CN111091385A - Weight-based object identification method and device and electronic equipment - Google Patents
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Abstract
The application provides an object identification method and device based on weight and electronic equipment, relates to the technical field of data identification, and solves the technical problem that the identification result accuracy of the user danger degree is low. The method comprises the following steps: determining a plurality of user objects, and converting the relationship among the user objects into a relationship network graph; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the node represents the user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the number of contact times among a plurality of user objects; dividing the nodes according to the weights and the labels to obtain a plurality of communities; judging a target community to which an object to be identified belongs in a plurality of communities; and identifying the risk data of the object to be identified according to the risk data of the target community.
Description
Technical Field
The present application relates to the field of data identification technologies, and in particular, to a weight-based object identification method and apparatus, and an electronic device.
Background
Currently, in a scenario where the risk level of a certain user needs to be identified, the process of identifying the risk level needs to consider many aspects. For example, the fraud risk level of the loan individual is calculated by the individual's historical loan performance, basic income expenditure conditions, demographic information, and the like.
However, in the process of performing fraud risk degree cluster division on a plurality of users, the consideration angle is single and cannot be close to the actual situation, which results in erroneous identification of the risk degree of the user, and the accuracy of the identification result of the risk degree of the user is easily low.
Disclosure of Invention
The invention aims to provide a weight-based object identification method, a weight-based object identification device and electronic equipment, and aims to solve the technical problem that the identification result of the danger degree of a user is low in accuracy.
In a first aspect, an embodiment of the present application provides a weight-based object identification method, where the method includes:
determining a plurality of user objects, and converting the relationship among the user objects into a relationship network graph; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the node represents the user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the number of contact times among a plurality of user objects;
dividing the nodes according to the weights and the labels to obtain a plurality of communities;
judging a target community to which an object to be identified belongs in a plurality of communities;
and identifying the risk data of the object to be identified according to the risk data of the target community.
In a possible implementation, the step of dividing the plurality of nodes according to the weight to obtain a plurality of communities includes:
calculating the weight sum of all second nodes in the community to which the adjacent nodes of the first nodes belong and connecting edges between the second nodes and the first nodes aiming at each first node in the relational network graph;
and if the total weight is greater than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong.
In one possible implementation, the neighboring node is a node in the relationship network graph, where a node distance between the neighboring node and the first node is smaller than a preset distance.
In a possible implementation, the step of dividing the plurality of nodes according to the weight to obtain a plurality of communities further includes:
if the difference value between the first weight sum and the second weight sum is smaller than a second preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a first community and a second community simultaneously;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weighted sum is the weighted sum of the connecting edges between all the nodes in the second community and the first node.
In a possible implementation, the step of dividing the plurality of nodes according to the weight to obtain a plurality of communities further includes:
if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a third community and a fourth community simultaneously;
the third community and the fourth community are two communities to which one adjacent node of the first node simultaneously belongs;
the third weighted sum is the weighted sum of the connecting edges between all the nodes in the third community and the first node; the fourth weighted sum is the weighted sum of the connecting edges between all the nodes in the fourth community and the first node.
In a possible implementation, before the step of dividing the plurality of nodes according to the weights and the labels to obtain a plurality of communities, the method further includes:
and carrying out data standardization processing on the weight of each connecting edge in the relational network graph.
In one possible implementation, the number of contacts includes any one or more of:
the number of call communication times, the number of short message communication times and the number of network interaction times.
In a second aspect, there is provided a weight-based object recognition apparatus, including:
the determining module is used for determining a plurality of user objects and converting the relationship among the user objects into a relationship network diagram; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the node represents the user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the number of contact times among a plurality of user objects;
the dividing module is used for dividing the nodes according to the weights and the labels to obtain a plurality of communities;
the judging module is used for judging a target community to which an object to be identified belongs in the plurality of communities;
and the identification module is used for identifying the risk data of the object to be identified according to the risk data of the target community.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, this embodiment of the present application further provides a computer-readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect.
The embodiment of the application brings the following beneficial effects:
the object identification method, the device and the electronic equipment based on the weight can determine a plurality of user objects and convert the relationship among the user objects into a relationship network graph comprising a plurality of nodes, wherein the nodes representing the user objects are marked with labels representing risk data of the user objects, the nodes correspond to connecting edges with different weights representing the number of times of contact among the user objects, then the nodes are divided according to the weights and the labels to obtain a plurality of communities, then the target community to which the object to be identified belongs is judged in the communities to identify the risk data of the object to be identified according to the risk data of the target community, in the scheme, the number of times of contact among the user objects is combined through the different weights of the connecting edges among the nodes, and various abnormal conditions can be considered, the divided communities are more accurate and reasonable, and accuracy of the risk data identification result of the object to be identified is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart diagram of a weight-based object identification method provided in an embodiment of the present application;
FIG. 2 is a schematic view of another flowchart of a weight-based object identification method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an object identification apparatus based on weight according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram illustrating an electronic device provided in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "comprising" and "having," and any variations thereof, as referred to in the embodiments of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Currently, in a real social network, if the two people are not connected again, the strength of the relationship between the two people is increased according to the increase of the number of times of connection and is decreased along with the time, and finally, the relationship link is disconnected and decreased. Therefore, the fraud risk is only measured from the perspective of a single individual, the risk propagation among related individuals cannot be utilized to identify the potential high-risk loan behavior, and the prior art cannot perform effective risk identification when encountering data-packaging loan persons.
Based on this, the embodiment of the application provides an object identification method and device based on weight and electronic equipment. The method can solve the technical problem of low accuracy of the identification result of the danger degree of the user.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of an object identification method based on weight according to an embodiment of the present disclosure. As shown in fig. 1, the method includes:
s110, determining a plurality of user objects, and converting the relationship among the user objects into a relationship network diagram.
It should be noted that the relational graph includes a plurality of nodes, the nodes are labeled with labels, and the plurality of nodes correspond to connecting edges with different weights.
Wherein the nodes represent user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the number of times of contact among a plurality of user objects. In this step, the relationship data between the plurality of user objects may be converted into the relationship network graph data, and the weight of the initial connection edge may be determined according to the contact number C.
In practice, the association between two nodes may include many forms of communication, such as telephony, network interoperability, and the like.
And S120, dividing the nodes according to the weights and the labels to obtain a plurality of communities.
Therein, the community may be a community in a community discovery algorithm, similar to a cluster. As shown in fig. 2, the weight in this step is a weight for indicating the number of times of contact between the plurality of user objects in step S110. The label in this step is the label used to represent the risk data of the user object in step S110.
S130, judging a target community to which the object to be identified belongs in the plurality of communities.
It should be noted that the object to be recognized may be a user to be recognized. The target communities are communities in the relationship network graph.
And S140, identifying the risk data of the object to be identified according to the risk data of the target community.
In the embodiment of the application, the weights of the connecting edges are different, and the number of times of contact between the sample objects affects the weights. For example, the user a frequently contacts the risky user B, and the risky user B applies for a car loan or other loan on the package, but the risky user B is intelligible and thus does not contact the user a during the packaging period, which may cause the relationship between the user a and the risky user B to be overlooked. The object identification method based on the weight provided by the embodiment of the application can be suitable for scenes sensitive to the number of times and the strength of contact, such as a relationship network formed by a vehicle, an insurance developer, a driver, an insurance beneficiary and the like in insurance fraud.
The above steps are described in detail below.
In some embodiments, the step S120 may include the following steps:
aiming at each first node in the relational network graph, calculating the weight sum of all second nodes in the community to which the adjacent nodes of the first node belong and connecting edges between the second nodes and the first nodes;
and if the total weight is larger than a first preset weight value, determining that the first node is divided into communities to which the adjacent nodes belong.
Illustratively, a point node C in the relationship network graph is randomly selected, and all nodes and connecting edges with a step length of 1 from the node C are obtained, which is equivalent to obtaining all neighbor nodes of the node C and the relations between the neighbor nodes. The sum of the connection edge weights between the node C and all nodes in the community to which one neighbor node belongs determines whether the node C is divided into the communities to which the neighbor nodes belong.
Since all the communities to which the neighbor nodes belong are connected with the node C, in the embodiment of the present application, which community a finally belongs to is determined according to the sum SW of the weights of the connections between the neighbor nodes and the node C.
By calculating the weight sum of the connecting edges among all the nodes in the community, all the nodes in the community can be combined more comprehensively and comprehensively, so that the dividing process of the nodes is more comprehensive, and the dividing result of the community is more reasonable and accurate.
In some embodiments, the neighboring node is a node in the relationship network graph whose distance from the node to the first node is less than a preset distance.
The neighboring node may be defined as a node having a sufficiently small node distance from the first node. For example, a node distance from the first node is one unit distance of the node. By defining the adjacent nodes, the partitioning process of the nodes is more precise and accurate, so that the accuracy of the partitioning result of the nodes is guaranteed.
In some embodiments, the step S120 may further include the following steps:
if the difference value between the first weight sum and the second weight sum is smaller than the second preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a first community and a second community simultaneously;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weighted sum is the weighted sum of the connecting edges between all the nodes in the second community and the first node.
In practical applications, a threshold delta (w1) may also be set, and if SW1-SW2< delta (w1), it is determined that node C belongs to both community 1 and community 2. Wherein, SW1 is the sum of the weights of the connecting edges between all the nodes in the community 1 to which the adjacent node 1 belongs and the node C, and SW2 is the sum of the weights of the connecting edges between all the nodes in the community 2 to which the adjacent node 2 belongs and the node C.
In the embodiment of the application, the comparison of the sum of the weights among the communities is performed, the target nodes are simultaneously divided into the communities under the condition that the comparison difference is small, the condition that the target nodes are simultaneously affiliated to the plurality of nodes can be considered, the division process of the nodes is combined more comprehensively, and the community division result is more accurate.
In some embodiments, the step S120 may further include the following steps:
if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a third community and a fourth community simultaneously;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; the fourth weighted sum is the weighted sum of the connecting edges between all the nodes in the fourth community and the first node.
In the embodiment of the present application, another threshold delta (w2) may be set, and if SW3-SW4< delta (w2), it is determined that node C belongs to both community 3 and community 4. Wherein, SW3 is the sum of the weights of the connecting edges between all nodes in the community 3 to which the adjacent point 2 belongs and the node C, and SW4 is the sum of the weights of the connecting edges between all nodes in the other community 4 to which the adjacent point 2 belongs and the node C.
Through the comparison of the sum of the weights among the communities, when the comparison difference is small, the nodes are simultaneously divided into the communities, the condition that the nodes belong to the nodes at the same time can be considered, the division process of the nodes is combined more comprehensively, and the community division result is more accurate.
In some embodiments, before step S120, the method may further include the steps of:
and carrying out data standardization processing on the weight of each connecting edge in the relational network graph.
The weights of all the connecting edges in the relation network graph can be weighted values defined by a unified weight standard through data standardization processing, so that the weighted value of each connecting edge is more accurate, and the accuracy of community division results is guaranteed.
In some embodiments, the number of contacts includes any one or more of: the number of call communication times, the number of short message communication times and the number of network interaction times.
The contact way in the embodiment of the application is not limited to any communication form, and can be an interactive way in any way, such as terminal conversation communication, terminal short message communication, terminal network communication and the like. Therefore, the weight value determined according to the contact times can better meet the actual situation among the sample objects.
Fig. 3 provides a schematic structural diagram of an object recognition apparatus based on weight. As shown in fig. 3, the weight-based object recognition apparatus 300 includes:
a determining module 301, configured to determine multiple user objects, and convert relationships between the multiple user objects into a relationship network diagram; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the nodes represent user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the number of contact times among a plurality of user objects;
a dividing module 302, configured to divide the multiple nodes according to the weights and the labels to obtain multiple communities;
the judging module 303 is configured to judge, in the multiple communities, a target community to which an object to be identified belongs;
and the identification module 304 is configured to identify risk data of the object to be identified according to the risk data of the target community.
In some embodiments, the partitioning module 302 is specifically configured to:
aiming at each first node in the relational network graph, calculating the weight sum of all second nodes in the community to which the adjacent nodes of the first node belong and connecting edges between the second nodes and the first nodes;
and if the total weight is larger than a first preset weight value, determining that the first node is divided into communities to which the adjacent nodes belong.
In some embodiments, the neighboring node is a node in the relationship network graph whose distance from the node to the first node is less than a preset distance.
In some embodiments, the partitioning module 302 is further configured to:
if the difference value between the first weight sum and the second weight sum is smaller than the second preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a first community and a second community simultaneously;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weighted sum is the weighted sum of the connecting edges between all the nodes in the second community and the first node.
In some embodiments, the partitioning module 302 is further configured to:
if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a third community and a fourth community simultaneously;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; the fourth weighted sum is the weighted sum of the connecting edges between all the nodes in the fourth community and the first node.
In some embodiments, the apparatus further comprises:
and the processing module is used for carrying out data standardization processing on the weight of each connecting edge in the relational network diagram.
In some embodiments, the number of contacts includes any one or more of:
the number of call communication times, the number of short message communication times and the number of network interaction times.
The weight-based object identification device provided by the embodiment of the application has the same technical characteristics as the weight-based object identification method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
As shown in fig. 4, an electronic device 400 provided in an embodiment of the present application includes: a processor 401, a memory 402 and a bus, the memory 402 storing machine-readable instructions executable by the processor 401, the processor 401 and the memory 402 communicating via the bus when the electronic device is running, the processor 401 executing the machine-readable instructions to perform the steps of the weight-based object recognition method as described above.
Specifically, the memory 402 and the processor 401 can be general-purpose memories and processors, which are not limited to specific ones, and the weight-based object recognition method can be performed when the processor 401 runs a computer program stored in the memory 402.
In response to the above weight-based object recognition method, embodiments of the present application further provide a computer-readable storage medium storing machine executable instructions that, when invoked and executed by a processor, cause the processor to perform the steps of the above weight-based object recognition method.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A weight-based object recognition method, the method comprising:
determining a plurality of user objects, and converting the relationship among the user objects into a relationship network graph; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the node represents the user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the number of contact times among a plurality of user objects;
dividing the nodes according to the weights and the labels to obtain a plurality of communities;
judging a target community to which an object to be identified belongs in a plurality of communities;
and identifying the risk data of the object to be identified according to the risk data of the target community.
2. The method of claim 1, wherein the step of dividing the plurality of nodes into a plurality of communities according to the weights comprises:
calculating the weight sum of all second nodes in the community to which the adjacent nodes of the first nodes belong and connecting edges between the second nodes and the first nodes aiming at each first node in the relational network graph;
and if the total weight is greater than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong.
3. The method according to claim 2, wherein the neighboring node is a node in the relational network graph whose node distance from the first node is smaller than a preset distance.
4. The method of claim 2, wherein the step of dividing the plurality of nodes according to the weight to obtain a plurality of communities further comprises:
if the difference value between the first weight sum and the second weight sum is smaller than a second preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a first community and a second community simultaneously;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weighted sum is the weighted sum of the connecting edges between all the nodes in the second community and the first node.
5. The method of claim 2, wherein the step of dividing the plurality of nodes according to the weight to obtain a plurality of communities further comprises:
if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value in the weight sum larger than the first preset weight value, determining that the first node is divided into a third community and a fourth community simultaneously;
the third community and the fourth community are two communities to which one adjacent node of the first node simultaneously belongs;
the third weighted sum is the weighted sum of the connecting edges between all the nodes in the third community and the first node; the fourth weighted sum is the weighted sum of the connecting edges between all the nodes in the fourth community and the first node.
6. The method of claim 1, wherein before the step of dividing the plurality of nodes into a plurality of communities according to the weights and the labels, the method further comprises:
and carrying out data standardization processing on the weight of each connecting edge in the relational network graph.
7. The method of claim 1, wherein the number of contacts comprises any one or more of:
the number of call communication times, the number of short message communication times and the number of network interaction times.
8. A weight-based object recognition apparatus, comprising:
the determining module is used for determining a plurality of user objects and converting the relationship among the user objects into a relationship network diagram; the relational network graph comprises a plurality of nodes, the nodes are marked with labels, and connecting edges with different weights are correspondingly arranged among the nodes; the node represents the user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the number of contact times among a plurality of user objects;
the dividing module is used for dividing the nodes according to the weights and the labels to obtain a plurality of communities;
the judging module is used for judging a target community to which an object to be identified belongs in the plurality of communities;
and the identification module is used for identifying the risk data of the object to be identified according to the risk data of the target community.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to execute the method of any of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860655A (en) * | 2020-07-23 | 2020-10-30 | 中国联合网络通信集团有限公司 | User processing method, device and equipment |
| CN112653669A (en) * | 2020-12-04 | 2021-04-13 | 智网安云(武汉)信息技术有限公司 | Network terminal security threat early warning method and system and network terminal management device |
| CN116012169A (en) * | 2022-12-21 | 2023-04-25 | 南京睿聚科技发展有限公司 | Method and system for screening risk of insurance claim settlement based on position data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166731A (en) * | 2014-08-29 | 2014-11-26 | 河海大学常州校区 | Discovering system for social network overlapped community and method thereof |
| CN104715034A (en) * | 2015-03-16 | 2015-06-17 | 北京航空航天大学 | Weighed graph overlapping community discovery method based on central persons |
| CN108648094A (en) * | 2018-05-08 | 2018-10-12 | 阿里巴巴集团控股有限公司 | A kind of community discovery method, device and equipment |
| CN109784636A (en) * | 2018-12-13 | 2019-05-21 | 中国平安财产保险股份有限公司 | Fraudulent user recognition methods, device, computer equipment and storage medium |
| CN109978710A (en) * | 2019-04-11 | 2019-07-05 | 重庆大学 | Overlapping community division method based on K- core iteration factor and community's degree of membership |
-
2019
- 2019-12-13 CN CN201911291671.7A patent/CN111091385B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104166731A (en) * | 2014-08-29 | 2014-11-26 | 河海大学常州校区 | Discovering system for social network overlapped community and method thereof |
| CN104715034A (en) * | 2015-03-16 | 2015-06-17 | 北京航空航天大学 | Weighed graph overlapping community discovery method based on central persons |
| CN108648094A (en) * | 2018-05-08 | 2018-10-12 | 阿里巴巴集团控股有限公司 | A kind of community discovery method, device and equipment |
| CN109784636A (en) * | 2018-12-13 | 2019-05-21 | 中国平安财产保险股份有限公司 | Fraudulent user recognition methods, device, computer equipment and storage medium |
| CN109978710A (en) * | 2019-04-11 | 2019-07-05 | 重庆大学 | Overlapping community division method based on K- core iteration factor and community's degree of membership |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860655A (en) * | 2020-07-23 | 2020-10-30 | 中国联合网络通信集团有限公司 | User processing method, device and equipment |
| CN111860655B (en) * | 2020-07-23 | 2023-05-30 | 中国联合网络通信集团有限公司 | User processing method, device and equipment |
| CN112653669A (en) * | 2020-12-04 | 2021-04-13 | 智网安云(武汉)信息技术有限公司 | Network terminal security threat early warning method and system and network terminal management device |
| CN116012169A (en) * | 2022-12-21 | 2023-04-25 | 南京睿聚科技发展有限公司 | Method and system for screening risk of insurance claim settlement based on position data |
| CN116012169B (en) * | 2022-12-21 | 2024-03-22 | 南京睿聚科技发展有限公司 | Method and system for screening risk of insurance claim settlement based on position data |
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