CN113312467A

CN113312467A - Information mining method based on intelligent business big data and cloud computing AI (Artificial Intelligence) service system

Info

Publication number: CN113312467A
Application number: CN202110708205.5A
Authority: CN
Inventors: 赵天硕; 黄义宝; 田巧玲
Original assignee: Dongguan Huixuehuiwan Education Technology Co ltd
Current assignee: Dongguan Huixuehuiwan Education Technology Co ltd
Priority date: 2021-06-25
Filing date: 2021-06-25
Publication date: 2021-08-27

Abstract

The embodiment of the application provides an information mining method based on intelligent business big data and a cloud computing AI service system, because conversation intentions reflected by any two intention points with binding relationship in an intention point space have conversation circulation operation in a target intelligent business conversation process, and the conversation intentions with more conversation circulation operations generally have more intention interest possibility, the conversation intention strength of the conversation intentions reflected by each intention point can be more accurately determined based on the conversation circulation information between the intention points in the intention point space. Then, the precision of the dialog intention strength of each dialog intention can be improved, so that the precision of the interest dialog track data can be effectively improved, and the precision of the interest content features can be improved; in addition, the whole interest point prediction process can be automatically completed in real time, the interest content characteristics of the target intelligent business conversation process can be automatically identified, and the time cost of interest point prediction is reduced.

Description

Information mining method based on intelligent business big data and cloud computing AI (Artificial Intelligence) service system

Technical Field

The application relates to the technical field of information mining, in particular to an information mining method based on intelligent business big data and a cloud computing AI (Artificial intelligence) service system.

Background

Big data is complex and various, the updating speed is abnormally high, and the scale is larger. But has high analytical new value, so that the research of big data plays a decisive role in the future development trend of the business, such as how to perform information mining to decide the interesting conversation content of the user so as to improve the trend of future business updating.

In the related art, driven by factors such as the current big data technology, how to perform retrospective information mining based on the dialogue trajectory data in the dialogue intention is an important problem to be researched and solved urgently in the current field.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present application aims to provide an information mining method based on smart business big data and a cloud computing AI service system.

In a first aspect, the present application provides an information mining method based on smart business big data, which is applied to a cloud computing AI service system, where the cloud computing AI service system is in communication connection with a plurality of smart business registration terminals, and the method includes:

extracting a conversation intention cluster from a target intelligent business conversation process to be mined, wherein the conversation intention cluster comprises a plurality of conversation intents, and the plurality of conversation intents at least comprise conversation track data;

adopting the plurality of conversation intentions to construct an intention point space of the target intelligent business conversation process, wherein the intention point space comprises a plurality of intention points; one intention point reflects one conversation intention, and the conversation intentions reflected by any two intention points with binding relationship have conversation circulation operation in the target intelligent business conversation process;

calculating the conversation intention strength of the conversation intention reflected by each intention point based on the conversation circulation information among the intention points in the intention point space;

and selecting interest conversation track data of the target intelligent business conversation process from the conversation intention cluster according to the conversation intention strength of each conversation intention, constructing an interest prediction feature of the target intelligent business conversation process by adopting the interest conversation track feature of the interest conversation track data, inputting the interest prediction feature into an interest point prediction network, and obtaining a predicted interest point set of the target intelligent business conversation process, wherein the interest prediction feature is used for indicating the interest content feature of the target intelligent business conversation process.

In a second aspect, an embodiment of the present application further provides an information mining system based on smart business big data, where the information mining system based on smart business big data includes a cloud computing AI service system and a plurality of smart business registration terminals in communication connection with the cloud computing AI service system;

the cloud computing AI service system is used for:

Based on any one of the above aspects, the application may extract a plurality of dialog intents from the target intelligent business dialog flow, and construct an intention point space of the target intelligent business dialog flow by using the plurality of dialog intents. Because the conversation intentions reflected by any two intention points with binding relationship in the intention point space have conversation circulation operation in the target intelligent business conversation process, the conversation intentions with more conversation circulation operations generally have more intention interest possibility; therefore, the conversation intention strength of the conversation intention reflected by each intention point can be more accurately determined based on the conversation circulation information between the intention points in the intention point space. Then, interest dialog track data of the target intelligent business dialog process can be selected from the dialog intention clusters according to the dialog intention strength of each dialog intention, and interest prediction features used for indicating interest content features of the target intelligent business dialog process are constructed by using the interest dialog track features of the interest dialog track data. Therefore, the precision of the interested dialogue track data can be effectively improved by improving the precision of the dialogue intention strength of each dialogue intention, so that the precision of the interested content characteristics is improved; in addition, the whole interest point prediction process can be automatically completed in real time, the interest content characteristics of the target intelligent business conversation process can be automatically identified, and the time cost of interest point prediction is reduced.

Drawings

Fig. 1 is a schematic view of an application scenario of an intelligent business big data-based information mining system according to an embodiment of the present application;

fig. 2 is a schematic flowchart of an information mining method based on smart business big data according to an embodiment of the present application;

fig. 3 is a schematic block diagram of structural components of a cloud computing AI service system for implementing the above intelligent business big data-based information mining method according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interactive schematic diagram of an intelligent business big data-based information mining system 10 according to an embodiment of the present application. The information mining system 10 based on the intelligent business big data may include a cloud-computing AI service system 100 and an intelligent business registration terminal 200 communicatively connected to the cloud-computing AI service system 100. The intelligent business big data based information mining system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the intelligent business big data based information mining system 10 may also include only at least some of the components shown in fig. 1 or may also include other components.

In an embodiment that can be implemented independently, the cloud computing AI service system 100 and the intelligent business registration terminal 200 in the intelligent business big data based information mining system 10 can cooperatively perform the intelligent business big data based information mining method described in the following method embodiment, and the detailed description of the method embodiment can be referred to in the following steps of the cloud computing AI service system 100 and the intelligent business registration terminal 200.

To solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information mining method based on smart business big data according to an embodiment of the present application, and the information mining method based on smart business big data according to the present embodiment may be executed by the cloud computing AI service system 100 shown in fig. 1, and the information mining method based on smart business big data is described in detail below.

Step S110, extracting a conversation intention cluster from the target intelligent business conversation process to be mined.

Research shows that for any intelligent business conversation process, the intelligent business conversation process usually includes a large number of conversation intents, and the large number of conversation intents may include one or more conversation track data; the session trace data is session log information formed by session process records, and further forms session trace big data. And optionally, one or more non-dialog trajectory data may also be included in the large number of dialog intents; by non-dialog trace data is meant dialog log information consisting of other operational behavior related information besides dialog process records, where the other operational behavior related information may include, but is not limited to: the operation behavior is located in the service, the operation behavior source, the operation behavior interaction mode and the like. Accordingly, the non-dialog trace data may be dialog log information composed of the service where the operation behavior is located, dialog log information composed of the source of the operation behavior, and so on.

Because these large numbers of conversation intentions are used to describe the operation behavior information (such as conversation process records, the business where the operation behavior is located, the operation behavior source, etc.) involved in the intelligent business conversation process, and the interest content features of the intelligent business conversation process are used to perform predictive analysis on the relevant content of the main operation behavior (i.e. the key operation behavior) involved in the intelligent business conversation process; it can be seen that the conversation intention in the intelligent business conversation process is connected with the interesting content characteristics of the intelligent business conversation process to a certain extent. Therefore, the embodiment of the application provides a technical solution for determining the content of interest characteristics of the intelligent business conversation process through the conversation intention included in the intelligent business conversation process. Based on the technical scheme, when there is a mining request about the dialog content characteristics of the target intelligent business dialog flow, the cloud computing AI service system 100 may obtain the target intelligent business dialog flow to be mined. Then, a dialogue intention cluster can be extracted from the target intelligent business dialogue process through the step; the dialog intent cluster herein can include a plurality of dialog intents, and the plurality of dialog intents includes at least dialog trajectory data. In an embodiment, the cloud-computing AI service system 100 may perform dialog intention clustering on the target intelligent business dialog process, and match an initial dialog intention cluster obtained by the dialog intention clustering with the at least one intention feature library to match and extract an initial dialog intention contained in the initial dialog intention cluster and located in the at least one dialog intention reference model. And then, determining the conversation intention in the target intelligent business conversation process according to the matched and extracted initial conversation intention, thereby constructing a conversation intention cluster for obtaining the target intelligent business conversation process.

And step S120, constructing an intention point space of the target intelligent business conversation process by adopting a plurality of conversation intents.

In an independently implementable embodiment, the target intelligent business conversation process may include a plurality of intent points in an intent point space; one intention point reflects one conversation intention, and the conversation intentions reflected by any two intention points with binding relationship have conversation circulation operation in the target intelligent business conversation process. That is, in the target intelligent business conversation process, there are intent points corresponding to two conversation intentions of the conversation circulation operation, and binding relationship exists in the intent point space. Wherein, the dialog circulation operation mentioned here can include any one of the following meanings:

in an embodiment, the above-mentioned dialog circulation operation may refer to: in the process of carrying out conversation circulation on the target intelligent business conversation process by adopting a conversation guide reference service, two conversations intend to simultaneously appear in the circulation process behavior of the conversation guide reference service. The number of quoting times of the dialogue guiding quoting service can be set according to an empirical value or a maximum dialogue number range in a target intelligent business dialogue process; for example, the maximum dialog time range in the target intelligent business dialog process is 20 page active interest dimensions, the reference time can be set to be less than or equal to 20 page active interest dimensions. For example, a dialogue guide reference service with 5 reference times as active interest dimensions of pages is adopted to perform dialogue circulation on a target intelligent business dialogue process; and setting a plurality of dialog intents including: the dialog intentions Q, W, E, R … … assume that the dialog intentions Q and W can be considered to have a dialog flow operation in the target intelligent business dialog flow because they can appear in the dialog guidance reference service at the same time during the dialog flow. Since the dialog intention W and the dialog intention E can simultaneously appear in the dialog guidance referencing service during the dialog flow, the dialog intention W and the dialog intention E can be considered to have a dialog flow operation in the target intelligent business dialog flow. Since the dialog intention E and the dialog intention R cannot simultaneously appear in the dialog guidance reference service, it can be considered that the dialog intention E and the dialog intention R do not have a dialog flow operation in the target intelligent business dialog flow, and so on.

In another embodiment, the above-mentioned dialog flow operation may refer to: in the process of carrying out conversation circulation on a target intelligent business conversation process by adopting a conversation guide reference service, two conversation intents simultaneously appear in the conversation guide reference service, and the relation that the relevant parameters of a conversation application environment between the two conversation intents are larger than the preset relevant parameter values. The dialog application environment related parameters between the two dialog intents can be obtained by calculation according to the interesting dialog track characteristics of the two dialog intents; the dialog application environment related parameter between the two dialog intents can be used for reflecting the dialog application environment feature correlation degree between the two dialog intents, and the dialog application environment related parameter is in direct proportion to the dialog application environment feature correlation degree; that is, the greater the dialog application environment-related parameter between two dialog intents, the greater the dialog application environment feature correlation between the two dialog intents. And, the preset relevant parameter values can be set according to the actual service conditions. For example, let the preset relevant parameter value be K, the dialog application environment relevant parameter between the dialog intention Q and the dialog intention W be kQW, the dialog application environment relevant parameter between the dialog intention W and the dialog intention E be kWE, and the dialog application environment relevant parameter between the dialog intention E and the dialog intention R be kER; and kQW < K, kWE > K, kER < K. Still take over the above example: since the dialog application environment-related parameter (i.e., kQW) between the dialog intention Q and the dialog intention W is smaller than the preset-related parameter value (K), the cloud computing AI service system 100 may consider the dialog intention Q and the dialog intention W not to have a dialog circulation operation in the target intelligent business dialog flow although the dialog intention Q and the dialog intention W may simultaneously appear in the dialog guidance referencing service. Since the dialog application environment-related parameter (i.e., kWE) between the dialog intention W and the dialog intention E is greater than the preset-related parameter value (K), and the dialog intention W and the dialog intention E may simultaneously appear in the dialog guidance reference service, the cloud computing AI service system 100 may consider the dialog intention W and the dialog intention E to have a dialog circulation operation in the target intelligent business dialog flow, and so on. Therefore, when judging whether the two conversation intentions have the conversation circulation operation in the target intelligent business conversation process or not, the embodiment not only considers the distance of the coverage range of the two conversation intentions in the target intelligent business conversation process through the conversation guide reference service, but also considers the conversation application environment characteristic correlation degree between the two conversation intentions, so that the judgment accuracy of the conversation circulation operation can be effectively improved, and the precision of the intention point space is improved.

Based on the above description, in the process of implementing the step S120, the cloud computing AI service system 100 may first construct an initial intent point space of the target intelligent business dialog flow by using a plurality of dialog intents; the initial intent point space includes a plurality of intent points, each of which reflects a dialog intent. Next, the cloud computing AI service system 100 may select at least one pair of combinations of streamed dialog intention objects from among the plurality of dialog intents, the combination of streamed dialog intention objects being a combination of dialog intention objects composed of two dialog intents having a dialog stream operation in the target intelligent business dialog flow. Then, the cloud computing AI service system 100 can walk the combination of the dialog intent objects for each pair of flows; for the combination of the currently circulated dialog intention objects which are currently circulated, two intention points for recording two dialog intentions in the combination of the currently circulated dialog intention objects can be respectively connected in the initial intention point space; when the combination of the dialog intention objects of each circulation is wandered away, the intention point space of the target intelligent business dialog process can be obtained. For example, it may be assumed that the plurality of dialog intents includes: dialog intentions Q (recorded with intent point Q), W (recorded with intent point W), E (recorded with intent point E), R (recorded with intent point R), E (recorded with intent point E) … …; and there are a total of 5 pairs of combinations of streamed dialog intent objects from the plurality of dialog intents, which are respectively: (dialog intention Q, dialog intention W), (dialog intention Q, dialog intention R), (dialog intention W, dialog intention E), and (dialog intention R, dialog intention E). Then, the cloud computing AI service system 100 may connect the intention point Q and the intention point W, connect the intention point Q and the intention point R, connect the intention point W and the intention point E, and connect the intention point R and the intention point E, respectively, in the initial intention point space, thereby obtaining the intention point space of the target intelligent business conversation process.

Step S130, based on the dialog circulation information between the intention points in the intention point space, the dialog intention strength of the dialog intention reflected by each intention point is calculated.

In an embodiment which can be implemented independently, the dialogue circulation operation refers to the relation that two dialogue intentions appear in one dialogue guide reference service at the same time or refers to the relation that two dialogue intentions appear in one dialogue guide reference service at the same time and a dialogue application environment related parameter between the two dialogue intentions is larger than a preset related parameter value; therefore, it can be known that the higher the occurrence frequency of the dialog intentions with more dialog flow operations in the target intelligent business dialog flow, the more possible the intention interest of the dialog intentions with more dialog flow operations can be considered. Based on this, when the cloud-computing AI service system 100 executes step S130, for example, the number of dialog flow times of the dialog flow operation included in each dialog intention may be counted according to the dialog flow information between the respective intention points in the intention point space, and the dialog intention strength of each dialog intention may be determined according to the number of dialog flow times corresponding to each dialog intention on the basis of the principle that the number of dialog flow times and the dialog intention strength are in positive correlation.

In an embodiment which can be implemented independently, the number of dialog circulation corresponding to each dialog intention can be directly used as the dialog intention strength of each dialog intention. Or carrying out regularized conversion on the conversation circulation times corresponding to each conversation intention to obtain the conversation intention strength of each conversation intention; the regularized conversion is processing for mapping the number of times of the dialog flow to a range of 0 to 1. Or, carrying out weight fusion on the conversation circulation times corresponding to each conversation intention by adopting a conversation intention strength parameter to obtain the conversation intention strength of each conversation intention; wherein, the dialogue intention intensity parameter can be set according to the actual business situation. For example, referring to the foregoing example, if the intention point Q is mapped to the intention point W and the intention point Q is mapped to the intention point R, it may be statistically determined that the dialog flow operation of the dialog intention Q reflected by the intention point Q has a dialog flow number of 2; the number of dialog circulation times can be directly used as the dialog intention strength of the dialog intention Q (i.e. the dialog intention strength is 2), or the dialog intention strength parameter (e.g. 1.5) is used to perform weight fusion on the number of dialog circulation times to obtain the dialog intention strength of the dialog intention Q (i.e. the dialog intention strength is 3), and so on.

In another embodiment, which can be implemented independently, it is shown through research that if two dialog intents have a dialog flow operation in the target intelligent business dialog flow, the dialog intention strengths of the two dialog intents will generally affect each other because the two dialog intents are simultaneously present. Based on this, when the cloud computing AI service system 100 executes step S130, for the dialog intention reflected by any arbitrary point, the dialog intention strength of the dialog intention resulting in any arbitrary point can be calculated by combining the dialog intention strength of the dialog intention reflected by the cascaded arbitrary point having a binding relationship with the arbitrary point, so as to improve the accuracy of the dialog intention strength. In an independently implementable embodiment, for a dialog intention reflected by any intention point, at least one cascade intention point having a binding relationship with the any intention point can be determined based on dialog flow information between the intention points in an intention point space; then, the dialogue intention strength of the dialogue intention reflected by any of the cascade intention points is calculated according to the dialogue intention strength of the dialogue intention reflected by each cascade intention point.

The specific implementation manner of calculating the conversation intention strength of the conversation intention reflected by any one of the cascade intention points according to the conversation intention strength of the conversation intention reflected by each cascade intention point can include any one of the following:

the first implementation mode comprises the following steps: the cloud computing AI service system 100 may calculate an initial value of the dialog intention reflected by any one of the intent points according to the number of dialog transitions of the dialog transition operation of the dialog intention reflected by any one of the intent points. Secondly, the times of the dialog intention reflected by any one of the mind points and the dialog intention reflected by each cascade mind point appearing in the dialog guide reference service at the same time can be respectively counted, and the counted times are respectively subjected to regularized conversion to obtain the intention evaluation index of each cascade mind point. For example, for the dialog intention Q reflected by the intention point Q, the intention point Q has two cascade intention points of an intention point W and an intention point R; if the number of times that the dialogue intention Q and the dialogue intention W reflected by the intention point W simultaneously appear in the dialogue guide reference service is 15, the number of times that the dialogue intention Q and the dialogue intention R reflected by the intention point R simultaneously appear in the dialogue guide reference service is 5; the intention evaluation index of the intention point W is 15/(15+5) to 0.75, and the intention evaluation index of the intention point R is 5/(15+5) to 0.25. After the intention evaluation indexes of all the cascade intention points are obtained, the intention evaluation indexes of all the cascade intention points can be adopted to carry out weighted summation on the conversation intention strength of all the cascade intention points; for example, if the dialog intention strength of the intention point W is 0.4 and the dialog intention strength of the intention point R is 0.2, 0.4 × 0.75+0.2 × 0.25 may be executed, or 0.35 may be executed. Then, the numeric value obtained by weighted summation and the initial value of the dialogue intention reflected by any one of the intention points can be summed to obtain the dialogue intention strength of the dialogue intention reflected by any one of the intention points.

The second embodiment: the cloud-computing AI service system 100 may further calculate the conversation intention strength of the conversation intention reflected by any of the cascaded intention points according to the conversation intention strength of the conversation intention reflected by the respective cascaded intention points.

In another embodiment, since the dialog application environment-related parameter can be used to indicate the dialog application environment feature correlation between two dialog intents, research shows that for any dialog intention, the greater the dialog application environment feature correlation between other dialog intents and the dialog intention, the greater the influence of the dialog intention strength of the other dialog intention on the dialog intention strength of the dialog intention will be. Based on this, when the cloud computing AI service system 100 executes step S130, for the dialog intention reflected by any arbitrary point, the dialog intention strength of the dialog intention reflected by any arbitrary point can be calculated by combining the dialog intention strength of the dialog intention reflected by the cascade arbitrary point having a binding relationship with the arbitrary point and the dialog application environment related parameters between the dialog intention reflected by any arbitrary point and the dialog intents reflected by the respective cascade arbitrary points, so as to further improve the precision of the dialog intention strength. In an embodiment, for a dialog intention reflected by any intention point, at least one cascade intention point having a binding relationship with any intention point can be determined based on dialog flow information between the intention points in the intention point space. Then, the dialogue application environment related parameters between the dialogue intention reflected by any one of the mind points and the dialogue intentions reflected by all the cascade mind points can be calculated; and calculating the conversation intention strength of the conversation intention reflected by any one of the cascade intention points according to the calculated conversation application environment related parameters and the conversation intention strength of the conversation intention reflected by each cascade intention point.

According to the calculated conversation application environment related parameters and the conversation intention strength of the conversation intention reflected by each cascade intention point, the specific implementation mode of calculating the conversation intention strength of the conversation intention reflected by any intention point can comprise any one of the following steps:

the first implementation mode comprises the following steps: the cloud computing AI service system 100 may calculate an initial value of the dialog intention reflected by any one of the intent points according to the number of dialog transitions of the dialog intention reflected by any one of the intent points. Secondly, the conversation intention strength of each cascade intention point can be weighted and summed by adopting each conversation application environment related parameter respectively. For example, for the dialog intention Q reflected by the intention point Q, the intention point Q has two cascade intention points of an intention point W and an intention point R; and the intensity of the dialogue intention of the intention point W is 0.4, and the intensity of the dialogue intention of the intention point R is 0.2. If the dialog application environment related parameter between the dialog intention Q and the dialog intention W reflected by the intention point W is kQW, the dialog application environment related parameter between the dialog intention Q and the dialog intention R reflected by the intention point R is kQR; 0.4 × kQW +0.2 × kQR may be performed. Then, the numerical value obtained by weight fusion and the initial value of the dialogue intention reflected by any intention point can be summed to obtain the dialogue intention strength of the dialogue intention reflected by any intention point.

The second embodiment: the cloud computing AI service system 100 may further calculate the conversation intention strength of the conversation intention reflected by any one of the cascade intention points according to the calculated conversation application environment related parameters and the conversation intention strengths of the conversation intents reflected by the respective cascade intention points.

Step S140, selecting interest dialogue track data of the target intelligent business dialogue process from the dialogue intention cluster according to the dialogue intention strength of each dialogue intention, and constructing an interest prediction feature of the target intelligent business dialogue process by using the interest dialogue track feature of the interest dialogue track data, wherein the interest prediction feature is used for indicating the interest content feature of the target intelligent business dialogue process.

Since the interesting content features of the target intelligent business conversation process are used for performing predictive analysis on the relevant content of the main operation behaviors (i.e., key operation behaviors) involved in the target intelligent business conversation process, the cloud computing AI service system 100 can select the conversation track data with the maximum conversation intention strength from the conversation intention clusters according to the conversation intention strengths of the conversation intents after obtaining the conversation intention strengths of the conversation intents. Interest prediction features of the target intelligent business conversation process can then be constructed using the interest conversation track features of the interest conversation track data, the interest prediction features indicating content of interest features of the target intelligent business conversation process.

The specific implementation of selecting the interest dialog track data from the dialog intention cluster by the cloud computing AI service system 100 may be as follows: screening all conversation track data included in the conversation intention cluster, and then selecting the conversation track data with the maximum conversation intention intensity from the screened conversation track data as the interesting conversation track data of the target intelligent business conversation process. Alternatively, the cloud computing AI service system 100 may first select a plurality of election dialog trajectory segments of the target intelligent business dialog flow from the dialog intention cluster according to the dialog trajectory segment segmentation rules and according to the dialog intention strengths of the respective dialog intents. The plurality of election conversation track segments comprise at least one interest conversation track data, and the conversation track segment segmentation rule can be set according to business requirements. For example, a dialog track segment segmentation rule can be set for indicating that dialog intents of a target range are selected from the dialog intention cluster as election dialog track segments in descending order of the dialog intention strength; or a dialog track segment segmentation rule can be set for indicating that from the dialog intention cluster, a dialog intention with a dialog intention strength greater than a dialog intention strength threshold value is selected as an election dialog track segment, and the like.

According to the method and the device, aiming at the target intelligent business conversation process to be mined, a plurality of conversation intents can be extracted from the target intelligent business conversation process, and an intention point space of the target intelligent business conversation process is constructed by adopting the plurality of conversation intents. Because the conversation intentions reflected by any two intention points with binding relationship in the intention point space have conversation circulation operation in the target intelligent business conversation process, the conversation intentions with more conversation circulation operations generally have more intention interest possibility; therefore, the conversation intention strength of the conversation intention reflected by each intention point can be more accurately determined based on the conversation circulation information between the intention points in the intention point space. Then, interest dialog track data of the target intelligent business dialog process can be selected from the dialog intention clusters according to the dialog intention strength of each dialog intention, and interest prediction features used for indicating interest content features of the target intelligent business dialog process are constructed by using the interest dialog track features of the interest dialog track data. Therefore, the precision of the interested dialogue track data can be effectively improved by improving the precision of the dialogue intention strength of each dialogue intention, so that the precision of the interested content characteristics is improved; in addition, the whole interest point prediction process can be automatically completed in real time, the interest content characteristics of the intelligent service conversation process can be automatically identified, and the time cost of interest point prediction is reduced.

Based on the description of the above embodiment, the embodiment of the present application further provides another information mining method based on smart business big data, which can be implemented independently, and can be executed by the above mentioned cloud computing AI service system 100. For example, the information mining method based on intelligent business big data may include the following steps S210-S290, which are described in detail below.

Step S210, extracting a conversation intention cluster from the target intelligent business conversation process to be mined.

In an embodiment that can be implemented independently, the cloud computing AI service system 100 may perform dialog intention clustering on a target intelligent business dialog process to be mined to obtain an initial dialog intention cluster; the originating dialog intent cluster includes a plurality of originating dialog intents therein, and each originating dialog intent in the originating dialog intent cluster has an intent feature.

After obtaining the initial dialog intention cluster, the cloud computing AI service system 100 may screen a plurality of trigger dialog intents from the initial dialog intention cluster according to at least one dialog intention reference model; the triggering dialog intention referred to herein refers to an initial dialog intention that exists in the at least one dialog intention reference model, i.e., the triggering dialog intention refers to a dialog intention that exists in both the at least one dialog intention reference model and the target intelligent business dialog flow. In an independently implementable embodiment, the cloud computing AI service system 100 can screen a plurality of trigger dialog intents from the starting dialog intent cluster directly according to at least one dialog intent reference model; for example, in an independently implementable embodiment, the cloud computing AI service system 100 can walk around each originating dialog intent in the cluster of originating dialog intents; matching the starting dialogue intention of the current walk with at least one dialogue intention reference model to detect whether the starting dialogue intention of the current walk exists in the at least one dialogue intention reference model; and if so, taking the starting dialog intention of the current wandering as the trigger dialog intention. In another embodiment, which can be implemented independently, since some error intentions may exist in the starting dialog intention cluster, the error intentions are intentions which need to be eliminated and have no practical meaning.

Based on this, when the cloud computing AI service system 100 screens out a plurality of trigger dialog intents from the initial dialog intention cluster according to at least one dialog intention reference model, the initial dialog intention of the target intention feature may be screened out from the initial dialog intention cluster; the target intent characteristics mentioned herein may be specified according to actual business conditions, which may include at least one of: intent trigger behavior, intent destination behavior, and intent data sources. Then, the cloud computing AI service system 100 may screen out a plurality of trigger dialog intents from the initial dialog intents of the target intention feature according to the at least one dialog intention reference model, and the specific implementation thereof is similar to the aforementioned specific implementation of the step of "screening out a plurality of trigger dialog intents from the initial dialog intention cluster directly according to the at least one dialog intention reference model", and the detailed description thereof is omitted here.

After the plurality of trigger dialog intents are screened out, a dialog intention cluster (which can be represented by M') of the target intelligent business dialog flow can be constructed by using the plurality of trigger dialog intents. In an independently implementable embodiment, a plurality of triggering dialog intents can be directly adopted to construct a dialog intention cluster of a target intelligent business dialog process; in this embodiment, the dialog intents in the dialog intent cluster are the triggering dialog intents, and the statistical distribution amount of the dialog intents is equal to the statistical distribution amount of the triggering dialog intents. In another embodiment, some triggering dialogue intents which are adjacent in the target intelligent business dialogue process and have special meanings may exist in the screened triggering dialogue intents; for these triggering dialog intents, they will typically appear in the dialog intent reference model at the same time, and the intents that are composed after their intent contents are associated together are more analytically meaningful than a single triggering dialog intent. For example, for triggering dialog intents "Q" and "W", they would typically appear in the dialog intent reference model at the same time, and "Q + W" is more analytically meaningful than "Q" and "W". In this case, the cloud computing AI service system 100 can associate the content of the triggering dialog intentions together and use the dialog intentions after the content of the intentions is associated as the dialog intentions to improve the accuracy of the subsequent topic identification. Based on this, when the cloud computing AI service system 100 constructs a dialog intention cluster of the target intelligent business dialog flow by using a plurality of trigger dialog intents, it may determine whether there is a trigger dialog intention satisfying the intention content association condition in the plurality of trigger dialog intents; the intention content association condition here may include: the dialog intention coverage in the target intelligent business dialog flow is matched and exists in the same dialog intention reference model. If the triggering dialogue intents meeting the intention content association condition exist in the plurality of triggering dialogue intents, performing intention content association processing on the triggering dialogue intents meeting the intention content association condition; and adding the conversation intention cluster subjected to the intention content association processing and the triggering conversation intention which is not subjected to the intention content association processing into the conversation intention cluster of the target intelligent business conversation process as conversation intents. If there is no trigger dialog intention satisfying the intention content association condition among the plurality of trigger dialog intents, each trigger dialog intention can be added as a dialog intention to a dialog intention cluster of the target intelligent business dialog flow.

Step S220, an intention point space of the target intelligent business conversation process is constructed by adopting a plurality of conversation intents.

In specific implementation, an initial intention point space of a target intelligent business conversation process can be constructed by adopting a plurality of conversation intents; the initial intent point space includes a plurality of intent points, each of which reflects a dialog intent. Secondly, at least one pair of combinations of the circulating conversation intention objects can be selected from the plurality of conversation intentions, and the circulating conversation intention object combination refers to the combination of the conversation intention objects formed by two conversation intentions with a conversation circulating operation in the target intelligent business conversation process. Then, at least one intention point subregion can be determined from the initial intention point space according to the combination of the at least one pair of circulated dialogue intention objects; wherein, any arbitrary point sub-area can include: two intent points of two dialog intents in a combination of a pair of streamed dialog intent objects are recorded, respectively. Then, two intent points in each intent point sub-area can be connected in the initial intent point space respectively, so as to obtain the intent point space of the target intelligent business conversation process.

In step S230, based on the dialog circulation information between the respective intention points in the intention point space, the dialog intention strength of the dialog intention reflected by the respective intention points is calculated.

Step S240, selecting a plurality of election conversation track segments of the target intelligent business conversation process from the conversation intention cluster according to the conversation intention strength of each conversation intention according to the conversation track segment segmentation rule.

In an embodiment, the cloud computing AI service system 100 may select a target range of dialog intents from the dialog intention cluster as a plurality of election dialog trajectory segments of the target intelligent business dialog flow in descending order of the dialog intention strength. In another independently implementable embodiment, the cloud computing AI service system 100 can select a conversation intention from the conversation intention cluster with a conversation intention strength greater than a conversation intention strength threshold as the plurality of election conversation track segments of the target intelligent business conversation process. Wherein the plurality of election dialog track segments include at least one interest dialog track data. For convenience of illustration, the dialog intents of the target range selected from the dialog intention clusters by the plurality of election dialog track segments in descending order of the dialog intention strength will be described as an example.

Step S250, selecting the interest dialog trajectory data with the highest dialog intention intensity from the at least one interest dialog trajectory data as the interest dialog trajectory data of the target intelligent business dialog process.

Step S260, the interest prediction characteristics of the target intelligent business conversation process are constructed by adopting the interest conversation track characteristics of the interest conversation track data, and the interest prediction characteristics are used for indicating the interest content characteristics of the target intelligent business conversation process.

In an embodiment, which can be implemented independently, the cloud computing AI service system 100 may first invoke the interest dialog track feature generation model to obtain the interest dialog track features of the interest dialog track data. In an embodiment, the interest dialog track feature of the dialog intention can be determined by combining the contextual data of the dialog intention or by combining the functions of the dialog intention in different dimensions, so that the dialog intention can have the same interest dialog track feature in different dimensions, without limitation.

After the interest conversation track characteristics of the interest conversation track data are obtained, the interest conversation track characteristics of the interest conversation track data can be adopted to construct interest prediction characteristics of the target intelligent business conversation process. In an embodiment which can be independently implemented, related non-conversation track data corresponding to interest conversation track data can be obtained from a conversation intention cluster; the so-called relevant non-conversational trajectory data fulfils the following condition: in the intent point space, intent points for recording related non-dialog track data are mapped with intent points for recording interest dialog track data. Secondly, the interest dialogue track characteristics of the interest dialogue track data and the interest dialogue track characteristics of the related non-dialogue track data can be obtained; it should be noted that any of the interest dialog trajectory features mentioned herein may be generated by the cloud computing AI service system 100 by calling the interest dialog trajectory feature generation model, and any of the interest dialog trajectory features may be a P-dimensional vector; the value of P may be set according to empirical values, for example P200. Then, the interest dialogue track characteristics of the interest dialogue track data and the interest dialogue track characteristics of the related non-dialogue track data can be spliced to obtain the interest prediction characteristics of the target intelligent business dialogue process; in an embodiment, which can be implemented independently, the interest dialogue track features of the interest dialogue track data and the interest dialogue track features of the related non-dialogue track data can be accumulated according to each dimension to obtain the interest prediction features in the target medical treatment text. For example, let the interest conversation track data be "live cable commercial skip data", and its corresponding interest conversation track characteristic be G (l0) ═ l01, l02, …, l 0P; relevant non-conversational trajectory data includes: "Q", "W", "E", and "R", and the corresponding interest dialog track features are as follows in order: g (l1) ═ G (l11, l12, …, l1P), G (l2) ═ l21, l22, …, l2P, G (l3) ═ l31, l32, …, l3P, and G (l4) ═ l41, l42, …, l 4P. Then, the individual interest dialogue track features are added up in each dimension to obtain the interest prediction features g (l01+ l11+ l21+ l31+ l41, l02+ l12+ l22+ l32+ l42, …, l0P + l1P + l2P + l3P + l 4P).

Step S270, obtaining the interest dialogue track characteristics of each election dialogue track segment, and calculating the characteristic correlation degree between the interest dialogue track characteristics and the interest prediction characteristics of each election dialogue track segment.

Step S280, selecting the interest dialogue track section of the target intelligent business dialogue process from the plurality of election dialogue track sections according to the feature correlation degree between the interest dialogue track feature and the interest prediction feature of each election dialogue track section.

In the specific implementation process of steps S210-S280, the cloud computing AI service system 100 may first invoke the interest dialog track feature generation model to represent each election dialog track segment as a P-dimensional vector, so as to obtain the interest dialog track features (i.e., P-dimensional vectors) of each election dialog track segment. Secondly, a feature correlation algorithm can be adopted to calculate the feature correlation between the interest conversation track features and the interest prediction features of each election conversation track segment. Then, according to the feature correlation degree between the interest conversation track feature and the interest prediction feature of each election conversation track segment, the election conversation track segment with the feature correlation degree larger than a preset correlation degree threshold value is selected from the multiple election conversation track segments to serve as the interest conversation track segment of the target intelligent business conversation process. It should be noted that the preset correlation threshold mentioned herein may be set according to actual traffic conditions, for example, may be set to 0.85, etc.; also, the interest dialog track segment selected through steps S210-S280 may or may not include the aforementioned interest dialog track data, which is not limited in this respect.

Therefore, through steps S210 to S280, it can be realized that the feature correlation between the interest conversation track feature and the interest prediction feature of the interest conversation track segment is greater than the preset correlation threshold, so as to prevent the election conversation track segment unrelated to the interest content feature from being selected as the interest conversation track segment, and if the interest content feature of the target intelligent business conversation process is located on the "Q" related operation behavior, the election conversation track segment "W" unrelated to the interest content feature can be prevented from being selected as the interest conversation track segment of the target intelligent business conversation process. Therefore, the interest dialogue track segment obtained through the steps S210-S280 can reflect the cheating main track of the target intelligent business dialogue process, and the precision of the interest dialogue track segment is improved; and moreover, interest conversation track sections of the target intelligent business conversation process all belong to the same interest content characteristic, and the matching of interest conversation dimensions among the interest conversation track sections is ensured.

Step S290, mapping the target intelligent business conversation process and the interest conversation track segment.

In a specific implementation, after the interest dialog track segment of the target intelligent business dialog flow is selected by the cloud computing AI service system 100 through the above steps S210 to S280, the target intelligent business dialog flow and the interest dialog track segment may be mapped. In an independently implementable embodiment, the cloud computing AI service system 100 can directly add and associate the interest dialog track segment to the dialog track segment list associated with the target intelligent business dialog flow; alternatively, the cloud computing AI service system 100 can add the interest conversation track features of each interest conversation track segment to the list of conversation track segments associated with the target intelligent business conversation process. By mapping the target intelligent business conversation process and the interest conversation track segment, information mining processing can be carried out on the target intelligent business conversation process according to the interest conversation track segment.

It should be noted that the above-mentioned interest prediction features are global deep learning prediction features (i.e. main deep learning prediction features) of the target intelligent business conversation process, and the interest content features are main conversation content features of the target intelligent business conversation process. In an alternative embodiment, when the statistical distribution quantity of the interest dialogue trajectory data extracted from the target intelligent business dialogue process is multiple, the target intelligent business dialogue process is indicated to have multiple dialogue content characteristics. In this case, the cloud computing AI service system 100 may further select interest conversation track data of the target intelligent business conversation process from at least one interest conversation track data, the conversation intention strength of which is less than that of the interest conversation track data. Second, the interest conversation track features of the interest conversation track data can be used to construct deep learning prediction features (i.e., non-essential deep learning prediction features) of the conversation trend of the target intelligent business conversation process, which are used to indicate the conversation content features of the conversation trend of the target intelligent business conversation process. Then, the target intelligent business conversation process, the interest prediction feature and the deep learning prediction feature of the conversation trend can be mapped to the interest trend evaluation application, so that when an interest trend evaluation request exists, the interest trend evaluation processing can be carried out according to the interest prediction feature and the deep learning prediction feature of the conversation trend.

In an independently implementable embodiment, the cloud computing AI service system 100 may further calculate global deep learning prediction features and deep learning prediction features of a conversation trend of other intelligent business conversation processes by using the above method steps, and associate each of the calculated deep learning prediction features to an interest trend evaluation application; the impromptu trend assessment application further includes at least one other intelligent business conversation process, and each other intelligent business conversation process has a corresponding global deep learning prediction feature and a corresponding deep learning prediction feature of a conversation trend. When there is an interest trend evaluation request, the cloud computing AI service system 100 may obtain an interest trend evaluation feature of the interest trend evaluation information carried by the interest trend evaluation request. Secondly, each intelligent business conversation process in the interest trend evaluation application and at least one deep learning prediction characteristic of each intelligent business conversation process can be obtained; each intelligent business conversation process is provided with a business putting weight, and at least one deep learning prediction feature of each intelligent business conversation process comprises a global deep learning prediction feature of each intelligent business conversation process and a deep learning prediction feature of a conversation trend. Then, the feature correlation degree between each deep learning prediction feature and the interest trend evaluation feature of each intelligent business conversation process can be respectively calculated, and the business putting weight of each intelligent business conversation process is updated according to the calculated feature correlation degree. In an embodiment, for any intelligent business conversation process, the deep learning prediction feature with the maximum feature correlation degree can be determined from at least one deep learning prediction feature of any intelligent business conversation process according to the feature correlation degree between each deep learning prediction feature and the interest trend evaluation feature of any intelligent business conversation process. If the deep learning prediction feature with the maximum feature correlation degree is the global deep learning prediction feature of any intelligent business conversation process, the business launching weight of any intelligent business conversation process can be improved so as to update the business launching weight of any intelligent business conversation process; if the deep learning prediction feature with the maximum feature correlation degree is the deep learning prediction feature of the conversation trend of any intelligent business conversation process, the business putting weight of any intelligent business conversation process can be reduced so as to update the business putting weight of any intelligent business conversation process.

Based on the updating principle, after the service delivery weight of each intelligent service conversation process is updated, the cloud computing AI service system 100 can sort each intelligent service conversation process in a descending order according to the updated service delivery weight of each intelligent service conversation process; and selecting the intelligent service conversation process positioned at the head as the intelligent service conversation process to be analyzed to output. Therefore, the intelligent business conversation process with the conversation content characteristics can be subjected to ascending or descending weight sequencing according to the matching condition of the conversation content characteristics, and the accuracy of information mining is improved.

According to the method and the device, aiming at the target intelligent business conversation process to be mined, a plurality of conversation intents can be extracted from the target intelligent business conversation process, and an intention point space of the target intelligent business conversation process is constructed by adopting the plurality of conversation intents. Because the conversation intentions reflected by any two intention points with binding relationship in the intention point space have conversation circulation operation in the target intelligent business conversation process, the conversation intentions with more conversation circulation operations generally have more intention interest possibility; therefore, the conversation intention strength of the conversation intention reflected by each intention point can be more accurately determined based on the conversation circulation information between the intention points in the intention point space. Then, interest dialog track data of the target intelligent business dialog process can be selected from the dialog intention clusters according to the dialog intention strength of each dialog intention, and interest prediction features used for indicating interest content features of the target intelligent business dialog process are constructed by using the interest dialog track features of the interest dialog track data. Therefore, the precision of the interested dialogue track data can be effectively improved by improving the precision of the dialogue intention strength of each dialogue intention, so that the precision of the interested content characteristics is improved; in addition, the whole interest point prediction process can be automatically completed in real time, the interest content characteristics of the intelligent service conversation process can be automatically identified, and the time cost of interest point prediction is reduced. Moreover, after the interest prediction features are obtained, the cloud computing AI service system 100 may further perform a purification process on the dialog track segment of the target intelligent business dialog flow based on the feature correlation between the election dialog track segment and the interest prediction features to obtain an interest dialog track segment with matching of interest dialog dimensions, so that the accuracy of the interest dialog track segment can be effectively improved.

In an embodiment that can be implemented independently, the present application further provides an information pushing method based on smart business big data and interest prediction, which may include the following steps.

Step S310, a plurality of hot interest items under each e-commerce promotion service are obtained according to the predicted interest point set of each target intelligent service conversation process under each e-commerce promotion service.

Step S320, determining interest tendency distribution information of a plurality of hotspot interest items.

The method comprises the steps that a plurality of hot spot interest items exist, at least one interest tendency exists in the plurality of hot spot interest items, and the interest tendency distribution information indicates the statistical distribution quantity of each interest tendency of the plurality of hot spot interest items.

Step S330, clustering the plurality of hot interest items according to the interest tendency distribution information of the plurality of hot interest items to obtain at least two hot interest item clusters, wherein the hot interest item clusters comprise at least one interest tendency hot interest item.

In an embodiment, each hot spot interest item group may include all the hot spot interest items with interest tendency in the plurality of hot spot interest items, and the hot spot interest items with the same interest tendency in each hot spot interest item group may be one or more than one.

Step S340, performing e-commerce information push rule configuration on at least two hot spot interest item groups, so that the hot spot interest item groups located in the same active interest dimension in the at least two hot spot interest item groups are associated with corresponding information push rules, and the hot spot interest item groups located in the same passive interest dimension are associated with corresponding information push rules, and then performing information push based on the configured e-commerce information push rules.

In each hot interest item group, different hot interest items can be arranged and distributed according to the passive interest dimension direction. And associating the corresponding information pushing rules with the hot interest item groups positioned in the same active interest dimension.

In an embodiment, the cloud computing AI service system may first perform an initial e-commerce information push rule configuration on the at least two hot spot interest item groups, so as to determine the active interest dimension number configured by the e-commerce information push rule for the at least two hot interest item groups, the statistical distribution quantity of the hot interest item groups of each active interest dimension, and the hot interest item groups of each active interest dimension (i.e. determining the active interest dimension number and the passive interest dimension number), and then, at least one of interest information push crowd and interest information push domains of the at least two hot interest item groups are configured in an associated manner, so that the hot interest item groups in the same active interest dimension are associated with corresponding information push rules, and the hotspot interest item groups positioned in the same passive interest dimension are associated with corresponding information pushing rules.

By means of the design, after the cloud computing AI service system determines the interest tendency distribution information of a plurality of hot interest items, the plurality of hot interest items can be clustered according to the interest tendency distribution information of the plurality of hot interest items to obtain at least two hot interest item groups, and then the at least two hot interest item groups are subjected to E-commerce information pushing rule configuration, so that the hot interest item groups in the at least two hot interest item groups in the same active interest dimension are associated with corresponding information pushing rules, and the hot interest item groups in the same passive interest dimension are associated with corresponding information pushing rules. Therefore, the association configuration process of the hotspot interest items can be simplified, and the information pushing precision is improved.

Fig. 3 is a schematic diagram illustrating a hardware structure of a cloud computing AI service system 100 for implementing the above intelligent business big data based information mining method according to an embodiment of the present application, where as shown in fig. 3, the cloud computing AI service system 100 may include a processing chip 110 and a machine-readable storage medium 120; the machine-readable storage medium 120 has stored thereon executable code, which when executed by the processing chip 110, causes the processing chip 110 to perform the steps of the above embodiment of the information mining method based on intelligent business big data.

Actually, the cloud computing AI service system may further include a communication interface 140, wherein the processing chip 110, the machine-readable storage medium 120 and the communication interface 140 are connected through the bus 130, and the communication interface 140 is used for communicating with other devices.

In addition, the present application provides a non-transitory machine-readable storage medium having executable code stored thereon, which when executed by a processor of an electronic device, causes the processor to implement at least the steps of the aforementioned embodiment of the intelligent business big data based information mining method.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims

1. An information mining method based on intelligent business big data is characterized by being applied to a cloud computing AI service system, wherein the cloud computing AI service system is in communication connection with a plurality of intelligent business registration terminals, and the method comprises the following steps:

extracting a conversation intention cluster from a target intelligent business conversation process to be mined of the intelligent business registration terminal, wherein the conversation intention cluster comprises a plurality of conversation intents, and the plurality of conversation intents at least comprise conversation track data;

2. The intelligent business big data-based information mining method of claim 1, wherein the constructing of the interest prediction feature of the target intelligent business conversation process using the interest conversation track feature of the interest conversation track data comprises:

obtaining relevant non-dialogue track data corresponding to the interest dialogue track data from the dialogue intention cluster, wherein the relevant non-dialogue track data meet the following conditions: in the intention point space, an intention point for recording the related non-dialog track data is mapped with an intention point for recording the interest dialog track data;

obtaining interest conversation track characteristics of the interest conversation track data and interest conversation track characteristics of the related non-conversation track data;

and splicing the interest conversation track characteristics of the interest conversation track data and the interest conversation track characteristics of the related non-conversation track data to obtain the interest prediction characteristics of the target intelligent business conversation process.

3. The intelligent business big data-based information mining method according to claim 1 or 2, wherein the selecting of the interest conversation track data of the target intelligent business conversation process from the conversation intention cluster according to the conversation intention strength of each conversation intention comprises:

selecting the dialogue intents in a target range from the dialogue intention cluster according to the descending order of the dialogue intention strength as a plurality of election dialogue track segments of the target intelligent business dialogue process; or selecting a dialogue intention of which the dialogue intention strength is greater than a dialogue intention strength threshold value from the dialogue intention cluster as a plurality of election dialogue track sections of the target intelligent business dialogue process, wherein the election dialogue track sections comprise at least one interest dialogue track data;

and selecting the interest conversation track data with the highest conversation intention intensity from the at least one interest conversation track data as the interest conversation track data of the target intelligent business conversation process.

4. The intelligent business big data-based information mining method according to claim 3, wherein the method further comprises:

obtaining interest conversation track characteristics of each election conversation track segment, and calculating the characteristic correlation degree between the interest conversation track characteristics of each election conversation track segment and the interest prediction characteristics;

selecting an interest conversation track section of the target intelligent business conversation process from the plurality of election conversation track sections according to the feature correlation degree between the interest conversation track features of each election conversation track section and the interest prediction features; wherein the feature correlation between the interest dialogue track feature of the interest dialogue track segment and the interest prediction feature is greater than a preset correlation threshold;

and mapping the target intelligent business conversation process and the interest conversation track segment so as to perform information mining processing on the target intelligent business conversation process according to the interest conversation track segment.

5. The intelligent business big data-based information mining method according to claim 3, wherein the interest prediction feature is a global deep learning prediction feature of the target intelligent business conversation process, and the interest content feature is a main conversation content feature of the target intelligent business conversation process; the method further comprises the following steps:

selecting interest conversation track data of the target intelligent business conversation process from the at least one interest conversation track data, wherein the conversation intention strength of the interest conversation track data is smaller than that of the interest conversation track data;

establishing deep learning prediction characteristics of the conversation trend of the target intelligent business conversation process by adopting the interest conversation track characteristics of the interest conversation track data, wherein the deep learning prediction characteristics of the conversation trend of the target intelligent business conversation process are used for indicating conversation content characteristics of the conversation trend of the target intelligent business conversation process;

mapping the target intelligent business conversation process, the interest prediction feature and the deep learning prediction feature of the conversation trend to an interest trend evaluation application so as to perform interest trend evaluation processing according to the interest prediction feature and the deep learning prediction feature of the conversation trend when an interest trend evaluation request exists;

the interest trend evaluation application further comprises at least one other intelligent business conversation process, and each other intelligent business conversation process is provided with a corresponding global deep learning prediction feature and a deep learning prediction feature of a corresponding conversation trend; the method further comprises the following steps:

when an interest trend evaluation request exists, obtaining interest trend evaluation characteristics of interest trend evaluation information carried by the interest trend evaluation request;

obtaining each intelligent business conversation process in the interest trend evaluation application and at least one deep learning prediction feature of each intelligent business conversation process; each intelligent business conversation process is provided with a business putting weight, and at least one deep learning prediction feature of each intelligent business conversation process comprises a global deep learning prediction feature of each intelligent business conversation process and a deep learning prediction feature of a conversation trend;

respectively calculating the feature correlation degree between each deep learning prediction feature of each intelligent business conversation process and the interest trend evaluation feature, and updating the business putting weight of each intelligent business conversation process according to the calculated feature correlation degree;

sorting the intelligent service conversation flows in a descending order according to the updated service delivery weight of each intelligent service conversation flow;

selecting the first intelligent service conversation process as the intelligent service conversation process to be analyzed and outputting the intelligent service conversation process;

wherein, the updating of the service delivery weight of each intelligent service conversation process according to the calculated feature correlation degree comprises:

for any intelligent business conversation process, determining a deep learning prediction feature with the maximum feature correlation degree from at least one deep learning prediction feature of any intelligent business conversation process according to the feature correlation degree between each deep learning prediction feature of any intelligent business conversation process and the interest trend evaluation feature;

if the deep learning prediction feature with the maximum feature correlation degree is the global deep learning prediction feature of any intelligent business conversation process, improving the business putting weight of any intelligent business conversation process;

and if the deep learning prediction feature with the maximum feature correlation degree is the deep learning prediction feature of the conversation trend of any intelligent business conversation process, reducing the business putting weight of any intelligent business conversation process.

6. The intelligent business big data-based information mining method according to claim 1 or 2, wherein the constructing an intention point space of the target intelligent business conversation process by using the plurality of conversation intents comprises:

constructing an initial intention point space of the target intelligent business conversation process by adopting the plurality of conversation intentions, wherein the initial intention point space comprises a plurality of intention points, and each intention point reflects a conversation intention;

selecting at least one pair of combinations of circulating conversation intention objects from the plurality of conversation intents, wherein the circulating conversation intention object combination is a combination of conversation intention objects formed by two conversation intents with conversation circulation operations in the target intelligent business conversation process;

determining at least one intention point subregion from the initial intention point space according to a combination of the at least one pair of circulated dialog intention objects, wherein any intention point subregion comprises:

recording two intention points of two dialog intentions in a combination of a pair of circulated dialog intention objects respectively;

respectively connecting two intention points in each intention point sub-area in the initial intention point space to obtain an intention point space of the target intelligent business conversation process;

wherein the selecting at least one combination of streamed dialog intent objects from the plurality of dialog intents comprises:

determining a first dialog intent coverage of a first dialog intent in the target intelligent business dialog flow, the first dialog intent being any of the plurality of dialog intents;

obtaining a second dialogue intention from the plurality of dialogue intents according to a first dialogue intention coverage range of the first dialogue intention, wherein the range loss of the second dialogue intention between the second dialogue intention coverage range and the first dialogue intention coverage range in the target intelligent business dialogue process is smaller than a preset range loss threshold value;

calculating a conversation application environment-related parameter between the first conversation intention and the second conversation intention, the conversation application environment-related parameter indicating a conversation application environment feature correlation between the first conversation intention and the second conversation intention;

if the dialog application environment related parameter between the first dialog intention and the second dialog intention is larger than a preset related parameter value, determining that the first dialog intention and the second dialog intention have the dialog circulation operation in the target intelligent business dialog flow, and constructing a combination of dialog intention objects in one circulation by adopting the first dialog intention and the second dialog intention.

7. The method for mining information based on intelligent business big data according to claim 1 or 2, wherein the calculating of the dialogue intention strength of the dialogue intention reflected by each intention point based on the dialogue circulation information between the intention points in the intention point space comprises:

aiming at a conversation intention reflected by any intention point, determining at least one cascade intention point which has a binding relationship with the any intention point based on conversation circulation information among all the intention points in the intention point space;

calculating conversation application environment related parameters between the conversation intention reflected by any one of the meaning points and the conversation intention reflected by each cascade meaning point;

and calculating the conversation intention strength of the conversation intention reflected by any one of the cascade intention points according to the calculated conversation application environment related parameters and the conversation intention strength of the conversation intention reflected by each cascade intention point.

8. The method for mining information based on big intelligent business data according to claim 1 or 2, wherein the extracting of the conversation intention cluster from the target intelligent business conversation process to be mined comprises:

carrying out conversation intention clustering on a target intelligent service conversation process to be mined to obtain an initial conversation intention cluster, wherein the initial conversation intention cluster comprises a plurality of initial conversation intents;

screening a plurality of triggering dialog intents from the starting dialog intention cluster according to at least one dialog intention reference model, wherein the triggering dialog intents refer to the starting dialog intents existing in the at least one dialog intention reference model;

constructing a dialogue intention cluster of the target intelligent business dialogue process by adopting the plurality of trigger dialogue intents;

wherein each originating dialog intent in the originating dialog intent cluster has an intent feature; the screening of a plurality of triggering dialog intents from the starting dialog intention cluster according to at least one dialog intention reference model comprises:

screening out an initial dialog intention of a target intention characteristic from the initial dialog intention cluster, wherein the target intention characteristic comprises at least one of the following items: intent trigger behavior, intent destination behavior, and intent data sources;

screening a plurality of triggering dialog intents from the starting dialog intents of the target intention characteristics according to at least one dialog intention reference model;

wherein the constructing of the dialog intention cluster of the target intelligent business dialog process by using the plurality of trigger dialog intents includes:

if the triggering dialogue intents meeting the intention content association condition exist in the plurality of triggering dialogue intents, performing intention content association processing on the triggering dialogue intents meeting the intention content association condition;

adding the conversation intention cluster subjected to the intention content association processing and the triggering conversation intention which is not subjected to the intention content association processing into the conversation intention cluster of the target intelligent business conversation process as conversation intents;

if the triggering dialogue intents meeting the intention content association condition do not exist in the plurality of triggering dialogue intents, all the triggering dialogue intents are added to a dialogue intention cluster of the target intelligent business dialogue process as dialogue intents; wherein the intention content association condition includes: the dialog intention coverage in the target intelligent business dialog flow is matched and exists in the same dialog intention reference model.

9. The intelligent business big data-based information mining method according to any one of claims 1-8, wherein after the step of inputting the interest prediction features into the interest point prediction network to obtain the predicted interest point set of the target intelligent business conversation process, the method further comprises:

acquiring a plurality of hot interest items under each electronic commerce promotion service according to the predicted interest point set of each target intelligent service conversation process under each electronic commerce promotion service;

determining interest tendency distribution information of the plurality of hot interest items, wherein at least one interest tendency of the plurality of hot interest items exists, and the interest tendency distribution information indicates the statistical distribution quantity of each interest tendency of the plurality of hot interest items;

clustering the plurality of hot interest items according to the interest tendency distribution information of the plurality of hot interest items to obtain at least two hot interest item clusters, wherein the hot interest item clusters comprise the hot interest items with at least one interest tendency;

e-commerce information push rule configuration is carried out on the at least two hot spot interest item groups, so that the hot spot interest item groups in the at least two hot spot interest item groups in the same active interest dimension are associated with corresponding information push rules, and the hot spot interest item groups in the same passive interest dimension are associated with corresponding information push rules;

the configuring of e-commerce information push rules for the at least two hot interest item groups, so that the hot interest item groups located in the same active interest dimension in the at least two hot interest item groups are associated with corresponding information push rules, and the hot interest item groups located in the same passive interest dimension are associated with corresponding information push rules, includes:

according to a hotspot interest data source of a hotspot interest item included by each of the at least two hotspot interest item groups, performing initial e-commerce information push rule configuration on the at least two hotspot interest item groups;

at least one of an interest information pushing domain and an interest information pushing crowd of each of the at least two hot interest item groups is configured in an associated manner, so that the hot interest item groups in the at least two hot interest item groups located in the same active interest dimension are associated with corresponding information pushing rules, and the hot interest item groups in the same passive interest dimension are associated with corresponding information pushing rules;

the initial e-commerce information push rule configuration is performed on the at least two hot interest item groups according to the hot interest data source of the hot interest item included in each of the at least two hot interest item groups, and includes:

for each of the at least two hot spot interest item groups, determining hot spot progress position information of the hot spot interest item group in a target hot spot distribution network according to the hot spot progress position information of the hot spot interest items in the hot spot interest item groups in the target hot spot distribution network;

and sequencing the at least two hot interest item groups according to the hot progress position information of the at least two hot interest item groups to obtain a hot interest item cluster.

10. A cloud computing AI service system comprising a machine-readable storage medium, a processor; wherein the machine-readable storage medium has stored thereon executable code, which when executed by the processor, causes the processor to perform the intelligent business big data based information mining method of any one of claims 1 to 9.