CN117391864A - Risk identification method and device based on data flow direction, electronic equipment and medium - Google Patents
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Abstract
The invention relates to artificial intelligence, and discloses a risk identification method based on data flow direction, which comprises the following steps: performing data risk judgment on each initial service data in the initial service data set to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the initial service data to obtain a risk marking service data set; constructing a risk data reference table according to the processed data set and the risk marking service data set after the initial service data set is processed; performing regional risk judgment on the initial service data according to the data use region corresponding to the initial service data to obtain a regional risk judgment result; and carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result. The invention further provides a risk identification device based on the data flow direction, electronic equipment and a storage medium. The risk identification method and the risk identification device can improve the accuracy of risk identification.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a risk identification method and apparatus based on data flow direction, an electronic device, and a storage medium.
Background
In the field of financial technology, there are typically generated a lot of data related to financial business, which has a flowing nature and can generate a value corresponding thereto. In order to ensure the safety and reliability of the data in the flowing process, the access, use and circulation data are generally managed and controlled in a user authority audit mode, and the data security is graded in a layering and grading mode. However, this approach may lead to an inability to accurately locate the final flow of data as the number of users of the financial data increases, and the concealment of the data continues to increase, thereby failing to accurately rank the risk and security of the data. Therefore, it is highly desirable to provide a risk identification method with higher accuracy.
Disclosure of Invention
The invention provides a risk identification method, a risk identification device, electronic equipment and a storage medium based on a data flow direction, and the method and the device are mainly used for improving the accuracy of risk identification.
In order to achieve the above object, the present invention provides a risk identification method based on data flow direction, including:
Acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set;
performing data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
acquiring data use areas corresponding to different initial service data in the initial service data set, and performing area risk judgment on the initial service data according to the data use areas to obtain an area risk judgment result;
and carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
Optionally, the performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set includes:
Acquiring a risk judgment reference comparison table corresponding to a preset risk judgment rule, wherein the risk judgment reference comparison table comprises a reference entity and a risk grade corresponding to the reference entity;
extracting service entities corresponding to different initial service data in the initial service data set, and comparing the service entities with reference entities in the risk judgment reference comparison table;
taking the risk level corresponding to the reference entity with consistent comparison as the data risk level corresponding to the initial service data;
and counting the frequency of risk grades corresponding to different initial service data in the initial service data set, and carrying out grade identification on the initial service data set according to the frequency and a preset identification rule to obtain the data risk grade corresponding to the initial service data set.
Optionally, the step of performing level discrimination on the initial service data set according to the frequency and a preset discrimination rule to obtain a data risk level corresponding to the initial service data set includes:
when the risk level of any one initial service data in the initial service data set is high risk, the data risk level corresponding to the initial service data set is high risk;
When the risk level of the initial service data in the initial service data set is that the frequency of the medium risk is greater than a preset threshold value, the data risk level corresponding to the initial service data set is that the medium risk;
when the initial business data in the initial business data set does not meet the condition that the risk level of any one of the initial business data is high risk and does not meet the condition that the frequency of the risk level of the initial business data is medium risk is larger than a preset threshold value, the data risk level corresponding to the initial business data set is low risk.
Optionally, the constructing a risk data reference table according to the processing data set and the risk marking service data set includes:
extracting risk labels in the risk marking service data set, and adding the risk labels into the processing data set to obtain a processing label set;
and carrying out field filtering on the data in the preset database to obtain a filtered database, and inserting a tag table corresponding to the processing tag set into the filtered database to obtain a risk data reference table.
Optionally, the performing area risk judgment on the initial service data according to the data use area to obtain an area risk judgment result includes:
When the data use area is an external network area, the area risk judgment result is high risk;
and when the data use area is an intranet area, carrying out area content identification on the data use area, and generating an area risk judging result according to the area content identification result.
Optionally, the identifying the area content of the data using area, generating an area risk judging result according to the result of identifying the area content, including:
when the result of the regional content identification is that data circulation exists, the regional risk judgment result is high risk;
when the result of the regional content identification is that data exchange exists, the regional risk judgment result is a medium risk;
and when the result of the regional content identification is that no data flow exists and no data exchange exists, the regional risk judgment result is low risk.
Optionally, the performing flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result includes:
identifying data flow directions among different service data in the initial service data set to obtain a service data flow direction, wherein the service data flow direction is determined by any two initial service data in the initial service data set;
Identifying the corresponding flow direction risk level of the business data flow according to the risk data reference table and the regional risk judgment result;
carrying out scheme recommendation on the initial service data according to a preset recommendation algorithm and the flow direction risk level to obtain a recommendation scheme;
and outputting the flow direction risk level and the recommended scheme as the final risk identification result.
In order to solve the above problem, the present invention further provides a risk identification device based on a data flow direction, the device including:
the data risk judging module is used for acquiring an initial service data set, carrying out data risk judgment on each initial service data in the initial service data set according to a preset risk judging rule to obtain a data risk grade corresponding to the initial service data set, and adding a risk label corresponding to the data risk grade to the corresponding initial service data to obtain a risk marking service data set;
the reference table construction module is used for carrying out data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
The regional risk judging module is used for acquiring data use regions corresponding to different initial service data in the initial service data set, and judging regional risk of the initial service data according to the data use regions to obtain regional risk judging results;
and the flow direction risk identification module is used for carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data flow direction based risk identification method described above.
In order to solve the above-mentioned problems, the present invention also provides a storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned data flow direction based risk identification method.
In the embodiment of the invention, the flow direction risk identification is carried out on the business data in the initial business data set by constructing the generated risk data reference table and the regional risk judgment result, so that not only can the information such as the data source, the safety risk level and the like be known at each flow stage in the circulation of the data, but also the information such as the safety risk level, the upstream and downstream safety level relationship and the like where each node in the circulation of the data is known can be ensured, the safety of the data is kept after the data passes through each processing process, and the non-damage of the data processing safety information is realized. Therefore, the risk identification method, the risk identification device, the electronic equipment and the storage medium based on the data flow direction can solve the problem of low accuracy of improving risk identification.
Drawings
Fig. 1 is a flow chart of a risk identification method based on data flow according to an embodiment of the present invention;
FIG. 2 is a detailed flow chart of one of the steps shown in FIG. 1;
FIG. 3 is a functional block diagram of a risk identification device based on data flow according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the risk identification method based on data flow direction according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a risk identification method based on a data flow direction. The execution subject of the risk identification method based on the data flow direction includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the risk identification method based on the data flow direction may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a risk identification method based on data flow direction according to an embodiment of the present invention is shown. In this embodiment, the risk identification method based on the data flow direction includes the following steps S1 to S4:
s1, acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set.
In the embodiment of the present invention, the initial service data set refers to service source data when different financial services are processed in the field of financial technology, and the initial service data set includes source data corresponding to different service scenarios, for example, may be in a deposit service scenario or a financial service scenario.
Specifically, the performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set includes:
S11, acquiring a risk judgment reference comparison table corresponding to a preset risk judgment rule, wherein the risk judgment reference comparison table comprises a reference entity and a risk grade corresponding to the reference entity;
s12, extracting service entities corresponding to different initial service data in the initial service data set, and comparing the service entities with reference entities in the risk judgment reference comparison table;
s13, taking the risk level corresponding to the reference entity with consistent comparison as the data risk level corresponding to the initial service data;
and S14, counting the frequency of risk grades corresponding to different initial service data in the initial service data set, and carrying out grade identification on the initial service data set according to the frequency and a preset identification rule to obtain a data risk grade corresponding to the initial service data set.
In detail, the risk determination reference table includes a reference entity and a risk level corresponding to the reference entity, where the reference entity may be a name, a phone number, a transaction amount, a transaction number, whether plaintext, etc., and the corresponding risk level may be a low risk, a medium risk, and a high risk.
Further, the step of performing level discrimination on the initial service data set according to the frequency and a preset discrimination rule to obtain a data risk level corresponding to the initial service data set includes:
when the risk level of any one initial service data in the initial service data set is high risk, the data risk level corresponding to the initial service data set is high risk;
when the risk level of the initial service data in the initial service data set is that the frequency of the medium risk is greater than a preset threshold value, the data risk level corresponding to the initial service data set is that the medium risk;
when the initial business data in the initial business data set does not meet the condition that the risk level of any one of the initial business data is high risk and does not meet the condition that the frequency of the risk level of the initial business data is medium risk is larger than a preset threshold value, the data risk level corresponding to the initial business data set is low risk.
In detail, the preset threshold is 10%, that is, when the risk level of the initial service data is that the frequency of the medium risk is greater than 10%, the data risk level corresponding to the initial service data set is that the medium risk.
S2, carrying out data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set.
In the embodiment of the invention, since the initial business data set is the source data, the source data is processed in different business processing processes under the financial scene in the financial science and technology field, and the data processing process comprises, but is not limited to, data sorting and data screening of the initial business data.
Specifically, the constructing a risk data reference table according to the processing data set and the risk marking service data set includes:
extracting risk labels in the risk marking service data set, and adding the risk labels into the processing data set to obtain a processing label set;
and carrying out field filtering on the data in the preset database to obtain a filtered database, and inserting a tag table corresponding to the processing tag set into the filtered database to obtain a risk data reference table.
In detail, performing field filtering on data in a preset database refers to performing risk field filtering on a table structure field and a preset number of rows before and after table data in the database.
S3, acquiring data use areas corresponding to different initial service data in the initial service data set, and judging the area risk of the initial service data according to the data use areas to obtain an area risk judging result.
In the embodiment of the invention, the data use area of the initial service data comprises an extranet, an intranet, an extranet and the like.
Specifically, the performing area risk judgment on the initial service data according to the data use area to obtain an area risk judgment result includes:
when the data use area is an external network area, the area risk judgment result is high risk;
and when the data use area is an intranet area, carrying out area content identification on the data use area, and generating an area risk judging result according to the area content identification result.
Further, the performing area content recognition on the data use area, generating an area risk judging result according to the result of the area content recognition, includes:
when the result of the regional content identification is that data circulation exists, the regional risk judgment result is high risk;
when the result of the regional content identification is that data exchange exists, the regional risk judgment result is a medium risk;
and when the result of the regional content identification is that no data flow exists and no data exchange exists, the regional risk judgment result is low risk.
In detail, data circulation refers to designing different departments and organizing data exchange in an intranet, and data exchange refers to that data in the intranet can reach an extranet after being circulated through processing.
And S4, carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
In the embodiment of the present invention, the performing flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result includes:
identifying data flow directions among different service data in the initial service data set to obtain a service data flow direction, wherein the service data flow direction is determined by any two initial service data in the initial service data set;
identifying the corresponding flow direction risk level of the business data flow according to the risk data reference table and the regional risk judgment result;
carrying out scheme recommendation on the initial service data according to a preset recommendation algorithm and the flow direction risk level to obtain a recommendation scheme;
and outputting the flow direction risk level and the recommended scheme as the final risk identification result.
In detail, the risk data reference table and the regional risk judgment result are used for carrying out flow direction risk identification, so that risk identification can be carried out from different dimensions, and further an accurate final risk identification result is obtained.
In the embodiment of the invention, the flow direction risk identification is carried out on the business data in the initial business data set by constructing the generated risk data reference table and the regional risk judgment result, so that not only can the information such as the data source, the safety risk level and the like be known at each flow stage in the circulation of the data, but also the information such as the safety risk level, the upstream and downstream safety level relationship and the like where each node in the circulation of the data is known can be ensured, the safety of the data is kept after the data passes through each processing process, and the non-damage of the data processing safety information is realized. Therefore, the risk identification method based on the data flow direction can solve the problem of low accuracy of improving risk identification.
Fig. 3 is a functional block diagram of a risk identification device based on data flow according to an embodiment of the present invention.
The risk identification device 100 based on data flow direction of the present invention may be installed in an electronic device. The risk identification device 100 based on data flow direction may include a data risk determination module 101, a reference table construction module 102, a regional risk determination module 103, and a flow direction risk identification module 104 according to the implemented functions. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data risk judging module 101 is configured to obtain an initial service data set, perform data risk judgment on each initial service data in the initial service data set according to a preset risk judging rule to obtain a data risk level corresponding to the initial service data set, and add a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set;
the reference table construction module 102 is configured to perform data processing on the initial service data set to obtain a processed data set, and construct a risk data reference table according to the processed data set and the risk marking service data set;
the region risk judging module 103 is configured to obtain data usage regions corresponding to different initial service data in the initial service data set, and perform region risk judgment on the initial service data according to the data usage regions, so as to obtain a region risk judging result;
the flow direction risk identification module 104 is configured to perform flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result, so as to obtain a final risk identification result.
In detail, the specific embodiments of each module of the risk identification apparatus 100 based on the data flow direction are as follows:
step one, acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set.
In the embodiment of the present invention, the initial service data set refers to service source data when different financial services are processed in the field of financial technology, and the initial service data set includes source data corresponding to different service scenarios, for example, may be in a deposit service scenario or a financial service scenario.
Specifically, the performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set includes:
acquiring a risk judgment reference comparison table corresponding to a preset risk judgment rule, wherein the risk judgment reference comparison table comprises a reference entity and a risk grade corresponding to the reference entity;
Extracting service entities corresponding to different initial service data in the initial service data set, and comparing the service entities with reference entities in the risk judgment reference comparison table;
taking the risk level corresponding to the reference entity with consistent comparison as the data risk level corresponding to the initial service data;
and counting the frequency of risk grades corresponding to different initial service data in the initial service data set, and carrying out grade identification on the initial service data set according to the frequency and a preset identification rule to obtain the data risk grade corresponding to the initial service data set.
In detail, the risk determination reference table includes a reference entity and a risk level corresponding to the reference entity, where the reference entity may be a name, a phone number, a transaction amount, a transaction number, whether plaintext, etc., and the corresponding risk level may be a low risk, a medium risk, and a high risk.
Further, the step of performing level discrimination on the initial service data set according to the frequency and a preset discrimination rule to obtain a data risk level corresponding to the initial service data set includes:
when the risk level of any one initial service data in the initial service data set is high risk, the data risk level corresponding to the initial service data set is high risk;
When the risk level of the initial service data in the initial service data set is that the frequency of the medium risk is greater than a preset threshold value, the data risk level corresponding to the initial service data set is that the medium risk;
when the initial business data in the initial business data set does not meet the condition that the risk level of any one of the initial business data is high risk and does not meet the condition that the frequency of the risk level of the initial business data is medium risk is larger than a preset threshold value, the data risk level corresponding to the initial business data set is low risk.
In detail, the preset threshold is 10%, that is, when the risk level of the initial service data is that the frequency of the medium risk is greater than 10%, the data risk level corresponding to the initial service data set is that the medium risk.
And secondly, carrying out data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set.
In the embodiment of the invention, since the initial business data set is the source data, the source data is processed in different business processing processes under the financial scene in the financial science and technology field, and the data processing process comprises, but is not limited to, data sorting and data screening of the initial business data.
Specifically, the constructing a risk data reference table according to the processing data set and the risk marking service data set includes:
extracting risk labels in the risk marking service data set, and adding the risk labels into the processing data set to obtain a processing label set;
and carrying out field filtering on the data in the preset database to obtain a filtered database, and inserting a tag table corresponding to the processing tag set into the filtered database to obtain a risk data reference table.
In detail, performing field filtering on data in a preset database refers to performing risk field filtering on a table structure field and a preset number of rows before and after table data in the database.
Step three, acquiring data use areas corresponding to different initial service data in the initial service data set, and carrying out area risk judgment on the initial service data according to the data use areas to obtain an area risk judgment result.
In the embodiment of the invention, the data use area of the initial service data comprises an extranet, an intranet, an extranet and the like.
Specifically, the performing area risk judgment on the initial service data according to the data use area to obtain an area risk judgment result includes:
When the data use area is an external network area, the area risk judgment result is high risk;
and when the data use area is an intranet area, carrying out area content identification on the data use area, and generating an area risk judging result according to the area content identification result.
Further, the performing area content recognition on the data use area, generating an area risk judging result according to the result of the area content recognition, includes:
when the result of the regional content identification is that data circulation exists, the regional risk judgment result is high risk;
when the result of the regional content identification is that data exchange exists, the regional risk judgment result is a medium risk;
and when the result of the regional content identification is that no data flow exists and no data exchange exists, the regional risk judgment result is low risk.
In detail, data circulation refers to designing different departments and organizing data exchange in an intranet, and data exchange refers to that data in the intranet can reach an extranet after being circulated through processing.
And step four, carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
In the embodiment of the present invention, the performing flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result includes:
identifying data flow directions among different service data in the initial service data set to obtain a service data flow direction, wherein the service data flow direction is determined by any two initial service data in the initial service data set;
identifying the corresponding flow direction risk level of the business data flow according to the risk data reference table and the regional risk judgment result;
carrying out scheme recommendation on the initial service data according to a preset recommendation algorithm and the flow direction risk level to obtain a recommendation scheme;
and outputting the flow direction risk level and the recommended scheme as the final risk identification result.
In detail, the risk data reference table and the regional risk judgment result are used for carrying out flow direction risk identification, so that risk identification can be carried out from different dimensions, and further an accurate final risk identification result is obtained.
In the embodiment of the invention, the flow direction risk identification is carried out on the business data in the initial business data set by constructing the generated risk data reference table and the regional risk judgment result, so that not only can the information such as the data source, the safety risk level and the like be known at each flow stage in the circulation of the data, but also the information such as the safety risk level, the upstream and downstream safety level relationship and the like where each node in the circulation of the data is known can be ensured, the safety of the data is kept after the data passes through each processing process, and the non-damage of the data processing safety information is realized. Therefore, the risk identification device based on the data flow direction can solve the problem of low accuracy of improving risk identification.
Fig. 4 is a schematic structural diagram of an electronic device for implementing a risk identification method based on a data flow direction according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a risk identification program based on a data flow direction.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a risk identification program based on a data flow direction, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a risk recognition program based on a data flow direction, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 4 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The data flow based risk identification program stored in the memory 11 of the electronic device 1 is a combination of instructions which, when run in the processor 10, can implement:
acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set;
Performing data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
acquiring data use areas corresponding to different initial service data in the initial service data set, and performing area risk judgment on the initial service data according to the data use areas to obtain an area risk judgment result;
and carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a storage medium if implemented in the form of software functional units and sold or used as separate products. The storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set;
performing data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
acquiring data use areas corresponding to different initial service data in the initial service data set, and performing area risk judgment on the initial service data according to the data use areas to obtain an area risk judgment result;
and carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for risk identification based on data flow direction, the method comprising:
acquiring an initial service data set, performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set, and adding a risk tag corresponding to the data risk level to the corresponding initial service data to obtain a risk marking service data set;
Performing data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
acquiring data use areas corresponding to different initial service data in the initial service data set, and performing area risk judgment on the initial service data according to the data use areas to obtain an area risk judgment result;
and carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
2. The risk identification method based on data flow direction according to claim 1, wherein the performing data risk judgment on each initial service data in the initial service data set according to a preset risk judgment rule to obtain a data risk level corresponding to the initial service data set comprises:
acquiring a risk judgment reference comparison table corresponding to a preset risk judgment rule, wherein the risk judgment reference comparison table comprises a reference entity and a risk grade corresponding to the reference entity;
extracting service entities corresponding to different initial service data in the initial service data set, and comparing the service entities with reference entities in the risk judgment reference comparison table;
Taking the risk level corresponding to the reference entity with consistent comparison as the data risk level corresponding to the initial service data;
and counting the frequency of risk grades corresponding to different initial service data in the initial service data set, and carrying out grade identification on the initial service data set according to the frequency and a preset identification rule to obtain the data risk grade corresponding to the initial service data set.
3. The risk identification method based on data flow direction according to claim 2, wherein the step of performing level identification on the initial service data set according to the frequency and a preset identification rule to obtain a data risk level corresponding to the initial service data set includes:
when the risk level of any one initial service data in the initial service data set is high risk, the data risk level corresponding to the initial service data set is high risk;
when the risk level of the initial service data in the initial service data set is that the frequency of the medium risk is greater than a preset threshold value, the data risk level corresponding to the initial service data set is that the medium risk;
when the initial business data in the initial business data set does not meet the condition that the risk level of any one of the initial business data is high risk and does not meet the condition that the frequency of the risk level of the initial business data is medium risk is larger than a preset threshold value, the data risk level corresponding to the initial business data set is low risk.
4. The method for risk identification based on data flow direction according to claim 1, wherein said constructing a risk data reference table from said process data set and said risk marking service data set comprises:
extracting risk labels in the risk marking service data set, and adding the risk labels into the processing data set to obtain a processing label set;
and carrying out field filtering on the data in the preset database to obtain a filtered database, and inserting a tag table corresponding to the processing tag set into the filtered database to obtain a risk data reference table.
5. The method for recognizing risk based on data flow direction according to claim 1, wherein said performing region risk judgment on said initial service data according to said data usage region to obtain a region risk judgment result comprises:
when the data use area is an external network area, the area risk judgment result is high risk;
and when the data use area is an intranet area, carrying out area content identification on the data use area, and generating an area risk judging result according to the area content identification result.
6. The method for recognizing risk based on data stream according to claim 5, wherein the performing area content recognition on the data usage area, generating an area risk judging result based on the result of the area content recognition, comprises:
When the result of the regional content identification is that data circulation exists, the regional risk judgment result is high risk;
when the result of the regional content identification is that data exchange exists, the regional risk judgment result is a medium risk;
and when the result of the regional content identification is that no data flow exists and no data exchange exists, the regional risk judgment result is low risk.
7. The method for risk identification based on data flow direction as set forth in claim 1, wherein said performing flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result includes:
identifying data flow directions among different service data in the initial service data set to obtain a service data flow direction, wherein the service data flow direction is determined by any two initial service data in the initial service data set;
identifying the corresponding flow direction risk level of the business data flow according to the risk data reference table and the regional risk judgment result;
carrying out scheme recommendation on the initial service data according to a preset recommendation algorithm and the flow direction risk level to obtain a recommendation scheme;
And outputting the flow direction risk level and the recommended scheme as the final risk identification result.
8. A data flow direction based risk identification device, the device comprising:
the data risk judging module is used for acquiring an initial service data set, carrying out data risk judgment on each initial service data in the initial service data set according to a preset risk judging rule to obtain a data risk grade corresponding to the initial service data set, and adding a risk label corresponding to the data risk grade to the corresponding initial service data to obtain a risk marking service data set;
the reference table construction module is used for carrying out data processing on the initial service data set to obtain a processing data set, and constructing a risk data reference table according to the processing data set and the risk marking service data set;
the regional risk judging module is used for acquiring data use regions corresponding to different initial service data in the initial service data set, and judging regional risk of the initial service data according to the data use regions to obtain regional risk judging results;
and the flow direction risk identification module is used for carrying out flow direction risk identification on the service data in the initial service data set based on the risk data reference table and the regional risk judgment result to obtain a final risk identification result.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data flow based risk identification method of any one of claims 1 to 7.
10. A storage medium storing a computer program, wherein the computer program when executed by a processor implements the data flow direction based risk identification method according to any one of claims 1 to 7.
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