CN112486710A

CN112486710A - Information acquisition method based on big data and artificial intelligence and digital content service platform

Info

Publication number: CN112486710A
Application number: CN202011499106.2A
Authority: CN
Inventors: 夏红梅
Original assignee: Individual
Current assignee: Zhejiang Panshi Information Technology Co ltd
Priority date: 2020-12-17
Filing date: 2020-12-17
Publication date: 2021-03-12
Anticipated expiration: 2040-12-17
Also published as: CN112486710B; CN113238878A; CN113254231A

Abstract

The embodiment of the application provides an information acquisition method based on big data and artificial intelligence and a digital content service platform, wherein at least two key operation acquisition node business hierarchies are used as a target business hierarchy acquisition plan for indicating acquisition configuration of acquisition feature items of acquisition page objects represented by the key operation acquisition nodes of the business hierarchies. Then, the operation distribution of the key acquisition nodes can be updated by adopting the target business layered acquisition plan, and the updated operation distribution of the key acquisition nodes is sent to the acquisition configuration process of the software acquisition plan, so that the acquisition configuration process of the software acquisition plan can acquire and configure the acquisition characteristic items of the acquisition page objects represented by the key operation acquisition nodes of the business layers according to the indication of the target business layered acquisition plan in the process of configuring the acquisition process of the business functional area, thereby reducing the times of re-calling the acquisition configuration, saving the induction time of the acquisition data and improving the execution efficiency of the acquisition plan.

Description

Information acquisition method based on big data and artificial intelligence and digital content service platform

Technical Field

The application relates to the technical field of information acquisition, in particular to an information acquisition method based on big data and artificial intelligence and a digital content service platform.

Background

In the information acquisition process, the inventor researches and discovers that in the current acquisition configuration scheme, each key operation acquisition node needs to be circularly and independently subjected to acquisition configuration, so that the number of times of re-calling of each acquisition configuration is large. However, the inventor has found that, in fact, development and use of many software function services generally have an aggregation characteristic of service layering, and how to reduce the number of times of recalling acquisition configuration to save the induction time of acquiring data and improve the execution efficiency of an acquisition plan is a technical problem to be solved in the field.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide an information acquisition method and a digital content service platform based on big data and artificial intelligence, which can perform acquisition configuration on acquisition feature items of acquisition page objects represented by key operation acquisition nodes of business hierarchy according to the indication of a target business hierarchy acquisition plan, thereby reducing the number of times of re-calling acquisition configuration, saving the induction time of acquisition data, and improving the execution efficiency of the acquisition plan.

In a first aspect, the present application provides an information acquisition method based on big data and artificial intelligence, which is applied to a digital content service platform, where the digital content service platform is in communication connection with a plurality of digital content subscription devices, and the method includes:

acquiring a service function area acquisition process obtained by a current information push source based on an information push strategy;

acquiring large data label information acquired by a plurality of key operation acquisition nodes in key acquisition node operation distribution of the service functional area acquisition process; each key operation acquisition node is used for representing one or more acquisition page objects which need to be acquired in the acquisition preparation process of the service functional area, and the acquisition feature items of the acquisition page objects represented by each key operation acquisition node need to be acquired and activated; the collected big data label information of any key operation collecting node is used for reflecting the collection type relation between the any key operation collecting node and other key operation collecting nodes;

according to the collected big data label information of each key operation collection node, layering at least two key operation collection node services into a target service layered collection plan, wherein the target service layered collection plan is used for indicating collection configuration of collection characteristic items of collection page objects represented by the key operation collection nodes of the service layers;

updating the running distribution of the key acquisition nodes by adopting the target business layered acquisition plan, sending the updated running distribution of the key acquisition nodes to an acquisition configuration process of a software acquisition plan, indicating the acquisition configuration process of the software acquisition plan according to the indication of the target business layered acquisition plan, acquiring and configuring the acquisition characteristic items of the acquisition page objects represented by the business layered key operation acquisition nodes in the acquisition preparation process of the business functional area acquisition process, and outputting the acquisition configuration result.

In a possible implementation manner of the first aspect, the plurality of key operation acquisition nodes correspond to a target information acquisition chain, and the target information acquisition chain is obtained by connecting a plurality of acquisition migration chains to each key operation acquisition node based on an acquisition chain distribution relationship of each key operation acquisition node in the key acquisition node operation distribution;

the acquisition chain distribution relationship is used for indicating that: a key operation collection node matches the collection relationship of other key operation collection nodes along at least one collection operation chain in the key collection node operation distribution;

the collection plan data label information of any key operation collection node comprises at least one of the following items: the acquisition plan partition sequence of any key operation acquisition node and the inverse acquisition plan partition sequence of any key operation acquisition node;

the acquisition plan partition in the acquisition plan partition sequence of any key operation acquisition node is as follows: the key operation acquisition nodes are covered by all forward acquisition modes from the first key operation acquisition node in the target information acquisition chain to any one key operation acquisition node;

the acquisition plan partition sequence of any key operation acquisition node separates the acquisition plan partition which is the first of the key operation acquisition nodes from the acquisition plan partition sequence of any key operation acquisition node and is the first acquisition plan partition of any key operation acquisition node;

the inverse acquisition plan partition in the inverse acquisition plan partition sequence of any key operation acquisition node is as follows: the key operation acquisition nodes are covered by all reverse acquisition modes from the first key operation acquisition node in the reverse acquisition relation corresponding to the target information acquisition chain to any one key operation acquisition node;

the inverse acquisition plan partition sequence of any key operation acquisition node separates the inverse acquisition plan partition which is the first of any key operation acquisition node from the inverse acquisition plan partition which is the first inverse acquisition plan partition of any key operation acquisition node;

the inverse acquisition relation is obtained by performing inverse processing on each acquisition migration chain in the target information acquisition chain;

the method for layering at least two key operation acquisition node services into a target service layered acquisition plan according to the acquired big data label information of each key operation acquisition node comprises the following steps:

constructing an acquisition plan data label network consisting of a plurality of key operation acquisition nodes according to the acquired big data label information of each key operation acquisition node;

extracting service hierarchical distribution information based on the acquisition plan data label network;

the service hierarchical distribution information includes: acquiring plan sequences required by multi-layer service layering, wherein at least one acquiring plan in each acquiring plan sequence is the key operation acquiring node;

and performing at least one layer of service hierarchical iterative processing on the plurality of key operation acquisition nodes according to the service hierarchical distribution information to obtain a target service hierarchical acquisition plan.

In a possible implementation manner of the first aspect, the constructing an acquisition plan data tag network composed of a plurality of key operation acquisition nodes according to the acquired big data tag information of each key operation acquisition node includes:

taking the first key operation acquisition node in the target information acquisition chain as a reference acquisition plan of an acquisition plan data label network, and determining the rest key operation acquisition nodes except the first key operation acquisition node in the target information acquisition chain in the plurality of key operation acquisition nodes;

acquiring a first acquisition plan partition of each remaining key operation acquisition node from an acquisition plan partition sequence in the acquired big data label information of each remaining key operation acquisition node;

determining the first acquisition type relation among all the key operation acquisition nodes according to the first acquisition plan partition of each remaining key operation acquisition node;

and adding the remaining key operation acquisition nodes to the reference acquisition plan according to the relationship of the first acquisition type so as to obtain an acquisition plan data label network.

In a possible implementation manner of the first aspect, the parent acquisition plan of each critical operation acquisition node in the acquisition plan data tag network except for the reference acquisition plan is: the first acquisition plan partition of each key operation acquisition node; w collection plan data label combinations exist in the key operation collection nodes, and one collection plan data label combination is associated with a collection plan sequence required by at least one service layer; wherein W is a positive integer;

the extracting service hierarchical distribution information based on the collection plan data label network comprises the following steps:

selecting a first key operation acquisition node from key operation acquisition nodes which are not subjected to targeted processing in the acquisition plan data label network according to the targeted processing sequence of the label priority;

detecting whether a W acquisition plan data label combination is formed by a second key operation acquisition node and a first key operation acquisition node according to an inverse acquisition plan partition sequence of each key operation acquisition node except for the last key operation acquisition node in the target information acquisition chain, wherein W belongs to [1, W ];

the second key operation acquisition node meets the following conditions: the second key operation acquisition node is the first acquisition plan partition of the first key operation acquisition node, and the first key operation acquisition node is the first inverse acquisition plan partition of the second key operation acquisition node;

if the target service hierarchy exists, selecting at least one key operation acquisition node from the plurality of key operation acquisition nodes according to the second key operation acquisition node, adding the at least one key operation acquisition node into an acquisition plan sequence required by the target service hierarchy associated with the w acquisition plan data label combination, and continuing to process the acquisition plan data label network in a targeted manner; and if not, reselecting the first key operation acquisition node until all key operation acquisition nodes in the acquisition plan data label network are processed in a targeted manner.

In a possible implementation manner of the first aspect, if the first collection plan data label combination exists, selecting at least one key operation collection node from the multiple key operation collection nodes according to the second key operation collection node, and adding the selected key operation collection node to a collection plan sequence required by a target service hierarchy associated with the w-th collection plan data label combination includes:

if yes, acquiring an extended acquisition plan sequence of the second key operation acquisition node from the acquisition plan data tag network;

if the extended acquisition plan sequence only comprises the first key operation acquisition node and an extended acquisition plan of the first key operation acquisition node, selecting the first key operation acquisition node and the second key operation acquisition node, and adding the first key operation acquisition node and the second key operation acquisition node into an acquisition plan sequence required by a target service hierarchy associated with the w-th acquisition plan data label combination;

and if the extended acquisition plan sequence comprises other extended acquisition plans except the first key operation acquisition node and the extended acquisition plan of the first key operation acquisition node, selecting the other extended acquisition plans to add to the acquisition plan sequence required by the target service hierarchy.

In a possible implementation manner of the first aspect, the selecting the first key operation acquisition node and the second key operation acquisition node, and adding the selected first key operation acquisition node and the second key operation acquisition node to an acquisition plan sequence required by a target service hierarchy associated with the w-th acquisition plan data label combination includes:

detecting whether a first historical acquisition plan sequence including the first key operation acquisition node exists in an acquisition plan sequence required by historical layer service layering associated with the previous w-1 acquisition plan data label combinations;

if the first historical acquisition plan sequence exists, adding a service hierarchical acquisition plan corresponding to the first historical acquisition plan sequence and the second key operation acquisition node into an acquisition plan sequence required by a target service hierarchy associated with the w acquisition plan data label combination;

and if the first historical acquisition plan sequence does not exist, adding the first key operation acquisition node and the second key operation acquisition node into the acquisition plan sequence required by the target service hierarchy.

In a possible implementation manner of the first aspect, the selecting the other extended acquisition plans to be added to the acquisition plan sequence required by the target service hierarchy includes:

detecting whether a second historical acquisition plan sequence exists in acquisition plan sequences required by historical layer service hierarchies associated with the previous w-1 acquisition plan data label combinations, wherein the second historical acquisition plan sequence comprises service hierarchy acquisition plans corresponding to other extended acquisition plans;

if the second historical acquisition plan sequence exists, adding a service hierarchical acquisition plan, the first key operation acquisition node and the second key operation acquisition node corresponding to the second historical acquisition plan sequence to an acquisition plan sequence required by the target service hierarchy;

and if the second historical acquisition plan sequence does not exist, adding the other extended acquisition plans to the acquisition plan sequence required by the target service hierarchy, and adding the service hierarchy acquisition plans of the other extended acquisition plan service hierarchies, the first key operation acquisition node and the second key operation acquisition node to the acquisition plan sequence required by the next label service hierarchy below the target service hierarchy associated with the w-th acquisition plan data label combination.

In a possible implementation manner of the first aspect, the updating, by using the target service hierarchical acquisition plan, the operation distribution of the key acquisition node includes:

adding the target service layered acquisition plan in the key acquisition node operation distribution, and connecting the target service layered acquisition plan and the service layered key operation acquisition nodes by adopting an acquisition and migration chain;

and adding a matched acquisition plan for the key operation acquisition nodes which are not layered by the service in the operation distribution of the key acquisition nodes, and adding a matched acquisition plan for the layered acquisition plan of the target service in the operation distribution of the key acquisition nodes.

In a possible implementation manner of the first aspect, the step of obtaining a service function area acquisition process obtained by a current information push source based on an information push policy includes:

acquiring an information push operation container of the digital content subscription equipment, and performing information push strategy analysis on the information push operation container through an information push service to obtain information push strategy information of information push sources in the information push operation container, wherein the information push operation container is a cloud computing container formed by information push strategies bound by the information push sources and generated based on a user feedback portrait;

analyzing a service functional area based on the information pushing strategy information of the information pushing source to obtain a target service functional area of the information pushing source;

analyzing the collected time-space sequence control record information of the information pushing operation container based on the collected time-space sequence control script to obtain the collected time-space sequence control record information of the information pushing source;

carrying out service function area updating on a target service function area of an information pushing source in the information pushing operation container and acquired time-sequence control recorded information of the information pushing source to obtain service function area updating information of the information pushing source, and carrying out information acquisition partition positioning on the information pushing strategy based on the service function area updating information of the information pushing source to obtain a current information acquisition strategy of the information pushing source;

performing script injection according to the current information acquisition strategy of the information push source to obtain a corresponding service function area acquisition process;

the step of acquiring the collected big data label information of a plurality of key operation collection nodes in the key collection node operation distribution of the service functional area collection process comprises the following steps:

extracting each key acquisition node in the acquisition process of the business functional area, constructing operation distribution of the key acquisition nodes according to the business relationship of each key acquisition node, acquiring the large data label information of each key acquisition node according to the acquisition type relationship between each key operation acquisition node and other key operation acquisition nodes, wherein the acquisition type relationship between each key operation acquisition node and other key operation acquisition nodes is acquired from process configuration information in the acquisition process of the business functional area.

In a second aspect, an embodiment of the present application further provides an information collecting apparatus based on big data and artificial intelligence, which is applied to a digital content service platform, where the digital content service platform is in communication connection with a plurality of digital content subscribing devices, and the apparatus includes:

the first acquisition module is used for acquiring a service function area acquisition process acquired by a current information push source based on an information push strategy;

the second acquisition module is used for acquiring the acquired big data label information of a plurality of key operation acquisition nodes in the key acquisition node operation distribution of the service functional area acquisition process; each key operation acquisition node is used for representing one or more acquisition page objects which need to be acquired in the acquisition preparation process of the service functional area, and the acquisition feature items of the acquisition page objects represented by each key operation acquisition node need to be acquired and activated; the collected big data label information of any key operation collecting node is used for reflecting the collection type relation between the any key operation collecting node and other key operation collecting nodes;

the service layering module is used for layering at least two key operation acquisition node services into a target service layering acquisition plan according to the acquired big data label information of each key operation acquisition node, and the target service layering acquisition plan is used for indicating acquisition configuration of acquisition feature items of acquisition page objects represented by the key operation acquisition nodes of the service layering;

and the acquisition configuration module is used for updating the running distribution of the key acquisition nodes by adopting the target service layered acquisition plan, sending the updated running distribution of the key acquisition nodes to an acquisition configuration process of a software acquisition plan, indicating the acquisition configuration process of the software acquisition plan to acquire and configure acquisition characteristic items of acquisition page objects represented by the service layered key operation acquisition nodes in the acquisition preparation process of the service functional area acquisition process according to the indication of the target service layered acquisition plan, and outputting an acquisition configuration result.

In a third aspect, an embodiment of the present application further provides an information acquisition system based on big data and artificial intelligence, where the information acquisition system based on big data and artificial intelligence includes a digital content service platform and a plurality of digital content subscription devices communicatively connected to the digital content service platform;

the digital content service platform is used for:

In a fourth aspect, an embodiment of the present application further provides a digital content service platform, where the digital content service platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one digital content subscription device, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the big data and artificial intelligence based information collection method in any one of the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer executes the big data and artificial intelligence based information acquisition method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, according to the collected big data tag information of a plurality of key operation collection nodes in the key operation collection node operation distribution of the service function area collection process, at least two key operation collection node services are layered into a target service layered collection plan, and the target service layered collection plan is used for indicating collection configuration of collection feature items of collection page objects represented by the key operation collection nodes of the service layers. Then, the operation distribution of the key acquisition nodes can be updated by adopting the target business layered acquisition plan, and the updated operation distribution of the key acquisition nodes is sent to the acquisition configuration process of the software acquisition plan, so that the acquisition configuration process of the software acquisition plan can acquire and configure the acquisition characteristic items of the acquisition page objects represented by the key operation acquisition nodes of the business layers according to the indication of the target business layered acquisition plan in the process of acquiring and configuring the acquisition process of the business functional area, thereby reducing the times of re-calling the acquisition configuration, saving the induction time of the acquisition data and improving the execution efficiency of the acquisition plan.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an information acquisition system based on big data and artificial intelligence provided in an embodiment of the present application;

FIG. 2 is a schematic flow chart of an information collection method based on big data and artificial intelligence provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of functional modules of an information acquisition device based on big data and artificial intelligence provided in an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a digital content service platform for implementing the above-described big data and artificial intelligence based information acquisition method according to an embodiment of the present application.

Detailed Description

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

FIG. 1 is an interactive schematic diagram of an information collection system 10 based on big data and artificial intelligence provided by an embodiment of the present application. The big data and artificial intelligence based information gathering system 10 may include a digital content service platform 100 and a digital content subscribing device 200 communicatively coupled to the digital content service platform 100. The big data and artificial intelligence based information gathering system 10 shown in FIG. 1 is only one possible example, and in other possible embodiments, the big data and artificial intelligence based information gathering system 10 may also include only some of the components shown in FIG. 1 or may also include other components.

Based on the inventive concept of the technical solution provided by the present application, the digital content service platform 100 provided by the present application may be applied to scenes such as smart medical, smart city management, smart industrial internet, general service monitoring management, etc. in which a big data technology or a cloud computing technology is applied, and for example, may also be applied to scenes such as but not limited to new energy automobile system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud automobile management platform, block chain financial data service platform, etc., but not limited thereto.

In this embodiment, the digital content service platform 100 and the digital content subscription device 200 in the big data and artificial intelligence based information collection system 10 may cooperatively perform the big data and artificial intelligence based information collection method described in the following method embodiment, and the detailed description of the method embodiment below may be referred to in the specific steps of the digital content service platform 100 and the digital content subscription device 200.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of an information collection method based on big data and artificial intelligence provided in an embodiment of the present application, and the information collection method based on big data and artificial intelligence provided in this embodiment may be executed by the digital content service platform 100 shown in fig. 1, and the information collection method based on big data and artificial intelligence is described in detail below.

Step S110, acquiring a business function area acquisition process obtained from the current information push source based on the information push strategy.

Step S120, acquiring large data label information acquired by a plurality of key operation acquisition nodes in the key acquisition node operation distribution of the service functional area acquisition process.

In this embodiment, each key operation acquisition node may be configured to represent one or more acquisition page objects that need to be acquired in an acquisition preparation process of the service functional area, and an acquisition feature item of an acquisition page object represented by each key operation acquisition node needs to be acquired and activated.

In this embodiment, the collected page object may refer to a specific page function element (e.g., live attention, commodity collection, etc.) of a collected data page (e.g., a browsing page of an e-commerce live broadcast).

In this embodiment, the collected big data tag information of any key operation collecting node is used to reflect the collection type relationship between any key operation collecting node and other key operation collecting nodes. For example, for e-commerce live broadcast service, a key operation acquisition node of live broadcast attention service, a key operation acquisition node of live broadcast ordering service, and a key operation acquisition node of live broadcast recommendation service have an acquisition type relationship, that is, after live broadcast attention, live broadcast ordering may be performed, and after live broadcast ordering, live broadcast recommendation of live broadcast goods may be performed.

And step S130, layering at least two key operation acquisition node services into a target service layered acquisition plan according to the acquired big data label information of each key operation acquisition node.

In this implementation, the target service hierarchical acquisition plan is used to instruct acquisition configuration of acquisition feature items of acquisition page objects represented by the key operation acquisition nodes of the service hierarchy. The service layering may refer to hierarchical clustering division performed on actually acquired services of the key operation acquisition nodes.

And step S140, updating the running distribution of the key acquisition nodes by adopting the target service hierarchical acquisition plan, and sending the updated running distribution of the key acquisition nodes to the acquisition configuration process of the software acquisition plan.

In this implementation, the updated key acquisition node operation distribution may be used to instruct the acquisition configuration process of the software acquisition plan to perform acquisition configuration on the acquisition feature items of the acquisition page object represented by the key operation acquisition nodes of the service hierarchy in the acquisition preparation process of the service functional area acquisition plan according to the instruction of the target service hierarchical acquisition plan, and output the acquisition configuration result.

In detail, in some possible implementation manners, the collection feature item may be configured according to a requirement of an actual software function, and may specifically be customized, or refer to a conventional collection configuration task in the prior art, which is not limited herein. In addition, the specific acquisition configuration logic of the acquisition configuration process of the software acquisition plan may perform adaptive configuration with reference to the acquisition feature items, and the content and form of the specific acquisition configuration are not the technical problems that the embodiments of the present application aim to solve, and will not be described in detail herein.

Based on the above steps, in this embodiment, at least two key operation collection node services may be layered into a target service layered collection plan according to the collected big data tag information of a plurality of key operation collection nodes in the key operation collection node operation distribution of the service functional area collection process, where the target service layered collection plan is used to instruct to collect and configure the collection feature items of the collection page object represented by the key operation collection nodes of the service layered. Then, the operation distribution of the key acquisition nodes can be updated by adopting the target business layered acquisition plan, and the updated operation distribution of the key acquisition nodes is sent to the acquisition configuration process of the software acquisition plan, so that the acquisition configuration process of the software acquisition plan can acquire and configure the acquisition characteristic items of the acquisition page objects represented by the key operation acquisition nodes of the business layers according to the indication of the target business layered acquisition plan in the process of acquiring and configuring the acquisition process of the business functional area, thereby reducing the times of re-calling the acquisition configuration, saving the induction time of the acquisition data and improving the execution efficiency of the acquisition plan.

In a possible implementation manner, before the embodiments of the present application are described in detail, the following first explains the respective defined terms so that those skilled in the art can clearly and completely realize the scheme of the embodiments of the present application.

In detail, the plurality of key operation collection nodes correspond to a target information collection chain. The target information acquisition chain is obtained by connecting a plurality of acquisition and migration chains with each key operation acquisition node based on the acquisition chain distribution relation of each key operation acquisition node in the key acquisition node operation distribution.

As such, in the art, the acquisition chain distribution relationship may be used to indicate: and one key operation acquisition node is matched with the acquisition relation of other key operation acquisition nodes along at least one acquisition operation chain in the key acquisition node operation distribution.

The collection plan data label information of any key operation collection node comprises at least one of the following items: an acquisition plan partition sequence for any key operational acquisition node and an inverse acquisition plan partition sequence for any key operational acquisition node.

It is worth to be noted that the acquisition plan partition in the acquisition plan partition sequence of any critical operation acquisition node may be understood as: and the key operation acquisition nodes are covered by all forward acquisition modes from the first key operation acquisition node to any key operation acquisition node in the target information acquisition chain.

The collection plan partition sequence of any key operation collection node is separated from the collection plan partition with the first key operation collection node and is the first collection plan partition of any key operation collection node.

In this embodiment, the inverse acquisition plan partition in the inverse acquisition plan partition sequence of any key operation acquisition node may be understood as: and the key operation acquisition nodes are covered by all reverse acquisition modes from the first key operation acquisition node to any key operation acquisition node in the reverse acquisition relation corresponding to the target information acquisition chain.

In this embodiment, the inverse acquisition plan partition sequence of any key operation acquisition node is separated from the first inverse acquisition plan partition of any key operation acquisition node, and is the first inverse acquisition plan partition of any key operation acquisition node.

In this embodiment, the inverse acquisition relationship refers to an acquisition relationship obtained by performing inverse processing on each acquisition migration chain in the target information acquisition chain.

Thus, for step S130, in the process of layering at least two key operation collection node services into a target service layered collection plan according to the collected big data tag information of each key operation collection node, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S131, constructing an acquisition plan data label network formed by a plurality of key operation acquisition nodes according to the acquired big data label information of each key operation acquisition node.

And a substep S132 of extracting service hierarchical distribution information based on the acquisition plan data label network.

In this embodiment, the service hierarchical distribution information may include: and acquiring plan sequences required by the multi-layer service layering, wherein at least one acquiring plan is a key operation acquiring node in each acquiring plan sequence.

And a substep S133 of performing at least one layer of service hierarchical iterative processing on the plurality of key operation acquisition nodes according to the service hierarchical distribution information to obtain a target service hierarchical acquisition plan.

For example, in a possible implementation manner, a w acquisition plan sequence required by a w-th service hierarchy may be determined according to service hierarchy distribution information, and a total acquisition plan hop count of the w-th acquisition plan sequence may be determined according to an acquisition plan hop count of each acquisition plan in the w-th acquisition plan sequence; and W belongs to [1, W ], wherein W is the distribution layer level number of the service hierarchical distribution information. And when the total number of acquisition plan jumping times of the w-th acquisition plan sequence is less than or equal to the acquisition plan jumping time threshold, performing service layering processing on each acquisition plan in the w-th acquisition plan sequence to obtain a w-th service layering acquisition plan. And if the current value of W is less than W and the total acquisition plan jump times of the W +1 acquisition plan sequence required by the W +1 th service hierarchy acquired according to the service hierarchy level information is greater than the acquisition plan jump time threshold, acquiring a target service hierarchy acquisition plan according to the W-th service hierarchy acquisition plan.

In one possible implementation, the substep S131 can be implemented by the following embodiments.

(1) And taking the first key operation acquisition node in the target information acquisition chain as a reference acquisition plan of an acquisition plan data label network, and determining the rest key operation acquisition nodes except the first key operation acquisition node in the target information acquisition chain in the plurality of key operation acquisition nodes.

(2) And acquiring the first acquisition plan partition of each remaining key operation acquisition node from the acquisition plan partition sequence in the acquired big data label information of each remaining key operation acquisition node.

(3) And determining the first acquisition type relationship among the key operation acquisition nodes according to the first acquisition plan partition of each remaining key operation acquisition node.

(4) And adding each remaining key operation acquisition node to the reference acquisition plan according to the relationship of the first acquisition type so as to obtain an acquisition plan data label network.

In a possible implementation manner, the parent acquisition plan of each key operation acquisition node in the acquisition plan data tag network except for the reference acquisition plan is as follows: the first acquisition plan partition for each critical operation acquisition node. W collection plan data label combinations exist in a plurality of key operation collection nodes, and one collection plan data label combination is associated with a collection plan sequence required by at least one business layer. Wherein W is a positive integer.

Thus, the substep S132 can be realized by the following embodiments.

(1) And selecting a first key operation acquisition node from key operation acquisition nodes which are not subjected to the targeted processing in the acquisition plan data label network according to the targeted processing sequence of the label priority.

(2) And detecting whether a W-th collection plan data label combination formed by a second key operation collection node and a first key operation collection node exists or not according to the inverse collection plan partition sequence of each key operation collection node except the last key operation collection node in the target information collection chain, wherein W belongs to [1, W ].

(3) The second key operation acquisition node meets the following conditions: the second critical operation collection node is the first collection plan partition of the first critical operation collection node, and the first critical operation collection node is the first inverse collection plan partition of the second critical operation collection node.

(4) If the target service hierarchy exists, selecting at least one key operation acquisition node from a plurality of key operation acquisition nodes according to a second key operation acquisition node, adding the at least one key operation acquisition node into an acquisition plan sequence required by the target service hierarchy associated with the w-th acquisition plan data label combination, and continuing to process the acquisition plan data label network in a targeted manner. And if not, reselecting the first key operation acquisition node until all key operation acquisition nodes in the acquisition plan data label network are processed in a targeted manner.

For example, if the acquisition plan data tag exists, an extended acquisition plan sequence of a second key operation acquisition node may be acquired from the acquisition plan data tag network, and if the extended acquisition plan sequence only includes a first key operation acquisition node and an extended acquisition plan of the first key operation acquisition node, the first key operation acquisition node and the second key operation acquisition node are selected and added to an acquisition plan sequence required by a target service hierarchy associated with a w-th acquisition plan data tag combination. Or if the extended acquisition plan sequence comprises other extended acquisition plans except the first key operation acquisition node and the extended acquisition plan of the first key operation acquisition node, selecting other extended acquisition plans to add to the acquisition plan sequence required by the target service hierarchy.

Illustratively, in the process of selecting a first key operation acquisition node and a second key operation acquisition node and adding the first key operation acquisition node and the second key operation acquisition node to an acquisition plan sequence required by a target service hierarchy associated with a w-th acquisition plan data tag combination, it may be detected whether a first historical acquisition plan sequence including the first key operation acquisition node exists in an acquisition plan sequence required by a historical layer service hierarchy associated with the previous w-1 acquisition plan data tag combinations.

For example, if there is a first historical acquisition plan sequence, adding a service hierarchical acquisition plan corresponding to the first historical acquisition plan sequence and a second key operation acquisition node to an acquisition plan sequence required by a target service hierarchy associated with a w-th acquisition plan data tag combination.

For another example, if there is no first historical acquisition plan sequence, then a first critical operation acquisition node and a second critical operation acquisition node are added to the acquisition plan sequence required by the target business hierarchy.

In a possible implementation manner, in the process of selecting other extended acquisition plans to add to the acquisition plan sequence required by the target service hierarchy, it may be specifically detected whether a second historical acquisition plan sequence exists in the acquisition plan sequences required by the historical layer service hierarchies associated with the previous w-1 acquisition plan data tag combinations, where the second historical acquisition plan sequence includes the service hierarchy acquisition plans corresponding to the other extended acquisition plans.

For example, if a second historical acquisition plan sequence exists, a service hierarchical acquisition plan, a first key operation acquisition node and a second key operation acquisition node corresponding to the second historical acquisition plan sequence are added to an acquisition plan sequence required by a target service hierarchy.

For another example, if there is no second historical acquisition plan sequence, then add the other extended acquisition plan to the acquisition plan sequence required for the target business hierarchy, and add the business hierarchy acquisition plan, the first critical operation acquisition node, and the second critical operation acquisition node of the other extended acquisition plan business hierarchy to the acquisition plan sequence required for the next tag business hierarchy below the target business hierarchy associated with the w-th acquisition plan data tag combination.

In one possible implementation manner, for step S140, in the process of updating the operation distribution of the key acquisition nodes by using the target business hierarchical acquisition plan, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S141 of adding a target service layered acquisition plan in the operation distribution of the key acquisition nodes and connecting the target service layered acquisition plan and the service layered key operation acquisition nodes by adopting an acquisition and migration chain.

And a substep S142, adding a matched acquisition plan for the key operation acquisition nodes which are not layered by the service in the operation distribution of the key acquisition nodes, and adding a matched acquisition plan for the layered acquisition plan of the target service in the operation distribution of the key acquisition nodes.

In a possible implementation manner, for step S110, in the process of acquiring the service functional area acquisition process obtained based on the current information push source of the information push policy, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S111, acquiring an information push operation container of the digital content subscription device 200, and performing information push policy analysis on the information push operation container through an information push service to obtain information push policy information of an information push source in the information push operation container.

And a substep S112, analyzing the service functional region based on the information pushing policy information of the information pushing source to obtain a target service functional region of the information pushing source.

And a substep S113, analyzing the collected time-space sequence control record information of the information push operation container based on the collected time-space sequence control script to obtain the collected time-space sequence control record information of the information push source.

And a substep S114 of updating the service function region of the target service function region of the information push source in the information push operation container and the acquisition time-sequence control record information of the information push source to obtain the update information of the service function region of the information push source, and performing information acquisition partition positioning on the information push strategy based on the update information of the service function region of the information push source to obtain the current information acquisition strategy of the information push source.

In this embodiment, the information push service may be understood as an information push program running in the cloud, for example, each information push module in the information push process may be configured in the cloud in advance, for example, a function module that needs to use information push policy analysis in this embodiment, and then this operation is performed. The operation of the function module for the specific information pushing policy resolution can be referred to the following detailed description of step S111.

In this embodiment, the information push operation container may be understood as a cloud computing container formed by information push policies bound to information push sources that are generated based on the user feedback representation. The information push policy may refer to a policy package generated by a large amount of big data when the digital content subscription apparatus 200 performs any information push software. The information push source may refer to a certain data source formed under the information push policy.

In this embodiment, the service function area may be used to represent service function logic distinguishing information of a service function corresponding to each information push source, and the acquisition time-space sequence control record information may be used to describe the service function logic distinguishing information for each acquisition unit in the acquisition time-space sequence control process.

In the information pushing process, the service function area needs to be considered, so that the operation of targeted acquisition configuration is performed according to the feature distribution of the service function area. In this way, in the embodiment, the target service functional area of the information push source and the acquisition time-space sequence control record information of the information push source are updated through the service functional area, so that the information push strategy information of the information push source and the acquisition time-space sequence control record information of the information push source are integrated, and the service functional area of the rich information push source is extracted, thereby facilitating the efficient acquisition time-space sequence control based on the service functional area of the information acquisition strategy in the information acquisition strategy updating process; in addition, the information acquisition partition positioning is carried out on the information push source through the target service functional area of the information push source to obtain the current information acquisition strategy of the information push source, so that the information acquisition strategy of the modular unit is convenient to update, and the update restart time of the information acquisition strategy after the information acquisition strategy fails to update is reduced.

In a possible implementation manner, for step S112, in the process of acquiring large data tag information of multiple key operation acquisition nodes in the operation distribution of key acquisition nodes in the acquisition process of the service functional area, each key acquisition node in the acquisition process of the service functional area may be extracted to construct the operation distribution of key acquisition nodes according to the service relationship of each key acquisition node, and the large data tag information acquired by each key acquisition node is acquired according to the acquisition type relationship between each key operation acquisition node and other key operation acquisition nodes.

It should be noted that the collection type relationship between each key operation collection node and other key operation collection nodes is obtained from the process configuration information in the collection process of the service functional area.

While certain alternative embodiments of the present application will be described below with reference to the above, it should be understood that the following description of the embodiments is only exemplary and should not be taken as an exhaustive identification of the features necessary to implement the present invention.

In a possible implementation manner, for step S111, in the process of performing information push policy analysis on the information push operation container through the information push service to obtain information push policy information of an information push source in the information push operation container, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1111, acquiring a policy container logical pointer data set bound by the push logical pointer controller of each push source in the information push operation container.

In this embodiment, it is worth to be noted that the policy container logical pointer data set includes policy container logical pointer data using each logical pointer rule as a reference unit, and the policy container logical pointer data includes a logical pointer data source condition of the logical pointer rule, a logical pointer data source result, and an index operation node of a logical pointer data source in the logical pointer rule. For example, the logical pointer rule may be used to represent a business scope related to a code editing configuration process, the logical pointer data source condition may be used to characterize a condition of the logical pointer data source (e.g., a condition may be used when a certain function code is called), and the logical pointer data source result may be used to characterize a running result indicated after the logical pointer data source.

For example, in the present embodiment, the push logical pointer controller may be a software program, and the push logical pointer controller refers to a program having a function of pushing logical pointer data.

Substep S1112, for each logical pointer rule, determining whether each stored structure interpretation information in the index operation node row is a new information acquisition policy update code acquisition plan according to each index operation node row of the plurality of index operation node rows in the index operation node of the logical pointer data source of the logical pointer rule from each push source, according to the interpretation vector representation of the stored structure interpretation information in the index operation node row, determining a logical pointer data page of each code resource packet corresponding to the index operation node row according to the acquisition plan type of the new information acquisition policy update code acquisition plan in the index operation node row, dividing the logical pointer data page of the code resource packet into a plurality of sub logical pointer data pages for the logical pointer data page of each code resource packet, according to the interpretation tag and the preset storage interpretation range of each stored structure interpretation information in each sub logical pointer data page, it is determined whether the logical pointer data page of the code resource package is a logical pointer data page of the target logical pointer package.

It is worth mentioning that each storage structure interpretation information updates the classified storage structure behavior corresponding to each information collection policy.

And a substep S1113, obtaining information acquisition strategy update classification template block information of each piece of storage structure interpretation information in a logic pointer data page of a preset information acquisition strategy update classification template matching target logic pointer program package, wherein the information acquisition strategy update classification template block information comprises an index main key function label of an information acquisition strategy and an index main key application label of the information acquisition strategy, and the preset information acquisition strategy update classification template comprises matching strategies corresponding to index main key use types of different information acquisition strategies.

Substep S1114 determines, according to the information collection policy update classification template block information of the index operation node of each logical pointer data source of each different logical pointer rule in the policy container logical pointer data set, the variable information of the update cycle variable of the information collection policy of the index main key of each information collection policy and the constant information of the update cycle constant of each information collection policy, and according to the variable information of the update cycle variable of the information collection policy of the index main key of each information collection policy and the constant information of the update cycle constant of each information collection policy in the logical pointer data page of the target logical pointer package, determines the index main key tag combination object of the information collection policy of each push source in the logical pointer rule, and selects the index sorting information located in the index selection object range of the index main key of the information collection policy of the index main key tag combination object of the information collection policy and the index main key located in the information collection policy And after the index sorting information of the index selection object range of the index main key of the information acquisition strategy of the label combination object is used as the acquisition time-space sequence control script information of each push source in the logic pointer rule, the acquisition time-space sequence control script information of each push source in all the logic pointer rules is gathered to obtain the information push strategy information of the information push sources in the information push operation container.

For example, an update cycle constant of an information collection policy refers to data whose value remains unchanged throughout the operation, and is usually given directly in a command or program, and the types of data used as the constant are numeric, character, date, logical, and monetary. For another example, the update cycle variable of the information collection policy refers to data of the value policy in the whole operation process, and has an abstraction of a storage space, and the update cycle variable of the information collection policy is a placeholder convenient to use and used for referencing a memory address, and the address can store modifiable program information during Script operation.

In a possible implementation manner, for step S112, in the process of performing service functional area analysis based on the information push policy information of the information push source to obtain the target service functional area of the information push source, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1121, obtaining primary key constraint index object allocation information of a primary key constraint index object set added to the logical pointer data source description information of each acquired spatio-temporal sequence control script information in the information push strategy information of the information push source, and determining a first primary key constraint key word set corresponding to the primary key constraint index object allocation information.

For example, there may only be one aggregated index in a table, but each column in the table may have its own non-aggregated index. If a primary key constraint is created in the table, SQL Server will automatically generate a uniqueness constraint for it. When creating primary key constraints, if a CLUSTERED key is formulated, a unique aggregate index will be generated for the table.

It should be noted that the primary key constraint index object allocation information includes relationship graph collection plan object information for calculating relationship information according to relationship graph rules determined by relationship graph rule input information and relationship graph rule output information of the primary key constraint index object set, and the first primary key constraint key set includes a high-low order of priorities of a plurality of relationship graph collection plans of the relationship graph collection plan object information.

Substep S1122 determines a first relation graph rule characteristic based on the relation graph rule input information and a second relation graph rule characteristic based on the relation graph rule output information of the logical pointer data source description information of each acquisition time-space sequence control script information.

And a substep S1123 of determining a constraint business function positioning parameter for performing constraint business function positioning on the first primary key constraint keyword set according to the collection plan priority relationship of the relation graphs of the first relation graph rule characteristic and the second relation graph rule characteristic.

And a substep S1124, performing constraint service function positioning on the first primary key constraint keyword set based on the constraint service function positioning parameter to obtain a second primary key constraint keyword set.

And a substep S1125, performing relational graph type division on the second primary key constraint keyword set to obtain a plurality of relational graph type division sets, and performing feature extraction on each relational graph type division set to obtain relational graph type division variables.

And a substep S1126 of dividing the service function region corresponding to the variable according to the plurality of relation graph types corresponding to the second main key constraint keyword set and determining the service function region for collecting the time-space sequence control script information.

And a substep S1127 of obtaining a target service functional area of the information push source based on each service functional area of the collected time-sequence control script information.

Further, in a possible implementation manner, for step S113, in the process of analyzing the collection spatio-temporal sequence control record information of the information push operation container based on the collection spatio-temporal sequence control script to obtain the collection spatio-temporal sequence control record information of the information push source, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1131, inputting the information pushing operation container into a preset acquisition time-space sequence control script, and obtaining the matching degree of the information pushing operation container matched with the update rule of each preset information acquisition strategy.

And a substep S1132, determining an update rule of the target information acquisition policy corresponding to the information push operation container according to the matching degree of the information push operation container to the update rule of each preset information acquisition policy.

For example, the update rule of the preset information collection policy with the matching degree greater than the preset matching degree threshold may be determined as the update rule of the target information collection policy corresponding to the information pushing operation container.

And a substep S1133 of extracting acquisition time-sequence control record information matched with each information push source from the update rule description information of the information acquisition policy of the update rule of the target information acquisition policy corresponding to the information push operation container.

For example, in the extraction process, the feature information with the structured description information, which is matched with each information push source, in the update rule description information of the information collection policy may be specifically extracted.

In a possible implementation manner, for step S114, in the process of performing service functional area update on the target service functional area of the information push source in the information push operation container and the collected time-sequence control record information of the information push source to obtain the service functional area update information of the information push source, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1141 of adding the target service functional area of the information push source and the collected time-space sequence control record information into a service functional area updating program, and determining a relation diagram knowledge diagram of the target service functional area of the information push source and the collected time-space sequence control record information corresponding to the updating base table of each information collection strategy from the service functional area updating program.

And a substep S1142 of combining the knowledge graphs of the relationship graphs for carrying out relationship graph type division according to the relationship labels of the base table relationship between the updated base tables of the information acquisition strategies in the knowledge graphs of the different relationship graphs to obtain at least one knowledge graph sequence of the relationship graphs.

In this embodiment, the relationship labels of the base table relationship of the updated base table of the information acquisition strategies in any two relationship map knowledge graphs in the same relationship map knowledge graph sequence cover the preset storage interpretation range.

And a substep S1143 of determining program relation map call information corresponding to the relation map knowledge map sequence aiming at the target service function area of the information push source and the collected time-sequence control record information from the service function area update program based on each relation map knowledge map in the relation map knowledge map sequence aiming at each relation map knowledge map sequence.

In this embodiment, the program relation diagram call information at least includes characteristic information data of each relation diagram distribution of each relation diagram knowledge diagram in the relation diagram knowledge diagram sequence for a target service function region of an information push source and collection time-sequence control record information, and the program relation diagram call information is used for determining a relation distribution result of a relation diagram relation between the target service function region of the information push source and collection time-sequence control record information corresponding to an update base table of an information collection strategy in each relation diagram knowledge diagram in the relation diagram knowledge diagram sequence.

And a substep S1144 of determining a relation distribution result of the relation graph relation corresponding to the update base table of the information acquisition strategy in each relation graph knowledge graph in the relation graph knowledge graph sequence based on the program relation graph call information, classifying the relation distribution result of the relation graph relation by adopting a preset base table relation classification model to obtain a classification result, and obtaining the service function area update information of the information push source according to the classification result.

In a possible implementation manner, still referring to step S114, in the process of performing information acquisition partition location on the information push policy based on the service functional area update information of the information push source to obtain the current information acquisition policy of the information push source, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1145 of obtaining the positioning information of the information acquisition partition of the information pushing source under the information pushing strategy.

In this embodiment, the information collection partition may refer to a logical partition of each information collection data area under the information pushing policy, for example, a service logical partition, a functional partition where a user subscribes to a service, and the like.

And a substep S1146 of obtaining information acquisition partition positioning items under the positioning information of the information acquisition partition and base table relationship configuration information corresponding to each information acquisition partition positioning item.

And a substep S1147 of overlaying and configuring the service functional area update information of the information push source under the base table relationship configuration information corresponding to each information acquisition partition positioning item to obtain a current information acquisition strategy of the information push source.

Fig. 3 is a schematic diagram of functional modules of an information acquisition apparatus 300 based on big data and artificial intelligence according to an embodiment of the present disclosure, in this embodiment, the information acquisition apparatus 300 based on big data and artificial intelligence may be divided into the functional modules according to an embodiment of a method executed by the digital content service platform 100, that is, the following functional modules corresponding to the information acquisition apparatus 300 based on big data and artificial intelligence may be used to execute each embodiment of the method executed by the digital content service platform 100. The big data and artificial intelligence based information collection device 300 may include a first obtaining module 310, a second obtaining module 320, a business layering module 330, and a collection configuration module 340, and the functions of the functional modules of the big data and artificial intelligence based information collection device 300 are described in detail below.

The first obtaining module 310 is configured to obtain a service functional area acquisition process obtained by a current information push source based on an information push policy. The first obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the first obtaining module 310, reference may be made to the detailed description of the step S110.

A second obtaining module 320, configured to obtain collected big data tag information of multiple key operation collection nodes in the key collection node operation distribution of the service functional area collection process; each key operation acquisition node is used for representing one or more acquisition page objects which need to be acquired in the acquisition preparation process of the service functional area, and the acquisition feature items of the acquisition page objects represented by each key operation acquisition node need to be acquired and activated; and the acquired big data label information of any key operation acquisition node is used for reflecting the acquisition type relation between the any key operation acquisition node and other key operation acquisition nodes. The second obtaining module 320 may be configured to perform the step S120, and for a detailed implementation of the second obtaining module 320, reference may be made to the detailed description of the step S120.

And the service layering module 330 is configured to layer at least two key operation acquisition node services into a target service layering acquisition plan according to the acquired big data tag information of each key operation acquisition node, where the target service layering acquisition plan is used to instruct acquisition and configuration of acquisition feature items of acquisition page objects represented by the key operation acquisition nodes of the service layering. The business layering module 330 may be configured to perform the step S130, and the detailed implementation of the business layering module 330 may refer to the detailed description of the step S130.

And the acquisition configuration module 340 is configured to update the running distribution of the key acquisition nodes by using the target service hierarchical acquisition plan, and send the updated running distribution of the key acquisition nodes to an acquisition configuration process of a software acquisition plan, where the updated running distribution of the key acquisition nodes is used to instruct the acquisition configuration process of the software acquisition plan to perform acquisition configuration on acquisition feature items of acquisition page objects represented by the service hierarchical key acquisition nodes in an acquisition preparation process of the service functional area acquisition process according to instructions of the target service hierarchical acquisition plan, and output an acquisition configuration result. The collection configuration module 340 may be configured to perform the step S140, and the detailed implementation manner of the collection configuration module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the first obtaining module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the first obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

Fig. 4 shows a hardware structure diagram of the digital content service platform 100 for implementing the big data and artificial intelligence based information collection method, according to an embodiment of the present disclosure, as shown in fig. 4, the digital content service platform 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 (for example, the first obtaining module 310, the second obtaining module 320, the service layering module 330, and the collecting configuration module 340 included in the big data and artificial intelligence based information collecting apparatus 300 shown in fig. 3), so that the processor 110 may execute the big data and artificial intelligence based information collecting method according to the above method embodiment, where 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 the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned digital content subscribing device 200.

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

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, an embodiment of the present application further provides a readable storage medium, where the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the method for acquiring information based on big data and artificial intelligence is implemented.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, particular push elements are used in this description to describe embodiments of this description. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a passive programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, as a stand-alone index arrangement, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences are processed, the use of alphanumeric characters, or the use of other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

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, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An information acquisition method based on big data and artificial intelligence is applied to a digital content service platform, wherein the digital content service platform is in communication connection with a plurality of digital content subscription devices, and the method comprises the following steps:

2. The big data and artificial intelligence based information acquisition method according to claim 1, wherein the plurality of key operation acquisition nodes correspond to a target information acquisition chain, and the target information acquisition chain is obtained by connecting each key operation acquisition node with a plurality of acquisition migration chains based on an acquisition chain distribution relationship of each key operation acquisition node in the key acquisition node operation distribution;

3. The big data and artificial intelligence based information collection method according to claim 2, wherein the constructing of the collection plan data label network composed of the plurality of key operation collection nodes according to the collection big data label information of each key operation collection node comprises:

4. The big-data and artificial-intelligence based information gathering method as claimed in claim 2, wherein the parent gathering plan of each key operational gathering node in the gathering plan data tag network except the reference gathering plan is: the first acquisition plan partition of each key operation acquisition node; w collection plan data label combinations exist in the key operation collection nodes, and one collection plan data label combination is associated with a collection plan sequence required by at least one service layer; wherein W is a positive integer;

5. The big data and artificial intelligence based information collection method according to claim 4, wherein if existing, selecting at least one key operation collection node from the plurality of key operation collection nodes according to the second key operation collection node, and adding the selected at least one key operation collection node to the collection plan sequence required by the target business hierarchy associated with the w-th collection plan data tag combination comprises:

6. The big data and artificial intelligence based information gathering method as claimed in claim 5, wherein said selecting the first and second critical operation gathering nodes to add to the gathering plan sequence required by the target business hierarchy associated with the w-th gathering plan data tag combination comprises:

7. The big data and artificial intelligence based information gathering method as claimed in claim 5, wherein said selecting the other extended gathering plans to add to the gathering plan sequence required by the target business hierarchy comprises:

8. The big data and artificial intelligence based information collection method according to any one of claims 1-7, wherein said updating the key collection node operation distribution using the target business hierarchical collection plan comprises:

9. The big data and artificial intelligence based information collection method according to any one of claims 1 to 8, wherein the step of obtaining a service functional area collection process obtained from a current information push source based on an information push policy comprises:

extracting each key acquisition node in the acquisition process of the service functional area to construct key acquisition node operation distribution according to the service relationship of each key acquisition node, and acquiring big data label information of each key acquisition node according to the acquisition type relationship between each key operation acquisition node and other key operation acquisition nodes, wherein the acquisition type relationship between each key operation acquisition node and other key operation acquisition nodes is acquired from process configuration information in the acquisition process of the service functional area.

10. A digital content service platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one digital content subscription device, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the big data and artificial intelligence based information collection method according to any one of claims 1 to 9.