CN112765435A

CN112765435A - Business data processing method and big data platform combining block chain and digital finance

Info

Publication number: CN112765435A
Application number: CN202110058252.XA
Authority: CN
Inventors: 王红建
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-08-22
Filing date: 2020-08-22
Publication date: 2021-05-07
Also published as: CN112015964A; CN112765434A; CN112015964B

Abstract

According to the business data processing method and the big data platform combining the block chain and the digital finance, firstly, a current authority request of business behavior information extracted by a first block chain node in target business data is obtained according to authority calling records, secondly, a historical authority request list of a second block chain link point relative to the first block chain node is determined based on pre-configured data crawler distribution information, then when a target authority request matched with the current authority request exists in the historical authority request list, calling environment parameters of the target authority request are determined, and finally when a data crawler script exists in the target business data based on the calling environment parameters, a data processing result output by the first block chain link point is intercepted. Therefore, the crawler script existing in the service data can be accurately and reliably detected, the corresponding anti-crawling operation can be carried out, and the user privacy data of the first block chain node is prevented from being illegally crawled along with the output data processing result.

Description

Business data processing method and big data platform combining block chain and digital finance

Technical Field

The application relates to the technical field of block chain finance, in particular to a business data processing method and a big data platform combining block chain and digital finance.

Background

With the development of network technologies, digital financial services have gradually replaced traditional financial services, providing convenient and efficient services for social development. Nowadays, the digital financial services include internet payment, mobile payment, online banking, financial services outsourcing and online loan, online insurance, online fund and other financial services. In addition, by applying the blockchain technology to the field of digital finance, the related user privacy data can be protected by utilizing the decentralization, traceability and non-falsification of the blockchain technology.

However, in actual application, each block chain node for performing business behavior processing can protect the private data of the user, but cannot cope with increasingly complex digital financial business scenes, so that some data written with abnormal crawlers crawl the private data of the user of the block chain node. Thus, although the blockchain technique can ensure that the user privacy data and the transaction behavior data are not tampered with, the user privacy data cannot be prevented from being crawled to some extent.

Disclosure of Invention

The application provides a service data processing method and a big data platform combining a block chain and digital finance, so as to solve the technical problems in the prior art.

In a first aspect, a business data processing method combining blockchain and digital finance is provided, which is applied to a big data platform, and the method at least includes:

acquiring a current permission request of service behavior information extracted by a first block chain node in target service data according to a permission calling record of the first block chain node in the process of processing the target service data sent by a second block chain node;

determining a historical permission request list of the second block chain link point relative to the first block chain node based on pre-configured data crawler distribution information;

judging whether a target permission request matched with the current permission request exists in the historical permission request list or not;

when judging that a target permission request matched with the current permission request exists in the historical permission request list, determining a calling environment parameter of the target permission request according to storage path information of request data corresponding to the target permission request in the first block chain node; detecting whether a data crawler script exists in the target service data or not based on the calling environment parameter; and intercepting a data processing result output by the first block link point based on the target service data when the calling environment parameter is detected to detect that the data crawler script exists in the target service data.

In a second aspect, a big data platform is provided for executing a business data processing method combining blockchain and digital finance, the big data platform is at least used for:

The method for processing service data and big data platform combining block chain and digital finance provided by the embodiment of the application firstly obtains the current authority request of the service behavior information extracted by the first block chain node in the target service data according to the authority calling record when the first block chain node processes the target service data sent by the second block chain node, secondly, determining a historical authority request list of the second block chain link point relative to the first block chain node based on the pre-configured data crawler distribution information, then when it is determined that there is a target permission request matching the current permission request in the historical permission request list, and finally, intercepting a data processing result output by the link point of the first block when detecting that the data crawler script exists in the target service data based on the calling environment parameter. Therefore, the crawler script existing in the service data can be accurately and reliably detected, the corresponding anti-crawling operation can be carried out, and the user privacy data of the first block chain node is prevented from being illegally crawled along with the output data processing result.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

FIG. 1 is a schematic diagram of a business data processing system incorporating blockchain and digital finance as illustrated in accordance with an exemplary embodiment of the present application.

Fig. 2 is a flow chart illustrating a method for processing business data in conjunction with blockchain and digital finance according to an exemplary embodiment of the present application.

Fig. 3 is a block diagram illustrating an embodiment of a business data processing apparatus incorporating blockchain and digital finance according to an exemplary embodiment of the present application.

Fig. 4 is a hardware structure diagram of a big data platform where the business data processing device of the present application combines the blockchain and digital finance.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

The inventor analyzes the service flows of the existing blockchain nodes, and finds that the service flows are generally limited to the functions of a consensus mechanism, an encryption algorithm, forgery prevention and the like. That is, the block link node usually can only perform "passive" data defense, and cannot actively perform detection of a crawler script on the service data to implement "active" data defense, so that it is difficult to avoid illegal crawling of the user privacy data.

In order to detect the crawler script for the service data to realize 'active' data defense and avoid illegal crawling of the user privacy data of the blockchain nodes, the embodiment of the invention provides a service data processing method and a big data platform combining a blockchain and digital finance, which can cloud the detection of the crawler script, so that uniform crawler script detection is performed on the service data corresponding to the blockchain links, thus accurately and reliably detecting the crawler script existing in the service data and performing corresponding anti-crawling operation, and thus avoiding illegal crawling of the user privacy data of the blockchain nodes.

To achieve the above objective, please first refer to fig. 1, which is a schematic diagram of a communication architecture of a business data processing system 100 combining blockchain and digital finance, wherein the business data processing system 100 may include a big data platform 200 and a plurality of blockchain nodes 400. Wherein the big data platform 200 and the plurality of blockchain nodes 400 are communicatively coupled to each other. Further, please refer to fig. 2, which is a flowchart of a method for processing business data in combination with blockchain and digital finance, the method may be applied to the big data platform 200 in fig. 1, and specifically may include the contents described in steps S21 to S24.

Step S21, obtaining a current permission request of the service behavior information extracted by the first blockchain node in the target service data according to a permission call record of the first blockchain node when processing the target service data sent by the second blockchain node.

For example, the target business data may be data of online payment behavior of the second block link point and the first block link point or other financial service behavior, including but not limited to payment verification data, payment authorization data, payment inquiry data, and the like.

Step S22, determining a historical permission request list of the second tile link point relative to the first tile link node based on pre-configured data crawler distribution information.

For example, the data crawler distribution information is set according to script codes of a plurality of different data crawler scripts, and the historical permission request list is used for recording a historical record that the second block link node requests permission of the first block link node to be opened.

Step S23, determining whether there is a target permission request matching the current permission request in the historical permission request list.

For example, the target permission request is used for representing that the permission request of the second block link point to the first block link node has "out-of-bounds" behavior, in other words, the permission request of the second block link point to the first block link node may have abnormal or excessive request permission.

Step S24, when it is judged that a target permission request matched with the current permission request exists in the historical permission request list, determining a calling environment parameter of the target permission request according to the storage path information of the request data corresponding to the target permission request in the first block chain node; detecting whether a data crawler script exists in the target service data or not based on the calling environment parameter; and intercepting a data processing result output by the first block link point based on the target service data when the calling environment parameter is detected to detect that the data crawler script exists in the target service data.

For example, the data processing result may be a processing result corresponding to response data made by the first block link node based on the target service data, and the data processing result may carry privacy information of the first block link node, such as user identity information, user decision factor information, and the like.

In specific implementation, by executing the contents described in the above steps S21-S24, a current permission request of service behavior information extracted from the target service data by the first blockchain node is first obtained according to a permission call record of the first blockchain node when processing the target service data sent by the second blockchain node, then a historical permission request list of the second blockchain node relative to the first blockchain node is determined based on pre-configured data crawler distribution information, then a call environment parameter of the target permission request is determined when it is determined that a target permission request matching the current permission request exists in the historical permission request list, and finally a data processing result output by the first blockchain node is intercepted when it is detected that a data crawler script exists in the target service data based on the call environment parameter. Therefore, the crawler script existing in the service data can be accurately and reliably detected, the corresponding anti-crawling operation can be carried out, and the user privacy data of the first block chain node is prevented from being illegally crawled along with the output data processing result.

In practical application, the inventor finds that the technical problem that the determined calling environment parameters have deviation usually occurs when the calling environment parameters are determined, so that missed detection occurs when the data crawler script is detected. To solve this technical problem, the determining, by the step S24, the call environment parameter of the target permission request according to the storage path information of the request data corresponding to the target permission request in the first blockchain node may specifically include the following contents described in the steps S241 to S243.

Step S241, splitting a request message for the target permission request according to message field distribution characteristics corresponding to the category mapping values of the target permission request in a preset mapping list to obtain message header information and message body information; and acquiring a target information segment with a directional identifier from the message main body information, and extracting message data in the target information segment to serve as request data corresponding to the target permission request.

Step S242, determining a corresponding storage area of the first block chain node in the big data platform based on a channel protocol layer text of a data transmission channel pre-established with the first block chain node; listing the storage addresses of the corresponding storage areas to obtain a storage address sequence, and mapping the storage address sequence according to the channel protocol layer text to obtain a mapping address sequence in the first block chain node; and pairing the multiple segments of message data in the request data with the mapping address sequence sequentially according to a time sequence to obtain a pairing result, and determining the storage path information of the request data in the first block chain node based on the pairing result.

Step S243, extracting a path parameter set that is not updated with the change of the request priority of the target permission request in the storage path information, and determining the calling environment parameter of the target permission request according to the path parameter set.

It can be understood that, when the contents described in steps S241 to S243 are applied, the request data in the target permission request can be accurately extracted, and the storage path information is determined according to a time sequence, so that the path parameter set that is not updated with the change of the request priority of the target permission request in the storage path information is extracted, and it is ensured that the determined calling environment parameter has no deviation, so that the problem of missed detection in the subsequent detection of the data crawler script can be avoided.

In a specific embodiment, in order to ensure the integrity and feature differentiation of the invocation environment parameters, the determining of the invocation environment parameters of the target permission request through the path parameter set described in step S243 may specifically include the following: extracting a first path distribution track and a second path distribution track in the path parameter set, and determining a calling environment parameter of the target permission request according to the first path distribution track and the second path distribution track; the first path distribution track is used for representing a distribution track corresponding to a dynamic parameter in the path parameter set, and the second path distribution track is used for representing a distribution track corresponding to a static parameter in the path parameter set. Therefore, the completeness and the feature discrimination of the call environment parameters can be ensured through the first path distribution track and the second path distribution track.

Further, step S2431-step S2434 provide a specific implementation manner for extracting a first path distribution trajectory and a second path distribution trajectory in the path parameter set, and determining a calling environment parameter of the target permission request according to the first path distribution trajectory and the second path distribution trajectory.

Step S2431, determining a plurality of path nodes corresponding to the path parameter set according to the parameter distribution labels corresponding to the path parameter set; the encapsulated path parameters in each path node are different, and each path node has different path priority.

Step S2432, sorting the path nodes according to the descending order of the path priorities to obtain a path node sorting sequence, grabbing a plurality of first path nodes in the path node sorting sequence as a first node set according to parameter characteristic intervals of path parameters encapsulated in the path nodes, and then determining the remaining second path nodes in the path node sorting sequence as a second node set; determining node redirection coefficients between each path node of the path node sequencing sequence in the second node set and each path node of the path node sequencing sequence in the first node set according to the path nodes of the path node sequencing sequence in the first node set and queue information of the path nodes, and migrating the path nodes of the path node sequencing sequence, which have redirection pair identifications between the path nodes in the second node set and the path nodes in the first node set, to the first node set according to the node redirection coefficients; and generating the first path distribution track and the second path distribution track according to the first node set which completes the node migration and the second node set which completes the node migration.

Step S2433, constructing first trajectory description information of the first path distribution trajectory and second trajectory description information of the second path distribution trajectory, and determining whether a correlation coefficient between the first trajectory description information and the second trajectory description information is lower than a preset coefficient; if the correlation coefficient between the first track description information and the second track description information is not lower than the preset coefficient, iteratively updating the second track description information according to the interactive track data of the first track description information until the correlation coefficient between the first track description information and the second track description information is lower than the preset coefficient; when a correlation coefficient between the first track description information and the second track description information is lower than the preset coefficient, the first track description information and the second track description information respectively include a plurality of pieces of calling data with different environment index factors.

Step S2434, selecting any calling data from the first track description information as calling data to be detected, and mapping the calling authority features of the calling data to be detected into target calling data with the maximum environment index factor in the second track description information so as to calculate the feature discrimination of the calling authority features in the target calling data; and determining the calling environment parameters of the target permission request according to the feature discrimination, the calling data in the first track description information and the calling data in the second track description information.

In this way, based on the above steps S2431 to S2434, the integrity and feature discrimination of the call environment parameter can be ensured.

In a possible case, the inventor finds that, when detecting the data crawler script, the data crawler script may not be detected accurately and reliably because the similarity between the data crawler script and the normal data script in the target service data is too high, so that the data crawler script may be highly "hidden", and to improve this technical problem, the detection of whether the data crawler script exists in the target service data based on the call environment parameter described in step S24 may exemplarily include the following steps a to e.

Step a, under the condition that data calling simulation needs to be carried out on the calling environment parameters, obtaining data transcoding configuration information of the target service data.

B, when the full-load field length corresponding to the data transcoding identifier in the data transcoding configuration information is smaller than the full-load field length in the target service data, determining whether a first data field which needs to be ignored and is in an adjustable state exists; wherein the first data field is a data field in the target service data.

And c, when first data fields needing to be ignored exist, generating a corresponding field ignoring identifier for each first data field, and adjusting the current state of the field ignoring identifier to be an activated state so that only the callable second data field is reserved in the target service data.

D, starting data calling simulation, and putting preset sample data into a calling pool; the sample data comprises first sample data and second sample data, wherein the first sample data is non-private data, and the second sample data is private data.

Step e, executing the service thread corresponding to the second data field to call the sample data in the call pool and obtain a call result; judging whether the second sample data exists in the calling result or not; if so, judging that a data crawler script exists in the target service data; if not, returning to the step of determining whether the first data field which needs to be ignored and is in the calling state exists or not until all data fields of the target business data are traversed.

It can be understood that through the steps a to e, when the data crawler script is detected, the similarity comparison between the data crawler script and a normal data script in target service data can be avoided, and the problem of strong concealment of the data crawler script is avoided laterally through data calling simulation, so that the data crawler script can be detected accurately and reliably based on the calling condition of the private data in sample data.

In an alternative embodiment, the step S21 may exemplarily include the following steps S211 to S214, where the current permission request for obtaining the service behavior information extracted by the first blockchain node in the target service data according to the permission invocation record of the first blockchain node when processing the target service data sent by the second blockchain node is described in the following step S21.

Step S211, acquiring a thread running record of the first block chain node when processing target service data sent by a second block chain node, and a service request record corresponding to the second block chain node; and integrating the thread running records and the service request records according to a time sequence pairing relationship to obtain two groups of record queues.

Step S212, obtaining authority execution data of each record queue and local thread running records corresponding to the record queues; wherein the local thread run record is part of the thread run record.

Step S213, calculating an authorization factor of authority when each record queue is affine-transformed into the thread running record according to the authority execution data of each record queue and the local thread running record, where the authorization factor includes a service behavior authorization factor.

Step S214, when the service behavior authorization coefficient falls into a set coefficient interval, acquiring a current permission request of the service behavior information extracted by the first block chain node in the target service data according to the record queue with the larger queue concentration in the two groups of record queues; and when the business behavior authorization coefficient does not fall into a set coefficient interval, acquiring the current permission request of the business behavior information extracted by the first block chain node in the target business data according to the record queue with the smaller queue concentration in the two groups of record queues.

In this way, the real-time performance and the reliability of the current permission request can be ensured based on the above steps S211 to S214.

In practical applications, in order to completely determine the historical permission request list, the historical permission request list of the second tile link point relative to the first tile link node is determined based on the preconfigured data crawler distribution information as described in step S22, which may be specifically implemented as described in steps S221 to S223 below.

Step S221, determining a URL queue of the data crawler distribution information, and searching sample distribution information, attribute distribution information and decision factor information of a plurality of text classifiers meeting the URL queue from a preset database according to the URL queue; the sample distribution information includes positive sample information and negative sample information.

Step S222, analyzing the service interaction report of the second block chain node based on the sample distribution information, the attribute distribution information, and the decision factor information of the plurality of text classifiers, and analyzing the interaction behavior data that simultaneously satisfies the interaction correlation among the sample distribution information, the attribute distribution information, and the decision factor information from the service interaction report of the second block chain node.

Step S223, determining the historical permission request list according to the link request data actively initiated by the second block link point in the interactive behavior data.

In this way, the history authority request list can be completely determined by applying the above-described steps S221 to S223.

In a possible implementation manner, the determination of whether the target permission request matching the current permission request exists in the historical permission request list described in step S23 may specifically include the following contents described in step S231 and step S232.

Step S231, listing each permission request to be matched in the historical permission request list, and calculating a request responsiveness of each permission request to be matched.

Step S232, judging whether the permission request to be matched is the target permission request or not according to the request responsivity of the permission request to be matched and the correlation coefficient of the current permission request; if the request responsiveness of the permission request to be matched and the correlation coefficient of the current permission request are greater than the target coefficient, determining that the permission request to be matched is the target permission request; and if the request responsiveness of the permission request to be matched and the correlation coefficient of the current permission request are less than or equal to the target coefficient, determining that the permission request to be matched is not the target permission request.

It can be understood that based on the above steps S231 and S232, it can be accurately determined whether there is a target permission request matching the current permission request in the history permission request list.

Based on the same inventive concept, a big data platform is provided, which is described in detail as follows.

A big data platform for performing a business data processing method that combines blockchain and digital finance, the big data platform at least for:

Alternatively, the determining, by the big data platform, the invocation environment parameter of the target permission request according to the storage path information of the request data corresponding to the target permission request in the first block chain node specifically includes:

splitting a request message for the target permission request according to message field distribution characteristics corresponding to the category mapping values of the target permission request in a preset mapping list to obtain message header information and message body information; acquiring a target information segment with a directional identifier from the message main body information and extracting message data in the target information segment to serve as request data corresponding to the target permission request;

determining a corresponding storage area of the first block chain node in the big data platform based on a channel protocol layer text of a data transmission channel pre-established with the first block chain node; listing the storage addresses of the corresponding storage areas to obtain a storage address sequence, and mapping the storage address sequence according to the channel protocol layer text to obtain a mapping address sequence in the first block chain node; sequentially pairing the multiple segments of message data in the request data with the mapping address sequence according to a time sequence to obtain a pairing result, and determining storage path information of the request data in the first block chain node based on the pairing result;

and extracting a path parameter set which is not updated along with the change of the request priority of the target permission request in the storage path information, and determining the calling environment parameter of the target permission request through the path parameter set.

Alternatively, the big data platform determining the call environment parameter of the target permission request through the path parameter set further comprises:

extracting a first path distribution track and a second path distribution track in the path parameter set;

determining a calling environment parameter of the target permission request according to the first path distribution track and the second path distribution track; the first path distribution track is used for representing a distribution track corresponding to a dynamic parameter in the path parameter set, and the second path distribution track is used for representing a distribution track corresponding to a static parameter in the path parameter set.

Alternatively, the extracting, by the big data platform, a first path distribution trajectory and a second path distribution trajectory in the path parameter set, and determining, according to the first path distribution trajectory and the second path distribution trajectory, the invocation environment parameter of the target permission request further includes:

determining a plurality of path nodes corresponding to the path parameter set according to the parameter distribution labels corresponding to the path parameter set; the path parameters encapsulated in each path node are different, and each path node has different path priority;

sequencing the path nodes according to the sequence of the path priorities from big to small to obtain a path node sequencing sequence, grabbing a plurality of first path nodes in the path node sequencing sequence according to parameter characteristic intervals of path parameters encapsulated in the path nodes to serve as a first node set, and then determining the remaining second path nodes in the path node sequencing sequence to serve as a second node set; determining node redirection coefficients between each path node of the path node sequencing sequence in the second node set and each path node of the path node sequencing sequence in the first node set according to the path nodes of the path node sequencing sequence in the first node set and queue information of the path nodes, and migrating the path nodes of the path node sequencing sequence, which have redirection pair identifications between the path nodes in the second node set and the path nodes in the first node set, to the first node set according to the node redirection coefficients; generating the first path distribution track and the second path distribution track according to a first node set which completes node migration and a second node set which completes node migration;

constructing first track description information of the first path distribution track and second track description information of the second path distribution track, and judging whether a correlation coefficient between the first track description information and the second track description information is lower than a preset coefficient; if the correlation coefficient between the first track description information and the second track description information is not lower than the preset coefficient, iteratively updating the second track description information according to the interactive track data of the first track description information until the correlation coefficient between the first track description information and the second track description information is lower than the preset coefficient; when a correlation coefficient between the first track description information and the second track description information is lower than the preset coefficient, the first track description information and the second track description information respectively comprise a plurality of pieces of calling data with different environment index factors;

selecting any calling data from the first track description information as calling data to be detected, mapping the calling authority features of the calling data to be detected into target calling data with the maximum environment index factor in the second track description information, and calculating the feature discrimination of the calling authority features in the target calling data; and determining the calling environment parameters of the target permission request according to the feature discrimination, the calling data in the first track description information and the calling data in the second track description information.

Alternatively, the big data platform detecting whether a data crawler script exists in the target service data based on the call environment parameter includes:

under the condition that data calling simulation needs to be carried out on the calling environment parameters, data transcoding configuration information of the target service data is obtained;

when the full-load field length corresponding to the data transcoding identifier in the data transcoding configuration information is smaller than the full-load field length in the target service data, determining whether a first data field which needs to be ignored and is in an adjustable state exists; wherein the first data field is a data field in the target service data;

when first data fields needing to be ignored exist, generating a corresponding field ignoring identifier for each first data field, and adjusting the current state of the field ignoring identifier to be an activated state so that only a callable second data field is reserved in the target service data;

starting data calling simulation, and putting preset sample data into a calling pool; the sample data comprises first sample data and second sample data, wherein the first sample data is non-private data, and the second sample data is private data;

executing a service thread corresponding to the second data field to call the sample data in the call pool and obtain a call result; judging whether the second sample data exists in the calling result or not; if so, judging that a data crawler script exists in the target service data; if not, returning to the step of determining whether the first data field which needs to be ignored and is in the calling state exists or not until all data fields of the target business data are traversed.

Based on the same inventive concept, there is also provided a schematic diagram of the business data processing apparatus 300 combining block chaining and digital finance as shown in fig. 3, and the description about the business data processing apparatus 300 is as follows.

A1. A business data processing apparatus 300 combining blockchain and digital finance, applied to a big data platform, the apparatus at least comprising:

a request obtaining module 310, configured to obtain, according to an authority call record of a first blockchain node when processing target service data sent by a second blockchain node, a current authority request of service behavior information extracted by the first blockchain node in the target service data; the method is specifically used for: acquiring a thread running record of the first block chain node when processing target service data sent by a second block chain node, and a service request record corresponding to the second block chain node; integrating the thread operation records and the service request records according to a time sequence pairing relationship to obtain two groups of record queues; acquiring authority execution data of each record queue and local thread running records corresponding to the record queues; wherein the local thread run record is part of the thread run record; according to the authority execution data of each record queue and the local thread running records, calculating an authority authorization coefficient when each record queue is affine transformed into the thread running records, wherein the authority authorization coefficient comprises a service behavior authorization coefficient; when the business behavior authorization coefficient falls into a set coefficient interval, acquiring a current permission request of business behavior information extracted by the first block chain node in the target business data according to a record queue with a larger queue concentration in the two groups of record queues; when the business behavior authorization coefficient does not fall into a set coefficient interval, acquiring a current permission request of the business behavior information extracted by the first block chain node in the target business data according to a record queue with the smaller queue concentration in the two groups of record queues;

a list determining module 320, configured to determine a historical permission request list of the second block link point relative to the first block link node based on preconfigured data crawler distribution information;

a request determining module 330, configured to determine whether a target permission request matching the current permission request exists in the historical permission request list;

the crawler detection module 340 is configured to, when it is determined that a target permission request matching the current permission request exists in the historical permission request list, determine, according to storage path information of request data corresponding to the target permission request in the first block chain node, a calling environment parameter of the target permission request; detecting whether a data crawler script exists in the target service data or not based on the calling environment parameter; and intercepting a data processing result output by the first block link point based on the target service data when the calling environment parameter is detected to detect that the data crawler script exists in the target service data.

A2. The apparatus of a1, crawler detection module 340 to:

A3. The apparatus of a2, the crawler detection module 340, further configured to:

A4. The apparatus of a3, the crawler detection module 340, further configured to:

A5. The apparatus of claim 1, the crawler detection module 340 to:

Based on the similar inventive concept, a business data processing system combining blockchain and digital finance is also provided, and the functionality of the system is described as follows.

A business data processing system combining block chain and digital finance comprises a big data platform and a plurality of block chain nodes which are in communication connection with each other;

the first block link point is for:

processing target service data sent by a second block chain node;

the big data platform is used for:

On the basis of the above, please refer to fig. 4 in combination, which provides a big data platform 200, comprising: a processor 210, and a memory 220 and a network interface 230 connected to the processor 210; the network interface 230 is connected with a nonvolatile memory 240 in the big data platform 200; the processor 210 retrieves a computer program from the non-volatile memory 240 via the network interface 230 and runs the computer program via the memory 220 to perform the above-described method.

On the basis, a readable storage medium applied to a computer is further provided, and a computer program is burned in the readable storage medium, and when the computer program runs in the memory 220 of the big data platform 200, the method is implemented.

The implementation process of the functions and actions of each module in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A business data processing method combining block chain and digital finance is characterized by being applied to a big data platform, and the method at least comprises the following steps:

when judging that a target permission request matched with the current permission request exists in the historical permission request list, determining a calling environment parameter of the target permission request according to storage path information of request data corresponding to the target permission request in the first block chain node; detecting whether a data crawler script exists in the target service data or not based on the calling environment parameter; intercepting a data processing result output by the first block link point based on the target service data when the calling environment parameter is detected to detect that the data crawler script exists in the target service data;

wherein:

the target service data comprises payment verification data, payment authorization data and payment inquiry data;

the data crawler distribution information is set according to script codes of a plurality of different data crawler scripts, and the historical permission request list is used for recording a historical record of permission opening of the second block chain link point for requesting the first block chain node.

2. The method according to claim 1, wherein determining the call environment parameter of the target permission request according to the storage path information of the request data corresponding to the target permission request in the first blockchain node specifically includes:

3. The method of claim 2, wherein determining the call context parameter of the target permission request via the set of path parameters, further comprises:

4. The method of claim 3, wherein extracting a first path distribution trace and a second path distribution trace in the path parameter set, and determining a call environment parameter of the target permission request according to the first path distribution trace and the second path distribution trace, further comprises:

5. The method of claim 1, wherein detecting whether a data crawler script exists in the target business data based on the call environment parameter comprises:

6. A big data platform for performing a business data processing method that combines blockchain and digital finance, the big data platform at least for:

wherein:

7. The big data platform according to claim 6, wherein the step of the big data platform determining, according to the storage path information of the request data corresponding to the target permission request in the first block chain node, the invocation environment parameter of the target permission request specifically includes:

8. The big data platform of claim 7, wherein the big data platform determining the invocation environment parameters of the target permission request through the set of path parameters further comprises:

9. The big data platform of claim 8, wherein the big data platform extracts a first path distribution trace and a second path distribution trace in the path parameter set, and determining the invocation environment parameter of the target permission request according to the first path distribution trace and the second path distribution trace further comprises:

10. The big data platform of claim 6, wherein the big data platform detecting whether a data crawler script exists in the target business data based on the call environment parameter comprises: