Dynamic pricing device and method for multifunctional demand pricing coupling relationship

Abstract

The invention discloses a dynamic pricing device and method for a multifunctional demand pricing coupling relation, and belongs to the technical field of dynamic pricing. The dynamic pricing method comprises the following steps: analyzing the change condition of each numerical value related to air ticket pricing prices during the operation period of an airline company, and distinguishing numerical values during interval periods according to the change condition of the numerical values; and pricing is performed on the air ticket pricing price values of the airline companies in different sections, so that the obtained air ticket pricing prices are more accurate. Compared with an existing grouping method which is not easy to clearly distinguish numerical values in interval periods, the method has the advantages that the numerical values in the interval periods can be decomposed by adding variation trend parameters and adding components of information points, so that the numerical values of the air ticket pricing prices executed according to each group in the grouping values are more accurate. The defects that in the prior art, operation of general aviation enterprises is hindered, and the actual income of the general aviation enterprises is far lower than the expected investment income are effectively avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic pricing, and in particular relates to a dynamic pricing device and method for a multifunctional demand pricing coupling relationship. Background Art

[0002] General aviation, as an important part of the aviation field, refers to civil aviation activities other than public air transport by civil aircraft, including short-distance passenger and cargo transportation, agricultural and forestry spraying, medical rescue, tourism and entertainment, aviation training and other flight activities. At present, my country is vigorously developing general aviation, with an average annual growth rate of more than 10% in the total number of flights, and short-distance air transport routes, low-altitude sightseeing and other fields are in the growth segment. Moreover, due to the outbreak of COVID-19, most of my country's flight training has been transferred from abroad to China, and the demand for general aviation has been further deepened. Data show that the overall number of general aviation enterprises in my country has grown steadily in recent years, with an average annual increase of nearly 40, and there are 601 as of 2021. According to different regional distributions, the number of general aviation enterprises in Central and South my country, East China and North China has exceeded 100, which shows that the competitive advantage of the general aviation industry in my country's more economically developed regions is more significant.

[0003] Although my country's general aviation industry continues to develop under the condition of continuous industry subsidies, about 60% of enterprises are still in the red. The reason is that in addition to the fact that the current "openness" of low-altitude airspace is far from meeting the needs of the development of the general aviation industry, on the other hand, my country's general aviation enterprises can only use the pricing method mentioned in the existing technical solution with patent publication number "CN106600326B" to set prices. There are many scientific problems such as lack of mature operation methods, that is, no consideration of demand, lack of demand values, and lack of centralized optimization configuration. These problems hinder the operation of general aviation enterprises, making their actual income far lower than the expected investment income.

[0004] In order to price airline tickets based on demand values ​​(such as the average of the number of ticket reservations and the discount factor), decision tree and other models are often used for pricing. However, such models often cannot fully address the complex connections and non-proportional changes between various factors, making the pricing values ​​inaccurate and unreliable.

[0005] Because the airline's air ticket prices will be affected to different extents under different discount coefficients, it is necessary to implement situation-based pricing for the air ticket prices. Usually, grouping is used to group the air ticket price values ​​collected at different time points. The airline will affect the air ticket prices under the influence of the discount coefficient. There will be some differences in the air ticket prices during the discount period and the period without discounts, and the value changes in the interval between them are very stable, and it is not easy to clearly distinguish the values ​​in the interval through grouping. Summary of the invention

[0006] In order to solve the defects existing in the prior art, the present invention proposes a dynamic pricing device and method coupled with multifunctional demand pricing, which analyzes the changes in various values ​​related to the ticket pricing price during the operation of the airline, distinguishes the values ​​of the interval period according to the changes in the values, and performs pricing on the ticket pricing price values ​​of airlines in different sections, so that the obtained ticket pricing price is more accurate. Compared with the current grouping, it is not easy to clearly distinguish the values ​​of the interval period. The present invention adds a change trend parameter to increase the components of the information point, so that the values ​​of the interval period can be decomposed, so that the values ​​of the ticket pricing price executed according to each group in the group value are more accurate. It effectively avoids the defects of the prior art that hinder the operation of general aviation enterprises and make their actual income far lower than the expected investment income.

[0007] The present invention uses the following technical solutions.

[0008] A dynamic pricing method with multifunctional demand pricing coupling connection, comprising:

[0009] Step 1: Collect the associated action value of the airline's air ticket pricing price;

[0010] Step 2: Obtain a number of interval periods based on the single change trend of the air ticket discount coefficient;

[0011] Step 3: Obtain several periods of change in the number of air ticket reservations based on a single change trend, and obtain the corresponding relationship between the interval period and the period of change in the number of air ticket reservations;

[0012] Step 4: Obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient;

[0013] Step 5: Associate the information points to the quaternary group according to the change trend parameter, group the information points in the quaternary group, and perform air ticket pricing according to the grouping value.

[0014] Preferably, in step 1, a numerical collection cycle is defined in advance, and the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price are periodically collected according to the numerical collection cycle, and are defined as E1, E2, and E3 respectively. The values ​​of the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price collected for the jth time are defined as E1(j), E2(j), and E3(j) respectively.

[0015] Preferably, in step 1, the numerical values ​​of the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price collected each time are regarded as an information point in a triplet, and the three components of the information point are the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price. The airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price are the associated effect values ​​of the airline's ticket pricing price.

[0016] Preferably, in step 1, the method for collecting the associated effect value of the airline's ticket pricing price is: the airline is equipped with a computer terminal dedicated to instantly entering its latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to refresh the original ticket reservation quantity, ticket discount coefficient and ticket pricing price respectively, and the computer terminal periodically transmits the latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to a processing terminal running a program that executes a dynamic pricing method with a multi-functional demand pricing coupling connection so that the processing terminal can collect it.

[0017] Preferably, step 2 specifically includes: defining a partial value interval s in advance, and for each collected air ticket discount coefficient, taking the s air ticket discount coefficients that are adjacent to and preceding the air ticket discount coefficient as the partial near-area values ​​of the air ticket discount coefficient.

[0018] Preferably, step 2 specifically further includes: obtaining the partial variation parameter of the air ticket discount coefficient according to the partial near-area value of the air ticket discount coefficient of each information point, and the equation is:

[0019]

[0020] In the equation, D2(j) represents the partial variation parameter of the air ticket discount coefficient of the j-th information point, s represents the pre-defined partial value interval, E2(j,k) and E2(j,k+1) respectively represent the k-th and k+1-th air ticket discount coefficients within the partial near-range value of the air ticket discount coefficient of the j-th information point.

[0021] Preferably, step 2 specifically further includes: defining a critical value U of a partial variation parameter in advanceD ;

[0022] The partial change parameter is higher than the partial change parameter critical value U D The air ticket discount coefficient is defined as the interval discount coefficient. If the interval discount coefficient is higher than s times continuously, the time zone corresponding to the interval discount coefficient that is higher than s times continuously is regarded as an interval.

[0023] By identifying the partial change parameters of the air ticket discount coefficient of all the collected information points, it is possible to obtain a number of discontinuous intervals, obtain the number of information points contained in each interval, and define the average number of information points contained in all intervals as the average capacity of the interval.

[0024] Preferably, step 3 specifically includes:

[0025] Using the method in step 2, obtain a number of change period reservation quantities according to the partial change parameters of each air ticket reservation quantity. If the change period reservation quantity is continuously higher than s times, the time zone corresponding to the continuous change period reservation quantity higher than s times is regarded as a reservation quantity change period;

[0026] For each interval period, the scheduled quantity change period with the highest overlap in the time length section of the interval period is obtained and regarded as the scheduled quantity change period corresponding to the interval period.

[0027] Preferably, step 4 specifically includes:

[0028] The number of discount coefficients during the interval period before the ticket discount coefficient of each information point is obtained. According to the number of discount coefficients during the interval period before the ticket discount coefficient of each information point and the partial change parameter of the ticket discount coefficient of each information point, the probability of each information point being in the interval area is obtained. The calculation equation is:

[0029]

[0030] In the equation, G j represents the probability that the jth information point is in the separated area, M j represents the number of discount coefficients in the interval before the ticket discount coefficient of the jth information point, represents the average capacity of the airline during the interval, D2(j) represents the partial change parameter of the ticket discount coefficient of the jth information point, and BZ(D2(j)) represents the standardization of D2(j) using the Z-Score method.

[0031] Preferably, step 4 specifically further includes:

[0032] According to the corresponding relationship between each interval period and the scheduled quantity change period, the starting time point and the ending time point of the interval period are obtained, respectively, minus the starting time point and the ending time point of the corresponding scheduled quantity change period, and the starting time point decrement and the ending time point decrement are obtained accordingly, and the average of all the starting time point decrements and the ending time point decrements is defined as the scheduled quantity change delay time interval

[0033] Preferably, step 4 specifically further includes:

[0034] The delay coefficient of the information point is obtained according to the single amplitude of the air ticket discount coefficient in the delay interval of the change of the number of reservations before each information point, and its equation is:

[0035]

[0036] In the equation, H j represents the delay coefficient of the jth information point, represents the time interval for delaying the change in the scheduled quantity, and E2(jl) and E2(jl-1) represent the air ticket discount coefficients of the jl-th and jl-1-th information points respectively.

[0037] Preferably, step 4 specifically further includes: obtaining information point change trend parameters according to the interval area probability and the delay coefficient of each information point, and the equation is:

[0038] I j =G j *H j

[0039] In the equation, I j Represents the trend parameter of the jth information point, G j represents the probability that the jth information point is in the separated area, H j Represents the delay coefficient of the j-th information point.

[0040] Preferably, step 5 specifically includes: adding the change trend parameter of each information point as a new component to each information point, so that the change trend parameter, the number of air ticket reservations, the air ticket discount coefficient and the air ticket pricing price together form a four-component attribute vector;

[0041] All information points in the quaternary group are cut by K-means method to obtain the final grouping value;

[0042] Confirm that each group in the final grouping value is a segment during the airline's operation period, obtain the group centroid of each group in the grouping value, face the most recently received information point, calculate the Pearson coefficient from the newly received information point to the group centroid of each group, take the group with the highest Pearson coefficient as the segment group of the most recently received information point, obtain the continuous information points belonging to the segment group before the most recently received information point, and arrange the ticket pricing of the continuous information point and the most recently received information point according to the collection time point, take the arranged ticket pricing price as the current segment value reference value, use the least squares method to perform ticket pricing on the current segment value reference value, and achieve the pricing of the airline ticket pricing price at the next point in time.

[0043] A dynamic pricing device with a multifunctional demand pricing coupling relationship, comprising:

[0044] A correlation module, which is used to collect correlation effect values ​​of airline ticket pricing prices;

[0045] An interval module, which is used to obtain a number of interval periods according to a single change trend of the air ticket discount coefficient;

[0046] A change module, which is used to obtain a number of change periods of the scheduled quantity according to a single change trend of the scheduled quantity of air tickets, and obtain the corresponding relationship between the interval period and the change period of the scheduled quantity;

[0047] A postponement module is used to obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient;

[0048] The pricing module is used to associate information points with four-tuples according to the changing trend parameters, group the information points in the four-tuples, and perform air ticket pricing according to the grouping values.

[0049] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0050] Under different conditions involving different factors, establishing different demand pricing methods to obtain the future development needs of general aviation is an important basis for general aviation transportation decision-making departments to formulate development strategies and plans. Accurately grasping the future demand for general aviation can minimize the hidden dangers of general aviation companies, reduce the operating costs of general aviation companies, and maintain a long-term basic balance between market supply and demand, thereby increasing the profits of general aviation companies. By analyzing the changes in the various values ​​related to the ticket pricing price during the operation of the airline, the values ​​of the interval period are distinguished according to the changes in the values, and the ticket pricing price values ​​of different sections of airlines are priced separately, so that the obtained ticket pricing price is more accurate. Compared with the current grouping, it is not easy to clearly distinguish the values ​​of the interval period. The present invention adds a change trend parameter, allows the components of the information point to be added, and allows the values ​​of the interval period to be decomposed, so that the values ​​of the ticket pricing price executed according to each group in the group value are more accurate. It effectively avoids the defects in the prior art that hinder the operation of general aviation enterprises and make their actual income far lower than the expected investment income. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of the dynamic pricing method of the multifunctional demand pricing coupling relationship described in the present invention;

[0052] Figure 2 It is a partial structural diagram of the dynamic pricing device of the multifunctional demand pricing coupling relationship described in the present invention. DETAILED DESCRIPTION

[0053] Under the condition of continuous industry subsidies, my country's general aviation industry continues to develop, but about 60% of enterprises are still in the red. The reason is that in addition to the fact that the current "openness" of low-altitude airspace is far from meeting the needs of the development of the general aviation industry, on the other hand, my country's general aviation enterprises can only use the pricing method mentioned in the existing technical solution with patent publication number "CN106600326B" to set prices. There are many scientific problems such as lack of mature network operation methods; lack of equipment and market operation data; scattered industrial resources, and no centralized optimization configuration. These problems hinder the operation of general aviation enterprises, making their actual income far lower than the expected investment income.

[0054] The present invention intends to identify general aviation demand on the basis of considering the transfer rules and coupled interactive effects of travelers' behavioral demand, and then implement an optimized pricing strategy to provide a reference basis for the realization of economic benefits in the general aviation industry.

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments expressed in the present invention are only some embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0056] like Figure 1 As shown, the dynamic pricing method of a multifunctional demand pricing coupling relationship described in the present invention includes:

[0057] General aviation is in a period of rapid development, and its demand is affected by many factors, one of which is the price issue. The result of each "update" pricing has an impact on travel demand at the current time point and subsequent time points. It can be seen that the formulation of a certain "update" price should not only consider the timely feedback of demand at the current time point, but also the long-term resistance of demand at subsequent time points. Because the immediate feedback results and long-term feedback results of demand respectively reflect the effects of the same "update" pricing decision on the current time point and the long-term process, how to integrate these two parts of the feedback results to propose a reasonable price decision is the key to the pricing process. Therefore, how to study the mutual influence mechanism in the pricing-demand coupling connection is another scientific problem that must be solved in this project.

[0058] In addition, in order to ensure the ideal solution of the above scientific problems, a key technical problem must be solved:

[0059] Because the influencing factors and their connections are relatively complicated, and the application of aviation enterprises must be simple and easy to operate in daily life, if the traditional mathematical expression method is used to describe the above problems, the model will be too complicated and difficult to solve. Therefore, how to construct the model as accurately and simply as possible is the key problem that the present invention needs to solve.

[0060] Step 1: Collect the associated action value of the airline's air ticket pricing price;

[0061] In a preferred but non-limiting embodiment of the present invention, during the operation of the airline, the company's air ticket pricing price is mainly affected by the number of air ticket reservations and the air ticket discount coefficient, so the real-time air ticket reservation number and the air ticket discount coefficient can be collected, and it is suitable to subsequently perform pricing on the current situation of the airline and the future air ticket pricing price based on the real-time air ticket reservation number and the air ticket discount coefficient.

[0062] In step 1, a numerical collection cycle is defined in advance. The numerical collection cycle can be once every ten minutes. The airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price are periodically collected according to the numerical collection cycle and are defined as E1, E2, and E3 respectively. The values ​​of the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price collected for the jth time are defined as E1(j), E2(j), and E3(j) respectively.

[0063] In a preferred but non-limiting embodiment of the present invention, in step 1, the values ​​of the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price collected each time are regarded as an information point in a triplet, and the three components of the information point are the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price, and the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price are the associated action values ​​of the airline's ticket pricing price. The airline's ticket reservation quantity and ticket discount coefficient are also demand values.

[0064] In a preferred but non-limiting embodiment of the present invention, in step 1, the method for collecting the associated action value of the airline ticket pricing price is: the airline is equipped with a computer terminal dedicated to instantly inputting its latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to refresh the original ticket reservation quantity, ticket discount coefficient and ticket pricing price respectively, and the computer terminal periodically transmits the latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to a processing terminal running a program that executes a dynamic pricing method for multi-functional demand pricing coupling every ten minutes so that the processing terminal can collect it. The processing terminal can be a computer.

[0065] Thus, the collection of relevant function values ​​of airline ticket pricing is achieved.

[0066] Step 2: Obtain a number of interval periods based on the single change trend of the air ticket discount coefficient;

[0067] The number of air ticket reservations is mainly affected by the air ticket discount coefficient. During the period between the airline's conversion between the two states of having a discount coefficient and not having a discount coefficient, the airline's air ticket discount coefficient gradually changes, which will also cause the number of air ticket reservations to change. The effect of the air ticket discount coefficient on the number of air ticket reservations is obtained based on the relationship between the change of the air ticket discount coefficient and the number of air ticket reservations.

[0068] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes: pre-defining a partial value interval s, the value of s can be six, and facing each collected air ticket discount coefficient, taking the s air ticket discount coefficients that are adjacent to the air ticket discount coefficient before and after as the partial near-area value of the air ticket discount coefficient. Here, the first and the second adjacent are the collection time points of the air ticket discount coefficients collected by the processing terminal, and are adjacent in order.

[0069] If the distribution near-zone value is expressed as a single change trend in time interval (time interval is the time interval between the first and last collection points of the ticket discount coefficient in the distribution near-zone value by the processing terminal), it means that the airline is in a gap period in this time interval. During the gap period of the airline, the changes in the airline's ticket discount coefficient and the number of ticket reservations can be more clearly grasped to analyze the relationship between the ticket discount coefficient and the number of ticket reservations.

[0070] In a preferred but non-limiting embodiment of the present invention, step 2 specifically further includes: obtaining a partial variation parameter of the air ticket discount coefficient according to the partial near-area value of the air ticket discount coefficient of each information point, and the equation is:

[0071]

[0072] In the equation, D2(j) represents the partial variation parameter of the air ticket discount coefficient of the j-th information point, s represents the predefined partial value interval, E2(j,k) and E2(j,k+1) respectively represent the k-th and k+1-th air ticket discount coefficients within the partial near-area value of the air ticket discount coefficient of the j-th information point, and C2(j) represents the quotient of the partial near-area values ​​of the air ticket discount coefficient of the j-th information point. When C2(j) is higher than one and the higher it is, the partial near-area value of the air ticket discount coefficient of the j-th information point will continue to increase (that is, continue to gradually increase). When C2(j) is lower than one and the lower it is, the partial near-area value of the air ticket discount coefficient of the j-th information point will be monotonically decreasing (that is, continue to gradually decrease). Therefore, when C2(j) is not lower than one, let the partial variation parameter D2(j) be C2(j), and when C2(j) is lower than one, let the partial variation parameter D2(j) be Let the partial change parameter be a value not less than one, and use the partial change parameter to represent the single change state of the air ticket discount coefficient in the sub-interval. The closer the partial change parameter is to one, it means that the difference between all adjacent values ​​in the partial close range value of the air ticket discount coefficient of the j-th information point is very low. At this time, it means that the airline is more likely to be in a stable period rather than a gap period in this sub-interval. The higher the partial change parameter is, it means that the difference between all adjacent values ​​in the partial close range value of the air ticket discount coefficient of the j-th information point is not low, and the airline is more likely to be in a gap period in this sub-interval.

[0073] In a preferred but non-limiting embodiment of the present invention, step 2 specifically further includes: defining a critical value U of a partial variable parameter in advance D , U D The value can be

[0074] The partial change parameter is higher than the partial change parameter critical value U D The air ticket discount coefficient is defined as the interval discount coefficient. If the interval discount coefficient is higher than s times continuously, it can be confirmed that the airline is in an interval. The time zone corresponding to the interval discount coefficient that is higher than s times continuously (the time zone is the time interval between the first discount coefficient collected by the processing terminal and the last discount coefficient collected by the processing terminal in the interval discount coefficient that is higher than s times continuously) is regarded as an interval.

[0075] By identifying the partial change parameters of the air ticket discount coefficient of all the collected information points, it is possible to obtain a number of discontinuous intervals, obtain the number of information points contained in each interval, and define the average number of information points contained in all intervals as the average capacity of the interval.

[0076] So I got some intervals from the airline.

[0077] Step 3: Obtain several periods of change in the number of air ticket reservations based on a single change trend, and obtain the corresponding relationship between the interval period and the period of change in the number of air ticket reservations;

[0078] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0079] Using the method in step 2, a number of booking numbers during the change period are obtained according to the partial change parameters of each ticket booking number. If the booking number during the change period is continuously higher than s times, it can be confirmed that the airline is in the booking number change period at this time, and the time zone corresponding to the booking number during the change period that is continuously higher than s times (the time zone is the time interval between the first booking number collected by the processing terminal and the last booking number collected by the processing terminal within the booking number during the change period that is continuously higher than s times) is regarded as a booking number change period;

[0080] Each interval period corresponds to a time interval, and each scheduled quantity change period also corresponds to a time interval. For each interval period, the scheduled quantity change period with the highest overlap with the time interval of the interval period is obtained and regarded as the scheduled quantity change period corresponding to the interval period. In this way, the interval period and the scheduled quantity change period are matched.

[0081] Thus, a number of predetermined periods of quantity changes are obtained, and the corresponding relationship between the intervals and the predetermined periods of quantity changes is obtained.

[0082] Step 4: Obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient;

[0083] However, only the existing airline intervals can be obtained through the segmented change parameters. The status of the airline can be obtained based on the high and low status of the value itself. The relevant values ​​of the airline's air ticket pricing can be divided into various sections through the grouping method. In actual use, the difference in the number of airline ticket bookings is very high. Airlines have good methods to maintain a relatively stable number of air ticket bookings (giving gifts, etc.). The change in the number of airline bookings is compared to the air ticket discount coefficient. There is a certain delay. This application obtains the change trend parameters of each information point based on the delay, which is suitable for implementing the difference in air ticket pricing values ​​of companies in different sections.

[0084] Since airlines mainly use marketing to promote the sale of tickets, the discount coefficient directly determines the airline's ticket pricing. The average capacity of the airline in each period is If the number of discount coefficients in the interval before the current information point is less than This means that the current information point is often exactly in the interval. If the number of discount coefficients of the interval before the current information point is much higher than This means that the current information point is less likely to be in the interval period.

[0085] In a preferred but non-limiting embodiment of the present invention, step 4 specifically comprises:

[0086] The number of discount coefficients during the interval period before the ticket discount coefficient of each information point is obtained. According to the number of discount coefficients during the interval period before the ticket discount coefficient of each information point and the partial change parameter of the ticket discount coefficient of each information point, the probability of each information point being in the interval area is obtained. The calculation equation is:

[0087]

[0088] In the equation, G j represents the probability that the jth information point is in the separated area, M j represents the number of discount coefficients in the interval before the ticket discount coefficient of the jth information point, represents the average capacity of the airline in the interval period, D2(j) represents the partial change parameter of the air ticket discount coefficient at the jth information point, BZ(D2(j)) represents the standardization of D2(j) using the Z-Score method, the higher the partial change parameter of the air ticket discount coefficient at the jth information point, and the number of discount coefficients in the interval period before (before) the air ticket discount coefficient of the jth information point is lower than When the jth information point is often in the interval area, the distribution change parameter of the air ticket discount coefficient at the jth information point is lower, and the number of discount coefficients in the interval period before the air ticket discount coefficient of the jth information point is much higher than , the jth information point will be less likely to be in the separated area.

[0089] The number of ticket reservations during the airline's operation period is mainly determined by the ticket discount coefficient, and the effect of the ticket discount coefficient on the number of ticket reservations has some delay. Even if the ticket discount coefficient continues to change, the number of ticket reservations will continue to increase or decrease for a period of time, and the company's ticket prices will still be affected by the changes in the number of reservations during this period. Therefore, the delay in the effect of the ticket discount coefficient on the number of ticket reservations is obtained based on the value affected by the ticket price collected.

[0090] In a preferred but non-limiting embodiment of the present invention, step 4 specifically further comprises:

[0091] According to the corresponding relationship between each interval period and the scheduled quantity change period, the starting time point and the ending time point of the interval period are obtained, respectively, minus the starting time point and the ending time point of the corresponding scheduled quantity change period, and the starting time point decrement and the ending time point decrement are obtained accordingly, and the average of all the starting time point decrements and the ending time point decrements is defined as the scheduled quantity change delay time interval

[0092] When facing each information point, if the number of reservations continues to change due to the delay of the air ticket discount coefficient, then the air ticket discount coefficient in the time interval of the delayed reservation number before the information point should also be a single change (a single change is a gradual increase or decrease) to allow the number of air ticket reservations to change.

[0093] In a preferred but non-limiting embodiment of the present invention, step 4 specifically further comprises:

[0094] The delay coefficient of the information point is obtained according to the single amplitude of the air ticket discount coefficient in the delay interval of the change of the number of reservations before each information point, and its equation is:

[0095]

[0096] In the equation, Hj represents the delay coefficient of the jth information point, represents the delay interval of the scheduled quantity change, E2(jl) and E2(jl-1) represent the air ticket discount coefficients of the jl-th and jl-1-th information points respectively, T2(j) represents the quotient of the air ticket discount coefficients in the delay interval of the scheduled quantity change before the j-th information point, when T2(j) is higher than one and the higher it is, the air ticket discount coefficient in the delay interval of the scheduled quantity change before the j-th information point will continue to increase, when T2(j) is lower than one and the lower it is, the air ticket discount coefficient in the delay interval of the scheduled quantity change before the j-th information point will continue to decrease, so when T2(j) is not lower than one, let the delay coefficient H j T j (2) When T2(j) is lower than 1, let the delay coefficient H j for Let the delay coefficient be a value not less than one, and use the delay coefficient to represent the single change of the air ticket discount coefficient within the delay time interval of the change in the reservation quantity before the j-th information point. The closer the delay coefficient is to one, it means that the difference between the air ticket discount coefficients in the delay time interval of the change in the reservation quantity before the j-th information point is very low, which means that the j-th information point is less likely to be a delayed segment of the change in the reservation quantity. The higher the delay coefficient is, it means that the air ticket discount coefficient will have a single change in the delay time interval of the change in the reservation quantity before the j-th information point, which means that the j-th information point is more likely to be a delay segment formed by the change in the air ticket discount coefficient and the change in the reservation quantity of the air ticket.

[0097] In a preferred but non-limiting embodiment of the present invention, step 4 specifically further includes: obtaining information point change trend parameters according to the interval area probability and the delay coefficient of each information point, and the equation is:

[0098] I j =G j *H j

[0099] In the equation, I j Represents the change trend parameter of the jth information point, G j represents the probability that the jth information point is in the separated area, H j Represents the delay coefficient of the j-th information point.

[0100] The trend parameter is obtained based on the interval probability of each value and the value delay coefficient. The higher the trend parameter, the more certain it is that the current value is in the interval period.

[0101] Thus, the changing trend parameters of the overall numerical values ​​are obtained.

[0102] Step 5: Associate the information points to the quaternary group according to the change trend parameter, group the information points in the quaternary group, and perform air ticket pricing according to the grouping value.

[0103] If the information points are grouped, the status of the airlines will be differentiated only according to the ticket discount coefficient, the number of ticket reservations and the company's ticket pricing price. It is not easy to clearly distinguish the values ​​in different periods. Therefore, the present application distinguishes the information points in different periods by changing the trend parameters.

[0104] In a preferred but non-limiting embodiment of the present invention, step 5 specifically includes: adding the change trend parameter of each information point as a new component to each information point, allowing the change trend parameter, the number of air ticket reservations, the air ticket discount coefficient and the air ticket pricing price to form a four-component attribute vector, and associating each information point into a four-component through the previous three-tuple;

[0105] All information points in the quadruple are cut by the K-means method to obtain the final grouping value; and due to the addition of variable trend parameters, information points in different sections can show obvious differences, and grouping can better distinguish information points in different sections.

[0106] It can be confirmed that each group in the last group value is a segment during the operation of the airline, and the group centroid of each group in the group value is obtained. Facing the most recently received information point, the Pearson coefficient from the newly received information point to the group centroid of each group is calculated, and the group with the highest Pearson coefficient is regarded as the segment group of the most recently received information point, and the continuous information point belonging to the segment group before the most recently received information point is obtained, and the ticket pricing price of the continuous information point and the most recently received information point are arranged according to the collection time point, and the arranged ticket pricing price is regarded as the current segment value reference value, and the least squares method is used to perform ticket pricing on the current segment value reference value to achieve the pricing of the airline ticket pricing price at the next time point. The newly received information point is the information point in the quaternary formed by the most recently received ticket reservation quantity, ticket discount coefficient, ticket pricing price and corresponding change trend parameters.

[0107] This resulted in coupled pricing of airline ticket prices.

[0108] This application analyzes the changes in various values ​​related to the air ticket pricing price during the operation of the airline, distinguishes the values ​​of the interval period according to the changes in the values, and performs pricing on the values ​​of the air ticket pricing price of different sections of the airline separately, so that the obtained air ticket pricing price is more accurate. Compared with the current grouping, it is not easy to clearly distinguish the values ​​of the interval period. The present invention adds a change trend parameter to increase the components of the information point, so that the values ​​of the interval period can be decomposed, so that the values ​​of the air ticket pricing price executed according to each group in the group value in the future are more accurate.

[0109] like Figure 2 As shown, the dynamic pricing device of a multifunctional demand pricing coupling relationship of the present invention comprises:

[0110] A correlation module, which is used to collect correlation effect values ​​of airline ticket pricing prices;

[0111] An interval module, which is used to obtain a number of interval periods according to a single change trend of the air ticket discount coefficient;

[0112] A change module, which is used to obtain a number of change periods of the scheduled quantity according to a single change trend of the scheduled quantity of air tickets, and obtain the corresponding relationship between the interval period and the change period of the scheduled quantity;

[0113] A postponement module is used to obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient;

[0114] The pricing module is used to associate information points with four-tuples according to the changing trend parameters, group the information points in the four-tuples, and perform air ticket pricing according to the grouping values.

[0115] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0116] Under different conditions involving different factors, establishing different demand pricing methods to obtain the future development needs of general aviation is an important basis for general aviation transportation decision-making departments to formulate development strategies and plans. Accurately grasping the future demand for general aviation can minimize the hidden dangers of general aviation companies, reduce the operating costs of general aviation companies, and maintain a long-term basic balance between market supply and demand, thereby increasing the profits of general aviation companies. By analyzing the changes in the various values ​​related to the ticket pricing price during the operation of the airline, the values ​​of the interval period are distinguished according to the changes in the values, and the ticket pricing price values ​​of different sections of airlines are priced separately, so that the obtained ticket pricing price is more accurate. Compared with the current grouping, it is not easy to clearly distinguish the values ​​of the interval period. The present invention adds a change trend parameter, allows the components of the information point to be added, and allows the values ​​of the interval period to be decomposed, so that the values ​​of the ticket pricing price executed according to each group in the group value are more accurate. It effectively avoids the defects in the prior art that hinder the operation of general aviation enterprises and make their actual income far lower than the expected investment income.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A dynamic pricing method for multifunctional demand pricing coupling relationship, characterized in that: include: Step 1: Collect the associated action value of the airline's air ticket pricing price; Step 2: Obtain a number of interval periods based on the single change trend of the air ticket discount coefficient; Step 3: Obtain several periods of change in the number of air ticket reservations based on a single change trend, and obtain the corresponding relationship between the interval period and the period of change in the number of air ticket reservations; Step 4: Obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient; Step 5: Associate the information points to the quaternary group according to the change trend parameter, group the information points in the quaternary group, and perform air ticket pricing according to the grouping value.

2. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 1 is characterized in that: In step 1, a value collection cycle is defined in advance, and the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price are periodically collected according to the value collection cycle, which are defined as E1, E2, and E3 respectively. The values ​​of the airline's ticket reservation quantity, ticket discount coefficient and ticket pricing price collected for the jth time are defined as E1(j), E2(j), and E3(j) respectively; In step 1, the values ​​of the number of airline ticket reservations, ticket discount coefficient and ticket pricing price collected each time are regarded as an information point in a triplet, and the three components of the information point are the number of airline ticket reservations, ticket discount coefficient and ticket pricing price, and the number of airline ticket reservations, ticket discount coefficient and ticket pricing price are the correlation values ​​of the airline ticket pricing price; In step 1, the method for collecting the associated effect value of the airline's ticket pricing price is: the airline is equipped with a computer terminal dedicated to instantly entering its latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to refresh the original ticket reservation quantity, ticket discount coefficient and ticket pricing price respectively, and the computer terminal periodically transmits the latest ticket reservation quantity, ticket discount coefficient and ticket pricing price to a processing terminal running a program that executes a dynamic pricing method with a multi-functional demand pricing coupling connection so that the processing terminal can collect it.

3. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 2 is characterized in that: Step 2 specifically includes: defining a partial value interval s in advance, and for each air ticket discount coefficient collected, taking the s air ticket discount coefficients that are adjacent to and preceding the air ticket discount coefficient as the partial near-area values ​​of the air ticket discount coefficient.

4. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 3 is characterized in that: Step 2 specifically includes: obtaining the partial change parameter of the air ticket discount coefficient according to the partial near-area value of the air ticket discount coefficient of each information point, and the equation is: In the equation, D2(j) represents the partial variation parameter of the air ticket discount coefficient of the j-th information point, s represents the pre-defined partial value interval, E2(j,k) and E2(j,k+1) respectively represent the k-th and k+1-th air ticket discount coefficients within the partial near-range value of the air ticket discount coefficient of the j-th information point.

5. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 4 is characterized in that: Step 2 specifically includes: defining a critical value U of a partial variable parameter in advance D ; The partial change parameter is higher than the partial change parameter critical value U D The air ticket discount coefficient is defined as the interval discount coefficient. If the interval discount coefficient is higher than s times continuously, the time zone corresponding to the interval discount coefficient that is higher than s times continuously is regarded as an interval. By identifying the partial change parameters of the air ticket discount coefficients of all the collected information points, we can obtain several discontinuous intervals, the number of information points contained in each interval, and define the average number of information points contained in all intervals as the interval capacity average M.

6. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 5 is characterized in that: Step 3 specifically includes: Using the method in step 2, obtain a number of change period reservation quantities according to the partial change parameters of each air ticket reservation quantity. If the change period reservation quantity is continuously higher than s times, the time zone corresponding to the continuous change period reservation quantity higher than s times is regarded as a reservation quantity change period; For each interval period, the scheduled quantity change period with the highest overlap in the time length section of the interval period is obtained and regarded as the scheduled quantity change period corresponding to the interval period.

7. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 6 is characterized in that: Step 4 specifically includes: The number of discount coefficients during the interval period before the ticket discount coefficient of each information point is obtained. According to the number of discount coefficients during the interval period before the ticket discount coefficient of each information point and the partial change parameter of the ticket discount coefficient of each information point, the probability of each information point being in the interval area is obtained. The calculation equation is: In the equation, G j represents the probability that the jth information point is in the separated area, M j represents the number of discount coefficients in the interval before the ticket discount coefficient of the jth information point, represents the average capacity of the airline during the interval, D2(j) represents the partial change parameter of the ticket discount coefficient of the jth information point, and BZ(D2(j)) represents the standardization of D2(j) using the Z-Score method.

8. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 7 is characterized in that: Step 4 specifically also includes: According to the corresponding relationship between each interval period and the scheduled quantity change period, the starting time point and the ending time point of the interval period are obtained, respectively, minus the starting time point and the ending time point of the corresponding scheduled quantity change period, and the starting time point decrement and the ending time point decrement are obtained accordingly, and the average of all the starting time point decrements and the ending time point decrements is defined as the scheduled quantity change delay time interval Step 4 specifically also includes: The delay coefficient of the information point is obtained according to the single amplitude of the air ticket discount coefficient in the delay interval of the change of the number of reservations before each information point, and its equation is: In the equation, H j represents the delay coefficient of the jth information point, represents the time interval for the change of the scheduled quantity to be postponed, E2(jl) and E2(jl-1) represent the air ticket discount coefficients of the jl-th and jl-1-th information points respectively; Step 4 specifically includes: obtaining the information point change trend parameter according to the interval area probability and the delay coefficient of each information point, and the equation is: I j =G j *H j In the equation, I j Represents the change trend parameter of the jth information point, G j represents the probability that the jth information point is in the separated area, H j Represents the delay coefficient of the jth information point.

9. The dynamic pricing method of multi-functional demand pricing coupling relationship according to claim 8 is characterized in that: Step 5 specifically includes: adding the change trend parameter of each information point as a new component to each information point, so that the change trend parameter, the number of air ticket reservations, the air ticket discount coefficient and the air ticket pricing price together form a four-component attribute vector; All information points in the quaternary group are cut by K-means method to obtain the final grouping value; Confirm that each group in the final grouping value is a segment during the airline's operation period, obtain the group centroid of each group in the grouping value, face the most recently received information point, calculate the Pearson coefficient from the newly received information point to the group centroid of each group, take the group with the highest Pearson coefficient as the segment group of the most recently received information point, obtain the continuous information points belonging to the segment group before the most recently received information point, and arrange the ticket pricing of the continuous information point and the most recently received information point according to the collection time point, take the arranged ticket pricing price as the current segment value reference value, use the least squares method to perform ticket pricing on the current segment value reference value, and achieve the pricing of the airline ticket pricing price at the next point in time.

10. A dynamic pricing device with multifunctional demand pricing coupling relationship, characterized in that: include: A correlation module, which is used to collect correlation effect values ​​of airline ticket pricing prices; An interval module, which is used to obtain a number of interval periods according to a single change trend of the air ticket discount coefficient; A change module, which is used to obtain a number of change periods of the scheduled quantity according to a single change trend of the scheduled quantity of air tickets, and obtain the corresponding relationship between the interval period and the change period of the scheduled quantity; A postponement module is used to obtain the probability of each information point being in the separated area, obtain the postponement interval of the scheduled quantity change according to the corresponding connection between each separated period and the scheduled quantity change period, obtain the postponement coefficient of each information point according to the single amplitude of the air ticket discount coefficient in the postponement interval of the scheduled quantity change before each information point, and obtain the change trend parameter of each information point according to the probability of each information point being in the separated area and the postponement coefficient; The pricing module is used to associate information points with four-tuples according to the changing trend parameters, group the information points in the four-tuples, and perform air ticket pricing according to the grouping values.

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