WO2019174141A1

WO2019174141A1 - Questionnaire generation method, server and computer readable storage medium

Info

Publication number: WO2019174141A1
Application number: PCT/CN2018/090644
Authority: WO
Inventors: 万晓辉
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-03-12
Filing date: 2018-06-11
Publication date: 2019-09-19
Also published as: CN108428152A

Abstract

A questionnaire generation method, an application server and a computer readable storage medium. The method comprises: receiving survey demand information and generating a questionnaire according to the survey demand information (S500); distributing the questionnaire to preset target users, the preset target users referring to users whose behavior logs or historical survey records match the survey demand information (S502); collecting the questionnaire filling results returned by the preset target users (S504); summarizing the questionnaire filling results, performing consensus analysis on the summarized questionnaire filling results to output a summarized questionnaire result (S506). The method automatically generates a questionnaire according to received survey demand information, and the operation is simple and convenient, and can also improve survey result effectiveness.

Description

Questionnaire generation method, server and computer readable storage medium

This application claims priority to Chinese Patent Application No. 201101200161.3, entitled "Questionnaire Generation Method, Server and Computer Readable Storage Media", filed on March 12, 2018, the entire contents of which are hereby incorporated by reference. Combined in this application.

Technical field

The present application relates to the field of Internet, and in particular, to a questionnaire generation method, a server, and a computer readable storage medium.

Background technique

Market research is the process of planning, collecting, and analyzing data related to marketing decisions and using the results of the analysis for marketing decisions. The basic task of market research is to provide management with information that can help solve marketing problems. Market research is also a key management tool for determining the needs and needs of customers and potential customers. It is a means for companies to establish long-term relationships. Good market research helps to ensure the survival and development of the company in the future. The existing market research is mainly achieved through questionnaires.

However, the existing questionnaires are complex in configuration and require high professional skills and experience to complete the problem. The error rate is high, and the human and material resources are extremely exhausted. There are also requirements for the research site and environment, and there is no choice for the research personnel. Good selection measures, survey accuracy often can not meet the actual research needs.

Summary of the invention

In view of this, the present application proposes a questionnaire generation, a server and a computer readable storage medium, which can realize the intelligent generation of the questionnaire, is beneficial to obtaining the true will of the customer, saves a lot of manpower and material resources, and makes the validity of the research result very good. Great improvement.

First, in order to achieve the above object, the present application provides a server, which includes a memory and a processor, and the memory stores a questionnaire generating system that can be run on the processor, and the questionnaire generating system is The processor implements the following steps when executed:

Receiving research demand information and generating a questionnaire according to the research demand information;

Distributing the questionnaire to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research demand information;

Collecting the results of the questionnaire returned by the preset target user; and

The results of the questionnaires are summarized, and the results of the questionnaires are filled out for consistency analysis to output the summarized questionnaire results.

In addition, in order to achieve the above object, the present application further provides a questionnaire generating method, which is applied to a server, and the method includes:

Further, in order to achieve the above object, the present application further provides a computer readable storage medium storing a questionnaire generating system, the questionnaire generating system being executable by at least one processor, so that The at least one processor performs the steps of the questionnaire generation method as described above.

Compared with the prior art, the questionnaire generating method, the server and the computer readable storage medium proposed by the present application firstly receive survey demand information and generate a questionnaire according to the survey demand information; secondly, distribute the questionnaire To the preset target user; further, collecting the questionnaire filling result of the preset target user returning; finally, summarizing the questionnaire filling result, and performing the opinion consistency analysis on the summarized questionnaire filling result, to output the summary Post-question results. In this way, the questionnaire can be automatically generated, and the system can generate different questionnaires according to different research demand information and select target users suitable for the research purpose to conduct network research, and the operation is simple, and the questionnaire generation is intelligent, which is beneficial to obtaining the true will of the customer. It saves a lot of manpower and material resources, and the effectiveness of the research results is greatly improved.

DRAWINGS

1 is a schematic diagram of an optional application environment of each embodiment of the present application;

2 is a schematic diagram of an optional hardware architecture of the server of the present application;

3 is a schematic diagram of a program module of a first embodiment of a questionnaire generating system of the present application;

4 is a schematic diagram of a program module of a second embodiment of the questionnaire generating system of the present application;

FIG. 5 is a schematic diagram of an implementation process of a first embodiment of a method for generating a questionnaire according to the present application; FIG.

FIG. 6 is a schematic diagram of an implementation process of a second embodiment of a questionnaire generating method of the present application.

Reference mark:

终端设备Terminal Equipment	11
服务器server	22
网络The internet	33
存储器Memory	1111
处理器processor	1212
网络接口Network Interface	1313
调查问卷生成系统 Questionnaire generation system	100100
生成模块 Build module	101101

分发模块 Distribution module	102102
收集模块 Collection module	103103
汇总分析模块 Summary analysis module	104104
调整模块 Adjustment module	105105

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

detailed description

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. . Thus, features defining "first" or "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

Referring to FIG. 1 , it is a schematic diagram of an optional application environment of each embodiment of the present application.

In this embodiment, the present application is applicable to an application environment including, but not limited to, the terminal device 1, the server 2, and the network 3. The terminal device 1 may be 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 device, an in-vehicle device, etc. Mobile devices such as, and fixed terminals such as digital TVs, desktop computers, notebooks, servers, and the like. The server 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server. The server 2 may be a stand-alone server or a server cluster composed of multiple servers. The network 3 may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, Wireless or wired networks such as 5G networks, Bluetooth, Wi-Fi, and call networks.

The server 2 can be communicatively connected to one or more of the terminal devices 1 through the network 3 for data transmission and interaction.

Referring to FIG. 2, it is a schematic diagram of an optional hardware architecture of the server 2 of the present application.

In this embodiment, the server 2 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13 that can communicate with each other through a system bus. It is pointed out that Figure 2 only shows the server 2 with the components 11-13, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the server 2, such as a hard disk or memory of the server 2. In other embodiments, the memory 11 may also be an external storage device of the server 2, such as a plug-in hard disk equipped on the server 2, a smart memory card (SMC), and a secure digital (Secure) Digital, SD) cards, flash cards, etc. Of course, the memory 11 can also include both the internal storage unit of the server 2 and its external storage device. In this embodiment, the memory 11 is generally used to store an operating system installed on the server 2 and various types of application software, such as program codes of the questionnaire generating system 100. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used to control the overall operation of the server 2, such as performing control and processing related to data interaction or communication with the terminal device 1. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, such as running the questionnaire generation system 100 and the like.

The network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the server 2 and other electronic devices. In this embodiment, the network interface 13 is mainly used to connect the server 2 to one or more of the terminal devices 1 through the network 3, and the server 2 and the one or more terminal devices 1 Establish a data transmission channel and communication connection between.

So far, the hardware structure and functions of the devices related to this application have been described in detail. Hereinafter, various embodiments of the present application will be made based on the above description.

First, the present application proposes a questionnaire generating system 100.

Referring to FIG. 3, it is a program module diagram of the first embodiment of the questionnaire generating system 100 of the present application.

In this embodiment, the questionnaire generating system 100 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the questionnaire generating operation of each embodiment of the present application can be implemented. . In some embodiments, the questionnaire generation system 100 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 3, the questionnaire generation system 100 can be divided into a generation module 101, a distribution module 102, a collection module 103, and a summary analysis module 104. among them:

The generating module 101 is configured to receive survey demand information and generate a questionnaire according to the survey demand information.

In an embodiment, the research requirement information may include a research target group, a research topic, a research question type, and the like. The user can input the research request information through the terminal device 1, and the terminal device 1 can forward the received research request information to the server 2. The terminal device 1 can set an interactive interface to guide the user to input the research requirement information, thereby obtaining information such as the target group, the theme, the type of the research question, and the research restriction factors. For example, the interactive interface may be provided with input fields such as "this research topic", "objects expected to be researched", and a combination of selectable question types (multiple choice questions, multiple choice questions + quiz questions).

In an embodiment, the generating module 101 may use a deep learning algorithm to establish a questionnaire generating model, and train the questionnaire generating model, so that the questionnaire may be automatically generated according to the survey demand information, and the generating module 101 may acquire a training sample by generating a record or accessing a network through a historical questionnaire to train the questionnaire generation model.

For example, the generating module 101 accesses the network to obtain a plurality of questionnaire training samples, each questionnaire training sample includes a questionnaire sample and a training feature, and the training feature may include a research target group, a research topic, a research question type, and the like. The generating module 101 converts the training features of each questionnaire training sample into training vectors, and trains the questionnaire generating model by using the training vectors and the questionnaire samples of each questionnaire training sample, the questionnaire generating model The training layer is preferably implemented based on a nonlinear function (for example, a Sigmoid function); the generating module 101 further converts the received survey demand information into a demand feature vector, and inputs the demand feature vector to the questionnaire generating model. Further, a questionnaire corresponding to the survey request information can be obtained.

The distribution module 102 is configured to distribute the questionnaire to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research requirement information.

In an embodiment, the distribution module 102 can distribute the questionnaire to the preset target user through a specific transmission channel. The specific transmission channel may refer to a specific software client such as a preset target user, mail, instant messaging, etc., thereby informing and guiding the preset target user to participate in the questionnaire. The preset target user may have multiple attributes. The attributes may be a user login time, a login duration, a login location, and a visited website. The distribution module 102 can find a collection of users that meet the research purpose and the research needs according to different research purposes and research needs of the user.

In an embodiment, the distribution module 102 may analyze the behavior log of the user, obtain the attribute of the preset target user, and extract the user whose attribute corresponds to the research purpose as the preset target user. . The behavior log of the preset target user may be an interest in the research purpose and a browsing habit of the user. The distribution module 102 may further use, as the preset target user, a user whose user's evaluation result reaches a preset standard according to the research record of the user. The research record may be the degree of seriousness of the user's answer and the number of answers. The behavior log and/or historical research record of the preset target user matches the research target group and/or the research topic.

The collecting module 103 is configured to collect a questionnaire filling result returned by the preset target user.

In an embodiment, the collecting module 103 collects the questionnaire filling result returned by the preset target user. After the distribution module 102 distributes the generated questionnaire to the preset target user through the designated transmission channel, the preset target user may fill in the questionnaire at any time and anywhere.

The summary analysis module 104 is configured to summarize the questionnaire filling results, and perform an opinion consistency analysis on the summarized questionnaire filling results to output the summarized questionnaire results.

In an embodiment, the summary analysis module 104 saves the questionnaire filling result to a database after receiving the questionnaire filling result returned by the preset target user; when the result analysis is needed, from the All the results of the survey are recalled and summarized in the database, and then based on the summary of the results of all the questionnaires, the degree of dispersion of the opinions of all the surveyed personnel can be visually displayed, and the results are summarized in a visual form.

In an embodiment, the summary analysis module 104 performs an opinion consistency analysis on the summarized questionnaire completion results by using an Isomap dimensionality reduction algorithm and a similarity algorithm to output the aggregated questionnaire results.

For example, a vector is used to represent the survey results of each researcher, and the Isomap dimension reduction method is used to convert the high-dimensional vector into a low-dimensional vector for coordinate display under the condition that the distance between the vectors is constant. By calculating the quantitative relationship between vectors, the similarities and differences between different researchers are expressed. Define the distance between vector X and vector Y as:

If the i-th component of X, Y is the same, the contribution of the component to the distance is 0; if different, the contribution of the component to the distance is greater than 0, and the greater the difference in components, the greater their contribution to the distance. Using the distance between vectors can describe the differences in the opinions of the various investigators. The larger the distance value, the greater the difference in the opinions of the respondents. The similarity calculation method evaluates the degree of divergence of the group opinions: if the distance is small, the similarity is large; if the distance is large, the similarity is small.

In an embodiment, different analysis methods may be set for different problems, and a quantitative and qualitative combination method may be adopted for the closedness problem, for example, methods such as percentage, frequency and similarity analysis, dimensionality reduction analysis, and analytic hierarchy process may be used. Combine. For the open problem, qualitative analysis methods can be used, mainly including clustering, interval division, and similarity analysis methods. Finally, Isomap dimension reduction is performed on all the options of all the questions, and then the similarity calculation method is used to summarize the degree of dispersion of the overall opinions. If the opinions of all the respondents are basically the same for a certain question in the questionnaire, the results can be consistent. Sexual opinion. If there is a logical inconsistency, a reminder of inconsistency is issued, and a corresponding question is given, and then the summary analysis module 104 re-sends the results and analyzes the results, until the group opinions are basically the same, and finally the opinions are The relevant content of the analysis is presented in a visual form.

Through the above-mentioned program modules 101-104, the questionnaire generating system 100 proposed by the present application first receives the research demand information and generates a questionnaire according to the research demand information; secondly, distributes the questionnaire to a preset target user; Furthermore, the results of the questionnaires returned by the preset target users are collected; finally, the results of the questionnaires are summarized, and the results of the questionnaires are filled out for consistency analysis to output the summarized questionnaire results. In this way, the questionnaire can be automatically generated, and the system can generate different questionnaires according to different research demand information and select target users suitable for the research purpose to conduct network research, and the operation is simple, and the questionnaire generation is intelligent, which is beneficial to obtaining the true will of the customer. It saves a lot of manpower and material resources, and the effectiveness of the research results is greatly improved.

Referring to FIG. 4, it is a program module diagram of the second embodiment of the questionnaire generating system 100 of the present application. In this embodiment, the questionnaire generating system 100 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the questionnaire generating operation of each embodiment of the present application can be implemented. . In some embodiments, the questionnaire generation system 100 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 4, the questionnaire generation system 100 can be divided into a generation module 101, a distribution module 102, a collection module 103, a summary analysis module 104, and an adjustment module 105. Each of the program modules 101-104 is the same as the first embodiment of the questionnaire generating system 100 of the present application, and an adjustment module 105 is added thereto. among them:

The adjustment module 105 is configured to receive a questionnaire adjustment request and perform detail adjustment on the generated questionnaire according to the questionnaire adjustment request.

In an embodiment, the adjustment module 105 may receive a user questionnaire adjustment request and perform detail adjustment on the generated questionnaire according to the questionnaire adjustment request. The questionnaire may include a plurality of questionnaire questions, and the detail adjustment may be a modification, deletion, addition of a questionnaire question, setting an association relationship of the questionnaire questions, and changing the order of the questionnaire questions.

For example, the adjustment module 105 can adjust the proportion of some research questions related to a specific product, set the relationship of the questionnaire questions, modify the description of some problems, change the order of the questionnaire questions, set options for closed questions, and set the matrix. Question elements, etc. The adjustment module 105 can also specify a certain questionnaire question corresponding to the answer by the workflow mode to jump to the specified question, or set the analysis of the descriptive questionnaire answer and jump to the specified question, allowing the expression and picture of the subjective question. Answer the question.

In an embodiment, when the user sends a questionnaire adjustment request to select a detailed adjustment of the questionnaire, the adjustment module 105 receives the user's setting requirements and/or acquires the user's previous questionnaire records and/or the user's basics. The information is adjusted in detail. After setting the adjustment details for the selected questionnaire, the system can automatically generate the modified questionnaire and form a preview. If the questionnaire is not satisfied with the modified questionnaire, the questionnaire can be further adjusted. When the detailed adjustment problem is performed, the adjustment module 105 can also promptly remind the researcher how to set the topic so that the obtained research result is better.

Through the above-mentioned program modules 101-105, the questionnaire generating system 100 proposed by the present application first receives the research demand information and generates a questionnaire according to the research demand information; secondly, receives the questionnaire adjustment request and adjusts the request according to the questionnaire. The generated questionnaire is adjusted in detail; further, the questionnaire is distributed to the preset target user; and further, the questionnaire of the preset target user is collected to fill in the result; and finally, the questionnaire is filled in. Summarize and analyze the results of the completed questionnaires to output the aggregated questionnaire results. In this way, the detailed adjustment of the generated questionnaire according to the user's needs can be realized, and the user's research needs can be further adapted, which is beneficial to obtaining the true will of the customer and further improving the effectiveness of the research result.

In addition, the present application also proposes a questionnaire generation method.

Referring to FIG. 5, it is a schematic flowchart of the implementation of the first embodiment of the questionnaire generating method of the present application. In this embodiment, the order of execution of the steps in the flowchart shown in Fig. 5 may be changed according to different requirements, and some steps may be omitted.

Step S500, receiving research demand information and generating a questionnaire according to the research demand information.

In an embodiment, the questionnaire generation model may be established by using a deep learning algorithm, and the questionnaire generation model may be trained, and then the questionnaire may be automatically generated according to the research requirement information, and the record may be generated through a historical questionnaire. The network is used to obtain training samples to train the questionnaire generation model.

In an embodiment, the network may be accessed to obtain a plurality of questionnaire training samples, each questionnaire training sample includes a questionnaire sample and a training feature, and the training feature may include information such as a research target group, a research topic, and a type of research question; Converting the training features of each questionnaire training sample into training vectors, and training the questionnaire generating model by using the training vectors and the questionnaire samples of each questionnaire training sample, wherein the training layer of the questionnaire generating model is preferably based on nonlinearity a function (for example, a Sigmoid function) is implemented; the received research demand information is converted into a demand feature vector, and the demand feature vector is input to the questionnaire generation model, thereby obtaining a survey corresponding to the survey demand information. Questionnaire.

In step S502, the questionnaire is distributed to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research requirement information.

In an embodiment, the questionnaire may be distributed to the preset target user via a specific transmission channel. The specific transmission channel may refer to a specific software client such as a preset target user, mail, instant messaging, etc., thereby informing and guiding the preset target user to participate in the questionnaire. The preset target user may have multiple attributes. The attributes may be a user login time, a login duration, a login location, and a visited website. It is also possible to find a collection of users that meet the research purpose and research needs according to the purpose of the user's research and the needs of the research.

In an embodiment, the behavior log of the user may be analyzed, the attribute of the preset target user is obtained, and the user whose attribute is corresponding to the research purpose is extracted as the preset target user. The behavior log of the preset target user may be an interest in the research purpose and a browsing habit of the user. The user who has reached the preset standard of the user's evaluation result may also be used as the preset target user according to the research record of the user. The research record may be the degree of seriousness of the user's answer and the number of answers. The behavior log and/or historical research record of the preset target user matches the research target group and/or the research topic.

Step S504, collecting a questionnaire filling result returned by the preset target user.

In an embodiment, the questionnaire filling result returned by the preset target user is collected. After the generated questionnaire is distributed to the preset target user through the designated transmission channel, the preset target user can fill in the questionnaire at any time and anywhere.

In step S506, the questionnaire filling result is summarized, and the result of the questionnaire filling is completed, and the consensus analysis is performed to output the summarized questionnaire result.

In an embodiment, after receiving the questionnaire filling result of the preset target user return, the questionnaire filling result is saved to a database; when the result analysis is needed, the database is called out from the database. All the results of the survey are summarized and then summarized according to the results of all the questionnaires. The dispersion of the opinions of all the surveyed personnel can be visually displayed, and the results are summarized in a visual form.

In an embodiment, the Isomap dimension reduction algorithm and the similarity algorithm are used to perform an opinion consistency analysis on the summarized questionnaire completion results to output the summarized questionnaire results.

In an embodiment, a vector is used to represent the survey results of each researcher, and the Isomap dimension reduction method is used to convert the high-dimensional vector into a low-dimensional vector for coordinate display under the condition that the distance between the vectors is constant. Then, by calculating the quantitative relationship between the vectors, the similarities and differences between the different researchers are expressed. Define the distance between vector X and vector Y as:

In an embodiment, different analysis methods may be set for different problems, and a quantitative and qualitative combination method may be adopted for the closedness problem, for example, methods such as percentage, frequency and similarity analysis, dimensionality reduction analysis, and analytic hierarchy process may be used. Combine. For the open problem, qualitative analysis methods can be used, mainly including clustering, interval division, and similarity analysis methods. Finally, Isomap dimension reduction is performed on all the options of all the questions, and then the similarity calculation method is used to summarize the degree of dispersion of the overall opinions. If the opinions of all the respondents are basically the same for a certain question in the questionnaire, the results can be consistent. Sexual opinion. If there is a logical inconsistency, then a reminder of inconsistency will be issued, and the corresponding questions will be given, and then the results will be summarized and analyzed, until the group opinions are basically the same, and finally the relevant content of the opinion analysis is visualized. The form is expressed.

Through the above steps S500-S506, the questionnaire generating method proposed by the present application first receives the research demand information and generates a questionnaire according to the research demand information; secondly, distributes the questionnaire to a preset target user; Collecting the results of the questionnaires returned by the preset target users; finally, summarizing the results of the questionnaires, and performing consistency analysis on the results of the completed questionnaires to output the summarized questionnaire results. In this way, the questionnaire can be automatically generated, and the system can generate different questionnaires according to different research demand information and select target users suitable for the research purpose to conduct network research, and the operation is simple, and the questionnaire generation is intelligent, which is beneficial to obtaining the true will of the customer. It saves a lot of manpower and material resources, and the effectiveness of the research results is greatly improved.

Referring to FIG. 6, it is a schematic flowchart of the implementation of the second embodiment of the questionnaire generating method of the present application. In this embodiment, the order of execution of the steps in the flowchart shown in FIG. 6 may be changed according to different requirements, and some steps may be omitted.

Step S508, receiving a questionnaire adjustment request and performing detail adjustment on the generated questionnaire according to the questionnaire adjustment request.

In an embodiment, a user questionnaire adjustment request may be received and details of the generated questionnaire may be adjusted according to the questionnaire adjustment request. The questionnaire may include a plurality of questionnaire questions, and the detail adjustment may be a modification, deletion, addition of a questionnaire question, setting an association relationship of the questionnaire questions, and changing the order of the questionnaire questions.

In an embodiment, it is possible to adjust the proportion of some research questions related to a specific product, set the relationship of the questionnaire questions, modify the description of some problems, replace the order of the questionnaire questions, set options for closed questions, and set matrix questions. Elements, etc. You can also specify the answer to a questionnaire question through workflow to jump to the specified question, or you can set the analysis of the descriptive questionnaire answer and jump to the specified question, allowing the expression of the subjective question and the picture to answer.

In an embodiment, when the user sends a questionnaire adjustment request to select a detailed adjustment of the questionnaire, the user's setting requirements are received and/or the user's previous questionnaire record and/or the user's basic information are obtained for detailed adjustment. After setting the adjustment details for the selected questionnaire, the system can automatically generate the revised questionnaire and form a preview. If the questionnaire is not satisfied with the modified questionnaire, the questionnaire can be further adjusted. When the details are adjusted, the reporter can also be reminded in real time how to set the topic so that the research results obtained are better.

In an embodiment, the behavior log of the user may be analyzed, the attribute of the preset target user is obtained, and the user whose attribute is corresponding to the research purpose is extracted as the preset target user. The behavior log of the preset target user may be an interest in the research purpose and a browsing habit of the user. The user who has reached the preset standard of the user's evaluation result may also be used as the preset target user according to the research record of the user. The research record may be the degree of seriousness of the user's answer and the number of answers. The behavior log or historical research record of the preset target user matches the research target group or the research topic.

Through the above steps S500-S508, the questionnaire generating method proposed by the present application first receives the survey demand information and generates a questionnaire according to the survey demand information; secondly, receives the questionnaire adjustment request and adjusts the request according to the questionnaire. The questionnaire is adjusted in detail; further, the questionnaire is distributed to the preset target user; further, the questionnaire is returned to the preset target user, and finally, the results of the questionnaire are summarized. The results of the questionnaires are filled out and the opinions are consistently analyzed to output the summarized questionnaire results. In this way, it is also possible to adjust the details of the student's questionnaire according to the actual needs of the user, further adapting the user's research needs, and is beneficial to obtaining the true will of the customer, and further improving the effectiveness of the research result.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A questionnaire generating method is applied to a server, wherein the method comprises:

Receiving research demand information and generating a questionnaire according to the research demand information;

Distributing the questionnaire to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research demand information;

Collecting the results of the questionnaire returned by the preset target user; and

The results of the questionnaires are summarized, and the results of the questionnaires are filled out for consistency analysis to output the summarized questionnaire results.
The method of claim 1, wherein the step of receiving survey request information and generating a questionnaire based on the survey demand information comprises:

Using a deep learning algorithm to build a questionnaire generation model, and training the questionnaire generation model according to the questionnaire training sample;

Receiving the survey demand information and converting the survey demand information into a demand feature vector; and inputting the demand feature vector to the questionnaire generation model to obtain a questionnaire corresponding to the survey demand information.
The method according to claim 1, wherein the research requirement information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target and/or The users whose research topics match.
The method according to claim 2, wherein the research demand information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target and/or The users whose research topics match.
The method of claim 1 wherein said questionnaire generating method further comprises the steps of:

Receiving a questionnaire adjustment request and performing detailed adjustment on the generated questionnaire according to the questionnaire adjustment request;

The questionnaire includes a plurality of questionnaire questions, and the detail adjustment includes: modifying, deleting, increasing the questionnaire question, setting the association relationship of the questionnaire questions, and changing the order of the questionnaire questions.
The method of claim 2, wherein the questionnaire generating method further comprises the steps of:

Receiving a questionnaire adjustment request and performing detailed adjustment on the generated questionnaire according to the questionnaire adjustment request;

The questionnaire includes a plurality of questionnaire questions, and the detail adjustment includes: modifying, deleting, increasing the questionnaire question, setting the association relationship of the questionnaire questions, and changing the order of the questionnaire questions.
The method according to claim 1 or 2, wherein the step of summarizing the questionnaire filling results and performing the opinion consistency analysis on the summarized questionnaire filling results to output the summarized questionnaire results includes :

Summarizing the results of the questionnaire; and

The Isomap dimension reduction algorithm and the similarity algorithm are used to perform an opinion consistency analysis on the results of the summarized questionnaires to output the summarized questionnaire results.
A server, comprising: a memory, a processor, wherein the memory stores a questionnaire generating system operable on the processor, when the questionnaire generating system is executed by the processor Implement the following steps:

Receiving research demand information and generating a questionnaire according to the research demand information;

Distributing the questionnaire to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research demand information;

Collecting the results of the questionnaire returned by the preset target user; and

The results of the questionnaires are summarized, and the results of the questionnaires are filled out for consistency analysis to output the summarized questionnaire results.
The server according to claim 8, wherein the step of receiving the research request information and generating the questionnaire according to the research demand information comprises:

Using a deep learning algorithm to build a questionnaire generation model, and training the questionnaire generation model according to the questionnaire training sample;

Receiving the survey demand information and converting the survey demand information into a demand feature vector; and

The demand feature vector is input to the questionnaire generation model to obtain the questionnaire corresponding to the survey demand information.
The server according to claim 8, wherein the research requirement information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target and/or the The users whose research topics match.
The server according to claim 9, wherein the research requirement information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target and/or The users whose research topics match.
The server according to claim 8, wherein when said questionnaire generating system is executed by said processor, the following steps are further implemented:

Receiving a questionnaire adjustment request and performing detailed adjustment on the generated questionnaire according to the questionnaire adjustment request;

The detail adjustment includes: modifying, deleting, adding a questionnaire question, setting an association relationship of the questionnaire question, and changing the order of the questionnaire question.
The server according to claim 9, wherein when said questionnaire generating system is executed by said processor, the following steps are further implemented:

Receiving a questionnaire adjustment request and performing detailed adjustment on the generated questionnaire according to the questionnaire adjustment request;

The detail adjustment includes: modifying, deleting, adding a questionnaire question, setting an association relationship of the questionnaire question, and changing the order of the questionnaire question.
The server according to claim 8 or 9, wherein the step of summarizing the questionnaire filling results and performing the opinion consistency analysis on the summarized questionnaire filling results to output the summarized questionnaire results includes :

Summarizing the results of the questionnaire; and

The Isomap dimension reduction algorithm and the similarity algorithm are used to analyze the results of the summarized questionnaires to output a consensus analysis result.
A computer readable storage medium storing a questionnaire generating system, the questionnaire generating system being executable by at least one processor, the questionnaire generating system being implemented by the processor The following steps:

Receiving research demand information and generating a questionnaire according to the research demand information;

Distributing the questionnaire to a preset target user, where the preset target user refers to a user whose behavior log or historical research record matches the research demand information;

Collecting the results of the questionnaire returned by the preset target user; and

The results of the questionnaires are summarized, and the results of the questionnaires are filled out for consistency analysis to output the summarized questionnaire results.
The computer readable storage medium according to claim 15, wherein the step of receiving research request information and generating a questionnaire according to the research demand information comprises:

Using a deep learning algorithm to build a questionnaire generation model, and training the questionnaire generation model according to the questionnaire training sample;

Receiving the survey demand information and converting the survey demand information into a demand feature vector; and

The demand feature vector is input to the questionnaire generation model to obtain the questionnaire corresponding to the survey demand information.
The computer readable storage medium according to claim 15, wherein the research requirement information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target. And/or users who match the research topic.
The computer readable storage medium according to claim 16, wherein the research requirement information comprises a research target and a research topic, and the preset target user refers to a behavior log and/or a historical research record and the research target. And/or users who match the research topic.
The computer readable storage medium according to claim 15 or 16, wherein when the questionnaire generating system is executed by the processor, the following steps are further implemented:

Receiving a questionnaire adjustment request and performing detailed adjustment on the generated questionnaire according to the questionnaire adjustment request;

The detail adjustment includes: modifying, deleting, adding a questionnaire question, setting an association relationship of the questionnaire question, and changing the order of the questionnaire question.
The computer readable storage medium according to claim 15 or 16, wherein said summarizing the results of said questionnaires and performing a consensus analysis on the results of the completed questionnaires to output the summarized questionnaires The steps of the result include:

Summarizing the results of the questionnaire; and

The Isomap dimension reduction algorithm and the similarity algorithm are used to analyze the results of the summarized questionnaires to output a consensus analysis result.