CN114090663B

CN114090663B - User demand prediction method applying artificial intelligence and big data optimization system

Info

Publication number: CN114090663B
Application number: CN202111496052.9A
Authority: CN
Inventors: 贾小奇
Original assignee: Qingshan Information Technology Development Shenzhen Co ltd
Current assignee: Qingshan Information Technology Development Shenzhen Co ltd
Priority date: 2021-12-08
Filing date: 2021-12-08
Publication date: 2022-06-21
Anticipated expiration: 2041-12-08
Also published as: CN114090663A

Abstract

The embodiment of the application provides a user demand prediction method and a big data optimization system applying artificial intelligence, by using the user operation behavior characteristics to call the contact behavior log, the candidate contact behavior log sequence related to the user operation behavior log is obtained, the processing performance of the candidate contact behavior log sequence is improved, then, associated behavior vector mining is carried out, the processing performance of analyzing the log sequence of the first mining behavior is improved, then, associated behavior characteristic mining is carried out, the precision of the second mining behavior log sequence obtained by screening is ensured, finally, reference user demand distribution related to the second mining behavior log sequence is obtained, the reference user demand distribution is determined as predicted user demand related to the user operation behavior log, therefore, the processing performance of user demand prediction is improved on the basis of ensuring the distribution precision of the reference user demand.

Description

User demand prediction method applying artificial intelligence and big data optimization system

Technical Field

The application relates to the technical field of artificial intelligence and big data, in particular to a user demand prediction method and a big data optimization system applying the artificial intelligence.

Background

With the development of internet network information technology, various internet online services are generated along with the operation of internet online platforms. In order to improve the utilization rate of service products, each internet online platform needs to deliver various internet contents to different users, so as to achieve a higher conversion rate.

At present, most of internet content push logics of internet online platforms are user demand analysis, and targeted push is performed based on user demand analysis prediction results so as to provide personalized content push experience for different users. Therefore, it is important to provide a method for improving the processing performance of user demand prediction on the basis of ensuring the distribution accuracy of user demand.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present application aims to provide a user demand prediction method and a big data optimization system applying artificial intelligence.

In a first aspect, the present application provides a user demand prediction method applying artificial intelligence, which is applied to a big data optimization system, and the method includes:

responding to a user demand forecasting request, wherein the user demand forecasting request is correspondingly associated with a user operation behavior log, and performing behavior tendency vector mining on the user operation behavior log to obtain user operation behavior characteristics;

performing attention vector mining on the user operation behavior characteristics to obtain attention behavior vectors related to the user operation behavior logs;

acquiring a reference operation behavior log, related reference operation behavior features and related reference attention behavior vectors of each reference user requirement, and calling a contact behavior log from the reference operation behavior log of each reference user requirement according to the user operation behavior features and the related reference operation behavior features of the reference operation behavior log of each reference user requirement to obtain a candidate contact behavior log sequence related to the user operation behavior log;

performing association behavior vector mining from the candidate contact behavior log sequence according to the attention behavior vector and a reference attention behavior vector related to the candidate contact behavior log sequence to obtain a first mining behavior log sequence related to the user operation behavior log, and performing association behavior feature mining from the first mining behavior log sequence according to the reference operation behavior feature related to the user operation behavior feature and the first mining behavior log sequence to obtain a second mining behavior log sequence related to the user operation behavior log;

and acquiring reference user demand distribution related to the second mining behavior log sequence, and determining the reference user demand distribution as the predicted user demand related to the user operation behavior log.

In a second aspect, an embodiment of the present application further provides a user demand prediction system applying artificial intelligence, where the user demand prediction system applying artificial intelligence includes a big data optimization system and a plurality of intelligent medical registration platforms in communication connection with the big data optimization system;

the big data optimization system is configured to:

responding a user demand prediction request, wherein the user demand prediction request is correspondingly associated with a user operation behavior log, and performing behavior tendency vector mining on the user operation behavior log to obtain user operation behavior characteristics;

carrying out attention vector mining on the user operation behavior characteristics to obtain attention behavior vectors related to the user operation behavior logs;

According to the above aspects, the contact behavior log is called by using the user operation behavior characteristics, the candidate contact behavior log sequence related to the user operation behavior log is obtained, the processing performance of the analyzed candidate contact behavior log sequence is improved, then the associated behavior vector mining is carried out, the processing performance of the analyzed first mining behavior log sequence is improved, then the associated behavior characteristics mining is carried out, the precision of the screened second mining behavior log sequence is ensured, finally the reference user demand distribution related to the second mining behavior log sequence is obtained, the reference user demand distribution is determined as the predicted user demand related to the user operation behavior log, and therefore the processing performance of user demand prediction is improved on the basis of ensuring the precision of the reference user demand distribution.

Drawings

Fig. 1 is a schematic flowchart of a user demand prediction method using artificial intelligence according to an embodiment of the present disclosure.

Detailed Description

The architecture of the user demand prediction system 10 using artificial intelligence provided by an embodiment of the present application is described below. The user demand prediction system 10 employing artificial intelligence may include a big data optimization system 100 and an intelligent medical registration platform 200 communicatively coupled to the big data optimization system 100.

Referring to fig. 1, the big data optimization system 100 and the intelligent medical registration platform 200 of the system 10 for predicting user demand using artificial intelligence can cooperate to perform the method for predicting user demand using artificial intelligence as described in the following method embodiments, and the following method embodiments can be referred to in the detailed description of the steps of the system 100 and the intelligent medical registration platform 200.

Step S110, responding to the user demand forecasting request, wherein the user demand forecasting request is correspondingly associated with a user operation behavior log, and behavior tendency vector mining is carried out on the user operation behavior log to obtain the user operation behavior characteristics.

The user operation behavior log may be an operation behavior activity for expressing user requirements of the relevant users, where the operation behavior activity refers to a segment of recording user operation behaviors through various online service platform systems, and for example, each user operation behavior record may be recorded as one operation behavior activity. The user operation behavior feature may be expressed in a multi-modal feature.

For example, the big data optimization system may respond to a user demand prediction request, which is associated with a user operation behavior log. The big data optimization system can also search the corresponding user operation behavior log from the related log database after receiving the user demand prediction request. And then the big data optimization system performs behavior tendency vector mining on the user operation behavior log by using an operation behavior tendency decision network to obtain the user operation behavior characteristics. The operational behavior propensity decision network may be a model built through the AI network structure.

And step S120, carrying out attention vector mining on the user operation behavior characteristics to obtain an attention behavior vector related to the user operation behavior log.

The attention behavior vector is obtained by performing continuous attention vector mining on the user operation behavior characteristics. For example, the big data optimization system needs to perform continuous attention vector mining on the user operation behavior characteristics to obtain attention behavior vectors related to the user operation behavior logs.

Step S130, obtaining a reference operation behavior log required by each reference user, a related reference operation behavior feature and a related reference attention behavior vector, and calling a contact behavior log from the reference operation behavior log required by each reference user according to the user operation behavior feature and the related reference operation behavior feature of the reference operation behavior log required by each reference user to obtain a candidate contact behavior log sequence related to the user operation behavior log.

Wherein, the reference operation behavior log refers to operation behavior activity that has been generated with user requirements. The reference operation behavior characteristics refer to operation behavior characteristics obtained by performing behavior tendency vector mining on a reference operation behavior log. The reference attention behavior vector refers to an attention behavior vector obtained by performing attention vector mining on the reference operation behavior feature. The candidate contact behavior log sequence refers to a sequence of reference operation behavior logs returned by contact behavior log calling, and the candidate contact behavior log sequence needs to be further screened. The contact behavior log calling can be called and returned in the reference operation behavior log of each reference user requirement through a K-MEANS algorithm.

For example, the big data optimization system may obtain a reference operational behavior log for each reference user requirement, a reference operational behavior feature associated with each reference operational behavior log, and a reference attention behavior vector associated with each reference operational behavior log. And the big data optimization system uses the user operation behavior characteristics and the reference operation behavior characteristics related to each reference operation behavior log to determine the associated deviation metric, and returns the reference operation behavior log with the deviation metric larger than the preset deviation metric from the reference operation behavior log required by each reference user according to the obtained associated deviation metric related to each reference operation behavior characteristic, so as to obtain a candidate contact behavior log sequence related to the user operation behavior log.

Step S140, mining association behavior vectors from the candidate contact behavior log sequence according to the attention behavior vectors and reference attention behavior vectors related to the candidate contact behavior log sequence to obtain a first mining behavior log sequence related to the user operation behavior log, and mining association behavior features from the first mining behavior log sequence according to the reference operation behavior features related to the user operation behavior features and the first mining behavior log sequence to obtain a second mining behavior log sequence related to the user operation behavior log.

Wherein, the association behavior vector mining refers to screening by using an attention behavior vector from a candidate contact behavior log sequence. The first mining behavior log sequence is a reference operation behavior log sequence obtained by screening a reference operation behavior log in the candidate contact behavior log sequence through the attention behavior vector. And the associated behavior characteristic mining refers to a reference operation behavior log sequence obtained by screening the first mining behavior log sequence by using the user operation behavior characteristics. The second mining behavior log sequence refers to a reference operation behavior log which is obtained by screening and is most related to the user operation behavior log.

For example, the big data optimization system may determine an association deviation degree between the attention behavior vector and a reference attention behavior vector associated with each reference operation behavior log in the candidate contact behavior log sequence, perform screening from the candidate contact behavior log sequence according to the association deviation degree associated with each reference operation behavior log, and may use a reference operation behavior log larger than a preset threshold value of the association deviation degree between the attention behavior vectors as a first mining behavior log sequence associated with the user operation behavior log. The big data optimization system can also sort each reference operation behavior log according to the associated deviation degree related to each reference operation behavior log, and select the reference operation behavior log in the front sort to obtain a first mining behavior log sequence related to the user operation behavior log. Further, the big data optimization system determines the correlation deviation degree between the user operation behavior characteristic and the reference operation behavior characteristic related to the reference operation behavior log in the first mining behavior log sequence by using a correlation degree algorithm, then performs screening from the first mining behavior log sequence according to each correlation deviation degree to obtain a second mining behavior log sequence related to the user operation behavior log, may select each reference operation behavior log larger than a preset correlation degree threshold value, may also sort each reference operation behavior log in the first mining behavior log sequence according to each correlation deviation degree, and then selects the reference operation behavior log with the top sorting order to obtain the second mining behavior log sequence. The big data optimization system can also perform related behavior feature mining from the first mining behavior log sequence to obtain a plurality of second mining behavior log sequences related to the user operation behavior log.

Step S150, obtaining a reference user requirement distribution related to the second mining behavior log sequence, and determining the reference user requirement distribution as a predicted user requirement related to the user operation behavior log.

Wherein, the reference user requirement distribution refers to the predicted user requirement related to the second mining behavior log sequence, and the user requirement is already generated.

For example, the big data optimization system acquires reference user demand distribution related to the second mining behavior log sequence, and then directly determines the reference user demand distribution as predicted user demand related to the user operation behavior log. That is, the associated operational behavior activities make the generated predicted user demands the same, thereby avoiding generating related predicted user demands for each identical or associated operational behavior activity, and improving user demand prediction processing performance.

Based on the steps, the behavior tendency vector mining is carried out on the user operation behavior logs to obtain the user operation behavior characteristics, the attention vector mining is carried out on the user operation behavior characteristics to obtain the attention behavior vectors related to the user operation behavior logs, then the contact behavior logs are called from the reference operation behavior logs of each reference user requirement according to the user operation behavior characteristics and the reference operation behavior characteristics related to the reference operation behavior logs of each reference user requirement to obtain the candidate contact behavior log sequences related to the user operation behavior logs, the associated behavior vector mining is carried out from the candidate contact behavior log sequences to obtain the first mining behavior log sequences related to the user operation behavior logs, the associated behavior characteristics mining is carried out from the first mining behavior log sequences to obtain the second mining behavior logs related to the user operation behavior logs, finally, obtaining the reference user demand distribution related to the second mining behavior log sequence, determining the reference user demand distribution as the predicted user demand related to the user operation behavior log, namely, the contact behavior log is called by using the user operation behavior characteristics to obtain the candidate contact behavior log sequence related to the user operation behavior log, the processing performance of the candidate contact behavior log sequence is improved, then, associated behavior vector mining is carried out, the processing performance of analyzing the log sequence of the first mining behavior is improved, then, associated behavior characteristic mining is carried out, the precision of the second mining behavior log sequence obtained by screening is ensured, finally, reference user demand distribution related to the second mining behavior log sequence is obtained, the reference user demand distribution is determined as predicted user demand related to the user operation behavior log, therefore, the processing performance of user demand prediction is improved on the basis of ensuring the distribution precision of the reference user demand.

In an exemplary independent design idea, a user demand prediction request is associated with a plurality of user operation behavior logs, and the method further includes:

step S202, respectively carrying out behavior tendency vector mining on a plurality of user operation behavior logs to obtain a plurality of user operation behavior characteristics.

Step S204, conducting attention vector mining on the multiple user operation behavior characteristics, and obtaining attention behavior vectors related to the multiple user operation behavior logs.

For example, the big data optimization system may also perform demand prediction on a round of operation behavior activity at the same time, and at this time, the big data optimization system may obtain a plurality of user operation behavior logs carried in the user demand prediction request by analyzing the round of operation behavior activity, or may directly obtain the plurality of user operation behavior logs from the database. A plurality of user operation behavior logs and the like may also be obtained from the internet. And then respectively carrying out behavior tendency vector mining on the plurality of user operation behavior logs by using an operation behavior tendency decision network to obtain user operation behavior characteristics related to each user operation behavior log, and then carrying out continuous attention vector mining on each user operation behavior characteristic to obtain attention behavior vectors related to each user operation behavior characteristic.

Step S206, clustering the plurality of user operation behavior logs according to the plurality of user operation behavior characteristics to obtain a plurality of user operation behavior log clusters, and performing significant operation behavior activity selection from the plurality of user operation behavior log clusters to obtain significant operation behavior activities related to the plurality of user operation behavior log clusters.

The user operation behavior log cluster refers to a sequence of user operation behavior logs of the same cluster. And the user operation behavior log clusters of different clusters belong to different user operation behavior log clusters. The obvious operation behavior activity refers to a user operation behavior log which has the most obvious effect in the user operation behavior log cluster, and the global deviation degree between the obvious operation behavior activity and other user operation behavior logs in the user operation behavior log cluster is the smallest.

For example, the big data optimization system may cluster a plurality of user operation behavior logs through a plurality of user operation behavior features using a clustering algorithm. And after clustering is finished, different categories are obtained, each category is provided with a related user operation behavior log, and a plurality of user operation behavior log clusters are obtained. And then, selecting the significant operation behavior activity from each user operation behavior log cluster to obtain the significant operation behavior activity related to each user operation behavior log cluster.

Step S208, contact behavior logs are called from the reference operation behavior logs of each reference user requirement according to the user operation behavior characteristics related to the significant operation behavior activities and the reference operation behavior characteristics related to the reference operation behavior logs of each reference user requirement, and target candidate contact behavior log sequences related to the significant operation behavior activities are obtained.

The target candidate contact behavior log sequence is a candidate contact behavior log sequence obtained by screening user operation behavior features related to significant operation behavior activities. Each significant operational behavior activity results in a related sequence of target candidate contact behavior logs.

For example, after obtaining the significant operation behavior activity related to each user operation behavior log cluster, the big data optimization system then performs parallel processing on all the significant operation behavior activities, that is, determines the correlation deviation degree by using the user operation behavior characteristics related to the significant operation behavior activity and the reference operation behavior characteristics related to the reference operation behavior log of each reference user requirement, and calls the contact behavior log from the reference operation behavior log of each reference user requirement according to the correlation deviation degree to obtain a target candidate contact behavior log sequence related to the significant operation behavior activity. Namely, a reference operation behavior log which is larger than the correlation deviation threshold value is selected from the reference operation behavior logs required by each reference user, and a target candidate contact behavior log sequence related to the significant operation behavior activity is obtained.

Step S210, performing associated behavior vector mining from the target candidate contact behavior log sequence according to the attention behavior vector related to the significant operation behavior activity and the reference attention behavior vector related to the target candidate contact behavior log sequence to obtain a target first mining behavior log sequence related to the significant operation behavior activity, and performing associated behavior feature mining from the target first mining behavior log sequence according to the user operation behavior feature related to the significant operation behavior activity and the reference operation behavior feature related to the target first mining behavior log sequence to obtain the significant mining operation behavior activity related to the significant operation behavior activity.

The target first mining behavior log sequence is a first mining behavior log sequence obtained by mining association behavior vectors by using attention behavior vectors related to significant operation behavior activities. The significant mining operational behavior activity refers to a reference operational behavior log that is most associated with the significant operational behavior activity.

For example, the big data optimization system continues to perform association behavior vector mining and association behavior feature mining, namely, the attention behavior vector related to the significant operation behavior activity and the reference attention behavior vector related to each reference operation behavior log in the target candidate contact behavior log sequence are used for determining the association deviation degree of the attention vector mining, then each reference operation behavior log in the target candidate contact behavior log sequence is sorted according to each edit association deviation degree, and then the first target first mining behavior log sequence related to the significant operation behavior activity is obtained by selecting the reference operation behavior log in the front of the sorting. And then, determining the correlation deviation degree by using the user operation behavior characteristics related to the significant operation behavior activities and the reference operation behavior characteristics related to the reference operation behavior logs in the target first mining behavior log sequence, sequencing each reference operation behavior log in the target first mining behavior log sequence according to each correlation deviation degree, and then selecting the reference operation behavior log with the top sequencing to obtain the significant mining operation behavior activities related to the significant operation behavior activities.

Step S212, obtaining the significant user requirement related to the significant mining operation behavior activity, and determining the significant user requirement as the predicted user requirement related to the user operation behavior log in the user operation behavior log cluster related to the significant operation behavior activity.

Wherein the significant user requirement refers to a predicted user requirement that the significant mining operation behavior activity has generated.

For example, the big data optimization system acquires the significant user requirements related to the significant mining operation behavior activity from the database, and then determines the significant user requirements as the predicted user requirements related to each user operation behavior log in the user operation behavior log cluster related to the significant operation behavior activity. Namely, the predicted user requirement of each user operation behavior log in the user operation behavior log cluster is a significant user requirement.

Based on the design, when a plurality of user operation behavior logs are obtained, the user operation behavior logs are clustered, the obvious operation behavior activity is selected, and then the obvious mining operation behavior activity related to the obvious operation behavior activity is obtained through screening, so that the predicted user requirement related to each user operation behavior log in the user operation behavior log cluster related to the obvious operation behavior activity is obtained, the predicted user requirement related to each user operation behavior log in the user operation behavior log cluster can be obtained quickly, and the generation processing performance of the user requirement is improved.

In an exemplary independent design concept, step S206, clustering the multiple user operation behavior logs according to the multiple user operation behavior features to obtain multiple user operation behavior log clusters, includes:

step S302, obtaining a clustering label vector interval and a target clustering number.

Step S304, arbitrarily extracting the obtained target operation behavior characteristics from the multiple user operation behavior characteristics, determining the deviation degrees of the target operation behavior characteristics and the multiple user operation behavior characteristics, and determining the number of the user operation behavior characteristics of the deviation degrees in the clustering label vector interval.

The clustering label vector interval refers to an associated label vector interval used in determining the operation behavior log of the same cluster user during clustering. Namely, the user operation behavior log in the clustering label vector interval needs to be used as the same-cluster user operation behavior log. The target clustering number refers to the minimum configuration parameter of the user operation behavior logs in each category after clustering. The target operation behavior characteristic refers to the user operation behavior characteristic which needs to be clustered and determined currently.

For example, the big data optimization system may obtain the cluster label vector interval and the target cluster number directly based on the data server. The big data optimization system can also obtain the clustering label vector interval and the target clustering number uploaded by related developers. And then the big data optimization system walks each user operation behavior feature, namely, the target operation behavior feature is randomly extracted from the plurality of user operation behavior features, the degree of association deviation between the target operation behavior feature and the user operation behavior features except the target operation behavior feature in the plurality of user operation behavior features is determined by using a degree of association deviation algorithm, and the number of the user operation behavior features of which the degree of association deviation is in the clustering label vector interval is determined.

Step S306, when the number of the user operation behavior characteristics is larger than the target clustering number, determining the user operation behavior characteristics of the thermal connection from the plurality of user operation behavior characteristics according to the target operation behavior characteristics.

For example, the big data optimization system judges that the target operation behavior feature is a key operation segment when the number of the user operation behavior features is larger than the target clustering number. The target operational behavior signature is then used to determine a thermodynamic relationship of the user operational behavior signature from the plurality of user operational behavior signatures via the thermodynamic profile. And the big data optimization system judges that the target operation behavior feature is a non-key operation segment when the number of the user operation behavior features is not larger than the target clustering number, and at the moment, any one user operation behavior feature except the target operation behavior feature is selected from the plurality of user operation behavior features and determined as the target operation behavior feature for re-determination.

Step S308, obtaining a user operation behavior log cluster according to the user operation behavior log related to the target operation behavior characteristics and the user operation behavior log related to the thermodynamic connection user operation behavior characteristics.

For example, the big data optimization system obtains the user operation behavior log cluster according to the user operation behavior log related to the target operation behavior feature and the user operation behavior logs related to the user operation behavior features of all the thermal connections.

Step S310, determining each segment feature to be clustered from a plurality of user operation behavior features according to the user operation behavior log cluster, and randomly selecting a target segment feature to be clustered from each segment feature to be clustered.

The segment characteristics to be clustered refer to user operation behavior characteristics except the user operation behavior characteristics which are clustered.

For example, the big data optimization system deletes the user operation behavior characteristics related to each user operation behavior log in the user operation behavior log cluster from the plurality of user operation behavior characteristics to obtain the remaining user operation behavior characteristics, namely the characteristics of each segment to be clustered. And randomly selecting target fragment characteristics to be clustered from the characteristics of each fragment to be clustered.

Step S312, determining the target segment characteristics to be clustered as target operation behavior characteristics, and returning to the step of determining the deviation degrees of the target operation behavior characteristics and the plurality of user operation behavior characteristics for execution, and obtaining a plurality of user operation behavior log clusters when the clustering of the plurality of user operation behavior logs is completed.

For example, the big data optimization system determines the target segment characteristics to be clustered as target operation behavior characteristics, and returns to the step of determining the deviation degrees of the target operation behavior characteristics and the plurality of user operation behavior characteristics for cycle iteration execution, until all the user operation behavior logs are clustered completely, and a plurality of user operation behavior log clusters are obtained.

Based on the design, the clustering label vector interval and the target clustering number are obtained, then clustering determination is carried out by using the clustering label vector interval and the target clustering number, and a plurality of user operation behavior log clusters are obtained until all user operation behavior logs are clustered, so that the condition that different predicted user demands are generated for associated operation behavior activities to cause demand conflicts can be avoided, and the precision of user demand prediction applying artificial intelligence is improved.

In an exemplary independent design concept, step S206, namely, performing significant operation behavior activity selection from a plurality of user operation behavior log clusters to obtain significant operation behavior activities related to the plurality of user operation behavior log clusters includes:

step S402, determining the current user operation behavior log and the user operation behavior logs except the current user operation behavior log from the user operation behavior log cluster, and obtaining each remaining user operation behavior log.

Step S404, determining the global deviation degree of the current user operation behavior log and each remaining user operation behavior log according to the user operation behavior characteristics related to the current user operation behavior log and the user operation behavior characteristics related to each remaining user operation behavior log, and obtaining the current target deviation degree.

The current user operation behavior log refers to a user operation behavior log which is required to be actually a significant operation behavior activity or not at present. The rest user operation behavior logs refer to user operation behavior logs different from the current user operation behavior logs in the user operation behavior log cluster. The current target deviation degree refers to a target deviation degree related to the current user operation behavior log, and the target deviation degree is the sum of deviation degrees with other user operation behavior logs in the user operation behavior log cluster.

Step S406, each user operation behavior log in the user operation behavior log cluster is walked, and the current target deviation degree related to each user operation behavior log is obtained.

For example, the big data optimization system may determine, in parallel, a current target deviation degree associated with each user operation behavior log in the user operation behavior log cluster.

Step S408, comparing the current target deviation degree related to each user operation behavior log, and taking the user operation behavior log related to the minimum current target deviation degree as the significant operation behavior activity related to the user operation behavior log cluster.

For example, the big data optimization system may compare the current target deviation degrees related to each user operation behavior log, determine the minimum current target deviation degree from the current target deviation degrees, and then use the user operation behavior log related to the minimum current target deviation degree as the significant operation behavior activity related to the user operation behavior log cluster.

Based on the design, the current target deviation degree related to each user operation behavior log in the user operation behavior log cluster is determined, and then the user operation behavior log related to the minimum current target deviation degree is selected as the significant operation behavior activity related to the user operation behavior log cluster, so that the selected significant operation behavior activity is more accurate.

In an exemplary independent design idea, step S110, namely, performing behavior tendency vector mining on a user operation behavior log to obtain user operation behavior characteristics, includes the steps of:

and transmitting the user operation behavior log to an operation behavior tendency decision network for behavior tendency vector mining to obtain the user operation behavior characteristics, wherein the operation behavior tendency decision network is obtained by performing model convergence optimization by using reference operation behavior activities according to an AI network model.

For example, the big data optimization system uses reference operation behavior activities in advance to train through an AI network model, obtains an operation behavior tendency decision network, and deploys and uses the operation behavior tendency decision network. When the user operation behavior characteristic extraction method is needed to be used, the big data optimization system transmits the user operation behavior log to the operation behavior tendency decision network, and the operation behavior tendency decision network conducts behavior tendency vector mining to obtain the input user operation behavior characteristic.

In an exemplary independent design idea, step S120, namely, performing attention vector mining on the user operation behavior feature to obtain an attention behavior vector related to a user operation behavior log, includes the steps of:

performing feature expansion on the user operation behavior features to obtain expanded user operation behavior features; and determining related key behavior vectors under the behavior attention mining space according to the expanded user operation behavior characteristics, and obtaining the attention behavior vectors related to the user operation behavior logs.

For example, the big data optimization system may perform attention vector mining on the user operation behavior features, that is, feature expansion is performed on the user operation behavior features by using a feature expansion algorithm to obtain expanded user operation behavior features. And then determining related key behavior vectors under the behavior attention mining space by using the expanded user operation behavior characteristics, and obtaining the related attention behavior vectors of the user operation behavior logs. The feature expansion is carried out on the user operation behavior features to obtain expanded user operation behavior features, relevant key behavior vectors in a behavior attention mining space are determined according to the expanded user operation behavior features, and the attention behavior vectors relevant to the user operation behavior logs are obtained, so that the obtained attention behavior vectors are more accurate.

In an exemplary independent design idea, the method for predicting user demand by applying artificial intelligence provided by the embodiment of the present application further includes:

step S502, when the reference operation behavior log of each reference user requirement is not searched, acquiring a fuzzy prediction user requirement, wherein the fuzzy prediction user requirement is used for representing the trend user requirement obtained by the current fuzzy algorithm analysis.

For example, when the reference operation behavior log of each reference user requirement is not searched by the big data optimization system, it indicates that the user requirement generation is just started at this time, and all operation behavior activities have no relevant predicted user requirements. At the moment, the big data optimization system obtains the saved fuzzy prediction user requirement, and the fuzzy prediction user requirement is used for representing the trend user requirement obtained by analyzing based on the fuzzy algorithm.

Step S504, conducting demand derivation according to the fuzzy prediction user demand, and obtaining a first target fuzzy prediction user demand.

For example, the big data optimization system performs a requirement derivation operation on the requirement vector related to the requirement of the fuzzy prediction user, for example, a mapping operation of other associated requirements may be performed to obtain the requirement of the fuzzy prediction user after the requirement derivation, that is, the first target requirement of the fuzzy prediction user.

Step S506, the first target fuzzy prediction user requirement is determined as a prediction user requirement related to the user operation behavior log.

For example, the big data optimization system saves the first target fuzzy prediction user requirement and determines the first target fuzzy prediction user requirement as the prediction user requirement related to the user operation behavior log.

In an exemplary independent design idea, when an operation behavior activity is subjected to associated query and a candidate contact behavior log sequence is not queried, it is indicated that no associated reference operation behavior log exists in the reference operation behavior log, at this time, a big data optimization system obtains a fuzzy prediction user requirement, performs requirement derivation according to the fuzzy prediction user requirement, obtains a target fuzzy prediction user requirement, and determines the target fuzzy prediction user requirement as a prediction user requirement related to the operation behavior activity.

Based on the design, the big data optimization system uses fuzzy prediction user requirements to generate prediction user requirements related to operation behavior activities, so that the generated prediction user requirements have more reference values.

In an exemplary independent design idea, step S130, obtaining a candidate contact behavior log sequence related to a user operation behavior log by calling the contact behavior log from each reference operation behavior log of each reference user requirement according to the user operation behavior feature and the reference operation behavior feature related to the reference operation behavior log of each reference user requirement, includes:

and step S602, performing feature expansion on the user operation behavior characteristics to obtain target expanded user operation behavior characteristics.

Step S604, determining each cluster set vector from the reference operation behavior characteristics related to the reference operation behavior log required by each reference user, determining the set association deviation degree of the target expansion user operation behavior characteristics and each cluster set vector, and determining candidate cluster set vectors from each cluster set vector according to the set association deviation degree.

The vector in the clustering set refers to a vector related to the center of the label to which each reference operation behavior feature belongs. The candidate cluster set vector refers to a cluster set vector related to a category most relevant to the operation behavior characteristics of the target extension user. The concentrated association degree of deviation refers to the degree of deviation determined by using a degree of deviation association degree algorithm.

For example, the big data optimization system performs a coarse return, that is, the big data optimization system performs feature expansion on the user operation behavior feature by using a feature expansion algorithm to obtain a target expanded user operation behavior feature. And then, clustering determination is carried out in advance by using the reference operation behavior characteristics related to the reference operation behavior log required by each reference user to obtain a clustering result, and then a vector in each clustering set is determined. When the vector selection method is required to be used, the big data optimization system obtains each cluster concentrated vector, determines the concentrated association deviation degree of the target expansion user operation behavior characteristic and each cluster concentrated vector, then selects the minimum concentrated association deviation degree from each concentrated association deviation degree, and determines the cluster concentrated vector related to the minimum concentrated association deviation degree as a candidate cluster concentrated vector.

Step S606, obtaining each same cluster reference operation behavior characteristic related to the vector in the candidate cluster set, and determining the association deviation degree of the target expansion user operation behavior characteristic and each same cluster reference operation behavior characteristic.

The same cluster reference operation behavior feature refers to a reference operation behavior feature that is the same cluster as the vector in the candidate cluster set. The association deviation degree refers to the deviation degree between the operation behavior feature of the target extension user and the reference operation behavior feature, and the smaller the deviation degree is, the higher the association degree is, and the larger the deviation degree is, the smaller the association degree is.

For example, the big data optimization system acquires each same-cluster reference operation behavior feature related to the vector in the candidate clustering set from the database, and then determines the association deviation degree between the target expansion user operation behavior feature and each same-cluster reference operation behavior feature by using a deviation degree association degree algorithm.

Step S608, selecting the same-cluster reference operation behavior feature not greater than the target deviation from each same-cluster reference operation behavior feature according to the association deviation, and using the reference operation behavior log related to the same-cluster reference operation behavior feature not greater than the target deviation as a candidate contact behavior log sequence related to the user operation behavior log.

For example, the big data optimization system compares each degree of correlation deviation with a target degree of deviation, which is a maximum value that is preset to the degree of correlation deviation at the time of search. And then selecting a reference operation behavior log related to the same cluster of reference operation behavior characteristics with the associated deviation degree not greater than the target deviation degree as a candidate contact behavior log sequence related to the user operation behavior log.

Based on the design, the candidate cluster concentrated vectors are determined from each cluster concentrated vector, and then the reference operation behavior log which is not more than the target deviation degree and is related to the cluster reference operation behavior feature is determined from each cluster reference operation behavior feature to serve as the candidate contact behavior log sequence related to the user operation behavior log, so that the processing performance of the candidate contact behavior log sequence obtained through searching is improved.

In an exemplary independent design idea, in step S140, performing association behavior vector mining from the candidate contact behavior log sequence according to a reference attention behavior vector associated with the attention behavior vector and the candidate contact behavior log sequence to obtain a first mining behavior log sequence associated with the user operation behavior log, the method includes the steps of:

determining the association deviation degree of the attention behavior vector and a reference attention behavior vector related to each action of the operation to be mined in the candidate contact behavior log sequence respectively, and obtaining the editing association deviation degree related to each action of the operation to be mined; and screening each behavior activity to be mined according to the edit association deviation degree and the set target deviation degree related to each behavior activity to be mined, so as to obtain a first mining behavior log sequence related to the user operation behavior log.

The editing association deviation degree is a deviation degree determined by using the attention behavior vector and is used for representing the degree of association between the attention behavior vectors, and the smaller the deviation degree is, the higher the degree of association is, and the larger the deviation degree is, the smaller the degree of association is. The set target deviation degree is a preset minimum deviation degree when the attention vector mining feature screening is performed.

For example, the big data optimization system determines the association deviation degree of the attention behavior vector and a reference attention behavior vector related to each to-be-mined operation behavior activity in the candidate contact behavior log sequence by using a hamming deviation degree algorithm, obtains the editing association deviation degree related to each to-be-mined operation behavior activity, compares each editing association deviation degree with a set target deviation degree, and takes the to-be-mined operation behavior activity with the editing association deviation degree smaller than the preset editing association deviation degree as a reference operation behavior log in the first mining behavior log sequence. In an exemplary independent design idea, the big data optimization system may also sequence each edit association deviation degree from small to large, and then select the operation behavior activity to be mined related to the edit association deviation degree in the front sequence as a reference operation behavior log in the first mining behavior log sequence.

In an exemplary independent design idea, step S140, performing related behavior feature mining from a first mining behavior log sequence according to a reference operation behavior feature related to a user operation behavior feature and the first mining behavior log sequence, to obtain a second mining behavior log sequence related to a user operation behavior log, includes the steps of:

determining the association deviation degree of the user operation behavior characteristic and a reference operation behavior characteristic related to each first mining operation behavior activity in the first mining behavior log sequence respectively to obtain the association deviation degree related to each first mining operation behavior activity; and determining the minimum association deviation degree from the association deviation degrees related to each first mining operation behavior activity, and taking the first mining operation behavior activity related to the minimum association deviation degree as a second mining behavior log sequence related to the user operation behavior log.

The association deviation degree is a deviation degree determined by using the operation behavior characteristics and is used for representing the contact degree between the operation behavior characteristics, and the smaller the deviation degree is, the higher the contact degree is, the larger the deviation degree is, and the smaller the contact degree is.

For example, the big data optimization system may determine, by using a euclidean deviation algorithm, an association deviation between the user operation behavior feature and a reference operation behavior feature associated with each first mining operation behavior activity in the first mining behavior log sequence, and obtain an association deviation associated with each first mining operation behavior activity. And then sequentially ascending each association deviation degree, and selecting the first mining operation behavior activity related to the minimum association deviation degree as a second mining behavior log sequence related to the user operation behavior log.

Based on the design, the editing association deviation degree is determined, and then each action activity to be mined is screened by using the deviation degree, so that a first mining action log sequence is obtained. And then determining a second mining behavior log sequence from the first mining behavior log sequence by using the association deviation degree through the time association deviation degree, so that the processing performance of obtaining the second mining behavior log sequence is improved.

In an exemplary independent design idea, the method for predicting user demand by applying artificial intelligence provided by the embodiment of the present application further includes the steps of:

when the minimum correlation deviation degree is larger than a preset correlation deviation degree threshold value, acquiring fuzzy prediction user requirements; carrying out requirement derivation according to the fuzzy prediction user requirement to obtain a second target fuzzy prediction user requirement and configuring; and determining the second target fuzzy prediction user requirement as the prediction user requirement related to the user operation behavior log.

The preset association deviation threshold is a preset minimum association deviation, is used for representing the minimum deviation when the reference operation behavior log is associated with the user operation behavior log, and is larger than the preset association deviation threshold, which indicates that the reference operation behavior log is not associated with the user operation behavior log, and when the preset association deviation threshold is not larger than the preset association deviation threshold, indicates that the reference operation behavior log is associated with the user operation behavior log.

For example, when the minimum association deviation degree is greater than a preset association deviation degree threshold, it is indicated that the reference operation behavior log related to the minimum association deviation degree is not associated with the user operation behavior log, at this time, the big data optimization system obtains a saved fuzzy prediction user requirement, and the fuzzy prediction user requirement is saved after the requirement derivation is performed last time. And at the moment, carrying out requirement derivation by using the fuzzy prediction user requirement, obtaining and configuring a second target fuzzy prediction user requirement, and determining the second target fuzzy prediction user requirement as a prediction user requirement related to the user operation behavior log.

Based on the design, when the correlation deviation degree of the second mining behavior log sequence is larger than a preset correlation deviation degree threshold value, acquiring fuzzy prediction user requirements; and carrying out requirement derivation according to the fuzzy prediction user requirement to obtain a second target fuzzy prediction user requirement, and determining the second target fuzzy prediction user requirement as the prediction user requirement related to the user operation behavior log. The accuracy of user demand prediction by applying artificial intelligence is improved.

In an exemplary independent design idea, determining a minimum association deviation degree from association deviation degrees related to each first mining operation behavior activity, and taking the first mining operation behavior activity related to the minimum association deviation degree as a second mining behavior log sequence related to a user operation behavior log, the method includes:

step S702, ascending the correlation deviation degree related to each first mining operation behavior activity to obtain a correlation deviation degree arrangement set.

Step S704, a plurality of association deviation degrees are sequentially selected from the association deviation degree arrangement set, and target first mining operation behavior activities related to the association deviation degrees are obtained.

For example, the big data optimization system may also sort each first mining operation behavior activity according to the associated deviation degree related to each first mining operation behavior activity in the order from small to large to obtain a sorting result of the first mining operation behavior activities, and then select the first mining operation behavior activity in the top of the sorting result of the first mining operation behavior activities to obtain a plurality of target first mining operation behavior activities.

Step S706, obtaining reference user requirements respectively related to each target first mining operation behavior activity, and counting the number of target first mining operation behavior activities related to the same reference user requirement in the reference user requirements respectively related to each target first mining operation behavior activity.

For example, the big data optimization system obtains a reference user requirement related to each target first mining operation behavior activity, then matches the reference user requirement related to each target first mining operation behavior activity to obtain target first mining operation behavior activities which are matched consistently, then counts the number of target first mining operation behavior activities with the same reference user requirement to obtain the number of target first mining operation behavior activities related to all the same reference user requirements.

Step S708, comparing the quantities of the target first mining operation behavior activities related to the same reference user requirement, and using the first mining operation behavior activity related to the maximum quantity of the second mining behavior log sequence as the second mining behavior log sequence related to the user operation behavior log.

For example, the big data optimization system selects a first mining operation behavior activity related to the maximum target first mining operation behavior activity amount as a second mining behavior log sequence related to the user operation behavior log. For example, 5 target first mining operation behavior activities are selected, wherein three target first mining operation behavior activities having the same reference user requirement are selected, the remaining two first mining operation behavior activities are the same reference user requirement, and at this time, the three first mining operation behavior activities having the same reference user requirement are used as a second mining behavior log sequence related to the user operation behavior log. The second mining behavior log sequence is selected in a voting mode, the same reference user requirement is determined as the predicted user requirement of the user operation behavior log, and the accuracy of user requirement prediction applying artificial intelligence is improved.

In an exemplary independent design concept, after step S150, that is, after obtaining a reference user requirement distribution related to a second mining behavior log sequence, and determining the reference user requirement distribution as a predicted user requirement related to a user operation behavior log, the method further includes:

and performing correlation configuration on the user operation behavior log, the user operation behavior characteristics related to the user operation behavior log, the attention behavior vector related to the user operation behavior log and the predicted user requirements related to the user operation behavior log.

For example, the big data optimization system performs correlation matching on the user operation behavior log, the user operation behavior characteristics related to the user operation behavior log, the attention behavior vector related to the user operation behavior log and the predicted user requirements related to the user operation behavior log, and performs subsequent deployment processing by using the user operation behavior log as a reference operation behavior log.

In an exemplary independent design idea, an embodiment of the present application further provides an operation behavior activity pushing method based on artificial intelligence, including the following steps:

step S802, a user requirement set of candidate operation behavior activities and past trust operation behavior activities is obtained.

Wherein the candidate operation behavior activity is an operation behavior activity to be determined whether pushing is required. Past trusted operational behavior activities refer to operational behavior activities that have already acknowledged a push. The user requirement set of the past trust operation behavior activity comprises a plurality of user requirements of the past trust operation behavior activity, and the user requirements of the past trust operation behavior activity refer to predicted user requirements related to the past trust operation behavior activity.

For example, the big data optimization system may directly obtain the user requirement sets of the candidate operation behavior activities and the past trust operation behavior activities based on the data server, or may obtain the uploaded candidate operation behavior activities, and then obtain the user requirement sets of the past trust operation behavior activities from the database. The big data optimization system may also obtain a set of user requirements for candidate operational behavior activities and past trust operational behavior activities from the business big data optimization system.

Step S804, obtaining the predicted user requirements related to the candidate operation behavior activities, wherein the candidate operation behavior characteristics are obtained by performing behavior tendency vector mining on the candidate operation behavior activities, the candidate operation behavior characteristics are subjected to attention vector mining to obtain the attention behavior vectors related to the candidate operation behavior activities, the reference operation behavior log of each reference user requirement, the related reference operation behavior characteristics and the related reference attention behavior vectors are obtained, the contact behavior log is obtained from the reference operation behavior log of each reference user requirement according to the candidate operation behavior characteristics and the reference operation behavior characteristics related to the reference operation behavior log of each reference user requirement, the candidate contact behavior sequences related to the candidate operation behavior activities are obtained, and the contact behavior log sequences are obtained from the candidate contact behavior log sequences according to the reference attention behavior vectors related to the candidate contact behavior log sequences and the reference attention behavior vectors related to the candidate contact behavior log sequences For vector mining, a first mining behavior log sequence related to the candidate operation behavior activity is obtained, associated behavior feature mining is carried out from the first mining behavior log sequence according to the candidate operation behavior feature and a reference operation behavior feature related to the first mining behavior log sequence, a second mining behavior log sequence related to the candidate operation behavior activity is obtained, a reference user demand distribution related to the second mining behavior log sequence is obtained, and the reference user demand distribution is determined as a predicted user demand related to the candidate operation behavior activity.

For example, the big data optimization system may obtain the predicted user requirement related to the candidate operation behavior activity by obtaining a stored predicted user requirement from a database, where the user requirement is generated in advance. The big data optimization system may also generate a predicted user demand related to the candidate operation behavior activity after acquiring the candidate operation behavior activity, that is, the user demand predicted by any one embodiment of the above-described user demand prediction method applying artificial intelligence may be used. For example, the big data optimization system may perform behavior tendency vector mining on the candidate operation behavior activities to obtain candidate operation behavior features, perform attention vector mining on the candidate operation behavior features to obtain attention behavior vectors related to the candidate operation behavior activities, obtain a reference operation behavior log, related reference operation behavior features, and related reference attention behavior vectors of each reference user requirement, perform contact behavior log retrieval from the reference operation behavior log of each reference user requirement according to the candidate operation behavior features and the reference operation behavior features related to the reference operation behavior log of each reference user requirement to obtain candidate contact behavior log sequences related to the candidate operation behavior activities, perform associated behavior vector mining from the candidate contact behavior logs according to the reference attention behavior vectors related to the candidate contact behavior logs and the reference attention behavior vectors related to the candidate contact behavior logs, obtaining a first mining behavior log sequence related to the candidate operation behavior activity, performing associated behavior feature mining from the first mining behavior log sequence according to the candidate operation behavior feature and a reference operation behavior feature related to the first mining behavior log sequence, obtaining a second mining behavior log sequence related to the candidate operation behavior activity, obtaining a reference user demand distribution related to the second mining behavior log sequence, and determining the reference user demand distribution as a predicted user demand related to the candidate operation behavior activity.

Step S806, determining a degree of association between the predicted user requirement related to the candidate operation behavior activity and the user requirement of the past trust operation behavior activity in the set of user requirements of the past trust operation behavior activity.

For example, the big data optimization system determines the degree of association between the predicted user requirement related to the candidate operation behavior activity and the user requirement of each past trust operation behavior activity in the user requirement set of the push operation behavior activity by using an association deviation algorithm, and obtains the degree of association for each user requirement. The set of user requirements for past trusted operational behavior activities is a sequence of predicted user requirement formations that have been associated with the obtained operational behavior activities.

And step S808, when the contact degree is not greater than the target contact degree, pushing the candidate operation behavior activities to the relevant user demand processing service.

For example, the target contact degree refers to a preset threshold value for characterizing the association between the candidate operation behavior activities and the past trust operation behavior activities. If the candidate operation behavior activity is associated with a past trusted operation behavior activity, it is not necessary that the candidate operation behavior activity be pushed. Namely, when any one of all the contact degrees is greater than the target contact degree, the big data optimization system does not push the candidate operation behavior activities, and the pushed operation behavior activities are prevented from being pushed to the user again. And only when all the contact degrees are not greater than the target contact degree, the candidate operation behavior activity is the operation behavior activity which is not pushed, and at the moment, the big data optimization system can push the candidate operation behavior activity to the relevant user demand processing service.

According to the design, a user requirement set of candidate operation behavior activities and past trust operation behavior activities is obtained, then a predicted user requirement related to the candidate operation behavior activities is obtained, and then the degree of relation between the predicted user requirement related to the candidate operation behavior activities and the user requirement of the past trust operation behavior activities in the user requirement set of the past trust operation behavior activities is determined; when the contact degree is not greater than the target contact degree, the candidate operation behavior activities are pushed to the related user demand processing service, the processing performance of generating the user demands is improved, the predicted user demands related to the candidate operation behavior activities can be rapidly obtained, then the contact degree is determined, and when the contact degree is not greater than the target contact degree, the candidate operation behavior activities are pushed to the related user demand processing service, so that repeated operation behavior activities can be prevented from being pushed, and the pushing processing performance of the operation behavior activities is improved.

In an exemplary independent design idea, an operational behavior activity anomaly analysis method based on artificial intelligence is provided, which may include the following steps:

acquiring an abnormal user requirement set of operation behavior activity to be analyzed and abnormal operation behavior activity, and generating a user requirement to be analyzed of the operation behavior activity to be analyzed, wherein behavior tendency vector mining is performed on the operation behavior activity to be analyzed to acquire operation behavior characteristics to be analyzed, attention vector mining is performed on the operation behavior characteristics required by the user to acquire an attention behavior vector related to the operation behavior activity required by the user, a reference operation behavior log, related reference operation behavior characteristics and related reference attention behavior vectors required by each reference user are acquired, a contact behavior log is called from the reference operation behavior log required by each reference user according to the operation behavior characteristics required by the user and the reference operation behavior characteristics related to the reference operation behavior log of each reference user to acquire a candidate contact behavior log sequence related to the operation behavior activity required by the user, performing associated behavior vector mining from the candidate contact behavior log sequence according to the reference attention behavior vector related to the attention behavior vector and the candidate contact behavior log sequence to obtain a first mining behavior log sequence related to the user demand operation behavior activity, performing associated behavior characteristic mining from the first mining behavior log sequence according to the reference operation behavior characteristic related to the user demand operation behavior characteristic and the first mining behavior log sequence to obtain a second mining behavior log sequence related to the user demand operation behavior activity, obtaining reference user demand distribution related to the second mining behavior log sequence, and determining the reference user demand distribution as the user demand to be analyzed related to the operation behavior activity to be analyzed;

and determining the Hamming deviation degree of the user requirement to be analyzed and the abnormal user requirement set, obtaining abnormal association degree, and determining the operation behavior activity to be analyzed as the abnormal operation behavior activity when the abnormal association degree is greater than a preset abnormal threshold value.

The abnormal user demand refers to a predicted user demand of the abnormal operation behavior activity generated in advance. Each abnormal user requirement is included in the abnormal user requirement set. The abnormal association degree is used for representing the association degree between the operation behavior activity to be analyzed and the abnormal operation behavior activity, and the higher the association degree is, the higher the possibility that the operation behavior activity to be analyzed is the abnormal operation behavior activity is.

For example, when analyzing the operation behavior activity to be analyzed, the big data optimization system may obtain an abnormal user requirement set of the operation behavior activity to be analyzed and the abnormal operation behavior activity, generate a user requirement to be analyzed of the operation behavior activity to be analyzed, then determine hamming deviation degrees of the user requirement to be analyzed and the abnormal user requirement set, obtain an abnormal association degree, and when the abnormal association degree is greater than a preset abnormal threshold value, determine the operation behavior activity to be analyzed as the abnormal operation behavior activity. And when all the abnormal association degrees are not greater than the preset abnormal threshold value, determining the operation behavior activity to be analyzed as normal operation behavior activity. When the abnormal contact degree is determined, the abnormal contact degree of all the abnormal user requirements in the abnormal user requirement set can be rapidly determined, so that the processing performance of analyzing the abnormal operation behavior activity can be improved.

In an exemplary independent design idea, a user demand prediction method applying artificial intelligence is provided, which specifically includes the following steps:

step S902, obtaining a plurality of user operation behavior logs, and transmitting the plurality of user operation behavior logs to an operation behavior tendency decision network in parallel to perform behavior tendency vector mining, so as to obtain a plurality of user operation behavior characteristics.

And step S904, performing feature expansion on the multiple user operation behavior characteristics to obtain expanded user operation behavior characteristics, determining related key behavior vectors in a behavior attention mining space according to the expanded user operation behavior characteristics, and obtaining related attention behavior vectors of multiple user operation behavior logs.

Step S906, clustering the plurality of user operation behavior logs according to the plurality of user operation behavior characteristics to obtain a plurality of user operation behavior log clusters, and selecting the significant operation behavior activities from the plurality of user operation behavior log clusters to obtain the significant operation behavior activities related to the plurality of user operation behavior log clusters.

Step S908, calling a contact behavior log from the reference operation behavior log of each reference user requirement according to the user operation behavior feature related to the significant operation behavior activity and the reference operation behavior feature related to the reference operation behavior log of each reference user requirement, so as to obtain a target candidate contact behavior log sequence related to the significant operation behavior activity.

Step S910, performing association behavior vector mining from the target candidate contact behavior log sequence according to the attention behavior vector related to the significant operation behavior activity and the reference attention behavior vector related to the target candidate contact behavior log sequence to obtain a target first mining behavior log sequence related to the significant operation behavior activity, and performing association behavior feature mining from the target first mining behavior log sequence according to the user operation behavior feature related to the significant operation behavior activity and the reference operation behavior feature related to the target first mining behavior log sequence to obtain the significant mining operation behavior activity related to the significant operation behavior activity.

Step S912, obtaining a significant user requirement related to the significant mining operation behavior activity, and determining the significant user requirement as a predicted user requirement related to the user operation behavior log in the user operation behavior log cluster related to the significant operation behavior activity.

And executing the steps S908 to S912 in parallel to obtain the predicted user demand related to the user operation behavior log in each user operation behavior log cluster related to the significant operation behavior activity, thereby further improving the user demand prediction processing performance applying artificial intelligence.

Step S100, a predicted user requirement sequence aiming at a user operation behavior log transmitted in response is obtained, first user portrait feature distribution of a corresponding target user is determined according to the predicted user requirement sequence, and a user interaction tracking request is transmitted to each content push service in a preset user community service based on the first user portrait feature distribution so as to request each content push service to perform initial interaction tracking on the target user in a corresponding user community.

In an exemplary design, the first user representation feature distribution includes a first user representation feature that does not cover user representation features corresponding to one or more user community interaction activities of the target user among a plurality of preset user community interaction activities. For example, the social interaction activities of the users may include, but are not limited to, purchasing behavior interaction activities, video live behavior interaction activities, and the like, and these interaction activities may form an overall social interaction tag set for describing social characteristics of the target users.

Step S200, obtaining an initial interaction tracking log generated by each content pushing service in an initial interaction tracking process, and obtaining second user portrait feature distribution of the target user based on the initial interaction tracking log.

In an exemplary design approach, the initial interaction tracking log comprises one or more user community interaction logs with a knowledge point relevance of the first user portrait feature distribution greater than a first target knowledge point relevance;

step S300, determining one or more content push services in the preset user community service to be target content push services based on the second user portrait feature distribution. For example, it may be determined that the content push service corresponding to one or more social interaction logs of the target users involved in obtaining the second user profile feature distribution is determined to be the target content push service.

Step S400, transmitting a content pushing configuration rule to the target content pushing service based on the second user portrait feature distribution so as to request the target content pushing service to perform corresponding Internet content pushing on the target user.

Based on the steps, the first user portrait feature distribution which does not contain the user portrait features corresponding to one or more user community interactive activities of the target user in the preset user community interactive activities is sent to each content pushing service, each content pushing service executes initial interactive tracking aiming at the target user according to the first user portrait feature distribution, and further analysis is carried out based on the initial interactive tracking log after the initial interactive tracking log is generated, so that the second user portrait feature distribution of the target user is accurately obtained, and the user portrait features related to community interaction are timely supplemented. In this way, a content push configuration rule may be delivered to the target content push service based on the second user portrait feature distribution to request the target content push service to perform corresponding internet content push on the target user, so as to improve reliability of internet content push.

In an exemplary design concept, for step S200, obtaining a second user portrait feature distribution of the target user based on the initial interaction tracking log may be implemented as follows.

Step S2001, obtaining, from the initial interaction tracking log generated by each content push service, a user community interaction log generated by each content push service, where a degree of association of a knowledge point with the first user portrait feature distribution is greater than a first target degree of association of the knowledge point.

Step S2002, for each user community interaction log, mining a network according to user portrait characteristics, and mining an interaction intention vector set under each community interaction label from the user community interaction log according to an interaction intention mining structure, which is included in the user portrait characteristics and corresponds to each of a plurality of community interaction labels.

And step S2003, carrying out user portrait mapping on the interaction intention vector set under each community interaction tag according to a user portrait mapping structure included in the user portrait feature mining network, and obtaining the user portrait features corresponding to the interaction intention vector set under each community interaction tag.

Step S2004, calculating the association degree of the knowledge points of the user community interaction log and the first user portrait feature distribution based on the user portrait feature corresponding to the interaction intention vector set under the community interaction label and the first user portrait feature corresponding to the first user portrait feature distribution according to a knowledge point association analysis structure included in the user portrait feature mining network, and determining the user community interaction log as a key community interaction log if the association degree of the knowledge points is greater than a second target association degree of the knowledge points. Wherein the second target knowledge point relevance is greater than the first target knowledge point relevance.

In an exemplary design idea, the association degree of the second target knowledge point needs to be greater than the association degree of the first target knowledge point, so as to realize accurate mining analysis.

Step S2005, a preset overall community interaction tag set is obtained, where the overall community interaction tag set includes a plurality of user community interaction activities for the target user.

Step S2006, determining a supplemental community interaction tag for the target user based on the set of overall community interaction tags and the first user profile feature of the first user profile feature distribution.

In an exemplary design idea, the supplementary community interaction tag may be configured to determine which user community interaction activity is not covered in the first user profile feature by matching or comparing each user profile feature in the first user profile feature distribution with each user community interaction activity in the entire community interaction tag set, and then determine the corresponding user community interaction activity as the supplementary community interaction tag. Therefore, the supplementary community interaction tag can be supplemented in the follow-up process, and the first user portrait feature distribution can be supplemented and updated to obtain the second user portrait feature distribution.

Step S2007, obtaining user supplementary behavior feature data under the supplementary community interaction tag from the key community interaction log, and performing supplementary updating on the first user portrait feature distribution based on the user supplementary behavior feature data to obtain the second user portrait feature distribution.

In an exemplary design, after determining the supplementary community interaction tags, the content push service may extract, according to the key community interaction log, user supplementary behavior feature data corresponding to the supplementary community interaction tags. For example, the user supplemental behavioral characteristic data may be converted into corresponding user profile characteristics, and the user profile characteristics may be added to the second user profile characteristic distribution after the first user profile characteristic distribution is optimized.

According to the above, in step S300, the determining that one or more content push services within the preset user community service are determined as target content push services based on the second user profile feature distribution may include:

and determining one or more content push services generating the at least one association community interaction log to be the target content push service based on one or more association community interaction logs corresponding to the user supplementary behavior feature data in the second user portrait feature distribution. For example, the target content push service is determined based on the content push service ID according to the content push service ID carried by the association community interaction log.

In an embodiment which can be based on an independent concept, the embodiment of the application also provides a user portrait feature mining method based on artificial intelligence, and the method comprises the following steps.

(1) In an exemplary design approach, a reference training data set is obtained that includes a plurality of reference community interaction logs having reference user imagery features. The reference user profile feature may refer to a user profile feature corresponding to user information determined in advance based on the user information in the reference community interaction log, and may include user profile features corresponding to a plurality of user community interaction activities, for example.

(2) Obtaining an initialized AI training network, wherein the initialized AI training network comprises an interactive intention mining structure, a user portrait mapping structure and a knowledge point association analysis structure.

In an exemplary design concept, the initialization AI training network may be, without limitation, a convolutional neural network, a cyclic convolutional neural network, a residual neural network, or the like.

(3) And aiming at each reference community interaction log, acquiring an interaction intention vector set of the reference community interaction log under the interaction activities of a plurality of user communities according to the interaction intention mining structure.

In an exemplary design idea, an interaction intention vector set under each user community interaction activity may be sequentially extracted for the reference community interaction log according to the interaction intention mining structure. In an exemplary design concept, the interaction intention mining structure may include a plurality of convolution kernels, each convolution kernel being used for correspondingly extracting an interaction intention vector set under at least one user community interaction activity.

(4) And carrying out user portrait mapping on the interaction intention vector set under each user community interaction activity according to the user portrait mapping structure to obtain the user portrait characteristics corresponding to the interaction intention vector set under each user community interaction activity.

In an exemplary design idea, the interaction intention vector set may be subjected to user portrait mapping according to a preset user portrait mapping manner.

(5) And obtaining a predicted user portrait feature based on the user portrait feature corresponding to the interaction intention vector set under the user community interaction activity according to the knowledge point association analysis structure.

(6) Determining a first learning convergence assessment indicator based on the predicted user profile feature and the reference user profile feature.

In an exemplary design approach, for example, the first learning convergence assessment index may be determined based on a knowledge point association between a user profile feature corresponding to each user community interaction activity in the predicted user profile feature and a reference user profile feature corresponding to each user community interaction activity in the reference user profile feature. For example, in an exemplary design approach, the learning convergence assessment index may be used to characterize a degree of knowledge point association between a user profile feature corresponding to each user community interaction activity in the predicted user profile feature and a reference user profile feature corresponding to each user community interaction activity in the reference user profile feature.

(7) And circularly updating the network weight information of the initialized AI training network based on the first learning convergence evaluation index until the first learning convergence evaluation index matches a first training termination requirement, and obtaining the initialized AI training network matching the network deployment requirement as the user portrait feature mining network.

In an exemplary design approach, the first learning convergence evaluation indicator is determined by a first knowledge point relevance of each user portrait feature in each of the predicted user portrait features and corresponding user portrait features in the reference user portrait feature, and the first training termination requirement includes that the first knowledge point relevance represented by the first learning convergence evaluation indicator is greater than a first target relevance threshold.

In an exemplary design idea, an embodiment of the present application further provides an AI network configuration method based on artificial intelligence, which is described in detail as follows.

(11) A reference training data set is obtained, wherein the reference training data set comprises a plurality of reference community interaction logs with reference user image features. In an exemplary design approach, the reference training data set may be the same as a reference library used to train the user representation feature mining network.

(12) And acquiring an initialized AI training network, and carrying out network initial optimization on the initialized AI training network according to the public network weight information to obtain the optimized initialized AI training network.

(13) And aiming at each reference community interaction log, acquiring an interaction intention vector set of the reference community interaction log under a plurality of user community interaction activities according to the initialized AI training network, and determining a second learning convergence evaluation index based on the interaction intention vector set under the user community interaction activities and the user portrait characteristics included by the reference user portrait characteristics.

In an exemplary design approach, for example, the second learning convergence assessment index may be determined based on a knowledge point association between a user profile feature corresponding to each user community interaction activity in the predicted user profile feature and a reference user profile feature corresponding to each user community interaction activity in the reference user profile feature. In other words, in an exemplary design concept, the second learning convergence evaluation index may be used to characterize a knowledge point association between a user profile feature corresponding to each user community interaction activity in the predicted user profile features and a reference user profile feature corresponding to each user community interaction activity in the reference user profile features.

(14) And circularly updating the optimized network weight information of the initialized AI training network based on the second learning convergence evaluation index until the second learning convergence evaluation index matches a second training termination requirement, and obtaining the initialized AI training network matching the network deployment requirement as a target AI network.

In an exemplary design idea, the second learning convergence evaluation index is determined by a relevance between an interaction intention vector set under each user community interaction activity and a second knowledge point of each corresponding user portrait feature in the reference user portrait features, the second training termination requirement includes that the relevance of the second knowledge point represented by the second learning convergence evaluation index is greater than a second target relevance threshold, and the second target relevance threshold is smaller than the first target relevance threshold.

(15) Configuring the target AI network to each content push service to request the content push service to perform interactive tracking identification on users in the preset user community service based on the target AI network so as to generate the initial interactive tracking log for the content push service.

In an exemplary design idea, the manner of obtaining the reference training data set may be implemented by the following steps.

(111) And obtaining a community interaction log under the target application service environment according to the plurality of content push services, and obtaining a plurality of community interaction logs.

(112) And adding each community interaction log serving as a reference community interaction log into a preset reference interaction log data set.

(113) And extracting an interaction intention vector set of each reference community interaction log in the reference interaction log data set under the interaction activities of a plurality of user communities to obtain the interaction intention vector distribution corresponding to each reference community interaction log. Wherein the interaction intention vector distribution may include one or more sets of interaction intention vectors for users in the reference community interaction log.

(114) And based on the interaction intention vector distribution corresponding to each reference community interaction log, performing data cleaning on the reference community interaction logs in the reference interaction log data set to obtain a cleaned reference interaction log data set. In an exemplary design idea, in order to avoid that the network performance is affected by too much missing feature data of users in the reference community interaction logs, data cleaning needs to be performed on the reference community interaction logs in the reference interaction log data set based on the interaction intention vector distribution, so as to clean the reference community interaction logs that do not meet the condition.

In an exemplary design approach, in (114), based on the interaction intention vector distribution corresponding to each reference community interaction log, data cleaning is performed on the reference community interaction logs in the reference interaction log data set, and the cleaned reference interaction log data set may include:

determining whether missing characteristic data exists in the interaction intention vector distribution corresponding to each reference community interaction log;

if missing feature data exists, deleting the reference community interaction log from the reference training data set;

the missing feature data comprise interaction intention vector sets which do not cover the previously set user community interaction activities in the interaction intention vector distribution corresponding to the reference community interaction log or interaction intention vector sets which do not cover the preset number of user community interaction activities.

(115) And obtaining reference user portrait characteristics corresponding to the reference community interaction logs based on the interaction intention vector distribution corresponding to each reference community interaction log in the cleaned reference interaction log data set, and performing related configuration on the user portrait characteristics and the reference community interaction logs in the reference interaction log data set to obtain the reference training data set.

The step of acquiring the reference training data set may further include the following steps.

Firstly, a part of the reference community interaction logs in the reference training data set is copied as candidate community interaction logs.

And secondly, performing feature derivation on user portrait features corresponding to one or more user community interaction activities in the user portrait features corresponding to the candidate community interaction logs, wherein the feature derivation comprises derivation of the corresponding user portrait features by using connection portrait features or addition of corresponding associated user portrait features.

And then, adding the candidate community interaction logs after feature derivation as derived training data objects to the reference training data set, and performing random scrambling adjustment on the reference training data set added to the derived training data objects to obtain an expanded number of reference training data sets.

In an exemplary design idea, taking the number of the social interaction activities of the multiple users as N as an example, the feature derivation of the user portrait features corresponding to one or more social interaction tags in the user portrait features corresponding to the candidate social interaction logs may be implemented according to the following manners:

firstly, adding the candidate community interaction log into a reference data set configured in advance to obtain a candidate community interaction log set;

secondly, determining a quantitative value required by each user community interaction activity to perform feature derivation;

thirdly, acquiring a corresponding number of candidate community interaction logs from the candidate community interaction log set based on a quantitative value required for feature derivation of the Mth user community interaction activity, and performing feature derivation on user portrait features corresponding to the Mth user community interaction activity and corresponding to the candidate community interaction logs to acquire an Mth derived reference data set; wherein M is a natural number which is more than or equal to 1 and less than or equal to N.

Based on the above steps, the adding the candidate community interaction logs after feature derivation as derived training data objects to the reference training data set, and performing random scrambling adjustment on the reference training data set added to the derived training data objects to obtain an extended number of reference training data sets, including:

and sequentially adding the obtained Mth derived reference data set to the reference training data set, and after the Nth derived reference data set is added to the reference training data set, performing random scrambling adjustment on the reference training data set.

Alternatively, in one exemplary design approach, the user profile characteristics of at least two user community interaction activities may be feature-derived simultaneously. Accordingly, the obtaining of the reference training data set by performing feature derivation on the user portrait features corresponding to one or more user community interaction activities in the user portrait features corresponding to the candidate community interaction logs may further include:

(1) determining one or more community interaction activity clusters which are obtained by combining at least two user community interaction activities and correspond to preset characteristic derivation;

(2) and aiming at each community interaction activity cluster, acquiring at least one corresponding candidate community interaction log from the candidate community interaction logs, and performing multi-feature derivation on user portrait features in the acquired candidate community interaction logs, which correspond to the user community interaction activities in the community interaction activity cluster.

Further, in step S200, community interaction logs generated in content partitions of the content push service that are almost impossible to be related to by the target user at present may be cleaned based on the behavioral interest nodes and behavioral interest content corresponding to the first user portrait feature distribution and the initial interaction tracking logs generated by each content push service, respectively.

According to the above inventive concept, in an exemplary design idea, the first user profile feature distribution further includes a first behavior interest feature for the target user, and the initial interaction tracking log further includes a second behavior interest feature corresponding to a user community interaction log generated by each content push service. The first behavior interest feature may include a first behavior interest node (e.g., a behavior generation interest node of the first user representation feature distribution) corresponding to the target user or the first user representation feature distribution, and a first behavior interest content (e.g., a current location of the target user). The second behavior interest feature may be a second behavior interest node corresponding to the user community interaction log generated by the content push service (the generation interest node of the user community interaction log) and a corresponding second behavior interest content (the generation content of the user community interaction log).

Accordingly, the obtaining of the initial interaction tracking log generated by each content push service in the initial interaction tracking process and the obtaining of the second user portrait feature distribution of the target user based on the initial interaction tracking log in step S200 may include the following steps.

Step S2011, a support value is output for each content push service based on the first behavior interest feature and the second behavior interest feature, and a service support value between each content push service and the target user is obtained.

For example, a first behavior interest node and a first behavior interest content corresponding to the target user may be first obtained based on the first behavior interest feature;

then, acquiring a second behavior interest node and second behavior interest content corresponding to the user community interaction log generated by each content push service based on the second behavior interest characteristics;

secondly, according to a community subject knowledge graph corresponding to the preset user community service, determining a content skipping route and a corresponding skipping cost for the target user to skip from a first content area corresponding to the first behavior interest content to a second content area corresponding to the second behavior interest content based on the community subject knowledge graph;

and finally, determining a service support value corresponding to each content push service by the target user based on the jump cost and the behavior interest correlation values of the first behavior interest node and the second behavior interest node.

Step S2012, a user community interaction log generated by the content push service whose corresponding service support value is smaller than the target support value is cleaned, so as to obtain a candidate output community interaction log set.

And step S2013, based on each user community interaction log in the candidate output community interaction log set, obtaining second user portrait feature distribution of the target user.

For example, in step S2013, the second user profile feature distribution may be obtained according to the following steps:

aiming at each user community interaction log in the candidate output community interaction log set, mining a network according to user portrait characteristics, and mining an interaction intention vector set under each community interaction label from the user community interaction logs according to interaction intention mining structures which are included in the user portrait characteristics and respectively correspond to a plurality of community interaction labels;

according to a user portrait mapping structure included in the user portrait feature mining network, carrying out user portrait mapping on an interaction intention vector set under each community interaction label to obtain a user portrait feature corresponding to the interaction intention vector set under each community interaction label;

calculating the association degree of the knowledge points of the user community interaction log and the first user portrait feature distribution based on the user portrait feature corresponding to the interaction intention vector set under the community interaction label and the first user portrait feature corresponding to the first user portrait feature distribution according to a knowledge point association analysis structure included in the user portrait feature mining network, and determining the user community interaction log as a key community interaction log if the association degree of the knowledge points is greater than a second target knowledge point association degree, wherein the second target knowledge point association degree is greater than the first target knowledge point association degree;

acquiring a preset overall community interaction tag set, wherein the overall community interaction tag set comprises a plurality of user community interaction activities aiming at the target user;

determining a supplemental community interaction tag for the target user based on the set of overall community interaction tags and a first user profile feature of the first user profile feature distribution;

and acquiring user supplementary behavior feature data under the supplementary community interaction label from the key community interaction log, and performing supplementary updating on the first user portrait feature distribution based on the user supplementary behavior feature data to acquire the second user portrait feature distribution.

In an embodiment, which may be according to the independent concept, the big data optimization system 100 may include: a processor 101 and a machine-readable storage medium 102. Wherein the machine-readable storage medium 102 is used for storing a program that supports the big data optimization system 100 to execute the method for predicting user demand by applying artificial intelligence provided in any one of the foregoing embodiments, and the processor 101 is configured to execute the program stored in the machine-readable storage medium 102.

The program comprises one or more computer instructions, wherein the one or more computer instructions, when executed by the processor 101, enable all or part of the steps of any of the foregoing embodiments.

The architecture of the big data optimization system 100 may further include a communication unit 103, which is used for the big data optimization system 100 to communicate with other devices or communication networks (e.g. the intelligent medical registration platform 200).

In addition, the present application provides a computer storage medium for storing computer software instructions for the big data optimization system 100, which includes a program for executing the method for predicting user demand using artificial intelligence in any of the above method embodiments.

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. A user demand forecasting method applying artificial intelligence is applied to a big data optimization system, and the method comprises the following steps:

2. The method according to claim 1, wherein the user demand prediction request is associated with a plurality of user operation behavior logs; the method further comprises the following steps:

respectively carrying out behavior tendency vector mining on the plurality of user operation behavior logs to obtain a plurality of user operation behavior characteristics;

carrying out attention vector mining on the plurality of user operation behavior characteristics to obtain attention behavior vectors related to the plurality of user operation behavior logs;

clustering the user operation behavior logs according to the user operation behavior characteristics to obtain a plurality of user operation behavior log clusters, and selecting significant operation behavior activities from the user operation behavior log clusters to obtain significant operation behavior activities related to the user operation behavior log clusters;

calling a contact behavior log from the reference operation behavior log of each reference user requirement according to the user operation behavior characteristics related to the significant operation behavior activity and the reference operation behavior characteristics related to the reference operation behavior log of each reference user requirement, and obtaining a target candidate contact behavior log sequence related to the significant operation behavior activity;

performing associated behavior vector mining from the target candidate contact behavior log sequence according to the attention behavior vector related to the significant operation behavior activity and the reference attention behavior vector related to the target candidate contact behavior log sequence to obtain a target first mining behavior log sequence related to the significant operation behavior activity, and performing associated behavior feature mining from the target first mining behavior log sequence according to the user operation behavior feature related to the significant operation behavior activity and the reference operation behavior feature related to the target first mining behavior log sequence to obtain the significant mining operation behavior activity related to the significant operation behavior activity;

acquiring a significant user requirement related to the significant mining operation behavior activity, and determining the significant user requirement as a predicted user requirement related to a user operation behavior log in a user operation behavior log cluster related to the significant operation behavior activity;

the clustering the user operation behavior logs according to the user operation behavior characteristics to obtain a plurality of user operation behavior log clusters includes:

acquiring a clustering label vector interval and the number of target clusters;

randomly extracting target operation behavior characteristics from the plurality of user operation behavior characteristics, determining the deviation degrees of the target operation behavior characteristics and the plurality of user operation behavior characteristics, and determining the number of the user operation behavior characteristics of the deviation degrees in the clustering label vector interval;

if the number of the user operation behavior features is determined to be larger than the target clustering number, determining the user operation behavior features of the thermal connection from the plurality of user operation behavior features according to the target operation behavior features;

obtaining a user operation behavior log cluster according to the user operation behavior log related to the target operation behavior characteristic and the user operation behavior log related to the user operation behavior characteristic of the thermal connection;

determining each fragment feature to be clustered from the plurality of user operation behavior features according to the user operation behavior log cluster, and randomly selecting a target fragment feature to be clustered from each fragment feature to be clustered;

determining the target fragment characteristics to be clustered as the target operation behavior characteristics, and returning to the step of determining the deviation degrees of the target operation behavior characteristics and the plurality of user operation behavior characteristics for execution, and obtaining the plurality of user operation behavior log clusters until all the user operation behavior logs are clustered;

the step of selecting the significant operation behavior activity from the plurality of user operation behavior log clusters to obtain the significant operation behavior activity related to the plurality of user operation behavior log clusters comprises the following steps:

determining a current user operation behavior log and user operation behavior logs except the current user operation behavior log from the user operation behavior log cluster, and obtaining each remaining user operation behavior log;

determining the global deviation degree of the current user operation behavior log and each remaining user operation behavior log according to the user operation behavior characteristics related to the current user operation behavior log and the user operation behavior characteristics related to each remaining user operation behavior log, and obtaining a current target deviation degree;

walking each user operation behavior log in the user operation behavior log cluster to obtain a current target deviation degree related to each user operation behavior log;

and comparing the current target deviation degree related to each user operation behavior log, and taking the user operation behavior log related to the minimum current target deviation degree as the significant operation behavior activity related to the user operation behavior log cluster.

3. The method for predicting user demand by applying artificial intelligence according to claim 1, wherein the performing behavior tendency vector mining on the user operation behavior log to obtain user operation behavior characteristics comprises:

4. The method for predicting user demand by applying artificial intelligence according to claim 1, wherein the performing attention vector mining on the user operation behavior features to obtain an attention behavior vector related to the user operation behavior log comprises:

performing feature expansion on the user operation behavior features to obtain expanded user operation behavior features;

and determining related key behavior vectors under a behavior attention mining space according to the extended user operation behavior characteristics, and obtaining the attention behavior vectors related to the user operation behavior logs.

5. The method of claim 1, further comprising:

when the reference operation behavior log of each reference user requirement is not searched, acquiring fuzzy prediction user requirements, wherein the fuzzy prediction user requirements are used for representing tendency user requirements obtained by analysis based on a fuzzy algorithm;

carrying out demand derivation according to the fuzzy prediction user demand to obtain a first target fuzzy prediction user demand;

and determining the first target fuzzy prediction user requirement as the prediction user requirement related to the user operation behavior log.

6. The method for predicting user demand by applying artificial intelligence according to claim 1, wherein the obtaining of the candidate contact behavior log sequence related to the user operation behavior log by calling the contact behavior log from the reference operation behavior log of each reference user demand according to the user operation behavior feature and the reference operation behavior feature related to the reference operation behavior log of each reference user demand comprises:

performing feature expansion on the user operation behavior features to obtain target expansion user operation behavior features;

determining each cluster concentration vector from the reference operation behavior characteristics related to the reference operation behavior log required by each reference user, determining the concentrated association deviation degree of the target expansion user operation behavior characteristics and each cluster concentration vector, and determining candidate cluster concentration vectors from each cluster concentration vector according to the concentrated association deviation degree;

acquiring each same-cluster reference operation behavior characteristic related to the vector in the candidate cluster set, and determining the association deviation degree of the target expansion user operation behavior characteristic and each same-cluster reference operation behavior characteristic;

and selecting the same cluster reference operation behavior characteristics which are not more than the target deviation degree from each same cluster reference operation behavior characteristic according to the association deviation degree, and taking the reference operation behavior log which is not more than the target deviation degree and is related to the same cluster reference operation behavior characteristics as a candidate contact behavior log sequence related to the user operation behavior log.

7. The method for predicting demand of a user by applying artificial intelligence according to claim 1, wherein the performing association behavior vector mining from the candidate contact behavior log sequence according to the reference attention behavior vector associated with the candidate contact behavior log sequence to obtain the first mining behavior log sequence associated with the user operation behavior log comprises:

determining the association deviation degree of the attention behavior vector and a reference attention behavior vector related to each to-be-mined operation behavior activity in the candidate contact behavior log sequence respectively, and obtaining the editing association deviation degree related to each to-be-mined operation behavior activity;

and screening each behavior activity to be mined according to the editing association deviation degree and the set target deviation degree related to each behavior activity to be mined to obtain a first mining behavior log sequence related to the user operation behavior log.

8. The method for predicting user demand by applying artificial intelligence according to claim 1, wherein the performing associated behavior feature mining from the first mining behavior log sequence according to the reference operation behavior feature related to the first mining behavior log sequence to obtain the second mining behavior log sequence related to the user operation behavior log comprises:

determining the association deviation degree of the user operation behavior characteristic and a reference operation behavior characteristic related to each first mining operation behavior activity in the first mining behavior log sequence respectively to obtain the association deviation degree related to each first mining operation behavior activity;

and determining the minimum association deviation degree from the association deviation degrees related to each first mining operation behavior activity, and taking the first mining operation behavior activity related to the minimum association deviation degree as a second mining behavior log sequence related to the user operation behavior log.

9. The method of claim 8, further comprising:

if the minimum association deviation degree is determined to be larger than a preset association deviation degree threshold value, acquiring fuzzy prediction user requirements;

carrying out requirement derivation according to the fuzzy prediction user requirement to obtain a second target fuzzy prediction user requirement and configuring;

determining the second target fuzzy prediction user requirement as a prediction user requirement related to the user operation behavior log;

wherein the determining a minimum association deviation degree from the association deviation degrees related to each first mining operation behavior activity, and using the first mining operation behavior activity related to the minimum association deviation degree as a second mining behavior log sequence related to the user operation behavior log includes:

ascending the correlation deviation degree related to each first mining operation behavior activity to obtain a correlation deviation degree arrangement set;

selecting a plurality of associated deviation degrees from the associated deviation degree arrangement set in sequence, and obtaining target first mining operation behavior activities related to the associated deviation degrees;

acquiring reference user requirements respectively related to each target first mining operation behavior activity, and counting the number of target first mining operation behavior activities related to the same reference user requirement in the reference user requirements respectively related to each target first mining operation behavior activity;

and comparing the target first mining operation behavior activity quantity related to the same reference user requirement, and taking the first mining operation behavior activity related to the maximum second mining behavior log sequence quantity as a second mining behavior log sequence related to the user operation behavior log.

10. A big data optimization system, comprising a processor and a machine-readable storage medium, wherein the machine-readable storage medium has a computer program stored therein, the computer program being loaded and executed by the processor to implement the method for predicting user demand using artificial intelligence according to any of claims 1-9.