Air market space insight method, apparatus, device, storage medium and product

Abstract

The application discloses an aviation market space insight method, device, equipment, storage medium and product, relates to the technical field of big data, and comprises the following steps: determining two target regions to be observed and a time period to be observed; acquiring base station data of the two target regions in the time period to be observed; when one or more users have base station data in the two target regions in the time period to be observed, the user is regarded as a target user; determining target data of the target user based on target base station data of the target user, wherein the target data is used for reflecting travel characteristics of the target user; and obtaining an aviation market insight result based on the target data. The application can efficiently and quickly determine the target user who has personnel flow in the two target regions, and then efficiently realize aviation market insight based on data generated by the target user, thereby improving the data processing and analysis efficiency for aviation market insight.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, apparatus, equipment, storage medium and product for spatial insight into the aviation market. Background Technology

[0002] Against the backdrop of rapid development in the civil aviation industry, a precise grasp of aviation market demand and a comprehensive understanding of passenger migration patterns are crucial for airlines' strategic planning, flight scheduling, and marketing. However, achieving this goal highly depends on the collection and analysis of massive amounts of multi-dimensional data.

[0003] Currently, the civil aviation industry mainly relies on its own data systems to collect and analyze passenger information, including passenger identity information, ticket purchase records, flight status, passenger preferences, etc. This data can, to some extent, reflect the changing trends of passenger travel behavior and market demand.

[0004] However, faced with complex and diverse data samples, conventional technologies encounter significant challenges in coordinating and utilizing data, struggling to quickly and accurately filter out information crucial for insights into the aviation market. This bottleneck in data processing efficiency not only slows down the identification of market trends but also affects airlines' ability to respond rapidly to market opportunities and risks. Therefore, improving the efficiency of data processing and analysis for aviation market insights has become a critical issue that urgently needs to be addressed.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a method, apparatus, device, storage medium, and product for spatial insight into the aviation market, aiming to solve the technical problem of how to improve the efficiency of data processing and analysis for insights into the aviation market.

[0007] To achieve the above objectives, this application proposes a method for spatial insight into the aviation market, the method comprising:

[0008] Identify the two target regions and the time period to be analyzed;

[0009] Acquire base station data for the two target regions within the time period to be observed;

[0010] When one or more users have base station data in both target areas during the period to be observed, the user is considered the target user.

[0011] Based on the target base station data of the target user, target data of the target user is determined, wherein the target data is used to reflect the travel characteristics of the target user;

[0012] Based on the target data, insights into the aviation market are obtained.

[0013] In one embodiment, the target data includes: a user profile; the step of determining the target data of the target user based on the target base station data of the target user includes:

[0014] The basic attribute data of the target user are obtained from the target base station data according to the preset tag template;

[0015] The user profile of the target user is obtained by inputting the basic attribute data into the tag template.

[0016] In one embodiment, the target data includes: travel mode; the step of determining the target user's target data based on the target user's target base station data includes:

[0017] The travel duration of the target user is determined based on the time points in the target base station data.

[0018] Obtain the travel distance of the target user;

[0019] The travel speed of the target user is obtained by dividing the travel distance by the travel duration.

[0020] The travel mode corresponding to the travel speed is determined according to a preset mapping relationship, wherein the mapping relationship is the correspondence between speed range and travel mode.

[0021] In one embodiment, the target data includes: travel trajectory; the step of determining the target user's target data based on the target user's target base station data includes:

[0022] The trajectory information in the target base station data is sorted in chronological order to obtain a trajectory sequence;

[0023] Traverse the trajectory sequence, compare the current trajectory information with the next trajectory information, and if the next trajectory information is not equal to the current trajectory information, update the next trajectory information to the current trajectory information;

[0024] If the next trajectory information is equal to the current trajectory information, and the time difference between the next trajectory information and the current trajectory information matches a preset time threshold, then the next trajectory information is updated to the current trajectory information.

[0025] After traversing the trajectory sequence, the travel trajectory of the target user is obtained.

[0026] In one embodiment, the target data includes: travel purpose; the step of determining the target user's target data based on the target user's target base station data includes:

[0027] The location information in the target base station data is classified into daytime location information and nighttime location information according to a preset time threshold;

[0028] The travel purpose of the target user is determined based on the daytime location information and the nighttime location information.

[0029] In one embodiment, the target data includes: travel preferences; the step of determining the target user's target data based on the target user's target base station data includes:

[0030] Match the target user's target flight information with the time and location information in the target base station data;

[0031] Obtain other flight information similar to the target flight information, wherein the transit locations of the other flight information include the transit locations of the target flight information;

[0032] The travel preferences of the target user are determined based on the target flight information and the other flight information.

[0033] Furthermore, to achieve the above objectives, this application also proposes an aviation market spatial insight device, which includes:

[0034] The insight determination module is used to determine the two target regions to be analyzed and the time period to be analyzed.

[0035] The acquisition module is used to acquire base station data for the two target areas within the time period to be observed.

[0036] The target determination module is used to identify one or more users as target users when base station data exists in both target areas during the time period to be observed.

[0037] The data determination module is used to determine the target data of the target user based on the target base station data of the target user, wherein the target data is used to reflect the travel characteristics of the target user;

[0038] The results module is used to obtain aviation market insights based on the target data.

[0039] In addition, to achieve the above objectives, this application also proposes an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the aviation market space insight method as described above.

[0040] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the aviation market spatial insight method described above.

[0041] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the aviation market space insight method described above.

[0042] This application provides a method for spatial insight into the aviation market. First, it identifies two target regions to be analyzed and a time period for the insight. Then, it acquires base station data for each of the two target regions within the insight period. If one or more users have base station data in both target regions within the insight period, it indicates that the user appeared in both target regions during the insight period, and this user is designated as the target user. Based on the target user's target base station data, the target user's target data can be determined. The target data reflects the user's travel characteristics. Based on this target data, aviation market insight results can be obtained, and airlines can adjust their market strategies accordingly.

[0043] In summary, this application filters base station data by setting two target regions and a time period for analysis. It identifies base station data of target users appearing in the two target regions during the analysis period. Compared to traditional methods that analyze aviation information data, this application filters the required target base station data from a large dataset, significantly reducing the computational load of data analysis. Furthermore, it efficiently and quickly identifies target users with population movement in the two target regions, thereby enabling efficient aviation market insights based on the data generated by these target users and improving the efficiency of data processing and analysis for aviation market insights. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1A flowchart illustrating the first embodiment of the aviation market spatial insight method of this application;

[0047] Figure 2 A schematic diagram of the travel mode calculation process provided for an embodiment of the aviation market spatial insight method of this application;

[0048] Figure 3 A schematic diagram of the travel trajectory calculation process provided for an embodiment of the aviation market spatial insight method of this application;

[0049] Figure 4 A schematic diagram of the trajectory update process provided for an embodiment of the aviation market spatial insight method of this application;

[0050] Figure 5 This is a schematic diagram of the module structure of the aviation market spatial insight device according to an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the aviation market space insight method in this application embodiment.

[0052] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0054] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application embodiment is as follows: determine two target regions and time periods to be analyzed; obtain base station data for the two target regions within the time period to be analyzed; when one or more users have base station data in both target regions within the time period to be analyzed, designate the user as the target user; determine the target data of the target user based on the target base station data of the target user, wherein the target data is used to reflect the travel characteristics of the target user; and obtain aviation market insight results based on the target data.

[0056] Against the backdrop of rapid development in the civil aviation industry, a precise grasp of aviation market demand and a comprehensive understanding of passenger migration patterns are crucial for airlines' strategic planning, flight scheduling, and marketing. However, achieving this goal highly depends on the collection and analysis of massive amounts of multi-dimensional data.

[0057] Currently, the civil aviation industry mainly relies on its own data systems to collect and analyze passenger information, including passenger identity information, ticket purchase records, flight status, passenger preferences, etc. This data can, to some extent, reflect the changing trends of passenger travel behavior and market demand.

[0058] However, faced with complex and diverse data samples, conventional technologies encounter significant challenges in coordinating and utilizing data, struggling to quickly and accurately filter out information crucial for insights into the aviation market. This bottleneck in data processing efficiency not only slows down the identification of market trends but also affects airlines' ability to respond rapidly to market opportunities and risks. Therefore, improving the efficiency of data processing and analysis for aviation market insights has become a critical issue that urgently needs to be addressed.

[0059] To address the aforementioned issues, this application provides a method for spatial insight into the aviation market. This application first identifies two target regions to be analyzed and the insight period to be observed. Then, it acquires base station data for each of the two target regions within the insight period. If one or more users have base station data in both target regions within the insight period, it indicates that the user appeared in both target regions during the insight period, and this user is designated as the target user. Based on the target user's target base station data, the target user's target data can be determined. The target data reflects the user's travel characteristics. Based on this target data, aviation market insight results can be obtained, allowing airlines to adjust their market strategies accordingly.

[0060] In summary, this application filters base station data by setting two target regions and a time period for analysis. It identifies base station data of target users appearing in the two target regions during the analysis period. Compared to traditional methods that analyze aviation information data, this application filters the required target base station data from a large dataset, significantly reducing the computational load of data analysis. Furthermore, it efficiently and quickly identifies target users with population movement in the two target regions, thereby enabling efficient aviation market insights based on the data generated by these target users and improving the efficiency of data processing and analysis for aviation market insights.

[0061] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses an electronic device as an example to illustrate this embodiment and the subsequent embodiments.

[0062] Based on this, embodiments of this application provide a method for spatial insight into the aviation market, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the aviation market spatial insight method of this application.

[0063] In this embodiment, the aviation market spatial insight method includes steps S10 to S50:

[0064] Step S10: Determine the two target regions to be observed and the time period to be observed;

[0065] In this embodiment, in order to reduce the amount of data to be calculated and improve the targeting of data insights, the target scope of the analysis is first defined, that is, two specific geographical regions are selected as the areas to be analyzed, and a specific time period is determined as the time frame for analysis.

[0066] Step S20: Obtain base station data for the two target areas within the time period to be observed;

[0067] In this embodiment, after determining the insight time period and two target regions, all base station data for the two target regions within the selected time period are extracted from the mobile operator's database.

[0068] Step S30: When one or more users have base station data in both target areas within the time period to be observed, the user is taken as the target user.

[0069] In this embodiment, by comparing and analyzing base station data, users who have activity records in both target areas within a selected time period are selected as target users. By using the filtering conditions, one or more users who travel between the two target areas within a selected time period can be selected.

[0070] Step S40: Based on the target base station data of the target user, determine the target data of the target user, wherein the target data is used to reflect the travel characteristics of the target user;

[0071] In this embodiment, the base station data of the target user is further analyzed to extract key information reflecting their travel characteristics, such as basic attribute data, travel mode, specific travel trajectory, travel purpose, flight preferences, and customer preferences. Among these, the basic attribute data can be used to generate user profiles to attract customers through targeted advertising in the aviation market; the travel mode and specific travel trajectory can be used to generate travel profiles to provide a basis for decision-making in the aviation market to increase or decrease routes; the travel purpose can be used to provide data reference for aviation-hotel-catering services; and flight preferences and customer preferences can be used to recommend preferred flight information to the target user.

[0072] In one feasible implementation, the target data includes: user profile; step S40 may include steps A10 to A20:

[0073] Step A10: Obtain the basic attribute data of the target user from the target base station data according to the preset tag template;

[0074] In this embodiment, a predefined tag template is used, which contains various tags to describe user characteristics, such as age, gender, occupation, and city of residence. Then, basic attribute information related to the target user is extracted from the target base station data. This information corresponds to the tags in the tag template. For example, the base station data includes the user's identity information, which includes the user's age, gender, address, and other information.

[0075] Step A20: Input the basic attribute data into the tag template to obtain the user profile of the target user.

[0076] In this embodiment, the acquired basic attribute data is filled into the tag template. Through algorithm or manual review, a detailed user profile is generated for each target user based on the matching degree and logical relationship of the data. This user profile not only includes the user's basic attributes, but may also include deeper user characteristics inferred from these attributes, such as the user's travel preferences and spending power.

[0077] Specifically, based on location data, user attribute data, and user group data using target tags to identify user groups provided by mobile operators, user profiles are created. These user profiles include: basic attribute tags, travel preference tags, and travel value tags. Combining this with the actual business scenario of flight route planning, correlation analysis is performed on mobile operator CRM (Customer Relationship Management), location, SMS, call, and internet access data to form a basic tagging system. This system primarily covers users' basic attributes, travel preferences, travel value, and enterprise tags, and is used for customer profile construction and target customer selection for precision marketing.

[0078] For example, mobile operator CRM, location, SMS, call, and internet access data can be input into a pre-set tag template to analyze users' basic attribute tags, travel preferences, and travel value. The analysis logic is as follows:

[0079] Basic attribute tags: By associating basic attribute data of mobile operators with signaling data, we can determine the user's age, gender, city of origin, city of residence, occupation, etc.

[0080] Travel preferences: By linking basic attribute data and signaling data of mobile operators' users, we can analyze users' travel modes, airlines, flight segments, etc.

[0081] Travel Value: By linking basic attribute data of mobile operator users with signaling data, we can analyze passenger value, loyalty, credit status, etc.

[0082] The final output will be the user profile, including the following Tables 1, 2, and 3:

[0083] Table 1 Basic Attribute Labels

[0084]

[0085]

[0086] Table 2 Travel Preferences

[0087]

[0088]

[0089] Table 3. Travel Value

[0090]

[0091]

[0092] In this implementation, user profiles are created using basic attribute data. By incorporating these user profiles as part of the target data, the entire analysis process not only provides insights into macro trends in the aviation market based on user geographic location data but also delves into the individual user level, understanding the travel needs and behavioral patterns of different user groups. This granular analysis helps airlines, airports, and other related companies more accurately target customer groups, develop personalized marketing strategies and service plans, thereby improving customer satisfaction and market competitiveness.

[0093] In another feasible implementation, the target data includes: travel mode; step S40 may include steps B10 to B40:

[0094] Step B10: Determine the travel duration of the target user based on the time points in the target base station data;

[0095] In this embodiment, we first need to extract time point information from the target base station data. These time points may represent the moments when the user interacts with the base station, such as the time when the mobile signal switches base stations. By calculating the differences between these time points, we can obtain the user's travel duration. For example, if a user moves from one base station area to another, the difference between the time of the last communication with the first base station and the time of the first communication with the second base station is the approximate travel duration for the user.

[0096] Step B20: Obtain the travel distance of the target user;

[0097] In this embodiment, after determining the duration of the user's trip, it is also necessary to determine the target user's travel distance. There are many ways to determine the travel distance. For example, the shortest distance from one base station to another can be calculated as the travel distance. Alternatively, the user's movement trajectory (which can be obtained from multiple base station data points) can be combined with the API (Application Programming Interface) provided by the map service to calculate a more accurate travel distance.

[0098] Step B30: Divide the travel distance by the travel time to obtain the travel speed of the target user;

[0099] In this embodiment, the user's travel speed is obtained by dividing the travel distance by the travel time. The resulting speed value helps us understand the user's movement speed, whether they are walking, cycling, driving, or using public transportation.

[0100] Step B40: Determine the travel mode corresponding to the travel speed according to a preset mapping relationship, wherein the mapping relationship is the correspondence between speed range and travel mode.

[0101] In this embodiment, the user's travel mode is determined according to a preset mapping relationship, which is a correspondence between a speed range and a travel mode.

[0102] For example, to help understand the implementation process of the aviation market spatial insight method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 2 , Figure 2 A simplified flowchart illustrating a method for spatial insights into the aviation market is provided, specifically:

[0103] First, based on the desired time and location, filter the mobile phone number, travel time, and departure point of the traveler; then manage the location and city code table to find the longitude and latitude information of the departure city and destination city; then use the longitude and latitude of the two places to calculate the spatial coordinates of the two places, and then calculate the distance between the two points using a specific formula. When calculating the distance, the Haversine formula can be used, which is a formula that can be used to determine the great circle distance between two points on Earth (that is, the distance of the shortest path on the sphere). The basic steps of distance calculation are: (1) Obtain the longitude and latitude coordinates of the two cities. Usually, these coordinates are given in decimal form, such as 40.7128 degrees north latitude and 74.0060 degrees west longitude; (2) Convert all the longitude and latitude from decimal to radians, because most trigonometric functions use radians instead of degrees when calculating. The conversion formula is: radians = degrees × π / 180 degrees; (3) Calculate the difference in latitude and longitude between the two locations, apply the sine function to these two differences and calculate the square, calculate the average latitude of the two locations and apply the cosine function to this value, combine the above results and apply the square root function. Multiply the above results by the Earth's radius (usually the average radius is about 6,371 kilometers) to get the great circle distance between the two points;

[0104] Then, the mode of transportation is determined by travel speed. Travel speed = distance between two locations / (travel end time - travel start time), where the travel start time is the last time the user's signal disappears from the base station at the departure point, and the travel end time is the first time the user's signal appears at the base station at the destination. Then, the speed range is determined, and travel speeds are categorized as greater than or equal to 300 km / h, [200, 300) km / h, [120, 200) km / h, [80, 120) km / h, and others, thus corresponding to various modes of transportation such as air travel, high-speed rail travel, train travel, and car travel.

[0105] When calculating distances, map APIs (such as Google Maps API, Amap API, etc.) can be used to obtain more accurate route and distance information. For transportation mode identification, additional information can be provided to make the travel mode judgment more accurate. For example, information on user interactions with base stations can be supplemented to determine whether the user has been traveling along railway lines for an extended period, thus better distinguishing between train and car travel.

[0106] In this embodiment, by calculating the user's travel speed, the user's travel mode can be inferred from the target base station data.

[0107] In another feasible implementation, the target data includes: travel trajectory; step S40 may include steps C10 to C40:

[0108] Step C10: Sort the trajectory information in the target base station data according to time order to obtain a trajectory sequence;

[0109] In this embodiment, trajectory information is extracted from the target base station data. This trajectory information typically includes the location and timestamp of the user's base station. All trajectory information is sorted according to the timestamp to ensure that each trajectory in the trajectory sequence is arranged in chronological order.

[0110] Step C20: Traverse the trajectory sequence, compare the current trajectory information with the next trajectory information, and if the next trajectory information is not equal to the current trajectory information, update the next trajectory information to the current trajectory information.

[0111] In this embodiment, the sorted trajectory sequence is traversed, and the current trajectory information is compared with the next trajectory information. If the position information of the next trajectory information is different from that of the current trajectory information, the next trajectory information is updated to the current trajectory information.

[0112] Step C30: If the next trajectory information is equal to the current trajectory information, and the time difference between the next trajectory information and the current trajectory information matches a preset time threshold, then the next trajectory information is updated to the current trajectory information.

[0113] In this embodiment, if the position information of the next trajectory information is the same as that of the current trajectory information, and the time difference between the two trajectory information matches a preset time threshold, then the next trajectory information is updated to the current trajectory information.

[0114] Step C40: After completing the traversal of the trajectory sequence, the travel trajectory of the target user is obtained.

[0115] In this embodiment, the travel trajectory of the target user can be obtained after traversing each trajectory information.

[0116] For example, to help understand the implementation process of the aviation market spatial insight method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart illustrating a method for spatial insights into the aviation market is provided, specifically:

[0117] When analyzing the travel trajectories of more than one user, a time series analysis is used to iterate through the trajectory information of all users. Starting from the earliest travel time of a user, the process iterates in chronological order. If the information for the next stage of the user's journey can be found, the corresponding time, city name, and other information are stored. This process continues until the last trajectory information for a user is retrieved, thus completing the trajectory identification for that user. This process is repeated until the trajectories of all users have been traversed.

[0118] When updating user tracking data, please refer to the following for details. Figure 4 , Figure 4 This is a schematic diagram of the trajectory update process involved in the embodiment of the aviation market spatial insight method of this application, such as... Figure 4 As shown, when updating the trajectory of a single user, the process first retrieves the user's last completed trajectory information (`latest`). Then, it retrieves all the user's signaling, call detail records (CDRs), and traffic data, sorting them in ascending time order. Next, it retrieves the information for the user's next trajectory (`next`) and checks if `latest` and `next` are the same. If they are not equal, it saves the last record of the previous position (`latest`) and the first record of the next position (`next`). Next is then updated to `latest`, retaining the first and last records with the time field set to `ctime`. The process of retrieving the information for the user's next trajectory (`next`) and checking if `latest` and `next` are the same is repeated. If they are equal, a time difference of 30 minutes is used, and next is updated to `latest`, retaining the first and last records with the time field set to `ctime`. The process of retrieving the information for the user's next trajectory (`next`) and checking if `latest` and `next` are the same is repeated. After completion, the `latest` data is updated (and `next` is updated to `latest`).

[0119] In this embodiment, determining the user's travel trajectory can provide a basis for decision-making regarding increasing or decreasing flight routes in the aviation market.

[0120] In another feasible implementation, the target data includes: travel purpose; step S40 may include steps D10 to D20:

[0121] Step D10: Classify the location information in the target base station data into daytime location information and nighttime location information according to a preset time threshold;

[0122] In this embodiment, the target base station data typically contains the user's location information at different times. The location information can be divided by setting a time threshold (for example, 6 a.m. to 6 p.m. is daytime, and 6 p.m. to 6 a.m. the next day is nighttime). Then, the location information in the target base station data is classified according to this time threshold. Daytime location information refers to the user's location during the daytime period, while nighttime location information refers to the user's location during the nighttime period.

[0123] Step D20: Determine the travel purpose of the target user based on the daytime location information and the nighttime location information.

[0124] In this embodiment, after obtaining the target user's daytime and nighttime location information, we can further analyze this information to determine the user's travel purpose. For example, if the user's daytime location information is mainly concentrated in commercial or office areas, while the nighttime location information is concentrated in hotels, we can preliminarily infer that the user's travel purpose may be a business trip. Conversely, if the user's daytime and nighttime location information are both concentrated in tourist or leisure areas, we can infer that the user's travel purpose may be leisure or tourism.

[0125] Through the model capability analysis described above, we can answer questions about users' origin, destination, mode of transportation choice, and travel purpose. The output data can serve as a general basis for understanding the spatiotemporal space of flight routes; the mode of transportation choices for population migration can serve as a basis for understanding and analyzing the market space of flight routes. For example, a large number of local passengers arrive in city A by air, and some then travel to the nearby city B by ground transportation within a short period of time. In this case, assessing whether to increase, reduce, or optimize local routes to city A based on passenger volume, and planning direct local flights to city B, provides a very solid basis and has significant market potential.

[0126] In another feasible implementation, the target data includes: travel preferences; step S40 may include steps E10 to E30:

[0127] Step E10: Match the target user's target flight information with the time and location information in the target base station data;

[0128] In this embodiment, the time and location information in the target base station data are used in combination with the flight database to attempt to match the target user's possible flight information. The matching process includes a comprehensive comparison of information such as time, location and flight number.

[0129] Step E20: Obtain other flight information similar to the target flight information, wherein the transit points of the other flight information include the transit points of the target flight information;

[0130] In this embodiment, after determining the target flight information, information on other flights similar to it is further obtained. Similarity may include flight time, take-off and landing locations, transit points, flight type (such as direct or connecting flights), etc. In particular, the transit point feature is important because the transit point often reflects the flight's route selection and coverage area, and is an important basis for judging flight similarity.

[0131] Step E30: Determine the travel preferences of the target user based on the target flight information and the other flight information.

[0132] In this embodiment, after obtaining the target flight information and similar flight information, we can determine the travel preferences of the target users by comparing and analyzing the characteristics and differences of these flights. Travel preferences may include flight price preferences, flight time preferences (such as early morning flights vs. late evening flights), flight type preferences (such as direct flights vs. connecting flights), and transit point preferences (such as preferring to pass through certain specific cities or regions). This preference information is of great value to airlines because it reflects the travel needs and preferences of target users, providing a basis for airlines to develop more precise marketing strategies and service plans.

[0133] Specifically, a flight matching model is implemented by integrating data from the civil aviation industry and analysis results from mobile operators. If the time interval between a user's phone being switched off in the departure city and the actual flight departure is within 30 minutes, and the time interval between the phone being switched on in the destination city and the actual flight landing is within 10 minutes, then the user is considered to be on that flight. This allows the system to obtain the user's flight information.

[0134] In addition, analyzing passengers' air travel records over the past year can reveal users' preferences when choosing flights. The calculation logic for these preferences is as follows:

[0135] Time-sensitive passenger selection rules: Passengers who choose flights with good time slots when there are two or more flights on a route.

[0136] Service-sensitive passenger selection rules: Passengers who choose the higher-priced flight when there are two or more flights with different prices at the same time.

[0137] Price-sensitive passenger selection criteria: Passengers who have two or more flights on a route with at least one departure time, or passengers who have flights with good departure times but still choose to forgo flights with good departure times and opt for lower-priced flights.

[0138] In another example, by generating tags, analyzing user migration, and assessing customer preferences, passenger flow across various modes of transportation, including air, ground transportation (rail, road), and air-ground intermodal transport, can be visualized. Furthermore, a 360-degree travel profile of passengers can be created, supporting analysis of air, rail, and road passenger flow on routes A and B, as well as analysis of passenger origin attributes and their willingness to travel by air, thereby achieving the goal of gaining insights into the aviation market. For instance, if passengers on a local route to city A have an origin and destination (O&D) of both local and city A within a certain period, the opening of a high-speed rail line connecting local and cities A, B, and C will change the O&D of passengers on the local-city A route. Air passengers traveling from local to city A will decrease significantly due to the high-speed rail opening, while passengers traveling from local to city B will rely heavily on air-rail intermodal transport because there are no direct flights. In this case, optimizing the local-city A route and opening a new local-city B route provides a strong basis for expansion into the aviation market.

[0139] In another example, based on the above analysis of local passenger migration and combined with a penetration analysis of the actual O&D (Operations and Demand) of local airport passengers, the local airport's route network can complete preliminary planning and design for a certain period. However, at the same time, the impact of more passenger attributes and other transportation factors such as competing airports and high-speed rail lines in the surrounding area should also be considered.

[0140] With the support of route matching models and flight preference models, passenger profiles for air travel can be further explored, enabling the design and adjustments of air travel products and services that are more in line with passenger attributes and differentiated from other modes of transportation. This will help expand the aviation market and tap into more potential passengers.

[0141] Meanwhile, by analyzing the actual O&D (Operations and Demand) of passengers at competing airports and high-speed rail lines in the surrounding area, we can avoid vicious competition among passengers with the same O&D, and conduct differentiated marketing and guidance for travelers from different regions. This can also help local airports design and adjust their route networks, and promote the healthy and positive development of the regional aviation industry.

[0142] The data on user migration from city A to city B is obtained, and then correlated with the basic user attribute data in the mobile operator data to obtain the basic attribute tags of the user group, including: age characteristics, gender, city of origin, city of residence, occupation, etc.

[0143] The process of determining travel preference tags is as follows: Using location data, transportation modes and travel trajectories are identified to obtain information on user migration from city A to city B. For air travelers, flight preference and price preference models are used to match user travel and arrival times with flight schedule data from the civil aviation industry, filtering out the flights selected by the user, and combining this with flight price data from the civil aviation industry. For non-air travelers, the latitude and longitude corresponding to the user's location migration data are correlated with the latitude and longitude information corresponding to actual roads and railways to determine the user's travel mode, such as car or high-speed rail, thereby obtaining travel preference tags.

[0144] The process of determining travel value tags is as follows: By identifying transportation modes and travel trajectories through location data, the migration of user groups from city A to city B can be obtained. For this batch of users, information such as user travel frequency, travel preferences, and length of stay in airport shopping malls and airline VIP lounges is analyzed to obtain passenger value information such as whether the user is a frequent flyer, high-spending passenger, or VIP customer. Passenger credit is obtained through mobile operator user information scoring. The choice of transportation modes during the travel period is statistically analyzed to determine whether the passenger is a loyal air travel passenger. Through the above analysis, passenger travel value tags are obtained.

[0145] The above are just a few possible implementations of step S40 provided in this embodiment. This embodiment does not specifically limit step S40.

[0146] Step S50: Based on the target data, obtain aviation market insights.

[0147] In this embodiment, finally, based on the travel characteristics of users traveling between two target regions reflected in the obtained target data, information such as potential trends in the aviation market and changes in user demand can be extracted to form aviation market insights. These results can provide strong support for the strategic planning and marketing of airlines, airports and other related enterprises.

[0148] This application filters base station data by setting two target regions and a time period for analysis. It identifies base station data of target users who appear in the two target regions during the analysis period. Compared with traditional methods that analyze aviation information data, this application filters the required target base station data from a large amount of data through the filtering conditions, which greatly reduces the computational workload of data analysis. Furthermore, it can efficiently and quickly identify target users with personnel movement in the two target regions, thereby achieving efficient aviation market insights based on the data generated by the target users and improving the efficiency of data processing and analysis for aviation market insights.

[0149] As an example, by screening target customer groups, acquiring user group tags, and analyzing user migration patterns, a comprehensive view of passenger flow across various modes of transportation, including air, ground transportation (rail, road), and air-ground intermodal transport, can be achieved. This addresses the issue of incomplete data on ground transportation and air-ground intermodal transport in market analysis within the civil aviation industry. Furthermore, it enables the creation of passenger profiles, supporting the analysis of air, rail, and road passenger flow on routes A and B. This allows for analysis of user preferences when choosing between air travel and other modes of transportation (rail, road), identifying competitive dynamics with surrounding airports, and helping airports and airlines develop effective competitive strategies, such as improving service quality, optimizing flight schedules, or providing convenient ground transportation connections. Passenger source attribute analysis and passenger air travel purpose analysis provide insights into the entire market space (air, rail, road) for routes A and B. By combining passenger travel purpose, travel preferences, and passenger value tags, potential passengers for routes A and B can be identified, thus providing insights into the market capacity of these routes. Finally, personalized marketing enables the reach of potential passengers, completing a closed loop from market space insights to reaching potential high-value passengers.

[0150] In summary, by leveraging mobile operator data and models, and conducting in-depth analysis of civil aviation industry data, the meticulous steps outlined above enable a theoretical understanding and analysis of the aviation market's potential. This approach represents one way to leverage external data to empower the development of the civil aviation industry.

[0151] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the aviation market space insight method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0152] This application also provides an aviation market spatial insight device, please refer to... Figure 5 The aviation market spatial insight device includes:

[0153] The insight determination module 10 is used to determine the two target regions to be analyzed and the time period to be analyzed.

[0154] The acquisition module 20 is used to acquire base station data of the two target areas within the time period to be observed;

[0155] The target determination module 30 is used to identify the user as the target user when one or more users have base station data in both target areas during the time period to be observed.

[0156] The data determination module 40 is used to determine the target data of the target user based on the target base station data of the target user, wherein the target data is used to reflect the travel characteristics of the target user;

[0157] Results module 50 is used to obtain aviation market insights based on the target data.

[0158] Optionally, the target data includes: user profiles; the data determination module 40 can also be used for:

[0159] The basic attribute data of the target user are obtained from the target base station data according to the preset tag template;

[0160] The user profile of the target user is obtained by inputting the basic attribute data into the tag template.

[0161] Optionally, the target data includes: travel mode; the data determination module 40 can also be used for:

[0162] The travel duration of the target user is determined based on the time points in the target base station data.

[0163] Obtain the travel distance of the target user;

[0164] The travel speed of the target user is obtained by dividing the travel distance by the travel duration.

[0165] The travel mode corresponding to the travel speed is determined according to a preset mapping relationship, wherein the mapping relationship is the correspondence between speed range and travel mode.

[0166] Optionally, the target data includes: travel trajectory; the data determination module 40 can also be used for:

[0167] The trajectory information in the target base station data is sorted in chronological order to obtain a trajectory sequence;

[0168] Traverse the trajectory sequence, compare the current trajectory information with the next trajectory information, and if the next trajectory information is not equal to the current trajectory information, update the next trajectory information to the current trajectory information;

[0169] If the next trajectory information is equal to the current trajectory information, and the time difference between the next trajectory information and the current trajectory information matches a preset time threshold, then the next trajectory information is updated to the current trajectory information.

[0170] After traversing the trajectory sequence, the travel trajectory of the target user is obtained.

[0171] Optionally, the target data includes: travel purpose; the data determination module 40 can also be used for:

[0172] The location information in the target base station data is classified into daytime location information and nighttime location information according to a preset time threshold;

[0173] The travel purpose of the target user is determined based on the daytime location information and the nighttime location information.

[0174] Optionally, the target data includes: travel preferences; the data determination module 40 can also be used for:

[0175] Match the target user's target flight information with the time and location information in the target base station data;

[0176] Obtain other flight information similar to the target flight information, wherein the transit locations of the other flight information include the transit locations of the target flight information;

[0177] The travel preferences of the target user are determined based on the target flight information and the other flight information.

[0178] The aviation market spatial insight device provided in this application, employing the aviation market spatial insight method in the above embodiments, can solve the technical problem of how to improve the efficiency of data processing and analysis for aviation market insights. Compared with the prior art, the beneficial effects of the aviation market spatial insight device provided in this application are the same as those of the aviation market spatial insight method provided in the above embodiments, and other technical features in the aviation market spatial insight device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0179] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aviation market space insight method in Embodiment 1 above.

[0180] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic devices in these embodiments may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0181] like Figure 6 As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. While electronic devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0182] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0183] The electronic device provided in this application, employing the aviation market spatial insight method in the above embodiments, can solve the technical problem of how to improve the efficiency of data processing and analysis for aviation market insights. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the aviation market spatial insight method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0184] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0185] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0186] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the aviation market space insight method in the above embodiments.

[0187] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0188] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0189] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: determine two target regions and a time period to be analyzed; acquire base station data for the two target regions within the time period to be analyzed; when one or more users have base station data in both target regions within the time period to be analyzed, designate the user as a target user; determine target data for the target user based on the target base station data of the target user, wherein the target data reflects the travel characteristics of the target user; and obtain aviation market insight results based on the target data.

[0190] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0192] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0193] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described aviation market spatial insight method, thereby solving the technical problem of how to improve the efficiency of data processing and analysis for aviation market insights. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the aviation market spatial insight method provided in the above embodiments, and will not be repeated here.

[0194] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aviation market space insight method described above.

[0195] The computer program product provided in this application solves the technical problem of how to improve the efficiency of data processing and analysis for aviation market insights. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the aviation market spatial insight method provided in the above embodiments, and will not be repeated here.

[0196] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for spatial insight into the aviation market, characterized in that, The method includes: Identify the two target regions and the time period to be analyzed; Acquire base station data for the two target regions within the time period to be observed; When one or more users have base station data in both target areas during the period to be observed, the user is considered the target user. Based on the target base station data of the target user, target data of the target user is determined, wherein the target data is used to reflect the travel characteristics of the target user; Based on the target data, insights into the aviation market are obtained.

2. The aviation market spatial insight method as described in claim 1, characterized in that, The target data includes: user profile; the step of determining the target data of the target user based on the target base station data of the target user includes: The basic attribute data of the target user are obtained from the target base station data according to the preset tag template; The user profile of the target user is obtained by inputting the basic attribute data into the tag template.

3. The aviation market spatial insight method as described in claim 1, characterized in that, The target data includes: travel mode; the step of determining the target user's target data based on the target user's target base station data includes: The travel duration of the target user is determined based on the time points in the target base station data. Obtain the travel distance of the target user; The travel speed of the target user is obtained by dividing the travel distance by the travel duration. The travel mode corresponding to the travel speed is determined according to a preset mapping relationship, wherein the mapping relationship is the correspondence between speed range and travel mode.

4. The aviation market spatial insight method as described in claim 1, characterized in that, The target data includes: travel trajectory; the step of determining the target user's target data based on the target user's target base station data includes: The trajectory information in the target base station data is sorted in chronological order to obtain a trajectory sequence; Traverse the trajectory sequence, compare the current trajectory information with the next trajectory information, and if the next trajectory information is not equal to the current trajectory information, update the next trajectory information to the current trajectory information; If the next trajectory information is equal to the current trajectory information, and the time difference between the next trajectory information and the current trajectory information matches a preset time threshold, then the next trajectory information is updated to the current trajectory information. After traversing the trajectory sequence, the travel trajectory of the target user is obtained.

5. The aviation market spatial insight method as described in claim 1, characterized in that, The target data includes: travel purpose; the step of determining the target user's target data based on the target user's target base station data includes: The location information in the target base station data is classified into daytime location information and nighttime location information according to a preset time threshold; The travel purpose of the target user is determined based on the daytime location information and the nighttime location information.

6. The aviation market spatial insight method as described in claim 1, characterized in that, The target data includes: travel preferences; the step of determining the target user's target data based on the target user's target base station data includes: Match the target user's target flight information with the time and location information in the target base station data; Obtain other flight information similar to the target flight information, wherein the transit locations of the other flight information include the transit locations of the target flight information; The travel preferences of the target user are determined based on the target flight information and the other flight information.

7. An aviation market spatial insight device, characterized in that, The device includes: The insight determination module is used to determine the two target regions to be analyzed and the time period to be analyzed. The acquisition module is used to acquire base station data for the two target areas within the time period to be observed. The target determination module is used to identify one or more users as target users when base station data exists in both target areas during the time period to be observed. The data determination module is used to determine the target data of the target user based on the target base station data of the target user, wherein the target data is used to reflect the travel characteristics of the target user; The results module is used to obtain aviation market insights based on the target data.

8. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the aviation market space insight method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the aviation market space insight method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the aviation market space insight method as described in any one of claims 1 to 6.

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