CN109753600A - Handle the method, apparatus asked questions and storage medium - Google Patents
Handle the method, apparatus asked questions and storage medium Download PDFInfo
- Publication number
- CN109753600A CN109753600A CN201811565501.9A CN201811565501A CN109753600A CN 109753600 A CN109753600 A CN 109753600A CN 201811565501 A CN201811565501 A CN 201811565501A CN 109753600 A CN109753600 A CN 109753600A
- Authority
- CN
- China
- Prior art keywords
- keyword
- temperature
- value
- index
- characterization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012512 characterization method Methods 0.000 claims description 29
- 238000012790 confirmation Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 5
- 230000000052 comparative effect Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000013507 mapping Methods 0.000 description 5
- 241000208340 Araliaceae Species 0.000 description 4
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 4
- 235000003140 Panax quinquefolius Nutrition 0.000 description 4
- 235000008434 ginseng Nutrition 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
This disclosure relates to which a kind of handle the method, apparatus asked questions and storage medium, which comprises obtain the number that the keyword in described problem, the temperature of the keyword and the keyword occur in described problem；Based on the temperature, the number and the tightness factor, confirm that the keyword meets preset condition, wherein, if the keyword is present in default word set, the tightness factor is then set as the first numerical value, if the keyword is not present in default word set, the tightness factor is set as second value, first numerical value is greater than the second value；Export information relevant to the keyword.The process can be based on program automatic running, information relevant to keyword can be exported within a short period of time to browse for user, so that user before obtaining manual answering, can first pass through browsing relevant information and find the information being conducive to the solution of problems, or even directly search out the answer of problem.
Description
Technical field
This disclosure relates to technical field of information processing, and in particular, to a kind of to handle the method, apparatus asked questions and deposit
Storage media.
Background technique
It, can be by the application program of terminal or webpage to correlation currently, user is when a certain professional domain encounters problem
The expert in field puts question to, and expert answers user and asks a question and will answer display in terminal, to solve the doubt of user.For example,
The problem of user is in terms of property tax expert consulting property tax.
In the related technology, due to puing question to quantity more, and expert may simultaneously non-full-time answer a question, therefore answer a question
When have lag on certain time, user needs to wait the long period, cannot obtain relevant answer in time.
Summary of the invention
The method, apparatus asked questions and storage medium are handled purpose of this disclosure is to provide a kind of, for solving correlation
In technology, the technical issues of user cannot obtain the answer of relevant issues in time.
To achieve the goals above, the embodiment of the present disclosure in a first aspect, providing a kind of handles the method that asks questions, institute
The method of stating includes:
Obtain what the keyword in described problem, the temperature of the keyword and the keyword occurred in described problem
Number；
Based on the temperature, the number and the tightness factor, confirm that the keyword meets preset condition, wherein if
The keyword is present in default word set, then sets the tightness factor as the first numerical value, if the keyword is not present in
Default word set then sets the tightness factor as second value, and first numerical value is greater than the second value；
Export information relevant to the keyword.
Optionally, the temperature for obtaining the keyword, comprising:
The number that is at least occurred in third party's data based on the keyword, characterization keyword the searching on network
The index and the characterization keyword of rope temperature calculate the temperature of the keyword in the index of local search temperature.
Optionally, the temperature of the keyword is calculated based on following formula:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is table
Levying the index of search temperature of the keyword on network, q is index of the characterization keyword in local search temperature, α,
Beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.
Optionally, the temperature of the keyword is calculated based on following formula:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party
The number occurred in data, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword
In the index of local search temperature, α, beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the ginseng for reducing the temperature
Numerical value.
Optionally, described to be based on the temperature, the number and the tightness factor, confirm that the keyword meets default item
Part, comprising:
The relating value of the keyword is calculated based on following formula:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is time that the keyword occurs in described problem
Number, f are the tightness factor, and t is the temperature of the keyword；
The size for comparing the relating value and threshold value, when the relating value is greater than or equal to the threshold value, described in confirmation
Keyword meets preset condition.
Optionally, the method also includes:
Export the information of manual answering's described problem.
The second aspect of the embodiment of the present disclosure, provides a kind of device for handling and asking questions, and described device includes:
Obtain module, be configured as obtain described problem in keyword, the keyword temperature and the keyword
The number occurred in described problem；
Confirmation module is configured as confirming that the keyword meets based on the temperature, the number and the tightness factor
Preset condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value, if institute
It states keyword and is not present in default word set, then set the tightness factor as second value, first numerical value is greater than described
Second value；
First output module is configured as output information relevant to the keyword.
Optionally, the acquisition module is also configured to
The number that is at least occurred in third party's data based on the keyword, characterization keyword the searching on network
The index and the characterization keyword of rope temperature calculate the temperature of the keyword in the index of local search temperature.
Optionally, the acquisition module is additionally configured to calculate the temperature of the keyword based on following formula:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is table
Levying the index of search temperature of the keyword on network, q is index of the characterization keyword in local search temperature, α,
Beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.
Optionally, the acquisition module is additionally configured to calculate the temperature of the keyword based on following formula:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party
The number occurred in data, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword
In the index of local search temperature, α, beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the ginseng for reducing the temperature
Numerical value.
Optionally, the confirmation module includes:
Computational submodule is set to the relating value that the keyword is calculated based on following formula by coordination:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is time that the keyword occurs in described problem
Number, f are the tightness factor, and t is the temperature of the keyword；
Comparative sub-module is configured as the size of relating value and threshold value described in comparison, when the relating value is greater than or equal to
When the threshold value, confirm that the keyword meets preset condition.
Optionally, described device further include:
Second output module is configured as the information of output manual answering's described problem.
The third aspect of the embodiment of the present disclosure provides a kind of computer readable storage medium, is stored thereon with computer journey
The step of sequence, which realizes any one of above-mentioned first aspect the method when being executed by processor.
The fourth aspect of the embodiment of the present disclosure provides a kind of device for handling and asking questions, comprising:
Memory is stored thereon with computer program；
Processor, it is any in above-mentioned first aspect to realize for executing the computer program in the memory
The step of item the method.
Through the above technical solutions, obtaining the keyword in described problem, the temperature of the keyword and the pass first
The number that keyword occurs in described problem；It is then based on the temperature, the number and the tightness factor, confirms the key
Word meets preset condition；And when the keyword meets preset condition, information relevant to the keyword is exported.The process
It can be based on program automatic running, information relevant to keyword can be exported within a short period of time and browsed for user, so that user
Before obtaining manual answering, browsing relevant information can be first passed through and find the information being conducive to the solution of problems, or even directly sought
It finds a solution to the problem.
Other feature and advantage of the disclosure will the following detailed description will be given in the detailed implementation section.
Detailed description of the invention
Attached drawing is and to constitute part of specification for providing further understanding of the disclosure, with following tool
Body embodiment is used to explain the disclosure together, but does not constitute the limitation to the disclosure.In the accompanying drawings:
Fig. 1 is a kind of flow chart for handling the method asked questions shown according to an exemplary embodiment.
Fig. 2A is a surface chart of application program shown according to an exemplary embodiment.
Fig. 2 B is another surface chart of application program shown according to an exemplary embodiment.
Fig. 3 is during the method that a kind of processing shown according to an exemplary embodiment asks questions includes the steps that based on institute
Temperature, the number and the tightness factor are stated, confirms that the keyword meets the flow chart of preset condition.
Fig. 4 is a kind of another flow chart for handling the method asked questions shown according to an exemplary embodiment.
Fig. 5 is a kind of block diagram for handling the device asked questions shown according to an exemplary embodiment.
Fig. 6 is a kind of block diagram for handling confirmation module in the device asked questions shown according to an exemplary embodiment.
Fig. 7 is a kind of another block diagram for handling the device asked questions shown according to an exemplary embodiment.
Fig. 8 is a kind of another block diagram for handling the device asked questions shown according to an exemplary embodiment.
Specific embodiment
It is described in detail below in conjunction with specific embodiment of the attached drawing to the disclosure.It should be understood that this place is retouched
The specific embodiment stated is only used for describing and explaining the disclosure, is not limited to the disclosure.
Fig. 1 is a kind of flow chart for handling the method asked questions shown according to an exemplary embodiment, such as Fig. 1 institute
Show, this method can be applied to application program shown in Fig. 2A, and this approach includes the following steps.
In step s 11, the keyword in acquisition described problem, the temperature of the keyword and the keyword are described
The number occurred in problem；
In step s 12, it is based on the temperature, the number and the tightness factor, it is default to confirm that the keyword meets
Condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value, if the pass
Keyword is not present in default word set, then sets the tightness factor as second value, first numerical value is greater than described second
Numerical value；
In step s 13, information relevant to the keyword is exported.
Firstly, user can propose problem by application program or webpage, after user's proposition problem, step S11 is executed,
Obtain the number that the keyword in described problem, the temperature of the keyword and the keyword occur in described problem.Its
In, the keyword in acquisition problem can be obtained using the existing method for extracting keyword, and the temperature of keyword is for characterizing
Keyword search degree whithin a period of time and attention rate can use the calculation method of existing keyword popularity, can also be with
The calculation method of keyword popularity is established according to the actual situation.
Optionally, the temperature for obtaining the keyword, comprising:
The number that is at least occurred in third party's data based on the keyword, characterization keyword the searching on network
The index and the characterization keyword of rope temperature calculate the temperature of the keyword in the index of local search temperature.
Wherein, third party's data is the data that obtains from third party, for example, from the relevant regulation file of network acquisition,
The data such as knowledge mapping, encyclopaedia entry are also possible to the data manually provided for calculating the number of keyword appearance.Characterization institute
The index for stating search temperature of the keyword on network can be one in the indexes such as Baidu's index, 360 indexes, search dog index
Kind, the average value of the index of keyword whithin a period of time, such as this in nearest one week can be used to refer to when calculating temperature
Several average value, i.e., the arithmetic mean of instantaneous value of the daily index in seven days in the past.The keyword is characterized in local search temperature
Index in local nearest one week searching times or the calculated parameter of the searching times can be based on for keyword, local
The application program or the terminal where webpage that can be the method for realizing that the processing asks questions.The keyword is provided in third party
The index and the characterization keyword of search temperature of the number, the characterization keyword occurred in material on network are searched locally
The index of rope temperature characterizes the temperature of keyword to a certain extent, can be based on the number and two index structures
Suitable mathematical model is built to calculate the temperature of keyword.
In a kind of possible embodiment, the temperature of the keyword is calculated based on following formula:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is table
Levying the index of search temperature of the keyword on network, q is index of the characterization keyword in local search temperature, α,
Beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.The value of α, beta, gamma can be distinguished
Being 0.1,0.45,0.45, n can be for 1000.[] is to be rounded symbol, and [x] is represented less than or the maximum integer equal to x.Certainly,
In other embodiments, it can also be rounded mode using other, such as select ceiling function [x], [x] expression is greater than or equal to
The smallest positive integral of x.
In alternatively possible embodiment, the temperature of the keyword is calculated based on following formula:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party
The number occurred in data, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword
In the index of local search temperature, α, beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the ginseng for reducing the temperature
Numerical value.α, the value of beta, gamma, which can be respectively 0.1,0.45,0.45, n, to be 1000.[] is to be rounded symbol, and [x] is represented less than
Or the maximum integer equal to x.Certainly, in other embodiments, it can also be rounded mode using other, such as select ceiling letter
Number [x], [x] indicate the smallest positive integral for being greater than or equal to x.When the event for occurring being affected for keyword popularity, such as
When there is large variation in relevant policies, can taking human as adjusting parameter p, allow calculated t more accurately to reflect pass
The temperature of keyword.
For example, as shown in Fig. 2, user proposes problem " discovery when paying vehicle-purchase tax, the vehicle purchase actually paid
It is fewer than the vehicle-purchase tax that practical buying car settlement price calculates to set tax, does is may I ask preferential tax revenue? ", extract keyword " vehicle
Purchase tax ", " settlement price ", " preferential tax revenue ", the number occurred is respectively 3,1,1, can establish binary group set KS
(Keyword Set) store above- mentioned information, as KS be<vehicle-purchase tax, 3>,<settlement price, 1>,<preferential tax revenue, 1>},<
Vehicle-purchase tax, 3 > indicate that the number that keyword " vehicle-purchase tax " occurs in the problem is 3.Based on any of the above-described calculating heat
The formula of degree calculates the temperature t of vehicle-purchase tax.
It, can be by these keys if the two or more keywords extracted are adjacent in the problem when extracting keyword
The problem of word merges into a combination keyword, and combination keyword often can more accurately express user.Such as the above problem
In, keyword " vehicle " and " purchase tax " are extracted, since " vehicle " and " purchase tax " is adjacent in problem, therefore the two is merged
For a combination keyword " vehicle-purchase tax ".
Obtain what the keyword in described problem, the temperature of the keyword and the keyword occurred in described problem
After number, step S12 is executed, is based on the temperature, the number and the tightness factor, it is default to confirm that the keyword meets
Condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value, if the pass
Keyword is not present in default word set, then sets the tightness factor as second value, first numerical value is greater than described second
Numerical value.When keyword is present in default word set, show that keyword is important, therefore sets the first numerical value and be greater than second number
Value.
The word set that default word set can be established for the keyword extracted based on third party's data, such as choose existing regulation
The data such as file, knowledge mapping and encyclopaedia entry extract keyword and establish default word set, optionally, calculate in the default word set
The temperature of keyword so in step s 11 if keyword is present in default word set, can be obtained directly from default word set
Take the temperature of keyword.Keyword and temperature can also be stored using binary group in default word set, such as < vehicle-purchase tax, and 200
>,<preferential tax revenue, 230>,<settlement price, 49>.Wherein,<vehicle-purchase tax, 200>indicate keyword " vehicle-purchase tax "
Temperature be 200.
Optionally, as shown in figure 3, being based on the temperature, the number and the tightness factor, confirm that the keyword meets
Preset condition includes the following steps.
In step S121, the relating value of the keyword is calculated based on following formula:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is time that the keyword occurs in described problem
Number, f are the tightness factor, and t is the temperature of the keyword；
In step S122, the size of the relating value and threshold value, when the relating value is greater than or equal to the threshold
When value, confirm that the keyword meets preset condition.
Relating value score characterizes the significance level of its corresponding keyword to a certain extent.
Continue to use above-mentioned example, KS be<vehicle-purchase tax, 3>,<settlement price, 1>,<preferential tax revenue, 1>}, if the pass
Keyword is present in default word set, then sets the tightness factor as 1, if the keyword is not present in default word set, setting
The tightness factor is 0.5.By calculating, if keyword " vehicle-purchase tax ", " preferential tax revenue ", " settlement price "
Score value is respectively 600,230,49, threshold value 100, then keyword " vehicle-purchase tax ", " preferential tax revenue " meet default item
Part.And then in step s 13, information relevant to keyword " vehicle-purchase tax " and " preferential tax revenue " is exported.
By taking keyword " vehicle-purchase tax " as an example, as shown in Figure 2 A, information relevant to " vehicle-purchase tax " can be to know
Know map, which is placed in application program to meet the keyword " vehicle-purchase tax " of preset condition for central keyword
Interface center, and keyword " vehicle-purchase tax " around relevant to " vehicle-purchase tax " word " taxpayer " of generation,
" tax rate " etc., wherein the method for generating word relevant to keyword is the prior art, and this will not be repeated here.By clicking boundary
" more multichannel chromatogram " button on face can choose the knowledge mapping for showing other keywords such as " preferential tax revenue ", click other phases
The word of pass such as " taxpayer ", can show the knowledge mapping with " taxpayer " for central keyword, accordingly user
It is recognized that more correlation words, and utilizes correlation word relevant search information.
Further, it is also possible to based on the keyword search for meeting preset condition associated documents and display is exported, as shown in Figure 2 A,
Above-mentioned example is continued to use, the keyword for meeting preset condition is " vehicle-purchase tax " and " preferential tax revenue ", then based on " vehicle is purchased
Tax " and " preferential tax revenue " search associated documents are simultaneously shown in below knowledge mapping, and clicking associated documents may browse through associated documents
Detailed content.
The keyword in described problem, the temperature of the keyword and the keyword is obtained first to go out in described problem
Existing number；It is then based on the temperature, the number and the tightness factor, confirms that the keyword meets preset condition；And
When the keyword meets preset condition, information relevant to the keyword is exported.The process can be automatic based on program
Operation, can export within a short period of time information relevant to keyword for user browse so that user obtain manual answering it
Before, browsing relevant information can be first passed through and find the information being conducive to the solution of problems, or even directly search out the answer of problem.
Fig. 4 is a kind of another flow chart for handling the method asked questions shown according to an exemplary embodiment, such as Fig. 4
Shown, this approach includes the following steps.
In the step s 21, the keyword in acquisition described problem, the temperature of the keyword and the keyword are described
The number occurred in problem；
In step S22, it is based on the temperature, the number and the tightness factor, it is default to confirm that the keyword meets
Condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value, if the pass
Keyword is not present in default word set, then sets the tightness factor as second value, first numerical value is greater than described second
Numerical value；
In step S23, information relevant to the keyword is exported.
In step s 24, the information of manual answering's described problem is exported.
The answer that the information of manual answering's described problem can make for the expert of related fields or technical staff, in one kind
In possible mode, after the expert of related fields sees the problem, information is answered in input, after input, by where user
Terminal show the answer information.Since manual answering is slower, therefore the output of the information of manual answering's described problem is generally later than
The output of information relevant to keyword.User can be before the information of output manual answering's described problem, browsing and key
The relevant information of word.
As shown in Figure 2 B, the answer information of manual answering's problem is exported below problem, and shows the position of answerer, institute
Belong to the information such as unit.
It is worth noting that for simple description, therefore, it is stated as a systems for embodiment of the method shown in Fig. 4
The combination of actions of column, but those skilled in the art should understand that, the disclosure is not limited by the described action sequence.Its
It is secondary, those skilled in the art should also know that, the embodiments described in the specification are all preferred embodiments, related dynamic
Make necessary to the not necessarily disclosure.
Fig. 5 is a kind of block diagram for handling the device asked questions shown according to an exemplary embodiment, as shown in figure 5,
The device 100 includes:
Obtain module 110, be configured as obtain described problem in keyword, the keyword temperature and the key
The number that word occurs in described problem；
Confirmation module 120 is configured as confirming the keyword based on the temperature, the number and the tightness factor
Meet preset condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value,
If the keyword is not present in default word set, the tightness factor is set as second value, first numerical value is greater than
The second value；
First output module 130 is configured as output information relevant to the keyword.
Optionally, the acquisition module 110 is also configured to
The number that is at least occurred in third party's data based on the keyword, characterization keyword the searching on network
The index and the characterization keyword of rope temperature calculate the temperature of the keyword in the index of local search temperature.
Optionally, the acquisition module 110 is additionally configured to calculate the temperature of the keyword based on following formula:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is table
Levying the index of search temperature of the keyword on network, q is index of the characterization keyword in local search temperature, α,
Beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.
Optionally, the acquisition module 110 is additionally configured to calculate the temperature of the keyword based on following formula:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party
The number occurred in data, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword
In the index of local search temperature, α, beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the ginseng for reducing the temperature
Numerical value.
Optionally, as shown in fig. 6, the confirmation module 120 includes:
Computational submodule 121 is set to the relating value that the keyword is calculated based on following formula by coordination:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is time that the keyword occurs in described problem
Number, f are the tightness factor, and t is the temperature of the keyword；
Comparative sub-module 122 is configured as the size of relating value and threshold value described in comparison, when the relating value is greater than or waits
When the threshold value, confirm that the keyword meets preset condition.
Optionally, as shown in fig. 7, it includes obtaining module 110, confirmation module 120, the first output module that the device 100, which removes,
Outside 130, further includes:
Second output module 140 is configured as the information of output manual answering's described problem.
About the device in above-described embodiment, wherein modules execute the concrete mode of operation in related this method
Embodiment in be described in detail, no detailed explanation will be given here.
Fig. 8 is a kind of another block diagram for handling the device asked questions shown according to an exemplary embodiment.Such as Fig. 8 institute
Show, which may include: processor 701, memory 702.The device 700 can also include multimedia component 703, defeated
Enter/export one or more of (I/O) interface 704 and communication component 705.
Wherein, processor 701 is used to control the integrated operation of the device 700, is asked questions with to complete above-mentioned processing
All or part of the steps in method.Memory 702 is for storing various types of data to support the behaviour in the device 700
Make, these data for example may include the instruction of any application or method for operating on the device 700, Yi Jiying
With the relevant data of program, such as contact data, the message of transmitting-receiving, picture, audio, video etc..The memory 702 can be with
It is realized by any kind of volatibility or non-volatile memory device or their combination, such as static random access memory
(Static Random Access Memory, abbreviation SRAM), electrically erasable programmable read-only memory (Electrically
Erasable Programmable Read-Only Memory, abbreviation EEPROM), Erasable Programmable Read Only Memory EPROM
(Erasable Programmable Read-Only Memory, abbreviation EPROM), programmable read only memory
(Programmable Read-Only Memory, abbreviation PROM), and read-only memory (Read-Only Memory, referred to as
ROM), magnetic memory, flash memory, disk or CD.Multimedia component 703 may include screen and audio component.Wherein
Screen for example can be touch screen, and audio component is used for output and/or input audio signal.For example, audio component may include
One microphone, microphone is for receiving external audio signal.The received audio signal can be further stored in storage
Device 702 is sent by communication component 705.Audio component further includes at least one loudspeaker, is used for output audio signal.I/O
Interface 704 provides interface between processor 701 and other interface modules, other above-mentioned interface modules can be keyboard, mouse,
Button etc..These buttons can be virtual push button or entity button.Communication component 705 is used for the device 700 and other equipment
Between carry out wired or wireless communication.Wireless communication, such as Wi-Fi, bluetooth, near-field communication (Near Field
Communication, abbreviation NFC), 2G, 3G or 4G or they one or more of combination, therefore corresponding communication
Component 705 may include: Wi-Fi module, bluetooth module, NFC module.
In one exemplary embodiment, device 700 can be by one or more application specific integrated circuit
(Application Specific Integrated Circuit, abbreviation ASIC), digital signal processor (Digital
Signal Processor, abbreviation DSP), digital signal processing appts (Digital Signal Processing Device,
Abbreviation DSPD), programmable logic device (Programmable Logic Device, abbreviation PLD), field programmable gate array
(Field Programmable Gate Array, abbreviation FPGA), controller, microcontroller, microprocessor or other electronics member
Part realization, the method asked questions for executing above-mentioned processing.
In a further exemplary embodiment, a kind of computer readable storage medium including program instruction is additionally provided, it should
The step of method that above-mentioned processing asks questions is realized when program instruction is executed by processor.For example, this computer-readable is deposited
Storage media can be the above-mentioned memory 702 including program instruction, and above procedure instruction can be held by the processor 701 of device 700
Row is to complete the method that above-mentioned processing asks questions.
The preferred embodiment of the disclosure is described in detail in conjunction with attached drawing above, still, the disclosure is not limited to above-mentioned reality
The detail in mode is applied, in the range of the technology design of the disclosure, a variety of letters can be carried out to the technical solution of the disclosure
Monotropic type, these simple variants belong to the protection scope of the disclosure.
It is further to note that specific technical features described in the above specific embodiments, in not lance
In the case where shield, can be combined in any appropriate way, in order to avoid unnecessary repetition, the disclosure to it is various can
No further explanation will be given for the combination of energy.
In addition, any combination can also be carried out between a variety of different embodiments of the disclosure, as long as it is without prejudice to originally
Disclosed thought equally should be considered as disclosure disclosure of that.
Claims (14)
1. a kind of handle the method asked questions, which is characterized in that the described method includes:
Obtain time that the keyword in described problem, the temperature of the keyword and the keyword occur in described problem
Number；
Based on the temperature, the number and the tightness factor, confirm that the keyword meets preset condition, wherein if described
Keyword is present in default word set, then sets the tightness factor as the first numerical value, if the keyword is not present in presetting
Word set then sets the tightness factor as second value, and first numerical value is greater than the second value；
Export information relevant to the keyword.
2. the method according to claim 1, wherein the temperature for obtaining the keyword, comprising:
Search heat of the number, the characterization keyword at least occurred in third party's data based on the keyword on network
The index and the characterization keyword of degree calculate the temperature of the keyword in the index of local search temperature.
3. according to the method described in claim 2, it is characterized in that, calculating the temperature of the keyword based on following formula:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is characterization institute
The index of search temperature of the keyword on network is stated, q is index of the characterization keyword in local search temperature, α, beta, gamma
The weight of respectively s, i, q, α < β≤γ, n are the parameter value for reducing the temperature.
4. according to the method described in claim 2, it is characterized in that, calculating the temperature of the keyword based on following formula:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party's data
The number of middle appearance, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword at this
The index of temperature, α are searched in ground, and beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.
5. the method according to claim 1, wherein it is described based on the temperature, the number and tightness because
Son confirms that the keyword meets preset condition, comprising:
The relating value of the keyword is calculated based on following formula:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is the number that the keyword occurs in described problem, f
For the tightness factor, t is the temperature of the keyword；
The size for comparing the relating value and threshold value confirms the key when the relating value is greater than or equal to the threshold value
Word meets preset condition.
6. the method according to claim 1, wherein the method also includes:
Export the information of manual answering's described problem.
7. a kind of handle the device that asks questions, which is characterized in that described device includes:
Obtain module, be configured as obtain described problem in keyword, the keyword temperature and the keyword in institute
State the number occurred in problem；
Confirmation module is configured as that it is default to confirm that the keyword meets based on the temperature, the number and the tightness factor
Condition, wherein if the keyword is present in default word set, set the tightness factor as the first numerical value, if the pass
Keyword is not present in default word set, then sets the tightness factor as second value, first numerical value is greater than described second
Numerical value；
First output module is configured as output information relevant to the keyword.
8. device according to claim 7, which is characterized in that the acquisition module is also configured to
Search heat of the number, the characterization keyword at least occurred in third party's data based on the keyword on network
The index and the characterization keyword of degree calculate the temperature of the keyword in the index of local search temperature.
9. device according to claim 8, which is characterized in that the acquisition module is additionally configured to based on following formula
Calculate the temperature of the keyword:
T=[(s* α+i* β+q* γ)/n]
Wherein, t is the temperature of the keyword, and s is the number that the keyword occurs in third party's data, and i is characterization institute
The index of search temperature of the keyword on network is stated, q is index of the characterization keyword in local search temperature, α, beta, gamma
The weight of respectively s, i, q, α < β≤γ, n are the parameter value for reducing the temperature.
10. device according to claim 8, which is characterized in that the acquisition module is additionally configured to based on following formula
Calculate the temperature of the keyword:
T=[(p+s* α+i* β+q* γ)/n]
Wherein, p is the parameter for characterizing manual intervention, and t is the temperature of the keyword, and s is the keyword in third party's data
The number of middle appearance, i are the index of search temperature of the characterization keyword on network, and q is to characterize the keyword at this
The index of temperature, α are searched in ground, and beta, gamma is respectively s, and the weight of i, q, α < β≤γ, n are the parameter value for reducing the temperature.
11. device according to claim 7, which is characterized in that the confirmation module includes:
Computational submodule is set to the relating value that the keyword is calculated based on following formula by coordination:
Score=total*f*t
Wherein, score is the relating value of the keyword, and total is the number that the keyword occurs in described problem, f
For the tightness factor, t is the temperature of the keyword；
Comparative sub-module is configured as the size of relating value and threshold value described in comparison, when the relating value is more than or equal to described
When threshold value, confirm that the keyword meets preset condition.
12. device according to claim 7, which is characterized in that described device further include:
Second output module is configured as the information of output manual answering's described problem.
13. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The step of any one of claims 1 to 6 the method is realized when execution.
14. a kind of handle the device asked questions characterized by comprising
Memory is stored thereon with computer program；
Processor, for executing the computer program in the memory, to realize any one of claims 1 to 6 institute
The step of stating method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811565501.9A CN109753600A (en) | 2018-12-20 | 2018-12-20 | Handle the method, apparatus asked questions and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811565501.9A CN109753600A (en) | 2018-12-20 | 2018-12-20 | Handle the method, apparatus asked questions and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109753600A true CN109753600A (en) | 2019-05-14 |
Family
ID=66402969
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811565501.9A Pending CN109753600A (en) | 2018-12-20 | 2018-12-20 | Handle the method, apparatus asked questions and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109753600A (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020180806A1 (en) * | 2001-06-04 | 2002-12-05 | Inventec Appliances Corp. | System and method for upgrading input and inquiry efficiency |
| CN101751458A (en) * | 2009-12-31 | 2010-06-23 | 暨南大学 | Network public sentiment monitoring system and method |
| CN101847159A (en) * | 2010-05-11 | 2010-09-29 | 中兴通讯股份有限公司 | Terminal equipment and method for updating word stock thereof |
| CN101923544A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for monitoring and displaying Internet hot spots |
| US20110137918A1 (en) * | 2009-12-09 | 2011-06-09 | At&T Intellectual Property I, L.P. | Methods and Systems for Customized Content Services with Unified Messaging Systems |
| CN102194015A (en) * | 2011-06-30 | 2011-09-21 | 重庆新媒农信科技有限公司 | Retrieval information heat statistical method |
| CN102375835A (en) * | 2010-08-17 | 2012-03-14 | 腾讯科技（深圳）有限公司 | Information searching system and method |
| CN103164394A (en) * | 2012-07-16 | 2013-06-19 | 上海大学 | Text similarity calculation method based on universal gravitation |
| CN104240164A (en) * | 2014-09-29 | 2014-12-24 | 南京提坦信息科技有限公司 | Legal consulting method and legal consulting system based on big data analysis |
| CN107943935A (en) * | 2017-11-23 | 2018-04-20 | 北京天广汇通科技有限公司 | Processing method, device and the computer-readable recording medium of data |
| CN108090131A (en) * | 2017-11-23 | 2018-05-29 | 北京洪泰同创信息技术有限公司 | It teaches the method for pushing of auxiliary resource data and teaches the pusher of auxiliary resource data |
-
2018
- 2018-12-20 CN CN201811565501.9A patent/CN109753600A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020180806A1 (en) * | 2001-06-04 | 2002-12-05 | Inventec Appliances Corp. | System and method for upgrading input and inquiry efficiency |
| CN101923544A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for monitoring and displaying Internet hot spots |
| US20110137918A1 (en) * | 2009-12-09 | 2011-06-09 | At&T Intellectual Property I, L.P. | Methods and Systems for Customized Content Services with Unified Messaging Systems |
| CN101751458A (en) * | 2009-12-31 | 2010-06-23 | 暨南大学 | Network public sentiment monitoring system and method |
| CN101847159A (en) * | 2010-05-11 | 2010-09-29 | 中兴通讯股份有限公司 | Terminal equipment and method for updating word stock thereof |
| CN102375835A (en) * | 2010-08-17 | 2012-03-14 | 腾讯科技（深圳）有限公司 | Information searching system and method |
| CN102194015A (en) * | 2011-06-30 | 2011-09-21 | 重庆新媒农信科技有限公司 | Retrieval information heat statistical method |
| CN103164394A (en) * | 2012-07-16 | 2013-06-19 | 上海大学 | Text similarity calculation method based on universal gravitation |
| CN104240164A (en) * | 2014-09-29 | 2014-12-24 | 南京提坦信息科技有限公司 | Legal consulting method and legal consulting system based on big data analysis |
| CN107943935A (en) * | 2017-11-23 | 2018-04-20 | 北京天广汇通科技有限公司 | Processing method, device and the computer-readable recording medium of data |
| CN108090131A (en) * | 2017-11-23 | 2018-05-29 | 北京洪泰同创信息技术有限公司 | It teaches the method for pushing of auxiliary resource data and teaches the pusher of auxiliary resource data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210311751A1 (en) | Machine-learning models applied to interaction data for determining interaction goals and facilitating experience-based modifications to interface elements in online environments | |
| CN107911798A (en) | Information push method, device and terminal | |
| CN109144997A (en) | Data correlation method, device and storage medium | |
| CN107360536A (en) | Control method, terminal device and the computer-readable recording medium of terminal device | |
| CN107153847A (en) | Predict method and computing device of the user with the presence or absence of malicious act | |
| CN108694238A (en) | Business data processing method, device based on block chain and storage medium | |
| CN111400465B (en) | Generation method and device of customer service robot, electronic equipment and medium | |
| CN109767110A (en) | A kind of risk control system optimization method, device, equipment and storage medium | |
| US8381131B1 (en) | System, method, and computer program for displaying tasks as interactive thumbnails for interaction therewith by a user | |
| CN108920543A (en) | query and interaction method and device, computer device and storage medium | |
| CN109727047A (en) | A kind of method and apparatus, data recommendation method and the device of determining data correlation degree | |
| CN107547748A (en) | A kind of picture management method, terminal and computer-readable recording medium | |
| CN107450808A (en) | The mouse pointer localization method and computing device of a kind of browser | |
| CN107256224B (en) | A kind of generation method of the element structure of knowledge, searching method, apparatus and system | |
| CN118626724A (en) | A method and device for processing business function recommendations | |
| CN114997448A (en) | A business processing method and device | |
| CN109753600A (en) | Handle the method, apparatus asked questions and storage medium | |
| CN112732886A (en) | Session management method, device, system and medium | |
| CN107153705A (en) | The application program sort method and server of a kind of web browser | |
| Xie et al. | Arithmetic operations on triangular fuzzy numbers via credibility measures: An inverse distribution approach | |
| CN106447504A (en) | Friend making method and apparatus | |
| CN109785155A (en) | Method and Related product based on medical insurance reimbursement model adjustment medical insurance strategy | |
| CN104239296B (en) | The remote control method and system of multi-screen adapter browser | |
| CN112232046B (en) | Method and device for displaying repeated items of table | |
| CN103812994A (en) | Mobile phone display information processing method and apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190514 |
|
| RJ01 | Rejection of invention patent application after publication |