An electronic verification question order design optimization method and device and a storage medium

Abstract

The present application relates to an electronic verification question order design optimization method, device and storage medium, the method comprises the following steps: designing a questionnaire according to the business process or behavior logic, and estimating the probability of each question option being clicked; the designed questions are grouped according to the question logic; each group of questions is independently optimized in question order, and after the optimization of all question groups is completed, the click volume of each group is summed up to estimate the expected click volume of the entire questionnaire. Based on the greedy algorithm, the actual questionnaire and its question design are optimized, the number of clicks of the associated question options in the verification is analyzed, the best path is designed, the operation steps and time are reduced, and the scientificity and rationality of the questionnaire design are improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic questionnaires, and in particular to a method, device and storage medium for optimizing the sequence design of electronic verification questions. Background Art

[0002] Electronic questionnaires, a common technical tool in the current verification field, play an important role in improving work efficiency and expanding the scope of data collection. In actual applications, the design of electronic questionnaires is mainly based on the verification process or human behavioral logic. This design method may meet basic needs when dealing with regular-sized projects, but for large-scale verification projects, the order of questions in the questionnaire will have a significant impact on the verification work. An unreasonable question order may increase the number of system operation steps for the verifier, reduce the cooperation of the respondents, affect the accuracy and completeness of the data, and thus hinder the efficient implementation of the verification work. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device and storage medium for optimizing the sequence of electronic verification questions, so as to solve the problem that the sequence of questions in existing electronic questionnaires may increase the system operation steps of the verification personnel, reduce the cooperation of the respondents, affect the accuracy and completeness of the data, and thus restrict the efficient implementation of the verification work.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for optimizing the sequence design of electronic verification questions, comprising:

[0006] Design questionnaire questions according to business processes or behavioral logic, and estimate the probability of each question option being clicked;

[0007] Group the designed questions according to the logic of the questions;

[0008] The order of questions is optimized independently for each group of questions. After all question groups are optimized, the expected number of clicks for the entire questionnaire is estimated by adding up the number of clicks for each group.

[0009] In one embodiment, each set of problems independently optimizes the problem sequence, including:

[0010] According to the click probability or click count of each question option in the same group, take each question as the first question of the questionnaire and calculate the click probability or click count after the order is adjusted;

[0011] Sort each solution to find the sequence with the least clicks;

[0012] Re-modify the questions and question option descriptions according to the question sorting to complete the optimization of one question group.

[0013] In one embodiment, the click probability or click count of each question option in the same group includes:

[0014] Calculate the click probability / number of clicks for each layer:

[0015]

[0016] Calculate the total click probability / total number of clicks for the questionnaire:

[0017] , where N represents the number of clicks at each level, X is the number of clicks at this level (the number of clicks at the end of this level + the number of clicks associated with the next level), and n is the question level.

[0018] In one embodiment, the method of taking each question as the first question of the questionnaire, taking question n as the first question of the questionnaire as an example, includes:

[0019] Divide the options for question n into three parts, including some options that will end question n directly after selecting them , flow to some options of question n+1 and flow to some of the new options for question n-1 ;

[0020] The questionnaire was split into three groups of questions, reorganized and expanded.

[0021] In one embodiment, the question options of question n are divided into three parts, including:

[0022] Delete the options related to question n in question n-1;

[0023] Add the options in question n-1 that are not related to question n to question n;

[0024] The order of other questions remains unchanged.

[0025] In one embodiment, calculating the click probability or number of clicks after the order is adjusted includes:

[0026] Some options for ending the calculation problem n directly Number of clicks:

[0027] ;

[0028] Calculate the partial options that flow to question n+1 Number of clicks:

[0029] ;

[0030] Calculate the total number of hits for the new question tree:

[0031] , where question n-1 is assumed to be the mth question, N represents the number of clicks at each level, P is some of the options for question n, X is the number of clicks at this level, and n is the level of the question.

[0032] In one embodiment, sorting the solutions to find the sequence with the least clicks includes:

[0033] Calculate the difference between the number of clicks on the unreordered questionnaire and the total number of clicks on the new question tree:

[0034] ,in, , let question n-1 be the mth question, N represents the number of clicks at each level, P is some options of question n, X is the number of clicks at this level, and n is the level of the question;

[0035] turn up The maximum value of .

[0036] In one embodiment, estimating the expected number of clicks for the entire questionnaire includes:

[0037] Estimate the probability of each question option being clicked through pilot sampling;

[0038] Estimate the probability of each question option being clicked based on past experience.

[0039] An electronic verification question sequence design optimization device, comprising:

[0040] The questionnaire design module is used to design questionnaire questions according to business processes or behavioral logic, and estimate the probability of each question option being clicked;

[0041] Logical grouping module, used to group the designed questions according to question logic;

[0042] The question optimization module is used to independently optimize the order of questions for each group of questions. After all question groups are optimized, the total number of clicks for each group can be used to estimate the expected number of clicks for the entire questionnaire.

[0043] A storage medium stores instructions, which, when executed by a processor, implement the steps of the method described in the claim.

[0044] It can be seen from the above technical solution that compared with the existing technology, the present invention has at least the following advantages and positive effects: an electronic verification question sequence design optimization method, device and storage medium in an embodiment of the present invention are based on a greedy algorithm, combined with the actual questionnaire and its question design situation for optimization, and by analyzing the number of clicks on related question options in the verification, the optimal path is designed, thereby reducing the operation steps and time, and improving the scientificity and rationality of the questionnaire design. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0046] Figure 1 Schematic diagram of a flow chart of a method for optimizing the sequence of electronic verification questions according to one embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a serial problem in one embodiment of the present invention;

[0048] Figure 3 A schematic diagram of adjusting the order of questions for the present invention;

[0049] Figure 4 Schematic diagram of the inclusion relationship of options in each question of the questionnaire of the present invention;

[0050] Figure 5 A schematic diagram of a problem tree formed after the present invention is restructured and expanded;

[0051] Figure 6 A table reflecting the number of clicks on the address verification questionnaire illustrated in the present invention;

[0052] Figure 7 A table showing the number of clicks after the address verification questionnaire is optimized as exemplified in the present invention;

[0053] Figure 8 A table showing the number of clicks after the questions and question options of the address verification questionnaire illustrated in the present invention are readjusted;

[0054] Figure 9 This is a structural diagram of the device for designing and optimizing the sequence of electronic verification questions according to the present invention.

[0055] The following are the descriptions of the reference numerals:

[0056] ,After selecting, question 2 ends directly with some options; , go to some newly added options in question 1; , go to some options of question 3; , Question 1; 210, Questionnaire design module; 220, Logical grouping module; 230, Question optimization module. DETAILED DESCRIPTION

[0057] In order to more clearly illustrate the purpose, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The example implementation methods can be implemented in various forms and should not be understood as being limited to the examples described herein. On the contrary, these implementation methods are provided to make the present invention more comprehensive and complete, and to fully convey the concepts of the example implementation methods to those skilled in the art.

[0058] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, it will be appreciated by those skilled in the art that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0059] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0060] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0061] The present invention is described in detail below with reference to specific embodiments.

[0062] Example 1

[0063] The present invention provides an optimization method for the sequence design of electronic verification problems, referring to Figure 1 , Figure 1 Schematic diagram of the process of designing and optimizing the sequence of electronic verification questions according to the present invention.

[0064] The present invention is based on a greedy algorithm and is optimized in combination with actual questionnaires and question design conditions, aiming to provide designers with a more practical and streamlined path design solution.

[0065] The core of the greedy algorithm is to take the optimal decision in each step. For questionnaire question sorting, this means choosing the question that will minimize the expected increase in subsequent clicks when selecting the next question. To achieve this, it is necessary to calculate the expected number of subsequent clicks or the probability of being clicked for each question among the remaining unsorted questions.

[0066] Depend on Figure 1 It can be seen that a method for optimizing the sequence design of electronic verification questions includes the following steps:

[0067] S1. Design questionnaire questions according to business processes or behavioral logic, and estimate the probability of each question option being clicked;

[0068] It should be noted that the questionnaire content includes questions, question options and question paths.

[0069] It should be noted that estimating the probability of each question option being clicked includes estimating the probability of each question option being clicked through pilot sampling and estimating the probability of each question option being clicked through past experience.

[0070] It should be noted that, in other embodiments, estimating the probability of each question option being clicked includes estimating the probability of each question option being clicked through pilot sampling.

[0071] It should be noted that, in other embodiments, estimating the probability of each question option being clicked includes estimating the probability of each question option being clicked based on past experience.

[0072] S2. Group the designed questions according to the logic of the questions;

[0073] Questions that follow the same logic are grouped together. These groups are logically unrelated or have minimal correlation. This allows for independent optimization without interfering with each other. For example, when verifying business information, questions related to business address (address, house number, room number, etc.) and business scale (office space, personnel, equipment, etc.) can be grouped separately.

[0074] S3. Optimize the order of questions for each group of questions independently. After all question groups are optimized, add up the click volume of each group to estimate the expected click volume of the entire questionnaire.

[0075] It should be noted that this embodiment only takes single-choice questions as an example. For multiple-choice questions, if there is no multiple-choice combination jump (for example, when options A and B are selected in question Q1 at the same time, it jumps to question Q2, and when B and C are selected, it jumps to question Q3. This design is very complicated in both questionnaire design and questionnaire filling, and rarely appears in real scenarios. Therefore, this invention does not discuss this situation), multiple-choice questions are viewed only from the perspective of question path. The algorithm of multiple-choice questions and single-choice questions is the same. A certain option corresponds to which question is the next, but multiple-choice questions will correspond to multiple questions at a time, which has no effect on the number of clicks.

[0076] Optimizing the sequence of questions for a single question group includes the following steps:

[0077] S301, based on the click probability or click quantity of each question option in the same group, take each question as the first question of the questionnaire and calculate the click probability or click quantity after the order is adjusted;

[0078] It should be noted that there are multiple definitions of probability in this invention, such as the probability of a question being answered, the probability of an option being selected, and whether the probability of an option being selected is the percentage of total clicks or the percentage of clicks on the current question. The description is too complex, so we will directly use click count to express it. Using the number of clicks is simpler to calculate, and for questionnaires with fewer questions, a quick calculation can also be performed using a table.

[0079] The options for a question can be divided into two categories: the first category is jump, where you select a different option to jump to another question; the other category is end, where you select a different option to end the questionnaire or a series of subsequent questions. It can be seen that the total number of clicks on the entire questionnaire is affected by the number of question levels. Take a serial question as an example:

[0080] refer to Figure 2 The number of clicks on each layer is equal to the sum of the number of clicks on the options at the end of this layer and the number of clicks on the options at this layer that will access the questions in the next layer. Calculating the number of clicks on the questionnaire includes the following steps:

[0081] 1. Calculate the number of clicks per layer:

[0082] ,

[0083] 2. Calculate the total number of clicks on the questionnaire:

[0084] , where N represents the number of clicks at each level, X is the number of clicks at this level (the number of clicks at the end of this level + the number of clicks associated with the next level), and n is the question level.

[0085] It should be noted that it can be clearly concluded from the above formula:

[0086] (1) The number of clicks will double with each additional level, so the design should minimize the number of question levels;

[0087] (2) Try to reduce the number of clicks at the lower levels, that is, ask the questions with the most clicks as early as possible. ;

[0088] (3) When the number of layers and the number of clicks per layer conflict, the click volume of different solutions is calculated and compared according to the formula.

[0089] Each question is the first question of the questionnaire, adjust the order of questions, refer to Figure 3 , take question 2 as the first question in the questionnaire as an example to describe it.

[0090] Taking question 2 as the starting question, the questions and questionnaire effects are the same, but the number of clicks on each node question is different.

[0091] In actual questionnaire design, when the order of questions changes, we must also make corresponding adjustments to the questionnaire options to ensure the integrity of the verification results. The relationship between the options in each question is shown in Figure 4. Figure 4 It can be seen that:

[0092] 1. Question 2 is part of Question 1, so after answering Question 2, there is no need to have the options related to Question 2 in Question 1;

[0093] 2. For the parts of question 1 that are not related to question 2, you need to add options to question 2 so that these situations can be related to question 1;

[0094] 3. The order of other questions remains unchanged, so the options are not affected.

[0095] Therefore, the options for question two need to be deleted from question one, which are related to question two, and options for question one that are not related to question two are added to question two, while the order of other questions remains unchanged.

[0096] Taking Question 2 as the first question in the questionnaire as an example, it includes:

[0097] 1. From the above analysis, it is easy to conclude that the choices in question 2 can be divided into three parts. After selecting, question 2 will end directly with some options. (Click volume is ), go to some options of question three (Click volume is ), go to the newly added options for question 1 (Click volume is ), and since these three parts are logically unrelated, the questionnaire can be split into three independent question groups 、 、 .

[0098] 2. After reorganizing the structure and expanding it, Figure 5 Schematic problem tree.

[0099] S302: Sort the solutions to find the sequence with the least clicks;

[0100] Depend on Figure 5 It can be seen that we only need to calculate 、 The number of clicks on the two corresponding question trees can be For the mth problem, the derivation formula is as follows:

[0101] ;

[0102] .

[0103] From the above inclusion relationship, we can know that only the adjusted order and the question options of the corresponding question node are affected, without affecting the click volume of the option at the end of the current level question. Replace the corresponding .

[0104] ;

[0105] .

[0106] Therefore, the number of hits for the new question tree is:

[0107] , where question n-1 is assumed to be the mth question, N represents the number of clicks at each level, P is some of the options for question n, X is the number of clicks at this level, and n is the level of the question.

[0108] Sort each solution to find the order with the least clicks, including:

[0109] 1. Calculate the difference between the number of clicks on the unreordered questionnaire and the total number of clicks on the new question tree:

[0110] ,in, , let question n-1 be the mth question, N represents the number of clicks at each level, P is some options of question n, X is the number of clicks at this level, and n is the level of the question;

[0111] 2. Find The maximum value of , we can find the optimal questionnaire sorting.

[0112] S303. Re-modify the description of questions and question options according to the question ranking to complete the optimization of one question group.

[0113] For example:

[0114] Take an address verification question as an example. The purpose is to verify whether the company is operating at the registered address. The original question and click volume assumptions are as follows: Figure 6 A table showing the number of clicks on the address verification questionnaire.

[0115] We can see that there are 35 verification records in total, with 100 clicks. The number of clicks for each question is as follows:

[0116] ,

[0117] Starting with questions 1-4 respectively, we can get the following equation:

[0118] ,

[0119] because , starting from the third question as the first question of the questionnaire should be the best choice, and its click volume changes, such as Figure 7 The table shows the number of clicks after the address verification questionnaire is optimized.

[0120] The newly added questions are designed to enable jumping to the next question after the order of questions is adjusted. For example, in question "3. Operation status of the task enterprise", the newly added option 1 is designed to enable jumping to "2. Can the designated address be reached?". The number of clicks for this option is the sum of the number of clicks for "2. Can the designated address be reached?", that is, the sum of the number of clicks for options "No" and "New option 2": 10 times.

[0121] In order to conform to the thinking / language logic, the description of the questions and question options needs to be readjusted, such as Figure 8 The table shows the number of clicks after the questions and question options of the address verification questionnaire have been readjusted.

[0122] Through the above adjustments, the same number of tasks will have 60 clicks, which is 40 fewer than the original question clicks. This means that the operator can save 40 times of question switching and selection.

[0123] Example 2

[0124] refer to Figure 9 The present invention provides an electronic verification problem sequence design optimization device, Figure 9 This is a structural diagram of the device for designing and optimizing the sequence of electronic verification questions according to the present invention.

[0125] Depend on Figure 9 It can be seen that an electronic verification question sequence design optimization device includes: a questionnaire design module 210, a logic grouping module 220 and a question optimization module 230.

[0126] The questionnaire design module 210 is used to design questionnaire questions according to business processes or behavioral logic, and estimate the probability of each question option being clicked;

[0127] The logic grouping module 220 is used to group the designed questions according to question logic;

[0128] The question optimization module 230 is used to optimize the order of questions for each group of questions independently. After all question groups are optimized, the expected click volume of the entire questionnaire can be estimated by summing up the click volume of each group.

[0129] Example 3

[0130] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the embodiment can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in most cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD-ROM) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server or network device, etc.) to execute the method described in the present invention.

[0131] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present invention are indicated by the claims. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing the sequence design of electronic verification questions, characterized in that: include: Design questionnaire questions according to business processes or behavioral logic, and estimate the number of clicks on each question option; Group the designed questions according to the logic of the questions; The order of questions is optimized independently for each question group. After all question groups are optimized, the click volume of each group is summed up to estimate the expected click volume of the entire questionnaire. Each set of problems independently optimizes the order of problems, including: According to the number of clicks on each question option in the same group, take each question as the first question of the questionnaire and calculate the number of clicks after the order is adjusted; The first question of the questionnaire is question a, which includes dividing the options of question a into three parts, including some options that will end question a directly after selection. , go to some options of question a+1 and flow to some of the new options in question a-1 ; Split the questionnaire into three groups of questions, reorganize the structure, and expand it; The method of dividing the options of question a into three parts includes: deleting the options related to question a in question a-1; adding the options of the parts of question a-1 that are not related to question a to question a; and keeping the order of other questions unchanged; Sort each solution to find the sequence with the least clicks; Re-modify the questions and question option descriptions according to the question sorting to complete the optimization of one question group.

2. The method for optimizing the sequence design of electronic verification questions according to claim 1, characterized in that: The number of clicks on each question option in the same group, including: Calculate the number of clicks per layer: ， Calculate the total number of clicks on the questionnaire: ,in, is the number of clicks at layer i, = the sum of the number of clicks at the end of this layer and the number of clicks associated with the next layer, i=1, 2...n, n is the problem level.

3. The method for optimizing the sequence design of electronic verification questions according to claim 1, characterized in that: The method of sorting the various solutions to find the sequence with the least click volume includes: Calculate the difference between the number of clicks on the questionnaire that was not reordered and the total number of clicks on the new question tree; Find the maximum difference between the number of clicks of the unreordered questionnaire and the total number of clicks of the new question tree.

4. An electronic verification problem sequence design optimization device, characterized in that: include: The questionnaire design module is used to design questionnaire questions according to business processes or behavioral logic, and estimate the number of times each question option is clicked; Logical grouping module, used to group the designed questions according to question logic; The question optimization module is used to optimize the order of questions for each group of questions independently. After all question groups are optimized, the click volume of each group is summed up to estimate the expected click volume of the entire questionnaire; Wherein, the problem optimization module includes: The click count calculation module calculates the number of clicks after the order is adjusted based on the number of clicks on each question option in the same group and taking each question as the first question in the questionnaire; The first question of the questionnaire is question a, which includes dividing the options of question a into three parts, including some options that will end question a directly after selection. , go to some options of question a+1 and flow to some of the new options in question a-1 ; Split the questionnaire into three groups of questions, reorganize the structure, and expand it; The method of dividing the options of question a into three parts includes: deleting the options related to question a in question a-1; adding the options of the parts of question a-1 that are not related to question a to question a; and keeping the order of other questions unchanged; The sorting module sorts each solution to find the sequence with the least clicks; Re-modify the module, re-modify the questions and question option descriptions according to the question sorting, and complete the optimization of a question group.

5. A storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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