CN117611205A - Data analysis method and device based on big data and storage medium - Google Patents
Data analysis method and device based on big data and storage medium Download PDFInfo
- Publication number
- CN117611205A CN117611205A CN202311411874.1A CN202311411874A CN117611205A CN 117611205 A CN117611205 A CN 117611205A CN 202311411874 A CN202311411874 A CN 202311411874A CN 117611205 A CN117611205 A CN 117611205A
- Authority
- CN
- China
- Prior art keywords
- data
- solution
- software
- user
- standard
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000007405 data analysis Methods 0.000 title claims abstract description 13
- 239000000243 solution Substances 0.000 claims abstract description 117
- 238000006243 chemical reaction Methods 0.000 claims abstract description 35
- 239000003637 basic solution Substances 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 17
- 239000012086 standard solution Substances 0.000 claims description 38
- 238000004590 computer program Methods 0.000 claims description 5
- 239000000758 substrate Substances 0.000 claims 3
- 238000010586 diagram Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 235000019633 pungent taste Nutrition 0.000 description 2
- 239000002585 base Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000001212 derivatisation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Stored Programmes (AREA)
Abstract
The invention provides a data analysis method, a device and a storage medium based on big data, which are used for receiving problem data of corresponding target software uploaded by each user device, calling a solution template and sending the solution template to the user device, and acquiring basic solution data of the user device according to the solution template; performing image conversion processing on the basic answer data according to a standard conversion strategy to obtain standard answer data, and associating the standard answer data with the problem data; acquiring a plurality of user software of the demand equipment, generating primary customization information corresponding to the demand equipment according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information; generating guide data according to the first-level customization information and the second-level customization information, responding to the trigger information of the demand equipment on the guide data, calling corresponding standard answer data and sending the corresponding standard answer data to the demand equipment.
Description
Technical Field
The present invention relates to data analysis technologies, and in particular, to a data analysis method and apparatus based on big data, and a storage medium.
Background
Along with the popularization of computers and intelligent terminals, the development scale of application software is increasingly enlarged, the functions of the software are increasingly enriched, and the method is suitable for continuous derivatization of various application software of the intelligent terminals and brings great impetus to the intelligent development of society.
However, various problems may occur when a user uses various application software, for example, a problem such as flashing back may occur when the software a is used, in the prior art, when a user solves a problem occurring in the application software, a solution of a corresponding software problem is usually searched through a network, but the solution obtained in this way may be messy and invalid, so that the efficiency of the user in solving the corresponding software problem is low.
Therefore, how to combine user equipment data to customize and generate a problem solving strategy assists the user to solve the problem effectively becomes a problem to be solved in the present day.
Disclosure of Invention
The embodiment of the invention provides a data analysis method, a data analysis device and a storage medium based on big data, which can be combined with user equipment data customization to generate a problem solving strategy to assist a user to solve the problem efficiently.
In a first aspect of an embodiment of the present invention, a data analysis method based on big data is provided, including:
receiving problem data of corresponding target software uploaded by each user device, calling a solution template, sending the solution template to the user device, and acquiring basic solution data of the user device according to the solution template;
performing image conversion processing on the basic solution data according to a standard conversion strategy to obtain standard solution data, and associating the standard solution data with the problem data;
acquiring a plurality of user software of a demand device, generating primary customization information corresponding to the demand device according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information;
generating guide data according to the primary customization information and the secondary customization information, responding to triggering information of the demand equipment on the guide data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
Optionally, in one possible implementation manner of the first aspect, receiving problem data of corresponding target software uploaded by each user device, and sending an invoking solution template to the user device, and obtaining basic solution data of the user device according to the solution template, where the method includes:
receiving problem data of corresponding target software uploaded by each user equipment, and obtaining the number of steps of each user equipment based on the problem data input;
constructing corresponding number of step slots and content slots based on the number of steps, and generating a solution template according to the step slots and the content slots;
the solution template is called and sent to the corresponding user equipment, and solution steps and solution contents input by the user equipment based on step grooves and content grooves in the solution template are received;
and obtaining the corresponding answering step and answering content as sub answering data, and generating basic answering data corresponding to the corresponding problem data according to each sub answering data.
Optionally, in one possible implementation manner of the first aspect, performing image conversion processing on the basic solution data according to a standard conversion policy to obtain standard solution data, and associating the standard solution data with the problem data includes:
calling initial filling images with the same number as the steps, wherein the initial filling images comprise character filling areas and image filling areas;
filling each sub-answer data into each character filling area to obtain a character filling image corresponding to each answer step;
calling a plurality of software interfaces corresponding to the target software, and generating a solution image corresponding to the solution step according to the software interfaces and the solution content;
filling the answer image into an image filling area in the text filling image corresponding to the answer step to obtain a standard conversion image;
and arranging the standard conversion images according to the step sequence of the solving steps to obtain standard solving data, and associating the standard solving data with the problem data.
Optionally, in one possible implementation manner of the first aspect, invoking a plurality of software interfaces corresponding to the target software, generating a solution image corresponding to the solution step according to the software interfaces and the solution content, including:
determining interface keywords corresponding to the software interfaces, and traversing the solution content based on the interface keywords;
if the answer content contains the answer keywords corresponding to the interface keywords, determining that the interface keywords corresponding to the answer keywords in the software interface are positioning keywords;
generating a positioning frame corresponding to the positioning keyword, positioning the positioning frame into the software interface, and performing highlighting display on the positioning keyword to obtain a highlighting interface, and generating a solution image corresponding to the solution content according to the highlighting interface.
Optionally, in one possible implementation manner of the first aspect, the method further includes:
if the answer content does not contain answer keywords corresponding to the interface keywords, acquiring the text similarity of each interface keyword and the answer content, and determining the interface keywords with the text similarity larger than the preset text similarity as recommendation keywords;
and generating calibration reminding information according to the recommended keywords and sending the calibration reminding information to the first user side.
Optionally, in one possible implementation manner of the first aspect, acquiring a plurality of user software of a demand device, generating primary customization information corresponding to the demand device according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information, including:
acquiring the use times of each piece of user software in the demand equipment, and acquiring a software heat value of the corresponding piece of user software according to the ratio of the use times to the reference use times;
arranging all the user software from large to small according to the software heat value to obtain first-level customization information corresponding to the required equipment;
and acquiring problem data corresponding to each piece of user software in the primary customization information, and generating secondary customization information corresponding to the corresponding user software according to the problem data.
Optionally, in one possible implementation manner of the first aspect, generating guide data according to the first-level customization information and the second-level customization information, responding to triggering information of the demand device on the guide data, calling the corresponding standard solution data, and sending the standard solution data to the demand device, including:
generating primary guide data according to the primary customization information, and generating secondary guide data according to the secondary customization information, wherein the guide data comprises primary guide data and secondary guide data;
receiving primary trigger information of the first-level guide data of the corresponding user software by the demand equipment, and calling second-level guide data corresponding to the user software according to the primary trigger information and sending the second-level guide data to the demand equipment;
acquiring secondary trigger information of the demand equipment on corresponding problem data in the secondary guide data, and invoking a plurality of standard answer data corresponding to the problem data, wherein the trigger information comprises primary trigger information and secondary trigger information;
and determining the adoption amount of each piece of standard solution data, and transmitting each piece of standard solution data to the demand equipment after arranging the standard solution data from large to small according to the adoption amount.
Optionally, in one possible implementation manner of the first aspect, the method further includes:
if the optimal solution data exists in the problem data corresponding to the user software, generating combined solution data according to the optimal solution data and the standard solution data, and sending the combined solution data to the demand equipment;
and the optimal solution data is positioned at the top position of the display interface during display.
In a second aspect of the embodiments of the present invention, there is provided a data analysis device based on big data, including:
the basic module is used for receiving the problem data of the corresponding target software uploaded by each user equipment, calling a solution template and sending the solution template to the user equipment, and acquiring basic solution data of the user equipment according to the solution template;
the conversion module is used for carrying out image conversion processing on the basic answer data according to a standard conversion strategy to obtain standard answer data, and associating the standard answer data with the problem data;
the customization module is used for acquiring a plurality of pieces of user software of the demand equipment, generating primary customization information corresponding to the demand equipment according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information;
and the guiding module is used for generating guiding data according to the primary customization information and the secondary customization information, responding to the triggering information of the demand equipment on the guiding data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
In a third aspect of the embodiments of the present invention, there is provided a readable storage medium having stored therein a computer program for implementing the method of the first aspect and the various possible aspects of the first aspect when executed by a processor.
The beneficial effects of the invention are as follows:
1. the invention can combine user equipment data customization to generate the problem solving strategy, and assist the user to solve the problem efficiently. The invention can generate the basic answer data corresponding to each user equipment, then perform image conversion processing on the basic answer data to obtain the standard answer data, and correlate the standard answer data with the corresponding question data, so that the standard answer data corresponding to each question data can be quickly fetched, the efficiency of the user in checking the answer data is improved, and the answer data corresponding to the question data can be intuitively displayed through the converted image data. After standard answer data corresponding to each problem data are obtained, a plurality of user software and problem data in the demand equipment are customized according to the checking information of the demand equipment, and primary customization information corresponding to the user software and secondary customization information corresponding to the problem data are obtained, so that corresponding customization data can be customized for different users according to the use habits of the users.
2. When the basic answer data is generated, the answer template is called to enable the user to input corresponding answer steps and answer contents, so that the user can be more standardized when inputting the answer data, and the obtained basic answer data can be more standard. When the basic answering data is subjected to image conversion processing, the software interface corresponding to the answering content of each answering step is obtained, and then the keywords corresponding to the answering content in the software interface are highlighted and displayed to generate the corresponding answering image, so that the content of each answering step can be displayed more intuitively through the image data, and a user can better understand the content of the answering step through the image data.
3. When the user is guided through the guiding data, the method and the device can arrange the user software from large to small according to the using times of the user on the user software, so that the user software frequently used by the user can be arranged in front during arrangement, the user can conveniently check the corresponding user software, and the efficiency of the user in checking the answering data is improved. When the standard answer data corresponding to the corresponding problem data is called according to the triggering information of the user on the guide data, the invention also arranges the standard answer data from large to small according to the adoption amount of each standard answer data and then sends the arranged standard answer data to the demand equipment for the user to check, so that the user can check the standard answer data with large adoption amount preferentially, and the user can adopt the solution with high credibility preferentially. When the optimal solution data exists in the problem data, the invention also combines the optimal solution data with the standard solution data and then sends the combined optimal solution data to the demand equipment for the user to check, so that more solutions can be provided for the user to refer.
Drawings
FIG. 1 is a diagram of boot data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data analysis device based on big data according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The execution bodies of the present application may include, but are not limited to, at least one of: user equipment, network equipment, etc. The user equipment may include, but is not limited to, computers, smart phones, personal digital assistants (Personal Digital Assistant, abbreviated as PDA), and the above-mentioned electronic devices. The network device may include, but is not limited to, a single network server, a server group of multiple network servers, or a cloud of a large number of computers or network servers based on cloud computing, where cloud computing is one of distributed computing, and a super virtual computer consisting of a group of loosely coupled computers. This embodiment is not limited thereto. The method comprises the steps S1 to S4, and specifically comprises the following steps:
s1, receiving problem data of corresponding target software uploaded by each user device, calling a solution template, sending the solution template to the user device, and acquiring basic solution data of the user device according to the solution template.
In practical application, the software problems of each software may be different, some users may answer the software problems of the corresponding software, but because some users may be disordered in answering, in order to perform standardization processing on the answering data of the users, the solution may call the answering template to send the answering template to the user equipment, so that the users can input corresponding basic answering data according to the answering template, and then the standard answering data is obtained after the subsequent standardization processing on the basic answering data, so that the obtained answering data can be standardized, and the efficiency of other users in checking the corresponding answering data is improved.
The specific implementation manner of step S1 based on the above embodiment may be:
s11, receiving problem data of corresponding target software uploaded by each user equipment, and obtaining the number of steps of each user equipment based on the input of the problem data.
Generally, the problem data will generally have corresponding solving steps, so after obtaining the problem data corresponding to the corresponding target software in the user equipment, the number of steps input by the user equipment based on the problem data may be obtained, for example, the problem a may have 3 solving steps, and the corresponding number of steps is 3.
S12, constructing corresponding number of step slots and content slots based on the number of steps, and generating a solution template according to the step slots and the content slots.
It should be noted that the step slots and the content slots are correspondingly arranged, for example, when the number of steps is 3, 3 mutually corresponding step slots and content slots can be constructed.
S13, the solution template is called and sent to the corresponding user equipment, and solution steps and solution contents input by the user equipment based on step grooves and content grooves in the solution template are received.
In practical application, the form of the solution step and the solution content may be what the solution content corresponding to the first step is, what the solution content corresponding to the second step is, and the user may input the corresponding solution content in the corresponding content slot according to the solution corresponding to each step.
S14, obtaining the corresponding answering steps and answering contents as sub answering data, and generating basic answering data corresponding to the problem data according to each sub answering data.
It will be appreciated that the problem data may correspond to a plurality of solution steps, and thus a plurality of sub-solution data may be aggregated in generating the base solution data corresponding to the problem data.
By the method, the user can be more standardized when inputting the answer data.
S2, performing image conversion processing on the basic answer data according to a standard conversion strategy to obtain standard answer data, and associating the standard answer data with the problem data.
When the basic solution data is subjected to standard conversion, the scheme is to perform image conversion processing on the basic solution data to obtain the standard solution data, so that the obtained standard solution data can be more visual.
Specifically, step S2 may be implemented through steps S21 to S25, which are specifically as follows:
s21, calling initial filling images with the same number as the steps, wherein the initial filling images comprise character filling areas and image filling areas.
It will be appreciated that the basic solution data may include a plurality of solution steps, and the images corresponding to each solution step may be different, so that when the basic solution data is subjected to image conversion, the scheme performs image conversion for each solution step.
Specifically, the scheme can call the initial filling images with the same number as the steps, and then update the initial filling images through the basic solution data, so that the image conversion of the basic solution data can be realized.
S22, filling each sub-answer data into each character filling area to obtain a character filling image corresponding to each answer step.
In practical application, the text filling area in the initial filling image can be above the image filling area, so that sub-answer data and image data corresponding to the sub-answer data can be better displayed.
S23, a plurality of software interfaces corresponding to the target software are called, and a solution image corresponding to the solution step is generated according to the software interfaces and the solution content.
It can be understood that the software interfaces corresponding to each target software may be different, so that when the answer image corresponding to the corresponding answer step is generated, a plurality of software interfaces corresponding to the target software may be called first, then the software interface corresponding to the answer step is found through the answer content, and finally the answer image corresponding to the corresponding answer step is generated through the answer content and the corresponding software interface.
In some embodiments, step S23 may be implemented by the following steps, specifically as follows:
s231, determining interface keywords corresponding to the software interfaces, and traversing the answer content based on the interface keywords.
In practical application, the interface keyword may be an indication text corresponding to the application in the software interface, for example, there may be indication text of setting, contact person, my, etc. in the software a, and the interface keyword may be these indication text. Wherein, a software interface may correspond to a plurality of interface keywords.
It can be understood that the answer content may be a corresponding operation performed on an application in the software interface, such as clicking, opening or closing, so that in order to intuitively display the answer content, the software interface corresponding to the answer content may be found out to generate a corresponding interface operation image, so that a user may better understand the answer step through image data.
S232, if the answer keywords corresponding to the interface keywords exist in the answer content, determining that the interface keywords corresponding to the answer keywords in the software interface are positioning keywords.
If the answer content contains the answer keyword corresponding to the interface keyword, the software interface corresponding to the interface keyword and the answer content are mutually corresponding, so that the interface keyword corresponding to the answer keyword in the software interface can be used as the positioning keyword, the positioning keyword can be highlighted later, and a user can quickly find out the application corresponding to the positioning keyword to perform corresponding operation.
Furthermore, on the basis of the above embodiment, the present solution further includes the following steps:
and if the answer content does not contain the answer keywords corresponding to the interface keywords, acquiring the text similarity of each interface keyword and the answer content, and determining the interface keywords with the text similarity larger than the preset text similarity as recommendation keywords.
If the answer content does not have the answer keywords corresponding to the interface keywords, the answer content is indicated to be without the corresponding software interface, and the reason for this situation may be that the answer content input by the user has errors, for example, the user may input the indication text of the corresponding application in error. In this case, in order to improve the accuracy of the answer content, the scheme also generates corresponding calibration reminding information to remind the user.
It can be understood that if the text similarity between the interface keywords and the answer content is greater than the preset text similarity, it is explained that the corresponding interface keywords are likely to be input by the user, so that the corresponding interface keywords can be used as recommended keywords, and then the recommended keywords are sent to the user for corresponding calibration selection.
And generating calibration reminding information according to the recommended keywords and sending the calibration reminding information to the first user side.
S233, generating a positioning frame corresponding to the positioning keyword, positioning the positioning frame to the software interface, performing protruding display on the positioning keyword to obtain a protruding interface, and generating a solution image corresponding to the solution content according to the protruding interface.
In practical application, when the positioning frame corresponding to the positioning keyword is generated, the positioning frame can be correspondingly adjusted according to the positioning keyword, for example, when the positioning keyword is more, the positioning frame can be correspondingly enlarged, and when the positioning keyword is less, the positioning frame can be correspondingly reduced.
When the positioning keywords are highlighted, the positioning keywords can be subjected to frame selection through the positioning frame, and the positioning frame is highlighted through the preset pixel values, so that the positioning keywords selected by the positioning frame can be highlighted, and in practical application, the preset pixel values can be pixel values corresponding to red.
After the protruding interface is obtained, the image corresponding to the protruding interface can be intercepted to obtain a solution image.
And S24, filling the answer image into an image filling area in the text filling image corresponding to the answer step, so as to obtain a standard conversion image.
In some embodiments, when the solution image is filled into the corresponding image filling area, the center point of the solution image may be positioned by the center point of the image filling area, and the solution image may be filled into the corresponding image filling area.
S25, arranging the standard conversion images according to the step sequence of the solving steps to obtain standard solving data, and associating the standard solving data with the problem data.
It can be understood that in practical application, the answer steps have the corresponding step sequence, so that the standard answer data can be obtained by arranging each standard conversion image according to the step sequence, and the standard answer data can be associated with the question data, so that the standard answer data corresponding to the question data can be quickly fetched in the follow-up process and sent to the corresponding user for viewing.
In addition, in other embodiments, when generating the standard solution data, the standard solution data corresponding to each device model may be generated according to the device model preset by the staff, so that the corresponding standard solution data may be subsequently called according to the device model of the required device and sent to the corresponding user for viewing.
It can be understood that the device models are different, and the corresponding software interfaces may come in and go out, so that in order to more individually set the standard solution data, the standard solution data can correspond to the subsequent requirement devices, and standard solution data corresponding to a plurality of device models can be generated, so that the corresponding users can view the corresponding standard solution data in a targeted manner.
For example, if the staff sets 10 device models, standard solution data corresponding to the 10 device models can be respectively generated and associated with corresponding problem data, so that standard solution data corresponding to the problem data can be called according to the device model of the demand device and sent to the demand device.
S3, acquiring a plurality of pieces of user software of the demand equipment, generating primary customization information corresponding to the demand equipment according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information.
The requirement equipment refers to equipment for checking answer data, and it can be understood that the use habits of each user may be different, so that user software in the requirement equipment corresponding to different users may be different, and in order to perform customization processing on each user, the scheme can generate first-level customization information according to the user software in the requirement equipment, and generate second-level customization information according to the problem data corresponding to each user software in the first-level customization information, so that a plurality of software data and problem data can be customized by combining the use habits of the corresponding users.
The specific implementation manner of step S3 based on the above embodiment may be:
s31, obtaining the use times of the user software in the demand equipment, and obtaining the software heat value of the corresponding user software according to the ratio of the use times to the reference use times.
It will be appreciated that if the number of uses of the user software is greater, the more frequently the user may use the corresponding user software, and thus the greater the software hotness value of the corresponding user software may be set.
S32, arranging the user software from large to small according to the software heat value to obtain first-level customization information corresponding to the required equipment.
It can be understood that the more frequently the user software with a higher software hotness value is likely to be used, the more problems are likely to occur, so that the corresponding user software can be arranged in front during arrangement, thereby being convenient for a user to view the corresponding user software and improving the efficiency of the user in viewing the solution data.
S33, acquiring problem data corresponding to each piece of user software in the primary customization information, and generating secondary customization information corresponding to the corresponding piece of user software according to the problem data.
In practical application, the user software is different, and the corresponding problem data may be different, so that secondary customized information corresponding to each user software can be generated according to the problem data, and the problem data corresponding to the corresponding user software can be quickly searched through the secondary customized information, so that the efficiency of the user in checking the answer data is improved.
S4, generating guide data according to the primary customization information and the secondary customization information, responding to triggering information of the demand equipment on the guide data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
After the primary customization information and the secondary customization information are obtained, corresponding guide data can be generated to guide a user to check corresponding answer data. Specifically, the scheme can call corresponding standard solution data according to the trigger information of the user on the guide data based on the demand equipment, and the corresponding standard solution data is sent to the demand equipment for the user to check.
The specific implementation manner of step S4 based on the above embodiment may be:
s41, generating primary guide data according to the primary customization information, and generating secondary guide data according to the secondary customization information, wherein the guide data comprises primary guide data and secondary guide data.
Referring to fig. 1, a schematic diagram of booting data is provided in an embodiment of the present invention. In practical applications, the primary guidance data and the secondary guidance data may be displayed in a list form for each user software and problem data, for example, in the display manner in fig. 1.
S42, receiving first-level trigger information of the first-level guide data of the requirement equipment for the corresponding user software, retrieving second-level guide data corresponding to the user software according to the first-level trigger information, and sending the second-level guide data to the requirement equipment.
As shown in fig. 1, if the user clicks the user software 1, a plurality of problem data corresponding to the user software 1 can be called according to the first-level trigger information of the user on the user software 1 and sent to the demand device for viewing by the user.
S43, acquiring secondary trigger information of the demand equipment on corresponding problem data in the secondary guide data, and calling a plurality of standard answer data corresponding to the problem data, wherein the trigger information comprises primary trigger information and secondary trigger information.
In practical application, a user can slide and check a plurality of problem data in a display interface, then trigger and select corresponding problem data, or search corresponding problem data through a search bar, and then trigger and select the searched problem data.
S44, determining the adoption amount of each piece of standard solution data, and transmitting each piece of standard solution data to the demand equipment after arranging the standard solution data from large to small according to the adoption amount.
It can be understood that there may be a plurality of standard answer data corresponding to the question data, and the more the adopted amount is, the greater the question resolution of the corresponding standard answer data may be, so that the plurality of standard answer data may be sorted by the adopted amount and then sent to the demand device, so that the user may be able to view the standard answer data with the more adopted amount preferentially.
In addition, on the basis of the above embodiment, the present solution further includes the following embodiments:
and if the optimal solution data exists in the problem data corresponding to the user software, generating combined solution data according to the optimal solution data and the standard solution data, and sending the combined solution data to the demand equipment.
And the optimal solution data is positioned at the top position of the display interface during display.
It will be appreciated that in practical applications, there may be official optimal solution data for some problem data, so if it is determined that there is optimal solution data for problem data corresponding to user software, the optimal solution data and standard solution data may be combined to generate combined solution data and send the combined solution data to the demand device.
It will also be appreciated that the problem resolution of the optimal solution data may be higher and the confidence level may be higher than that of the standard solution data, so that the optimal solution data may be placed at the top of the display interface for preferential display during display.
Through the method, the answer data corresponding to the problem data can be quickly retrieved and sent to the demand equipment for the user to check, so that the efficiency of the user in checking the answer data can be improved.
Referring to fig. 2, a schematic structural diagram of a data analysis device based on big data according to an embodiment of the present invention includes:
the basic module is used for receiving the problem data of the corresponding target software uploaded by each user equipment, calling a solution template and sending the solution template to the user equipment, and acquiring basic solution data of the user equipment according to the solution template;
the conversion module is used for carrying out image conversion processing on the basic answer data according to a standard conversion strategy to obtain standard answer data, and associating the standard answer data with the problem data;
the customization module is used for acquiring a plurality of pieces of user software of the demand equipment, generating primary customization information corresponding to the demand equipment according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information;
and the guiding module is used for generating guiding data according to the primary customization information and the secondary customization information, responding to the triggering information of the demand equipment on the guiding data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
The present invention also provides a readable storage medium having stored therein a computer program for implementing the methods provided by the various embodiments described above when executed by a processor.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). In addition, the ASIC may reside in a user device. The processor and the readable storage medium may reside as discrete components in a communication device. The readable storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tape, floppy disk, optical data storage device, etc.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, the execution instructions being executed by the at least one processor to cause the device to implement the methods provided by the various embodiments described above.
In the above embodiment of the apparatus, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A data analysis method based on big data, comprising:
receiving problem data of corresponding target software uploaded by each user device, calling a solution template, sending the solution template to the user device, and acquiring basic solution data of the user device according to the solution template;
performing image conversion processing on the basic solution data according to a standard conversion strategy to obtain standard solution data, and associating the standard solution data with the problem data;
acquiring a plurality of user software of a demand device, generating primary customization information corresponding to the demand device according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information;
generating guide data according to the primary customization information and the secondary customization information, responding to triggering information of the demand equipment on the guide data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
receiving problem data of corresponding target software uploaded by each user device, calling a solution template and sending the solution template to the user device, and acquiring basic solution data of the user device according to the solution template, wherein the method comprises the following steps:
receiving problem data of corresponding target software uploaded by each user equipment, and obtaining the number of steps of each user equipment based on the problem data input;
constructing corresponding number of step slots and content slots based on the number of steps, and generating a solution template according to the step slots and the content slots;
the solution template is called and sent to the corresponding user equipment, and solution steps and solution contents input by the user equipment based on step grooves and content grooves in the solution template are received;
and obtaining the corresponding answering step and answering content as sub answering data, and generating basic answering data corresponding to the corresponding problem data according to each sub answering data.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
performing image conversion processing on the basic solution data according to a standard conversion strategy to obtain standard solution data, and associating the standard solution data with the problem data, wherein the method comprises the following steps:
calling initial filling images with the same number as the steps, wherein the initial filling images comprise character filling areas and image filling areas;
filling each sub-answer data into each character filling area to obtain a character filling image corresponding to each answer step;
calling a plurality of software interfaces corresponding to the target software, and generating a solution image corresponding to the solution step according to the software interfaces and the solution content;
filling the answer image into an image filling area in the text filling image corresponding to the answer step to obtain a standard conversion image;
and arranging the standard conversion images according to the step sequence of the solving steps to obtain standard solving data, and associating the standard solving data with the problem data.
4. The method of claim 3, wherein the step of,
invoking a plurality of software interfaces corresponding to the target software, and generating a solution image corresponding to the solution step according to the software interfaces and the solution content, wherein the solution image comprises the following steps:
determining interface keywords corresponding to the software interfaces, and traversing the solution content based on the interface keywords;
if the answer content contains the answer keywords corresponding to the interface keywords, determining that the interface keywords corresponding to the answer keywords in the software interface are positioning keywords;
generating a positioning frame corresponding to the positioning keyword, positioning the positioning frame into the software interface, and performing highlighting display on the positioning keyword to obtain a highlighting interface, and generating a solution image corresponding to the solution content according to the highlighting interface.
5. The method as recited in claim 4, further comprising:
if the answer content does not contain answer keywords corresponding to the interface keywords, acquiring the text similarity of each interface keyword and the answer content, and determining the interface keywords with the text similarity larger than the preset text similarity as recommendation keywords;
and generating calibration reminding information according to the recommended keywords and sending the calibration reminding information to the first user side.
6. The method of claim 1, wherein the step of determining the position of the substrate comprises,
acquiring a plurality of user software of a demand device, generating primary customization information corresponding to the demand device according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information, wherein the method comprises the following steps:
acquiring the use times of each piece of user software in the demand equipment, and acquiring a software heat value of the corresponding piece of user software according to the ratio of the use times to the reference use times;
arranging all the user software from large to small according to the software heat value to obtain first-level customization information corresponding to the required equipment;
and acquiring problem data corresponding to each piece of user software in the primary customization information, and generating secondary customization information corresponding to the corresponding user software according to the problem data.
7. The method of claim 6, wherein the step of providing the first layer comprises,
generating guide data according to the primary customization information and the secondary customization information, calling the corresponding standard answer data to send to the demand equipment in response to the trigger information of the demand equipment on the guide data, and comprising the following steps:
generating primary guide data according to the primary customization information, and generating secondary guide data according to the secondary customization information, wherein the guide data comprises primary guide data and secondary guide data;
receiving primary trigger information of the first-level guide data of the corresponding user software by the demand equipment, and calling second-level guide data corresponding to the user software according to the primary trigger information and sending the second-level guide data to the demand equipment;
acquiring secondary trigger information of the demand equipment on corresponding problem data in the secondary guide data, and invoking a plurality of standard answer data corresponding to the problem data, wherein the trigger information comprises primary trigger information and secondary trigger information;
and determining the adoption amount of each piece of standard solution data, and transmitting each piece of standard solution data to the demand equipment after arranging the standard solution data from large to small according to the adoption amount.
8. The method as recited in claim 7, further comprising:
if the optimal solution data exists in the problem data corresponding to the user software, generating combined solution data according to the optimal solution data and the standard solution data, and sending the combined solution data to the demand equipment;
and the optimal solution data is positioned at the top position of the display interface during display.
9. A big data based data analysis device, comprising:
the basic module is used for receiving the problem data of the corresponding target software uploaded by each user equipment, calling a solution template and sending the solution template to the user equipment, and acquiring basic solution data of the user equipment according to the solution template;
the conversion module is used for carrying out image conversion processing on the basic answer data according to a standard conversion strategy to obtain standard answer data, and associating the standard answer data with the problem data;
the customization module is used for acquiring a plurality of pieces of user software of the demand equipment, generating primary customization information corresponding to the demand equipment according to the user software, acquiring corresponding problem data based on the primary customization information, and generating secondary customization information;
and the guiding module is used for generating guiding data according to the primary customization information and the secondary customization information, responding to the triggering information of the demand equipment on the guiding data, calling the corresponding standard answer data and sending the standard answer data to the demand equipment.
10. A readable storage medium, characterized in that a computer program is stored in the readable storage medium, which computer program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311411874.1A CN117611205B (en) | 2023-10-27 | 2023-10-27 | Data analysis method and device based on big data and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311411874.1A CN117611205B (en) | 2023-10-27 | 2023-10-27 | Data analysis method and device based on big data and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117611205A true CN117611205A (en) | 2024-02-27 |
| CN117611205B CN117611205B (en) | 2024-05-14 |
Family
ID=89958631
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311411874.1A Active CN117611205B (en) | 2023-10-27 | 2023-10-27 | Data analysis method and device based on big data and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117611205B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108287648A (en) * | 2018-01-31 | 2018-07-17 | 泰康保险集团股份有限公司 | Feedback management method, apparatus, electronic equipment and the computer storage media of software |
| US11163586B1 (en) * | 2021-02-23 | 2021-11-02 | Xactly Corporation | Automated configuration of application program instance |
| CN114328839A (en) * | 2021-12-31 | 2022-04-12 | 海南安博教育科技有限公司 | Question answering method, device, medium and electronic equipment |
| CN115630146A (en) * | 2022-10-10 | 2023-01-20 | 华南理工大学 | Method and device for automatically generating demand document based on human-computer interaction and storage medium |
-
2023
- 2023-10-27 CN CN202311411874.1A patent/CN117611205B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108287648A (en) * | 2018-01-31 | 2018-07-17 | 泰康保险集团股份有限公司 | Feedback management method, apparatus, electronic equipment and the computer storage media of software |
| US11163586B1 (en) * | 2021-02-23 | 2021-11-02 | Xactly Corporation | Automated configuration of application program instance |
| CN114328839A (en) * | 2021-12-31 | 2022-04-12 | 海南安博教育科技有限公司 | Question answering method, device, medium and electronic equipment |
| CN115630146A (en) * | 2022-10-10 | 2023-01-20 | 华南理工大学 | Method and device for automatically generating demand document based on human-computer interaction and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 张宇航;: "基于用户数据的个性化推荐模块的设计与实现", 湖北文理学院学报, no. 11, 15 November 2016 (2016-11-15), pages 26 - 30 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117611205B (en) | 2024-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR20210070363A (en) | Interaction message processing method and apparatus, computer device and storage medium | |
| CN111666328B (en) | Method, device, computer equipment and storage medium for displaying visual chart in real time | |
| CN109190062B (en) | Crawling method and device for target corpus data and storage medium | |
| WO2012161928A1 (en) | Techniques to automatically search selected content | |
| CN114528044B (en) | Interface calling method, device, equipment and medium | |
| EP3961426A2 (en) | Method and apparatus for recommending document, electronic device and medium | |
| CN111476013A (en) | Information collection method, information collection device, information collection medium, and electronic device | |
| CN109791545A (en) | The contextual information of resource for the display including image | |
| CN113869063A (en) | Data recommendation method and device, electronic equipment and storage medium | |
| CN117909560A (en) | Search method, training device, training equipment, training medium and training program product | |
| CN117611205B (en) | Data analysis method and device based on big data and storage medium | |
| US20220027419A1 (en) | Smart search and recommendation method for content, storage medium, and terminal | |
| CN112069267A (en) | Data processing method and device | |
| CN110018871A (en) | The operation indicating method, apparatus and computer readable storage medium of system | |
| CN111737288B (en) | Search control method, device, terminal equipment, server and storage medium | |
| EP4053713A1 (en) | Question and answer method and apparatus based on knowledge graph | |
| CN114281923A (en) | Medicine name searching method, device, equipment and storage medium | |
| CN114491342A (en) | Training method of personalized model, information display method and equipment | |
| CN117252730B (en) | Service subscription processing system, service subscription information processing method and device | |
| CN116012118B (en) | Product recommendation method and device | |
| CN117708304B (en) | Database question-answering method, equipment and storage medium | |
| WO2023125284A1 (en) | Search method and electronic device | |
| CN116383391B (en) | Text classification method and device | |
| CN113961558B (en) | Front-end data storage method, device and storage medium | |
| CN117851575B (en) | Large language model question-answer optimization method and device, electronic equipment and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |