CN-122023030-A - Risk determination method, apparatus, network device and storage medium

Abstract

The disclosure relates to the field of computer technology, and in particular, to a risk determining method, a risk determining device, a network device and a storage medium. The risk determination method comprises the steps of obtaining first flight information corresponding to a target flight, obtaining a target flight pay risk identification model corresponding to the target flight, and carrying out risk identification on the first flight information by adopting the target flight pay risk identification model to obtain pay risk information corresponding to the target flight. By adopting the method and the device, the accuracy of the payment risk information determination can be improved.

Inventors

• LIANG DONG

Assignees

• 人保信息科技有限公司

Dates

Publication Date

20260512

Application Date

20260204

Claims (10)

1. 1. A risk determination method, comprising: Acquiring first flight information corresponding to a target flight; acquiring a target flight paying risk identification model corresponding to the target flight; and carrying out risk identification on the first flight information by adopting the target flight risk identification model to acquire the risk information of the target flight.
2. 2. The method according to claim 1, wherein the method further comprises: acquiring second flight information of a flight set, wherein the flight set comprises a historical flight subset and an on-season flight subset, and the second flight information comprises a flight execution rate and a flight punctuation rate; Training an initial flight reimbursement risk identification model by adopting the second flight information, and acquiring the target flight reimbursement risk identification model under the condition that the output information of the initial flight reimbursement risk identification model meets information requirements or the training information of the initial flight reimbursement risk identification model meets information requirements.
3. 3. The method of claim 2, wherein the obtaining the second flight information for the set of flights further comprises: And preprocessing the second flight information to obtain a training data set, wherein the training data set comprises at least one of single type data and combined type data, and the training data set is used for training the initial flight identification model.
4. 4. The method of claim 2, wherein training an initial flight reimbursement risk identification model using the second flight information comprises: Extracting features of the second flight information to obtain a first feature set, wherein the first feature set comprises time-related features and classification features; Performing feature correlation analysis and importance analysis on the feature set to obtain a second feature set related to flight risk identification; and training an initial flight claim risk identification model by adopting the second characteristic set.
5. 5. The method of claim 1, wherein the obtaining a target flight claim risk identification model corresponding to the target flight comprises: Acquiring the airport type of a target airport corresponding to the target flight; Acquiring a model corresponding to the target flight; acquiring current season information; acquiring route information corresponding to the target flight; and acquiring a target flight paying risk identification model corresponding to the target flight according to at least one of the airport type, the machine type, the current season information and the route information.
6. 6. The method according to claim 1, wherein the method further comprises: Acquiring a risk level corresponding to the pay risk information and risk growth information corresponding to the pay risk information, wherein the risk growth information is used for indicating risk information of the pay risk information grown according to time variation; And acquiring a risk management strategy corresponding to the pay risk information according to the risk level and the risk growth information.
7. 7. The method according to claim 1, wherein the method further comprises: acquiring current season information; Acquiring environment information corresponding to the target flight; Acquiring a model corresponding to the target flight; And carrying out risk identification on the first flight information, the current season information, the environment information and the model by adopting the target flight risk identification model, and obtaining the risk information of the target flight.
8. 8. A risk determining apparatus, comprising: The information acquisition unit is used for acquiring first flight information corresponding to the target flight; the model acquisition unit is used for acquiring a target flight reimbursement risk identification model corresponding to the target flight; the risk acquisition unit is used for carrying out risk identification on the first flight information by adopting the target flight claim risk identification model to acquire claim risk information corresponding to the target flight.
9. 9. A network device, comprising: A processor; a memory for storing the processor-executable instructions; Wherein the processor is configured to execute the instructions to implement the risk determination method of any one of claims 1 to 7.
10. 10. A storage medium, wherein instructions in the storage medium, when executed by a processor of a network device, enable the network device to perform the risk determination method of any one of claims 1 to 7.

Description

Risk determination method, apparatus, network device and storage medium Technical Field The disclosure relates to the field of computer technology, and in particular, to a risk determining method, a risk determining device, a network device and a storage medium. Background With the development of science and technology, more and more services are provided for users, and improving the quality of service is an important point of attention for users. The flight taking-off and landing time, weather data and airport operation data can be analyzed by using big data, and the insurance claim situation of the flight is determined, but the insurance claim information is determined only based on the flight taking-off and landing time, the weather data and the airport operation data, so that the determination accuracy of the insurance claim information is poor. Disclosure of Invention The disclosure provides a risk determination method, a risk determination device, network equipment and a storage medium, so as to improve the accuracy of risk information determination of payment. The technical scheme of the present disclosure is as follows: According to a first aspect of embodiments of the present disclosure, there is provided a risk determining method, including: Acquiring first flight information corresponding to a target flight; acquiring a target flight paying risk identification model corresponding to the target flight; and carrying out risk identification on the first flight information by adopting the target flight risk identification model to acquire the risk information of the target flight. According to some embodiments, the method further comprises: acquiring second flight information of a flight set, wherein the flight set comprises a historical flight subset and an on-season flight subset, and the second flight information comprises a flight execution rate and a flight punctuation rate; Training an initial flight reimbursement risk identification model by adopting the second flight information, and acquiring the target flight reimbursement risk identification model under the condition that the output information of the initial flight reimbursement risk identification model meets information requirements or the training information of the initial flight reimbursement risk identification model meets information requirements. According to some embodiments, the acquiring the second flight information of the flight set further includes: And preprocessing the second flight information to obtain a training data set, wherein the training data set comprises at least one of single type data and combined type data, and the training data set is used for training the initial flight identification model. According to some embodiments, the training the initial flight odds identification model using the second flight information includes: Extracting features of the second flight information to obtain a first feature set, wherein the first feature set comprises time-related features and classification features; Performing feature correlation analysis and importance analysis on the feature set to obtain a second feature set related to flight risk identification; and training an initial flight claim risk identification model by adopting the second characteristic set. According to some embodiments, the obtaining a target flight claim risk identification model corresponding to the target flight includes: Acquiring the airport type of a target airport corresponding to the target flight; Acquiring a model corresponding to the target flight; acquiring current season information; acquiring route information corresponding to the target flight; and acquiring a target flight paying risk identification model corresponding to the target flight according to at least one of the airport type, the machine type, the current season information and the route information. According to some embodiments, the method further comprises: Acquiring a risk level corresponding to the pay risk information and risk growth information corresponding to the pay risk information, wherein the risk growth information is used for indicating risk information of the pay risk information grown according to time variation; And acquiring a risk management strategy corresponding to the pay risk information according to the risk level and the risk growth information. According to some embodiments, the method further comprises: acquiring current season information; Acquiring environment information corresponding to the target flight; Acquiring a model corresponding to the target flight; And carrying out risk identification on the first flight information, the current season information, the environment information and the model by adopting the target flight risk identification model, and obtaining the risk information of the target flight. According to a second aspect of embodiments of the present disclosure, there is provided a risk determining apparatus, comprising: T