CN112990324A

CN112990324A - Resource pushing method based on big data online mode and deep learning service system

Info

Publication number: CN112990324A
Application number: CN202110308787.8A
Authority: CN
Inventors: 李光伟
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-03-23
Filing date: 2021-03-23
Publication date: 2021-06-18

Abstract

The embodiment of the disclosure provides a resource pushing method and a deep learning service system based on a big data online mode, and a target training model is called to obtain resource pushing configuration information for configuring big data information service in a second online mode stage based on a service use trend characteristic of a first online mode stage, a static service scene characteristic of the first online mode stage and a dynamic service scene characteristic of the first online mode stage, so that the pushing behavior of a subscribed service item in the big data information service is dynamically adjusted and controlled in combination with the dynamic service scene characteristic, and the service pushing service process of the subscribed service item is intelligently and accurately predicted.

Description

Resource pushing method based on big data online mode and deep learning service system

Technical Field

The disclosure relates to the technical field of big data, and exemplarily relates to a resource pushing method and a deep learning service system based on a big data online mode.

Background

The intelligent big data information service can help the user to finely control the service pushing process of the service information service, and an important research direction is provided for obtaining a more excellent service pushing control strategy. Service push strategies in the related art mainly fall into two categories: the first type is from setting rules in the business pushing process by combining past business pushing data and inherent professional knowledge, which is also the most widely adopted mode in the application process of the current big data information service; the second type is derived from data mining, and the best business pushing strategy is obtained by mining the relation between the control strategy and the business pushing value on the past business pushing data set in the modes of deep learning and the like. In the related art, it is difficult to realize intelligent and accurate prediction of a service pushing service process of a subscription service item.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide a resource pushing method and a deep learning service system based on a big data online mode.

In a first aspect, the present disclosure provides a resource pushing method based on a big data line mode, which is applied to a deep learning service system, where the deep learning service system is in communication connection with a plurality of business service devices, and the method includes:

acquiring service use trend characteristics of a service item subscribed by a service user of the service equipment in a first on-line mode stage in a big data information service, static service scene characteristics of the big data information service in the first on-line mode stage and dynamic service scene characteristics of the big data information service in the first on-line mode stage;

calling a target training model based on the service use trend characteristic of the first online mode stage, the static service scene characteristic of the first online mode stage and the dynamic service scene characteristic of the first online mode stage to obtain resource pushing configuration information for configuring the big data information service in a second online mode stage, wherein the second online mode stage is in the next development and update stage of the first online mode stage;

and applying the resource pushing configuration information to the big data information service in the second on-line mode stage.

In a possible embodiment, the method further comprises:

constructing a labeling feature object of the target training model based on service use trend features of subscribed service items in a plurality of online mode stages in the big data information service, static service scene features of the big data information service in the plurality of online mode stages, and dynamic service scene features of the big data information service in the plurality of online mode stages;

and training the target training model based on the labeled feature object to obtain the target training model for predicting the resource pushing configuration information.

For example, in a possible embodiment, the step of constructing the labeled feature object of the target training model based on the business usage trend feature of the subscribed business items in the big data information service in the multiple online mode stages, the static service scenario feature of the big data information service in the multiple online mode stages, and the dynamic service scenario feature of the big data information service in the multiple online mode stages includes:

performing the following for any of the plurality of past online mode phases:

acquiring a service use trend characteristic of a next online mode stage of the online mode stage, a static service scene characteristic of the next online mode stage of the online mode stage and a dynamic service scene characteristic of the next online mode stage of the online mode stage;

combining service use trend characteristics of subscribed service items in the big data information service in the passing online mode stage, static service scene characteristics of the big data information service in the passing online mode stage and dynamic service scene characteristics of the passing online mode stage into a first feature vector of the passing online mode stage;

combining the service use trend feature of a next online mode stage of the online mode stage, the static service scene feature of the next online mode stage of the online mode stage and the dynamic service scene feature of the next online mode stage of the online mode stage into a second feature vector of the next online mode stage;

constructing a labeled feature object of the past online mode stage based on the first feature vector of the past online mode stage, resource pushing configuration information used for configuring the big data information service in the past online mode stage, and the second feature vector of the next past online mode stage;

and fusing the plurality of labeled characteristic objects in the mode stage on the past line to obtain the labeled characteristic object of the target training model.

For example, in one possible embodiment, the method further comprises:

storing the marked feature object of the past online mode stage to an extended subscription area;

the step of training the target training model based on the labeled feature object includes:

when the number of the labeled feature objects in the past online mode stage in the extended subscription area reaches a set threshold value, acquiring a plurality of labeled feature objects in the past online mode stage from the extended subscription area, and training the target training model based on the plurality of labeled feature objects in the past online mode stage.

For example, in a possible embodiment, the training a target training model based on the labeled feature object of the business service process of the business item subscribed in the big data information service to obtain the target training model for predicting the resource pushing configuration information includes:

constructing a model updating reference function of the target training model based on the labeled characteristic object of any one of the labeled characteristic objects in the mode stage on the previous line and the labeled evaluation information of the mode stage on the previous line;

and updating the parameters of the target training model until the model updating reference function is converged, and taking the updated parameters of the target training model when the model updating reference function is converged as the parameters of the target training model for predicting the resource pushing configuration information.

For example, in a possible embodiment, before the model updating reference function for constructing the target training model, the method further includes:

calling the target training model to perform prediction processing based on the labeled feature object in any past online mode stage in the labeled feature objects to obtain prediction resource pushing configuration information for configuring the big data information service in the past online mode stage;

obtaining the prediction evaluation information of the mode stage on the past line based on the prediction resource pushing configuration information;

the model updating reference function for constructing the target training model comprises the following steps:

and constructing a model updating reference function of the target training model based on the labeled characteristic object of the past online mode stage, the predicted evaluation information of the past online mode stage and the labeled evaluation information of the past online mode stage.

acquiring service use trend characteristics of the next online mode stage in the marking characteristic objects of the online mode stage;

calling a subscribed business item simulator model based on the business use trend characteristics of the next online mode stage to determine business use trend estimation information brought by the business service process of the subscribed business item in the online mode stage, and taking the business use trend estimation information as the marked evaluation information of the online mode stage.

For example, in a possible embodiment, before the using the estimated service usage trend information as the labeled evaluation information of the past online mode stage, the method further includes:

acquiring resource pushing configuration information used for configuring the big data information service in the past online mode stage;

calling the subscription business item simulator model based on resource pushing configuration information for configuring the big data information service in the past online mode stage to determine reference business information associated with indexes required by the resource pushing configuration information;

the using the service usage trend pre-estimation information as the marking evaluation information of the past online mode stage comprises the following steps:

and taking the characteristic difference between the service use trend estimated information and the quoted service information as the marked evaluation information of the mode stage on the past line.

In a possible embodiment, the step of applying the resource push configuration information to the big data information service in the second in-line mode stage includes:

in the second online mode stage, acquiring an update instruction of resource pushing configuration information aiming at the big data information service; wherein the update instruction is used for indicating that push state update is executed in the big data information service aiming at the current push information text information in the resource push configuration information;

when the current push information text information is in a first push configuration state, determining an update mode corresponding to the current push information text information and all update service micro-services in an update activation state in the update mode, requesting all the update service micro-services to respond to the update instruction, and executing push state update aiming at the current push information text information;

when the current push information text message is in a second push configuration state, determining a current push configuration instruction of the current push information text message in resource push configuration information, and when the current push configuration instruction meets a preset update level, executing push state update aiming at the current push information text message.

For example, in one possible embodiment, the method further comprises:

acquiring historical pushing behavior information of the current pushing information text information in a preset pushing cycle period and pushing behavior feedback information corresponding to each historical pushing behavior information;

performing push index change calculation on the current push information text information according to the historical push behavior information and the push behavior feedback information to obtain push index change evaluation information corresponding to the current push information text information in each push cycle period;

calculating importance parameters of each piece of push index change evaluation information to obtain at least one group of push index importance parameters, and determining current push index change evaluation information to be estimated and related push index change evaluation information of a period adjacent to the current push index change evaluation information; wherein the relevant push index change evaluation information is push index change evaluation information of a previous period adjacent to the current push index change evaluation information;

determining expected push effective conversion parameters corresponding to the current push index change evaluation information and the related push index change evaluation information according to the push index importance parameters, and performing push value estimation on the current push information text information according to all the expected push effective conversion parameters and the push index change evaluation information corresponding to the current push information text information to obtain expected push value data corresponding to the current push information text information;

acquiring historical push value data corresponding to historical push information text information, and determining current push expected data of the current push information text information according to the historical push value data; the historical push information text information is the previous push information text information corresponding to the current push information text information;

calculating the push value degree of the current push expected data according to the expected push value data to obtain a push value influence parameter of the current push information text information;

and estimating the value of the current pushed information text message according to the pushed value influence parameters to obtain a reference pushed value corresponding to the current pushed information text message.

For example, in a possible embodiment, the obtaining historical push value data corresponding to the historical push information text information includes:

determining a target historical information updating item corresponding to the current pushed information text information, and acquiring a historical information updating data packet stored by the target historical information updating item; wherein, the historical information updating data packet is a data packet of all historical push information text information stored in the target historical information updating item;

acquiring an update serial number of the historical information update data packet fed back by the target historical information update item, and decoding the historical information update data packet by using the update serial number; acquiring a counting period starting node and a counting period ending node of the historical push information text information;

determining a target statistical period according to the statistical period starting node and the statistical period ending node;

obtaining target historical push subscription information of the decoded historical information updating data packet in the target counting period, and analyzing historical push value data corresponding to the current push information text information in the target historical push subscription information.

For example, in one possible embodiment, the determining current push expectation data for the current push information text message based on the historical push value data comprises:

acquiring a push value calculation strategy of the current push information text information;

and processing the historical push value data by using the push value calculation strategy to generate current push expected data of the current push information text information.

In a second aspect, an embodiment of the present disclosure further provides a resource pushing system based on a big data line mode, where the resource pushing system based on the big data line mode includes a deep learning service system and a plurality of business service devices in communication connection with the deep learning service system;

the deep learning service system is used for:

According to any one of the aspects, the resource pushing configuration information for configuring the big data information service in the second on-line mode stage is obtained by calling the target training model based on the service use trend feature of the first on-line mode stage, the static service scene feature of the first on-line mode stage and the dynamic service scene feature of the first on-line mode stage, and the pushing behavior of the subscribed service item in the big data information service is dynamically adjusted and controlled in combination with the dynamic service scene feature, so that the service pushing service process of the subscribed service item is intelligently and accurately predicted.

Drawings

Fig. 1 is a schematic application scenario diagram of a resource pushing system based on a big data line mode according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a resource pushing method based on a big data line mode according to an embodiment of the present disclosure;

fig. 3 is a schematic functional module diagram of a resource pushing apparatus based on a big data line mode according to an embodiment of the present disclosure;

fig. 4 is a schematic block diagram of structural components of a deep learning service system for implementing the above-described resource pushing method based on a large data line mode according to an embodiment of the present disclosure.

Detailed Description

The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.

Fig. 1 is an interaction diagram of a resource pushing system 10 based on a big data line mode according to an embodiment of the present disclosure. The resource pushing system 10 based on the big data line mode can comprise a deep learning service system 100 and a business service device 200 which is in communication connection with the deep learning service system 100. The resource pushing system 10 based on the big data line mode shown in fig. 1 is only one possible example, and in other possible embodiments, the resource pushing system 10 based on the big data line mode may also only include at least part of the components shown in fig. 1 or may also include other components.

In a separate embodiment, the deep learning service system 100 and the business service device 200 in the resource pushing system 10 based on the big data line mode can cooperatively perform the resource pushing method based on the big data line mode described in the following method embodiment, and the detailed description of the method embodiment can be referred to in the following steps of the deep learning service system 100 and the business service device 200.

Some terms referred to in the embodiments of the present disclosure will be described first.

Resource pushing configuration information: and configuring item factors for influencing the pushing behavior of the subscription service item, such as pushing frequency setting, pushing hierarchy setting, pushing interval period setting, pushing duration range setting and the like.

Service use trend characteristics: the method is used for representing the service use stage of the service process of the subscribed service item or representing the increase or slow-down situation of the service use of the subscribed service item, such as the service usage amount of the subscribed service item, the service usage rate of the subscribed service item, the service update amount of the subscribed service item, and the like.

Dynamic service scenario features: the method is used for representing dynamic scene conditions except for big data information service, such as global pushing frequency, global scene utilization rate and the like.

Static service scenario features: the method is used for representing static scene conditions in the big data information service, such as the passive push frequency of the big data information service, the push level in the big data information service and the like.

Referring to the foregoing background, the service push strategy in the related art has the following problems:

firstly, a service push strategy generated by setting rules in a service push process by combining past service push data and inherent professional knowledge is generally a fixed logic strategy, cannot be dynamically changed along with the service push process, and has poor adaptability, and the control granularity of the service push strategy is also relatively coarse, generally is a combination of some logics, for example, when a real-time push frequency exceeds a certain push frequency threshold value, the control of fine granularity cannot be performed by combining all feature vectors, for example, specific push frequency setting of each hour and the like.

Second, although the business pushing strategy based on data mining does not need intrinsic knowledge intervention, the relation is directly established through the big data information service state data and the business pushing result data in the business pushing process, but the method needs a large amount of data for training and is difficult to collect, the collected business pushing data are generally pushed according to certain fixed strategy businesses, the change is small, a better strategy is difficult to find on the basis, the data about the business information service in the business pushing data are also small, and the characteristics of the business information service are difficult to be used as an important factor to determine the next business pushing measure.

Based on this, in order to solve the technical problem in the foregoing background, fig. 2 is a flowchart illustrating a resource pushing method based on a big data online mode according to an embodiment of the present disclosure, and the resource pushing method based on a big data online mode according to the present embodiment may be executed by the deep learning service system 100 shown in fig. 1, which is described in detail below.

In the following embodiments, the dynamic service scene characteristic of the first online mode stage may be real-time dynamic scene information acquisition obtained by the third application, that is, the dynamic service scene characteristic of the first online mode stage is a real dynamic scene state of the first online mode stage, or may be previous synchronous dynamic scene data, that is, the dynamic service scene characteristic of the first online mode stage is a dynamic scene state simulated by the first online mode stage. The first on-line mode stage is a past on-line mode stage relative to the second on-line mode stage, and units of the first on-line mode stage and the second on-line mode stage can be flexibly set according to actual development and a service iteration cycle.

The static service scene characteristics are obtained by sensing the pushing behavior in the big data information service through the service script, and the service use trend characteristics are obtained by sensing the trend of subscribing service items in the big data information service through the service script.

Step S110, obtaining the service use trend characteristic of the subscribed service item in the first on-line mode stage, the static service scene characteristic of the big data information service in the first on-line mode stage, and the dynamic service scene characteristic of the big data information service in the first on-line mode stage.

For example, the deep learning service system 100 may obtain the static service scenario characteristic and the service usage trend characteristic of the past online mode stage (the first online mode stage) relative to the current online mode stage (the second online mode stage) through the service script in the big data information service, obtain the dynamic service scenario characteristic of the first online mode stage and the dynamic service scenario characteristic of the second online mode stage through the third application, automatically generate the configuration instruction for the big data information service based on the service usage trend characteristic of the first online mode stage, the static service scenario characteristic of the first online mode stage and the dynamic service scenario characteristic of the first online mode stage, and obtain the service usage trend characteristic of the first online mode stage, the static service scenario characteristic of the first online mode stage and the dynamic service scenario characteristic of the first online mode stage by analyzing the configuration instruction for the big data information service, and calling a target training model to perform dynamic optimization of resource pushing configuration information based on the service use trend characteristic of the first online mode stage, the static service scene characteristic of the first online mode stage and the dynamic service scene characteristic of the first online mode stage.

Step S120, a target training model is called based on the service use trend characteristic of the first online mode stage, the static service scene characteristic of the first online mode stage and the dynamic service scene characteristic of the first online mode stage to obtain resource pushing configuration information for configuring the big data information service in the second online mode stage, wherein the second online mode stage is in the next development and update stage of the first online mode stage.

For example, the service usage trend feature of the first online mode stage, the static service scenario feature of the first online mode stage, and the dynamic service scenario feature of the first online mode stage are input into the target training model, so that the target training model learns the following strategies: the resource pushing configuration information and the dynamic service scene characteristics of the big data information service are used as the static service scene characteristics of the big data information service, the static service scene characteristics of the big data information service are used as the service use trend characteristics of the subscribed service items, prediction processing is carried out through a target training model, the resource pushing configuration information used for configuring the big data information service in the second on-line mode stage, namely the resource pushing configuration information used for configuring the big data information service in the next on-line mode stage is obtained, the pushing behavior of the subscribed service items in the big data information service is dynamically adjusted and controlled in combination with the dynamic service scene characteristics, and intelligent and accurate prediction of the pushing behavior of the subscribed service items is further achieved.

Illustratively, in a separate embodiment, step S120 may be implemented by steps S121-S123: performing the following processing based on the target training model: in step S121, based on the service usage trend characteristic of the first on-line mode stage, determining a preset service usage trend characteristic that satisfies the push update requirement in the second on-line mode stage; in step S122, determining a static service scene characteristic of the second online mode stage when the preset service usage trend characteristic is realized; in step S123, the resource pushing configuration information for configuring the big data information service in the second on-line mode stage is determined based on the linkage relationship between the dynamic service scenario feature of the second on-line mode stage and the static service scenario feature of the second on-line mode stage where the resource pushing configuration information for configuring the big data information service in the second on-line mode stage jointly acts on.

For example, the deep learning service system 100 obtains the service usage trend feature of the first online mode stage, the static service scenario feature of the first online mode stage, and the dynamic service scenario feature of the first online mode stage by analyzing the configuration command for the big data information service, and calling a target training model to perform prediction processing, based on the strategy learned by the target training model, firstly performing prediction processing on service use trend characteristics based on the service use trend characteristics of the first on-line mode stage to obtain preset service use trend characteristics meeting the push updating requirement in the second on-line mode stage, the push update requirement may be set in advance according to an actual scene, for example, the push update requirement is that the service conversion rate of the subscription service item is maximized, or that the net worth of the service brought by the subscription service item is maximized. After the preset service use trend characteristic of the second on-line mode stage is obtained, the static service scene characteristic of the second on-line mode stage when the preset service use trend characteristic is realized is determined, finally, the dynamic service scene characteristic of the second on-line mode stage, which is learned based on the target training model, and the resource pushing configuration information used for configuring the big data information service in the second on-line mode stage jointly act on the linkage relation of the static service scene characteristic of the second on-line mode stage, and the resource pushing configuration information used for configuring the big data information service in the second on-line mode stage is determined, so that the pushing behavior of the subscribed service items in the big data information service is intelligently controlled.

In a single embodiment, the service usage trend characteristic of the first online mode stage is that the service usage rate of the subscribed service item is 50%, the service usage trend characteristic of the first online mode stage is predicted based on the pushing update requirement of the maximum net value of the service brought by the subscribed service item, the preset service usage trend characteristic of the second online mode stage is obtained as the service usage rate of 75%, in order to realize the preset service usage trend characteristic of the second online mode stage, the static service scenario characteristic of the second online mode stage is that the big data information service pushing frequency reaches 8 times/second, the pushing tier reaches 5 span levels, and the dynamic service scenario characteristic of the second online mode stage is obtained through the dynamic scenario information acquisition of the third party application as the global pushing frequency is 5 times/second, based on the linkage relationship that the dynamic service scene characteristics of the second on-line mode stage and the resource pushing configuration information for configuring the big data information service in the second on-line mode stage jointly act on the static service scene characteristics of the second on-line mode stage, the resource pushing configuration information of the second on-line mode stage is determined to be that the pushing frequency is set to be started to reach 8 times/second, the pushing level is set to be started to reach 5 span levels, and the pushing interval period is set to be 1 hour.

In a separate embodiment, the target training model includes a first processing unit, a second processing unit, and a feature processing unit; performing feature association processing on the service use trend feature of the first on-line mode stage based on the first processing unit to obtain a preset service use trend feature which meets the push updating requirement in the second on-line mode stage; correspondingly, based on the characteristic association relationship between the static service scene characteristic of the big data information service and the service use trend characteristic of the subscribed service item, the second processing unit performs characteristic association processing on the preset service use trend characteristic of the second on-line mode stage to obtain the static service scene characteristic of the second on-line mode stage when the preset service use trend characteristic of the second on-line mode stage is realized; correspondingly, based on the feature association relationship between the resource pushing configuration information and the dynamic service scene feature of the big data information service, which are included in the feature processing unit, and the static service scene feature of the big data information service, the static service scene feature of the second on-line mode stage is mapped to the resource pushing configuration information used for configuring the big data information service in the second on-line mode stage.

In an independent embodiment, the service usage trend feature of the first online mode stage can be input into a first processing unit in a target training model, the service usage trend feature of the first online mode stage is subjected to feature association processing through the first processing unit to obtain a preset service usage trend feature which meets the push updating requirement in the second online mode stage, the preset service usage trend feature is input into a second processing unit, the feature association relationship between the static service scene feature of the big data information service and the service usage trend feature of the subscribed service item is learned through the second processing unit, the feature association processing is carried out on the preset service usage trend feature of the second online mode stage to obtain the static service scene feature of the second online mode stage when the preset service usage trend feature of the second online mode stage is realized, and inputting the static service scene characteristics of the second on-line mode stage into the characteristic processing unit, and mapping the static service scene characteristics of the second on-line mode stage into the resource pushing configuration information for configuring the big data information service in the second on-line mode stage through the characteristic association relation between the resource pushing configuration information and the dynamic service scene characteristics of the big data information service learned by the characteristic processing unit and the static service scene characteristics of the big data information service. Therefore, the resource pushing configuration information of the second online mode stage can be predicted through the multilayer units in the target training model, intelligent and accurate prediction of the pushing behavior of the subscription business items is further achieved, and adjustment of the pushing behavior of the subscription business items in the big data information service in a manual regulation mode is avoided.

In a separate embodiment, the target training model includes a first processing unit, a second processing unit, and a feature processing unit; performing feature association processing on the service use trend feature of the first on-line mode stage based on the first processing unit to obtain a preset service use trend feature which meets the push updating requirement in the second on-line mode stage; correspondingly, based on the characteristic incidence relation between the static service scene characteristic of the big data information service and the service use trend characteristic change of the subscribed service item, the characteristic conversion processing is carried out on the characteristic difference between the service use trend characteristic of the first on-line mode stage and the preset service use trend characteristic of the second on-line mode stage, so as to obtain the static service scene characteristic of the second on-line mode stage when the preset service use trend characteristic of the second on-line mode stage is realized; correspondingly, based on the feature association relationship between the resource pushing configuration information and the dynamic service scene feature of the big data information service, which are included in the feature processing unit, and the static service scene feature of the big data information service, the static service scene feature of the second on-line mode stage is mapped to the resource pushing configuration information used for configuring the big data information service in the second on-line mode stage.

For example, the service usage trend feature of the first online mode stage may be input to a first processing unit in the target training model, the service usage trend feature of the first online mode stage is subjected to feature association processing by the first processing unit to obtain a preset service usage trend feature satisfying the push update requirement in the second online mode stage, the preset service usage trend feature is input to a second processing unit, a feature association relationship between the static service scenario feature of the big data information service learned by the second processing unit and the service usage trend feature change of the subscribed service item is obtained, the feature conversion processing is performed on the feature difference between the service usage trend feature of the first online mode stage and the preset service usage trend feature of the second online mode stage to obtain the static service scenario feature of the second online mode stage when the preset service usage trend feature of the second online mode stage is realized, the second processing unit inputs the static service scene characteristics of the second on-line mode stage into the characteristic processing unit, and the static service scene characteristics of the second on-line mode stage are mapped into the resource pushing configuration information for configuring the big data information service in the second on-line mode stage through the characteristic incidence relation between the resource pushing configuration information and the dynamic service scene characteristics of the big data information service, which are learned by the characteristic processing unit, and the static service scene characteristics of the big data information service. Therefore, the resource pushing configuration information of the second online mode stage can be predicted through the multilayer units in the target training model, intelligent and accurate prediction of the pushing behavior of the subscription business items is further achieved, and adjustment of the pushing behavior of the subscription business items in the big data information service in a manual regulation mode is avoided.

In an independent embodiment, the service usage trend characteristic of the first online mode stage is that the service usage rate of the subscribed service item is 50%, the preset service usage trend characteristic of the second online mode stage is that the service usage rate is 75%, the service usage trend characteristic of the subscribed service item changes to be 25% of the service usage rate, and in order to realize the change of the service usage trend characteristic of the subscribed service item, the static service scenario characteristic of the second online mode stage is that the pushing frequency of the big data information service reaches 8 times/second and the pushing hierarchy reaches 5 span levels, so that the relationship between the static service scenario characteristic of the big data information service and the change of the service usage trend characteristic of the subscribed service item in the actual growth process of the subscribed service item is learned through the second processing unit, and the relation between the static service scenario characteristic of the second online mode stage and the change of the service usage trend characteristic of the subscribed service scenario characteristic of the second online mode stage when the preset service usage trend characteristic of the second online mode stage is realized is accurately So as to accurately locate the resource pushing configuration information for configuring the big data information service in the second on-line mode stage.

In a separate embodiment, the feature processing unit includes a first feature extraction node, a second feature extraction node, a fully connected node, and a third feature extraction node; mapping the static service scenario features of the second on-line mode stage to resource push configuration information for configuring big data information service in the second on-line mode stage, comprising: performing feature extraction on the dynamic service scene features of the mode stage on the second line based on the first feature extraction node to obtain a first feature vector corresponding to the dynamic service scene features; performing feature extraction on the static service scene features of the mode stage on the second line based on a second feature extraction node to obtain a second feature vector corresponding to the static service scene features; determining a feature difference of the second feature vector and the first feature vector based on the fully connected nodes; and performing feature extraction on the feature difference based on the third feature extraction node to obtain resource pushing configuration information for configuring the big data information service in the second on-line mode stage.

For example, after the second processing unit inputs the static service scene features of the second on-line mode stage into the feature processing unit, based on the learned feature association relationship between the resource pushing configuration information and the dynamic service scene features of the big data information service and the static service scene features of the big data information service, feature extraction is performed on the dynamic service scene features of the second on-line mode stage through the first feature extraction node in the feature processing unit to obtain a first feature vector (i.e. a first convolution vector) corresponding to the dynamic service scene features, feature extraction is performed on the static service scene features of the second on-line mode stage through the second feature extraction node in the feature processing unit to obtain a second feature vector (i.e. a second convolution vector) corresponding to the static service scene features, and then the second feature vector is differentiated from the first feature vector through the full-connection node, and finally, performing feature extraction on the feature difference through a third feature extraction node to obtain resource pushing configuration information for configuring the big data information service in the second on-line mode stage.

Step S130, applying the resource pushing configuration information to the big data information service in the second on-line mode stage.

For example, the deep learning service system 100 sends a configuration instruction for a big data information service to a server, the server receives the configuration instruction for the big data information service, calls a target training model to perform prediction processing, obtains resource pushing configuration information of a second on-line mode stage, and feeds back the resource pushing configuration information of the second on-line mode stage to the deep learning service system 100, the deep learning service system 100 applies the resource pushing configuration information to the big data information service in the second on-line mode stage through a control device, so that static service scenario features of the first on-line mode stage are converted into static service scenario features of the second on-line mode stage, and under the combined action of the static service scenario features of the second on-line mode stage and the dynamic service scenario features of the second on-line mode stage, service usage trend features of the first on-line mode stage are converted into service usage trend features of the second on-line mode stage The method has potential characteristics, so that the growth of subscription business items in the big data information service is automatically controlled.

In a separate embodiment, steps S140-S150 may also be included: in step S140, constructing a labeled feature object of the target training model based on the service usage trend feature of the subscribed service item in the big data information service at the multiple online mode stages, the static service scene feature of the big data information service at the multiple online mode stages, and the dynamic service scene feature of the big data information service at the multiple online mode stages; in step S150, the target training model is trained based on the labeled feature object, so as to obtain a target training model for predicting the resource pushing configuration information.

For example, the characteristics of the current online mode stage (including the traffic usage trend characteristic, the static service scenario characteristic, and the dynamic service scenario characteristic) are taken as the characteristics of the past online mode stage of the next online mode stage. Features (including a business use trend feature, a static service scene feature and a dynamic service scene feature) in a business iteration stage of a business service process for subscribing business items in the big data information service can be used as a labeled feature object, a training target model, namely a plurality of past online mode stages belong to one business iteration stage of the business service process for subscribing business items, for example, the business service process period for subscribing business items is 30 stages, and the resource pushing configuration information is updated by taking the stages as a unit, so that the number of the past online mode stages is 30, namely, the 1 st stage, the 2 nd stage and the … th 30 th stage.

For example, the plurality of past online mode phases may be 1 st, 2 nd and 3 rd online mode phases, and the 1 st online mode phase is any one of the 1 st service iteration phases for subscribing the service item, the 2 nd online mode phase is any one of the 2 nd service iteration phases for subscribing the service item, and the 3 rd online mode phase is any one of the 3 rd service iteration phases for subscribing the service item.

In a separate embodiment, constructing the labeled feature object of the target training model based on the business usage trend feature of the subscribed business items in the big data information service in the multiple online mode stages, the static service scene feature of the big data information service in the multiple online mode stages, and the dynamic service scene feature of the big data information service in the multiple online mode stages includes: performing the following for any of a plurality of on-line mode phases: acquiring service use trend characteristics of a next online mode stage of the online mode stage, static service scene characteristics of the next online mode stage of the online mode stage and dynamic service scene characteristics of the next online mode stage of the online mode stage; combining service use trend characteristics of a subscribed service item in a big data information service in a past online mode stage, static service scene characteristics of the big data information service in the past online mode stage and dynamic service scene characteristics of the past online mode stage into a first feature vector of the past online mode stage; combining the service use trend characteristic of the next online mode stage of the online mode stage, the static service scene characteristic of the next online mode stage of the online mode stage and the dynamic service scene characteristic of the next online mode stage of the online mode stage into a second feature vector of the next online mode stage; constructing a labeled feature object of a past online mode stage based on a first feature vector of the past online mode stage, resource pushing configuration information for configuring big data information service in the past online mode stage and a second feature vector of a next past online mode stage; and fusing the labeled characteristic objects of the plurality of passing online mode stages to obtain the labeled characteristic object of the target training model.

For example, when the traffic usage trend feature of a certain past online mode phase is x1, the static service scenario feature is y1, and the dynamic service scenario feature is z1, the feature vector of the past online mode phase is s1[ x1, y1, z1 ]. When the traffic usage trend feature of the next online mode phase of the online mode phase is x2, the static service scene feature is y2, and the dynamic service scene feature is z2, the feature vector of the next online mode phase of the online mode phase is S2[ x2, y2, z2 ]. When the resource pushing configuration information for configuring the big data information service in the past online mode phase is a1, the service usage trend feature S1 of the past online mode phase, the resource pushing configuration information for configuring the big data information service in the past online mode phase is a1, and the feature vector S2 of the next past online mode phase of the past online mode phase are combined into the labeled feature object [ S1, a1, S2] of the past online mode phase. When there are N past online mode stages, the feature objects are marked as [ S1, a1, S2], …, [ sN-1, aN-1, sN ], [ sN, aN, termination state ], wherein N is a natural number greater than 2, and the termination state represents a training termination condition.

In the process of dynamically adjusting and controlling the pushing behavior of the subscription business items in the big data information service, the marked feature objects in the past online mode stage can be stored in the extended subscription area to collect the marked feature objects, so that when the number of the marked feature objects in the past online mode stage in the extended subscription area reaches a set threshold value, a plurality of marked feature objects in the past online mode stage are obtained from the extended subscription area, and the target training model is trained based on the marked feature objects in the past online mode stage.

In order to save the extended subscription area, the extended subscription area may be periodically cleaned, for example, when the validity period of the tagged feature object in the extended subscription area reaches, the tagged feature object is deleted; obtaining the importance degree of the labeled feature object in the expanded subscription area (for example, the change degree of the service use trend feature in the mode stage on the adjacent line in the labeled feature object indicates that the labeled feature object is more important when the change degree is larger, which indicates that the service process of the subscribed service item is better), and deleting the labeled feature object from the expanded subscription area when the importance degree of the labeled feature object is lower than the importance degree threshold.

In a separate embodiment, training a target training model based on a labeled feature object of a business service process of a subscription business item in a big data information service to obtain the target training model for predicting resource pushing configuration information includes: constructing a model updating reference function of the target training model based on the labeled characteristic object of any one past online mode stage in the labeled characteristic objects and the labeled evaluation information of the past online mode stage; and updating parameters of the target training model until the model updating reference function is converged, and taking the updated parameters of the target training model when the model updating reference function is converged as the parameters of the target training model for predicting the resource pushing configuration information.

For example, by using a reinforcement learning algorithm, after determining a value of a model update reference function of a target training model based on a labeled feature object of any one of the past online mode stages and labeled evaluation information (for example, a net business value actually brought by a subscription business item in the past online mode stage or an actual business conversion rate of the subscription business item in the past online mode stage), it may be determined whether the value of the model update reference function of the target training model exceeds a preset threshold, when the value of the model update reference function of the target training model exceeds the preset threshold, an error signal of the target training model is determined based on the model update reference function of the target training model, error information is reversely propagated in the target training model, and model parameters of each layer are updated in a propagation process.

Explaining backward propagation, namely inputting the characteristic-labeled object data into an input layer of the neural unit model, passing through a hidden layer, finally reaching an output layer and outputting a result, which is a forward propagation process of the neural unit model, calculating an error between the output result and an actual value because the output result of the neural unit model has an error with an actual result, and reversely propagating the error from the output layer to the hidden layer until the error is propagated to the input layer, wherein in the process of the backward propagation, the value of a model parameter is adjusted according to the error; and continuously iterating the process until convergence. Wherein, the target training model belongs to the nerve unit model.

In a separate embodiment, before constructing the model update reference function of the target training model, the method further includes: calling a target training model to perform prediction processing based on a labeled feature object in any past online mode stage in the labeled feature objects to obtain prediction resource pushing configuration information for configuring big data information service in the past online mode stage; pushing configuration information based on the predicted resources to obtain predicted evaluation information of a past online mode stage; and constructing a model updating reference function of the target training model based on the labeled characteristic object of the mode stage on the previous line, the prediction evaluation information of the mode stage on the previous line and the labeled evaluation information of the mode stage on the previous line.

For example, in the training stage, a target training model is called to perform prediction processing, prediction resource pushing configuration information used for configuring a big data information service in a past online mode stage is obtained, and prediction evaluation information of the past online mode stage is obtained based on the prediction resource pushing configuration information, for example, based on the prediction resource pushing configuration information, service use trend characteristics which can be reached by a subscription service item in the big data information service are predicted, and service use trend prediction information brought by the service use trend characteristics which can be reached by the subscription service item is used as the prediction evaluation information; predicting service use trend characteristics which can be achieved by a subscription service item in the big data information service based on the predicted resource pushing configuration information, taking service use trend predicted information brought by the service use trend characteristics which can be achieved by the subscription service item as predicted evaluation information, predicting quoted service information associated with indexes required by the resource pushing configuration information, and taking the characteristic difference between the service use trend predicted information and the quoted service information as marked evaluation information. And finally, constructing a model updating reference function of the target training model based on the labeled characteristic object of the mode stage on the previous line, the prediction evaluation information of the mode stage on the previous line and the labeled evaluation information of the mode stage on the previous line.

In an independent embodiment, a first processing unit in a target training model is used for performing feature association processing on service use trend characteristics of a past online mode stage to obtain preset service use trend characteristics meeting push updating requirements in a next past online mode stage, and static service scene characteristics of the next past online mode stage are mapped into predicted resource push configuration information for configuring large data information services in the next past online mode stage based on feature association relations between resource push configuration information and dynamic service scene characteristics of large data information services and static service scene characteristics of the large data information services, wherein the resource push configuration information and the dynamic service scene characteristics are included in the feature processing unit in the target training model.

For example, after determining the value of the model update reference function (e.g., cross entropy loss function) of the target training model based on the labeled feature object of any past online mode stage, the predicted evaluation information of the past online mode stage, and the labeled evaluation information of the past online mode stage (e.g., the net business value actually brought by the subscription business item in the past online mode stage, or the actual business transformation rate of the subscription business item in the past online mode stage), it may be determined whether the value of the model update reference function of the target training model exceeds a preset threshold, and when the value of the model update reference function of the target training model exceeds the preset threshold, and determining an error signal of the target training model based on the model updating reference function of the target training model, reversely propagating the error information in the target training model, and updating the model parameters of each layer in the propagation process.

In a separate embodiment, before constructing the model update reference function of the target training model, the method further includes: the method comprises the steps of obtaining service use trend characteristics of a next online mode stage in a labeled characteristic object of the online mode stage, calling a subscription service project simulator model based on the service use trend characteristics of the next online mode stage to determine service use trend estimation information brought by a service process of a subscription service project in the online mode stage, and taking the service use trend estimation information as labeled evaluation information of the online mode stage. For example, the actual service conversion rate of the subscribed service item in the past online mode stage is used as the labeled evaluation information of the past online mode stage.

In a separate embodiment, before constructing the model update reference function of the target training model, the method further includes: acquiring service use trend characteristics of a next online mode stage in a marking characteristic object of the online mode stage, and calling a subscription service project simulator model based on the service use trend characteristics of the next online mode stage to determine service use trend estimation information brought by a service process of a subscription service project in the online mode stage; acquiring resource pushing configuration information for configuring big data information service in a past online mode stage; calling a subscription business project simulator model based on resource pushing configuration information for configuring big data information service in a past online mode stage to determine reference business information associated with indexes required by the resource pushing configuration information, and taking business use trend prediction information as marking evaluation information of the past online mode stage, wherein the method comprises the following steps: and taking the characteristic difference between the service use trend estimated information and the quoted service information as the marking evaluation information of the past online mode stage. For example, the net business value actually brought by the subscription business item in the past online mode stage is used as the labeled evaluation information of the past online mode stage.

In a possible embodiment, the foregoing step S130 can be further realized by the following exemplary steps.

Step S131, obtain the update command of the resource pushing configuration information for the big data information service.

In this embodiment, the deep learning service system 100 may receive an update instruction sent by the front-end service node, where the update instruction is used to instruct to perform a push status update on the current push information text information in the resource push configuration information.

Step S132, when the current push information text message is in the first push configuration state, determining the update mode corresponding to the current push information text message and all update service microservices in the update mode in the update activation state, and requesting all update service microservices to respond to the update instruction, and executing push state update for the current push information text message.

Step S133, when the current push information text message is in the second push configuration state, determining a current push configuration instruction of the current push information text message in the resource push configuration information, and when the current push configuration instruction meets a preset update level, executing push state update on the current push information text message.

In this embodiment, a status information identifier is stored in the current push information text message, and the status information identifier is used to indicate the status of the current push information text message; for example, when the status information identifier indicates that the current pushed information text message is in the first push configuration state, the deep learning service system 100 determines that the current pushed information text message is in the complete push activation state, and at this time, the deep learning service system 100 may determine an update mode corresponding to the current pushed information text message and all update services in the update mode that are in the update activation state.

Wherein, an update tag can be stored in the current push information text message, and the update tag can be used for indicating a corresponding update mode; the deep learning service system 100 may store an update label mapping table, where the update label mapping table records a correspondence between a plurality of update labels and a plurality of update modes; the deep learning service system 100 can obtain an update mode corresponding to the update tag information stored in the current push information text information by querying the update tag mapping table; and, the deep learning service system 100 may send query information to the application service of the determined update mode to request response information returned by all the update business micro-services in the update mode.

Next, the deep learning service system 100 may determine the working state of each update service microservice in the update mode according to the condition that each update service microservice returns response information; for example, the deep learning service system 100 may determine that the update service microservice that feeds back the response information within the set time is in the update activated state, and the update service microservice that does not feed back the response information beyond the set time is not in the update activated state; thus, after the deep learning service system 100 determines all the update service microservices in the update activation state in the update mode, the deep learning service system 100 may request all the update service microservices to respond to the update request, and perform the update of the push state for the current push information text information.

In addition, when the status information identifier indicates that the current pushed information text information is in the second pushing configuration state, the deep learning service system 100 determines that the current pushed information text information is in a partial pushing activation state, that is, the current pushed information text information contains some data which is not pushed and activated, at this time, the deep learning service system 100 can determine a current pushing configuration instruction of the current pushed information text information in the resource pushing configuration information, and look up a pre-configured update level policy table, where update levels corresponding to multiple signaling are recorded in the update level policy table; when the deep learning service system 100 determines the update level corresponding to the current push configuration instruction by searching the update level policy table, and when the deep learning service system 100 determines that the update level corresponding to the current push configuration instruction meets the preset update level, the deep learning service system 100 determines that the current push information text information meets the update requirement, and then the deep learning service system 100 executes push state update for the current push information text information.

It can be seen that, in the solution provided by the present disclosure, by obtaining an update instruction of resource push configuration information for a big data information service, where the update instruction is used to instruct to perform push status update on current push information text information in the resource push configuration information; under the condition that the current pushed information text information is in a first pushing configuration state, determining an updating mode corresponding to the current pushed information text information and all updating service micro-services in an updating activation state in the updating mode, requesting all updating service micro-services to respond to an updating instruction, and executing pushing state updating aiming at the current pushed information text information; in addition, under the condition that the current push information text message is in the second push configuration state, a current push configuration instruction of the current push information text message in the resource push configuration message is determined, and when the current push configuration instruction meets a preset update level, the push state update is executed aiming at the current push information text message. Therefore, the push reliability of the push information text information is improved and data pollution is avoided by judging two conditions that the current push information text information is in the first push configuration state or the second push configuration state and responding to the update instruction of the current push information text information according to different conditions to execute push state update.

In a separate embodiment, for different working states of the update mode, after the deep learning service system 100 executes step S131, it may further obtain resource pushing description information of the resource pushing configuration information.

Next, the deep learning service system 100 may obtain the working state of the update mode in the above manner; moreover, when the deep learning service system 100 obtains the instruction indicating that the update mode is in the update maintenance mode, the current push information text message is switched from the first push configuration state to the second push configuration state, and the current push configuration instruction of the current push information text message in the resource push configuration information is obtained according to the resource push description information, so as to execute step S133.

In this embodiment, the current push configuration instruction may be recorded in a specified field of the resource push description information.

In addition, when the deep learning service system 100 obtains the information indicating that the update mode is in the update non-maintenance mode, the current push information text information is switched from the second push configuration state to the first push configuration state, and the management content description data in the resource push configuration information is obtained according to the resource push description information; thereby performing step S132.

In a separate embodiment, when the deep learning service system 100 executes step S133 to determine the current push configuration command of the current push information text message in the resource push configuration message, the following scheme may be adopted:

first, the deep learning service system 100 may use an information text configuration source of the current pushed information text information in the resource pushing configuration information as a target configuration source, and determine a traversal configuration source range by using a preset information text configuration source range in the current pushed information text information as a fixed range.

In this embodiment, the preset information text configuration source range may be recorded in the preset configuration source range in the current push information text message.

Next, the deep learning service system 100 may determine a high heat range of the configuration source of the current push information text information from the traversal configuration source range according to the heat range of the configuration source of the current push information text information; wherein, the high heat range of the allocation source is the high heat range of the push allocation command stored in the current push information text message.

In this embodiment, the configuration source heat range may be stored in the metadata of the current push info text message.

The deep learning service system 100 may then search for the current push configuration instructions for the current push informational text message from the configuration source high heat range.

Next, when the current push configuration instruction is not searched from the high-heat range of the configuration source, the deep learning service system 100 may use the information text configuration source of the current push information text information in the resource push configuration information as the target configuration source, use the corresponding information text index of the current push information text information as the target operation object, and divide the information text index according to the set index type to obtain a plurality of push information text indexes.

Then, the deep learning service system 100 may obtain an index type code corresponding to each of the plurality of pushed information text indexes, and merge all the index type codes according to the order of the pushed information text indexes corresponding to each of the index type codes, so as to generate a current pushing configuration instruction of the current pushed information text information.

In addition, in a separate embodiment, in order to evaluate the overall value of the big data information service, the information processing method may further include the following steps for the current pushed information text information corresponding to the big data information service:

first, the deep learning service system 100 may obtain respective historical pushing behavior information of the current pushing information text information in a preset pushing cycle period and pushing behavior feedback information corresponding to each piece of historical pushing behavior information.

In this embodiment, the predetermined push cycle period may be five cycle periods in the past for the big data information service; the historical push behavior information may include information referencing control information during the push behavior within each preset push cycle period.

Next, the deep learning service system 100 may perform push index change calculation on the current push information text information according to the historical push behavior information and the push behavior feedback information, so as to obtain push index change evaluation information corresponding to the current push information text information in each push cycle period.

In this embodiment, the deep learning service system 100 may perform service index increase calculation on the current pushed information text information according to the historical pushed behavior information and the pushed behavior feedback information by using a least square fitting, for example, so as to obtain the pushed index change evaluation information corresponding to the current pushed information text information in each pushing cycle period.

Then, the deep learning service system 100 may perform importance parameter calculation on each piece of push index change evaluation information to obtain at least one set of push index importance parameters.

Next, the deep learning service system 100 may determine current push index change evaluation information to be estimated and related push index change evaluation information of a period adjacent to the current push index change evaluation information; the relevant push index change evaluation information is push index change evaluation information of a previous period adjacent to the current push index change evaluation information.

Then, the deep learning service system 100 may determine, according to the push index importance parameter, an expected push effective conversion parameter corresponding to each of the current push index change evaluation information and the related push index change evaluation information.

Next, the deep learning service system 100 may estimate a push value of the current push information text message according to all the expected push effective transformation parameters and the push index change evaluation information corresponding to the expected push effective transformation parameters, so as to obtain expected push value data corresponding to the current push information text message.

Then, the deep learning service system 100 may obtain historical push value data corresponding to the historical push information text information, and determine current push expected data of the current push information text information according to the historical push value data; the historical push information text message is a previous push information text message corresponding to the current push information text message.

Next, the deep learning service system 100 may perform a push value degree calculation on the current push expected data according to the expected push value data to obtain a push value influence parameter of the current push information text information.

Then, the deep learning service system 100 may perform value estimation on the current pushed information text message according to the pushed value influence parameter to obtain a reference pushed value corresponding to the current pushed information text message.

Therefore, based on the scheme provided by the embodiment of the disclosure, the push value of the current push information text information is estimated, and the overall value of the current push information text information of the big data information service can be estimated based on the estimated push value, so that the index of the big data information service is objectively estimated, and the reliability of service expansion is improved.

As a possible implementation manner, in order to improve reliability of obtaining the historical pushed value data and prevent data from being leaked, the deep learning service system 100 may adopt the following manner when obtaining the historical pushed value data corresponding to the historical pushed information text information:

firstly, the deep learning service system 100 can determine a target historical information update item corresponding to the current pushed information text information, and obtain a historical information update data packet stored by the target historical information update item; wherein, the historical information updating data packet is a data packet of all historical push information text information stored in the target historical information updating item.

Next, the deep learning service system 100 may obtain the update sequence number of the history information update packet fed back by the target history information update item.

The deep learning service system 100 can then decode the historical information update packet using the update sequence number.

Next, the deep learning service system 100 may obtain a statistical period start node and a statistical period end node of the historical pushed information text information.

Then, the deep learning service system 100 may determine the target statistical period according to the statistical period start node and the statistical period end node.

Next, the deep learning service system 100 may obtain the target historical push subscription information of the decoded historical information update data packet in the target statistical period.

Then, the deep learning service system 100 can analyze the historical push value data corresponding to the current push information text information from the target historical push subscription information.

In addition, in a separate embodiment, the deep learning service system 100 may employ the following scheme when determining the current push expected data of the current push informational text message according to the historical push value data:

the deep learning service system 100 may first obtain a push value calculation policy of the current push information text information, and then process the historical push value data by using the push value calculation policy to generate current push expected data of the current push information text information.

In this embodiment, the push value calculation policy may be pre-selected and stored in the metadata of the current push information text information, and the deep learning service system 100 may obtain the push value calculation policy by analyzing the metadata of the current push information text information, where the push value calculation policy may be used to indicate a value calculation scheme corresponding to the current push information text information, and based on the push value calculation policy, may calculate the current push expected data of the current push information text information.

Fig. 3 is a schematic functional block diagram of a resource pushing apparatus 300 based on a big data line mode according to an embodiment of the present disclosure, and the functions of the functional blocks of the resource pushing apparatus 300 based on the big data line mode are described in detail below.

An obtaining module 310, configured to obtain a service usage trend feature of a service item subscribed by a service user of a service device in a big data information service in a first online mode stage, a static service scenario feature of the big data information service in the first online mode stage, and a dynamic service scenario feature of the big data information service in the first online mode stage;

a calling module 320, configured to call a target training model based on a service usage trend feature of the first online mode stage, a static service scene feature of the first online mode stage, and a dynamic service scene feature of the first online mode stage, so as to obtain resource pushing configuration information used for configuring a big data information service in the second online mode stage, where the second online mode stage is at a next development and update stage of the first online mode stage;

the application module 330 is configured to apply the resource pushing configuration information to the big data information service in the second online mode stage.

Fig. 4 shows a hardware structure diagram of a deep learning service system 100 for implementing the resource pushing method based on the mode on the big data line, which is provided by the embodiment of the present disclosure, and as shown in fig. 4, the deep learning service system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute a resource pushing method based on a big data line mode according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control transceiving actions of the transceiver 140, so as to perform data transceiving with the business service device 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the deep learning service system 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.

In addition, the embodiment of the present disclosure also provides a readable storage medium, where a computer execution instruction is preset in the readable storage medium, and when a processor executes the computer execution instruction, the resource pushing method based on the mode on the big data line is implemented.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, in a separate embodiment, not limiting, alternative configurations of the embodiments of the present specification are contemplated as being consistent with the teachings of the present specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A resource pushing method based on a big data online mode is applied to a deep learning service system, wherein the deep learning service system is in communication connection with a plurality of business service devices, and the method comprises the following steps:

2. The method according to claim 1, wherein the step of invoking a target training model based on the service usage trend feature of the first online mode stage, the static service scenario feature of the first online mode stage, and the dynamic service scenario feature of the first online mode stage to obtain resource pushing configuration information for configuring the big data information service in the second online mode stage comprises:

performing the following processing based on the target training model:

determining a preset service use trend characteristic which meets a pushing and updating requirement in the second on-line mode stage based on the service use trend characteristic of the first on-line mode stage, and determining a static service scene characteristic of the second on-line mode stage when the preset service use trend characteristic is realized;

and determining resource pushing configuration information for configuring the big data information service in the second on-line mode stage based on a linkage relation between the dynamic service scene characteristics of the second on-line mode stage and the static service scene characteristics of the second on-line mode stage where the resource pushing configuration information for configuring the big data information service commonly acts.

3. The resource pushing method based on the big data online mode according to claim 2, wherein the target training model comprises a first processing unit and a feature processing unit;

the step of determining a preset service use trend characteristic which meets the push update requirement in the second on-line mode stage based on the service use trend characteristic of the first on-line mode stage includes:

performing feature association processing on the service use trend feature of the first on-line mode stage based on the first processing unit to obtain a preset service use trend feature which meets a push updating requirement in the second on-line mode stage;

the step of determining resource push configuration information for configuring the big data information service in the second in-line mode stage includes:

and mapping the static service scene characteristics of the second on-line mode stage to the resource pushing configuration information for configuring the big data information service in the second on-line mode stage based on the feature association relationship between the resource pushing configuration information and the dynamic service scene characteristics of the big data information service, which are included in the feature processing unit, and the static service scene characteristics of the big data information service.

4. The big data online mode-based resource pushing method according to claim 3, wherein the target training model further comprises a second processing unit;

the step of determining the static service scene characteristics of the second online mode stage when the preset service usage trend characteristics are realized includes:

based on the feature association relationship between the static service scene features of the big data information service and the service usage trend features of the subscribed service items, the second processing unit performs feature association processing on the preset service usage trend features of the second on-line mode stage to obtain the static service scene features of the second on-line mode stage when the preset service usage trend features of the second on-line mode stage are realized;

or, based on the feature association relationship between the static service scene feature of the big data information service and the service usage trend feature change of the subscribed service item, the feature conversion processing is performed on the feature difference between the service usage trend feature of the first online mode stage and the preset service usage trend feature of the second online mode stage, so as to obtain the static service scene feature of the second online mode stage when the preset service usage trend feature of the second online mode stage is realized.

5. The resource pushing method based on the big data online mode according to claim 3, wherein the feature processing unit comprises a first feature extraction node, a second feature extraction node, a full connection node and a third feature extraction node;

the step of mapping the static service scenario features of the second on-line mode stage to resource pushing configuration information used for configuring the big data information service in the second on-line mode stage includes:

performing feature extraction on the dynamic service scene features of the second on-line mode stage based on the first feature extraction node to obtain a first feature vector corresponding to the dynamic service scene features;

performing feature extraction on the static service scene features of the second on-line mode stage based on the second feature extraction node to obtain a second feature vector corresponding to the static service scene features;

determining feature differences of the second feature vector and the first feature vector based on the fully-connected node;

and performing feature extraction on the feature difference based on the third feature extraction node to obtain resource pushing configuration information for configuring the big data information service in the second on-line mode stage.

6. The method for pushing resources based on the big data line mode according to claim 1, wherein the method further comprises:

7. The big data online mode-based resource pushing method according to claim 6, wherein the step of constructing the labeled feature object of the target training model based on the business usage trend feature of the subscribed business items in the big data information service in the plurality of past online mode stages, the static service scenario feature of the big data information service in the plurality of past online mode stages, and the dynamic service scenario feature of the big data information service in the plurality of past online mode stages comprises:

performing the following for any of the plurality of past online mode phases:

8. The method according to any of claims 1-7, wherein the step of applying the resource pushing configuration information to the big data information service in the second mode-on-line stage comprises:

9. The method according to claim 1, wherein after the step of obtaining the update instruction of the resource push configuration information for the big data information service, the method further comprises:

acquiring resource pushing description information of the resource pushing configuration information;

when the current push configuration instruction is obtained, the current push information text information is switched from a first push configuration state to a second push configuration state when the update mode is indicated to be in an update maintenance mode, and the current push configuration instruction of the current push information text information in the resource push configuration information is obtained according to the resource push description information;

when the current push information text information is obtained and used for indicating that the update mode is in the update non-maintenance mode, switching the current push information text information from a second push configuration state to a first push configuration state, and obtaining the management content description data in the resource push configuration information according to the resource push description information;

wherein the determining a current push configuration instruction of the current push information text message in the resource push configuration message includes:

taking an information text configuration source of the current pushed information text information in the resource pushing configuration information as a target configuration source, and taking a preset information text configuration source range in the current pushed information text information as a fixed range to determine a traversal configuration source range;

determining a high-heat-degree range of a configuration source of the current push information text information from the traversal configuration source range according to the configuration source heat-degree range of the current push information text information; wherein, the high-heat range of the configuration source is the high-heat range of the push configuration instruction stored in the current push information text message;

searching a current pushing configuration instruction of the current pushing information text information from the configuration source high-heat range;

when the current pushing configuration instruction is not searched from the high-heat range of the configuration source, taking an information text configuration source of the current pushing information text information in the resource pushing configuration information as a target configuration source, taking an information text index corresponding to the current pushing information text information as a target operation object, and dividing according to set index types to obtain a plurality of pushing information text indexes;

and acquiring an index category code corresponding to each pushed information text index in the pushed information text indexes, and fusing all the index category codes according to the sequence of the pushed information text indexes corresponding to each index category code to generate a current pushing configuration instruction of the current pushed information text information.

10. A deep learning service system, comprising:

a memory for storing executable instructions;

a processor, configured to execute the executable instructions stored in the memory, and implement the resource pushing method based on the big data line mode according to any one of claims 1 to 9.