Network questionnaire pushing method and device, electronic equipment and storage medium

Abstract

The invention provides a network questionnaire pushing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining user operation data corresponding to the user identification information of a network user, and determining the user preference probability of the network user according to the user operation data; obtaining a questionnaire theme corresponding to the user preference probability, configuring first weights of a plurality of question groups in the questionnaire theme according to the user preference probability, and determining first push priorities of the question groups according to the first weights; obtaining a preset operation result corresponding to the user identification information, configuring a second weight of a plurality of questions in the question group according to the preset operation result, and determining a second push priority of the plurality of questions in the question group according to the second weight; and pushing the network questionnaire to the network user according to the first pushing priority and the second pushing priority. According to the method, more accurate answer results can be collected, and the accuracy and efficiency of questionnaire survey are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for pushing an online questionnaire, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] An online survey questionnaire is a research tool that publishes and collects data through an Internet platform. It allows the publisher to send questions to the target group in electronic form and collect feedback quickly and conveniently.

[0003] Among them, the questionnaire usually includes multiple questions, and the publisher of the questionnaire needs to arrange the order of the questions reasonably to ensure the logical coherence. Among them, the question types can include: multiple-choice questions (single-choice, multiple-choice), fill-in-the-blank questions, scale questions (such as scoring), and ranking questions.

[0004] In the related art, the number of questions and the order of multiple questions presented in the online questionnaire during the presentation process are usually fixed and cannot be flexibly adapted to various types of users. Summary of the invention

[0005] The present invention provides a method and device for pushing a network questionnaire, an electronic device, a computer-readable storage medium, and a computer program product.

[0006] In a first aspect, the present disclosure provides a method for pushing a network questionnaire, the method comprising:

[0007] Acquire user operation data corresponding to user identification information of a network user, and determine a user preference probability of the network user according to the user operation data;

[0008] Obtaining a questionnaire topic corresponding to the user preference probability, configuring first weights of multiple question groups in the questionnaire topic according to the user preference probability, and determining first push priorities of the multiple question groups according to the first weights;

[0009] Obtaining a preset calculation result corresponding to the user identification information, configuring second weights of multiple questions in the question group according to the preset calculation result, and determining second push priorities of the multiple questions in the question group according to the second weights;

[0010] The network questionnaire is pushed to the network user according to the first push priorities of the multiple question groups and the second push priorities of the multiple questions in the question groups.

[0011] In an optional implementation, the network questionnaire is a questionnaire corresponding to a cloud gaming application, the network user is a cloud gaming user, the user operation data includes: multiple groups of interactive operation data triggered for multiple gaming function modules in the cloud gaming application; and the user preference probability includes: multiple function preference probabilities corresponding to the multiple gaming function modules;

[0012] The function preference probability corresponding to the game function module is determined by: acquiring the interaction operation data corresponding to the game function module, calculating the interaction depth value corresponding to the interaction operation data, and determining the function preference probability corresponding to the game function module according to the interaction depth value; wherein the interaction depth value is determined according to the interaction frequency, interaction level and / or interaction duration;

[0013] Furthermore, there is a preset mapping relationship between the multiple question groups in the questionnaire topic and the multiple game function modules; then the first weights of the multiple question groups are determined according to the preset mapping relationship and the function preference probability.

[0014] In an optional implementation, the questionnaire structure of the online questionnaire is a tree structure, the tree structure includes multiple levels, and each level has at least one node; wherein each node has a parent node and / or a child node, and each node corresponds to a question group or a question in a question group in the online questionnaire;

[0015] Furthermore, the node is pre-configured with a node control logic; wherein the node control logic includes: a node extraction logic, and / or a node display logic; wherein the node extraction logic is used to control a strategy for extracting and displaying corresponding nodes, and the node display logic is used to control a display weight of corresponding nodes;

[0016] Then, according to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups, pushing the network questionnaire to the network user includes:

[0017] According to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups, screening a plurality of target question groups and a plurality of target questions from a preset candidate question library;

[0018] According to the multiple nodes in the tree structure and the node control logic of each node, the push order of the multiple target question groups and the multiple target questions in the online questionnaire is determined.

[0019] In an optional implementation manner, before pushing the network questionnaire to the network user, the method further includes:

[0020] In response to an access request triggered by the network user for preset questionnaire access information, determining a language type corresponding to a user type of the network user;

[0021] Obtaining original questionnaire data corresponding to the questionnaire access information, and in the case where the original questionnaire data does not match the language type, performing language conversion processing on the original questionnaire data to obtain target questionnaire data matching the language type; wherein the target questionnaire data is used to generate the online survey questionnaire;

[0022] The questionnaire access information includes: a questionnaire access link and / or a questionnaire access information code.

[0023] In an optional implementation manner, before pushing the network questionnaire to the network user, the method further includes:

[0024] In response to an access request triggered by the network user for preset questionnaire access information, determining a plurality of sub-questionnaires associated with the questionnaire access information;

[0025] Selecting a target sub-questionnaire from the plurality of sub-questionnaires for generating the online survey questionnaire;

[0026] The target sub-questionnaire is selected according to at least one of the following: a questionnaire weight pre-configured for the sub-questionnaire, a user preference probability of the network user, and a preset calculation result corresponding to the user identification information.

[0027] In an optional implementation, after pushing the network questionnaire to the network user, the method further includes:

[0028] In response to a resource loading request triggered by a target resource in the network questionnaire, verifying whether the network user has access rights to the target resource according to a preset verification condition, and if the verification result is yes, loading and displaying the target resource;

[0029] The target resource is a preset resource stored in a preset storage space; and the verification condition includes at least one of the following:

[0030] The network identification of the network user matches the preset whitelist;

[0031] The triggering time of the resource loading request matches the preset accessible time.

[0032] In an optional implementation, the preset operation result corresponding to the user identification information is generated according to a hash operation and / or a random algorithm;

[0033] Furthermore, configuring the second weights of the plurality of questions in the question group according to the preset calculation result includes:

[0034] According to a preset result-weight mapping relationship, a set of weights corresponding to the preset operation result is obtained, and second weights of the plurality of questions in the question group are configured according to the weight set.

[0035] In a second aspect, the present disclosure provides a device for pushing a network questionnaire, the device comprising:

[0036] a preference determination module, adapted to obtain user operation data corresponding to user identification information of a network user, and determine a user preference probability of the network user according to the user operation data;

[0037] A first weight configuration module, adapted to obtain a questionnaire topic corresponding to the user preference probability, configure first weights of multiple question groups in the questionnaire topic according to the user preference probability, and determine first push priorities of the multiple question groups according to the first weights;

[0038] A second weight configuration module, adapted to obtain a preset calculation result corresponding to the user identification information, configure second weights of the multiple questions in the question group according to the preset calculation result, and determine a second push priority of the multiple questions in the question group according to the second weight;

[0039] The push module is adapted to push the network questionnaire to the network user according to the first push priorities of the multiple question groups and the second push priorities of the multiple questions in the question groups.

[0040] In a third aspect, the present disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, and one or more of the computer programs are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for pushing online questionnaires.

[0041] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned method for pushing the online questionnaire when executed by a processor.

[0042] In a fifth aspect, the present disclosure provides a computer program product, which includes a computer-readable code, or a non-volatile computer-readable storage medium carrying a computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned method for pushing the online questionnaire.

[0043] The push method of the network survey questionnaire provided by the embodiment of the present disclosure can determine the user preference probability of the network user according to the user operation data of the network user, and accordingly, dynamically configure the first weights of multiple question groups in the questionnaire subject according to the user preference probability, so as to dynamically determine the first push priority of multiple question groups according to the user preference probability. Therefore, this method can reasonably configure the order of appearance of multiple question groups in the questionnaire subject according to user preference, so as to place the question group that the user is interested in and willing to answer in a relatively front position, so as to facilitate the collection of more accurate results. In addition, in order to avoid the result deviation caused by the fixed order between multiple questions within the question group, the present disclosure further configures the second weights of multiple questions in the question group according to the preset operation result corresponding to the user identification information, so that the order of appearance between multiple questions within the question group can be dynamically adjusted, thereby avoiding the result deviation to the greatest extent. It can be seen that since the present invention can dynamically configure the push priority between multiple question groups based on user preference probability, and can dynamically configure the push priority between multiple questions within the same question group according to the preset calculation results corresponding to the user identification information, it can ensure that the question groups and the push order between questions in the online survey questionnaire are more in line with user characteristics, which is conducive to collecting more accurate answer results and improving the accuracy and efficiency of the questionnaire survey.

[0044] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing detailed example embodiments with reference to the accompanying drawings, in which:

[0046] Figure 1 A flowchart of a method for pushing a network questionnaire provided by an embodiment of the present disclosure;

[0047] Figure 2 A schematic diagram showing a process of allocating a network questionnaire to a designated user in the first stage of an example of the present disclosure is shown;

[0048] Figure 3 A data structure organization diagram of a network questionnaire of an example of the present disclosure is shown;

[0049] Figure 4A schematic diagram showing the process of dynamically pushing a network questionnaire in the second stage of an example of the present disclosure is shown;

[0050] Figure 5 A block diagram of a network questionnaire push device provided by an embodiment of the present disclosure;

[0051] Figure 6 A block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] Figure 1 FIG. 1 is a flow chart showing a method for pushing a network questionnaire provided by an embodiment of the present disclosure. Figure 1 As shown, the method specifically comprises the following steps:

[0054] Step S110: acquiring user operation data corresponding to the user identification information of the network user, and determining the user preference probability of the network user according to the user operation data.

[0055] The network user is a user corresponding to a preset network application, which may be a game application, for example. The user identification information of the network user is used to uniquely identify the user in the game application, for example, it may be various identification information such as the game account number and user ID of the network user.

[0056] According to the user identification information, the user operation data triggered by the network user can be obtained. The user operation data includes: game interaction operation data triggered by the network user through various input devices such as mouse, keyboard, game controller, etc. Accordingly, the user preference probability of the network user can be determined according to the user operation data.

[0057] Among them, the user preference probability refers to the quantitative expression of the tendency shown by the user when selecting a game or a game function. It can be seen that the user preference probability is used to characterize the preference of network users for a specified game or a specified functional module in a game, which can be determined by counting the interactive operations of network users for a specified game or a specified functional module in a game during a historical period. For example, if the user's interaction frequency is determined to be high according to the user operation data, it means that the user preference probability is large; if the user's interaction frequency is determined to be low according to the user operation data, it means that the user preference probability is small. In addition, in addition to being determined based on information such as the user's interaction frequency and interaction depth, the user preference probability can also be determined based on the user's interaction quality, where the user interaction quality can be characterized by a quality score, and the specific scoring criteria can be flexibly configured by the service provider of the cloud game application. For example, it can be comprehensively evaluated based on multiple factors such as the user's interaction time, interaction level, and interaction method. Among them, the scoring criteria configured by the service providers of different cloud game applications (corresponding to different game manufacturers) are usually different, and are specifically set according to the business characteristics and business needs of the service provider.

[0058] In short, the present disclosure does not limit the specific connotation of user operation data, as long as it can directly or indirectly reflect the user's preference.

[0059] Step S120: Obtain the questionnaire topic corresponding to the user preference probability, configure the first weights of multiple question groups in the questionnaire topic according to the user preference probability, and determine the first push priority of the multiple question groups according to the first weights.

[0060] Among them, multiple questionnaire topics can be pre-configured, and accordingly, the questionnaire topic suitable for the current network user is determined according to the calculation result of the user preference probability. For example, when the designated game includes multiple cloud games, the cloud games or game function modules that the user is interested in can be determined according to the user preference probability, and then the questionnaire topic is determined according to the cloud games or game function modules that the user is interested in.

[0061] Since the same questionnaire topic contains multiple question groups, in order to ensure that the appearance order between the question groups is more reasonable, in this step, the first weights of the multiple question groups in the questionnaire topic are configured according to the user preference probability. The first weights are used to determine the first push priorities of the multiple question groups.

[0062] Among them, assuming that the questionnaire topic contains M question groups, and a total of N question groups are presented in an online questionnaire, when N is equal to M, the first push priority is mainly used to represent the push order of the N question groups; when N is less than M, the first push priority is also used to filter out N question groups from the M question groups according to the priority level. It can be seen that the first push priority is used to represent whether each question group is pushed and the push order.

[0063] Step S130: Obtain a preset calculation result corresponding to the user identification information, configure second weights of the multiple questions in the question group according to the preset calculation result, and determine a second push priority of the multiple questions in the question group according to the second weights.

[0064] The preset operation result corresponding to the user identification information may be in various forms such as a hash operation result, a random operation result, etc. Accordingly, the mapping relationship between various operation results and the second weights of the multiple questions may be pre-configured, so as to configure the second weights of the multiple questions in the question group according to the preset operation result, and the second weights are used to determine the second push priority of the multiple questions in the question group.

[0065] Assuming that a question group contains m questions, and a total of n questions are presented in a question group, when n is equal to m, the first push priority is mainly used to characterize the push order of the n questions; when n is less than m, the first push priority is also used to filter the n questions from the m questions according to the priority. It can be seen that the second push priority is used to characterize whether each question in the question group is pushed and the push order.

[0066] Step S140: Pushing the network questionnaire to network users according to the first push priorities of the multiple question groups and the second push priorities of the multiple questions in the question groups.

[0067] The online survey questionnaire is dynamically generated online and pushed to network users. When it is generated, N question groups can be selected from the M question groups included in the questionnaire subject according to the first push priority of multiple question groups, and push weights of the N question groups can be configured respectively. In addition, according to the second push priority of multiple questions in the question group, n questions can be selected from the m questions included in each question group, and push weights of the n questions can be configured respectively, so as to determine the question groups and the presentation order of the questions in the online survey questionnaire.

[0068] Optionally, during the display process, the online questionnaire can display multiple question groups in sequence according to a first push priority (which can also be represented by a first push order), and during the display process of each question group, multiple questions can be displayed in sequence according to a second push priority (which can also be represented by a second push order).

[0069] It can be seen that in the embodiment of the present disclosure, the user preference probability of the network user can be determined according to the user operation data of the network user, and accordingly, the first weights (also called question group weights) of the multiple question groups in the questionnaire subject are dynamically configured according to the user preference probability, so as to dynamically determine the first push priority of the multiple question groups according to the user preference probability. Therefore, this method can reasonably configure the order of appearance of the multiple question groups in the questionnaire subject according to the user preference, so as to place the question groups that the user is interested in and willing to answer in a relatively forward position, so as to facilitate the collection of more accurate results. In addition, in order to avoid the result deviation caused by the fixed order between the multiple questions within the question group, the present disclosure further configures the second weights (also called question weights) of the multiple questions in the question group according to the preset operation results corresponding to the user identification information, so that the order of appearance between the multiple questions within the question group can be dynamically adjusted, thereby avoiding the result deviation to the greatest extent. It can be seen that since the present invention can dynamically configure the push priority between multiple question groups based on user preference probability, and can dynamically configure the push priority between multiple questions within the same question group according to the preset calculation results corresponding to the user identification information, it can ensure that the question groups and the push order between questions in the online survey questionnaire are more in line with user characteristics, which is conducive to collecting more accurate answer results and improving the accuracy and efficiency of the questionnaire survey.

[0070] It should be noted that in the above embodiment, two push priorities (the first push priority and the second push priority) are used as an example for explanation. In actual situations, more priorities can be set. For example, the push efficiency of multiple question groups and / or multiple questions within the question group in the historical push process can also be pre-counted (such as determined by factors such as the effective answer ratio and the answer quality), so as to configure the third push priority for the question group and / or question according to the push efficiency. For another example, the relevance between multiple question groups and / or multiple questions within the question group and the core business modules in the cloud game can also be pre-determined, and the fourth push priority can be configured for the question group and / or question according to the size of the relevance. Among them, the types and number of core business modules can be dynamically adjusted as the game goes online. Accordingly, in the process of pushing question groups and / or questions, the push order between question groups and / or questions can be further determined in combination with the third push priority and the fourth push priority.

[0071] It can be seen that the present invention is configured with at least two push priorities, namely a first push priority determined according to the user preference probability, and a second push priority determined according to the preset calculation result corresponding to the user identification information. The push weights of the question group and / or the questions within the question group are configured through at least two push priorities, so as to reasonably determine whether the question group and / or the questions need to be pushed and the order of pushing according to the push weight, so that the question setting in the online questionnaire can better meet user needs and business needs.

[0072] Among them, considering that a single push priority cannot flexibly adapt to various business needs, the embodiment of the present disclosure can improve the push effectiveness of questions in the online survey questionnaire by configuring at least two push priorities, and avoid poor questionnaire collection results due to unreasonable question settings and incomplete considerations.

[0073] It can be seen that the network questionnaire in the embodiment of the present disclosure can be pushed in the following ways: on the one hand, according to the user preference probability, a first push priority of multiple question groups and / or questions is configured; on the other hand, according to the preset operation result corresponding to the user identification information, a second push priority of multiple question groups and / or questions is configured, so as to generate and push the network questionnaire by combining the first push priority and the second push priority. It can be seen that in the process of pushing each question group and question in the network questionnaire, the push weight of the question group and the question can be comprehensively determined according to the above-mentioned at least two push priorities, and then a reasonable questionnaire is dynamically generated. Of course, it is more preferred that the number of push priorities is configured to be more. When the number of push priorities is more, the network questionnaire can be better adapted to the specific needs in a specific business scenario, thereby improving the efficiency of the survey. In addition, optionally, the questionnaire subject of the questionnaire can also be further configured according to one or more push priorities. The present disclosure does not limit the specific details.

[0074] In addition, in order to achieve better technical effects, those skilled in the art may also make various changes and modifications to the above embodiments:

[0075] In an optional implementation, the online questionnaire is a questionnaire corresponding to a cloud gaming application, and the network user is a cloud gaming user. Since cloud gaming usually includes multiple game function modules, and different game function modules correspond to different game functions (such as different game levels, different game types, etc.), the user operation data includes: multiple groups of interactive operation data triggered for multiple game function modules in the cloud gaming application, wherein one game function module corresponds to at least one group of interactive operation data, and the group of interactive operation data is used to characterize the operation type, operation frequency, and other related information of the interactive operation triggered by the network user for the game function module.

[0076] Accordingly, the user preference probability may include: multiple function preference probabilities corresponding to multiple game function modules, each function preference probability is used to characterize the network user's preference for the game function module. The function preference probability corresponding to the game function module can be determined by: obtaining the interactive operation data corresponding to the game function module, calculating the interactive depth value corresponding to the interactive operation data, and determining the function preference probability corresponding to the game function module according to the interactive depth value. The interactive depth value can also be further determined in combination with the user interaction quality mentioned above.

[0077] Among them, the interactive operation data corresponding to the game function module is used to characterize the various types of interactive operations triggered by the network user for the game function module. Correspondingly, the interactive depth value is used to characterize the degree of interaction between the network user and the game function module. For example, the interactive depth value can be determined based on the interaction frequency, interaction level and / or interaction duration. Under normal circumstances, the higher the interaction frequency, the deeper the interaction level, and the longer the interaction duration, the greater the interactive depth value, that is, the greater the probability of function preference; the lower the interaction frequency, the shallower the interaction level, and the shorter the interaction duration, the smaller the interactive depth value, that is, the smaller the probability of function preference. Among them, the interactive level is used to characterize the depth and breadth of interaction between users and cloud games. For example, cloud games usually contain multiple different levels of web pages, and the next level of web pages need to jump through the previous level of web pages. It can be seen that in the case of multi-level web pages, users can only interact with the next level of web pages after interacting with the previous level of web pages. It can be seen from this that the larger the number of levels of web pages interacted by the user, the more interested the user is in the functional module, that is, the larger the interaction depth value, the greater the probability of function preference; conversely, if the smaller the number of levels of web pages interacted by the user, the less interested the user is in the functional module, that is, the smaller the interaction depth value, the smaller the probability of function preference.

[0078] Wherein, in the case where the user preference probability includes multiple function preference probabilities, there is usually a preset mapping relationship between the multiple question groups in the questionnaire topic and the multiple game function modules, and therefore, the first weights of the multiple question groups can be determined according to the preset mapping relationship and the function preference probability. Wherein, the mapping relationship between the multiple question groups and the multiple game function modules can be a one-to-one mapping relationship or a one-to-many mapping relationship. In the case where the mapping relationship between the multiple question groups and the multiple game function modules is a one-to-many mapping relationship, a question group is associated with at least two game function modules, and accordingly, the first weight of any question group can be determined in the following manner: first, at least two game function modules having a mapping relationship with the question group are selected from the multiple game function modules. Optionally, the above-mentioned preset mapping relationship is also used to determine the mapping weight between each game function module and the question group. Accordingly, the mapping weight of each game function module in the at least two game function modules is determined respectively. Wherein, the greater the mapping weight, the closer the association between the game function module and the question group. Then, the function preference probabilities corresponding to at least two game function modules are obtained, and the function preference probabilities corresponding to the at least two game function modules are weighted according to the mapping weight of each game function module in the at least two game function modules to obtain the first weight of the question group. For example, the function preference probabilities corresponding to the at least two game function modules can be weighted with the mapping weight of each game function module to obtain the first weight of the question group. It can be seen that the first weight of the question group is comprehensively determined based on the function preference probability of the game function module associated with the question group (used to characterize the current network user's preference for the game function module) and the mapping weight of the game function module (used to characterize the association between the game function module and the current question group).

[0079] It can be seen that since the function preference probability in the present disclosure is associated with the game function module in the cloud game, the rationality of the configuration of the questionnaire content related to the cloud game can be ensured.

[0080] In an optional implementation, the questionnaire structure of the online questionnaire is a tree structure, which includes multiple levels, and each level has at least one node; wherein each node has a parent node and / or a child node. Among them, the root node has no parent node, only child nodes; the leaf node has no child nodes, only parent nodes. Each node can correspond to a question group or a question in a question group in the online questionnaire. It can be seen that in order to facilitate the management of the online questionnaire, the online questionnaire can be configured as a tree-shaped data organization structure. In addition, the corresponding node control logic can be pre-configured for each node. Among them, the node control logic is used to control the presentation method of the corresponding node.

[0081] For example, the node control logic may include: node extraction logic, and / or node display logic. The node extraction logic is used to control the strategy of extracting and displaying the corresponding node, that is, each node has a certain probability of being extracted, and only the extracted nodes are displayed, while the nodes that are not extracted are not displayed. The node display logic is used to control the display weight of the corresponding node. For example, the display weight can be used to determine the importance of the problem displayed by the node. Specifically, according to the display weight of the node, the problem (or problem group) whose problem weight (or problem group weight) matches the display weight can be selected from the corresponding multiple problems (or problem groups) for display. In the process of selecting problems according to weight, the depth-first traversal order and / or the breadth-first traversal order can be used for selection.

[0082] Accordingly, when pushing a network questionnaire to a network user according to the first push priority of multiple question groups and the second push priority of multiple questions in the question group, it can be achieved in the following way: First, according to the first push priority of multiple question groups and the second push priority of multiple questions in the question group, multiple target question groups and multiple target questions are screened from a preset candidate question library. Then, according to the multiple nodes in the tree structure and the node control logic of each node, the push order of the multiple target question groups and multiple target questions in the network questionnaire is determined. It can be seen that, first, according to the first push priority and the second push priority, multiple target question groups and multiple target questions are screened from the candidate question library corresponding to the questionnaire topic. Since a large number of question groups and questions can be stored in the candidate question library corresponding to the same questionnaire topic, the question groups and questions with greater relevance to the current network user are first screened out according to the push priority of each question group and question.

[0083] Among them, the total number of questions of the multiple target question groups and multiple target questions obtained by screening can be the same as or different from the total number of nodes contained in the tree-shaped hierarchical structure of the online questionnaire. In the case where the two are the same, the multiple target question groups and multiple target questions obtained by screening can all be pushed to the user. In the case where the two are different, if the total number of questions is greater than the total number of nodes, it is necessary to select from the corresponding multiple questions in the process of displaying each node (that is, there are some questions that are not displayed); if the total number of questions is less than the total number of nodes, it is necessary to determine whether the node is displayed for each node (that is, there are some nodes that are not displayed). It can be seen that in the case where the two are different, the node extraction logic in the node control logic corresponding to the node can be used to determine whether the node is extracted, so that the extracted nodes are displayed, and the unextracted nodes are not displayed; and the node display logic in the node control logic corresponding to the node can be used to determine the importance of the questions displayed by the node, so that in the display process of the node, the questions matching the importance are selected for display.

[0084] It can be seen that by configuring the online questionnaire as a tree-shaped data organization structure and configuring the corresponding node control logic for each node, the flexibility of the displayed content in the questionnaire can be improved. For example, if the number of question groups included in the online questionnaire or the number of questions in any question group needs to be adjusted, it is only necessary to adjust the corresponding nodes in the tree-shaped data organization structure (for example, a node can be deleted or the node control logic of a node can be adjusted), and the adjustment method is flexible.

[0085] The node control logic is intended to control the traversal order between multiple nodes. Specifically, the node control logic may be implemented by using a depth-first traversal method and / or a breadth-first traversal method.

[0086] In an optional implementation, before pushing the network questionnaire to the network user, the following operations may be further performed: in response to the access request triggered by the network user for the preset questionnaire access information, determine the language type corresponding to the user type of the network user; obtain the original questionnaire data corresponding to the questionnaire access information, and when the original questionnaire data does not match the language type, perform language conversion processing on the original questionnaire data to obtain the target questionnaire data matching the language type; wherein the target questionnaire data is used to generate the network questionnaire. Wherein, the questionnaire access information includes: a questionnaire access link, and / or a questionnaire access information code (such as a QR code, etc.). It can be seen that this method can automatically detect the language type corresponding to the user type of the network user, thereby automatically converting the original questionnaire data into the target questionnaire data matching the language type, so as to facilitate the user to read. Wherein, the user type can be determined based on multiple factors such as the user IP address and the language type used by the user in the game application, and the present disclosure does not limit this. In the related art, a questionnaire link of a questionnaire product can only be configured in one language. However, for cloud games covering users in multiple countries, there may be more than 15 languages ​​for questionnaire configuration requirements. Therefore, combined with the actual business deployment of cloud game applications, the function of quickly configuring multilingual questionnaires for a questionnaire link can be realized. In specific implementation, a benchmark questionnaire result (i.e., original questionnaire data) can be configured, the corresponding questionnaire structure (such as Excel data) can be exported, and then the multilingual difference part can be imported into the questionnaire structure to quickly generate multiple questionnaires in different languages. In addition, the correspondence between different questionnaires and different languages ​​can also be configured to achieve the effect of associating questionnaires in multiple languages ​​in the same questionnaire link. It can be seen that the configuration efficiency of multilingual questionnaires is improved by cloning questionnaires and multilingual questionnaires. In addition, through the support of multiple languages, the experience of overseas users filling out questionnaires can also be improved.

[0087] In an optional implementation, before pushing the network questionnaire to the network user, the following operations may be further performed: in response to the access request triggered by the network user for the preset questionnaire access information, multiple sub-questionnaires associated with the questionnaire access information are determined; and a target sub-questionnaire for generating the network questionnaire is selected from the multiple sub-questionnaires. The target sub-questionnaire may be selected based on at least one of the following: the questionnaire weight pre-configured by the sub-questionnaire, the user preference probability of the network user, and the preset calculation result corresponding to the user identification information. In the field of cloud gaming, since there are many game function modules involved and the number of users covered by each function module is also large, different questionnaires need to be placed for different recovery ratios when the questionnaire is released. Conventional questionnaires usually do not have a recovery volume of millions or even tens of millions and complex recovery requirements. Therefore, in order to adapt to the complex characteristics of the cloud gaming field, a weight distribution algorithm can be used to accurately recover the answer results of questionnaires of different proportions.

[0088] In specific implementation, a special questionnaire can be set up, and multiple sub-questionnaires can be integrated through the questionnaire access information (such as a QR code or link) of the special questionnaire, and the weight of each sub-questionnaire is pre-configured. When a user accesses the questionnaire through the questionnaire access information, the sub-questionnaire to be displayed is dynamically determined according to the weight. In addition, information such as whether to refresh to other sub-questionnaires after the page is refreshed can also be pre-configured to flexibly control the refresh method of multiple sub-questionnaires.

[0089] It can be seen that through weighted questionnaires and fixed allocations, survey data can be collected in multiple dimensions, and users are almost unaware. In addition, due to the huge volume of content updated each time in cloud games, the number of questions in each related questionnaire is also extremely large. In order to avoid the accuracy of the questionnaire recovery results affected by the order effect (for example, the user may lose patience when answering the later questions and affect the accuracy), in the disclosed embodiment, multiple questions can also be classified and some questions can be extracted by type for push, and different types and / or the same type can appear randomly to avoid the order effect. For example, multiple question groups can be pre-configured, and several questions of the same type are a question group. The weight of each question can be set in the group. When the user accesses the questionnaire, the range of questions required by the user is determined according to the weight. Multiple question groups can also be combined as a theme, and the weight of each question group can be flexibly set in the theme. When the user accesses the questionnaire to answer questions, the question group to be answered by the user can be determined according to the weight. By organizing questions in question groups and organizing question groups by themes, questionnaire designers can reduce 80% of the repeated configuration of questionnaires, and at the same time, the number of questions answered by users and the efficiency of recovery can be greatly improved.

[0090] In an optional implementation, after pushing the network questionnaire to the network user, in order to improve information security, the following operations may be further performed: in response to the resource loading request triggered for the target resource in the network questionnaire, verify whether the network user has the access right to the target resource according to the preset verification condition, and load and display the target resource if the verification result is yes. If the verification result is no, the user is prompted that there is no access right to avoid information leakage. The target resource may be a preset resource stored in a preset storage space. Accordingly, when the network user accesses the resource in the questionnaire, it is determined whether the resource to be accessed is the above-mentioned target resource according to whether the storage address of the resource is located in the preset storage space. Alternatively, the target resource may also be a preset type of resource, for example, the preset type may be a picture type, a video type, a multimedia static resource type, etc. involving confidential information, which may be specifically configured according to the confidentiality of the game resources in the cloud game application. The verification condition includes at least one of the following: (1) The network identification of the network user matches the preset whitelist: For example, a whitelist may be pre-configured to store the identification information of legitimate network users. (2) The trigger time of the resource loading request matches the preset accessible time: For example, the accessible time and inaccessible time of the resource can be pre-configured, so as to avoid users from accessing the resource during the inaccessible time based on time verification, thereby improving security.

[0091] For example, in order to improve the security of questionnaire resources, it can be achieved by verifying the public network access time and verifying the office IP (i.e., a specific implementation form of the identification information of the network user). Since cloud games involve many confidential multimedia static resources such as pictures and videos, these resources may be internally surveyed in the form of questionnaires, or may be allowed to be accessed externally only after a fixed time. Therefore, such resources have strong confidentiality requirements. In order to avoid leakage, all static resources in the questionnaire (i.e., the above-mentioned preset types of resources) can be configured into a specific directory. For this directory, set the public network accessible time and the office network IP list. When you need to view the files in this directory, the system will verify whether the current time is within the set public network accessible time. If not, it will check whether the access IP is the office network IP. If it matches, it will allow access to the corresponding resource; otherwise, it will prompt that the resource does not exist. Through the above method, the security of resources can be greatly improved to avoid the leakage of confidential information due to questionnaire surveys.

[0092] In an optional implementation, the preset operation result corresponding to the user identification information can be generated according to a hash operation and / or a random algorithm; and when configuring the second weights of multiple questions in the question group according to the preset operation result, a set of weight sets corresponding to the preset operation result can be obtained according to a preset result weight mapping relationship, and the second weights of the multiple questions in the question group can be configured according to the weight set. For example, the result weight mapping relationship can be pre-configured to determine a set of weight sets corresponding to each preset operation result, so as to achieve the effect that different preset operation results correspond to different weight sets, so as to avoid the sequence effect mentioned above.

[0093] For ease of understanding, the implementation of the above embodiment is introduced below by taking an example as an example. This example is illustrated by taking a questionnaire survey in a cloud game application scenario as an example. In conventional game solutions, games are usually run locally on client devices. Taking a mobile phone as an example, players need to install the game package (i.e., game-related resource data required for game operation such as game installation package) locally on the mobile phone, and the mobile phone renders the game screen in real time according to the player's operation. Therefore, all computing operations are concentrated on the player's mobile phone device. However, the above method has at least the following problems: In order to improve the quality of the game screen, more computing power is required, which leads to a series of problems in the hardware configuration of terminal devices such as mobile phones due to the inability to meet the computing requirements. For example, the mobile phone may have various problems such as overheating, insufficient memory, and jamming. It can be seen that if the user cannot replace the game client device with a higher configuration, he cannot experience better game performance. Therefore, how to improve the quality and fluency of the game screen as much as possible under limited hardware configuration has become a technical problem that needs to be solved in the game field. In order to solve the above problems, a processing link is added to the cloud game application scenario, the game package is installed on a dedicated cloud game server, and then the game screen is transmitted to terminal devices such as mobile phones in real time. In this way, the hardware configuration requirements for mobile terminals in the game scene are greatly reduced. It can be seen that by installing the game package on the cloud server and transferring the calculation process to the cloud server, the hardware pressure of the client device can be transferred to the cloud server, so that users can experience better game performance without upgrading the client device with better performance. For example, the player user can transmit various types of user operation instructions triggered by the user through the touch device, keyboard and mouse device, handle device and / or gyroscope device to the cloud server through the client device. The cloud server performs calculations corresponding to the user operation instructions in the cloud according to the instruction parameters (specifically including instruction type, text / picture corresponding to the instruction, etc.) and user information (such as user account, etc.) contained in the received user operation instructions. For example, rendering calculation, compression processing, resource management, game operation, etc. can all be implemented by the cloud server, thereby greatly reducing the pressure on the client. Correspondingly, the cloud server transmits the audio and video streams and other data calculated according to the user operation instructions to the client, and the client obtains the game screen to be displayed through decoding, decompression and other processing, and displays it directly on the client. It can be seen that in cloud games, the main computing overhead in the game is realized by the cloud server. The client only needs to transmit operation instructions and receive the game screen calculated by the cloud server, which greatly reduces the hardware requirements for the game client. This allows users to experience better gaming effects without upgrading hardware configuration.

[0094] Accordingly, in this example, it is necessary to collect the user operation instructions of cloud game users (i.e. the specific implementation form of the user operation data mentioned above) in order to determine user preferences and achieve more accurate questionnaire push. In this example, a "question group" refers to a set of questions that can be operated as a whole and control the display method of the questions it contains. A "topic" can contain multiple question groups, and the display method of the question groups can be controlled at the same time.

[0095] In related technologies, questionnaire delivery is usually achieved in the following way: first, determine a questionnaire theme, then design relevant questions around the theme, and there will be a certain logical relationship between the questions. After the configuration is completed, a single questionnaire delivery is carried out, and the results are analyzed after the user fills in the questionnaire. The result analysis of a normal questionnaire has a certain degree of credibility when it reaches a certain order of magnitude, and it will not be much improved when the amount of data is higher. At the same time, a single questionnaire delivery may have unreasonable question design, which may cause the recovery of the results to be affected by unknown factors. Due to the lack of reference analysis, the accuracy of the results is affected.

[0096] In the application scenario of this example, there are many functional modules in the game product. Therefore, in the questionnaire delivery phase, we hope to collect user feedback and opinions in as many dimensions as possible, which requires multiple questionnaire delivery. However, the willingness of the same user to fill out the questionnaire is limited. Multiple questionnaire delivery will lead to a low questionnaire recovery rate and high configuration costs. At the same time, based on user considerations, it is not necessary for all users to fill out a questionnaire. On the basis of high data credibility, we hope to deliver different questionnaires based on more dimensions.

[0097] Based on the above problems, in order to improve the configuration efficiency and collection rate of questionnaires as much as possible, and to improve the accuracy of data as much as possible, this example adopts a combination of fixed weight allocation and preference control to implement the question allocation method after the user enters the questionnaire:

[0098] First, for the allocation of users, hashing and user preferences are used to control the purpose of user diversion. This method can collect as many questionnaires of different dimensions as possible under large data volume, and at the same time, the user preference control can reach the users involved in the questionnaire questions as accurately as possible, thereby improving the credibility of the questionnaire data. In specific implementation, users can be initially diverted through the hash algorithm. The hash algorithm can map user identification information (such as user ID) to a fixed and limited space, and each user will be assigned a unique hash value. In this way, under large data volume, users can be dispersed into different groups or segments through hash values, providing a basis for subsequent user preference control. In addition, further segmentation can be carried out in combination with user preferences: on the basis of preliminary diversion, users can be divided more finely by collecting and analyzing user preference data (such as browsing history, purchase history, interest tags, etc.). These preference data can reflect the interests, needs and behavior patterns of users, thereby helping to determine which users are more likely to be interested in specific questionnaire questions or more likely to provide valuable answers. The above method helps to improve the recovery rate of questionnaires: by accurately targeting the target user group, the questionnaire can be distributed more effectively, thereby improving the recovery rate of the questionnaire. Users are more likely to respond to questionnaires related to their interests or needs. In addition, the credibility of the questionnaire data can be enhanced: since the questionnaire is distributed to user groups with specific preferences, the recovered data is more likely to reflect the real thoughts and opinions of these users, which helps to reduce the bias caused by improper sample selection and improve the credibility of the questionnaire data. In addition, it also helps to optimize resource allocation: through hash and user preference control, resources such as questionnaire distribution channels, survey time, etc. can be allocated more reasonably, which helps to reduce survey costs while improving survey efficiency.

[0099] Secondly, for the merging and grouping of questions, the random allocation of question groups can be used to experiment with the answering effects of different question groups. At the same time, this method can also perform multi-dimensional data recovery under large amounts of data. By grouping questions, the recovery rate of questionnaires and data accuracy can be improved while improving the efficiency of personnel allocation for questionnaire configuration. In specific implementation, similar or related questions can be grouped into the same logical grouping according to the topic, nature or purpose of the question, which helps to maintain the logic and coherence of the questionnaire and makes it easier for respondents to understand and respond when filling in the answers. In addition, in order to avoid the order effect, the question groups can be arranged in a random or rotating manner to reduce the deviation caused by the order of the questions. For example, some questions may affect the respondents' answers due to their position or the relationship between the context, and this effect can be reduced by grouping and random allocation.

[0100] In this example, since a large number of Internet users are involved and a large number of Internet questionnaires are to be surveyed, it needs to be divided into two stages: in the first stage, the Internet questionnaires are first assigned to designated users; in the second stage, the display order of each question in the Internet questionnaire is dynamically configured to push the corresponding Internet questionnaire to the designated users.

[0101] Figure 2 Figure 1 shows a flow chart of distributing online questionnaires to designated users in the first stage. Figure 2 As shown, the method for distributing the online questionnaire includes the following steps:

[0102] Step S201: Calculate the user preference value of the network user according to the user operation data of the network user.

[0103] Specifically, the user preference value of the user is calculated based on the user operation data. In addition, when a game includes multiple different game function modules, the user's function preference values ​​for multiple different game function modules can be further calculated. It can be seen that the user preference value can include multiple function preference values. Accordingly, the user preference threshold and the function preference threshold corresponding to each game function module can be pre-configured. If the user preference value is greater than the user preference threshold, it means that the user is more interested in the game; similarly, if the function preference value is greater than the function preference threshold, it means that the user is more interested in the game function module.

[0104] Specifically, the user preference value is a comprehensive calculation of the user's overall use of the function based on information such as the time and depth of the user in the game. The user preference threshold can be determined in the following way: rank all users who have used the function, and use the value of a certain proportion of the number of users as the threshold. For example, the user preference value ranked in a specified position (such as the top 30%) can be used as the user preference threshold. Similarly, the function preference value can be used to comprehensively calculate the user's overall use of the game function module based on information such as the time and depth used by the user in the corresponding game function module, and rank all users who have used the game function module, and use the value of a certain proportion of the number of users as the function preference threshold. For example, in the case where a cloud game includes multiple function modules, it is necessary to pre-configure multiple function preference thresholds, so that the multiple function preference values ​​are compared with the corresponding function preference thresholds respectively to obtain the user's preference for each function module.

[0105] Step S202: Determine the user preference according to the comparison result between the user preference value and the user preference threshold.

[0106] Wherein, in the case that the user preference value includes a plurality of function preference values, the user's function preference for the function module is further determined according to the comparison result between the function preference value and the function preference threshold.

[0107] Step S203: when the user preference value is greater than the user preference threshold, assign the corresponding questionnaire to the user according to the user preference.

[0108] The user preference value being greater than the user preference threshold includes: at least one function preference value being greater than the corresponding function preference threshold. It can be seen that if the user preference value is greater than the user preference threshold, it means that the user is interested in the cloud game, or at least interested in a specified function module in the cloud game, and therefore, a questionnaire with a corresponding topic can be assigned to the user based on his or her interest point.

[0109] Step S204: when the user preference value is not greater than the user preference threshold, the corresponding questionnaire is assigned to the user according to the hash algorithm.

[0110] If the user preference value is not greater than the user preference threshold, it means that the user is not interested in the cloud game or the specified functional module therein, and therefore, the user's interest point cannot be accurately determined. At this time, the corresponding questionnaire can be assigned to the user through the hash algorithm.

[0111] It can be seen from this that the user allocation method can use hash user identification to assign users to a certain questionnaire according to weight. At the same time, based on the overall preference analysis of the user group, a preference threshold is set so that users above the threshold are assigned to questionnaire questions on a certain topic, and users below the threshold are fixedly assigned according to weight.

[0112] Among them, in the above method, the hash algorithm can be used to uniquely identify the user, thereby ensuring that each user has a fixed and unique identifier, which is convenient for subsequent data processing and tracking. Accordingly, the user can be fixedly assigned to a certain questionnaire according to the weight corresponding to the hash. For example, according to the pre-set weight allocation rule, the user is fixedly assigned to a certain questionnaire, so that certain important user groups or users with specific characteristics can receive specific questionnaires. In addition, the overall preferences of the user group can also be analyzed, for example, by collecting and analyzing the user's historical behavior data, browsing history, purchase history, etc. Understanding the user's preferences helps to push questionnaires more accurately. Among them, a preference threshold can be set according to the results of the user preference analysis, and the threshold can be used to distinguish the user's interest or participation in a specific topic. Accordingly, for users above the preference threshold, a questionnaire title of a certain topic is specified. These users may be more interested in or more involved in a specific topic, so pushing relevant questionnaires to them can improve the recovery rate and data quality. For users below the preference threshold, they are fixedly assigned to other questionnaires according to the weight, so that all users can be covered and targeted distribution can be carried out according to their characteristics and interests.

[0113] It can be seen from this that Figure 2 The process shown can be used as a specific implementation method for obtaining the questionnaire topic corresponding to the user preference probability in step S120 mentioned above. In other words, in step S120, if the topic that the user is interested in is determined according to the user preference probability (that is, the user preference value is greater than the user preference threshold), the questionnaire topic is determined according to the user preference probability; if the topic that the user is interested in is not determined according to the user preference probability (that is, the user preference value is less than the user preference threshold), the questionnaire topic is determined according to the hash algorithm.

[0114] The online survey questionnaire in this example contains multiple question groups, and the merging and grouping of questions can be organized in a tree-like data structure. Several child nodes can be configured under each tree node (a tree node can be a question group or a specific question, and a leaf node must be a specific question). At the same time, the display logic (i.e., node control logic) for controlling child nodes can be configured on non-leaf nodes based on the overall preference analysis of the user group. Among them, the display logic may include extraction logic (i.e., node extraction logic) and the display weight of each child node (i.e., node display logic). Figure 3 The data structure organization diagram of the online questionnaire is shown in FIG. Figure 3 As shown, the online survey questionnaire includes multiple nodes at different levels, wherein the root node corresponds to all question groups, the middle node corresponds to a certain question group or a subdivided group under the question group, and the leaf node corresponds to a specific question in the question group.

[0115] Among them, the use of tree data structure to organize the merging and grouping of questions is conducive to the flexible configuration of questions. It can not only clearly display the hierarchical relationship between questions, but also realize the personalized structure of questionnaire display by configuring the display logic of sub-nodes. Among them, each tree node can be a question group (non-leaf node) or a specific question (leaf node). Several sub-nodes can be configured under each tree node to form a hierarchical structure. This structure can conveniently represent the classification, sub-classification and relationship of specific questions. Among them, the display logic of sub-nodes can be configured in the following ways: first, analyze the overall preferences of the user group to understand the user's interest and attention to different questions or question groups, and then control the display of sub-nodes: configure the logic of controlling the display of sub-nodes on non-leaf nodes (question groups). Among them, the display logic includes two parts: extraction logic and display weight. The extraction logic can usually be configured in the following ways: according to the results of the user preference analysis, decide which sub-nodes (questions or question groups) should be displayed to the user. For example, conditions or rules can be set, such as when the user prefers a specific topic, display the sub-nodes related to the topic. The display weight logic can usually be configured in the following way: a display weight is configured for each child node, indicating the display priority or frequency of the node in the user interface. The weight can be set according to user preferences, the importance of the question, or the purpose of the survey. It can be seen that the tree data structure allows questionnaire designers to freely add, delete, or move nodes as needed, thereby flexibly organizing the questionnaire structure. In addition, by configuring the display logic, the display content of the questionnaire can be dynamically adjusted according to the user's preferences and interests, thereby improving user participation and answer quality. Among them, the setting of display weight can help questionnaire designers allocate resources reasonably, such as placing more important or more relevant questions in a more prominent position. The tree structure facilitates subsequent data analysis, and the recovered data can be easily classified and compared by question groups or specific questions.

[0116] Figure 4 The flowchart of dynamically pushing the online questionnaire in the second stage is shown in FIG. Figure 4 As shown in the push method of online questionnaire, based on Figure 3 The data structure organization diagram in is pushed, which specifically includes the following steps:

[0117] Step S401: According to the tree data structure of the online survey questionnaire, point to the first node in the tree data structure.

[0118] The first node may be a node located in the topmost layer.

[0119] Step S402: Determine whether the current node is displayed.

[0120] Specifically, whether the current node is displayed can be determined according to the extraction logic mentioned above.

[0121] Step S403: When the judgment result is no (ie the current node does not need to be displayed), further judge whether the current node is the last node in the tree data structure.

[0122] Specifically, it can be determined according to the extraction logic whether the current node needs to be displayed. If not, it is further determined whether the current node is the last node. If so, the question is directly terminated. If not, step S404 is executed to point to the next node in the tree data structure, and the judgment operation in step S402 is repeated.

[0123] Step S405: When the judgment result is yes (ie the current node needs to be displayed), further judge whether the current node is a problem node.

[0124] As mentioned above, tree nodes include two types: problem nodes and problem group nodes. If the current node is not a leaf node, it is usually not a problem node but a problem group node.

[0125] Step S406: When the judgment result is yes (ie the current node is a problem node), further judge whether the problem node needs to be displayed.

[0126] Specifically, whether the question node needs to be displayed can be determined based on the visibility status, sub-questions, options and other related information of the question node. The visibility status can be determined by the extraction logic or display weight mentioned above.

[0127] Step S407: When it is determined that the question node needs to be displayed, the question corresponding to the question node is displayed.

[0128] Step S408: Display the next page after the user finishes answering.

[0129] Specifically, after the user answers the question, the next page is displayed according to the question logic or option logic.

[0130] Step S409: Determine whether to trigger a jump operation or directly end the questionnaire.

[0131] Specifically, it can be determined whether to trigger a jump operation or directly end the questionnaire based on the jump logic between nodes and other contents.

[0132] Step S411: If it is determined that a jump operation needs to be triggered or the questionnaire needs to be terminated directly, the questionnaire is directly jumped or terminated. If it is determined that a jump operation needs to be triggered, step S412 is executed to point to the node after the jump, and step S402 is repeated.

[0133] Step S410: When it is determined that there is no need to trigger a jump operation or directly end the questionnaire, point to the next node in the tree data structure to repeat step S402.

[0134] If the judgment result of step S405 is no, that is, the current node is not a problem node (for example, the current node is a problem group node), step S413 is executed to process the extraction logic, update the child node status, and execute the next node.

[0135] Among them, in the process of user answering questions, when processing the answering logic, the depth-first traversal order can be used, and the node jump order can be based on the breadth-first traversal. For example, in step S401 and step S413, it mainly involves the processing of the answering logic, so the depth-first traversal can be used. For another example, in step S403 and step S404, it mainly involves the processing of the node jump logic, so the breadth-first traversal can be used.

[0136] Among them, in the process of users answering questions, the logic of depth-first search (DFS) and breadth-first search (BFS) can be combined to handle the order of answering questions and node jumps, so as to achieve an orderly and flexible answering experience. Among them, the answering logic can be processed by depth-first traversal (DFS): First, the order of answering questions is determined in the following way: When the user enters a question group (non-leaf node), the first question (or sub-question group) under the group is displayed first in the order of depth-first traversal, and the user is guided to answer. When the user finishes answering the current question, it automatically jumps to the next question under the group until all questions in the group are answered. DFS can ensure that users answer all related questions in a question group continuously, which helps to maintain the consistency and concentration of answering questions.

[0137] In addition, the node jumping order can be handled by breadth-first traversal (BFS): when the user completes all questions in a question group, jump to the next question group of the same level or return to the question group of the previous level in the order of breadth-first traversal, and display the first unanswered question under the group. If there are no unanswered questions at the current level, continue to jump to the upper level until an unanswered question is found or return to the root node of the questionnaire. BFS can ensure that after completing a question group, the user can extensively explore other question groups of the same level, which helps to cover all questions in the questionnaire. It can be seen that BFS provides an effective way to jump for situations where users need to compare or associate different question groups.

[0138] Figure 5A schematic diagram of a network questionnaire push device provided by another embodiment of the present disclosure is shown, the device comprising:

[0139] A preference determination module 51 is adapted to obtain user operation data corresponding to user identification information of a network user, and determine a user preference probability of the network user according to the user operation data;

[0140] A first weight configuration module 52, adapted to obtain a questionnaire topic corresponding to the user preference probability, configure first weights of a plurality of question groups in the questionnaire topic according to the user preference probability, and determine a first push priority of the plurality of question groups according to the first weights;

[0141] A second weight configuration module 53, adapted to obtain a preset calculation result corresponding to the user identification information, configure second weights of the multiple questions in the question group according to the preset calculation result, and determine a second push priority of the multiple questions in the question group according to the second weight;

[0142] The push module 54 is adapted to push the network questionnaire to the network user according to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups.

[0143] In an optional implementation, the network questionnaire is a questionnaire corresponding to a cloud gaming application, the network user is a cloud gaming user, the user operation data includes: multiple groups of interactive operation data triggered for multiple gaming function modules in the cloud gaming application; and the user preference probability includes: multiple function preference probabilities corresponding to the multiple gaming function modules;

[0144] The function preference probability corresponding to the game function module is determined by: acquiring the interaction operation data corresponding to the game function module, calculating the interaction depth value corresponding to the interaction operation data, and determining the function preference probability corresponding to the game function module according to the interaction depth value; wherein the interaction depth value is determined according to the interaction frequency, interaction level and / or interaction duration;

[0145] Furthermore, there is a preset mapping relationship between the multiple question groups in the questionnaire topic and the multiple game function modules; then the first weights of the multiple question groups are determined according to the preset mapping relationship and the function preference probability.

[0146] In an optional implementation, the questionnaire structure of the online questionnaire is a tree structure, the tree structure includes multiple levels, and each level has at least one node; wherein each node has a parent node and / or a child node, and each node corresponds to a question group or a question in a question group in the online questionnaire;

[0147] Furthermore, the node is pre-configured with a node control logic; wherein the node control logic includes: a node extraction logic, and / or a node display logic; wherein the node extraction logic is used to control a strategy for extracting and displaying corresponding nodes, and the node display logic is used to control a display weight of corresponding nodes;

[0148] The push module is specifically suitable for:

[0149] According to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups, screening a plurality of target question groups and a plurality of target questions from a preset candidate question library;

[0150] According to the node control logic, the order in which the multiple target question groups and the multiple target questions are pushed in the online questionnaire is determined.

[0151] In an optional implementation, the preference determination module is further adapted to:

[0152] In response to an access request triggered by the network user for preset questionnaire access information, determining a language type corresponding to a user type of the network user;

[0153] Obtaining original questionnaire data corresponding to the questionnaire access information, and in the case where the original questionnaire data does not match the language type, performing language conversion processing on the original questionnaire data to obtain target questionnaire data matching the language type; wherein the target questionnaire data is used to generate the online survey questionnaire;

[0154] The questionnaire access information includes: a questionnaire access link and / or a questionnaire access information code.

[0155] In an optional implementation, the preference determination module is further adapted to:

[0156] In response to an access request triggered by the network user for preset questionnaire access information, determining a plurality of sub-questionnaires associated with the questionnaire access information;

[0157] Selecting a target sub-questionnaire from the plurality of sub-questionnaires for generating the online survey questionnaire;

[0158] The target sub-questionnaire is selected according to at least one of the following: a questionnaire weight pre-configured for the sub-questionnaire, a user preference probability of the network user, and a preset calculation result corresponding to the user identification information.

[0159] In an optional implementation, the push module is further adapted to:

[0160] In response to a resource loading request triggered by a target resource in the network questionnaire, verifying whether the network user has access rights to the target resource according to a preset verification condition, and if the verification result is yes, loading and displaying the target resource;

[0161] The target resource is a preset resource stored in a preset storage space; and the verification condition includes at least one of the following:

[0162] The network identification of the network user matches the preset whitelist;

[0163] The triggering time of the resource loading request matches the preset accessible time.

[0164] In an optional implementation, the preset operation result corresponding to the user identification information is generated according to a hash operation and / or a random algorithm;

[0165] Furthermore, the second weight configuration module is specifically adapted to:

[0166] According to a preset result-weight mapping relationship, a set of weights corresponding to the preset operation result is obtained, and second weights of the plurality of questions in the question group are configured according to the weight set.

[0167] Figure 6 A schematic structural diagram of an electronic device according to another embodiment of the present disclosure is shown. The specific embodiment of the present disclosure does not limit the specific implementation of the electronic device.

[0168] like Figure 6 As shown, the electronic device may include: a processor (processor) 502 , a communication interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

[0169] in:

[0170] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0171] The communication interface 504 is used to communicate with other devices such as clients or other servers.

[0172] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above method embodiment.

[0173] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0174] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present disclosure. The one or more processors included in the electronic device may be processors of the same type, such as one or more CPUs. They may also be processors of different types, such as one or more CPUs and one or more ASICs.

[0175] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0176] The program 510 may be specifically used to enable the processor 502 to execute the corresponding operations in the above method embodiment.

[0177] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device or other device. Various general purpose devices may also be used together with the teachings based thereon. According to the above description, it is apparent that the structure required for constructing such devices is. In addition, the present disclosure is not directed to any particular programming language either. It should be understood that various programming languages ​​may be utilized to implement the disclosure described herein, and the description of the specific languages ​​above is intended to disclose the best mode of implementation of the present disclosure.

[0178] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0179] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various disclosed aspects, in the above description of exemplary embodiments of the present disclosure, various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed disclosure requires more features than are expressly recited in each claim.

[0180] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0181] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present disclosure and form different embodiments. For example, any one of the claimed embodiments may be used in any combination.

[0182] The various component embodiments of the present disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the device according to the embodiments of the present disclosure. The present disclosure may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present disclosure may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0183] It should be noted that the above embodiments illustrate the present disclosure rather than limit the present disclosure, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present disclosure may be implemented by means of hardware including several different elements and by means of appropriately programmed computers. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for pushing a network questionnaire, the method comprising: Acquire user operation data corresponding to user identification information of a network user, and determine a user preference probability of the network user according to the user operation data; Obtaining a questionnaire topic corresponding to the user preference probability, configuring first weights of multiple question groups in the questionnaire topic according to the user preference probability, and determining first push priorities of the multiple question groups according to the first weights; Obtaining a preset calculation result corresponding to the user identification information, configuring second weights of multiple questions in the question group according to the preset calculation result, and determining second push priorities of the multiple questions in the question group according to the second weights; The network questionnaire is pushed to the network user according to the first push priorities of the multiple question groups and the second push priorities of the multiple questions in the question groups.

2. The method according to claim 1, wherein: The network questionnaire is a questionnaire corresponding to a cloud gaming application, the network user is a cloud gaming user, the user operation data includes: multiple sets of interactive operation data triggered for multiple gaming function modules in the cloud gaming application; and the user preference probability includes: multiple function preference probabilities corresponding to the multiple gaming function modules; The function preference probability corresponding to the game function module is determined by: acquiring the interaction operation data corresponding to the game function module, calculating the interaction depth value corresponding to the interaction operation data, and determining the function preference probability corresponding to the game function module according to the interaction depth value; wherein the interaction depth value is determined according to the interaction frequency, interaction level and / or interaction duration; Furthermore, there is a preset mapping relationship between the multiple question groups in the questionnaire topic and the multiple game function modules; then the first weights of the multiple question groups are determined according to the preset mapping relationship and the function preference probability.

3. The method according to any one of claims 1-2, wherein: The questionnaire structure of the online questionnaire is a tree structure, which includes multiple levels, and each level has at least one node; wherein each node has a parent node and / or a child node, and each node corresponds to a question group or a question in a question group in the online questionnaire; Furthermore, the node is pre-configured with a node control logic; wherein the node control logic includes: a node extraction logic, and / or a node display logic; wherein the node extraction logic is used to control a strategy for extracting and displaying corresponding nodes, and the node display logic is used to control a display weight of corresponding nodes; Then, according to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups, pushing the network questionnaire to the network user includes: According to the first push priorities of the plurality of question groups and the second push priorities of the plurality of questions in the question groups, screening a plurality of target question groups and a plurality of target questions from a preset candidate question library; According to the multiple nodes in the tree structure and the node control logic of each node, the push order of the multiple target question groups and the multiple target questions in the online questionnaire is determined.

4. The method according to any one of claims 1 to 3, wherein: Before pushing the network questionnaire to the network user, the method further includes: In response to an access request triggered by the network user for preset questionnaire access information, determining a language type corresponding to a user type of the network user; Obtaining original questionnaire data corresponding to the questionnaire access information, and in the case where the original questionnaire data does not match the language type, performing language conversion processing on the original questionnaire data to obtain target questionnaire data matching the language type; wherein the target questionnaire data is used to generate the online survey questionnaire; The questionnaire access information includes: a questionnaire access link and / or a questionnaire access information code.

5. The method according to any one of claims 1 to 4, wherein: Before pushing the network questionnaire to the network user, the method further includes: In response to an access request triggered by the network user for preset questionnaire access information, determining a plurality of sub-questionnaires associated with the questionnaire access information; Selecting a target sub-questionnaire from the plurality of sub-questionnaires for generating the online survey questionnaire; The target sub-questionnaire is selected according to at least one of the following: a questionnaire weight pre-configured for the sub-questionnaire, a user preference probability of the network user, and a preset calculation result corresponding to the user identification information.

6. The method according to any one of claims 1 to 5, wherein: After the network questionnaire is pushed to the network user, the method further includes: In response to a resource loading request triggered by a target resource in the network questionnaire, verifying whether the network user has access rights to the target resource according to a preset verification condition, and if the verification result is yes, loading and displaying the target resource; The target resource is a preset resource stored in a preset storage space; and the verification condition includes at least one of the following: The network identification of the network user matches the preset whitelist; The triggering time of the resource loading request matches the preset accessible time.

7. The method according to any one of claims 1 to 6, wherein: The preset operation result corresponding to the user identification information is generated according to a hash operation and / or a random algorithm; Furthermore, configuring the second weights of the plurality of questions in the question group according to the preset calculation result includes: According to a preset result-weight mapping relationship, a set of weights corresponding to the preset operation result is obtained, and second weights of the plurality of questions in the question group are configured according to the weight set.

8. A device for pushing a network questionnaire, the device comprising: a preference determination module, adapted to obtain user operation data corresponding to user identification information of a network user, and determine a user preference probability of the network user according to the user operation data; A first weight configuration module, adapted to obtain a questionnaire topic corresponding to the user preference probability, configure first weights of multiple question groups in the questionnaire topic according to the user preference probability, and determine first push priorities of the multiple question groups according to the first weights; A second weight configuration module, adapted to obtain a preset calculation result corresponding to the user identification information, configure second weights of the multiple questions in the question group according to the preset calculation result, and determine a second push priority of the multiple questions in the question group according to the second weight; The push module is adapted to push the network questionnaire to the network user according to the first push priorities of the multiple question groups and the second push priorities of the multiple questions in the question groups.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the method for pushing a network questionnaire as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the method for pushing a network questionnaire according to any one of claims 1 to 7.

11. A computer program product, characterized in that It includes a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the method for pushing a network questionnaire as described in any one of claims 1 to 7.

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