CN110717722A - Intelligent questionnaire survey method, system, electronic device and computer readable medium - Google Patents
Intelligent questionnaire survey method, system, electronic device and computer readable medium Download PDFInfo
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- 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
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Abstract
The present disclosure provides an intelligent questionnaire survey method, system, electronic device, and computer readable medium. The method comprises the following steps: acquiring questionnaire characteristic information; acquiring audience group characteristic information; and decomposing the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information, and distributing the question group to the audience group matched with the question group. The method, system, electronic device, and computer-readable medium can save the cost of questionnaire survey, improve questionnaire recovery, improve the pertinence, flexibility, and/or accuracy of questionnaire survey.
Description
Technical Field
The present disclosure relates to the technical field of computers, internet, big data, and the like, and in particular, to an intelligent questionnaire survey method, system, electronic device, and computer-readable medium.
Background
Currently, there are two general ways of questionnaire survey, one is offline survey, namely: the questionnaire is randomly issued in the physical area, so that the method is suitable for undifferentiated audiences, difficult in distinguishing audience groups and limited in propagation; another is an on-line survey, namely: the on-line survey system of the questionnaire star and the like has the basis of questionnaires, but is limited in propagation, relatively lacks guidance of methodology, and still the whole questionnaire distribution is the whole questionnaire distribution, and the target object pertinence of the questionnaire distribution is poor. In addition, overall questionnaire is also relatively poor in targeting and flexibility.
Disclosure of Invention
In view of the problems in the prior art, it is an object of the present disclosure to provide an intelligent questionnaire survey method, system, electronic device, and computer readable medium, which can save the cost of questionnaire survey, improve the recovery rate of questionnaire, and improve the pertinence, flexibility, and/or accuracy of questionnaire survey.
In a first aspect, an embodiment of the present disclosure provides an intelligent questionnaire survey method, including:
acquiring questionnaire characteristic information;
acquiring audience group characteristic information;
and decomposing the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information, and distributing the question group to the audience group matched with the question group.
In a second aspect, an embodiment of the present disclosure provides an intelligent questionnaire survey system, including:
the questionnaire characteristic information acquisition module is used for acquiring questionnaire characteristic information;
the audience group characteristic information acquisition module is used for acquiring audience group characteristic information;
and the questionnaire disassembling and matching distribution module is used for disassembling the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information and distributing the questionnaire to the audience group matched with the question group.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: the electronic device comprises a processor and a memory, wherein the memory is provided with a medium stored with program codes, and when the processor reads the program codes stored in the medium, the electronic device can execute the method provided by any embodiment of the disclosure.
In a fourth aspect, the embodiments of the present disclosure provide a computer-readable medium, which stores computer-readable instructions that can be executed by a processor to implement the method provided by any of the embodiments of the present disclosure.
Compared with the prior art, the method has the following beneficial effects:
according to the technical scheme, the overall questionnaire is disassembled into the problem groups and is sent to the matched audience groups, so that the questionnaire survey cost is saved, the questionnaire recovery rate is improved, and the pertinence, flexibility and/or accuracy of the questionnaire survey are improved.
Drawings
Fig. 1 is a schematic flow chart diagram of an intelligent questionnaire survey method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating a preferred embodiment of the intelligent questionnaire method according to the embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a preferred embodiment of the intelligent questionnaire survey system according to the embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
The present disclosure is described in further detail below. The following examples are merely illustrative of the present disclosure and do not represent or limit the scope of the claims that follow.
Detailed Description
The technical scheme of the disclosure is further explained by the specific implementation mode in combination with the attached drawings.
To better illustrate the present disclosure, and to facilitate an understanding of the technical solutions of the present disclosure, typical but non-limiting examples of the present disclosure are as follows: it should be specifically noted that the embodiments listed in the description of the present disclosure are only exemplary embodiments given for convenience of description, and should not be construed as the only correct embodiments of the present disclosure, nor as a restrictive description of the scope of the present disclosure.
The questionnaire is also called questionnaire, and is a tool for collecting data in social investigation research, and is in the form of a copy for systematically recording investigation content in the form of questions, and the essence of the questionnaire is a series of questions designed for collecting information such as attitude behavior characteristic value viewpoint or belief of people for a specific question.
Questionnaires can be classified into paper questionnaires and network questionnaires according to the difference of carriers. Paper questionnaires are traditional questionnaires and survey companies have been employed to distribute paper questionnaires by hiring workers to retrieve answers. This form of questionnaire has some disadvantages, including cumbersome analysis and statistical results, and high cost. Another type of web questionnaire is that users rely on online questionnaire websites that provide a series of services for designing questionnaires, issuing questionnaires, analyzing results, and the like. The method has the advantages of no regional limitation and relatively low cost, and the intelligent questionnaire survey disclosed by the disclosure is a solution provided for overcoming the defects and shortcomings of the existing network questionnaire survey method.
The intelligent questionnaire survey method of the present disclosure is explained below with reference to fig. 1 and 2.
An intelligent questionnaire survey method (as shown in figure 1) comprises: s1: acquiring questionnaire characteristic information; s2: acquiring audience group characteristic information; s3: and decomposing the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information, and distributing the question group to the audience group matched with the question group.
As a preferred embodiment, before obtaining the questionnaire characteristic information, a user creates a required questionnaire (as shown in fig. 2), the manner of creating the questionnaire may be created by logging in a questionnaire creation system for the user, the created process may call an existing questionnaire module in the system for the user, and an overall questionnaire is formed by combining a plurality of modules, and the process of calling the existing questionnaire module in the system includes direct calling and using the called questionnaire module after modifying or modifying. The mode of creating the questionnaire can also be that the created questionnaire is directly imported into a questionnaire survey system.
After the system creates or obtains the questionnaire, it is further necessary to obtain questionnaire feature information, which may be obtained by filling in tag information for describing questionnaire features by the user, for example: the user may fill in relevant information such as the design purpose of the questionnaire, the survey content, the audience group who wishes to survey, the survey area, and the like. In other preferred embodiments, the tag information may also be obtained in an automatic obtaining manner, which has the following advantages: the data processing efficiency is high, the method is suitable for big data processing, the automatic acquisition mode can be obtained by automatically extracting keywords in the questionnaire content and/or questionnaire description information by a system, and the extracted keywords can be obtained by means of word frequency statistics, sensitive word recognition, semantic association and the like; the automatic acquisition mode can also be an intelligent semantic analysis mode, wherein the intelligent semantic analysis mode refers to a process of generating corresponding label information according to the analysis of the whole text content of the questionnaire and the induction of key information of the questionnaire. The specific semantic analysis technique belongs to the existing mature technique, and is not described here again for the sake of economy.
The above describes the manual filling by a user or the automatic acquisition of questionnaire characteristic information by a machine. In some preferred embodiments, the questionnaire characteristics information may be obtained by a combination of manual user filling and automatic machine acquisition (see fig. 2). For example, a user fills in relevant information such as a questionnaire description, and then a machine-automated feature analysis is started, wherein the automatic feature analysis can automatically extract and/or intelligently analyze the keywords, and in the case that the machine-automated feature analysis cannot be started, the questionnaire feature information can be further manually filled in by the user, and in other preferable cases, for example, in the case that the result of the machine-automated feature analysis is not ideal, the questionnaire feature information can be further corrected by the manual filling in of the user. The combined manner of manual filling by the user and automatic acquisition by the machine to acquire the questionnaire characteristic information has the advantages that: the reliability, controllability, flexibility and accuracy of the system work are ensured.
In some more preferred embodiments, the step of obtaining the questionnaire characteristic information may be performed after the user completes the questionnaire description information to be designed, and before the formal questionnaire is created, that is, the questionnaire characteristic information is obtained only according to the questionnaire description information filled by the user, and the obtained manner may be manually filled by the user and/or automatically obtained by a machine.
The step of obtaining audience characteristic information may be manually setting audience characteristic information by a user, for example: people of a specific group are labeled with relevant information such as age, gender, area, personal preference, life style, professional information, family information, personal wealth and the like. In a preferred embodiment, the step of obtaining audience characteristic information may be obtained by the system according to historical data analysis. For example: the system history data shows that a person is female, behind 80, in a place of residence, Beijing, white collar, commonly go to Starbucks, commonly go to Shanghai, in a lodging hotel, at high grade, China Mobile 4G user, with an autonomous house, in repayment, love watching a comedy, in a school bus, attention to fashion, attention to wearable, love mobile phone payment, stock frying, love watching a movie, at home with a child kindergarten, love at high grade and medium grade cosmetics, love yoga, frequent exercise and the like, and then the following characteristic information can be added to the system according to the system analysis: small-resource women, life laws, high-medium consumption level, liking to try fresh things, 80 th later, white-collar, Beijing residents and other related labels.
As a preferred embodiment, the questionnaire characteristic information and audience segment characteristic information described earlier in this disclosure may be stored in a specific database to facilitate system invocation.
The step of decomposing the questionnaire into question groups based on the questionnaire characteristic information and the audience population characteristic information and distributing the question groups to the audience population matched with the question groups comprises the following steps:
and (3) decomposing the questionnaire into question groups:
assuming that a questionnaire with N questions exists and the goal is to put M questions, the disassembling mode is as follows:
problem group A: { Qa1, Qa2, Qa3, … Qai }, number i
Problem group B: { Qb1, Qb2, Qb3, … Qbj }, the number j
Problem group C: { Qc1, Qc2, Qc3, … Qck }, number k
…
Problem group X: { Qx1, Qx2, Qx3, … Qxq }, number q
It is necessary to satisfy i + j + k + … + q ═ M × N, that is, it is necessary to ensure that the total number of questions thrown is M × N and the number of questions thrown per question is M. Wherein i, j, k, …, q are all less than N.
A step of distributing the problem group to the audience group matched with the problem group:
the distribution process is a feature matching process of the problem group and the target users, and here, different matching algorithms can be adopted, which can be realized by calculating the similarity between two things, for example: there are two objects X, Y, each containing N-dimensional features, X ═ X1, X2, X3, … …, xn), Y ═ Y1, Y2, Y3, … …, yn), and the similarity between X and Y is calculated, and the specific algorithm may be euclidean distance method, manhattan distance method, minkowski distance method, cosine similarity method, pearson correlation coefficient method, or eigenvector euclidean distance.
Through the similarity calculation of the problem groups and the target users, different problem groups can be allocated to the target users with close characteristics; in some more preferred embodiments, it should be ensured that the same audience is not assigned two problem groups in the same survey, which is advantageous: the method and the device ensure that the user is not interfered by the junk information, and simultaneously improve the matching and sending efficiency of the questionnaire.
The target user, upon receiving the question group, performs questionnaire feedback and forms the feedback information into structured data that is output in a visual and/or machine-readable form. The visual output means that feedback information of a target user is output and represents related data in a human-readable data representation form such as a list, characters, graphics, images, and a data map. The machine-readable output refers to that the feedback information of the target user outputs related data in the forms of computer information interaction, transmission, identification, statistics and analysis.
As a preferred embodiment, feedback data which is output in a machine-readable manner may be further fed back to a questionnaire parsing matching distribution module (as shown in fig. 2), and the questionnaire parsing matching distribution module further optimizes a questionnaire parsing and matching distribution method according to the feedback structured data, and the specific process may be as follows: and counting the question feedback results of the feedback data, comparing the question feedback results with the feedback results of the overall questionnaire issuing survey, improving the recovery rate of the questionnaire through different disassembling modes, and finding out the optimal solution when the recovery rate of the questionnaire is the highest and the feedback results are closest to the results of the overall questionnaire issuing survey, namely the optimal questionnaire disassembling mode and the optimal distribution matching mode.
The specific implementation manner of counting the question feedback result of the feedback data and comparing the question feedback result with the feedback result of the overall questionnaire issuing survey may be as follows: two groups were set when the questionnaire was disassembled: the group A is issued in the form of all questions, and the group B is issued in the form of disassembled question groups. The two retrieved answer proportions are counted from the collected results, vector comparison is performed, and the two effects are approximated by rewriting the B-group problem group dismantling manner (e.g., controlling the number of problems per problem group, etc.).
The specific implementation mode of improving the recovery rate of the questionnaire through different disassembly modes can be as follows: similarly, basic disassembly is performed, for example, when full-problem disassembly (in fact, disassembly is not performed), the recovery rate V (the number of recovery problems in unit time) of the questionnaire is taken as a reference value, and after the parameters of the disassembly scale are rewritten, the change of V is compared, so that the disassembly mode is optimized.
The recovery rate of the questionnaire is improved through different disassembling modes, and the specific implementation mode of finding out the optimal solution when the recovery rate of the questionnaire is highest and the feedback result is closest to the result when the whole questionnaire is issued can be as follows: on the premise of ensuring the effect, the problem is recovered as soon as possible. Statistical regression analysis or the like can be performed on the results in the previous two steps in order to find the optimal solution. By the optimization, the accuracy of the data obtained by the questionnaire survey in the questionnaire parsing mode can be improved, and the questionnaire survey in the questionnaire parsing mode is close to the overall questionnaire survey in terms of the accuracy of the survey data to the maximum extent.
As a second aspect of the present disclosure, the present disclosure also provides a system corresponding to the aforementioned method steps, and many technical details submitted in the foregoing description of the method are also included in the system corresponding to the method, and are not expanded one by one in the description of the system for economy.
The system of the present disclosure may be a physical hardware device, a virtual device formed by software functional modules, or a device formed by combining hardware and software, but those skilled in the art should understand that the function of any virtual device formed by software functional modules is implemented without the support of the physical hardware device.
An intelligent questionnaire survey system (100), comprising: the questionnaire characteristic information acquisition module (101) is used for acquiring questionnaire characteristic information; the audience group characteristic information acquisition module (102) is used for acquiring audience group characteristic information; and the questionnaire disassembling and matching distribution module (103) is used for disassembling the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information and distributing the questionnaire to the audience group matched with the question group.
As will be understood by those skilled in the art, the questionnaire parsing matching distribution module (103) can receive the questionnaire characteristic information acquired by the questionnaire characteristic information acquisition module (101) and the audience population characteristic information acquired by the audience population characteristic information acquisition module (102), parse the questionnaire into problem groups based on the questionnaire characteristic information and the audience population characteristic information, and distribute the problem groups to the audience population matched with the problem groups. The specific questionnaire parsing and matching distribution process is the same as the method described in the present disclosure, and is not repeated here.
As a preferred embodiment, the system further includes a questionnaire creating system, configured to create a required questionnaire by a user before obtaining characteristic information of the questionnaire, where the manner of creating the questionnaire may be created by logging in the questionnaire creating system for the user, the created process may call an existing questionnaire module in the system for the user, and form an overall questionnaire by combining a plurality of modules, and the process of calling the existing questionnaire module in the system includes direct calling and using the called questionnaire module after modifying or modifying. The mode of creating the questionnaire can also be that the created questionnaire is directly imported into a questionnaire survey system.
After the system creates or obtains the questionnaire, it is further required to obtain questionnaire characteristic information, which is realized by a questionnaire characteristic information obtaining module (101), wherein the questionnaire characteristic information can be obtained by filling in tag information for describing questionnaire characteristics by a user, and in other preferred embodiments, the tag information can also be obtained by an automatic obtaining method. In some more preferred embodiments, the questionnaire characteristic information may be obtained by a combination of manual user filling and automatic machine acquisition. The combined manner of manual filling by the user and automatic acquisition by the machine to acquire the questionnaire characteristic information has the advantages that: the reliability, controllability, flexibility and accuracy of the system work are ensured.
In some more preferred embodiments, the questionnaire characteristic information may be obtained after the user completes the questionnaire description information to be designed, and before the formal questionnaire is created, that is, the questionnaire characteristic information is obtained only according to the questionnaire description information filled by the user, and the obtained mode may be manually filled by the user and/or automatically obtained by a machine.
As a preferred embodiment, the questionnaire characteristic information and the audience population characteristic information described above in the present disclosure may be stored in a specific database to facilitate system invocation, and it should be noted that the above-mentioned database may be included in the intelligent questionnaire system described in the present disclosure, and may be a separate database besides the intelligent questionnaire system.
The questionnaire disassembling, matching and distributing module (103) disassembles the questionnaire into problem groups based on the questionnaire characteristic information and the audience group characteristic information, and distributes the problem groups to the audience groups matched with the problem groups in the specific process that:
the process of breaking the questionnaire into problem groups:
assuming that a questionnaire with N questions exists and the goal is to put M questions, the disassembling mode is as follows:
problem group A: { Qa1, Qa2, Qa3, … Qai }, number i
Problem group B: { Qb1, Qb2, Qb3, … Qbj }, the number j
Problem group C: { Qc1, Qc2, Qc3, … Qck }, number k
…
Problem group X: { Qx1, Qx2, Qx3, … Qxq }, number q
It is required to satisfy i + j + k + … + q ≦ M × N, that is, it is required to ensure that the total number of the released topics is M × N, and the number of released topics per channel is M. Wherein i, j, k, …, q are all less than N.
The process of distributing problem groups to audience segments that match it:
the distribution process is a feature matching process of the problem group and the target users, and here, different matching algorithms can be adopted, which can be realized by calculating the similarity between two things, for example: there are two objects X, Y, each containing N-dimensional features, X ═ X1, X2, X3, … …, xn), Y ═ Y1, Y2, Y3, … …, yn), and the similarity between X and Y is calculated, and the specific algorithm may be euclidean distance method, manhattan distance method, minkowski distance method, cosine similarity method, pearson correlation coefficient method.
Through the similarity calculation of the problem groups and the target users, different problem groups can be allocated to the target users with close characteristics; in some more preferred embodiments, it should be ensured that the same audience is not assigned two problem groups in the same survey. The advantages are that: the method and the device ensure that the user is not interfered by the junk information, and simultaneously improve the matching and sending efficiency of the questionnaire.
As a preferred embodiment, the intelligent questionnaire survey system further comprises a survey data feedback module, which can receive feedback information of audience groups to the problem groups and form structured data. The structured data is output in a visual and/or machine readable form. The visual output means that feedback information of a target user is output and represents related data in a human-readable data representation form such as a list, characters, graphics, images, and a data map. The machine-readable output refers to that the feedback information of the target user outputs related data in the forms of computer information interaction, transmission, identification, statistics and analysis.
As a preferred embodiment, the survey data feedback module can further feed back feedback data which is output in a machine-readable manner to the questionnaire parsing matching distribution module (103), and the questionnaire parsing matching distribution module (103) further optimizes a questionnaire parsing and matching distribution mode according to the feedback structured data, and the specific process may be as follows: and counting the question feedback results of the feedback data, comparing the question feedback results with the feedback results of the overall questionnaire issuing survey, improving the recovery rate of the questionnaire through different disassembling modes, and finding out the optimal solution when the recovery rate of the questionnaire is the highest and the feedback results are closest to the results of the overall questionnaire issuing survey, namely the optimal questionnaire disassembling mode and the optimal distribution matching mode.
The specific implementation manner of counting the question feedback result of the feedback data and comparing the question feedback result with the feedback result of the overall questionnaire issuing survey may be as follows: two groups were set when the questionnaire was disassembled: the group A is issued in the form of all questions, and the group B is issued in the form of disassembled question groups. The two retrieved answer proportions are counted from the collected results, vector comparison is performed, and the two effects are approximated by rewriting the B-group problem group dismantling manner (e.g., controlling the number of problems per problem group, etc.).
The specific implementation mode of improving the recovery rate of the questionnaire through different disassembly modes can be as follows: similarly, basic disassembly is performed, for example, when full-problem disassembly (in fact, disassembly is not performed), the recovery rate V (the number of recovery problems in unit time) of the questionnaire is taken as a reference value, and after the parameters of the disassembly scale are rewritten, the change of V is compared, so that the disassembly mode is optimized.
The recovery rate of the questionnaire is improved through different disassembling modes, and the specific implementation mode of finding out the optimal solution when the recovery rate of the questionnaire is highest and the feedback result is closest to the result when the whole questionnaire is issued can be as follows: on the premise of ensuring the effect, the problem is recovered as soon as possible. Statistical regression analysis or the like can be performed on the results in the previous two steps in order to find the optimal solution. By the optimization, the accuracy of the data obtained by the questionnaire survey in the questionnaire parsing mode can be improved, and the questionnaire survey in the questionnaire parsing mode is closer to the overall questionnaire survey in terms of the accuracy of the survey data.
As another aspect of the present disclosure, there is also provided an electronic apparatus including: a processor and a memory, the memory having a medium (computer-readable storage medium) with program code stored therein, the electronic device being capable of performing the following method steps when the processor reads the program code stored in the medium: acquiring questionnaire characteristic information; acquiring audience group characteristic information; and decomposing the questionnaire into a problem group based on the questionnaire characteristic information and the audience group characteristic information, and distributing the problem group to the audience group matched with the problem group.
Further: the terminal device is also capable of performing any of the other methods described in this disclosure when the processor reads the program code stored in the medium.
Fig. 4 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure. As shown in fig. 4, a computer-readable storage medium 300 having non-transitory computer-readable instructions 301 stored thereon according to an embodiment of the present disclosure. The non-transitory computer readable instructions 301, when executed by a processor, perform all or a portion of the steps of the intelligent questionnaire survey method of the embodiments of the present disclosure previously described.
Fig. 5 is a diagram illustrating a hardware structure of an electronic device according to an embodiment of the present disclosure. The electronic device may be implemented in various forms, and the electronic device in the present disclosure may include, but is not limited to, mobile terminal devices such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation apparatus, a vehicle-mounted terminal device, a vehicle-mounted display terminal, a vehicle-mounted electronic rear view mirror, and the like, and fixed terminal devices such as a digital TV, a desktop computer, and the like.
As shown in fig. 5, the terminal device 1100 may include a processor 1120, an input unit 1130, a memory 1140, an output unit 1150, and the like. Fig. 5 shows an electronic device having various components, but it is understood that not all of the illustrated components are required to be implemented. More or fewer components may alternatively be implemented.
Among other things, the processor 1120 is used for executing the methods disclosed in the present disclosure, the input unit 1130 may generate key input data according to a command input by a user to control various operations of the electronic device, and the output unit 1150 provides an output signal. The memory 1140 may store software programs or the like for processing and controlling operations performed by the processor 1120, or may temporarily store data that has been output or is to be output. Memory 1140 may include at least one type of storage medium. Also, the electronic apparatus 1100 may cooperate with a network storage device that performs a storage function of the memory 1140 by way of a network connection. The processor 1120 generally controls the overall operation of the terminal device.
Various embodiments of the intelligent questionnaire methods presented in the present disclosure can be implemented using a computer-readable medium, such as computer software, hardware, or any combination thereof.
For a hardware implementation, various embodiments of the intelligent questionnaire survey method proposed by the present disclosure may be implemented by using at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microprocessor, and an electronic unit in which a microcontroller is designed to perform the functions described herein, and in some cases, various embodiments of the intelligent questionnaire survey method proposed by the present disclosure may be implemented in the processor 1120. For software implementation, various embodiments of the intelligent questionnaire method presented in the present disclosure may be implemented with a separate software module that allows for performing at least one function or operation. The software codes may be implemented by software applications (or programs) written in any suitable programming language, which may be stored in memory 1140 and executed by processor 1120.
The applicant declares that the present disclosure illustrates the detailed structural features of the present disclosure through the above-mentioned embodiments, but the present disclosure is not limited to the above-mentioned detailed structural features, i.e. it does not mean that the present disclosure must rely on the above-mentioned detailed structural features for implementation. It will be apparent to those skilled in the art that any modification of the present disclosure, equivalent substitutions of selected elements of the disclosure, additions of auxiliary elements, selection of particular means, etc., are within the scope and disclosure of the present disclosure.
The preferred embodiments of the present disclosure have been described in detail above, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (12)
1. An intelligent questionnaire survey method, characterized by comprising:
acquiring questionnaire characteristic information;
acquiring audience group characteristic information;
and decomposing the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information, and distributing the question group to the audience group matched with the question group.
2. The investigation method of claim 1, further comprising: receiving feedback information of audience groups to the problem groups and forming structured data.
3. The investigation method of claim 2, further comprising: outputting the structured data in a visual and/or machine readable form.
4. The investigation method of claim 2, further comprising: and optimizing a questionnaire disassembling and matching distribution mode according to the structural data of the feedback of the audience group to the problem group.
5. The method of claim 4, wherein optimizing the questionnaire parsing and matching distribution based on structured data of audience community feedback to the problem groups comprises: and comparing the statistical result with the statistical result of the overall questionnaire issuing, and adjusting and optimizing the questionnaire disassembling and matching distribution mode according to the comparison result.
6. The method of claim 5, wherein optimizing the questionnaire parsing based on structured data of audience segment feedback to the problem group comprises: when the questionnaire is distributed, the questionnaire is distributed to different audience groups in the form of whole questionnaire distribution and the form of problem group disassembly, the feedback structured data of the audience groups in different disassembly modes is received and compared, and the disassembly mode of the problem group is optimized according to the comparison result.
7. The method of claim 1, wherein obtaining questionnaire characteristics information comprises: and acquiring label information which is filled by a user and used for describing the questionnaire characteristics and/or automatically acquiring the questionnaire characteristic information through a machine.
8. The method of claim 1, wherein obtaining audience segment characteristics information comprises: and audience population characteristic information is obtained according to historical data analysis and/or is obtained in a manual setting mode.
9. The method as claimed in claim 1, wherein the matching of the problem groups and the audience groups is implemented by calculation of Euclidean distance and/or cosine similarity of feature vectors.
10. An intelligent questionnaire survey system, comprising:
the questionnaire characteristic information acquisition module is used for acquiring questionnaire characteristic information;
the audience group characteristic information acquisition module is used for acquiring audience group characteristic information;
and the questionnaire disassembling and matching distribution module is used for disassembling the questionnaire into a question group based on the questionnaire characteristic information and the audience group characteristic information and distributing the questionnaire to the audience group matched with the question group.
11. An electronic device, comprising: a processor and a memory, the memory having a medium with program code stored therein, the electronic device being capable of performing the method of any of claims 1-9 when the processor reads the program code stored in the medium.
12. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 9.
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