Flight delay insurance dynamic premium model iteration method based on multi-scale time-varying complexity

Abstract

The invention provides a flight delay insurance dynamic premium model iteration method based on multi-scale time-varying complexity, and the method comprises the steps: collecting and processing related data of flight information and weather information, and recognizing and evaluating the flight delay multi-scale time-varying complexity degree; based on the multi-scale time-varying complexity evaluation, constructing a flight delay risk evaluation model of a travel leg; based on the risk assessment model, constructing a flight delay dynamic premium model of the travel leg; based on the dynamic insurance premium model, calculating the risk rate and the dynamic insurance premium of the travel leg of the travel passenger in real time; and regularly evaluating the difference between the actual state of the recent flight delay and the multi-scale time-varying complexity risk evaluation model, and continuously iteratively optimizing the multi-scale time-varying complexity dynamic premium model. The invention provides a flight delay multi-scale time-varying complexity dynamic insurance premium model, which is used for dynamically evaluating the risk of a travel passenger who delays insurance of each insured flight in real time, dynamically calculating an insurance premium based on a multi-scale time-varying complexity risk evaluation result, and promoting the insurance premium to be matched with the specific risk of a travel leg of the travel passenger, so that the insurance premium can be accurately determined. The flight delay insurance operation quality is promoted to be improved, and the high-quality development of the flight delay insurance business is promoted.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an iterative method for a dynamic premium model of flight delay insurance based on multi-scale time-varying complexity. Background Art

[0002] Flight delay insurance originated from the value-added service of buying air tickets. Major insurance companies have long cooperated with financial institutions, travel service websites or airlines to develop flight delay insurance, but social awareness is not high and sales are low. It was not until the end of 2011 and the beginning of 2012 that flight delay insurance gradually attracted people's attention due to frequent flight delays. From 2011 to 2016, the premium income of flight delay insurance increased from 22 million yuan to about 950 million yuan, an increase of nearly 46 times in 5 years.

[0003] While the scale of flight delay insurance business has been growing rapidly, due to the high claims ratio, especially after the rainy seasons in 2017 and 2018, news of insurance companies removing flight delay insurance from their shelves has often appeared in the newspapers, and flight delay insurance products on many insurance platforms are difficult to search for.

[0004] The premiums of common flight delay insurance products are mostly divided into different levels according to the compensation limit, and the premiums are static fixed premiums. Such products do not match the specific risks of the outbound flight segments of travelers, which can easily induce moral risks, cause poor business operations, and make it difficult to achieve high-quality and sustainable development.

[0005] According to the research results of Nobel Prize winner in Economics Joseph Stiglitz [1] , the equilibrium state of customers with different risks in the competitive insurance market is separation equilibrium, that is, differentiated pricing. The developers of the present invention have done a lot of theoretical and practical research in long-term research and development, and thus proposed an iterative method for a dynamic premium model of flight delay insurance based on the specific risk changes of each travel passenger's travel segment and multi-scale time-varying complexity matching the risk, so as to solve one of the above technical problems. Summary of the invention

[0006] The purpose of the present invention is to provide an iterative method for a dynamic premium model of flight delay insurance with multi-scale time-varying complexity, which can solve some of the technical problems mentioned above. The specific scheme is:

[0007] Collect and process relevant data on flight information and weather information, and identify and evaluate the time-varying complexity of multi-scale flight delays (MTC-FD);

[0008] Based on the multi-scale time-varying complexity assessment results of flight delays in flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed;

[0009] Based on the flight delay risk assessment model for the travel segment, a multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed;

[0010] Calculate the specific risk rate and dynamic premium of the passenger's travel segment in real time;

[0011] A two-stage iterative optimization model of TS-MTC-FDIM is constructed to regularly evaluate the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and the multi-scale time-varying complexity dynamic premium model is continuously iterated and optimized.

[0012] Optionally, the collecting and processing of relevant data of flight information and weather information, and identifying and evaluating the multi-scale time-varying complexity degree MTC-FD of flight delays, includes:

[0013] Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale multi-fractal complexity degree of flight delays (MTC-FD-MF) is identified and evaluated.

[0014] Based on the related factors of multi-scale time-varying complexity of flight delays, the multi-scale chaotic complexity degree MTC-FD-CHAOS of flight delays is identified and evaluated.

[0015] Optionally, the identification and evaluation of the multi-scale multi-fractal complexity degree MTC-FD-MF of flight delays based on the multi-scale time-varying complexity related factors of flight delays includes:

[0016] The evaluation formula of surface multifractal time-varying complexity is as follows:

[0017]

[0018]

[0019]

[0020] The evaluation formula of the degree of essential multifractal time-varying complexity is as follows:

[0021]

[0022]

[0023]

[0024]

[0025] Optionally, the identification and evaluation of the multi-scale chaotic complexity degree MTC-FD-CHAOS of flight delays based on the multi-scale time-varying complexity related factors of flight delays includes:

[0026] The formula for the complexity of multiscale chaos is as follows:

[0027]

[0028] Optionally, the multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed based on the multi-scale time-varying complexity assessment result of flight delays of the flight segments, including:

[0029] Construct the flight delay basic risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model;

[0030] The basic risk assessment models are combined to construct the MTC-FDIRM composite risk assessment model.

[0031] Optionally, the MTC-FDBRM-PB delay risk assessment model is as follows:

[0032]

[0033] where x FD Represents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0034] Optionally, the MTC-FDBRM-TM delay duration evaluation model is as follows:

[0035]

[0036] where x FDRepresents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0037] Optionally, combining the basic risk assessment models to construct an MTC-FDIRM composite risk assessment model includes:

[0038] Calculate the risk rate R of whether the flight segment is delayed FDPB , the forecast of no delay is classified as low risk;

[0039] Calculate the flight delay risk rate R FDTM , when the forecast delay is long, it is classified as high risk, when the forecast delay is short, it is classified as low risk, and the rest are classified as medium risk;

[0040] Calculate the composite risk rate R of flight delay for each flight segment:

[0041]

[0042] Where A = {low risk (predicted no delay)}, B = {low risk (predicted delay)}, C = {medium and high risk (predicted delay)}

[0043] Optionally, the multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed based on the travel segment flight delay risk assessment model, including:

[0044] Evaluate the factors affecting the dynamic premium model for flight delays;

[0045] Based on the MTC-FDIRM flight delay composite risk assessment model and the aforementioned influencing factors, the MTC-FDDPM dynamic premium model is constructed as follows:

[0046]

[0047] Optionally, the real-time calculation of the specific risk rate and dynamic premium of the traveler's travel segment includes:

[0048] Write scripts or APIs based on the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0049] The script or API is called to calculate the specific risk rate and dynamic premium of the traveler's travel segment in real time.

[0050] Optionally, the TS-MTC-FDIM two-stage iterative optimization model is constructed to regularly evaluate the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and the multi-scale time-varying complexity dynamic premium model is continuously iteratively optimized, including:

[0051] Construct a two-stage iterative optimization model of TS-MTC-FDIM. The first stage model is:

[0052]

[0053] The second stage model is the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0054] The difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment is regularly evaluated through the TS-MTC-FDIM two-stage iterative optimization model.

[0055] Compared with the prior art, the above-mentioned scheme of the embodiment of the present invention has at least the following beneficial effects: the present invention uses a multi-scale time-varying complexity dynamic premium model for flight delays to dynamically evaluate the risk of each traveler who has insured flight delay insurance in real time, and dynamically calculates the premium based on the multi-scale time-varying complexity risk assessment results, thereby matching the premium with the specific risk of the traveler's travel segment, promoting the improvement of the operating quality of flight delay insurance, and promoting the high-quality development of flight delay insurance business. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0057] Figure 1 A flow chart of a model iteration method according to an embodiment of the present invention is shown;

[0058] Figure 2It shows a structural diagram of a model iteration device according to an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a connection structure of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0062] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0063] It should be understood that although the terms first, second, third, etc. may be used to describe ... in the embodiments of the present application, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of the present application, the first ... may also be referred to as the second ..., and similarly, the second ... may also be referred to as the first ....

[0064] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0065] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0066] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0067] Example 1

[0068] like Figure 1 As shown, the present invention provides an iterative method for a dynamic premium model of flight delay insurance based on multi-scale time-varying complexity, comprising the following method steps:

[0069] Step S101: collect and process relevant data of flight information and weather information, and identify and evaluate the multi-scale time-varying complexity degree of flight delay MTC-FD.

[0070] Optionally, the collecting and processing of flight information and weather information related data, and identifying and evaluating the multi-scale time-varying complexity of flight delay MTC-FD, includes the following sub-steps:

[0071] Step S101 - 1 : Identify and evaluate the multi-scale multi-fractal complexity degree MTC-FD-MF of flight delays based on the multi-scale time-varying complexity related factors of flight delays.

[0072] Optionally, the step of identifying and evaluating the multi-scale multi-fractal complexity degree MTC-FD-MF of flight delays based on the multi-scale time-varying complexity related factors of flight delays comprises the following sub-steps:

[0073] Step S101-1-1: Identify and evaluate the multi-scale multi-fractal complexity of flight delays based on the multi-scale time-varying complexity related factors of flight delays, including:

[0074] The evaluation formula of surface multifractal time-varying complexity is as follows:

[0075]

[0076]

[0077]

[0078] The evaluation formula of the degree of essential multifractal time-varying complexity is as follows:

[0079]

[0080]

[0081]

[0082]

[0083] In a specific case, the multi-fractal complexity of an airport is evaluated regularly or in real time according to its specific situation. The training is deployed in a cluster mode. After 10,000 simulations, the evaluation results are as shown in Table 1:

[0084]

[0085] Table 1 Multifractal complexity of an airport

[0086] Step S101-1-2: Identify and evaluate the multi-scale chaotic complexity degree MTC-FD-CHAOS of flight delays based on the multi-scale time-varying complexity related factors of flight delays, including:

[0087] The formula for the complexity of multiscale chaos is as follows:

[0088]

[0089] Step S103: Based on the multi-scale time-varying complexity assessment results of flight delays of the flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for the travel segments is constructed.

[0090] Optionally, based on the multi-scale time-varying complexity assessment results of flight delays of flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed, including the following sub-steps:

[0091] Step S103-1: construct a flight delay basic risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model.

[0092] Optionally, the construction of the flight delay basic risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model, includes the following sub-steps:

[0093] Step S103-1-1: The MTC-FDBRM-PB delay risk assessment model is as follows:

[0094]

[0095] where x FDRepresents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0096] Step S103-1-2: The MTC-FDBRM-TM delay duration evaluation model is as follows:

[0097]

[0098] where x FD Represents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0099] Step S103-2: Combining the basic risk assessment models to construct an MTC-FDIRM composite risk assessment model, including:

[0100] Calculate the risk rate R of whether the flight segment is delayed FDPB , the forecast of no delay is classified as low risk;

[0101] Calculate the flight delay risk rate R FDTM, when the forecast delay is long, it is classified as high risk, when the forecast delay is short, it is classified as low risk, and the rest are classified as medium risk;

[0102] Calculate the composite risk rate R of flight delay for each flight segment:

[0103]

[0104] Where A = {low risk (predicted no delay)}, B = {low risk (predicted delay)}, C = {medium and high risk (predicted delay)}

[0105] Step S105: Based on the travel segment flight delay risk assessment model, a multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed, including:

[0106] Evaluate the factors affecting the dynamic premium model for flight delays;

[0107] Based on the MTC-FDIRM flight delay composite risk assessment model and the aforementioned influencing factors, the MTC-FDDPM dynamic premium model is constructed as follows:

[0108]

[0109] Step S107: Real-time calculation of the specific risk rate and dynamic premium of the passenger's travel segment, including:

[0110] Write scripts or APIs based on the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0111] The script or API is called to calculate the specific risk rate and dynamic premium of the traveler's travel segment in real time.

[0112] Step S109: constructing a TS-MTC-FDIM two-stage iterative optimization model, regularly evaluating the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and continuously iteratively optimizing the multi-scale time-varying complexity dynamic premium model, including:

[0113] Construct a two-stage iterative optimization model of TS-MTC-FDIM. The first stage model is:

[0114]

[0115] The second stage model is the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0116] The model accuracy is continuously improved through the TS-MTC-FDIM two-stage iterative optimization model.

[0117] In a specific case, the risk of each passenger is dynamically assessed in real time according to the specific situation of each flight segment, and then the dynamic premium of the passenger is calculated based on the risk assessment results and the dynamic premium model. The dynamic premium plan for a flight segment (Airport A-Airport B) is as shown in Table 2 below:

[0118]

[0119] Table 2 Dynamic premium plan for a certain flight segment (Airport A-Airport B)

[0120] For example, input the historical data of the flight segment that has been verified and validated, and the composite risk rate vector of flight delay for a group of passengers on this flight segment is

[0121] R 1 =(0.05, 0.1, ..., 0.3)

[0122] Substitute it into the multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM, and calculate the corresponding dynamic premium

[0123] P 1 =(5, 10, ..., 30)

[0124] The present invention uses a multi-scale time-varying complexity dynamic premium model for flight delays to dynamically evaluate the risk of each traveler who has taken out flight delay insurance in real time, and dynamically calculates the premium based on the multi-scale time-varying complexity risk assessment results, so as to match the premium with the specific risk of the traveler's travel segment, promote the improvement of the operating quality of flight delay insurance, and promote the high-quality development of flight delay insurance business.

[0125] Example 2

[0126] like Figure 2 As shown, the present invention provides a flight delay insurance dynamic premium model iteration device based on multi-scale time-varying complexity, comprising:

[0127] Construction unit 1 201: Collect and process relevant data of flight information and weather information, and identify and evaluate the multi-scale time-varying complexity degree MTC-FD of flight delay.

[0128] Optionally, the construction unit 201 is further used for:

[0129] 1. Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale multifractal complexity degree of flight delays (MTC-FD-MF) is identified and evaluated.

[0130] Optionally, the identification and evaluation of the multi-scale multi-fractal complexity degree MTC-FD-MF of flight delays based on the multi-scale time-varying complexity related factors of flight delays includes:

[0131] First, the evaluation formula for the time-varying complexity of surface multifractals is as follows:

[0132]

[0133]

[0134]

[0135] Second, the evaluation formula for the degree of time-varying complexity of essential multifractals is as follows:

[0136]

[0137]

[0138]

[0139]

[0140] 2. Based on the multi-scale time-varying complexity related factors of flight delays, identify and evaluate the multi-scale chaotic complexity degree of flight delays MTC-FD-CHAOS, including:

[0141] The formula for the complexity of multiscale chaos is as follows:

[0142]

[0143] Construction Unit 203: Based on the multi-scale time-varying complexity assessment results of flight delays in flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed, including:

[0144] Optionally, the construction unit 203 is further used for:

[0145] 3. Construct the basic flight delay risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model.

[0146] Optionally, the construction of the flight delay basic risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model, includes:

[0147] First, the MTC-FDBRM-PB delay risk assessment model is as follows:

[0148]

[0149] where x FDRepresents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0150] Second, the MTC-FDBRM-TM delay duration evaluation model is as follows:

[0151]

[0152] where x FD Represents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

[0153] 4. Combining the basic risk assessment models to construct the MTC-FDIRM composite risk assessment model, including:

[0154] Calculate the risk rate R of whether the flight segment is delayed FDPB , the forecast of no delay is classified as low risk;

[0155] Calculate the flight delay risk rate R FDTM, when the forecast delay is long, it is classified as high risk, when the forecast delay is short, it is classified as low risk, and the rest are classified as medium risk;

[0156] Calculate the composite risk rate R of flight delay for each flight segment:

[0157]

[0158] Where A = {low risk (predicted no delay)}, B = {low risk (predicted delay)}, C = {medium and high risk (predicted delay)}

[0159] Construction unit three 205: Based on the travel segment flight delay risk assessment model, a multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed, including:

[0160] Evaluate the factors affecting the dynamic premium model for flight delays;

[0161] Based on the MTC-FDIRM flight delay composite risk assessment model and the aforementioned influencing factors, the MTC-FDDPM dynamic premium model is constructed as follows:

[0162]

[0163] Calculation unit 207: Real-time calculation of the specific risk rate and dynamic premium of the traveler's travel segment, including:

[0164] Write scripts or APIs based on the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0165] The script or API is called to calculate the specific risk rate and dynamic premium of the traveler's travel segment in real time.

[0166] Iterative optimization unit 209: constructs a TS-MTC-FDIM two-stage iterative optimization model, regularly evaluates the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and continuously iteratively optimizes the multi-scale time-varying complexity dynamic premium model, including:

[0167] Construct a two-stage iterative optimization model of TS-MTC-FDIM. The first stage model is:

[0168]

[0169] The second stage model is the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model.

[0170] The difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment is regularly evaluated through the TS-MTC-FDIM two-stage iterative optimization model.

[0171] The present invention uses a multi-scale time-varying complexity dynamic premium model for flight delays to dynamically evaluate the risk of each traveler who has taken out flight delay insurance in real time, and dynamically calculates the premium based on the multi-scale time-varying complexity risk assessment results, so as to match the premium with the specific risk of the traveler's travel segment, promote the improvement of the operating quality of flight delay insurance, and promote the high-quality development of flight delay insurance business.

[0172] Example 3

[0173] like Figure 3 As shown, this embodiment provides an electronic device, which is used in a model iteration method, and the electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein,

[0174] The memory stores instructions that can be executed by the one processor, and the instructions are executed by the at least one processor so that the at least one processor can: by setting the first virtual environment interface and the real avatar interface of the first user on the first screen of the display screen of the client, and setting the second virtual environment interface and the real avatar interface of the second user on the second screen of the display screen of the client, thereby splitting the interface corresponding to the first user account and the interface corresponding to the second user account into split screens, so that the interacting parties can see the entire interaction process more intuitively and in real time, which not only improves the fun of the interaction process, but also improves the accuracy of controlling the virtual character.

[0175] Example 4

[0176] An embodiment of the present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the interactive method in any of the above method embodiments.

[0177] Example 5

[0178] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0179] like Figure 3 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0180] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0181] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0182] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0183] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0184] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: by setting the first virtual environment interface and the real avatar interface of the first user on the first screen of the display screen of the client, and setting the second virtual environment interface and the real avatar interface of the second user on the second screen of the display screen of the client, the interface corresponding to the first user account and the interface corresponding to the second user account are displayed in split screens. In this way, the interacting parties can see the entire interaction process more intuitively and in real time, which not only improves the fun of the interaction process, but also improves the accuracy of controlling the virtual character.

[0185] Alternatively, the computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: by setting the first virtual environment interface and the real avatar interface of the first user on the first screen of the client's display screen, and setting the second virtual environment interface and the real avatar interface of the second user on the second screen of the client's display screen, the interface corresponding to the first user account and the interface corresponding to the second user account are displayed in a split screen. In this way, the interacting parties can see the entire interaction process more intuitively and in real time, which not only improves the fun of the interaction process, but also improves the accuracy of controlling the virtual character.

[0186] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0187] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0188] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0189] References

[0190] 1. Stiglitz, J. (1977). Monopoly, Nonlinear Pricing, and Imperfect information: The Insurance Market. Review of Economic Studies, 44, 407 - 430。

Claims

1. An iterative method for a dynamic premium model of flight delay insurance based on multi-scale time-varying complexity, characterized in that: include: Collect and process relevant data on flight information and weather information, and identify and evaluate the time-varying complexity of multi-scale flight delays (MTC-FD); Based on the multi-scale time-varying complexity assessment results of flight delays in flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed; Based on the flight delay risk assessment model for the travel segment, a multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed; Calculate the specific risk rate and dynamic premium of the passenger's travel segment in real time; A two-stage iterative optimization model of TS-MTC-FDIM is constructed to regularly evaluate the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and the multi-scale time-varying complexity dynamic premium model is continuously iterated and optimized.

2. The method according to claim 1, characterized in that: Collect and process relevant data on flight information and weather information, and identify and evaluate the multi-scale time-varying complexity of flight delays (MTC-FD), including: Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale multi-fractal complexity degree of flight delays (MTC-FD-MF) is identified and evaluated. Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale chaotic complexity degree MTC-FD-CHAOS of flight delays is identified and evaluated.

3. The method according to claim 2, characterized in that: Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale multi-fractal complexity of flight delays (MTC-FD-MF) is identified and evaluated, including: The evaluation formula of surface multifractal time-varying complexity is as follows: The evaluation formula of the degree of essential multifractal time-varying complexity is as follows:

4. The method according to claim 2, characterized in that: Based on the multi-scale time-varying complexity related factors of flight delays, the multi-scale chaotic complexity degree of flight delays MTC-FD-CHAOS is identified and evaluated, including: The formula for the complexity of multiscale chaos is as follows:

5. The method according to claim 1, characterized in that: Based on the multi-scale time-varying complexity assessment results of flight delays in flight segments, a multi-scale time-varying complexity flight delay risk assessment model MTC-FDIRM for travel segments is constructed, including: Construct the flight delay basic risk assessment model MTC-FDBRM, including the MTC-FDBRM-PB delay risk assessment model and the MTC-FDBRM-TM delay duration assessment model; The basic risk assessment models are combined to construct the MTC-FDIRM composite risk assessment model.

6. The method according to claim 5, characterized in that: The MTC-FDBRM-PB delay risk assessment model is as follows: g i ≥0, i=1, 2,…, l p≥0 where x FD Represents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

7. The method according to claim 5, characterized in that: The MTC-FDBRM-TM delay duration evaluation model is as follows: g i ≥0, i=1, 2,…, l where x FD Represents the independent variables of the function, including: multifractal complexity of temperature surface, multifractal complexity of humidity surface, multifractal complexity of flight delay surface at departure airport, multifractal complexity of flight delay surface at arrival airport, multifractal complexity of flight delay surface at airline, multifractal complexity of flight delay surface at flight segment, multifractal complexity of flight delay surface at departure time, essential multifractal complexity of temperature, essential multifractal complexity of humidity, essential multifractal complexity of flight delay at departure airport, essential multifractal complexity of flight delay at arrival airport, essential multifractal complexity of flight delay at airline, essential multifractal complexity of flight delay at flight segment, essential multifractal complexity of flight delay at departure time, chaotic complexity of temperature, chaotic complexity of humidity, chaotic complexity of flight delay at departure airport, chaotic complexity of flight delay at arrival airport, chaotic complexity of flight delay at airline, chaotic complexity of flight delay at flight segment, chaotic complexity of flight delay at departure time.

8. The method according to claim 5, characterized in that: The basic risk assessment models are combined to construct the MTC-FDIRM composite risk assessment model, including: Calculate the risk rate R of whether the flight segment is delayed FDPB ,The forecast of no delay is classified as low risk; Calculate the flight delay risk rate R FDTM ,When the predicted delay and the duration is long, it is classified as high risk, when the predicted delay and the duration is short, it is classified as low risk, and the rest are classified as medium risk; Calculate the composite risk rate R of flight delay for each flight segment: Among them, A = {low risk (predicted no delay}, B = {low risk (predicted delay)}, and C = {medium and high risk (predicted delay)}.

9. The method according to claim 1, characterized in that: Based on the flight delay risk assessment model for the travel segment, a multi-scale time-varying complexity flight delay dynamic premium model MTC-FDDPM for travelers is constructed, including: Evaluate the factors affecting the dynamic premium model for flight delays; Based on the MTC-FDIRM flight delay composite risk assessment model and the aforementioned influencing factors, the MTC-FDDPM dynamic premium model is constructed as follows: 0≤C(X)≤X 0≤η j ≤1,j=1,2 0≤η1+η2≤1.

10. The method according to claim 1, characterized in that: Real-time calculation of specific risk rates and dynamic premiums for travellers’ travel segments, including: Write scripts or APIs based on the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model. The script or API is called to calculate the specific risk rate and dynamic premium of the traveler's travel segment in real time.

11. The method according to claim 1, characterized in that: A two-stage iterative optimization model of TS-MTC-FDIM is constructed to regularly evaluate the difference between the actual status of recent flight delays and the multi-scale time-varying complexity risk assessment, and the multi-scale time-varying complexity dynamic premium model is continuously iterated and optimized, including: Construct a two-stage iterative optimization model of TS-MTC-FDIM. The first stage model is: The second stage model is the MTC-FDIRM flight delay composite risk assessment model and the MTC-FDDPM dynamic premium model. The model accuracy is continuously improved through the TS-MTC-FDIM two-stage iterative optimization model.

Images