CN114564522B - Intelligent push processing method and system based on block chain and big data mining - Google Patents
Intelligent push processing method and system based on block chain and big data mining Download PDFInfo
- Publication number
- CN114564522B CN114564522B CN202210218863.0A CN202210218863A CN114564522B CN 114564522 B CN114564522 B CN 114564522B CN 202210218863 A CN202210218863 A CN 202210218863A CN 114564522 B CN114564522 B CN 114564522B
- Authority
- CN
- China
- Prior art keywords
- cloud service
- article
- push
- linkage
- pushing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Probability & Statistics with Applications (AREA)
- Fuzzy Systems (AREA)
- Bioethics (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides an intelligent push processing method and system based on a block chain and big data mining, wherein in the process of performing deep learning optimization of interest decision on a target interest mining model based on a cloud service linkage event, the deep learning optimization stage distribution is performed on a cloud service linkage event log in the cloud service linkage event according to the feature learning cost, so that the feature learning effect can be improved when the deep learning optimization of the interest decision is performed on the target interest mining model, the interest mining precision is improved, and the precision of information push on a related target intelligent virtual service user group is improved subsequently.
Description
Technical Field
The invention relates to the technical field of cloud services, in particular to an intelligent pushing processing method and system based on a block chain and big data mining.
Background
The cloud service technology and the artificial intelligence are integrated, an intelligent ecological system is finally pursued, and mutual integration and intercommunication among different intelligent terminal devices, different system platforms and different application scenes are achieved in the system, so that everything is integrated.
In the related technology, interest mining is usually performed by combining a cloud service linkage behavior database related to big data analysis, and in the process of performing deep learning optimization of interest decision, the current optimization scheme does not consider allocation in a deep learning optimization stage, so that the characteristic learning effect is poor, the interest mining precision is influenced, and the subsequent precision of information push on a related target intelligent virtual service user group is influenced.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, the present invention provides an intelligent push processing method and system based on block chain and big data mining.
In a first aspect, the present invention provides an intelligent push processing method based on a block chain and big data mining, which is applied to an intelligent push processing system based on a block chain and big data mining, and the method includes:
acquiring a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database of a target intelligent virtual service user group;
mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on the selected cloud service scene interest mining model;
based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event so as to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event;
corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log group bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log group;
and in the distributed deep learning optimization stage, performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group in sequence, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event logs based on the target interest mining model, and performing information push on the target smart virtual service user group according to the target cloud service scene interest point distribution after obtaining the corresponding target cloud service scene interest point distribution.
In a second aspect, an embodiment of the present invention further provides an intelligent push processing system based on blockchain and big data mining, where the intelligent push processing system based on blockchain and big data mining includes a processor and a machine-readable storage medium, where the machine-readable storage medium has stored therein machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to implement the foregoing intelligent push processing method based on blockchain and big data mining.
Based on any one of the above aspects, in the process of performing deep learning optimization of interest decision on the target interest mining model based on the cloud service linkage event, the cloud service linkage event logs in the cloud service linkage event are subjected to deep learning optimization stage distribution according to the feature learning cost, so that the feature learning effect can be improved when performing deep learning optimization of interest decision on the target interest mining model, the interest mining precision is improved, and the precision of information push on the related target intelligent virtual service user group subsequently is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be extracted based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an intelligent push processing method based on a block chain and big data mining according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a structure of an intelligent push processing system based on blockchain and big data mining, configured to implement the intelligent push processing method based on blockchain and big data mining according to the embodiment of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a particular application and its requirements. It will be apparent to those skilled in the art that various changes can be made in the embodiments disclosed, and that the general principles defined in this disclosure may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Thus, the present invention is not limited to the described embodiments, but should be accorded the widest scope consistent with the claims.
Fig. 1 is a schematic flowchart of an intelligent push processing method based on a block chain and big data mining according to an embodiment of the present invention, and details of the intelligent push processing method based on a block chain and big data mining are described below.
Step S110, a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements is obtained from a cloud service linkage behavior database of a target smart virtual service user group.
For example, keyword field data logs can be called for a plurality of cloud service linkage events sequentially or simultaneously, and the cloud service linkage event logs in the cloud service linkage events are determined based on the called keyword field data logs, so that deep learning optimization of interest decision is performed on the target interest mining model according to the cloud service linkage event logs of the cloud service linkage events.
Step S120, mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on a cloud service scene interest mining model.
For example, the cloud service scene interest mining model may perform event interest mining on each cloud service linkage event log in the cloud service linkage event by using a deep learning network model in any related technology, so as to obtain an event interest variable bound to each cloud service linkage event log.
Step S130, based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event so as to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event.
For example, based on the event attention combination, the cloud service linkage event logs of the matched event attention features can be combined, so that the subsequent training effect is improved.
Step S140, corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log cluster bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log cluster.
And S150, performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group in sequence in the distributed deep learning optimization stage, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event log based on the target interest mining model, and performing information push on the target smart virtual service user group according to the target cloud service scene interest point distribution after obtaining the corresponding target cloud service scene interest point distribution.
Based on the above steps, in the process of performing deep learning optimization of interest decision on the target interest mining model based on the cloud service linkage event, the deep learning optimization stage distribution is performed on the cloud service linkage event logs in the cloud service linkage event according to the feature learning cost, so that the feature learning effect can be improved when performing deep learning optimization of interest decision on the target interest mining model, the interest mining precision is improved, and the precision of information push on the related target smart virtual service user group subsequently is improved.
In some embodiments, the aforementioned cloud business scenario interest mining model may include an event interest mining structure and an interest mining structure. Thus, in step S150, the following exemplary substeps may be implemented.
Step S151, transmitting the cloud service linkage event logs in each cloud service linkage event log group to the cloud service scene interest mining model, and outputting mining linkage interest points of each cloud service linkage event log.
Step S152, for all cloud service linkage event logs in each cloud service linkage event log group, carrying out loss analysis on mining linkage interest points of the cloud service linkage event logs and cloud service linkage interest points of the cloud service linkage events.
Step S153, if the mining linkage interest points of all the cloud service linkage event logs in any one cloud service linkage event log group have partial interest point loss, and/or if the mining linkage interest points of the cloud service linkage event logs in any one cloud service linkage event log group are different from the cloud service linkage interest points corresponding to the cloud service linkage events, updating model weight information of the interest mining structure and the cloud service scene interest mining model.
Step S154, obtaining iterative mining linkage interest points of all cloud service linkage event logs in each cloud service linkage event log cluster based on the cloud service scene interest mining model and the interest mining structure updated by the model weight, returning to the step of transmitting the cloud service linkage event logs in each cloud service linkage event log cluster to the cloud service scene interest mining model and outputting the mining linkage interest points of all cloud service linkage event logs in any cloud service linkage event log cluster until no interest point loss exists in the mining linkage interest points of all the cloud service linkage event logs in any cloud service linkage event log cluster, and outputting the interest mining model meeting the model deployment requirement.
In some embodiments, step S110 may be implemented by the following exemplary substeps.
Step S111, acquiring a cloud service linkage event which meets training deployment requirements and carries cloud service scene interest points from a cloud service linkage behavior database;
step S112, calling a key field data log of the cloud service linkage event according to a key field template of a linkage event log;
step S113, determining the cloud service linkage event log in the cloud service linkage event based on the called keyword field data log.
In some embodiments, in step S140, the determination scheme of the feature learning cost of each cloud service linkage event log group may be as in embodiment a and embodiment B described below.
Example A:
determining a global event attention variable from a cloud service linkage event log group, determining variable loss values of other event attention variables in the cloud service linkage event log group and the global event attention variable, obtaining an event attention variable with the maximum variable loss value between the cloud service linkage event log group and the global event attention variable, and determining the variable loss value between the maximum event attention variable and the global event attention variable as a learning cost value of the cloud service linkage event log group feature learning cost, wherein the variable loss value and the feature learning cost are in a positive correlation relationship.
Example B:
and determining a cyclic traversal quantity value corresponding to each cloud service linkage event log in each cloud service linkage event log group when the first interest mining of the cloud service linkage event log is confirmed based on the cloud service scene interest mining model, and obtaining a target reference coefficient of the cloud service linkage event log based on a preset reference coefficient corresponding to the cyclic traversal quantity value, wherein the cyclic traversal quantity value and the target reference coefficient are in a negative correlation relationship. And determining a target interest real value corresponding to each cloud service linkage event log group according to a target reference coefficient corresponding to each cloud service linkage event log of the cloud service linkage event log group. Determining the feature learning cost of each cloud service linkage event log group based on the interest real value corresponding to each cloud service linkage event log group, wherein the interest real value and the feature learning cost are in a negative correlation relationship.
In step S120, in some embodiments, the cloud service linkage event log in the cloud service linkage event may be input into an initial cloud service scene interest mining model, and deep learning optimization of an interest decision is performed on the initial cloud service scene interest mining model to obtain the cloud service scene interest mining model.
In step S130, in some embodiments, the present embodiment may sequentially or simultaneously combine event attention variables of cloud service linkage event logs included in each cloud service linkage event to obtain combined data of each cloud service linkage event.
In step S150, in some embodiments, some exemplary steps of pushing information of the target smart virtual service user group according to the target cloud service scenario interest point distribution may be implemented as follows.
Step S151, collecting at least one candidate cloud service pushing data matched with any one target cloud service scene interest point in the target cloud service scene interest point distribution;
step S152, comparing the portrait of the user group according to the push data of each candidate cloud service and the portrait of the user group of the target intelligent virtual service user group to obtain a portrait characteristic missing part related to each service push article in the push data of the candidate cloud service;
step S153, adding auxiliary article content to the missing part of the portrait characteristics related to each service push article in at least one candidate cloud service push data to obtain auxiliary push article information related to each service push article;
step S154, performing hotspot distribution analysis according to the candidate cloud service pushing data to acquire hotspot distribution information related to each service pushing article in the candidate cloud service pushing data;
step S155, determining pushing support degrees related to each service pushing article in the candidate cloud service pushing data according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to the auxiliary pushing article information;
step S156, determining push configuration information related to the service push article according to the auxiliary push article information and the push support degree related to the same service push article in at least one candidate cloud service push data, and performing information push of the target smart virtual service user group on the service push article based on the push configuration information.
Based on the above steps, in the embodiment, by performing user group portrait comparison on candidate cloud service pushed data of a target cloud service scene interest point, portrait feature missing parts of the candidate cloud service pushed data in the portrait direction of the user group can be mined, then auxiliary article content addition is performed based on the portrait feature missing parts, auxiliary pushed article information related to each service pushed article is obtained, and then the auxiliary pushed article information of each service pushed article is added, so that the pushed content richness of the target cloud service scene interest point can be improved. And performing hotspot distribution analysis on the relation of each service push article of each candidate cloud service push data, thereby determining the push support degree related to each service push article in the candidate cloud service push data, controlling the information push of the target intelligent virtual service user group on the service push articles based on the push support degree, and improving the reliability of the information push.
In some embodiments, adding the content of an auxiliary article to the missing part of the portrait feature related to each service push article in at least one candidate cloud service push data to obtain the information of the auxiliary push article related to each service push article includes: acquiring tendency keyword distribution related to each service pushing article in the candidate cloud service pushing data according to the article keyword distribution related to each service pushing article in the candidate cloud service pushing data and the label pushing tendency characteristic corresponding to the candidate cloud service pushing data; acquiring auxiliary tendency keyword distribution related to each service pushing article according to auxiliary tendency characteristics related to the same service pushing article in at least one candidate cloud service pushing data; determining auxiliary pushing article information related to each service pushing article according to tendency keyword distribution and auxiliary tendency keyword distribution related to the same service pushing article in at least one candidate cloud service pushing data;
in some embodiments, determining a pushing support degree related to each service pushing article in the candidate cloud service pushing data according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to the auxiliary pushing article information includes: determining the weighted refreshing number related to each service pushing article in the candidate cloud service pushing data according to the article refreshing number related to each service pushing article in the candidate cloud service pushing data and the article refreshing number corresponding to the auxiliary pushing article information; determining the pushing support degree related to each service pushing article in the candidate cloud service pushing data according to the corresponding relation between the weighted refreshing number of each service pushing article in the candidate cloud service pushing data and the preset pushing support degree, wherein the corresponding relation of the preset pushing support degree comprises the preset pushing support degrees corresponding to different refreshing number ranges;
in some embodiments, the determining, according to information and a push support degree of an auxiliary push article related to a same service push article in at least one candidate cloud service push data, push configuration information related to the service push article, and performing information push of the target smart virtual service user group on the service push article based on the push configuration information includes: determining derived extension strength for the auxiliary pushed article information according to the pushing support degree related to the same service pushed article in the at least one candidate cloud service pushing data; determining a target derived extension number of secondary push article information relevant for the service push article based on the derived extension strength; and performing derivative expansion on the auxiliary pushed article information based on the target derivative expansion quantity, taking the derivative expanded target auxiliary pushed article information as page auxiliary information of a pushed page corresponding to the service pushed article to obtain the pushed configuration information, and performing information pushing of the target intelligent virtual service user group on the service pushed article based on the pushed configuration information.
For example, in some embodiments, the target cloud business linkage event log may be obtained in the following manner.
Step W110, based on the acquired cloud service interaction requirement of the target cloud service user group, acquiring interaction requirement data of which the cloud service interaction requirement is matched with the subscription cloud service activity corresponding to the target cloud service user group.
In this embodiment, the target cloud service user group may be any cloud service user group, and the concept of the cloud service user group may be understood as a user group, for example, a family user group, a work user group, and the like. The cloud service interaction requirement can represent a cloud service requirement task initiated by the target cloud service user group, for example, a requirement task proposed for a certain family phase scene, such as a requirement task scene between returning home and sleeping, or a requirement task proposed for a certain work scene, such as a requirement task proposed based on a certain work initiation plan. On this basis, interaction demand data matching the cloud service interaction demand with subscription cloud service activities corresponding to the target cloud service user group can be obtained. The subscription cloud service activity can be understood as a cloud service activity subscribed by a target cloud service user group based on self needs, such as a cloud service activity related to movie plates in an intelligent home. The interaction demand data can be called from a cloud user database based on the cloud service interaction demand and used for representing specific content data of the interaction demand.
And W120, generating an interactive demand variable based on the interactive demand data, transmitting the interactive demand variable to a cloud service linkage decision network meeting network convergence requirements, and obtaining cloud service linkage decision information and the support degree of the cloud service linkage decision information.
The cloud service linkage decision information may include target linkage decision information, and the target linkage decision information represents that the cloud service interaction demand can be used for linkage configuration. The linkage configuration means that a plurality of cloud service elements can be subjected to linkage interaction to realize a specific linkage interaction scene.
Step W130, if the cloud service linkage decision information is determined to be the target linkage decision information, analyzing whether linkage interaction demand information is carried in the interaction demand data or not to obtain analysis information, generating linkage support degree based on the support degree and the analysis information, and if the linkage support degree is determined not to be smaller than the target support degree, mining an interaction demand knowledge graph of the interaction demand data.
Step W140, performing linkage knowledge node extraction on the interaction demand knowledge graph to obtain a linkage knowledge node sequence, performing cloud service element extraction on the interaction demand knowledge graph to obtain a cloud service element sequence, mining a corresponding linkage interaction event from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine the linkage interaction event as a suggested linkage interaction event of the interaction demand data, generating a cloud service linkage behavior database based on the suggested linkage interaction event, and obtaining a target cloud service linkage event log.
Based on the above steps, in this embodiment, based on the obtained cloud service interaction demand of the target cloud service user group, interaction demand data matching the cloud service interaction demand with subscription cloud service activities corresponding to the target cloud service user group is obtained, an interactive demand variable is generated based on the interaction demand data, the interactive demand variable is transmitted to a cloud service linkage decision network meeting network convergence requirements, support of cloud service linkage decision information and cloud service linkage decision information is obtained, therefore, if the cloud service linkage decision information is determined to be the target linkage decision information, whether linkage interaction demand information is carried in the interaction demand data is analyzed, analysis information is obtained, linkage support is generated based on the support and the analysis information, if it is determined that the linkage support is not less than the target support, an interaction demand knowledge graph of the interaction demand data is mined, linkage knowledge node extraction is performed on the interaction demand knowledge graph, a linkage knowledge node sequence is obtained, and cloud service element extraction is performed on the interaction demand knowledge graph, a cloud service element sequence is obtained, and based on the linkage knowledge node sequence and the cloud service element sequence, an interaction demand interaction event interaction recommendation of the interaction event is determined to be a corresponding linkage interaction event interaction demand data is determined. Therefore, when the interaction demand data based on the target cloud service user group is further decided to carry linkage interaction demand information, an interaction demand knowledge graph of the interaction demand data is mined, corresponding linkage interaction events are mined from a preset linkage interaction event sequence in sequence to be determined as suggested linkage interaction events of the interaction demand data, and the suggested linkage interaction events are uploaded to corresponding block chains, so that the linkage interaction events are mined as far as possible on the premise of considering the actual interaction demand of the user, the cloud service configuration is facilitated for the user, and the difficulty of manually configuring a cloud service linkage scene by the user can be reduced.
In an exemplary design idea, for step W120, in the process of generating an interactive demand variable based on the interactive demand data, in this embodiment, a target interactive demand item in the interactive demand data may be analyzed, each demand scenario rule in the target interactive demand item is read, a demand rule variable of the target interactive demand item is generated based on each demand scenario rule, data except the target interactive demand item in the interactive demand data is determined as candidate interactive data demand data, a cloud service demand node of the target interactive demand item in the interactive demand data is determined, and the interactive demand variable is obtained based on the cloud service demand node and the demand rule variable.
In an exemplary design idea, for step W130, this embodiment may obtain an interaction rule of the target interaction requirement item, determine an interaction scene feature corresponding to the interaction rule as a target interaction scene feature of the target interaction requirement item, and obtain a feature vector of the target interaction scene feature. And then, matching the feature vector with all linkage feature vectors in a preset linkage feature vector, and if the feature vector is determined to be matched with any one linkage feature vector, outputting the analysis information as the linkage interaction demand information carried in the interaction demand data.
In an exemplary design idea, for step W130, in the process of parsing the interaction requirement knowledge graph of the interaction requirement data, the following exemplary sub-steps may be implemented.
Substep W131, analyzing the interaction demand fragment data of each interaction demand fragment in each interaction demand in the interaction demand data to obtain an interaction demand fragment data set of each interaction demand;
substep W132, performing cloud service simulation operation on past cloud service configuration data of the interaction demand segment data set and a to-be-generated knowledge graph according to a plurality of cloud service software scenes to obtain cloud service simulation operation data of the interaction demand segment data set in the to-be-generated knowledge graph, where the cloud service software scenes of the interaction demand segment data set include at least one;
substep W133, corresponding to each interactive demand fragment data set, respectively performing cloud service knowledge entity identification on the corresponding past cloud service configuration data and cloud service simulation operation data to obtain a cloud service knowledge entity of the past cloud service configuration data and a cloud service knowledge entity of the cloud service simulation operation data;
substep W134, obtaining entity association attributes between cloud service knowledge entities of past cloud service configuration data and cloud service knowledge entities of cloud service simulation operation data corresponding to the same interaction requirement fragment data set, extracting cloud service simulation operation data of which the entity association attributes meet preset entity association attributes from the cloud service simulation operation data of the interaction requirement fragment data set, and obtaining first cloud service simulation operation data of each interaction requirement fragment data set;
substep W135, determining connectivity of cloud service activity information of corresponding past cloud service configuration data and first cloud service simulation operation data, corresponding to each interactive demand segment data set;
substep W136, extracting cloud service simulation operation data with connectivity of the cloud service activity information greater than a target connectivity from the first cloud service simulation operation data of each interactive demand segment data set, to obtain second cloud service simulation operation data of each interactive demand segment data set;
and a substep W137, determining target key knowledge entities of the interactive demand fragment data sets and knowledge entity attributes among the key knowledge entities according to the second cloud service simulation operation data of each interactive demand fragment data set so as to construct a corresponding interactive demand knowledge graph.
In an exemplary design idea, in step W140, this embodiment may analyze the knowledge entities that are intersected in the interaction demand knowledge graph to obtain a linkage knowledge node sequence, extract cloud service equipment objects covered by each knowledge entity in the interaction demand knowledge graph to obtain a cloud service element sequence, and mine a corresponding linkage interaction event from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine a suggested linkage interaction event of the interaction demand data, and upload the suggested linkage interaction event to a corresponding block chain.
Each preset linkage interaction event in the preset linkage interaction event sequence is provided with a corresponding linkage knowledge node and a corresponding cloud service element, and the suggested linkage interaction event of the interaction demand data is obtained based on a matching result by matching the linkage knowledge node sequence and the cloud service element sequence with the linkage knowledge node and the cloud service element covered by each preset linkage interaction event in the preset linkage interaction event sequence.
In an exemplary design idea, an embodiment of the present application further provides a training method for a cloud service linkage decision network, including the following steps.
Step W101, obtaining an initialized neural network model, wherein the initialized neural network model comprises a decision model unit;
step W102, acquiring reference collection data, wherein the reference collection data comprises reference collection interaction demand data, reference linkage decision information and corresponding demand support;
step W103, dividing the reference collected data into a first data set and a second data set, optimizing weight information in the decision model unit based on the first data set to obtain a training linkage decision network, and determining decision cost information of the training linkage decision network based on the second data set;
step W104, if the decision cost information is determined to be larger than the target decision cost, the weight information of the training linkage decision network is continuously optimized based on the second data set until the decision cost information of the training linkage decision network is not smaller than or equal to the target decision cost, and the cloud service linkage decision network is obtained.
In an exemplary design idea, in a process of generating a linkage support degree based on the support degree and the analysis information, a first influence coefficient of the cloud service linkage decision network may be obtained, and the first support degree of the interaction demand data is determined based on the support degree and a weighting support degree of the first influence coefficient. Then, a metric value corresponding to the analysis information is obtained, a second influence coefficient of the linkage interaction demand information is obtained, a second support degree of the interaction demand data is determined based on the metric value and the weighting support degree of the second influence coefficient, an adding support degree of the first support degree and the second support degree is calculated, and the linkage support degree is obtained.
Fig. 2 illustrates a hardware structure of the intelligent push processing system 100 based on blockchain and big data mining for implementing the intelligent push processing method based on blockchain and big data mining based on blockchain technology according to an embodiment of the present application, and as shown in fig. 2, the intelligent push processing system 100 based on blockchain and big data mining may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.
In one possible design, the intelligent push processing system 100 based on blockchain and big data mining may be a single server or a group of servers. The set of servers may be centralized or distributed (e.g., intelligent push processing system 100 based on blockchains and big data mining may be a distributed system). In some embodiments, the intelligent push processing system 100 based on blockchain and big data mining may be local or remote. For example, the intelligent push processing system 100 based on blockchain and big data mining may access information and/or data stored in the machine-readable storage medium 120 via a network. As another example, the intelligent push processing system 100 based on blockchain and big data mining may be directly connected to the machine-readable storage medium 120 to access stored information and/or data. In some embodiments, the intelligent push processing system 100 based on blockchain and big data mining may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
Machine-readable storage medium 120 may store data and/or instructions. In some embodiments, the machine-readable storage medium 120 may store data obtained from an external terminal. In some embodiments, the machine-readable storage medium 120 may store data and/or instructions for use by or in connection with the blockchain and big data mining based intelligent push processing system 100 to perform or perform the exemplary methods described in this application. In some embodiments, the machine-readable storage medium 120 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read-write memories can include Random Access Memory (RAM). Exemplary RAM may include active random access memory (DRAM), double data rate synchronous active random access memory (DDR SDRAM), passive random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM), among others. Exemplary read-only memories may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (perrom), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (dvd-ROM), and the like. In some embodiments, the machine-readable storage medium 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the intelligent push processing method based on blockchain and big data mining based on blockchain technology according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the communication unit 140.
For a specific implementation process of the processor 110, reference may be made to various method embodiments executed by the intelligent push processing system 100 based on the block chain and big data mining, which have similar implementation principles and technical effects, and details of this embodiment are not described herein again.
In addition, an embodiment of the present application further provides a readable storage medium, where the readable storage medium has preset therein computer-executable instructions, and when a processor executes the computer-executable instructions, the method for processing intelligent push based on blockchain and big data mining based on blockchain technology as described above is implemented.
It should be understood that the foregoing description is for purposes of illustration only and is not intended to limit the scope of the present disclosure. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the description of the invention. However, such modifications and variations do not depart from the scope of the present invention.
While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art in view of this disclosure that the above disclosure is intended to be exemplary only and is not intended to limit the invention. Various modifications, improvements and adaptations of the present invention may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.
Also, the present invention has been described using specific terms to describe embodiments of the invention. For example, "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 invention. 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 of the features, structures, or characteristics of one or more embodiments of the present invention may be combined as suitable.
Moreover, those skilled in the art will recognize that aspects of the present invention may be illustrated and described in terms of several patentable species or situations, including any new and useful process, machine, article, or material combination, or any new and useful modification thereof. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module", or "system". Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media, with computer-readable program code embodied therein.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination thereof.
Computer program code required for the operation of various parts of the present invention may be written in any one or more programming languages, including a subject oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, etc., a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, an active programming language such as Python, ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, or as a stand-alone software package, partly on the user's computer, partly on a remote computer, or entirely on the remote computer or on an intelligent push processing system based on blockchains and big data mining. 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 of the process are described, the use of letters or other designations herein is not intended to limit the order of the processes and methods of the invention unless otherwise indicated by the claims. While certain presently contemplated useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, 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 of the invention. 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 intelligent push processing system or mobile device based on blockchain and big data mining.
Similarly, it should be noted that in the foregoing description of embodiments of the invention, 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. Similarly, it should be noted that in the preceding description of embodiments of the invention, 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.
Claims (9)
1. An intelligent push processing method based on a block chain and big data mining is characterized by being applied to an intelligent push processing system based on the block chain and the big data mining, and the method comprises the following steps:
acquiring a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database of a target smart virtual service user group;
mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on a selected cloud service scene interest mining model;
based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event so as to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event;
corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log group bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log group;
performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group in sequence in an allocated deep learning optimization stage, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event logs based on the target interest mining model, and performing information push on the target smart virtual service user group according to the target cloud service scene interest point distribution after obtaining the corresponding target cloud service scene interest point distribution;
the step of pushing information to the target intelligent virtual service user group according to the target cloud service scene interest point distribution comprises the following steps:
collecting at least one candidate cloud service push data matched with any one target cloud service scene interest point in the target cloud service scene interest point distribution;
comparing the portrait of the user group according to the push data of each candidate cloud service and the portrait of the user group of the target smart virtual service user group to obtain a portrait feature missing part related to each service push article in the push data of the candidate cloud service;
adding auxiliary article content to the missing part of portrait characteristics related to each service push article in at least one candidate cloud service push data to obtain auxiliary push article information related to each service push article;
performing hotspot distribution analysis according to the candidate cloud service pushing data to acquire hotspot distribution information related to each service pushing article in the candidate cloud service pushing data;
determining pushing support degrees related to each service pushing article in the candidate cloud service pushing data according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to the auxiliary pushing article information;
according to the auxiliary article pushing information and the pushing support degree related to the same service pushing article in at least one candidate cloud service pushing data, the pushing configuration information related to the service pushing article is determined, and information pushing of the target intelligent virtual service user group is carried out on the service pushing article based on the pushing configuration information.
2. The intelligent push processing method based on block chains and big data mining according to claim 1, wherein the cloud service scenario interest mining model comprises an event attention mining structure and an interest mining structure;
the step of performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group comprises the following steps:
transmitting the cloud service linkage event logs in each cloud service linkage event log group to the cloud service scene interest mining model, and outputting mining linkage interest points of each cloud service linkage event log;
for all cloud service linkage event logs in each cloud service linkage event log group, carrying out loss analysis on mining linkage interest points of the cloud service linkage event logs and cloud service linkage interest points of the cloud service linkage events;
if the mining linkage interest points of all the cloud service linkage event logs in any one cloud service linkage event log group have partial interest point loss, and/or if the mining linkage interest points of the cloud service linkage event logs in any one cloud service linkage event log group are different from the cloud service linkage interest points corresponding to the cloud service linkage events, updating model weight information of the interest mining structure and the cloud service scene interest mining model;
and obtaining iterative mining linkage interest points of all cloud service linkage event logs in each cloud service linkage event log group based on the cloud service scene interest mining model and the interest mining structure updated by the model weight, returning to transmit the cloud service linkage event logs in each cloud service linkage event log group to the cloud service scene interest mining model, and outputting the mining linkage interest points of all the cloud service linkage event logs until no interest point loss exists in the mining linkage interest points of all the cloud service linkage event logs in any one cloud service linkage event log group, and outputting the interest mining model meeting the model deployment requirement.
3. The intelligent push processing method based on the block chain and the big data mining as claimed in claim 1, wherein the step of obtaining the cloud service linkage event log in the cloud service linkage event carrying the cloud service scene interest point satisfying the training deployment requirement from the cloud service linkage behavior database of the target smart virtual service user group comprises:
acquiring a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database;
calling a key field data log of the cloud service linkage event according to a key field template of the linkage event log;
and determining the data log of the called key field as a cloud service linkage event log in the cloud service linkage event.
4. The intelligent push processing method based on the block chain and the big data mining as claimed in claim 1, wherein the step of determining the feature learning cost of each cloud service linkage event log group comprises:
determining a global event attention variable from a cloud service linkage event log group;
determining variable loss values of other event concerned variables in the cloud service linkage event log group and the global event concerned variables;
obtaining an event concern variable with the largest variable loss value between the event concern variable and the global event concern variable in the cloud service linkage event log group, and determining the variable loss value between the largest event concern variable and the global event concern variable as a learning cost value of the feature learning cost of the cloud service linkage event log group, wherein the variable loss value and the feature learning cost are in a positive correlation relationship.
5. The intelligent push processing method based on the block chain and the big data mining as claimed in claim 1, wherein the step of determining the feature learning cost of each cloud service linkage event log group comprises:
determining a cyclic traversal quantity value of each cloud service linkage event log in each cloud service linkage event log group when the first interest mining of the cloud service linkage event log is confirmed based on the cloud service scene interest mining model;
acquiring a target reference coefficient of the cloud service linkage event log based on a preset reference coefficient corresponding to the cyclic traversal quantity value, wherein the cyclic traversal quantity value and the target reference coefficient are in a negative correlation relationship;
corresponding to each cloud service linkage event log group, determining a target interest real value corresponding to the cloud service linkage event log group according to a target reference coefficient corresponding to each cloud service linkage event log of the cloud service linkage event log group;
determining the feature learning cost of each cloud service linkage event log group based on the interest real value corresponding to each cloud service linkage event log group, wherein the interest real value and the feature learning cost are in a negative correlation relationship.
6. The intelligent push processing method based on the blockchain and big data mining according to any one of claims 1 to 5, wherein before the mining of the event attention variable of the cloud service linkage event log in each cloud service linkage event based on the selected cloud service scenario interest mining model, the method further comprises:
and inputting the cloud service linkage event log in the cloud service linkage event into an initial cloud service scene interest mining model, and performing deep learning optimization of interest decision on the initial cloud service scene interest mining model to obtain the cloud service scene interest mining model.
7. The intelligent push processing method based on the block chain and the big data mining according to any one of claims 1 to 5, wherein the step of performing event attention combination on event attention variables of the cloud service linkage event logs of each cloud service linkage event comprises:
and combining event attention variables of the cloud service linkage event logs included in each cloud service linkage event in sequence or simultaneously to obtain combined data of each cloud service linkage event.
8. The intelligent pushing processing method based on the block chain and the big data mining as claimed in claim 1, wherein the adding of the auxiliary article content to the missing part of the portrait feature related to each service push article in the at least one candidate cloud service push data to obtain the auxiliary push article information related to each service push article comprises:
obtaining tendency keyword distribution related to each service push article according to article keyword distribution related to each service push article in the candidate cloud service push data and label push tendency characteristics corresponding to the candidate cloud service push data;
acquiring auxiliary tendency keyword distribution related to each service pushing article according to auxiliary tendency characteristics related to the same service pushing article in at least one candidate cloud service pushing data;
determining auxiliary push article information related to each service push article according to tendency keyword distribution and auxiliary tendency keyword distribution related to the same service push article in at least one candidate cloud service push data;
the determining, according to hotspot distribution information related to each service push article in the candidate cloud service push data and hotspot distribution information corresponding to the auxiliary push article information, a push support degree related to each service push article in the candidate cloud service push data includes:
determining the weighted refreshing number related to each service pushing article in the candidate cloud service pushing data according to the article refreshing number related to each service pushing article in the candidate cloud service pushing data and the article refreshing number corresponding to the auxiliary pushing article information;
determining the push support degree related to each service push article in the candidate cloud service push data according to the weighted refresh number and the preset push support degree corresponding relation related to each service push article in the candidate cloud service push data, wherein the preset push support degree corresponding relation comprises preset push support degrees corresponding to different refresh number ranges;
the method for pushing the information of the target intelligent virtual service user group comprises the following steps of determining pushing configuration information related to a service pushing article according to auxiliary pushing article information and pushing support degree related to the same service pushing article in at least one candidate cloud service pushing data, and pushing the information of the target intelligent virtual service user group to the service pushing article based on the pushing configuration information, wherein the pushing configuration information comprises the following steps:
determining derived extension strength for the auxiliary pushed article information according to the pushing support degree related to the same service pushed article in the at least one candidate cloud service pushing data;
determining a target derived extension number of auxiliary push article information related to the service push article based on the derived extension strength;
and performing derivative expansion on the auxiliary pushed article information based on the target derivative expansion quantity, taking the derivative expanded target auxiliary pushed article information as page auxiliary information of a pushed page corresponding to the service pushed article to obtain the pushed configuration information, and performing information pushing of the target intelligent virtual service user group on the service pushed article based on the pushed configuration information.
9. An intelligent push processing system based on blockchain and big data mining, which is characterized by comprising a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to realize the intelligent push processing method based on blockchain and big data mining of any one of claims 1 to 8.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211081915.0A CN115329204A (en) | 2022-03-08 | 2022-03-08 | Cloud business service pushing method and pushing processing system based on big data mining |
| CN202210218863.0A CN114564522B (en) | 2022-03-08 | 2022-03-08 | Intelligent push processing method and system based on block chain and big data mining |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210218863.0A CN114564522B (en) | 2022-03-08 | 2022-03-08 | Intelligent push processing method and system based on block chain and big data mining |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211081915.0A Division CN115329204A (en) | 2022-03-08 | 2022-03-08 | Cloud business service pushing method and pushing processing system based on big data mining |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114564522A CN114564522A (en) | 2022-05-31 |
| CN114564522B true CN114564522B (en) | 2022-11-15 |
Family
ID=81716947
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211081915.0A Withdrawn CN115329204A (en) | 2022-03-08 | 2022-03-08 | Cloud business service pushing method and pushing processing system based on big data mining |
| CN202210218863.0A Active CN114564522B (en) | 2022-03-08 | 2022-03-08 | Intelligent push processing method and system based on block chain and big data mining |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211081915.0A Withdrawn CN115329204A (en) | 2022-03-08 | 2022-03-08 | Cloud business service pushing method and pushing processing system based on big data mining |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN115329204A (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114840486B (en) * | 2022-06-28 | 2022-09-16 | 广州趣米网络科技有限公司 | User behavior data acquisition method and system and cloud platform |
| CN114969552B (en) * | 2022-07-06 | 2023-04-18 | 中图数字科技(北京)有限公司 | Big data mining method and AI prediction system for personalized information push service |
| CN115455300B (en) * | 2022-09-29 | 2023-04-14 | 临沂沂川网络科技有限公司 | Data pushing method and system based on artificial intelligence and cloud platform |
| CN116070111B (en) * | 2022-11-17 | 2023-08-04 | 北京东方通科技股份有限公司 | Auxiliary decision method and system for big data mining based on AI |
| CN115905702B (en) * | 2022-12-06 | 2023-10-10 | 雨果跨境(厦门)科技有限公司 | Data recommendation method and system based on user demand analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648031A (en) * | 2018-03-28 | 2018-10-12 | 苏州迪尔未来网络科技有限公司 | Products Show method and device |
| CN111028006A (en) * | 2019-12-02 | 2020-04-17 | 支付宝(杭州)信息技术有限公司 | Service delivery auxiliary method, service delivery method and related device |
| CN111723273A (en) * | 2019-03-18 | 2020-09-29 | 北京中电翔云信息技术有限公司 | Smart cloud retrieval system and method |
| CN112600893A (en) * | 2020-12-04 | 2021-04-02 | 褚萌萌 | Software application data mining method based on big data positioning and software service platform |
| CN113779409A (en) * | 2021-09-18 | 2021-12-10 | 苏州科知律信息科技有限公司 | Intention mining method and system based on big data |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113392173A (en) * | 2020-08-28 | 2021-09-14 | 郭举 | Information push updating method and system based on block chain and cloud service information platform |
| CN112711578B (en) * | 2020-12-30 | 2021-09-21 | 深圳市全景网络有限公司 | Big data denoising method for cloud computing service and cloud computing financial server |
| CN113361794A (en) * | 2021-06-21 | 2021-09-07 | 深圳市鸿业电线有限公司 | Information pushing method and AI (Artificial Intelligence) pushing system based on Internet e-commerce big data |
| CN113332729A (en) * | 2021-07-08 | 2021-09-03 | 创联无忧(广州)信息科技有限公司 | Cloud game vulnerability detection method based on deep learning and artificial intelligence server |
| CN113409096B (en) * | 2021-08-19 | 2021-11-16 | 腾讯科技（深圳）有限公司 | Target object identification method and device, computer equipment and storage medium |
| CN113761033B (en) * | 2021-09-13 | 2022-03-25 | 江苏楚风信息科技有限公司 | Information arrangement method and system based on file digital management |
| CN114117235A (en) * | 2021-12-07 | 2022-03-01 | 绥化市纯互联网商务有限公司 | E-commerce content pushing method adopting artificial intelligence analysis and E-commerce big data system |
-
2022
- 2022-03-08 CN CN202211081915.0A patent/CN115329204A/en not_active Withdrawn
- 2022-03-08 CN CN202210218863.0A patent/CN114564522B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648031A (en) * | 2018-03-28 | 2018-10-12 | 苏州迪尔未来网络科技有限公司 | Products Show method and device |
| CN111723273A (en) * | 2019-03-18 | 2020-09-29 | 北京中电翔云信息技术有限公司 | Smart cloud retrieval system and method |
| CN111028006A (en) * | 2019-12-02 | 2020-04-17 | 支付宝(杭州)信息技术有限公司 | Service delivery auxiliary method, service delivery method and related device |
| CN112600893A (en) * | 2020-12-04 | 2021-04-02 | 褚萌萌 | Software application data mining method based on big data positioning and software service platform |
| CN113779409A (en) * | 2021-09-18 | 2021-12-10 | 苏州科知律信息科技有限公司 | Intention mining method and system based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114564522A (en) | 2022-05-31 |
| CN115329204A (en) | 2022-11-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114564522B (en) | Intelligent push processing method and system based on block chain and big data mining | |
| US20210150372A1 (en) | Training method and system for decision tree model, storage medium, and prediction method | |
| CN114564566A (en) | Application cloud service linkage big data processing method and cloud service artificial intelligence system | |
| CN106250461A (en) | A kind of algorithm utilizing gradient lifting decision tree to carry out data mining based on Spark framework | |
| CN109325118B (en) | Unbalanced sample data preprocessing method and device and computer equipment | |
| CN110674144A (en) | User portrait generation method and device, computer equipment and storage medium | |
| CN113689292B (en) | User aggregation identification method and system based on image background identification | |
| CN113706180B (en) | Method and system for identifying cheating communities | |
| CN113515672A (en) | Data processing method and device, computer readable medium and electronic equipment | |
| CN114330703A (en) | Method, device and equipment for updating search model and computer-readable storage medium | |
| CN109767264A (en) | Product data method for pushing, device, computer equipment and storage medium | |
| CN114357319A (en) | Network request processing method, device, equipment, storage medium and program product | |
| JP2013073301A (en) | Distributed computer system and control method of the same | |
| CN112286977A (en) | Data pushing method, electronic equipment and system based on cloud computing | |
| CN112199896A (en) | Chip logic comprehensive optimization acceleration method based on machine learning | |
| CN112287799A (en) | Video processing method and device based on artificial intelligence and electronic equipment | |
| CN114691630B (en) | Smart supply chain big data sharing method and system | |
| CN116030115A (en) | Visual design image analysis method and system applied to AI | |
| CN113761033B (en) | Information arrangement method and system based on file digital management | |
| CN113282839B (en) | Internet data push processing method and system | |
| CN113656927B (en) | Data processing method, related device and computer storage medium | |
| CN115756821A (en) | Online task processing model training and task processing method and device | |
| CN107688582B (en) | Resource recommendation model obtaining method and device | |
| CN115455426A (en) | Business error analysis method based on vulnerability analysis model development and cloud AI system | |
| CN114490673A (en) | Data information processing method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220831 Address after: No. 32, Xinyin Street, Pingfang District, Harbin City, Heilongjiang Province, 150000 Applicant after: Zhang Weibin Address before: 150000 No. 54-25, Xinping street, Pingfang District, Harbin City, Heilongjiang Province Applicant before: Harbin Tengda Network Technology Development Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221031 Address after: 250101 6th Floor, Building 4, Baowei Science Park, No. 3003, Xinluo Street, Jinan Area, China (Shandong) Pilot Free Trade Zone, Jinan City, Shandong Province Applicant after: Shanyou digital technology (Shandong) Co.,Ltd. Address before: No. 32, Xinyin Street, Pingfang District, Harbin City, Heilongjiang Province, 150000 Applicant before: Zhang Weibin |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |