CN115438241B - Random information interaction display method, system and storage medium - Google Patents

Random information interaction display method, system and storage medium Download PDF

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CN115438241B
CN115438241B CN202211298488.1A CN202211298488A CN115438241B CN 115438241 B CN115438241 B CN 115438241B CN 202211298488 A CN202211298488 A CN 202211298488A CN 115438241 B CN115438241 B CN 115438241B
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苏炳锡
甘华
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Guangzhou Mingdao Cultural Industry Development Co ltd
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Abstract

The invention provides a random information interactive display method, a system and a storage medium, which relate to the field of data processing and comprise the following steps: acquiring basic information of a first scenic spot, wherein the basic information of the first scenic spot comprises scenic spot partition information and tour log information; acquiring first coordinate information and first browsing flow information to perform display probability distribution, and generating a first-level display probability; acquiring partition project information including second coordinate information, second browsing flow information and project travel information according to the scenic spot partition information; through the voice monitoring device, the interactive voice characteristic values including the project keyword characteristic values and the intention keyword characteristic values are obtained to carry out display probability distribution, a second-level display probability is generated, random display of project travel information is carried out on a first display panel, and a first display result is generated. The online pushing method and the device solve the technical problem that in the prior art, due to the fact that the online pushing of the text and travel information is low in combination degree with the user, the adaptability is poor.

Description

Random information interaction display method, system and storage medium
Technical Field
The invention relates to the technical field of data processing, in particular to a random information interaction display method, a system and a storage medium.
Background
The improvement of the experience of tourists in scenic spots is one of the main directions of the development of scenic spot service industry, and the tourists can be attracted in the scenic spots, so that cultural base information such as cultural base implications and classical events, which are symbolized by the scenic spots, is a great characteristic of attracting the tourists.
In a traditional scenic spot tour, a tour guide or an explainer often outputs the travel information of the scenic spot. With the continuous progress of science and technology, modes such as scanning codes to acquire online pushing of travel information appear so as to improve the visiting experience of users, but practice proves that the users are poor in applicability due to complex operation and low combination degree of the pushing of the travel information and the users.
In the prior art, the online pushing of the travel information has the technical problem of poor adaptability due to low combination degree with the user.
Disclosure of Invention
The application provides a random information interaction display method and system, which are used for solving the technical problem of poor adaptability caused by low combination degree with a user in the prior art for online pushing of text and travel information.
In view of the foregoing problems, the present application provides a method and a system for displaying random information interaction.
In a first aspect of the present application, a method for displaying random information interaction is provided, which is applied to a random information interaction display system, the system is in communication connection with a first display panel, the first display panel includes a voice monitoring device, and the method includes: acquiring first scenic spot basic information, wherein the first scenic spot basic information comprises scenic spot partition information and tour log information; acquiring first coordinate information and first browsing flow information according to the tour log information; traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information, and generating a first-level display probability; obtaining partition project information according to the scenic spot partition information, wherein the partition project information comprises second coordinate information, second browsing flow information and project travel information; acquiring an interactive voice characteristic value through a voice monitoring device, wherein the interactive voice characteristic value comprises a project keyword characteristic value and an intention keyword characteristic value; traversing the partition project information to perform display probability distribution according to the keyword characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to generate a second level display probability; and randomly displaying the project travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result.
In a second aspect of the present application, a random information interactive display system is provided, and is connected to a first display panel in communication, the first display panel includes a voice monitoring device, including: the system comprises a scenic spot information acquisition module, a scenic spot information acquisition module and a scenic spot information acquisition module, wherein the scenic spot information acquisition module is used for acquiring first scenic spot basic information, and the first scenic spot basic information comprises scenic spot partition information and tour log information; the log information extraction module is used for acquiring first coordinate information and first browsing flow information according to the tour log information; the first probability distribution module is used for traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information to generate a first-level display probability; the project information extraction module is used for acquiring partition project information according to the scenic spot partition information, wherein the partition project information comprises second coordinate information, second browsing flow information and project travel information; the voice characteristic value extraction module is used for acquiring an interactive voice characteristic value through a voice monitoring device, wherein the interactive voice characteristic value comprises a project keyword characteristic value and an intention keyword characteristic value; the second probability distribution module is used for traversing the partition project information to perform display probability distribution according to the keyword characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to generate a second level display probability; and the task execution module is used for randomly displaying the project travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result.
In a third aspect of the present application, a computer device is provided, the computer device comprising a memory and a processor, the memory having stored therein a computer program, which when executed by the processor, performs the steps of the method of the first aspect.
In a fourth aspect of the present application, a computer-readable storage medium is provided, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the technical scheme, each scenic spot partition of a scenic spot and the browsing log information of each partition are determined; performing text and travel information display probability distribution on each partition according to coordinate information and browsing flow information in a browsing log to obtain a first-level display probability; determining scenic spot project information in the subarea to obtain coordinate information, browsing flow and project text and travel information of each project point; collecting voice characteristic values of user interaction received by a voice monitoring device, wherein the voice characteristic values comprise project keyword characteristics and intention keyword characteristic values; performing display probability distribution according to the key word characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to obtain a second level display probability; fusing the two display probabilities to obtain the random push probability of each project; and further random display of project travel information is realized. The probability value is set according to the coordinate information of each project point, the arrival convenience of the user is considered, and the humanization is improved; setting a probability value according to the browsing flow, considering the popularity of scenic spots and being beneficial to improving the experience of users; the pushing probability of the user interest item text travel information is improved according to the user voice characteristic value, and the combination degree with the user is improved; random display is carried out on a plurality of projects in a plurality of scenic spots, the diversity of display results is improved, and the technical effects of improving the intelligence, individuation and humanization of project text and travel information display are achieved.
Drawings
Fig. 1 is a schematic flow chart of a random information interaction display method provided in the present application;
fig. 2 is a schematic flow chart illustrating a process of obtaining a first-level presentation probability in a random information interaction presentation method provided by the present application;
fig. 3 is a schematic flowchart illustrating a process of determining a second-level display probability in a stochastic information interactive display method according to the present application;
fig. 4 is a schematic structural diagram of a random information interaction display system according to the present application.
Fig. 5 is a schematic structural diagram of an exemplary computer device according to an embodiment of the present application.
Description of reference numerals: the system comprises a scenic spot information acquisition module 11, a log information extraction module 12, a first probability distribution module 13, a project information extraction module 14, a voice characteristic value extraction module 15, a second probability distribution module 16, a task execution module 17, a computer device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The application provides a random information interaction display method and system, which are used for solving the technical problem of poor adaptability caused by low combination degree with a user in the prior art for online pushing of text and travel information.
Example one
As shown in fig. 1, the present application provides a random information interaction display method, which is applied to a random information interaction display system, the system is in communication connection with a first display panel, the first display panel includes a voice monitoring device, and the method includes the steps of:
s100: acquiring first scenic spot basic information, wherein the first scenic spot basic information comprises scenic spot partition information and tour log information;
specifically, the first scenic spot refers to a preset scenic spot in which the cultural travel information needs to be randomly displayed; the first scenic spot basic information refers to basic information for facilitating random display and analysis of post-stepping travel information related to scenic spots, and includes but is not limited to: scenic spot partition information, browsing log information, item information in the scenic spot, item travel information in the scenic spot, and the like.
Scenic spot partition information refers to different areas that characterize a scenic spot, such as, for example: a famous mountain, a north peak, an east peak, a west peak, a south peak and a middle peak are five different subareas; the tour log information refers to the visitor browsing information of different partitions, including but not limited to: daily browsing number of tourists, coordinate information of partitions and the like; the intra-scenic spot item information refers to specific tour items within respective sub-areas of the scenic spot, such as, for example: different book corners in the library, etc.; the project travel information in the scenic spot refers to travel culture information such as stories, classical events, historical backgrounds and the like of scenic spot projects. And acquiring and storing the basic information of the first scenic spot, setting the basic information as a state to be responded, and waiting for later calling.
S200: acquiring first coordinate information and first browsing flow information according to the tour log information;
specifically, the first coordinate information refers to a plurality of groups of area positioning coordinate information which is extracted from the tour log information and corresponds to each subarea in the scenic spot one by one; the first browsing flow information refers to a plurality of groups of daily average visitor flow information in preset time periods, which are extracted from the tour log information and correspond to all the subareas in the scenic spot one by one. By collecting the first coordinate information and the first browsing flow information of each partition, a data reference standard is provided for the differential analysis of the subsequent partitions.
S300: traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information, and generating a first-level display probability;
further, as shown in fig. 2, traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing traffic information, and generating a first-level display probability, where the step S300 includes the steps of:
s310: acquiring third coordinate information according to the first display panel;
s320: traversing the first coordinate information according to the third coordinate information to generate first partition distance information, second partition distance information and Nth partition distance information;
s330: acquiring first partition browsing flow information, second partition browsing flow information and Nth partition browsing flow information according to the first browsing flow information;
s340: carrying out display probability distribution on the nth partition according to the nth partition distance information and the nth partition browsing flow information to generate a first-level display probability of the nth partition, wherein N belongs to N;
further, the step S340 includes the steps of performing display probability distribution on the nth partition according to the nth partition distance information and the nth partition browsing flow information to generate a first-level display probability of the nth partition, where:
s341: obtaining a first display probability distribution formula:
Figure DEST_PATH_IMAGE002AAA
Figure DEST_PATH_IMAGE004AAA
Figure DEST_PATH_IMAGE006AAA
wherein the content of the first and second substances,
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the probabilities are shown for the first level of the nth partition,
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the sub-probabilities are shown for the first of the nth partition,
Figure DEST_PATH_IMAGE009
a second presentation sub-probability for the nth partition,
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and
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are respectively as
Figure 396062DEST_PATH_IMAGE008
And
Figure 322430DEST_PATH_IMAGE009
the fusion weight parameter of (a) is,
Figure 402381DEST_PATH_IMAGE012
as the n-th partition distance information,
Figure DEST_PATH_IMAGE013
browsing flow information for the nth partition;
s342: and inputting the nth partition distance information and the nth partition browsing flow information into the first display probability distribution formula to generate the first-level display probability of the nth partition.
S350: adding the nth partition first level presentation probability to the first level presentation probability.
Specifically, the first-level display probability refers to a result of performing display probability distribution on each partition according to the first coordinate information and the first browsing flow information, and represents the random display probability of the travel information in each partition.
The detailed determination process is as follows:
the third coordinate information refers to coordinate data representing the position of the display panel; and calculating distances between the third coordinate information and the plurality of coordinates in the first coordinate information respectively, so as to obtain first partition distance information, second partition distance information and nth partition distance information which represent the positions of the display panels and the partition distances.
And extracting a plurality of subarea flows which correspond to the subareas one by one in the first browsing flow information, recording the subarea flows as the first subarea browsing flow information and the second subarea browsing flow information until the Nth subarea browsing flow information, wherein the day average flow in a preset time is represented, and the preset time is preferably one month.
The process of showing the probability distribution for the first level of any one of the first partition to the nth partition is as follows: the nth partition refers to any one of the first partition to the nth partition, and according to a first display probability distribution formula:
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Figure DEST_PATH_IMAGE017
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in the formula
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And
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are respectively as
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And
Figure 841880DEST_PATH_IMAGE009
the fusion weight parameter of (2) can be paired by the staff
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And
Figure 137043DEST_PATH_IMAGE009
the bias degree of the probability distribution is subjected to custom assignment, and the manageable characteristic of the display probability distribution is improved;
Figure 139634DEST_PATH_IMAGE012
as the n-th partition distance information,
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and browsing the flow information for the nth partition. Inputting the nth partition distance information and the nth partition browsing flow information of the nth partition
Figure 889601DEST_PATH_IMAGE010
And
Figure 396806DEST_PATH_IMAGE011
and setting the completed first display probability distribution formula to obtain the first-level display probability of the nth partition. Thus, the first-level display probability of any one of the first partition to the Nth partition can be determined, and is stored in one-to-one association with the corresponding partition and recorded as the first-level display probability.
According to the first display probability distribution formula, the farther the distance is, the less the flow is, the lower the display probability is, and the distance and the flow are considered, so that the humanization is improved for the display of the travel information, and the user experience feeling is improved.
S400: obtaining partition project information according to the scenic spot partition information, wherein the partition project information comprises second coordinate information, second browsing flow information and project travel information;
specifically, the partition item information refers to a plurality of items of information in any partition of the scenic spot, and includes second coordinate information representing item positions, second browsing flow information representing browsing flow of each item, which is obtained by further decomposing the flow information of the partition, and item travel information associated with each item one by one. After probability distribution is carried out on each partition in the first level, probability distribution in the second level is further carried out according to the subdivided items, and the random display refinement degree of the scenic spot text travel information is improved. And storing the second coordinate information, the second browsing flow information and the project text and travel information in a one-to-one correlation manner, setting the second coordinate information, the second browsing flow information and the project text and travel information as a state to be responded, and waiting for calling in the next step.
S500: acquiring an interactive voice characteristic value through a voice monitoring device, wherein the interactive voice characteristic value comprises a project keyword characteristic value and an intention keyword characteristic value;
specifically, the voice monitoring device is located beside the display interface and interacts with the user, the interaction mode is divided into active interaction and passive interaction, the active interaction is that the user actively inputs voice to the voice monitoring device for interaction, the passive interaction is that interaction in a preset range near the voice monitoring device is monitored, and the active interaction is active and the passive interaction is auxiliary. The interactive voice characteristic value refers to voice recognition of interactive voice, and the extracted voice characteristic value mainly includes a project keyword characteristic value and an intention keyword characteristic value, the project keyword characteristic value refers to a name of each project, and the intention keyword refers to evaluation of each project, as exemplified by: words like nice-looking, fun, bored, too many people, scenic, etc., are preferably matched by an expert-defined lexicon. The humanized interactive characteristics can be provided for the random display of the follow-up text travel information through the interactive voice characteristic value, and the fitness of a random display result and a user is improved.
S600: traversing the partition project information to perform display probability distribution according to the keyword characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to generate a second level display probability;
further, as shown in fig. 3, traversing the partition item information to perform display probability distribution according to the keyword feature value, the intention keyword feature value, the second coordinate information, and the second browsing traffic information, and generating a second-level display probability, where the step S600 includes the steps of:
s610: acquiring first project coordinate information and second project coordinate information until M project coordinate information according to the second coordinate information;
s620: traversing the first project coordinate information, the second project coordinate information and the Mth project coordinate information according to the third coordinate information to generate first project distance information, second project distance information and the Mth project distance information;
s630: acquiring first item browsing flow information, second item browsing flow information and up to Mth item browsing flow information according to the second browsing flow information;
s640: performing display probability distribution on the mth project according to the mth project distance information and the mth project browsing flow information to generate a second-level initial display probability of the mth project, wherein M belongs to M;
further, the displaying probability distribution is performed on the mth project according to the mth project distance information and the mth project browsing flow information, so as to generate an initial displaying probability of the mth project at the second level, and step S640 includes the steps of:
s641: obtaining a second display probability distribution formula:
Figure DEST_PATH_IMAGE021
Figure DEST_PATH_IMAGE023
Figure DEST_PATH_IMAGE025
wherein the content of the first and second substances,
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the second level initially shows probabilities for the mth item,
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a first presentation sub-probability of the probability is initially presented for the second level of the mth item,
Figure 534974DEST_PATH_IMAGE028
a second presentation sub-probability of the probability for the second level of the mth item is initially presented,
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and
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are respectively as
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And
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the weight parameter of the probability fusion is,
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as the m-th item of distance information,
Figure DEST_PATH_IMAGE033
browsing flow information for the mth project;
s642: and inputting the mth item distance information and the mth item browsing flow information into the second display probability distribution formula to generate a second-level initial display probability of the mth item.
S650: adjusting the mth item second-level initial display probability according to the keyword characteristic value and the intention keyword characteristic value to generate an mth item second-level display probability;
further, the adjusting the mth item second level initial display probability according to the keyword feature value and the intention keyword feature value to generate the mth item second level display probability, and the step S650 includes the steps of:
s651: acquiring an mth project keyword according to the keyword characteristic value;
s652: according to the mth project keyword, the intention keyword characteristics of the mth project are screened from the intention keyword characteristic values;
s653: judging whether the mth item intention keyword features comprise first type keyword frequency features or not, wherein the first type keywords represent that a user has browsing intention;
s654: if yes, adjusting the second display probability distribution formula to generate a third display probability distribution formula:
Figure DEST_PATH_IMAGE035
Figure DEST_PATH_IMAGE037
wherein the content of the first and second substances,
Figure 392498DEST_PATH_IMAGE038
a third presentation sub-probability characterizing the initial presentation probability of the second level of the mth item,
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is composed of
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Fuse weight parameters of, and
Figure 177101DEST_PATH_IMAGE039
is greater than
Figure 325185DEST_PATH_IMAGE040
，
Figure DEST_PATH_IMAGE041
A first type keyword frequency characteristic of an mth item;
s655: and inputting the frequency characteristics of the first type keywords into the third display probability distribution formula to generate the second-level display probability of the mth item.
S660: adding the mth item second level presentation probability into the second level presentation probability.
Specifically, the second-level display probability refers to a second-level random display probability for determining the project travel information under the comprehensive consideration of the keyword feature value, the intention keyword feature value, the second coordinate information, and the second browsing flow information. The detailed process of probability distribution is as follows:
the first item coordinate information, the second item coordinate information and the Mth item coordinate information refer to coordinate information which is extracted from the second coordinate information and corresponds to each item one by one, and M represents the total number of items in any one partition; the first item distance information, the second item distance information and the up-to-M item distance information refer to third coordinate information traversing the first item coordinate information, the second item coordinate information and the up-to-M item coordinate information, and determining the third coordinate information and the distance information of each item in any one partition; the first item browsing flow information, the second item browsing flow information and the up to M item browsing flow information refer to daily average human flow which is extracted from the second browsing flow information and corresponds to each item in any one partition one by one.
And calling a second display probability distribution formula:
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Figure DEST_PATH_IMAGE045
Figure DEST_PATH_IMAGE047
Figure 366085DEST_PATH_IMAGE029
and
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are respectively as
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And
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weight parameter of probability fusion according to the staff pair
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And
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the bias degree of the probability distribution is set in a self-defining way, the manageable characteristic of the probability distribution is improved,
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is the m-th item distance information,
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browsing traffic information for the mth item, where M is any one of the first item through the mth item. And calling the mth item distance information and the mth item browsing flow information from the first item to the mth item through a second display probability distribution formula, and inputting the mth item distance information and the mth item browsing flow information into the display probability distribution formula to obtain the second-level initial display probability of the mth item.
Further, according to the interactive characteristic value: and adjusting the initial display probability of the second level by the keyword characteristic value and the intention keyword characteristic value to obtain the display probability of the second level of the mth item, and traversing any item from the first item to the mth item to obtain all the display probabilities of the second level and marking as the display probability of the second level.
The adjustment process according to the speech feature value is detailed as follows: taking the m-th item as an example without limitation:
the mth item keyword refers to a keyword of the mth item, i.e., a name of the mth item; the mth item intention keyword feature refers to intention keyword feature values associated with the mth item one by one, and is exemplarily as follows: and hearing that the air plank road of the east peak is good to play, the item is the air plank road, and the good to play is the intention keyword characteristic value of the air plank road. The first type of keywords refer to keywords representing that the user has browsing intention, that is, positive evaluation is performed on the item, and if the user actively interacts with the query, the user is directly regarded as having browsing intention.
Determining whether the mth item intention keyword features comprise first type keywords or not, if yes, indicating that the mth item intention keyword features comprise the first type keywords, and calling the first type keyword frequency features, which means the occurrence frequency of the item keywords in a preset time period; by the third demonstration probability distribution formula:
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Figure DEST_PATH_IMAGE051
Figure 712042DEST_PATH_IMAGE052
the preference of the staff to the user intention value can be represented and can be set in a self-defined way, preferably
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Is greater than
Figure 205657DEST_PATH_IMAGE040
And improving the fitness of the random display result and the user, inputting the frequency characteristics of the first type of keywords into a third display probability distribution formula, namely completing adjustment, and generating a second-level display probability of the mth project. And adding the mth item second level presentation probability to the second level presentation probability.
If the first type of keyword is not included, no adjustment is made.
And processing the first item to the Mth item in the same way to obtain all the second-level display probabilities, setting the second-level display probabilities as a state to be responded, and waiting for calling in a later step.
S700: and randomly displaying the project travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result.
Further, the randomly displaying the project travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result, and step S700 includes the steps of:
s710: obtaining a probability fusion formula:
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wherein the content of the first and second substances,
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and
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are respectively as
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And
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a fusion weight index of (a);
s720: fusing the first level display probability and the second level display probability according to the probability fusion formula to generate a first display probability;
s730: and randomly displaying the project text travel information according to the first display probability to generate the first display result.
Specifically, will
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And
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according to a probability fusion formula:
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carrying out a fusion process in which,
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and
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are respectively as
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And
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the fusion weight index can be set by a worker in a self-defined mode, the first display probability represents a result obtained after the first level display probability and the second level display probability are fused, and the random display probability of the travel information of any item in any partition on the first display panel is represented. The first display result refers to a random display result determined according to the first display probability at any time. By considering information such as distance, flow, voice interaction characteristics and the like, the intelligent, humanized and individualized degrees of random display of scenic spot travel information are improved.
In summary, the embodiment of the present application has at least the following technical effects:
according to the method and the device, the probability value is set according to the coordinate information of each project point, the convenience of arrival of the user is considered, and humanization is improved; the probability value is set according to the browsing flow, the popularity of the scenic spot is considered, and the user experience is favorably improved; the pushing probability of the text and travel information of the user interest item is improved according to the voice characteristic value of the user, and the combination degree with the user is favorably improved; random display is carried out on a plurality of projects in a plurality of scenic spots, the diversity of display results is improved, and the technical effects of improving the intelligence, individuation and humanization of project and travel information display are achieved.
Example two
Based on the same inventive concept as the method for displaying random information interaction in the foregoing embodiments, as shown in fig. 4, the present application provides a system for displaying random information interaction, which is in communication connection with a first display panel, where the first display panel includes a voice monitoring device, and includes:
the system comprises a scenic spot information acquisition module 11, a scenic spot information acquisition module, a scenic spot information processing module and a tour log information processing module, wherein the scenic spot information acquisition module is used for acquiring first scenic spot basic information, and the first scenic spot basic information comprises scenic spot partition information and tour log information;
the log information extraction module 12 is configured to obtain first coordinate information and first browsing flow information according to the tour log information;
a first probability distribution module 13, configured to traverse the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing traffic information, and generate a first-level display probability;
the project information extraction module 14 is configured to obtain partition project information according to the scenic spot partition information, where the partition project information includes second coordinate information, second browsing flow information, and project travel information;
the voice characteristic value extraction module 15 is configured to obtain an interactive voice characteristic value through a voice monitoring device, where the interactive voice characteristic value includes a project keyword characteristic value and an intention keyword characteristic value;
a second probability distribution module 16, configured to traverse the partition item information to perform display probability distribution according to the keyword feature value, the intention keyword feature value, the second coordinate information, and the second browsing traffic information, and generate a second level display probability;
and the task execution module 17 is configured to randomly display the project travel information on the first display panel according to the first level display probability and the second level display probability, and generate a first display result.
Further, the first probability distribution module 13 performs steps including:
acquiring third coordinate information according to the first display panel;
traversing the first coordinate information according to the third coordinate information to generate first partition distance information, second partition distance information and Nth partition distance information;
acquiring first partition browsing flow information, second partition browsing flow information and Nth partition browsing flow information according to the first browsing flow information;
carrying out display probability distribution on the nth partition according to the nth partition distance information and the nth partition browsing flow information to generate a first-level display probability of the nth partition, wherein N belongs to N;
adding the nth partition first level presentation probability to the first level presentation probability.
Further, the first probability distribution module 13 performs the steps further including:
obtaining a first display probability distribution formula:
Figure DEST_PATH_IMAGE061
Figure DEST_PATH_IMAGE063
Figure DEST_PATH_IMAGE065
wherein the content of the first and second substances,
Figure 215243DEST_PATH_IMAGE007
the probabilities are shown for the first level of the nth partition,
Figure 371418DEST_PATH_IMAGE008
the sub-probabilities are shown for the first of the nth partition,
Figure 434052DEST_PATH_IMAGE009
second presentation sub-probability for nth partition，
Figure 308467DEST_PATH_IMAGE010
And
Figure 747539DEST_PATH_IMAGE011
are respectively as
Figure 40111DEST_PATH_IMAGE008
And
Figure 273646DEST_PATH_IMAGE009
the fusion weight parameter of (a) is,
Figure 635357DEST_PATH_IMAGE012
as the n-th partition distance information,
Figure 612540DEST_PATH_IMAGE013
browsing flow information for the nth partition;
and inputting the nth partition distance information and the nth partition browsing flow information into the first display probability distribution formula to generate the first-level display probability of the nth partition.
Further, the second probability distribution module 16 performs steps including:
acquiring first project coordinate information and second project coordinate information until M project coordinate information according to the second coordinate information;
traversing the first project coordinate information, the second project coordinate information and the Mth project coordinate information according to the third coordinate information to generate first project distance information, second project distance information and the Mth project distance information;
acquiring first item browsing flow information, second item browsing flow information and up to Mth item browsing flow information according to the second browsing flow information;
performing display probability distribution on the mth project according to the mth project distance information and the mth project browsing flow information to generate a second-level initial display probability of the mth project, wherein M belongs to M;
adjusting the mth item second-level initial display probability according to the keyword characteristic value and the intention keyword characteristic value to generate an mth item second-level display probability;
adding the mth item second level presentation probability into the second level presentation probability.
Further, the second probability distribution module 16 performs the steps further including:
obtaining a second display probability distribution formula:
Figure DEST_PATH_IMAGE067
Figure DEST_PATH_IMAGE069
Figure DEST_PATH_IMAGE071
wherein the content of the first and second substances,
Figure 884253DEST_PATH_IMAGE026
the second level initially shows probabilities for the mth item,
Figure 554269DEST_PATH_IMAGE027
a first presentation sub-probability of the probability is initially presented for the second level of the mth item,
Figure 416658DEST_PATH_IMAGE028
a second presentation sub-probability of the initial presentation probability for the second level of the mth item,
Figure 931953DEST_PATH_IMAGE029
and
Figure 182806DEST_PATH_IMAGE030
are respectively as
Figure 758144DEST_PATH_IMAGE031
And
Figure 94447DEST_PATH_IMAGE028
the weight parameter of the probability fusion is,
Figure 413433DEST_PATH_IMAGE032
is the m-th item distance information,
Figure 535104DEST_PATH_IMAGE033
browsing flow information for the mth item;
and inputting the mth item distance information and the mth item browsing flow information into the second display probability distribution formula to generate a second-level initial display probability of the mth item.
Further, the second probability distribution module 16 performs the steps further including:
acquiring an mth project keyword according to the keyword characteristic value;
according to the mth item keyword, the intention keyword characteristic of the mth item is screened from the intention keyword characteristic value;
judging whether the mth item intention keyword features comprise first type keyword frequency features or not, wherein the first type keywords represent that a user has browsing intention;
if yes, adjusting the second display probability distribution formula to generate a third display probability distribution formula:
Figure DEST_PATH_IMAGE073
Figure DEST_PATH_IMAGE075
wherein the content of the first and second substances,
Figure 609239DEST_PATH_IMAGE038
a third presentation sub-probability characterizing an initial presentation probability of the second level of the mth item,
Figure 167259DEST_PATH_IMAGE039
is composed of
Figure 306248DEST_PATH_IMAGE038
Fuse weight parameters of, and
Figure 266113DEST_PATH_IMAGE039
is greater than
Figure 183254DEST_PATH_IMAGE040
，
Figure 494150DEST_PATH_IMAGE041
A first type keyword frequency characteristic of an mth item;
and inputting the frequency characteristics of the first type keywords into the third display probability distribution formula to generate the mth item second-level display probability.
Further, the task execution module 17 performs steps including:
obtaining a probability fusion formula:
Figure DEST_PATH_IMAGE077
wherein the content of the first and second substances,
Figure 233567DEST_PATH_IMAGE055
and
Figure 313518DEST_PATH_IMAGE056
are respectively as
Figure 667139DEST_PATH_IMAGE007
And
Figure 199751DEST_PATH_IMAGE057
a fusion weight index of (a);
fusing the first level display probability and the second level display probability according to the probability fusion formula to generate a first display probability;
and randomly displaying the project text travel information according to the first display probability to generate the first display result.
EXAMPLE III
As shown in fig. 5, based on the same inventive concept as the random information interaction display method in the foregoing embodiment, the present application further provides a computer device 300, where the computer device 300 includes a memory 301 and a processor 302, the memory 301 stores a computer program, and the computer program, when executed by the processor 302, implements the steps of the method in the embodiment.
The computer device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the computer device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of programs in accordance with the teachings of the present application.
Communication interface 303, using any transceiver or like device, is used to communicate with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, etc.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read Only Memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement the method for displaying random information interaction provided in the foregoing embodiments of the present application.
Example four
Based on the same inventive concept as the random information interaction display method in the foregoing embodiment, the present application further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method in the first embodiment.
The specification and figures are merely exemplary of the application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.

Claims (8)

1. A random information interaction display method is characterized in that the method is applied to a random information interaction display system, the system is in communication connection with a first display panel, the first display panel comprises a voice monitoring device, and the method comprises the following steps:
acquiring first scenic spot basic information, wherein the first scenic spot basic information comprises scenic spot partition information and tour log information;
acquiring first coordinate information and first browsing flow information according to the tour log information;
traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information, and generating a first-level display probability;
acquiring partition project information according to the scenic spot partition information, wherein the partition project information comprises second coordinate information, second browsing flow information and project travel information;
acquiring an interactive voice characteristic value through a voice monitoring device, wherein the interactive voice characteristic value comprises a project keyword characteristic value and an intention keyword characteristic value;
traversing the partition project information to perform display probability distribution according to the keyword characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to generate a second level display probability;
randomly displaying the project travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result;
traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information, and generating a first-level display probability, including:
acquiring third coordinate information according to the first display panel;
traversing the first coordinate information according to the third coordinate information to generate first partition distance information, second partition distance information and Nth partition distance information;
acquiring first partition browsing flow information, second partition browsing flow information and Nth partition browsing flow information according to the first browsing flow information;
carrying out display probability distribution on the nth partition according to the nth partition distance information and the nth partition browsing flow information to generate a first-level display probability of the nth partition, wherein N belongs to N;
adding the nth partition first level presentation probability to the first level presentation probability;
the generating the first-level display probability of the nth partition according to the display probability distribution of the nth partition distance information and the nth partition browsing flow information comprises the following steps:
obtaining a first display probability distribution formula:
Figure DEST_PATH_IMAGE002AA
Figure DEST_PATH_IMAGE004AA
Figure DEST_PATH_IMAGE006AA
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE008A
the probability is shown for the first level of the nth partition,
Figure DEST_PATH_IMAGE010A
the sub-probabilities are shown for the first of the nth partition,
Figure DEST_PATH_IMAGE012A
a second presentation sub-probability for the nth partition,
Figure DEST_PATH_IMAGE014A
and
Figure DEST_PATH_IMAGE016A
are respectively as
Figure DEST_PATH_IMAGE010AA
And with
Figure DEST_PATH_IMAGE012AA
The fusion weight parameter of (a) is,
Figure DEST_PATH_IMAGE018A
as the n-th partition distance information,
Figure DEST_PATH_IMAGE020A
browsing flow information for the nth partition;
and inputting the nth partition distance information and the nth partition browsing flow information into the first display probability distribution formula to generate the first-level display probability of the nth partition.
2. The method of claim 1, wherein traversing the partition item information for a presentation probability distribution according to the keyword eigenvalue, the intention keyword eigenvalue, the second coordinate information, and the second browsing traffic information, and generating a second level presentation probability comprises:
acquiring first project coordinate information and second project coordinate information until M project coordinate information according to the second coordinate information;
traversing the first project coordinate information, the second project coordinate information and the Mth project coordinate information according to the third coordinate information to generate first project distance information, second project distance information and the Mth project distance information;
acquiring first item browsing flow information, second item browsing flow information and up to Mth item browsing flow information according to the second browsing flow information;
performing display probability distribution on the mth project according to the mth project distance information and the mth project browsing flow information to generate a second-level initial display probability of the mth project, wherein M belongs to M;
adjusting the mth item second-level initial display probability according to the keyword characteristic value and the intention keyword characteristic value to generate an mth item second-level display probability;
adding the mth item second-level presentation probability into the second-level presentation probability.
3. The method as claimed in claim 2, wherein the generating of the second-level initial display probability of the mth item according to the display probability distribution of the mth item based on the mth item distance information and the mth item browsing traffic information comprises:
obtaining a second display probability distribution formula:
Figure DEST_PATH_IMAGE022A
Figure DEST_PATH_IMAGE024A
Figure DEST_PATH_IMAGE026A
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE028A
the second level initially shows probabilities for the mth item,
Figure DEST_PATH_IMAGE030A
a first presentation sub-probability of the probability is initially presented for the second level of the mth item,
Figure DEST_PATH_IMAGE032A
a second presentation sub-probability of the probability for the second level of the mth item is initially presented,
Figure DEST_PATH_IMAGE034A
and
Figure DEST_PATH_IMAGE036A
are respectively as
Figure DEST_PATH_IMAGE038A
And
Figure DEST_PATH_IMAGE040A
the weight parameter of the probability fusion is,
Figure DEST_PATH_IMAGE042A
as the m-th item of distance information,
Figure DEST_PATH_IMAGE044A
browsing flow information for the mth project;
and inputting the mth item distance information and the mth item browsing flow information into the second display probability distribution formula to generate a second-level initial display probability of the mth item.
4. The method of claim 3, wherein the adjusting the mth item second-level initial presentation probability according to the keyword feature value and the intention keyword feature value to generate the mth item second-level presentation probability comprises:
acquiring an mth project keyword according to the keyword characteristic value;
according to the mth project keyword, the intention keyword characteristics of the mth project are screened from the intention keyword characteristic values;
judging whether the mth item intention keyword features comprise first type keyword frequency features or not, wherein the first type keywords represent that a user has browsing intention;
if yes, adjusting the second display probability distribution formula to generate a third display probability distribution formula:
Figure DEST_PATH_IMAGE046A
Figure DEST_PATH_IMAGE048A
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE028AA
the second level initially shows probabilities for the mth item,
Figure DEST_PATH_IMAGE030AA
a first presentation sub-probability of the probability is initially presented for the second level of the mth item,
Figure DEST_PATH_IMAGE032AA
a second presentation sub-probability of the probability for the second level of the mth item is initially presented,
Figure DEST_PATH_IMAGE034AA
and
Figure DEST_PATH_IMAGE036AA
are respectively as
Figure DEST_PATH_IMAGE038AA
And
Figure DEST_PATH_IMAGE040AA
the weight parameter of the probability fusion is,
Figure DEST_PATH_IMAGE042AA
as the m-th item of distance information,
Figure DEST_PATH_IMAGE044AA
browsing the traffic information for the mth item,
Figure DEST_PATH_IMAGE050A
a third presentation sub-probability characterizing an initial presentation probability of the second level of the mth item,
Figure DEST_PATH_IMAGE052A
is composed of
Figure DEST_PATH_IMAGE050AA
Fuse weight parameters of, and
Figure DEST_PATH_IMAGE054A
is greater than
Figure DEST_PATH_IMAGE056A
，
Figure DEST_PATH_IMAGE058A
A first type keyword frequency characteristic of an mth item;
and inputting the frequency characteristics of the first type keywords into the third display probability distribution formula to generate the mth item second-level display probability.
5. The method of claim 1, wherein the randomly presenting the item travel information at the first presentation panel according to the first and second hierarchical presentation probabilities to generate a first presentation result comprises:
obtaining a probability fusion formula:
Figure DEST_PATH_IMAGE060A
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE062A
and
Figure DEST_PATH_IMAGE064
are respectively as
Figure DEST_PATH_IMAGE066
And
Figure DEST_PATH_IMAGE068
the fusion weight index of (a) is,
Figure DEST_PATH_IMAGE070
the second level initially shows probabilities for the mth item,
Figure DEST_PATH_IMAGE072
displaying the probability for the first level of the nth partition;
fusing the first level display probability and the second level display probability according to the probability fusion formula to generate a first display probability;
and randomly displaying the project travel information according to the first display probability to generate the first display result.
6. A random information interaction display system is characterized in that the random information interaction display system is in communication connection with a first display panel, the first display panel comprises a voice monitoring device, and the system comprises:
the system comprises a scenic spot information acquisition module, a scenic spot information acquisition module and a scenic spot management module, wherein the scenic spot information acquisition module is used for acquiring first scenic spot basic information, and the first scenic spot basic information comprises scenic spot partition information and tour log information;
the log information extraction module is used for acquiring first coordinate information and first browsing flow information according to the tour log information;
the first probability distribution module is used for traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information to generate a first-level display probability;
the project information extraction module is used for acquiring partition project information according to the scenic spot partition information, wherein the partition project information comprises second coordinate information, second browsing flow information and project travel information;
the voice characteristic value extraction module is used for acquiring an interactive voice characteristic value through a voice monitoring device, wherein the interactive voice characteristic value comprises a project keyword characteristic value and an intention keyword characteristic value;
the second probability distribution module is used for traversing the partition project information to perform display probability distribution according to the keyword characteristic value, the intention keyword characteristic value, the second coordinate information and the second browsing flow information to generate a second level display probability;
the task execution module is used for randomly displaying the project and travel information on the first display panel according to the first level display probability and the second level display probability to generate a first display result;
traversing the scenic spot partition information to perform display probability distribution according to the first coordinate information and the first browsing flow information, and generating a first-level display probability, including:
acquiring third coordinate information according to the first display panel;
traversing the first coordinate information according to the third coordinate information to generate first partition distance information, second partition distance information and Nth partition distance information;
acquiring first partition browsing flow information, second partition browsing flow information and Nth partition browsing flow information according to the first browsing flow information;
carrying out display probability distribution on the nth partition according to the nth partition distance information and the nth partition browsing flow information to generate a first-level display probability of the nth partition, wherein N belongs to N;
adding the nth partition first level presentation probability to the first level presentation probability;
the generating the first-level display probability of the nth partition according to the display probability distribution of the nth partition distance information and the nth partition browsing flow information comprises the following steps:
obtaining a first display probability distribution formula:
Figure DEST_PATH_IMAGE074
Figure DEST_PATH_IMAGE076
Figure DEST_PATH_IMAGE078
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE008AA
the probability is shown for the first level of the nth partition,
Figure DEST_PATH_IMAGE010AAA
the sub-probabilities are shown for the first of the nth partition,
Figure DEST_PATH_IMAGE012AAA
a second presentation sub-probability for the nth partition,
Figure DEST_PATH_IMAGE014AA
and
Figure DEST_PATH_IMAGE016AA
are respectively as
Figure DEST_PATH_IMAGE010AAAA
And with
Figure DEST_PATH_IMAGE012AAAA
The fusion weight parameter of (a) is,
Figure DEST_PATH_IMAGE018AA
as the n-th partition distance information,
Figure DEST_PATH_IMAGE020AA
browsing flow information for the nth partition;
and inputting the nth partition distance information and the nth partition browsing flow information into the first display probability distribution formula to generate the first-level display probability of the nth partition.
7. A computer arrangement, characterized in that the computer arrangement comprises a memory and a processor, in which memory a computer program is stored, which computer program, when being executed by the processor, realizes the steps of the method as set forth in any one of the claims 1-5.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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