CN116993162A - Black-out prevention wind control decision making method, device, equipment and storage medium - Google Patents
Black-out prevention wind control decision making method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to the field of artificial intelligence technology and digital medical treatment, and discloses a black production prevention wind control decision method, which comprises the following steps: acquiring a product identifier of a service product to be used; judging the to-be-treated product based on the product identification whether the service product is used for the first time; when the service product to be used is first used, performing risk decision based on face comparison; when the service to be used the product is not to be used for the first time, judging whether the current equipment of the service product to be used changes or not; if it is in a variation of the method, the device, performing risk decision based on face comparison; and if the risk type does not change, acquiring a use area of the service product to be used and an area risk type corresponding to the use area, and performing risk decision on the service product to be used based on a preset risk rule corresponding to the area risk type. The application also provides a device, equipment and medium for making the decision for preventing the black yield from being wind-controlled, which can be used in the medical field, and improves the accuracy of making the decision for preventing the black yield from being wind-controlled of products such as medical service products.
Description
Technical Field
The application relates to an artificial intelligence technology, which can be used in the field of medical health, in particular to a black production prevention wind control decision method, a device, electronic equipment and a storage medium.
Background
In the medical science and technology field, because high lever class medical service products (such as O2O class performance service products such as physical examination, tooth cleaning, medicine diagnosis card) have the settlement cost lower, the line is high, characteristics such as usable number of times is many need prevent by the black product mechanically, consequently need prevent that the black product risk decision is decided when the product uses.
However, the existing anti-black production wind control decision method only carries out risk decision from a single dimension, and the decision dimension is single, so that the accuracy of the anti-black production wind control decision of products such as medical service products is low.
Disclosure of Invention
The application provides a method, a device, electronic equipment and a storage medium for making a decision for preventing black production and wind control, which mainly aim to improve the accuracy of making a decision for preventing black production and wind control of products such as medical service products.
Acquiring a product identifier of a service product to be used;
judging whether the service product to be used is used for the first time or not based on the product identification;
when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result;
when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
judging whether the current using equipment identifier is consistent with the historical using equipment identifier or not;
when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
when the current use equipment identifier is consistent with the historical use equipment identifier, a use area of the service product to be used and an area risk type corresponding to the use area are obtained, and risk decision is carried out on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
Optionally, the obtaining a face image of the currently used user of the service product to be used, and performing face comparison by using the obtained face image, to obtain a risk decision result, includes:
acquiring a face image of a current user of the service product to be used to obtain a face image of the user;
acquiring a face image of a user to which the service product to be used belongs, and obtaining a face image of a target user;
performing face comparison on the user face image and the target user face image to obtain a face comparison result;
and carrying out risk decision according to the face comparison result to obtain the risk decision result.
Optionally, the step of comparing the face image of the user with the face image of the target user to obtain a face comparison result includes:
extracting the facial features of the user facial images to obtain user facial feature vectors;
extracting the face features of the face image of the target user to obtain a face feature vector of the target user;
calculating the vector similarity of the user face feature vector and the target user face feature vector to obtain the face similarity;
judging whether the face similarity is larger than a preset face comparison threshold value, and determining the face comparison result according to the judgment result.
Optionally, the extracting the face feature of the using face image to obtain a using face feature vector includes:
inputting the face image of the user into a pre-constructed face recognition model;
extracting output values of all output nodes of a last full-connection layer in the face recognition model;
and combining all the output values into a vector form according to the sequence of the corresponding output nodes in the full-connection layer, so as to obtain the face feature vector of the user.
Optionally, the obtaining the current usage equipment identifier and the historical usage equipment identifier of the service product to be used includes:
acquiring the equipment identifier of the current equipment of the service product to be used, and obtaining the current equipment identifier;
and acquiring the equipment identifier of the last history use equipment of the service product to be used, and obtaining the history use equipment identifier.
Optionally, the obtaining the usage area of the service product to be used and the area risk type corresponding to the usage area includes:
acquiring a use area of the service product to be used;
judging whether the preset high risk area set contains the use area or not;
when the high risk area set contains the use area, the area risk type is high risk;
when the high-risk area set does not contain the use area, judging whether the preset risk area set contains the use area or not;
when the risk area set contains the use area, the area risk type is risk;
when the set of risk areas does not contain the use area, the area risk type is low risk.
In order to solve the above problems, the present application further provides a device for controlling and deciding black yielding wind, comprising:
the use judgment module is used for acquiring the product identification of the service product to be used; judging whether the service product to be used is used for the first time or not based on the product identification; when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result; when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
the equipment replacement judging module is used for judging whether the current used equipment identifier is consistent with the historical used equipment identifier or not; when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
the risk decision module is used for acquiring the use area of the service product to be used and the area risk type corresponding to the use area when the current use equipment identifier is consistent with the historical use equipment identifier, and performing risk decision on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
Optionally, the obtaining the usage area of the service product to be used and the area risk type corresponding to the usage area includes:
acquiring a use area of the service product to be used;
judging whether the preset high risk area set contains the use area or not;
when the high risk area set contains the use area, the area risk type is high risk;
when the high-risk area set does not contain the use area, judging whether the preset risk area set contains the use area or not;
when the risk area set contains the use area, the area risk type is risk;
when the set of risk areas does not contain the use area, the area risk type is low risk.
In order to solve the above-mentioned problems, the present application also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the computer program stored in the memory to realize the black production prevention wind control decision method.
In order to solve the above-mentioned problems, the present application further provides a computer readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the above-mentioned black yielding wind control decision method.
The embodiment of the application judges whether the service product to be used is used for the first time or not based on the product identifier; when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result; when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used; judging whether the current using equipment identifier is consistent with the historical using equipment identifier or not; when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result; when the current use equipment identifier is consistent with the historical use equipment identifier, a use area of the service product to be used and an area risk type corresponding to the use area are obtained, and risk decision is carried out on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result. And carrying out multidimensional risk decision on the service product to be used in three aspects of whether to be used for the first time, whether to change equipment and the risk type of the use area, wherein the dimensionality of the risk decision is more multiple, and the result of the risk decision is more accurate. Therefore, the black production wind control decision-making method, the black production wind control decision-making device, the electronic equipment and the readable storage medium provided by the embodiment of the application improve the accuracy of the black production wind control decision-making.
Drawings
FIG. 1 is a flow chart of a method for controlling decision of preventing black yielding according to an embodiment of the application;
FIG. 2 is a schematic diagram of a block diagram of an embodiment of a device for controlling a decision of preventing black yielding;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a method for implementing a black-out prevention wind control decision according to an embodiment of the present application;
the achievement of the objects, functional features and advantages of the present application 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 application.
The embodiment of the application provides a black production prevention wind control decision method. The execution main body of the anti-black-out wind control decision method comprises at least one of an electronic device, such as 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 anti-black yielding wind control decision method 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: the server can be an independent server, or can be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligent platforms, and the like.
Referring to fig. 1, which is a schematic flow chart of a method for controlling and deciding black yielding wind according to an embodiment of the present application, in an embodiment of the present application, the method for controlling and deciding black yielding wind includes the following steps:
s1, acquiring a product identifier of a service product to be used;
in the embodiment of the application, the service product to be used is an online medical service product to be used, the online medical service product is a medical service product (such as an O2O type performance service product of physical examination, tooth cleaning, medicine diagnosis card and the like) which can be used for multiple times, the product identifier is a product ID for identifying the service product to be used, the product identifier can be any one or more of text, symbol and number, and the type of the product identifier is not limited in the embodiment of the application.
Further, in the embodiment of the application, in order to ensure that the service product to be used is normally used by a user, but not maliciously applied by a blackout person, risk decision making is required to be performed on the service product to be used, so that the decision making can use the risk product to be used.
S2, judging whether the service product to be used is used for the first time or not based on the product identification;
in the embodiment of the application, in order to judge whether the service product to be used can be used, risk decision judgment is carried out on the service product to be used, so that whether the service product to be used is used for the first time is judged based on the product identifier.
In detail, in the embodiment of the present application, the determining whether the service product to be used is used for the first time based on the product identifier includes:
acquiring a product use record corresponding to the product identifier;
judging whether the product use record is empty or not;
when the product use record is empty, the service product to be used is not used for the first time;
and when the product use record is not empty, the service product to be used is used for the first time.
S3, when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and performing face comparison by utilizing the acquired face image to obtain a risk decision result;
in detail, in the embodiment of the present application, a face image of a currently used user of the service product to be used is obtained, and face comparison is performed by using the obtained face information, so as to obtain a risk decision result, including:
acquiring a face image of a current user of the service product to be used to obtain a face image of the user;
acquiring a face image of a user to which the service product to be used belongs, and obtaining a face image of a target user;
performing face comparison on the user face image and the target user face image to obtain a face comparison result;
and carrying out risk decision according to the face comparison result to obtain the risk decision result.
Further, in the embodiment of the present application, the step of comparing the face image of the user with the face image of the target user to obtain a face comparison result includes:
extracting the facial features of the user facial images to obtain user facial feature vectors;
extracting the face features of the face image of the target user to obtain a face feature vector of the target user;
calculating the vector similarity of the user face feature vector and the target user face feature vector to obtain the face similarity;
judging whether the face similarity is larger than a preset face comparison threshold value, and determining the face comparison result according to the judgment result.
Cosine is available in embodiments of the present application the algorithms such as similarity calculate vector similarity, the method for calculating the vector similarity is not limited in the embodiment of the application. In the embodiment of the application, the face comparison threshold is a real number larger than zero, and the embodiment of the application is not limited in other ways.
Specifically, in the embodiment of the present application, face feature extraction is performed on the face image of the user to obtain a face feature vector of the user, including:
inputting the face image of the user into a pre-constructed face recognition model;
extracting output values of all output nodes of a last full-connection layer in the face recognition model;
and combining all the output values into a vector form according to the sequence of the corresponding output nodes in the full-connection layer, so as to obtain the face feature vector of the user.
The face recognition model in the embodiment of the application is a deep learning model (such as a convolutional neural network model) capable of recognizing and classifying the face image, and the embodiment of the application is not limited to this.
Further, in the embodiment of the present application, the face feature extraction method for the target user face image is the same as the face feature extraction method for the user face image, and will not be described in detail herein.
In detail, in the embodiment of the present application, the determining whether the face similarity is greater than a preset face comparison threshold, and determining the face comparison result according to the determination result includes:
when the face similarity is larger than a preset face comparison threshold, the face comparison result is that the face comparison is consistent;
and when the face similarity is not greater than a preset face comparison threshold, the face comparison result is inconsistent.
In detail, in the embodiment of the present application, performing risk decision according to the face comparison result to obtain the risk decision result includes:
when the face comparison result is that the face comparison is consistent, the risk decision result is permission;
and when the face comparison result is inconsistent, the risk decision result is not allowed to be used.
S4, when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
the method for acquiring the current use equipment identifier and the historical use equipment identifier of the service product to be used comprises the following steps:
acquiring the equipment identifier of the current equipment of the service product to be used, and obtaining the current equipment identifier;
and acquiring the equipment identifier of the last history use equipment of the service product to be used, and obtaining the history use equipment identifier.
In the embodiment of the present application, the current usage device and the history usage device are electronic devices using the service product to be used, which may be mobile phones, computers, tablet computers, etc., and in the embodiment of the present application, the device identifier is identification information of the electronic device using the service product to be used, for example: the ua information of the device, and the embodiment of the application does not limit the type of the device identifier.
S5, judging whether the current using equipment identifier is consistent with the historical using equipment identifier;
in the embodiment of the application, in order to judge whether the equipment for using the service product to be used is consistent with the equipment for historical use, whether the equipment for using the service product to be used is changed is judged, and whether the current equipment identification is consistent with the equipment identification for historical use is judged.
In detail, in the embodiment of the application, whether the current use equipment identifier is consistent with the history use equipment identifier is judged by comparing whether the current use equipment identifier is identical with the history use equipment identifier.
S6, when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of the current use user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result;
in the embodiment of the application, when the current use equipment identifier is inconsistent with the historical use equipment identifier, the change of the use equipment of the service product to be used is indicated, and the face recognition verification needs to be carried out on the current use user of the service product to be used, so that the face image of the current use user of the service product to be used is acquired, and the acquired face image is utilized for carrying out face comparison, so that a risk decision result is obtained.
And S7, when the current use equipment identifier is consistent with the historical use equipment identifier, acquiring a use area of the service product to be used and an area risk type corresponding to the use area, and performing risk decision on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
The method for acquiring the service product to be used and the region risk type corresponding to the service product to be used comprises the following steps:
acquiring a use area of the service product to be used;
judging whether the preset high risk area set contains the use area or not;
when the high risk area set contains the use area, the area risk type is high risk;
when the high-risk area set does not contain the use area, judging whether the preset risk area set contains the use area or not;
when the risk area set contains the use area, the area risk type is risk;
when the set of risk areas does not contain the use area, the area risk type is low risk.
In the embodiment of the present application, the set of high risk areas is a preset set of high risk areas, and the set of risk areas is a preset set of medium risk areas.
Further, in the embodiment of the present application, performing risk decision on the service product to be used based on the preset risk rule corresponding to the regional risk type to obtain a risk decision result, including:
determining a preset risk rule corresponding to the regional risk type as a target risk rule;
extracting preset type risk information of the service product to be used;
judging whether the preset type risk information meets the target risk rule,
when the preset type risk information meets the target risk rule, a risk decision result is allowed to be used;
and when the preset type risk information does not meet the target risk rule, the risk decision result is that the use is not allowed.
Optionally, in the embodiment of the present application, the preset type risk information is a face comparison frequency of the user who uses the service product to be used, and the target risk rule is higher than a preset target face comparison frequency; such as: the region risk type can be any one of high risk, medium risk and low risk, and when the region risk type is high risk, the target risk rule is that the service product to be used has completed face comparison for 1 time in the same day; when the regional risk type is stroke risk, the target risk rule is that the service product to be used has completed face comparison for 1 time per week; and when the regional risk type is low risk, the target risk rule is that the service product to be used is subjected to face comparison for 1 time in the month.
In the embodiment of the application, in order to ensure that the service product to be used is not applied by a black-date staff, multidimensional risk decision is carried out on the service product to be used, the dimension of the risk decision is more multiple, and the result of the risk decision is more accurate.
FIG. 2 is a functional block diagram of the anti-black yielding wind control decision device of the application.
The black-out prevention wind control decision device 100 of the present application can be installed in an electronic device. Depending on the functions implemented, the black yielding wind control decision device may comprise a usage judgment module 101, a device replacement judgment module 102, and a risk decision module 103, which may also be referred to as a unit, refers to a series of computer program segments capable of being executed by a processor of an electronic device and of performing a fixed function, which are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the usage judging module 101 is configured to obtain a product identifier of a service product to be used; judging whether the service product to be used is used for the first time or not based on the product identification; when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result; when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
the device replacement judging module 102 is configured to judge whether the currently used device identifier is consistent with the historically used device identifier; when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
the risk decision module 103 is configured to obtain a usage area of the service product to be used and an area risk type corresponding to the usage area when the current usage equipment identifier is consistent with the historical usage equipment identifier, and perform risk decision on the service product to be used based on a preset risk rule corresponding to the area risk type, so as to obtain a risk decision result.
In detail, each module in the anti-black yielding wind control decision device 100 in the embodiment of the present application adopts the same technical means as the above-mentioned anti-black yielding wind control decision method in fig. 1, and can produce the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the anti-black-production wind control decision method according to the present application.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a black yielding wind control decision program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card 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 to store application software installed in an electronic device and various data, such as codes of a black-out prevention wind control decision program, but also to temporarily store data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. 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, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., a black-out prevention wind Control decision program, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (PerIPheralComponent 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 communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of 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 classification 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.
Optionally, the communication interface 13 may comprise 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.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or 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.
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 anti-black yielding wind control decision program stored in the memory 11 in the electronic device is a combination of a plurality of computer programs, when running in the processor 10, it may be implemented that:
acquiring a product identifier of a service product to be used;
judging the to-be-treated product based on the product identification whether the service product is used for the first time;
when the service product to be used is used for the first time, acquiring a face image of the current user of the service product to be used, performing face comparison by using the obtained face image to obtain a risk decision result;
when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
judging whether the current using equipment identifier is consistent with the historical using equipment identifier or not;
when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
when the current use equipment identifier is consistent with the historical use equipment identifier, a use area of the service product to be used and an area risk type corresponding to the use area are obtained, and risk decision is carried out on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. 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).
Embodiments of the present application may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring a product identifier of a service product to be used;
judging whether the service product to be used is used for the first time or not based on the product identification;
when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result;
when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
judging whether the current using equipment identifier is consistent with the historical using equipment identifier or not;
when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
when the current use equipment identifier is consistent with the historical use equipment identifier, a use area of the service product to be used and an area risk type corresponding to the use area are obtained, and risk decision is carried out on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present application, 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.
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.
In addition, each functional module in the embodiments of the present application 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 application is not limited to the details of the foregoing illustrative embodiments, and that the present application 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 application 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.
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 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 application and not for limiting the same, and although the present application 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 application without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. A method for preventing black production wind control decision, the method comprising:
acquiring a product identifier of a service product to be used;
judging whether the service product to be used is used for the first time or not based on the product identification;
when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result;
when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
judging whether the current using equipment identifier is consistent with the historical using equipment identifier or not;
when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
when the current use equipment identifier is consistent with the historical use equipment identifier, a use area of the service product to be used and an area risk type corresponding to the use area are obtained, and risk decision is carried out on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
2. The black-out prevention wind control decision method of claim 1, wherein the obtaining the face image of the currently used user of the service product to be used and comparing faces by using the obtained face image to obtain the risk decision result comprises:
acquiring a face image of a current user of the service product to be used to obtain a face image of the user;
acquiring a face image of a user to which the service product to be used belongs, and obtaining a face image of a target user;
performing face comparison on the user face image and the target user face image to obtain a face comparison result;
and carrying out risk decision according to the face comparison result to obtain the risk decision result.
3. The black yielding wind control decision method of claim 2, wherein the comparing the face image of the user with the face image of the target user to obtain a face comparison result comprises:
extracting the facial features of the user facial images to obtain user facial feature vectors;
extracting the face features of the face image of the target user to obtain a face feature vector of the target user;
calculating the vector similarity of the user face feature vector and the target user face feature vector to obtain the face similarity;
judging whether the face similarity is larger than a preset face comparison threshold value, and determining the face comparison result according to the judgment result.
4. The black-out prevention wind control decision method of claim 3, wherein said extracting the face feature of the user face image to obtain the user face feature vector comprises:
inputting the face image of the user into a pre-constructed face recognition model;
extracting output values of all output nodes of a last full-connection layer in the face recognition model;
and combining all the output values into a vector form according to the sequence of the corresponding output nodes in the full-connection layer, so as to obtain the face feature vector of the user.
5. The black out prevention wind control decision method of claim 1, wherein said obtaining the current usage equipment identity and the historical usage equipment identity of the service product to be used comprises:
acquiring the equipment identifier of the current equipment of the service product to be used, and obtaining the current equipment identifier;
and acquiring the equipment identifier of the last history use equipment of the service product to be used, and obtaining the history use equipment identifier.
6. The black yielding wind control decision method according to any one of claims 1 to 5, wherein the obtaining the usage area of the service product to be used and the area risk type corresponding to the usage area includes:
acquiring a use area of the service product to be used;
judging whether the preset high risk area set contains the use area or not;
when the high risk area set contains the use area, the area risk type is high risk;
when the high-risk area set does not contain the use area, judging whether the preset risk area set contains the use area or not;
when the risk area set contains the use area, the area risk type is risk;
when the set of risk areas does not contain the use area, the area risk type is low risk.
7. A black-out prevention wind control decision device, comprising:
the use judgment module is used for acquiring the product identification of the service product to be used; judging whether the service product to be used is used for the first time or not based on the product identification; when the service product to be used is used for the first time, acquiring a face image of a current user of the service product to be used, and comparing faces by utilizing the acquired face image to obtain a risk decision result; when the service product to be used is not used for the first time, acquiring a current use equipment identifier and a historical use equipment identifier of the service product to be used;
the equipment replacement judging module is used for judging whether the current used equipment identifier is consistent with the historical used equipment identifier or not; when the current use equipment identification is inconsistent with the historical use equipment identification, acquiring a face image of a current use user of the service product to be used, and carrying out face comparison by utilizing the acquired face image to obtain a risk decision result;
the risk decision module is used for acquiring the use area of the service product to be used and the area risk type corresponding to the use area when the current use equipment identifier is consistent with the historical use equipment identifier, and performing risk decision on the service product to be used based on a preset risk rule corresponding to the area risk type to obtain a risk decision result.
8. The black yielding wind control decision device of claim 7, wherein the obtaining the usage area of the service product to be used and the area risk type corresponding to the usage area comprises:
acquiring a use area of the service product to be used;
judging whether the preset high risk area set contains the use area or not;
when the high risk area set contains the use area, the area risk type is high risk;
when the high-risk area set does not contain the use area, judging whether the preset risk area set contains the use area or not;
when the risk area set contains the use area, the area risk type is risk;
when the set of risk areas does not contain the use area, the area risk type is low risk.
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 anti-black yielding wind control decision method according to any one of claims 1 to 6.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the anti-black yielding wind control decision method according to any one of claims 1 to 6.
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| CN202311012130.2A CN116993162A (en) | 2023-08-10 | 2023-08-10 | Black-out prevention wind control decision making method, device, equipment and storage medium |
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| CN202311012130.2A CN116993162A (en) | 2023-08-10 | 2023-08-10 | Black-out prevention wind control decision making method, device, equipment and storage medium |
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