CN112667893A

CN112667893A - Information push method based on intelligent identification and big data and block chain financial platform

Info

Publication number: CN112667893A
Application number: CN202011569272.5A
Authority: CN
Inventors: 吕维东
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-08-02
Filing date: 2020-08-02
Publication date: 2021-04-16
Also published as: CN111931050A; CN111931050B; CN112612960A

Abstract

The embodiment of the invention provides an information pushing method based on intelligent identification and big data and a block chain financial platform, which are used for realizing the continuous updating of pushing key elements of a pushing structure body by carrying out iterative calibration on the pushing structure body on business indexes in target identification pushing information, and configuring a preset information pushing model by fusing pushing source characteristics and corresponding source characteristic importance degree information, so that the accuracy of information pushing of the configured preset information pushing model is higher, the efficiency of data processing is greatly improved, and the analysis efficiency of information pushing is further improved.

Description

Information push method based on intelligent identification and big data and block chain financial platform

Technical Field

The invention relates to the technical field of big data, in particular to an information pushing method based on intelligent identification and big data and a block chain financial platform.

Background

The digital financial service terminals distributed all over the service points can provide convenient inquiry service of business push information for users, thereby facilitating the users to know various information and relevant service use conditions of the digital financial service in time.

In the traditional information pushing scheme, the characteristics of information pushing can be obtained through manual marking list rules, so that the service information which accords with the preference of the digital financial service terminal is pushed based on the characteristics.

However, through research of the inventor of the present invention, it is found that the manual marking cost is extremely high, and the marking speed is difficult to achieve the expected effect, so that the accuracy of information pushing and the analysis efficiency of information pushing are greatly affected.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present invention is to provide an information push method and a block chain financial platform based on intelligent recognition and big data, which can implement continuous update of push key elements of a push structure by performing iterative calibration on a push structure on a service index in target recognition push information, and configure a preset information push model by fusing a push source characteristic and corresponding source characteristic importance information, so that the accuracy of information push of the configured preset information push model is higher, the efficiency of data processing is greatly improved, and the analysis efficiency of information push is further improved.

In a first aspect, the present invention provides an information pushing method based on intelligent identification and big data, which is applied to a blockchain financial platform, where the blockchain financial platform is in communication connection with a plurality of digital financial service terminals, and the method includes:

acquiring target identification push information, and performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list, wherein the target identification push information is request recommendation information corresponding to service request information of the digital financial service terminal in a preset time period;

performing information identification on the target service index classification list to obtain an information push label list and corresponding historical push feedback parameters, and determining the information push label list with the historical push feedback parameters meeting preset matching rules as a target push reference strategy;

traversing the target service index classification list according to the target push reference strategy, circularly updating push key elements of a push structure, adding push nodes to the target service index classification list conforming to the target push reference strategy, and extracting push source characteristics and corresponding source characteristic importance degree information in the target service index classification list with the added push nodes;

and configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing node to obtain a configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model.

In a possible implementation manner of the first aspect, the step of performing information identification on the target service index classification list to obtain an information push tag list and a corresponding history push feedback parameter, and determining the information push tag list with the history push feedback parameter meeting a preset matching rule as a target push reference policy includes:

performing information identification on the target service index classification list to obtain a corresponding information push label list;

acquiring a first target push feedback parameter of a push structure body and a global push feedback parameter of the push structure body contained in each information push label list;

determining a corresponding first historical push feedback parameter according to the matching relation between the first target push feedback parameter and the global push feedback parameter;

and determining an information push label list with the first historical push feedback parameters matching preset parameter characteristics as a target push reference strategy.

In a possible implementation manner of the first aspect, the step of performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list includes:

performing segmentation and service index classification operations on the target identification push information to obtain a corresponding service index classification list;

acquiring a pushing key element of a pushing structure body, and determining the pushing key element in the service index classification list;

and calibrating a corresponding pushing structure body for the pushing key elements in the service index classification list to obtain a corresponding target service index classification list.

In a possible implementation manner of the first aspect, the step of traversing the target service index classification list according to the target push reference policy and circularly updating a push key element of a push structure includes:

determining a service index sequence matched with the information push tag list of the target push reference strategy in the target service index classification list;

acquiring a second target push feedback parameter of a push structure body and a global push feedback parameter of the push structure body contained in each service index sequence, and determining a corresponding second historical push feedback parameter according to the matching relation between the second target push feedback parameter and the global push feedback parameter;

determining a service index sequence with the second historical push feedback parameter larger than a second preset historical push feedback parameter threshold value as a target service index sequence, carrying out push structure body calibration on service indexes in the target service index sequence according to the target push reference strategy, and updating push key elements of a push structure body;

and re-executing the step of obtaining a second target push feedback parameter of the push structure body and a global push feedback parameter of the push structure body included in each service index sequence, iteratively calibrating the push structure body on the service indexes in the target service index sequence, and updating the push key elements of the push structure body until the iteration times meet a preset iteration threshold value.

In a possible implementation manner of the first aspect, the step of calibrating, according to the target push reference policy, a push structure for the service indicators in the target service indicator sequence, and updating push key elements of the push structure includes:

acquiring a calibration rule for calibrating a pushing structure body of each strategy unit in the target pushing reference strategy;

and carrying out pushing structure body calibration on the service indexes in the target service index sequence according to the calibration rule and a strategy unit, and updating pushing key elements of the pushing structure body.

In a possible implementation manner of the first aspect, the step of extracting the push source feature and the corresponding source feature importance degree information in the target service index classification list to which the push node is added includes:

determining the push source characteristics of the target service index classification list of the added push nodes;

and calculating the source characteristic importance degree information of the target service index classification list added with the push node.

In a possible implementation manner of the first aspect, the step of calculating source feature importance degree information of a target service index classification list of an add-push node includes:

acquiring the occurrence times of target service indexes in a target service index classification list added with push nodes, and acquiring the total number of push nodes appearing in the target identification push information;

determining corresponding frequent pattern item information according to the matching relation between the occurrence frequency of the target service index and the total number of the push nodes;

acquiring the global pushing quantity in the target identification pushing information, and acquiring the target service pushing quantity containing the target service index;

calculating a target matching relation between the global pushing quantity and the target service pushing quantity, and calculating the logarithm of the target matching relation to obtain a corresponding logarithm result;

and multiplying the frequent pattern item information by a logarithm result to obtain the importance degree of the target service index, and combining the importance degrees corresponding to the service indexes in the same target service index classification list to generate source characteristic importance degree information.

In a possible implementation manner of the first aspect, the request recommendation information is obtained by:

acquiring at least one business keyword list from business request information of the digital financial service terminal in a preset time period, wherein each business keyword label in each business keyword list belongs to the same business service scene, and each business keyword label corresponds to a business processing flow under the business service scene to which the business keyword label belongs;

performing entity relationship identification on the business keyword list based on each business processing flow under the business service scene to obtain entity relationship characteristics of each business keyword list and corresponding entity business item grades;

determining a form operation track of a business service scene corresponding to each business keyword label according to the entity relation characteristics and the corresponding entity business item grades;

marking form operation tracks corresponding to the business service scenes according to the business keywords, determining form tracking thermalization entries corresponding to the business service scenes by adopting an artificial intelligence model, generating recommended portrait characteristics of an information recommendation process corresponding to the business request information according to the form tracking thermalization entries corresponding to the business service scenes, and pushing corresponding request recommendation information to the digital financial service terminal according to the recommended portrait characteristics of the information recommendation process.

In a possible implementation manner of the first aspect, the step of determining, according to the entity relationship feature and the corresponding entity business item level, a form operation trajectory of a business service scenario to which each business keyword label corresponds includes:

screening the entity relation characteristics according to the entity relation characteristics and the corresponding entity business item grades to obtain marked entity relation characteristics of which the entity business item grades are greater than the preset entity business item grades;

obtaining a first form operation element list corresponding to a first entity relationship object and a second form operation element list corresponding to a second entity relationship object on a mark entity relationship characteristic, wherein the first form operation element list comprises a plurality of form operation nodes for performing form operation on related form areas in the mark entity relationship characteristic by the first entity relationship object, the second form operation element list comprises a plurality of form operation nodes for performing form operation on the related form areas in the mark entity relationship characteristic by the second entity relationship object, and each form operation node comprises a plurality of form operation node components;

based on the category of preset form operation nodes, performing Gaussian classification on a plurality of form operation nodes in the first form operation element list to obtain a first form operation element list after Gaussian classification; the preset form operation node category belongs to types corresponding to a plurality of form operation node components;

combining all form operation node components corresponding to each preset form operation node category in the first form operation element list after Gaussian classification into a first initial form operation node list;

removing the duplicate of the first initial form operation node list to obtain a first form operation node list, so as to obtain a first form operation node list corresponding to the preset form operation node category list;

combining each form operation node component in the first form operation node list into a first form operation node component list corresponding to the first entity relationship object, wherein the first form operation node component list corresponds to a preset form operation node category list, and the preset form operation node category type is a list formed by various form operation node categories used for form operation crawling;

extracting, from the second form operation element list, each form operation node component corresponding to each preset form operation node category in the preset form operation node category list, and combining the extracted component into a second form operation node component list corresponding to the second entity relationship object, where the second form operation node component list corresponds to the preset form operation node category list, and the first form operation node component list and the second form operation node component list are lists composed of form operation node components extracted from the corresponding form operation element list, respectively;

determining track nodes of the same form operation node components between the first form operation node component list and the second form operation node component list, and obtaining operation description values from track range values corresponding to the track nodes;

when the operation description value is larger than a preset description value threshold value, determining that the first entity relationship object and the second entity relationship object are form operation units;

using any two entity relationship elements in the marked entity relationship characteristic as a first entity relationship object and a second entity relationship object to perform form operation crawling, and obtaining a form operation unit list with form operation behaviors in the marked entity relationship characteristic until the detection of the entity relationship elements in the marked entity relationship characteristic is completed;

taking the track node of the entity relation element in the form operation unit list as the track node of the target form operation unit;

taking the track node of the entity relationship element corresponding to the marked entity relationship characteristic as a target total entity relationship element track node;

calculating track segmentation of the target form operation unit track node and the target total entity relationship element track node to obtain track segmentation characteristics corresponding to the mark entity relationship characteristics;

and when the track segmentation characteristics meet the preset segmentation length, determining the track formed by the track segmentation characteristics corresponding to the mark entity relation characteristics as the form operation track of the business service scene corresponding to each business keyword mark.

In a second aspect, an embodiment of the present invention further provides an information pushing apparatus based on smart identification and big data, which is applied to a blockchain financial platform, where the blockchain financial platform is in communication connection with a plurality of digital financial service terminals, and the apparatus includes:

the acquisition module is used for acquiring target identification push information, and performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list, wherein the target identification push information is request recommendation information corresponding to service request information of the digital financial service terminal in a preset time period;

the information identification module is used for carrying out information identification on the target service index classification list to obtain an information push label list and corresponding historical push feedback parameters, and determining the information push label list with the historical push feedback parameters meeting preset matching rules as a target push reference strategy;

the traversal updating module is used for traversing the target service index classification list according to the target pushing reference strategy, circularly updating pushing key elements of the pushing structure, adding pushing nodes to the target service index classification list conforming to the target pushing reference strategy, and extracting pushing source characteristics and corresponding source characteristic importance degree information in the target service index classification list with the added pushing nodes;

and the information pushing module is used for configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing node to obtain the configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model.

In a third aspect, an embodiment of the present invention further provides an information push system based on intelligent identification and big data, where the information push system based on intelligent identification and big data includes a blockchain financial platform and a plurality of digital financial service terminals communicatively connected to the blockchain financial platform;

the block chain financial platform is used for acquiring target identification push information, and performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list, wherein the target identification push information is request recommendation information corresponding to service request information of the digital financial service terminal in a preset time period;

the block chain financial platform is used for performing information identification on the target service index classification list to obtain an information push label list and corresponding historical push feedback parameters, and determining the information push label list with the historical push feedback parameters meeting preset matching rules as a target push reference strategy;

the block chain financial platform is used for traversing the target business index classification list according to the target pushing reference strategy, circularly updating pushing key elements of the pushing structure, adding pushing nodes to the target business index classification list conforming to the target pushing reference strategy, and extracting pushing source characteristics and corresponding source characteristic importance degree information in the target business index classification list with the added pushing nodes;

the block chain financial platform is used for configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing nodes to obtain a configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model.

In a fourth aspect, an embodiment of the present invention further provides a blockchain financial platform, where the blockchain financial 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 financial service terminal, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the method for pushing information based on smart identification and big data in the first aspect or any one of possible designs in the first aspect.

In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform an intelligent identification and big data based information pushing method in the first aspect or any one of the possible designs of the first aspect.

Based on any one of the aspects, the method comprises the steps of collecting target identification push information to carry out service index classification and push structure association processing to obtain a corresponding target service index classification list; calculating a target service index classification list to obtain an information push label list and historical push feedback parameters, and determining the information push label list with the historical push feedback parameters meeting preset matching rules as a target push reference strategy; circularly updating the push key elements of the push structure body to the target service index classification list according to the target push reference strategy; adding a push node for a target service index classification list conforming to a target push reference strategy, and extracting push source characteristics and source characteristic importance degree information in the target service index classification list added with the push node; and configuring the preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing node, and performing information pushing processing on the target service index classification list by the preset information pushing model after configuration. Therefore, iterative calibration of the pushing structure body is carried out on the business indexes in the target identification pushing information, continuous updating of pushing key elements of the pushing structure body is achieved, pushing source characteristics and corresponding source characteristic importance degree information are fused to configure the preset information pushing model, accuracy of information pushing of the configured preset information pushing model is higher, data processing efficiency is greatly improved, and analysis efficiency of information pushing is further 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 can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an information push system based on intelligent identification and big data according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of an information push method based on intelligent identification and big data according to an embodiment of the present invention;

fig. 3 is a schematic functional module diagram of an information pushing apparatus based on smart identification and big data according to an embodiment of the present invention;

fig. 4 is a block diagram illustrating a block chain financial platform for implementing the above-described information push method based on smart identification and big data according to an embodiment of the present invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is an interaction diagram of an information push system 10 based on smart identification and big data according to an embodiment of the present invention. The smart identification and big data based information push system 10 may include a blockchain financial platform 100 and a digital financial service terminal 200 communicatively connected to the blockchain financial platform 100. The smart identification and big data based information push system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the smart identification and big data based information push system 10 may also include only a part of the components shown in fig. 1 or may also include other components.

In this embodiment, the blockchain financial platform 100 and the digital financial services terminal 200 in the smart recognition and big data based information push system 10 may cooperatively perform the smart recognition and big data based information push method described in the following method embodiment, and the detailed description of the method embodiment may be referred to for the specific steps performed by the blockchain financial platform 100 and the digital financial services terminal 200.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information pushing method based on smart identification and big data according to an embodiment of the present invention, and the information pushing method based on smart identification and big data according to the embodiment may be executed by the blockchain financial platform 100 shown in fig. 1, and the information pushing method based on smart identification and big data is described in detail below.

Step S110, acquiring target identification push information, and performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list.

Step S120, performing information identification on the target service index classification list to obtain an information push label list and corresponding historical push feedback parameters, and determining the information push label list with the historical push feedback parameters meeting preset matching rules as a target push reference strategy.

Step S130, traversing the target service index classification list according to the target push reference strategy, circularly updating push key elements of the push structure, adding push nodes to the target service index classification list conforming to the target push reference strategy, and extracting push source characteristics and corresponding source characteristic importance degree information in the target service index classification list with the added push nodes.

Step S140, configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing node to obtain a configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model.

In this embodiment, the target identification push information may be request recommendation information corresponding to service request information of the digital financial service terminal 200 in a preset time period. For example, the digital financial services terminal 200 may be distributed throughout different websites to provide services for users, and the users may send service request information at any time during the operation process to obtain request recommendation information.

In this embodiment, the service index may refer to index information generated in a specific digital financial service process and used for measuring each service requested to be recommended, and the push structure may refer to structural configuration information used for performing a reference to a push policy in the specific digital financial service process, for example, the configuration information may be arranged in a preset calibrated fixed format.

In this embodiment, each information push tag in the information push tag list may be used to represent a classification list specifically referred to in a subsequent information push process, and the corresponding history push feedback parameter may refer to content feature information, such as evaluation information and collection information, fed back by the user before the information push tag is used as a reference object for subsequent push.

In this embodiment, the push key element may refer to a content node included in push information in a subsequent push process, each content node may correspond to one push node, the push source characteristic may be used to represent a characteristic of an information source in the subsequent push process, and the corresponding source characteristic importance degree information may be used to represent a weight occupied by the characteristic of the information source.

In this embodiment, in step S140, the preset information push model may be configured according to the push source characteristics and the source characteristic importance information, so as to obtain the configured preset information push model. For example, the push source characteristics, the source characteristic importance degree information, and the push nodes may be input into a preset information push model for training to obtain a corresponding information push node list, and the corresponding information push node list is compared with the push nodes, and then the model parameters of the preset information push model are continuously updated according to the comparison difference until a training termination condition is reached, and information push processing may be performed on the target service index classification list based on the configured preset information push model. For example, the configured preset information push model may be used to obtain a corresponding target push node for the target service index classification list, and then perform information push processing based on the push source content related to the target push node.

Based on the above steps, the embodiment performs iterative calibration of the push structure body on the service index in the target identification push information, so as to continuously update the push key elements of the push structure body, and fuses the push source characteristics and the corresponding source characteristic importance information to configure the preset information push model, so that the accuracy of information push of the configured preset information push model is higher, the data processing efficiency is greatly improved, and the analysis efficiency of information push is further improved.

In one possible implementation, step S120 may be implemented by the following exemplary substeps, which are described in detail below.

And a substep S121, performing information identification on the target service index classification list to obtain a corresponding information push label list.

And a substep S122, obtaining a first target push feedback parameter of the push structure body and a global push feedback parameter of the push structure body included in each information push label list.

And a substep S123, determining a corresponding first historical push feedback parameter according to a matching relationship between the first target push feedback parameter and the global push feedback parameter.

And a substep S124, determining an information push tag list with the first historical push feedback parameter matching the preset parameter characteristic as a target push reference policy.

In one possible implementation, step S110 may be implemented by the following exemplary substeps, which are described in detail below.

And a substep S111, performing segmentation and service index classification operations on the target identification push information to obtain a corresponding service index classification list.

And a substep S112, acquiring the pushing key elements of the pushing structure body, and determining the pushing key elements in the service index classification list.

And a substep S113, calibrating a corresponding pushing structure body for the pushing key elements in the service index classification list to obtain a corresponding target service index classification list.

In a possible implementation manner, for step S130, in the process of traversing the target service index classification list according to the target push reference policy and circularly updating the push key element of the push structure, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S131, determining a service index sequence matched with the information push tag list of the target push reference strategy in the target service index classification list.

And a substep S132, obtaining a second target push feedback parameter of the push structure and a global push feedback parameter of the push structure included in each service index sequence, and determining a corresponding second historical push feedback parameter according to a matching relationship between the second target push feedback parameter and the global push feedback parameter.

And a substep S133, determining a service index sequence with a second historical push feedback parameter greater than a second preset historical push feedback parameter threshold value as a target service index sequence, performing push structure calibration on a service index in the target service index sequence according to a target push reference strategy, and updating a push key element of the push structure.

For example, a calibration rule for calibrating the push structure body for each policy unit in the target push reference policy may be obtained. And then, calibrating the pushing structure body according to the business indexes in the target business index sequence according to the calibration rule and the strategy unit, and updating the pushing key elements of the pushing structure body.

And a substep S134, re-executing the step of obtaining the second target push feedback parameter of the push structure body and the global push feedback parameter of the push structure body included in each service index sequence, iteratively calibrating the push structure body on the service indexes in the target service index sequence, and updating the push key elements of the push structure body until the iteration times meet a preset iteration threshold value.

In one possible implementation manner, for step S130, in the process of extracting the push source feature and the corresponding source feature importance degree information in the target service index classification list of the added push node, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S135, determining the push source characteristics of the target service index classification list of the added push node.

And a substep S136, calculating the source characteristic importance degree information of the target service index classification list of the added push node.

For example, the number of occurrences of the target service index in the target service index classification list of the added push node may be obtained, and the total number of push nodes occurring in the target identification push information may be obtained. And then, determining corresponding frequent pattern item information according to the matching relation between the occurrence frequency of the target service index and the total number of the push nodes. On the basis, the global pushing quantity in the target identification pushing information is obtained, the target service pushing quantity containing the target service index is obtained, the target matching relation between the global pushing quantity and the target service pushing quantity is calculated, the logarithm of the target matching relation is calculated, and a corresponding logarithm result is obtained. And then multiplying the frequent pattern item information by a logarithm result to obtain the importance degree of the target service index, and combining the importance degrees corresponding to the service indexes in the same target service index classification list to generate source characteristic importance degree information.

In one possible implementation, for step S110, the request recommendation information may be obtained through the following steps, which are described in detail below.

Step S101, at least one business keyword list is obtained from business request information of the digital financial service terminal in a preset time period.

And S102, performing entity relationship identification on the business keyword list based on each business processing flow under the business service scene to obtain the entity relationship characteristics and the corresponding entity business item grades of each business keyword list.

And step S103, determining the form operation track of the business service scene corresponding to each business keyword label according to the entity relation characteristics and the corresponding entity business item grades.

And step S104, marking the form operation track corresponding to the business service scene according to each business keyword, determining the form tracking thermalization table entry corresponding to each business service scene by adopting an artificial intelligence model, and generating the recommended portrait characteristics of the information recommendation process corresponding to the business request information according to the form tracking thermalization table entry corresponding to each business service scene.

In this embodiment, each service keyword label in each service keyword list belongs to the same service scenario, and each service keyword label corresponds to a service processing flow in the service scenario to which it belongs. For example, the service keyword labels of the service scenes belonging to the same service scene can be obtained from the service request information of the digital financial service terminal in a preset time period, and the service keyword label belonging to each service scene is determined as a corresponding service keyword list.

Illustratively, the service request information may include a plurality of service keyword tags, each service keyword tag may refer to a related part-of-speech and semantic field sequence of the keyword, and these part-of-speech and semantic field sequences may be used to represent different information with classification attributes in the actual service request push. Similarly, for different service keyword labels, the types of the corresponding service scenarios are different, so that the service keyword labels can correspond to a certain service scenario one by one, that is, the service scenario can be used to represent the type of the service scenario field.

In this embodiment, the entity relationship may be used to indicate that a relationship field capable of indicating the associated service scenario service exists, the entity relationship characteristic may be used to indicate a relationship field sequence corresponding to the entity relationship, and the corresponding entity service event level may be used to indicate how frequently the relationship field sequence corresponding to the entity relationship has the frequent pattern item of the interaction tag between the entity relationship and the associated service scenario service.

In this embodiment, the form operation trajectory may be used to represent recorded information in units of a time axis of a generated form operation behavior, so that the form operation trajectory corresponding to a business service scenario may be labeled according to each business keyword, a form tracking thermalization table entry corresponding to each business service scenario may be determined by using an artificial intelligence model, and the form tracking thermalization table entries are used to represent thermalization items (for example, items with an operation cycle number exceeding two times) in a form operation process, so that recommended representation features of an information recommendation process corresponding to the business request information may be generated specifically according to the form tracking thermalization table entry corresponding to each business service scenario.

Based on the design, the embodiment extracts the entity relationship characteristics of each service keyword list in an entity relationship identification mode, and determines the form operation track of the service scene to which each service keyword label corresponds based on the entity service item level, so that each service processing flow is converted into an effective pushing classification basis. The form operation track corresponding to the business service scene is marked according to each business keyword, an artificial intelligence model is adopted to determine a form tracking thermalization table entry corresponding to each business service scene, a recommended portrait feature of an information recommendation process corresponding to the business request information is generated according to the form tracking thermalization table entry corresponding to each business service scene, and the corresponding request recommendation information is pushed to the digital financial service terminal according to the recommended portrait feature of the information recommendation process, so that request response recommendation can be effectively combined with the specific type of the business service scene according to the business request information, and the classification precision of information push is improved.

In one possible implementation, step S102 may be implemented by the following exemplary sub-steps, which are described in detail below.

And step S1021, traversing the service keyword labels in the service keyword lists for each service keyword list, extracting the service keyword description representation fusing each service processing flow under the service scene to which the service keyword list belongs from the service keyword labels, and determining the service logic information corresponding to the service keyword list according to the extracted service keyword description representation.

And a substep S1022, removing the setting description information included in each service keyword description representation in the service logic information, performing service pushing feature segmentation and splitting on the service keyword description representation from which the setting description information is removed, obtaining first service logic information, and determining the entity relationship strength of each service pushing feature segment according to the service logic strength of the service pushing feature segment in the service keyword description representation included in the first service logic information.

For example, the service logic strength of the service push feature segment in the service keyword description representation included in the first service logic information may refer to a length of an overlapping segment part of the service push feature segment in the service keyword description representation included in the first service logic information.

And a substep S1023, removing service pushing characteristic segments with entity relation strength smaller than a preset entity relation strength threshold value in the first service logic information to obtain second service logic information, taking the service pushing characteristic segments with the entity relation strength not smaller than the preset entity relation strength threshold value as first service pushing characteristic segments to obtain a first service pushing characteristic segment list, and determining a second service pushing characteristic segment list which corresponds to each first service pushing characteristic segment and is formed by service pushing characteristic segments connected after the first service pushing characteristic segments according to matching information of each first service pushing characteristic segment in the first service pushing characteristic segment list in the second service logic information.

And a substep S1024, determining whether the second service pushing feature segment list is empty, if the second service pushing feature segment list is empty, returning in a circulating manner, and if the second service pushing feature segment list is not empty, counting the entity relationship strength of each service pushing feature segment in the second service pushing feature segment list, and determining whether the entity relationship strength of each service pushing feature segment meets the minimum entity relationship strength condition.

And a substep S1025, if the entity relationship strength of the service pushing characteristic segment does not meet the minimum entity relationship strength condition, circularly returning, if the entity relationship strength of the service pushing characteristic segment meets the minimum entity relationship strength condition, splicing the service pushing characteristic segment with a first service pushing characteristic segment corresponding to a second service pushing characteristic segment list to obtain a new first service pushing characteristic segment, determining a second service pushing characteristic segment list of the new first service pushing characteristic segment, and executing circular recognition on the second service pushing characteristic segment list corresponding to the new first service pushing characteristic segment to obtain all target first service pushing characteristic segments meeting the minimum entity relationship strength condition and corresponding entity relationship strength.

For example, the data returned in the loop is all currently obtained target first service push feature segments meeting the minimum entity relationship strength condition and corresponding entity relationship strengths, all target first service push feature segments meeting the minimum entity relationship strength condition and corresponding entity relationship strengths are obtained, the target first service push feature segments are used as the entity relationship features of the service keyword list, and the entity relationship strengths of the target first service push feature segments in the second service push feature segment list are used as the entity service item levels corresponding to the entity relationship features.

In a possible implementation manner, for step S103, in order to accurately and comprehensively determine the entity relationship element having the form operation behavior, thereby improving coverage and accuracy of form operation crawling, and effectively determining the form operation trajectory of the business service scenario to which each business keyword label corresponds, the following exemplary sub-steps may be implemented. The detailed description is as follows.

And a substep S1031, screening the entity relation characteristics according to the entity relation characteristics and the corresponding entity business item grades to obtain the marked entity relation characteristics of which the grade is greater than the preset entity business item grade.

In the sub-step S1032, a first form operation element list corresponding to the first entity relationship object and a second form operation element list corresponding to the second entity relationship object are obtained in the marked entity relationship feature.

For example, the first form operation element list includes a plurality of form operation nodes for form operations by the first entity relationship object on related form regions in the tagged entity relationship feature, the second form operation element list includes a plurality of form operation nodes for form operations by the second entity relationship object on related form regions in the tagged entity relationship feature, and each form operation node includes a plurality of form operation node components.

And a substep S1033 of performing gaussian classification on the plurality of form operation nodes in the first form operation element list based on the preset form operation node categories to obtain a first form operation element list after the gaussian classification. And presetting the types of the form operation nodes, wherein the types of the form operation nodes belong to the types corresponding to the components of the plurality of form operation nodes.

In the sub-step S1034, the form operation node components corresponding to each preset form operation node category in the list of preset form operation node categories in the first form operation element list after gaussian classification are combined into a first initial form operation node list.

And a substep S1035 of performing deduplication on the first initial form operation node list to obtain a first form operation node list, thereby obtaining a first form operation node list corresponding to the preset form operation node category list, and combining each form operation node component in the first form operation node list into a first form operation node component list corresponding to the first entity relationship object.

For example, the first form operation node component list corresponds to a preset form operation node category list, and the preset form operation node category type is a list composed of various form operation node categories used for form operation crawling.

In the substep S1036, form operation node components corresponding to the respective types of the preset form operation nodes in the preset form operation node type list are extracted from the second form operation element list and combined into a second form operation node component list corresponding to the second entity relationship object.

For example, the second form operation node component list corresponds to a preset form operation node category list, and the first form operation node component list and the second form operation node component list are lists formed by form operation node components extracted from the corresponding form operation element list.

And a substep S1037 of determining a track node of the same form operation node component between the first form operation node component list and the second form operation node component list, obtaining an operation description value from a track range value corresponding to the track node, and determining the first entity relationship object and the second entity relationship object as a form operation unit when the operation description value is greater than a preset description value threshold value.

And a substep S1038 of using any two entity relationship elements in the marked entity relationship characteristic as a first entity relationship object and a second entity relationship object to perform form operation crawling, and obtaining a form operation unit list with form operation behaviors in the marked entity relationship characteristic until the detection of the entity relationship elements in the marked entity relationship characteristic is completed.

And a substep S1039, taking the track node of the entity relationship element in the form operation unit list as a track node of a target form operation unit, taking the track node of the entity relationship element corresponding to the marked entity relationship characteristic as a track node of a target total entity relationship element, calculating track segmentation of the track node of the target form operation unit and the track node of the target total entity relationship element to obtain a track segmentation characteristic corresponding to the marked entity relationship characteristic, and determining the track formed by the track segmentation characteristic corresponding to the marked entity relationship characteristic as the form operation track of each business keyword marked with a corresponding business service scene when the track segmentation characteristic meets a preset segmentation length.

Based on the steps, when the entity relation element with the form operation behavior performs the form operation, the same form operation node component exists between the corresponding form operation nodes; therefore, when the form operation crawling is carried out, a form operation element list consisting of a plurality of form operation nodes of the entity relationship elements is obtained, whether the form operation behavior exists in the entity relationship elements is determined according to whether the common attribute condition exists between the form operation node component lists corresponding to the operation lists among the entity relationship elements, and whether the entity relationship elements are form operation units is further determined.

In a possible implementation manner, further aiming at step S104, in the process of labeling the form operation trajectory corresponding to the business service scenario to which the business keyword belongs according to each business keyword and determining the form tracking thermalization entry corresponding to each business service scenario by using an artificial intelligence model, the following exemplary substeps are implemented, which are described in detail as follows.

And a substep S1041 of obtaining the initial business table entry of the corresponding track passing business segment and the track passing business segment from the form operation track of the business service scene corresponding to each business keyword label.

And a substep S1042 of classifying the track passing service section according to the pre-trained artificial intelligence model to obtain a classified service list item.

And a substep S1043 of comparing the initial service table entry with the classification service table entry to obtain table entry matching information.

And a substep S1044 of determining the form tracking thermalization table entry corresponding to each business service scene by adopting an artificial intelligence model according to the table entry matching information.

For example, the target tracking thermalization items corresponding to each item matching node may be obtained from the item matching information, and each target tracking thermalization item is used as a form tracking thermalization item corresponding to each business service scenario determined by using an artificial intelligence model in a manner of clustering and arranging.

In a possible implementation manner, still referring to step S004, in the process of generating the recommended portrait feature of the information recommendation process corresponding to the service request information according to the form tracking thermalization table entry corresponding to each service scenario, the following exemplary sub-steps may be implemented, which are described in detail below.

Substep S1045, obtaining table entry service access information, service access object information associated with the table entry service access information, and historical access object information from an information list mapped by the form tracking thermalization table entry corresponding to each service scenario, where the historical access object information includes access object information of at least one historical access request.

And a substep S1046 of inputting the service access object information and the historical access object information into a machine learning model, performing service pushing feature extraction on the service access object information through the machine learning model to obtain a first service pushing feature vector, and performing service pushing feature extraction on each historical access object information to obtain a second service pushing feature vector.

And a substep S1047 of performing a splicing process on the quantities in the first service push feature vector to obtain a first service access authentication vector for representing service access authentication of the service access object information, and performing a splicing process on the quantities in the second service push feature vector to obtain a second service access authentication vector for representing service access authentication of the history access object information.

And a substep S1048 of calculating a hamming distance between the first service access authentication vector and each second service access authentication vector, and using the calculated hamming distance as the hamming distance between the service access object information and the historical access object information.

And a substep S1049 of determining the calculated Hamming distance as a corresponding strong association degree when the corresponding business access object information is strongly associated with the historical access object information. The strong association is used to measure how well the business access object information is being associated with the historical access object information.

And a substep S10491 of calculating push service information of the service access object information to the table entry service access information based on the first service push feature vector and a third service push feature vector of the table entry service access information, and operating the push service information and the strong association degree to obtain push service configuration information of the table entry service access information for the service access object information and a push service region of the history access object information in the table entry service access information.

And a substep S10492 of determining recommended portrait feature information corresponding to the push service area in the push service configuration information according to the push service configuration information and the push service area corresponding to the strong association condition, and generating recommended portrait features of the information recommendation process corresponding to the service request information according to the extracted recommended portrait feature information.

In this way, the depth recognition of the recommended portrait feature pushing service is further carried out through the recognition mode with strong correlation, so that the recommended portrait feature information corresponding to the pushing service area is determined in the pushing service configuration information, the recommended portrait feature of the information recommendation process corresponding to the service request information is generated according to the extracted recommended portrait feature information, and the accuracy of pushing classification can be effectively improved.

Fig. 3 is a schematic diagram of functional modules of the information pushing apparatus 300 based on intelligent identification and big data according to an embodiment of the present invention, and this embodiment may divide the functional modules of the information pushing apparatus 300 based on intelligent identification and big data according to the method embodiment executed by the blockchain financial platform 100, that is, the following functional modules corresponding to the information pushing apparatus 300 based on intelligent identification and big data may be used to execute each method embodiment executed by the blockchain financial platform 100. The information pushing apparatus 300 based on smart identification and big data may include an obtaining module 310, an information identifying module 350, a traversal updating module 330, and an information pushing module 340, and the functions of the functional modules of the information pushing apparatus 300 based on smart identification and big data are described in detail below.

The obtaining module 310 is configured to obtain target identification push information, and perform service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list, where the target identification push information is request recommendation information corresponding to service request information of the digital financial service terminal 200 in a preset time period. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

The information identification module 350 is configured to perform information identification on the target service index classification list to obtain an information push tag list and corresponding history push feedback parameters, and determine the information push tag list with the history push feedback parameters meeting a preset matching rule as a target push reference policy. The information identification module 350 may be configured to perform the step S120, and the detailed implementation of the information identification module 350 may refer to the detailed description of the step S120.

And the traversal updating module 330 is configured to traverse the target service index classification list according to the target push reference policy, update the push key elements of the push structure in a circulating manner, add push nodes to the target service index classification list conforming to the target push reference policy, and extract the push source characteristics and corresponding source characteristic importance information in the target service index classification list to which the push nodes are added. The traversal update module 330 may be configured to perform the step S130, and the detailed implementation of the traversal update module 330 may refer to the detailed description of the step S130.

The information pushing module 340 is configured to configure a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information, and the pushing node to obtain a configured preset information pushing model, and perform information pushing processing on the target service index classification list based on the configured preset information pushing model. The information pushing module 340 may be configured to perform the step S140, and the detailed implementation manner of the information pushing 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 can be realized in the form of software called by 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 obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the 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.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram illustrating a hardware structure of the blockchain financial platform 100 for implementing the above-described intelligent recognition and big data based information push method according to an embodiment of the present invention, and as shown in fig. 4, the blockchain financial 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 obtaining module 310, the information identifying module 350, the traversal updating module 330, and the information pushing module 340 included in the intelligent identification and big data based information pushing apparatus 300 shown in fig. 3), so that the processor 110 may execute the intelligent identification and big data based information pushing 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 financial service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the blockchain financial platform 100, which implement the similar principle and technical effect, and the detailed description of the embodiments is omitted here.

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 invention are not limited to only one bus or one type of bus.

In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the information push method based on intelligent identification and big data is realized.

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, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily 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 situations, 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, apparatus, 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, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic 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 on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or medical services platform. 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 lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, 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 healthcare platform 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.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present 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 push method based on intelligent identification and big data is applied to a block chain financial platform which is in communication connection with a plurality of digital financial service terminals, and the method comprises the following steps:

configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing node to obtain a configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model;

the request recommendation information is obtained by the following steps:

marking form operation tracks corresponding to the business service scenes according to the business keywords, determining form tracking thermalization entries corresponding to each business service scene by adopting an artificial intelligence model, generating recommended portrait characteristics of an information recommendation process corresponding to the business request information according to the form tracking thermalization entries corresponding to each business service scene, and pushing corresponding request recommendation information to the digital financial service terminal according to the recommended portrait characteristics of the information recommendation process;

the block chain financial platform acquires service keyword labels of service scenes belonging to the same service scene from service request information of a digital financial service terminal in a preset time period, and determines the service keyword labels belonging to each service scene as a corresponding service keyword list;

the service request information comprises a plurality of service keyword labels, each service keyword label refers to a field sequence formed by the word property and the semantic meaning related to the keyword, and the field sequence formed by the word property and the semantic meaning is used for expressing different information with classification attributes in the actual service request pushing; the business service scene is used for representing the type of the business service scene field;

the entity relationship is used for representing a relationship field representing associated business scene service, the entity relationship characteristic is used for representing a relationship field sequence corresponding to the entity relationship, and the corresponding entity business item level is used for representing the frequency degree of the relationship field sequence corresponding to the entity relationship having the frequency mode item of the interactive label between the entity relationship and the associated business scene service;

the form operation track is used for representing the record information of the generated form operation behavior in the unit of time axis.

2. The intelligent identification and big data-based information push method according to claim 1, wherein the step of performing information identification on the target service index classification list to obtain an information push tag list and corresponding historical push feedback parameters, and determining the information push tag list with the historical push feedback parameters meeting preset matching rules as a target push reference policy comprises:

3. The information push method based on intelligent identification and big data according to claim 1, wherein the step of performing service index classification and push structure association processing on the target identification push information to obtain a corresponding target service index classification list comprises:

4. The information push method based on intelligent identification and big data according to any one of claims 1 to 3, wherein the step of traversing the target service index classification list according to the target push reference policy and circularly updating push key elements of a push structure body comprises:

5. The information pushing method based on intelligent identification and big data according to claim 4, wherein the step of calibrating the pushing structure body for the business indexes in the target business index sequence according to the target pushing reference strategy and updating the pushing key elements of the pushing structure body comprises:

6. The information push method based on intelligent identification and big data according to any one of claims 1-3, wherein the step of extracting push source characteristics and corresponding source characteristic importance degree information in the classification list of target service indexes of the added push nodes comprises:

7. The intelligent identification and big data-based information push method according to claim 6, wherein the step of calculating the source feature importance degree information of the target service index classification list of the added push node comprises:

8. The information pushing method based on intelligent identification and big data as claimed in claim 1, wherein the step of determining the form operation track of the business service scenario to which each business keyword label corresponds according to the entity relationship feature and the corresponding entity business item level comprises:

9. The information push system based on intelligent identification and big data is characterized by comprising a blockchain financial platform and a plurality of digital financial service terminals in communication connection with the blockchain financial platform;

the block chain financial platform is used for configuring a preset information pushing model according to the pushing source characteristics, the source characteristic importance degree information and the pushing nodes to obtain a configured preset information pushing model, and performing information pushing processing on the target service index classification list based on the configured preset information pushing model;

the service index refers to index information which is generated in the digital financial service process and used for measuring each service request to be recommended, the pushing structure refers to structural configuration information which is used for carrying out pushing strategy reference in the digital financial service process, and the structural configuration information refers to configuration information which is well arranged in a preset calibrated fixed format;

each information push label in the information push label list is used for representing a classification list which is specifically referred to subsequently in the information push process, and the corresponding historical push feedback parameter refers to content characteristic information fed back by a user before the information push label is used as a reference object for subsequent push;

the push key elements refer to content nodes contained in push information in a subsequent push process, each content node corresponds to one push node, the push source characteristics are used for representing the characteristics of an information source in the subsequent push process, and the corresponding source characteristic importance degree information is used for representing the weight occupied by the characteristics of the information source;

the request recommendation information is obtained by the following method:

10. A blockchain financial 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 through a bus system, the network interface is configured to be communicatively connected with at least one digital financial services terminal, 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 smart identification and big data based information push method according to any one of claims 1 to 9.