matched with the preset motion data, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
Optionally, the pushing, to the user terminal or a target app client corresponding to the user information, a feedback instruction corresponding to the user real-time control signal includes:
acquiring the position information of the user terminal;
accessing a map application by adopting an API port to obtain a position information set of a to-be-selected place;
screening places meeting conditions in the position information set of the place to be selected through a first preset distance to serve as target places;
and pushing the position information of the target location to the user terminal or a target app client corresponding to the user information, wherein the position information comprises the distance between the target location and the user terminal and the management information or external feature information of the target location.
Optionally, the method further includes:
receiving feedback information of the user terminal based on the position information of the target place, wherein the feedback information based on the position information of the target place is generated based on a voice instruction of a user of the user terminal, and the feedback information based on the position information of the target place comprises selection information of a plurality of target places in the position information of the target place;
generating a navigation request from the current position of the user terminal to the position indicated by the selection information according to the selection information;
and sending the navigation request to the user terminal so that the user terminal generates a voice navigation instruction based on the navigation request.
Optionally, before receiving the user real-time control signal of the user terminal, the method includes:
acquiring environment temperature information corresponding to the position information of the user terminal, and comparing the environment temperature information with a preset temperature;
and if the environmental temperature information is lower than the preset temperature, sending a user real-time control signal acquisition request to the user terminal.
Optionally, the pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the user information includes:
and pushing a heat transfer instruction to the user terminal or a target app client corresponding to the user information, wherein the heat transfer instruction comprises the step of closing the original heat dissipation function of the user terminal or controlling a battery management system of the user terminal to heat.
In a second aspect, the present invention further provides a customer information management apparatus based on deep data mining, including:
the receiving module is used for receiving a user real-time control signal of a user terminal;
the acquisition module is used for acquiring reciprocating motion data of the user side based on the user real-time control signal;
the prediction module is used for analyzing and predicting a user real-time control type through an action data analysis model based on the reciprocating action data, wherein the user real-time control type comprises a target control type and a conventional control type;
and the pushing module is used for recording the affiliated user information of the user terminal and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information if the user real-time control type is predicted to be the target control type.
In a third aspect, the present invention further provides an electronic system, which includes a memory and a processor, where the processor is configured to implement the steps of the customer information management method based on deep data mining according to the first aspect when executing a computer program stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the customer information management method based on deep data mining according to the first aspect.
According to the technical scheme, the customer information management method, the customer information management device and the customer information management system based on deep data mining provided by the embodiment of the invention receive the user real-time control signal of the user terminal; acquiring reciprocating motion data of a user side based on the user real-time control signal; analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type; if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information. Therefore, the feedback can be carried out based on the real-time control signal of the user, the real-time dynamic characteristics of the user are mined, and the data mining efficiency is improved. And because the feedback instruction is pushed based on the real-time control signal of the user, the feedback accuracy is higher, and the feedback timeliness is better.
Drawings
Fig. 1 is a flowchart of a customer information management method based on deep data mining according to an embodiment of the present application;
fig. 2 is a structural diagram of a customer information management apparatus based on deep data mining according to an embodiment of the present application;
fig. 3 is a schematic diagram of an embodiment of an electronic system according to an embodiment of the present application;
fig. 4 is a schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions provided by the embodiments of the present specification, the technical solutions of the embodiments of the present specification are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present specification are detailed descriptions of the technical solutions of the embodiments of the present specification, and are not limitations on the technical solutions of the embodiments of the present specification, and the technical features in the embodiments and examples of the present specification may be combined with each other without conflict.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The term "two or more" includes the case of two or more.
Referring to fig. 1, fig. 1 is a flowchart of a customer information management method based on deep data mining according to the present invention. As shown in fig. 1, the method comprises the following steps:
In step 101, a user real-time manipulation signal of a user terminal may be received. For example, when the temperature of the environment in which the user is located is low, the user may perform a rubbing action to generate heat. At the moment, the user terminal can reciprocate along with the hand rubbing action of the user. At this time, the user terminal can send the user real-time control signal to the customer information management device based on the deep data mining.
And 102, acquiring reciprocating motion data of the user side based on the user real-time control signal.
In step 102, the user-side reciprocating motion data can be obtained based on the user real-time manipulation signal.
And 103, analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type.
In step 103, the real-time user manipulation type can be analyzed and predicted based on the reciprocating motion data and through the motion data analysis model. The user real-time manipulation type may include a target manipulation type and a regular manipulation type. It should be noted that the target manipulation type may be a user-rubbing heating type, and the conventional manipulation type is a user manipulation type other than the user-rubbing heating type.
Optionally, the method further includes:
acquiring action data analysis sample data comprising action data and a user actual operation purpose;
training a neural network model through the motion data analysis sample data to obtain the motion data analysis model.
Further, motion data analysis sample data including motion data and a user's actual operation purpose may be acquired. For example, a plurality of motion data analysis sample data may be acquired, and each motion data analysis sample data may include motion data and a user actual operation purpose in a corresponding relationship. The neural network model can be trained through a plurality of motion data analysis sample data to obtain a motion data analysis model. Therefore, the neural network model is trained by using a large amount of motion data analysis sample data to obtain the motion data analysis model, and the accuracy of analyzing and predicting the real-time control type of the user by using the motion data analysis model can be ensured.
Optionally, the method further includes:
comparing the reciprocating motion data of the user side with preset motion data, wherein the reciprocating motion data of the user side comprises a reciprocating motion amplitude, a frequency and a duration;
if the reciprocating motion data of the user side is matched with the preset motion data, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
Furthermore, the reciprocating motion data of the user side can be compared with the preset motion data. The reciprocating motion data of the user side comprises reciprocating motion amplitude, frequency and duration, and the preset motion data can be standard hand rubbing motion data stored in advance. If the reciprocating motion data of the user side is matched with the preset motion data, namely if the reciprocating motion data of the user side is matched with the standard hand rubbing motion data, the fact that the user is rubbing hands can be determined, and the fact that the temperature of the environment where the user is located is low is indicated. At this time, the affiliated user information of the user terminal can be recorded, and a feedback instruction corresponding to the user real-time control signal is pushed to the user terminal or a target app client corresponding to the affiliated user information. Therefore, the reciprocating motion data of the user side can be compared with the preset motion data, and the feedback instruction is pushed when the reciprocating motion data of the user side and the preset motion data are matched. The comparison process is simple, and the calculation resources are saved. The real-time dynamic characteristics of the user are mined, and the data mining efficiency is improved.
And step 104, if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
In step 104, if the predicted user real-time manipulation type is the target manipulation type, that is, if the predicted user real-time manipulation type is the user hand-rubbing warming type, the user information of the user terminal may be recorded, and a feedback instruction corresponding to the user real-time manipulation signal is pushed to the user terminal or a target app client corresponding to the user information.
Optionally, the pushing, to the user terminal or a target app client corresponding to the user information, a feedback instruction corresponding to the user real-time control signal includes:
acquiring the position information of the user terminal;
accessing a map application by adopting an API port to obtain a position information set of a to-be-selected place;
screening places meeting conditions in the position information set of the place to be selected through a first preset distance to serve as target places;
and pushing the position information of the target location to the user terminal or a target app client corresponding to the user information, wherein the position information comprises the distance between the target location and the user terminal and the management information or external feature information of the target location.
Furthermore, the position information of the user terminal can be obtained, and the API port is used for accessing the map application to obtain the position information set of the to-be-selected place. The candidate places can be places which can be warmed such as shopping malls, milk tea shops or coffee shops. And then, screening the places meeting the conditions from the position information set of the places to be selected by the first preset distance to serve as target places. Then, the location information of the target location may be pushed to the user terminal or a target app client corresponding to the affiliated user information, and the location information may include a distance from the target location to the user terminal and business information or external feature information of the target location. The operation information of the target location can be the business hours of the target location, and the external characteristic information is used for facilitating the user to identify the target location. Therefore, if the real-time control type of the user is predicted to be the target control type, namely if the real-time control type of the user is predicted to be the hand-rubbing heating type of the user, the position information of the target place can be pushed to the user, for example, the position information of places such as a market, a milk tea shop or a coffee shop which can be heated is convenient for the user to find the place which can be heated in time, the feedback accuracy is high, and the feedback timeliness is good. Furthermore, places meeting the conditions can be screened from the position information set of the places to be selected through the first preset distance to serve as target places, heating places which are relatively close to the users can be pushed for the users, and the users can reach the heating places by moving for a short distance.
Optionally, the method further includes:
receiving feedback information of the user terminal based on the position information of the target place, wherein the feedback information based on the position information of the target place is generated based on a voice instruction of a user of the user terminal, and the feedback information based on the position information of the target place comprises selection information of a plurality of target places in the position information of the target place;
generating a navigation request from the current position of the user terminal to the position indicated by the selection information according to the selection information;
and sending the navigation request to the user terminal so that the user terminal generates a voice navigation instruction based on the navigation request.
Further, feedback information of the user terminal based on the position information of the target place can be received, and the feedback information based on the position information of the target place is generated based on the voice instruction of the user to which the user terminal belongs. The feedback information based on the position information of the target place includes selection information of a plurality of target places among the position information of the target places by the user. Next, a navigation request based on the current location of the user terminal to the location indicated by the selection information may be generated according to the selection information. Then, a navigation request can be sent to the user terminal so that the user terminal can generate voice navigation instructions based on the navigation request. In this way, considering that the temperature of the environment where the user is located is low, the terminal is not very flexible to be operated by hands, so that the user can be allowed to send feedback information based on the position information of the target location in a voice mode, and the feedback efficiency is improved. In addition, the user terminal can also generate a voice navigation instruction based on the navigation request, manual navigation of the user is not needed, and the navigation efficiency is improved.
Optionally, before receiving the user real-time control signal of the user terminal, the method includes:
acquiring environmental temperature information corresponding to the user terminal position information, and comparing the environmental temperature information with a preset temperature;
and if the environmental temperature information is lower than the preset temperature, sending a user real-time control signal acquisition request to the user terminal.
Furthermore, the environment temperature information corresponding to the position information of the user terminal can be obtained, and the environment temperature information is compared with the preset temperature. And if the environmental temperature information is lower than the preset temperature, sending a user real-time control signal acquisition request to the user terminal. If the environment temperature information is higher than the preset temperature, which indicates that the environment where the user is located is not very cold, the user real-time control signal acquisition request may not be sent to the user terminal. The computing resources can be saved, and the resource waste is avoided.
Optionally, the pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the user information includes:
and pushing a heat transfer instruction to the user terminal or a target app client corresponding to the user information, wherein the heat transfer instruction comprises the step of closing the original heat dissipation function of the user terminal or controlling a battery management system of the user terminal to heat.
Further, if the predicted user real-time control type is the target control type, that is, if the predicted user real-time control type is the user hand-rubbing heating type, the heat transfer instruction may be pushed to the user terminal or the target app client corresponding to the user information. The heat transfer instruction may include turning off an original heat dissipation function of the user terminal or controlling a battery management system of the user terminal to perform heating. Therefore, when the user real-time control type is predicted to be the user hand rubbing heating type, namely the environment where the user is predicted to be cold, the battery management system of the user terminal can be controlled to heat, and the user can warm hands by using heat generated by the battery management system. In addition, the original heat dissipation function of the user terminal is closed, so that the heat can be prevented from being wasted, the battery management system of the user terminal can be controlled to heat, and the heating efficiency is improved.
It should be noted that, in the prior art, mainly attention is paid to mining of inherent feature information of a user, and mining of real-time dynamic feature or demand information of the user is lacked, which results in low data mining efficiency, low accuracy rate of service push, untimely service push and the like.
In the application, the feedback can be carried out based on the real-time control signal of the user, the real-time dynamic characteristics of the user are mined, and the data mining efficiency is improved. And because the feedback instruction is pushed based on the real-time control signal of the user, the feedback accuracy is higher, and the feedback timeliness is better.
According to the technical scheme, the customer information management method based on deep data mining provided by the embodiment of the invention receives the user real-time control signal of the user terminal; acquiring reciprocating motion data of a user side based on the user real-time control signal; analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type; if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information. Therefore, the feedback can be carried out based on the real-time control signal of the user, the real-time dynamic characteristics of the user are mined, and the data mining efficiency is improved. And because the feedback instruction is pushed based on the real-time control signal of the user, the feedback accuracy is higher, and the feedback timeliness is better.
Referring to fig. 2, fig. 2 is a structural diagram of a customer information management apparatus based on deep data mining according to the present invention. As shown in fig. 2, the customer information management apparatus 200 based on deep data mining includes a receiving module 201, an obtaining module 202, a predicting module 203, and a pushing module 204, wherein:
a receiving module 201, configured to receive a user real-time control signal of a user terminal;
the acquisition module 202 is configured to acquire reciprocating motion data of a user side based on the user real-time control signal;
the prediction module 203 is used for analyzing and predicting a user real-time control type through an action data analysis model based on the reciprocating action data, wherein the user real-time control type comprises a target control type and a conventional control type;
the pushing module 204 is configured to record the user information of the user terminal if the user real-time control type is predicted to be the target control type, and push a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the user information.
The customer information management device 200 based on deep data mining can implement each process implemented by the customer information management device based on deep data mining in the method embodiment of fig. 1, and is not described herein again to avoid repetition. And the customer information management device 200 based on deep data mining can realize feedback based on the real-time control signal of the user, mine the real-time dynamic characteristics of the user, and improve the data mining efficiency. And because the feedback instruction is pushed based on the real-time control signal of the user, the feedback accuracy is higher, and the feedback timeliness is better.
As shown in fig. 3, the embodiment of the present application provides an electronic system 300, which includes a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320, wherein the processor 320 executes the computer program 311 to implement the following steps:
receiving a user real-time control signal of a user terminal;
acquiring reciprocating motion data of a user side based on the user real-time control signal;
analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type;
if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
In a specific implementation, when the processor 320 executes the computer program 311, any of the embodiments corresponding to fig. 1 may be implemented.
Since the electronic system described in this embodiment is a system for implementing a customer information management device based on deep data mining in this embodiment, based on the method described in this embodiment, those skilled in the art can understand the specific implementation manner of the electronic system of this embodiment and various variations thereof, so that how to implement the method in this embodiment for the electronic system is not described in detail herein, and as long as those skilled in the art implement the system used for implementing the method in this embodiment, they all belong to the scope of protection intended by this application.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating an embodiment of a computer-readable storage medium according to the present application.
As shown in fig. 4, the present embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored, the computer program 411 implementing the following steps when executed by a processor:
receiving a user real-time control signal of a user terminal;
acquiring reciprocating motion data of a user side based on the user real-time control signal;
analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type;
if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
In a specific implementation, the computer program 411 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 computer, 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.
An embodiment of the present application further provides a computer program product, where the computer program product includes computer software instructions, and when the computer software instructions are run on a processing device, the processing device is caused to execute a flow in the customer information management method based on deep data mining in the corresponding embodiment of fig. 1.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. The customer information management method based on deep data mining is characterized by comprising the following steps:
receiving a user real-time control signal of a user terminal;
acquiring reciprocating motion data of a user side based on the user real-time control signal;
analyzing and predicting a user real-time control type through a motion data analysis model based on the reciprocating motion data, wherein the user real-time control type comprises a target control type and a conventional control type;
if the user real-time control type is predicted to be the target control type, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
2. The method of claim 1, wherein the method further comprises:
acquiring action data analysis sample data comprising action data and a user actual operation purpose;
training a neural network model through the motion data analysis sample data to obtain the motion data analysis model.
3. The method of claim 1, wherein the method further comprises:
comparing the reciprocating motion data of the user side with preset motion data, wherein the reciprocating motion data of the user side comprises a reciprocating motion amplitude, a frequency and a duration;
if the reciprocating motion data of the user side is matched with the preset motion data, recording the affiliated user information of the user terminal, and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information.
4. The method of claim 1, wherein the pushing of the feedback instruction corresponding to the user real-time manipulation signal to the user terminal or a target app client corresponding to the user information comprises:
acquiring the position information of the user terminal;
accessing a map application by adopting an API port to obtain a position information set of a to-be-selected place;
screening places meeting conditions in the position information set of the place to be selected through a first preset distance to serve as target places;
and pushing the position information of the target location to the user terminal or a target app client corresponding to the user information, wherein the position information comprises the distance between the target location and the user terminal and the management information or external feature information of the target location.
5. The method of claim 4, wherein the method further comprises:
receiving feedback information of the user terminal based on the position information of the target place, wherein the feedback information based on the position information of the target place is generated based on a voice instruction of a user of the user terminal, and the feedback information based on the position information of the target place comprises selection information of a plurality of target places in the position information of the target place;
generating a navigation request from the current position of the user terminal to the position indicated by the selection information according to the selection information;
and sending the navigation request to the user terminal so that the user terminal generates a voice navigation instruction based on the navigation request.
6. The method of claim 4, wherein before receiving the user real-time manipulation signal of the user terminal, the method comprises:
acquiring environment temperature information corresponding to the position information of the user terminal, and comparing the environment temperature information with a preset temperature;
and if the environmental temperature information is lower than the preset temperature, sending a user real-time control signal acquisition request to the user terminal.
7. The method of claim 1, wherein the pushing of the feedback instruction corresponding to the user real-time manipulation signal to the user terminal or a target app client corresponding to the user information comprises:
and pushing a heat transfer instruction to the user terminal or a target app client corresponding to the user information, wherein the heat transfer instruction comprises the step of closing the original heat dissipation function of the user terminal or controlling a battery management system of the user terminal to heat.
8. A customer information management apparatus based on deep data mining, comprising:
the receiving module is used for receiving a user real-time control signal of a user terminal;
the acquisition module is used for acquiring reciprocating motion data of the user side based on the user real-time control signal;
the prediction module is used for analyzing and predicting a user real-time control type through an action data analysis model based on the reciprocating action data, wherein the user real-time control type comprises a target control type and a conventional control type;
and the pushing module is used for recording the affiliated user information of the user terminal and pushing a feedback instruction corresponding to the user real-time control signal to the user terminal or a target app client corresponding to the affiliated user information if the user real-time control type is predicted to be the target control type.
9. An electronic system comprising a memory, a processor, wherein the processor is configured to implement the method for customer information management based on deep data mining of any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements a customer information management method based on deep data mining according to any one of claims 1 to 7.
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