CN111241235A

CN111241235A - Network disk searching method based on intelligent voice and related products

Info

Publication number: CN111241235A
Application number: CN201911383928.1A
Authority: CN
Inventors: 王辉
Original assignee: Shenzhen Jiuzhou Electric Appliance Co Ltd
Current assignee: Shenzhen Jiuzhou Electric Appliance Co Ltd
Priority date: 2019-12-28
Filing date: 2019-12-28
Publication date: 2020-06-05

Abstract

The present disclosure provides a network disk searching method based on intelligent voice, the method includes the following steps: the electronic equipment collects voice data of a target object, and calls an artificial intelligence model to recognize the voice data to obtain the meaning of the voice data; the electronic equipment generates a search command according to the meaning and calls a database of the network disk; the electronic equipment executes the search command in the database to obtain the search result, and the search result is displayed. The technical scheme provided by the application has the advantage of good user experience.

Description

Network disk searching method based on intelligent voice and related products

Technical Field

The invention relates to the technical field of networks, in particular to a network disk searching method based on intelligent voice and a related product.

Background

With the development of internet technology, more and more companies launch online storage network disk services based on networks, and users can conveniently access network resources backed up by themselves anytime and anywhere. However, as internet resources are more and more abundant, data stored in the user network disk is also more, and each search needs to spend a certain energy, which affects user experience.

Disclosure of Invention

The embodiment of the invention provides a network disk searching method based on intelligent voice and a related product, which can improve the speed and the interestingness of resource retrieval.

In a first aspect, an embodiment of the present invention provides a network disk searching method based on intelligent voice, where the method includes the following steps:

the electronic equipment is wirelessly paired with the temperature sensor and the intelligent power switch to form a wireless networking;

the electronic equipment periodically inquires the temperature value of the temperature sensor;

and when the electronic equipment determines that the temperature value is greater than the threshold value, sending a closing command to the intelligent power switch, wherein the closing command is used for closing the power supply.

In a second aspect, an electronic device is provided, the electronic device comprising:

the communication unit is used for wirelessly pairing with the temperature sensor and the intelligent power switch to form a wireless networking; periodically inquiring the temperature value of the temperature sensor;

a processing unit for sending a shutdown command to the intelligent power switch when the temperature value is determined to be greater than the threshold value, the shutdown command being used for shutting down the power supply

In a third aspect, a computer-readable storage medium is provided, which stores a program for electronic data exchange, wherein the program causes a terminal to execute the method provided in the first or second aspect.

The embodiment of the invention has the following beneficial effects:

according to the technical scheme, the electronic equipment is wirelessly paired with the temperature sensor and the intelligent power switch to form a wireless networking; the electronic equipment periodically inquires the temperature value of the temperature sensor; and when the electronic equipment determines that the temperature value is greater than the threshold value, sending a closing command to the intelligent power switch, wherein the closing command is used for closing the power supply. Thus, dangerous things such as fire caused by over-high temperature of the household appliance are avoided, and the safety of the household appliance is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic structural diagram of an electronic device.

Fig. 2 is a flow chart diagram of a network disk searching method based on intelligent voice.

Fig. 3 is a schematic structural diagram of an electronic device.

Detailed Description

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 some, not all, embodiments of the present invention. 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.

The terms "first," "second," "third," and "fourth," etc. in the description and claims of the invention and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application, where the electronic device 100 includes a storage and processing circuit 110, and a sensor 170 connected to the storage and processing circuit 110, where the sensor 170 includes a front camera and a rear camera, where:

the electronic device 100 may include control circuitry, which may include storage and processing circuitry 110. The storage and processing circuitry 110 may be a memory, such as a hard drive memory, a non-volatile memory (e.g., flash memory or other electronically programmable read-only memory used to form a solid state drive, etc.), a volatile memory (e.g., static or dynamic random access memory, etc.), etc., and the embodiments of the present application are not limited thereto. Processing circuitry in storage and processing circuitry 110 may be used to control the operation of electronic device 100. The processing circuitry may be implemented based on one or more microprocessors, microcontrollers, digital signal processors, baseband processors, power management units, audio codec chips, application specific integrated circuits, display driver integrated circuits, and the like.

The storage and processing circuitry 110 may be used to run software in the electronic device 100, such as an Internet browsing application, a Voice Over Internet Protocol (VOIP) telephone call application, an email application, a media playing application, operating system functions, and so forth. Such software may be used to perform control operations such as, for example, camera-based image capture, ambient light measurement based on an ambient light sensor, proximity sensor measurement based on a proximity sensor, information display functionality based on status indicators such as status indicator lights of light emitting diodes, touch event detection based on a touch sensor, functionality associated with displaying information on multiple (e.g., layered) display screens, operations associated with performing wireless communication functionality, operations associated with collecting and generating audio signals, control operations associated with collecting and processing button press event data, and other functions in the electronic device 100, to name a few.

The electronic device 100 may include input-output circuitry 150. The input-output circuit 150 may be used to enable the electronic device 100 to input and output data, i.e., to allow the electronic device 100 to receive data from an external device and also to allow the electronic device 100 to output data from the electronic device 100 to the external device. The input-output circuit 150 may further include a sensor 170. The sensor 170 may further include an ambient light sensor, a proximity sensor based on light and capacitance, a fingerprint recognition module, a touch sensor (e.g., a touch sensor based on light and/or a capacitive touch sensor, where the touch sensor may be a part of a touch display screen or may be used independently as a touch sensor structure), an acceleration sensor, a camera, and other sensors.

Input-output circuit 150 may also include one or more display screens, and when multiple display screens are provided, such as 2 display screens, one display screen may be provided on the front of the electronic device and another display screen may be provided on the back of the electronic device, such as display screen 130. The display 130 may include one or a combination of liquid crystal display, organic light emitting diode display, electronic ink display, plasma display, display using other display technologies. The display screen 130 may include an array of touch sensors (i.e., the display screen 130 may be a touch display screen). The touch sensor may be a capacitive touch sensor formed by a transparent touch sensor electrode (e.g., an Indium Tin Oxide (ITO) electrode) array, or may be a touch sensor formed using other touch technologies, such as acoustic wave touch, pressure sensitive touch, resistive touch, optical touch, and the like, and the embodiments of the present application are not limited thereto.

The electronic device 100 may also include an audio component 140. The audio component 140 may be used to provide audio input and output functionality for the electronic device 100. The audio components 140 in the electronic device 100 may include a speaker, a microphone, a buzzer, a tone generator, and other components for generating and detecting sound.

The communication circuit 120 may be used to provide the electronic device 100 with the capability to communicate with external devices. The communication circuit 120 may include analog and digital input-output interface circuits, and wireless communication circuits based on radio frequency signals and/or optical signals. The wireless communication circuitry in communication circuitry 120 may include radio-frequency transceiver circuitry, power amplifier circuitry, low noise amplifiers, switches, filters, and antennas. For example, the wireless Communication circuitry in Communication circuitry 120 may include circuitry to support Near Field Communication (NFC) by transmitting and receiving Near Field coupled electromagnetic signals. For example, the communication circuit 120 may include a near field communication antenna and a near field communication transceiver. The communications circuitry 120 may also include a cellular telephone transceiver and antenna, a wireless local area network transceiver circuitry and antenna, and so forth.

The electronic device 100 may further include a battery, power management circuitry, and other input-output units 160. The input-output unit 160 may include buttons, joysticks, click wheels, scroll wheels, touch pads, keypads, keyboards, cameras, light emitting diodes and other status indicators, and the like.

A user may input commands through input-output circuitry 150 to control the operation of electronic device 100, and may use output data of input-output circuitry 150 to enable receipt of status information and other outputs from electronic device 100.

According to the technical scheme, resources in the network disk are quickly retrieved through voice, user voice is collected, keywords are analyzed and extracted, and matching is carried out according to the keywords and the resources in the network disk. The method mainly comprises three parts of uploading resources, label resources and voice retrieval resources.

Uploading resources

And the user finishes uploading the resource files to the network server through the mobile phone APP, the PC client or the browser.

Label resources

After the user finishes uploading the resources, the user can actively mark some labels on the resources, and can also extract the keywords of the resources through deep learning algorithms such as a picture recognition technology and the like, for example, the picture may contain GPS position information and shooting time, and the film contains information such as a lead actor and caption keywords. And associating and storing the extracted groups of label information with resource names, resource types, uploading time and resource unique identifiers in a database.

3. Voice retrieval resources

When a user searches resources through voice, the voice is converted into text by using an automatic voice recognition technology (ASR) technology, and the text is matched with resource associated information in a database after word segmentation and extraction so as to find a file required by the user. Multiple groups or multiple-wheel type of conversations can be designed to improve the user experience during feedback.

The electronic device provided by the application can comprise the following modules:

resource label module

The resource tag module is divided into three sub-modules:

① Manual Label Module

When uploading resources, a user can mark some self-defined labels on the materials, such as marking the lead actor information of a certain movie.

② automatic labeling module

After the user uploads the resources, the module extracts the titles of the resource files, or extracts keywords with high capturing repetition rate of the embedded subtitles of the pictures in the video by adopting an image recognition technology aiming at the video files, and tags the resources.

③ database association module

And the resource label information extracted according to ① and ② is associated with information such as resource file types, resource names, uploading time, unique identifiers and the like and is stored in a database.

Voice acquisition module

The voice acquisition module is used for acquiring the demand information of the user through a voice receiving module of the mobile phone APP or the intelligent sound box when the user searches the network disk resources through voice.

Voice keyword extraction module

The voice processing module converts the user voice received by the voice acquisition module into characters through an ASR technology, and extracts a plurality of groups of keyword information by using technologies such as word segmentation and the like.

Resource retrieval module

The resource retrieval module is used for matching the keyword information group submitted by the voice processing module with the information of the database saved in the resource tag module to find out the resource file required by the user.

Referring to fig. 2, fig. 2 provides a method for searching a network disk based on smart voice, which is shown in fig. 2 and includes the following steps:

step S201, the electronic equipment collects voice data of a target object, and calls an artificial intelligence model to recognize the voice data to obtain the meaning of the voice data;

step S202, the electronic equipment generates a search command according to the meaning and calls a database of the network disk;

step S203, the electronic device executes the search command in the database to obtain the search result, and displays the search result.

According to the technical scheme, the electronic equipment acquires voice data of a target object, and calls an artificial intelligence model to recognize the voice data to obtain the meaning of the voice data; the electronic equipment generates a search command according to the meaning and calls a database of the network disk; and executing the search command in the database to obtain the search result, and displaying the search result. According to the technical scheme, the data of the network disk are searched in a voice recognition mode, the searching speed is increased, and the user experience is improved.

In one alternative approach, the system may be configured such that,

the electronic equipment receives the resources uploaded by the target object, obtains the characteristic information of the resources after identifying the resources, adds the characteristic information as the label information of the resources and uploads the label information to the network disk.

In an alternative, the electronic device executing the search command in the database to obtain the search result may specifically include:

and the electronic equipment matches the keywords in the search command with the tag information in the database, and if the matching is successful, the resource corresponding to the tag information is determined to be a search result.

In an alternative, the invoking of the artificial intelligence model to recognize the voice data to obtain the meaning of the voice data may specifically include:

the voice of the user is converted into text information by ASR (speech recognition technology), the text information is used for obtaining a plurality of groups of keyword information by using a word segmentation algorithm, and the plurality of groups of keyword information are used as the meaning of the voice data.

In an optional scheme, the obtaining the feature information of the resource after identifying the resource specifically may include:

if the resource is the picture information, extracting the address coordinate of the picture information, identifying and determining the category of the picture through a classification identification algorithm for the picture information, and taking the category and the address coordinate as the characteristic information of the resource.

The classification recognition algorithm includes, but is not limited to: neural network recognition algorithms, machine learning algorithms, deep neural network algorithms, and the like. This category includes, but is not limited to; characters, scenery, etc.

Machine learning algorithms represent the basic structure of a learning system. The environment provides some information to the learning part of the system, the learning part uses the information to modify the knowledge base to improve the efficiency of the system execution part to complete the task, the execution part completes the task according to the knowledge base, and simultaneously feeds back the obtained information to the learning part. In a specific application, the environment, the knowledge base and the execution part determine specific work content, and the problem to be solved by the learning part is completely determined by the part 3. We describe the impact of these 3 sections on the design learning system separately below.

Knowledge can be represented in a variety of forms, such as feature vectors, first-order logic statements, production rules, semantic networks and frameworks, and so forth. These representations have their own features, and the following 4 aspects are taken into consideration when selecting the representation:

(1) the expression ability is strong.

(2) It is easy to reason.

(3) The knowledge base is easily modified.

(4) The knowledge representation is easily scalable.

One problem that may ultimately be addressed with knowledge bases is that learning systems cannot acquire knowledge from the air without any knowledge at all, and each learning system requires information provided by certain knowledge understanding environments, analyzes comparisons, makes assumptions, checks and modifies these assumptions. Thus, more precisely, the learning system is an extension and improvement of existing knowledge.

The execution part is the core of the whole learning system, because the action of the execution part is the action of the learning part aiming for improvement. There are 3 problems associated with the execution part: complexity, feedback, and transparency.

The learning strategy refers to an inference strategy adopted by the system in the learning process. A learning system always consists of two parts, learning and environment. The information is provided by the environment (such as books or teachers), and the learning part realizes information conversion, memorizes the information in an understandable form and obtains useful information from the information. In the learning process, the less inference the student (learning part) uses, the greater his reliance on the teacher (environment), and the heavier the teacher's burden. The classification standard of the learning strategy is classified according to the reasoning amount and difficulty degree required by students to realize information conversion, the compliance is simple to complex, and the classification standard is divided into the following six basic types from few to many:

1) mechanical learning (Rote learning)

The learner can directly draw the information provided by the environment without any inference or other knowledge conversion. Such as the chequer program of seegmuir, the LT system of neuter and simon. The primary consideration of such learning systems is how to index and utilize stored knowledge. The learning method of the system is to directly learn through a pre-programmed and constructed program, and the learner does not do any work, or to directly receive established facts and data to learn, and does not make any reasoning on the input information.

2) Teaching Learning (Learning from Learning or Learning by Learning told)

Students take information from the environment (teacher or other information sources such as textbooks, etc.), convert knowledge into an internally usable representation, and organically integrate new knowledge with the original knowledge. So students are required to have a certain degree of reasoning ability, but the environment still needs to do a lot of work. Teachers propose and organize knowledge in some form so that the knowledge owned by students can be continually increased. The learning method is similar to the teaching mode of schools in human society, and the learning task is to establish a system, so that the system can receive teaching and suggestions and effectively store and apply learned knowledge. Many expert systems use this method to achieve knowledge acquisition when building a knowledge base.

3) Deduction Learning (Learning by reduction)

The form of reasoning used by students is deductive reasoning. Reasoning is based on axiom, and a conclusion is deduced through logic transformation. This reasoning is a process of "fidelity" transformation and specialization (specialization) that allows students to gain useful knowledge in the reasoning process. Such learning methods include macro-operation learning, knowledge editing, and Chunking (Chunking) techniques. The reverse process of deductive reasoning is inductive reasoning.

4) Analogy Learning (Learning by analog)

By using the similarity of knowledge in two different domains (source domain, target domain), learning can be achieved by analogy, deriving the corresponding knowledge of the target domain from the knowledge of the source domain (including similar features and other properties). The analog learning system can transform an existing computer application system to be adapted to a new domain to perform similar functions that were not originally designed.

Analogy learning requires more reasoning than the three learning approaches described above. It generally requires that the available knowledge be retrieved from a knowledge source (source domain) and then converted to a new form for use in a new situation (target domain). Analogy learning plays an important role in the human science and technology development history, and many scientific findings are obtained through analogy.

5) Interpretation-based learning (EBL)

The student first constructs an explanation to explain that the target concept is met for the example according to the target concept provided by the teacher, an example of the concept, a field theory and an operational criterion, and then generalizes the explanation into a sufficient condition of the target concept to meet the operational criterion. EBL has been widely used for knowledge base refinement and to improve system performance.

6) Inductive Learning (Learning from induction)

Inductive learning is the presentation of some instance or counterexample of a concept by a teacher or environment, with students deriving a general description of the concept through inductive reasoning. Such learning can be much more reasoning effort than teaching learning and deductive learning because the environment does not provide a general conceptual description (e.g., axiom). Induction learning is also somewhat more reasoning than analog learning, as no one similar concept can be taken as a "source concept". Induction learning is the most basic, and a mature learning method is developed, and has been widely researched and applied in the field of artificial intelligence.

Presentation based on acquired knowledge

The knowledge acquired by the learning system may be: behavioral rules, descriptions of physical objects, problem solving strategies, various classifications, and other types of knowledge for task implementation. For the knowledge acquired in learning, there are mainly some expressions as follows:

1) algebraic expression parameters

The goal of learning is to adjust the parameters or coefficients of an algebraic expression in the form of a fixed function to achieve a desired performance.

2) Decision tree

The classes of objects are divided by a decision tree, where each internal node in the tree corresponds to an object attribute, and each edge corresponds to a selectable value of these attributes, and the leaf nodes of the tree correspond to each basic classification of the object.

3) Formal grammar

In learning to identify a particular language, a formal grammar for that language is formed by generalizing a series of expressions for that language.

4) Generative rules

Production rules, expressed as condition-action pairs, have been used very widely. The learning behavior in the learning system is mainly as follows: generate, generalize, specialize (Specialization), or synthesize production rules.

5) Formal logic expression

The basic components of a formal logic expression are propositions, predicates, variables, statements that constrain the range of variables, and embedded logic expressions.

6) Graph and network

Some systems employ graph matching and graph transformation schemes to efficiently compare and index knowledge.

7) Framework and schema (schema)

Each frame contains a set of slots that describe various aspects of things (concepts and individuals).

8) Computer program and other process code

Knowledge in this form is acquired with the aim of deriving a capability to implement a particular process, rather than to infer the internal structure of the process.

9) Neural network

This is mainly used in the coupling learning. The acquired knowledge is learned and finally generalized to a neural network.

Referring to fig. 3, fig. 3 provides an electronic device including:

the acquisition unit is used for acquiring voice data of a target object;

the processing unit is used for calling the artificial intelligence model to recognize the voice data to obtain the meaning of the voice data; generating a search command according to the meaning, and calling a database of the network disk; and executing the search command in the database to obtain the search result, and displaying the search result.

Optionally, the electronic device further includes:

the communication unit is used for receiving the resources uploaded by the target object;

the processing unit is further configured to obtain feature information of the resource after identifying the resource, add the feature information as tag information of the resource, and upload the added feature information to the network disk.

Optionally, the processing unit is configured to match the keyword in the search command with the tag information in the database, and determine that the resource corresponding to the tag information is a search result if the matching is successful.

Optionally, the processor is further configured to perform character conversion on the voice data through an ASR technology to obtain text information, obtain multiple sets of keyword information from the text information using a word segmentation algorithm, and use the multiple sets of keyword information as the meaning of the voice data.

Embodiments of the present invention also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.

Embodiments of the present invention also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the methods as recited in the above method embodiments.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules illustrated are not necessarily required to practice the invention.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric 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 invention 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 may be implemented in the form of hardware, or may be implemented in the form of a software program module.

The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; 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

1. A network disk searching method based on intelligent voice is characterized by comprising the following steps:

the electronic equipment collects voice data of a target object, and calls an artificial intelligence model to recognize the voice data to obtain the meaning of the voice data;

the electronic equipment generates a search command according to the meaning and calls a database of the network disk;

the electronic equipment executes the search command in the database to obtain the search result, and the search result is displayed.

2. The method of claim 1, further comprising:

3. The method of claim 1, wherein executing, by the electronic device, the search command in the database to obtain the search result specifically comprises:

4. The method of claim 1, wherein invoking the artificial intelligence model to recognize the voice data to obtain the meaning of the voice data specifically comprises:

the method comprises the steps of performing character conversion on voice data through an ASR technology to obtain text information, obtaining multiple groups of keyword information from the text information by using a word segmentation algorithm, and taking the multiple groups of keyword information as the meaning of the voice data.

5. The method according to claim 2, wherein the obtaining the feature information of the resource after identifying the resource specifically comprises:

6. An electronic device, characterized in that the electronic device comprises:

the acquisition unit is used for acquiring voice data of a target object;

7. The electronic device of claim 6, further comprising:

8. The electronic device of claim 6,

and the processing unit is used for matching the keywords in the search command with the tag information in the database, and determining the resource corresponding to the tag information as a search result if the matching is successful.

9. The electronic device of claim 6,

the processor is further used for performing character conversion on the voice data through an ASR technology to obtain text information, obtaining multiple groups of keyword information from the text information through a word segmentation algorithm, and taking the multiple groups of keyword information as the meaning of the voice data.

10. A computer-readable storage medium storing a program for electronic data exchange, wherein the program causes a method provided in any one of claims 1-5 to be performed.