CN113609142A - Automatic freight rate updating method and storage medium based on OTA platform data delivery - Google Patents
Automatic freight rate updating method and storage medium based on OTA platform data delivery Download PDFInfo
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Abstract
The invention relates to the technical field of air ticket industry, and discloses an automatic freight rate updating method and a storage medium based on OTA platform data delivery, which comprises the following steps of 1) obtaining freight rate data of each B2B platform; 2) storing the obtained original data; 3) splitting and comparing the data and setting a freight rate rule; 4) adjusting a data uploading OTA platform; 5) and updating the new, modified or deleted freight rate of the data source channel in real time. The method has the advantages that after the optimal data are automatically compared, the data are added or dropped according to the user setting rule, the data are automatically updated after the change, the accuracy and timeliness of data dropping are improved, the low efficiency of manual dropping and the complexity and timeliness of manual dropping data are reduced, the task system can be automatically matched and dropped by only setting the rule in advance, and the working efficiency and the accuracy are greatly improved.
Description
Technical Field
The invention relates to the technical field of air ticket industry, in particular to an automatic freight rate updating method and a storage medium based on OTA platform data delivery.
Background
At present, domestic civil aviation flights are more than thirty-five thousand flights per day, the price of each air route and each day is different, and each airline company can adjust the price change of each flight at any time according to the sale condition of the airplane-carrying type seat of each flight or due to various special reasons, such as flight cancellation, delay and the like.
The traditional agent puts flight freight rate data in each big OTA (where to go, take a journey, go the same journey, fly pigs, cattle on the way, make a party, travel, intelligent lines, flight managers, Jingdong and other channels, and then directly replaces the OTA with the OTA), the agent self acquires the price information of the flight from each channel, the price information is made into an OTA corresponding table to be uploaded, if the price of the flight changes, the price information needs to be manually edited and adjusted in an OTA background, because the air ticket freight rate data is huge and belongs to the data volume of the level of billions, the freight rate information of the flights cannot be maintained by manpower at all and cannot be updated in time, this results in high labor costs, low efficiency, loss due to untimely update rates, therefore, the change of flight price fluctuation is generated in a multi-element environment, and the difficulty for timely understanding the sales condition of each flight carrier is relatively high.
To solve the above problems. For this purpose, an automatic freight rate updating method and a storage medium based on OTA platform data delivery are provided.
Disclosure of Invention
The invention aims to provide an automatic freight rate updating method and a storage medium based on OTA platform data delivery, and the problems in the background art can be solved through the cooperation of the automatic freight rate updating method and the storage medium based on the method.
In order to achieve the purpose, the invention provides the following technical scheme: the automatic freight rate updating method based on OTA platform data delivery comprises the following steps:
1) automatically acquiring freight rate data of each B2B platform through API full volume or increment;
2) after splitting the acquired original data, storing the original data in a cache container by taking a platform name, a navigation department, a airline, a flight number and a cabin space as KEY, and marking the data needing to be updated or deleted in different states;
3) the system automatically updates the selected channel of the user in real time, acquires all effective data in the cache container according to the channel selected by the user, splits and compares the data, selects the optimal price data by taking the navigation department, the date, the flight number and the space as units, automatically updates the optimal pricing and price reduction rule, and calculates the optimal freight rate rule according to the navigation department, the airline, the date, the flight number, the space and the like;
4) the system organizes the adjusted freight rate data into a corresponding message through a format required by an API (application program interface) and uploads the message to an OTA (over the air) platform, and the system records the current uploading time after the uploading is successful;
5) the system executes an increment timing updating task once every 1-3 minutes, updates the situation of increasing, modifying or deleting the freight rate of a data source channel, firstly obtains the time point of the last updating record of the user from a database, obtains all data changed in the time through the time point, and then performs operations of increasing, modifying and deleting the freight rate data through the API of the OTA through the matching of the optimal matching rule and the charging and discharging rules set by the user;
6) and 1-5, directly and automatically extracting all original data from the storage medium, automatically transmitting future data to the storage medium in real time, and automatically updating the storage medium in real time.
Further, a method of setting a freight rate in step 3;
31) setting a flight number and a flight date and adding Y elements, wherein Y is a directly added RMB and then numerical value;
32) setting the price-adding algorithm according to the return point as Y1＝Y2-Y2*X％，Y2The original freight rate of a certain cabin is X, and the number of points is added according to the return points.
Further, all data sources in the steps 1-5 comprise a database module and a weather cache service module;
the database module stores the cabin FD freight rate data module, the historical price data module, the summer and autumn information data module, the winter and spring information data module, the historical punctuality rate data module and the future price data module in a classified manner;
the weather cache service module is used for storing the airport weather states of all flights in three years of history of each department.
Further, the berth FD freight rate data module is used for acquiring the berth of each current sale route of each navigation department and the berth FD freight rate published by each navigation department to the outside and putting the berth FD freight rate into the database as the standard freight rate;
the historical price data module is used for storing the data of the class level, the price and the remaining situation of each airline of each historical three-year flight of each department;
the summer and autumn information data module and the winter and spring information data module are used for storing the summer and autumn information data module and the winter and spring information data module;
the historical punctuality rate data module is used for storing all flight taking-off and landing moments of all flights of each driver for three years in history as historical punctuality rate data;
the future price data module is used for storing and polling to obtain the daily flight space, price and surplus of each future department of each department.
Furthermore, the flight space level, the price and the remaining space condition of the airlines of the summer and autumn information data module and the winter and spring information data module are matched with the weather cache service module, and the remaining space condition of the airlines, flight numbers and spaces under each navigation department in the same weather state is calculated by using the following formula;
wherein
The average rest positions of lower routes, flight numbers and cabin positions of each navigation department under different weather conditions, M is a middle route of each navigation department,Seat numbers of different cabin levels under the flight number, and n is the number of days in which the same weather condition appears in summer, autumn or winter and spring.
Further, the future price data module stores the future price, and the future price judging step is as follows;
11) firstly, matching the acquired future daily flight space and price with the standard freight rate space price of the same route of the driver in the standard freight rate library;
12) preferentially judging whether the acquired price information is in a standard price range or not;
13) simultaneously, the data which are retained in the previous round of screening are met, and a unique 8-bit ID and a previous navigation department, a previous route, a previous flight number, a previous cabin and a previous price are randomly generated according to each navigation department, route, flight number and cabin, and are stored into the established summer and autumn information data module and the winter and spring information data module respectively on the condition of the travel date;
14) dividing the air states of all flight airports of each airline department in the future every day into the 15 state grades and storing the state grades into a weather cache service module;
15) in the future price zone, after the summer and autumn information data module and the winter and spring information data module store data, triggering the default base number 0 of a safety value (SF) generated by the module for each piece of data, wherein the safety value (SF) is used as a reference base number in an early warning mechanism to determine whether risk exists or not to give an early warning prompt;
16) when polling to the next round, after meeting the judgment standard, respectively matching the weather conditions of the travel date in the previous summer and autumn information data module and the previous winter and spring information data module, and comparing the prices of the department, the airline, the flight number, the cabin and the like in the previous summer and autumn information data module and the previous 7 days in the previous winter and spring information data module with the rest position and the punctuality rate;
17) the current price and the seat number are less than or equal to the average price and the rest number trained in the historical data module, the current price and the rest number are taken as safety data, and the safety value (SF) is adjusted by adding 1;
18) and after the N rounds of verification are carried out, the safety value (SF) is calculated according to the current-day historical record times and the current-day average occurrence times of the navigation department data, and the safety value (SF) is calculated when new data and old data are compared before the price cache is stored.
Further, the calculation formula of step 12 is (T-L < alpha-T) | (L/y ≦ T/y)
Wherein T is the corresponding cabin standard promulgated freight rate, and L is the acquired cabin price corresponding to the daily flight. Alpha is the standard published freight rate of the last level of the corresponding cabin position, and Y is the published freight rate of the Y cabin of the airline department.
Further, the front part (T-L < alpha-T) judges whether the difference between the two is less than the difference between the last level and the current level of the compartment, and the discount between the price of the L and the standard Y compartment in the second half (L/Y is less than or equal to the discount between the published freight rate of the compartment and the standard Y compartment is required to be met. Thereby determining that the price falls within the set. Otherwise, the abnormal price is screened out for the first time in the calculation of the current round.
Further, when the prices of the same department, airline, cabin and travel date in the summer and autumn information data module and the winter and spring information data module are higher than the current price found in polling, the prices are regarded as the price change condition, the summer and autumn information data module and the winter and spring information data module which enter the historical data module again are matched with the same condition to determine whether the same price exists, if the same price exists, the real price is regarded, but the safety value (SF) is directly reduced to 0, and early warning is pushed; the prices of the same navigation department, air route, cabin space and travel date in the summer and autumn information data module and the winter and spring information data module are lower than the current price found in the polling, the prices are also considered to be changed, the prices are not compared with the historical data module at this time, the safety value (SF) is directly reduced to 0, and the warning is directly pushed.
The invention provides another technical scheme for realizing the technical scheme as follows:
a computer readable storage medium, on which a computer program is stored, and when being executed by a processor, the computer program implements the steps of any of the above-mentioned technical solutions of the method for automatically updating the freight rate based on OTA platform data delivery.
Compared with the prior art, the invention has the beneficial effects that:
1. the automatic freight rate updating method and the storage medium based on OTA platform data delivery provided by the invention have the advantages that after the automatic optimization, the freight rate is added or reduced according to the user setting rule, the freight rate is automatically updated after the change, the accuracy and timeliness of data delivery are improved, the manual delivery efficiency is reduced, the complexity and timeliness of data delivery are reduced, the task system can be automatically matched for delivery only by setting the rule in advance, and the working efficiency and the accuracy are greatly improved.
2. According to the OTA platform data release-based freight rate automatic updating method and the storage medium, the system is distributed by multiple servers, and is subjected to uninterrupted analysis and early warning in 24 hours in a circulating mode, compared with the existing data, the method is more intelligent and accurate, the agent price dynamic state is reminded, the future price trend is predicted by big data, the agent is reminded, the economic loss is reduced, the method is suitable for multi-scene application, the multi-terminal acquisition is more timely, the data operation is more purposeful, and the benefit maximization is increased.
Drawings
FIG. 1 is a schematic flow chart of the method for automatically updating the freight rate based on OTA platform data delivery according to the present invention;
FIG. 2 is a schematic diagram of a storage module of the storage medium of the present invention;
FIG. 3 is a flowchart illustrating a future price determining step of the storage medium according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the method for automatically updating the freight rate based on OTA platform data delivery,
1) automatically acquiring freight rate data of each B2B platform through API full volume or increment,
2) after splitting the acquired original data, storing the original data in a cache container by taking a platform name, a navigation department, a airline, a flight number and a cabin space as KEY, and marking the data needing to be updated or deleted in different states.
3) The system automatically updates the selected channel of the user in real time, acquires all effective data in the cache container according to the channel selected by the user, splits and compares the data, selects the optimal price data by taking the navigation department, the date, the flight number and the space as units, automatically updates the optimal pricing and price reduction rules, calculates the optimal freight rate rules according to the navigation department, the airline, the date, the flight number, the space and the like, and recommends the best freight rate rules to the client, so that the client can determine the freight rate rules or set the freight rate rules by himself or herself.
Setting a flight number and a flight date and adding Y elements, wherein Y is a directly added RMB and then numerical value; for example, if the user sets the flight number CZ555 for sale of 10 money added to the 12 th number of the flight number, the system automatically matches all the data meeting the flight number and date requirements in the data, adds 10 money to the original freight rate, and sets the return point pricing algorithm as Y1＝Y2-Y2*X％，Y2And X is the number of points added according to the return points for the original freight rate of a certain compartment, if the customer selects 1 point added according to the return points, the points are converted into money through calculation (a calculation rule: the freight rate of the corresponding compartment FD is the return points%) and added or subtracted, for example, if the original freight rate of the compartment Y is 1000, the calculation mode is 1000 plus 1% — 990.
4) The system organizes the adjusted freight rate data into a corresponding message through a format required by the API and uploads the message to the OTA platform, and the system records the current uploading time after the uploading is successful.
5) The system executes an increment timing updating task once every 1-3 minutes, updates the situation of increasing, modifying or deleting the freight rate of a data source channel, firstly obtains the time point of the last updating record of the user from a database, obtains all data changed in the time through the time point, and then performs operations of increasing, modifying and deleting the freight rate data through the API of the OTA through the matching of the optimal matching rule and the charging and discharging rules set by the user;
6) and 1-5, directly and automatically extracting all original data from the storage medium, automatically transmitting future data to the storage medium in real time, and automatically updating the storage medium in real time.
Channel data sources such as butt joint B2B, B2C, own are compared the optimum automatically and are put in according to user's setting rule with the price or reduce the price, have the change to update automatically, improve the accuracy and the promptness that the data was put in, reduce artifical input inefficiency, the loaded down with trivial details and the promptness of artifical input data, only need set up the rule in advance, start the task system and will match the input automatically, greatly improved work efficiency and accuracy.
All data sources in the steps 1-5 comprise a database module and a weather cache service module; the database module carries out classified storage on a cabin FD freight rate data module, a historical price data module, a summer and autumn information data module, a winter and spring information data module, a historical punctuality rate data module and a future price data module:
the storage method of the storage medium is to obtain the current cabin space of each air route sold by each navigation department and the externally published cabin space FD freight rate of each navigation department and put the FD freight rate data module of the cabin space as the standard freight rate.
And importing the cabin level, the price and the surplus situation of each airline every day of flight in three years of history of each department as historical price data into a historical price data module.
And dividing historical data of each navigation department in the historical price database into a summer and autumn information data module and a winter and spring information data module according to the cabin space level, the price and the remaining space condition of the air route.
The weather states of all flights and airports of each department for three years in history can be divided into 15 (thunderstorm, lightning, typhoon, dense fog, sand storm, sunny, cloudy, thunderstorm, rainstorm, snowfall, heavy snow, snowstorm, cloudy, haze and sleet) state levels and stored in the weather cache service module.
And importing all flight taking-off and landing moments of all flights of each navigation department for three years in history into a historical quasi-point rate data module as historical quasi-point rate data.
And respectively storing the historical punctual rates of all navigation departments into a summer and autumn information data module and a winter and spring information data module.
And the airline flight space level, price and remaining space conditions of the summer and autumn information data module and the winter and spring information data module are matched with the weather cache service module.
And calculating the remaining positions of the lower routes, the flight numbers and the cabins of each driver under the same weather condition according to the following formula.
Wherein
The average rest positions of the airlines, flight numbers and spaces under each navigation department in different weather states, M is the seat position of different space levels under the central airlines and flight numbers of each navigation department, and n is the number of days in which the same weather state appears in the days of the summer and autumn information data module and the winter and spring information data module.
And calculating the punctuation rate of the lower air route, the flight number and the cabin of each navigation department under the same weather condition according to the formula.
And generating a surplus position change model which takes the KEY, the air route, the flight number and the cabin position as the only reference to train according to the weather cache and the historical punctuation rate in the historical price data summer and autumn information data module and the winter and spring information data module.
And (5) producing price and residual position change models under different weather states according to the neural network training model.
And taking the conditions of the cabin, the price and the surplus of each daily flight of each future department of each department of the navigation as a future price data module by polling.
The future price judgment method comprises the following steps:
the acquired future daily flight space and price are first matched prior to the standard freight rate space price of the same route of the flight department in a standard freight rate warehouse (FD).
Preferentially judging whether the acquired price information is in a standard price range or not according to the following comparison formula,
(T-L＜α-T)||(L/y≤T/y)
wherein T is the corresponding cabin standard promulgated freight rate, and L is the acquired cabin price corresponding to the daily flight. Alpha is the standard published freight rate of the last level of the corresponding cabin, Y is the published freight rate of the Y cabin of the airline department, the front part (T-L < alpha-T) judges whether the difference between the two is less than the difference between the last level of the cabin and the current level, and the discount of the price of the L and the standard Y cabin which needs to meet the second half (L/Y is less than or equal to T/Y) is less than or equal to the discount of the published freight rate of the cabin and the standard Y cabin. Thereby determining that the price falls within the set. Otherwise, the abnormal price is screened out for the first time in the calculation of the current round.
Meanwhile, the data retained in the previous round of screening is met, and a unique 8-bit ID is randomly generated according to each navigation department, airline, flight number and cabin position, and the navigation department, airline, flight number, cabin position and price which are in front of the unique ID are stored into the established summer and autumn information data module and winter and spring information data module respectively under the condition of the travel date.
And dividing the airport weather states of all flights of each airline department in the future into the 15 state grades and storing the state grades into a weather cache service module.
In the future price zone, after the summer and autumn information data module and the winter and spring information data module store data, the default cardinality 0 of a safety value (SF) generated by the modules for each piece of data is triggered.
The safety value (SF) is used as a reference base in an early warning mechanism to determine whether risks exist or not to give an early warning prompt.
When polling is carried out in the next round, after the calculation of (T-L < alpha-T) | (L/y is less than or equal to T/y), the prices of the weather conditions respectively matched with the travel date in the previous summer and autumn information data module and the previous 7 days and the prices of the department, the airline, the flight number, the cabin and the like in the winter and spring information data module are compared with the rest position and the standard point rate conditions.
And the current price and the seat number are less than or equal to the average price and the rest number trained in the historical data module, the average price and the rest number are taken as safety data, and the safety value (SF) is adjusted by adding 1.
After the N rounds of verification, the safety value (SF) is calculated according to the current-day historical record times and the current-day average occurrence times of the navigation data, and the safety value (SF) is calculated when new data and old data are compared before the price cache is stored.
In some cases, when the current price obtained in polling is found to be lower than the prices of the same department, airline, cabin space and travel date in the existing summer and autumn information data module and winter and spring information data module, the current price is considered as a price change condition, whether the same condition matched in the summer and autumn information data module and the winter and spring information data module which enter the historical data module again is the same or not is judged, if the price is the same, the real price is considered, but the safety value (SF) is directly reduced to 0, and early warning is pushed.
When polling again until the price is still the same, the security value is adjusted up to 1, but no other security value (SF) calculation is performed this time, and the warning is pushed again.
When the price of the third polling is still the same, the third polling is calculated according to the normal daily historical record times and the average occurrence times of the daily navigation data, and the calculation is carried out when the new data and the old data are compared before the storage and the caching. This price is judged to be valid.
In some cases, when the current price obtained in polling is found to be higher than the prices of the same department, airline, cabin space, and travel date in the existing summer, autumn, summer, and autumn information data modules and winter and spring information data modules, which are also considered as price changes, this time will not be compared with the historical data module any more, and the safety value (SF) is directly lowered to 0, and the warning is directly pushed.
Also in some cases, in the polling process, the cabin space of the travel date, which is not the same as the cabin space of the summer and autumn information data module and the winter and spring information data module, appears in the navigation department and the air route, the cabin space is directly identified as an invalid state, and early warning is pushed.
In some cases, in the polling process, when the travel date relates to the time period of legal holidays, the frequent condition of price floating is more likely to occur due to the sharp increase of flight demand of holiday travel, and the acquired number of cabin equinox is necessarily larger than the calculated surplus number under the state of the same weather historical data (W).
Meanwhile, the corresponding real-time spare bit condition can be obtained through an Internet Booking Engine (IBE) of the Chinese navigation message, and the spare bit data searched in the polling is corrected more accurately. IBE is an open platform technology based on the internet, which provides a way for various user application systems to access the traditional Chinese airline business system, and is an interface using an api (application Programming interface) mode. The system provides a full-flow solution for the navigation department and the navigation department agent, and has services of inquiry, reservation, ticket drawing, ticket returning and changing, basic data and the like.
In some cases, in analyzing the future price trend, the price trends of the historical data module in summer and autumn and the price trends of the historical data module in winter and spring are compared with the future price trend, and when 80% of the prices of the historical data module are lower than the future price, the historical data module is used as an early warning.
The system has the advantages that the multi-server distribution type and 24-hour circulation uninterrupted analysis and early warning are realized, compared with the existing data, the data are more intelligent and accurate, the agent price dynamic state is reminded, the future price trend of big data prediction is reminded, the agent is reminded, the economic loss is reduced, the system is suitable for multi-scene application, the multi-terminal acquisition is more timely, the data operation is more purposeful, and the benefit maximization is increased.
The invention provides another technical scheme for realizing the technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for automatically updating a freight rate.
In summary, the following steps: the invention relates to an automatic freight rate updating method and a storage medium based on OTA platform data release, the automatic freight rate updating method based on OTA platform data release can be butted with data sources of B2B, B2C, own channels and the like, automatically compares the optimal data sources, adds or reduces the price according to the rules set by a user, automatically updates the data sources with change, improves the accuracy and timeliness of data release, reduces the low efficiency of manual release, the complexity and timeliness of the data release, only needs to set the rules in advance, automatically matches and releases by starting a task system, greatly improves the working efficiency and the accuracy, and the storage medium based on the automatic freight rate updating method is matched with a multi-server distributed system to continuously analyze and early warn in 24 hours in a circulating way, is more intelligent and accurate than the current data, reminds the price dynamic state of an agent, predicts the price trend of the future by big data, reminds the agent, and reduces the economic loss, the method is suitable for multi-scene application, and can achieve more timely acquisition of multiple terminals, more purposeful data operation and maximum benefit increase.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (10)
1. The method for automatically updating the freight rate based on OTA platform data delivery is characterized by comprising the following steps:
1) automatically acquiring freight rate data of each B2B platform through API full volume or increment;
2) after splitting the acquired original data, storing the original data in a cache container by taking a platform name, a navigation department, a airline, a flight number and a cabin space as KEY, and marking the data needing to be updated or deleted in different states;
3) the system automatically updates the selected channel of the user in real time, acquires all effective data in the cache container according to the channel selected by the user, splits and compares the data, selects the optimal price data by taking the navigation department, the date, the flight number and the space as units, automatically updates the optimal pricing and price reduction rule, and calculates the optimal freight rate rule according to the navigation department, the airline, the date, the flight number, the space and the like;
4) the system organizes the adjusted freight rate data into a corresponding message through a format required by an API (application program interface) and uploads the message to an OTA (over the air) platform, and the system records the current uploading time after the uploading is successful;
5) the system executes an increment timing updating task once every 1-3 minutes, updates the situation of increasing, modifying or deleting the freight rate of a data source channel, firstly obtains the time point of the last updating record of the user from a database, obtains all data changed in the time through the time point, and then performs operations of increasing, modifying and deleting the freight rate data through the API of the OTA through the matching of the optimal matching rule and the charging and discharging rules set by the user;
6) and 1-5, directly and automatically extracting all original data from the storage medium, automatically transmitting future data to the storage medium in real time, and automatically updating the storage medium in real time.
2. The OTA platform data placement-based freight rate automatic updating method according to claim 1, wherein the method for setting freight rate in step 3;
31) setting a flight number and adding Y elements to the flight number, wherein Y is a directly added RMB and then a numerical value;
32) setting the price-adding algorithm according to the return point as Y1＝Y2-Y2*X％，Y2The original freight rate of a certain cabin is X, and the number of points is added according to the return points.
3. The OTA platform data placement-based freight rate automatic updating method according to claim 1, wherein all data sources of steps 1-5 include a database module and a weather cache service module;
the database module stores the cabin FD freight rate data module, the historical price data module, the summer and autumn information data module, the winter and spring information data module, the historical punctuality rate data module and the future price data module in a classified manner;
the weather cache service module is used for storing the airport weather states of all flights in three years of history of each department.
4. The OTA platform data launch-based freight rate automatic updating method according to claim 3, wherein the cabin FD freight rate data module is used for acquiring the cabin of each current airline sold by each department of aviation and the cabin FD freight rate published by each department of aviation to be put into the database as the standard freight rate;
the historical price data module is used for storing the data of the class level, the price and the remaining situation of each airline of each historical three-year flight of each department;
the summer and autumn information data module and the winter and spring information data module are used for storing the summer and autumn information data module and the winter and spring information data module;
the historical punctuality rate data module is used for storing all flight taking-off and landing moments of all flights of each driver for three years in history as historical punctuality rate data;
the future price data module is used for storing and polling to obtain the daily flight space, price and surplus of each future department of each department.
5. The OTA platform data launch-based freight rate automatic updating method according to claim 4, wherein the airline flight space level, price and remaining space conditions of the summer and autumn information data module and the winter and spring information data module are matched with the weather cache service module, and the remaining space conditions of the airline, flight number and space under each navigation department in the same weather state are calculated by using the following formula;
wherein
The average rest positions of the airlines, flight numbers and spaces under each navigation department in different weather states, M is the seat position of different space levels under the central airlines and the flight numbers of each navigation department, and n is the number of days in summer, autumn or winter and spring with the same weather state.
6. The OTA platform data launch-based freight rate automatic updating method as claimed in claim 4, wherein the future price data module stores the future price, and the future price judgment steps are as follows;
11) firstly, matching the acquired future daily flight space and price with the standard freight rate space price of the same route of the driver in the standard freight rate library;
12) preferentially judging whether the acquired price information is in a standard price range or not;
13) simultaneously, the data which are retained in the previous round of screening are met, and a unique 8-bit ID and a previous navigation department, a previous route, a previous flight number, a previous cabin and a previous price are randomly generated according to each navigation department, route, flight number and cabin, and are stored into the established summer and autumn information data module and the winter and spring information data module respectively on the condition of the travel date;
14) dividing the air states of all flight airports of each airline department in the future every day into the 15 state grades and storing the state grades into a weather cache service module;
15) in the future price zone, after the summer and autumn information data module and the winter and spring information data module store data, triggering the default base number 0 of a safety value (SF) generated by the module for each piece of data, wherein the safety value (SF) is used as a reference base number in an early warning mechanism to determine whether risk exists or not to give an early warning prompt;
16) when polling to the next round, after meeting the judgment standard, respectively matching the weather conditions of the travel date in the previous summer and autumn information data module and the previous winter and spring information data module, and comparing the prices of the department, the airline, the flight number, the cabin and the like in the previous summer and autumn information data module and the previous 7 days in the previous winter and spring information data module with the rest position and the punctuality rate;
17) the current price and the seat number are less than or equal to the average price and the rest number trained in the historical data module, the current price and the rest number are taken as safety data, and the safety value (SF) is adjusted by adding 1;
18) and after the N rounds of verification are carried out, the safety value (SF) is calculated according to the current-day historical record times and the current-day average occurrence times of the navigation department data, and the safety value (SF) is calculated when new data and old data are compared before the price cache is stored.
7. The OTA platform data placement-based freight rate automatic updating method according to claim 6, wherein the calculation formula of step 12 is (T-L < α -T) | (L/y ≦ T/y)
Wherein T is the corresponding cabin standard promulgated freight rate, and L is the acquired cabin price corresponding to the daily flight. Alpha is the standard published freight rate of the last level of the corresponding cabin position, and Y is the published freight rate of the Y cabin of the airline department.
8. The OTA platform data placement-based freight rate automatic updating method as claimed in claim 7, wherein (T-L < α -T) the front part is to determine whether the difference between the two is smaller than the difference between the previous level and the current level of the bay, and the discount between the price of the second part (L/Y ≦ T/Y) L and the standard Y bay is required to be less than or equal to the published freight rate of the bay and the standard Y bay discount; therefore, the price is judged to be in the range of the set, otherwise, the abnormal price is screened out for the first time in the calculation of the current round.
9. The OTA platform data launch-based freight rate automatic updating method according to claim 8, wherein the prices of the same department, airline, cabin space, and travel date in the summer and autumn information data module and the winter and spring information data module are higher than the current price found in the polling, and the prices are considered to be changed, and the summer and autumn information data module and the winter and spring information data module which enter the history data module again are matched with the same conditions, and whether the same price is found, if the same price is found, the real price is considered, but the safety value (SF) is directly reduced to 0, and an early warning is pushed;
the prices of the same navigation department, air route, cabin space and travel date in the summer and autumn information data module and the winter and spring information data module are lower than the current price found in the polling, the prices are also considered to be changed, the prices are not compared with the historical data module at this time, the safety value (SF) is directly reduced to 0, and the warning is directly pushed.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the OTA platform data placement based rate automatic updating method according to any of the claims 1 to 9.
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