Electronic check problem sequence design optimization method and device and storage medium

Abstract

The invention relates to an electronic check question sequence design optimization method and device and a storage medium, and the method comprises the steps: designing questionnaire questions according to a business process or behavior logic, and estimating the probability that each question option is clicked; grouping the designed problems according to a problem logic; each group of questions independently optimizes the question sequence, and after all the question groups are optimized, the click rate of each group is added to estimate the expected click rate of the whole questionnaire. The method is based on the greedy algorithm, optimization is carried out by combining the actual questionnaire and the question design condition of the questionnaire, and the optimal path is designed by analyzing the click frequency of the associated question options in checking, so that the operation steps and time are reduced, and the scientificity and rationality of questionnaire design are improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic questionnaires, and more particularly, to an optimized method, device, and storage medium for designing the order of electronic verification questions. Background Art

[0002] As a commonly used technical means in the current verification field, electronic questionnaires play an important role in improving work efficiency and expanding the scope of data collection. In practical applications, the design of electronic questionnaires mainly refers to the verification process or human behavior logic. This design method may be able to meet the basic needs when dealing with projects of regular scale. However, for large-scale verification projects, the order of questions in the questionnaire will have a non-negligible impact on the verification work. An unreasonable question order may increase the system operation steps of the verifiers, reduce the cooperation degree of the respondents, affect the accuracy and integrity of the data, and thus restrict the efficient development of the verification work. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimized method, device, and storage medium for designing the order of electronic verification questions, so as to solve the problem that the existing order of questions in electronic questionnaires may increase the system operation steps of the verifiers, reduce the cooperation degree of the respondents, affect the accuracy and integrity of the data, and thus restrict the efficient development of the verification work.

[0004] To achieve the above object, the present invention adopts the following technical solutions: An optimized method for designing the order of electronic verification questions, comprising: Designing questionnaire questions according to the business process or behavior logic, and estimating the probability of each question option being clicked; Grouping the designed questions according to the question logic; Independently optimizing the question order for each group of questions. After all groups of questions are optimized, the estimated click volumes of each group are added up to estimate the expected click volume of the entire questionnaire.

[0005] In one embodiment, the independently optimizing the question order for each group of questions includes: According to the click probability or click count of each question option in the same group, taking each question as the first question of the questionnaire respectively, and calculating the click probability or click count after the order adjustment; Sorting each scheme to find the order with the smallest click volume; Modifying the questions and the descriptions of the question options according to the question order to complete the optimization of one group of questions.

[0006] In one embodiment, the click probability or click count of each question option in the same group includes: Calculating the click probability / click count for each layer:

[0007] Calculate the total click probability / total click times of the questionnaire: , where N represents the click times at each level, X is the click times at this level (the click times when this level ends + the click times associated with the lower level), and n is the question level.

[0008] In one embodiment, taking each question as the first question of the questionnaire and taking question n as the first question of the questionnaire as an example, it includes: Divide the question options of question n into three parts, including some options that directly end question n after selection , some options that flow to question n + 1 and some newly added options that flow to question n - 1 ; Split the questionnaire into three question groups, reorganize the structure, and expand it.

[0009] In one embodiment, the dividing the question options of question n into three parts includes: Delete the relevant options related to question n in question n - 1; Add the options in question n - 1 that are not related to question n to question n; Keep the order of other questions unchanged.

[0010] In one embodiment, the calculating the click probability or click times after adjusting the calculation order includes: Calculate the click volume of some options that directly end question n : ; Calculate the click volume of some options that flow to question n + 1 : ; Calculate the total click volume of the new question tree: , where it is assumed that question n - 1 is located at the mth question, N represents the click times at each level, P is some options of question n, X is the click times at this level, and n is the question level.

[0011] In one embodiment, the sorting each scheme to find the order with the smallest click volume includes: Calculate the difference between the click volume of the questionnaire without re - sorting and the total click volume of the new question tree: , where, , assume that question n - 1 is located at the m-th question, N represents the number of clicks at each level, P is a partial option of question n, X is the number of clicks at this level, and n is the question level; Find the maximum value.

[0012] In one embodiment, the estimated expected click volume of the entire questionnaire includes: Estimating the probability of each question option being clicked through pilot sampling; Estimating the probability of each question option being clicked through past experience.

[0013] An electronic verification question sequence design optimization device includes: A questionnaire design module for designing questionnaire questions according to business processes or behavioral logics and estimating the probability of each question option being clicked; A logic grouping module for grouping the designed questions according to question logics; A question optimization module for independently optimizing the question sequence for each group of questions. After all question groups are optimized, the total click volume of each group can be used to estimate the expected click volume of the entire questionnaire.

[0014] A storage medium stores instructions, and when the instructions are executed by a processor, the steps of the above-mentioned method in the claims are implemented.

[0015] As can be seen from the above technical solutions, compared with the prior art, the present invention has at least the following advantages and positive effects: An electronic verification question sequence design optimization method, device, and storage medium according to an embodiment of the present invention are optimized based on a greedy algorithm in combination with the actual questionnaire and its question design situation. By analyzing the click volume of associated question options during verification, the best path is designed, thereby reducing operation steps and time and improving the scientificity and rationality of questionnaire design. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 It is a flowchart of an electronic verification question sequence design optimization method in an embodiment of the present invention; Figure 2 It is a schematic diagram of serial questions in an embodiment of the present invention; Figure 3 It is a schematic diagram of adjusting the question sequence of the present invention; Figure 4It is a schematic diagram of the inclusion relationship of options in each question of the questionnaire of the present invention; Figure 5 It is a schematic diagram of the question tree formed after reorganizing the structure of the present invention and expanding it; Figure 6 It is a reflection table of the click times of the address verification question questionnaire illustrated by way of example in the present invention; Figure 7 It is a reflection table of the click times of the optimized address verification question questionnaire illustrated by way of example in the present invention; Figure 8 It is a reflection table of the click times of the address verification question questionnaire illustrated by way of example in the present invention after readjusting the questions and question options; Figure 9 It is a schematic diagram of the structure of the electronic verification question sequence design optimization device of the present invention.

[0018] The description of the reference numerals is as follows: Part of the options where question two directly ends after selection; Part of the options newly added that flow to question one; Part of the options that flow to question three; Question one; 210, questionnaire design module; 220, logic grouping module; 230, question optimization module. Detailed implementation manners

[0019] In order to more clearly explain the purpose, technical solution and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples described herein; on the contrary, providing these embodiments makes the present invention more comprehensive and complete, and conveys the concept of the exemplary embodiments to those skilled in the art in an all-round way.

[0020] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present invention. However, those skilled in the art will realize 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. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present invention.

[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the accompanying drawings are only illustrative and do not necessarily include all contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0023] The present invention will be described in detail below with reference to specific embodiments.

[0024] Embodiment 1 The present invention provides an optimization method for the order design of electronic verification questions. Refer to Figure 1 , Figure 1 which is a schematic flowchart of the optimization method for the order design of electronic verification questions of the present invention.

[0025] Based on the greedy algorithm, the present invention is optimized in combination with the actual questionnaire and its question design situation, aiming to provide a more practical and more concise path design scheme for designers.

[0026] The core of the greedy algorithm is to make the optimal decision in the current state at each step of selection. For the sorting of questionnaire questions, it is to select the question that can minimize the expected subsequent click volume when selecting the next question to be placed each time. To achieve this, it is necessary to calculate the expected subsequent click volume or click probability of each question among the remaining unsorted questions.

[0027] As can be seen from Figure 1 , an optimization method for the order design of electronic verification questions includes the following steps: S1. Design questionnaire questions according to the business process or behavioral logic, and estimate the probability of each question option being clicked; It should be noted that the questionnaire content includes questions, question options, and question paths.

[0028] 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.

[0029] 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.

[0030] It should be noted that in other embodiments, estimating the probability that each question option is clicked includes estimating the probability that each question option is clicked based on past experience.

[0031] S2. Group the designed questions according to the question logic; Questions that conform to the same logic are grouped into one group. After grouping, the questions in each group are logically independent or have extremely weak relevance, so that the questions in each group can be optimized separately without affecting each other. For example: When conducting an enterprise operation information verification, questions about business addresses (address, house number, room number, etc.) and questions about business scale (office area, number of personnel, equipment, etc.) can be divided into independent question groups.

[0032] S3. Independently optimize the question order for each group of questions. After all groups of questions are optimized, sum up the click volumes of each group to estimate the expected click volume of the entire questionnaire.

[0033] 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 involved (for example, when selecting options A and B in question Q1 at the same time, it jumps to question Q2, and when selecting B and C, it jumps to question Q3. This design is very complex both in questionnaire design and questionnaire filling and rarely appears in real scenarios. Therefore, this invention does not discuss this situation), from the perspective of the question path, the algorithms for multiple-choice questions and single-choice questions are the same, that is, which question corresponds to a certain option. It's just that multiple-choice questions will correspond to multiple questions at a time, which has no impact on the click volume.

[0034] Optimizing the question order for a single group of questions includes the following steps: S301. According to the click probability or click quantity of each question option in the same group, use each question as the first question of the questionnaire respectively, and calculate the click probability or click quantity after the order adjustment; It should be noted that since there are multiple definitions of probability in this invention, for example: the probability that a question is answered, the probability that an option is selected, and whether the probability that an option is chosen is the proportion of the total click volume or the proportion of the click volume of the current question. The description is too complex. Therefore, the click volume is directly used for expression later. Using quantity for calculation is simpler, and for questionnaires with fewer questions, it can also be quickly calculated through a table.

[0035] The options of a question can be divided into two categories: The first category is jump, selecting this option jumps to another question; the second category is end, selecting this option ends the questionnaire or this series of subsequent questions. Then it can be seen that the total click volume of the entire questionnaire is affected by the number of question levels. Taking a serial question as an example: Reference Figure 2, the click volume of 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 of this layer that will access the next-layer questions. Calculating the click volume of the questionnaire includes the following steps: 1. Calculate the click volume of each layer: , 2. Calculate the total click volume of the questionnaire: , where N represents the click volume of each level, X is the click volume of this layer (the number of clicks at the end of this layer + the number of clicks associated with the next layer), and n is the question level.

[0036] It should be noted that it can be clearly obtained from the above formula that: (1) The click volume will double for each additional level. Therefore, the design should try to reduce the number of question levels; (2) Try to reduce the click volume of the lower levels, that is, ask the questions with more click volumes as early as possible ; (3) When there is a conflict between the number of levels and the click volume of each layer, calculate and compare the click volumes of different schemes according to the formula.

[0037] Regarding each question as the first question of the questionnaire and adjusting the question order, refer to Figure 3 , and take question two as the first question of the questionnaire as an example for description.

[0038] Taking question two as the starting question, the questions and the questionnaire effect are the same, but the click volume of each node question is different.

[0039] In the actual questionnaire design, when the question order changes, we must also make corresponding adjustments to the questionnaire options to ensure the integrity of the verification results. The inclusion relationship of the options in each question is shown in Figure 4. From Figure 4 it can be seen that: 1. Question two itself is a part of question one. Therefore, after answering question two first, question one does not need to have the options related to question two anymore; 2. For the part of question one that has nothing to do with question two, options need to be added to question two so that these situations can be associated with question one; 3. The order of other questions remains unchanged, and the options are not affected.

[0040] Therefore, the steps of deleting the options related to question two in question one, adding the options of the part of question one that has nothing to do with question two in question two, and keeping the order of other questions unchanged are required for the question options of question two.

[0041] Taking question two as the first question of the questionnaire as an example, it includes: 1. From the above analysis, it is easy to conclude that the choices of question two can be divided into three parts, and some options directly end after selection (Click volume is ), go to some options of question 3 (Click volume is ), go to the newly added options in question 1 (Click volume is ), and since these three parts are logically unrelated, the questionnaire can be divided into three independent question groups , , .

[0042] 2. After reorganizing the structure and expanding it, Figure 5 Schematic problem tree.

[0043] S302, sorting the various solutions to find the sequence with the least number of clicks; Depend on Figure 5 It can be seen that we only need to calculate , The number of clicks on the corresponding two problem trees is sufficient. Let For the mth problem, the derivation formula is as follows: ; .

[0044] From the above inclusion relationship, we can know that only the adjusted order and the question options of the corresponding question node are affected, but the number of clicks on the options at the end of the current level of questions is not affected. Replace the corresponding .

[0045] ; .

[0046] Therefore, the number of hits for the new question tree is: , 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.

[0047] Sort each solution to find the order with the least clicks, including: 1. Calculate the difference between the number of clicks of the unreordered questionnaire and the total number of clicks of the new question tree: ,in, , let question n-1 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; 2. Find The maximum value can be used to find the optimal questionnaire sorting.

[0048] S303. Modify the questions and the descriptions of question options according to the question sorting to complete the optimization of a question group.

[0049] For example: Taking a certain address verification question as an example, the purpose is to verify whether the enterprise is operating at the registered address. The original question and the assumed click-through rate are as follows Figure 6 Shown is the reflection table of the click-through times of the address verification question questionnaire.

[0050] We can know that there are a total of 35 verification records, with 100 click-through times. The click-through times at the end of each question are: , Starting from questions 1-4 respectively and substituting into the above formula, we can get: , Since , starting from the third question as the starting question of the questionnaire should be the optimal choice. Its click-through rate changes, as shown in Figure 7 Shown is the reflection table of the click-through times of the optimized address verification question questionnaire.

[0051] The newly added question is for the total click-through times to be able to jump to the next question after the question order is adjusted. For example: In the question "3. Business operation situation of the task enterprise", the newly added question option 1 is to be able to jump to "2. Whether it is possible to reach the specified address". The click-through times of this option are the sum of the click-through times of "2. Whether it is possible to reach the specified address", that is, the sum of the click-through times of the option "No" and the "Newly added option 2": 10 times.

[0052] To conform to the thinking / language logic, it is necessary to readjust the questions and the descriptions of question options, as shown in Figure 8 Shown is the reflection table of the click-through times after readjusting the questions and the descriptions of question options of the address verification question questionnaire.

[0053] Through the above adjustments, for the same number of tasks, the click-through times are 60 times, a decrease of 40 times compared to the original questions. That is, it can save 40 times of question switching and selection for the operator.

[0054] Embodiment 2 Referring to Figure 9 , the present invention provides an electronic verification question sequence design optimization device. Figure 9 This is the structural schematic diagram of the electronic verification question sequence design optimization device of the present invention.

[0055] From Figure 9An electronic verification problem sequence design optimization device is known, including: a questionnaire design module 210, a logic grouping module 220, and a problem optimization module 230.

[0056] Among them, the questionnaire design module 210 is used to design questionnaire questions according to the business process or behavioral logic, and estimate the probability of each question option being clicked; The logic grouping module 220 is used to group the designed questions according to the problem logic; The problem optimization module 230 is used to independently optimize the problem sequence for each group of questions. After all the question groups are optimized, the total click volume of each group can be used to estimate the expected click volume of the entire questionnaire.

[0057] Embodiment 3 Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in most cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the method described in the present invention.

[0058] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present invention. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the claims. It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited 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 probability of each question option being clicked; Group the designed questions according to the logic of the questions; The order of questions is optimized independently for each group of questions. After all question groups are optimized, the number of clicks for each group is summed up to estimate the expected number of clicks for the entire questionnaire.

2. The method for optimizing the sequence design of electronic verification questions according to claim 1, characterized in that: Each set of problems independently optimizes the problem sequence, including: 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; 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 a question group.

3. The method for optimizing the sequence design of electronic verification questions according to claim 2, characterized in that: The click probability or click count of each question option in the same group includes: Calculate the click probability / number of clicks for each layer: ， Calculate the total click probability / total number of clicks for the questionnaire: , 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.

4. The method for optimizing the sequence design of electronic verification questions according to claim 2, characterized in that: The method of taking each question as the first question of the questionnaire, and taking question n as the first question of the questionnaire as an example, includes: Divide the options of question n into three parts, including some options that will end question n directly after selection , flow to some options of question n+1 and flow to question n-1 with some new options ; The questionnaire was split into three groups of questions, reorganized and expanded.

5. The method for optimizing the sequence design of electronic verification questions according to claim 4, characterized in that: The question options of question n are divided into three parts, including: Delete the relevant options in question n-1 related to question n; Add the options in question n-1 that are not related to question n to question n; The order of other questions remains unchanged.

6. The method for optimizing the sequence design of electronic verification questions according to claim 2, characterized in that: The calculation of click probability or click count after the order is adjusted includes: Some options for calculating problem n directly ending Number of clicks: ； Calculate the partial options that flow to problem n+1 Number of clicks: ； Calculate the total number of hits for the new question tree: , 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.

7. The method for optimizing the sequence design of electronic verification questions according to claim 2, 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 of the unreordered questionnaire and the total number of clicks of the new question tree: ,in, , let question n-1 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; turn up The maximum value of .

8. The method for optimizing the sequence design of electronic verification questions according to claim 1, characterized in that: The estimated expected click volume of the entire questionnaire includes: The probability of each question option being clicked was estimated through pilot sampling; Estimate the probability of each question option being clicked based on past experience.

9. 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 probability of each question option being clicked; Logical grouping module, used to group the designed questions according to the 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 expected click volume of the entire questionnaire can be estimated by adding up the click volume of each group.

10. A storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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