Method and apparatus for determining transit flight, network device and storage medium

Abstract

Embodiments of the present application provide a transfer flight determination method and device, network equipment and storage medium, the method comprising: constructing a target model of a quantum-enhanced multi-objective recommendation algorithm corresponding to target data; the target data includes flight price data, transfer flight time data and reliability evaluation data; a three-dimensional data set of time, airport state data and flight dynamic data is taken as input of the target model, and a plurality of calculation results are obtained; the airport state data includes weather data and airport Internet of Things data; the calculation result is time, price and reliability corresponding to a flight transfer scheme; a multi-path search algorithm is executed on the plurality of calculation results, and at least one target transfer scheme is obtained; and the target transfer scheme is sent to a second node. The present scheme solves the limitations of the centralized matching system in the prior art in terms of calculation efficiency and data reliability, and the problem of the bottleneck of the calculation performance of the pure blockchain scheme.

Description

Technical Field

[0001] This application relates to the field of flight and business travel technology, specifically to a method, apparatus, network equipment, and storage medium for determining transit flights. Background Technology

[0002] Existing methods for matching connecting flights mainly include optimization methods based on flight wave theory, optimization methods based on hit count, and simple airline-packaged connecting flight schemes. Methods based on flight wave theory rely excessively on the quantity and structure of flight waves, lacking effective calculation of potential connecting opportunities for random flight waves. Furthermore, airline-packaged connecting flight schemes typically cannot combine flights across different airlines, have limited flight combinations, and are time-consuming.

[0003] In actual flight matching, due to diversity and complexity, accurately assessing the cost-effectiveness of various resources is a challenge. Furthermore, unforeseen circumstances such as weather make it difficult to meet the needs of all users, meaning it's impossible to achieve a relatively optimal balance between user satisfaction and cost. While optimization models based on large-scale data can address these issues to some extent, traditional centralized matching systems have significant limitations in computational efficiency and data reliability, while pure blockchain solutions suffer from computational performance bottlenecks. Summary of the Invention

[0004] At least one embodiment of this application provides a method, apparatus, network device, and storage medium for determining transit flights, which addresses the limitations of centralized matching systems in terms of computational efficiency and data reliability, as well as the bottleneck in computational performance of pure blockchain solutions.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a method for determining transit flights, applied to a first node, including:

[0007] A target model is constructed corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data; the target model is constructed based on the quantum-enhanced multi-objective recommendation algorithm.

[0008] A three-dimensional dataset including time, airport status data, and flight dynamic data is used as input to the target model to obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan;

[0009] A multi-path search algorithm is executed on the multiple calculation results to obtain at least one target transit solution;

[0010] Send the target relay plan to the second node;

[0011] The first node is a node not on the blockchain, and the second node is a node on the blockchain.

[0012] Optionally, the target model corresponding to the flight price data is the flight price data with a penalty function set;

[0013] The target model corresponding to the transit flight time data is a function of the sum of flight transit time and flight time;

[0014] The target model corresponding to the reliability assessment data is a reliable assessment function related to weather data.

[0015] Optionally, the three-dimensional dataset is obtained by fusing the weather data, the airport IoT data, and the flight dynamic data within a preset time period using a data fusion algorithm with dynamic weights;

[0016] The dynamic weights are adjusted based on freshness, stability, and consistency.

[0017] The freshness is used to adjust the data based on its timeliness and age; the data age is the difference between the current time and the data timestamp.

[0018] The stability is used to adjust the data based on the data gradient and data type; the data gradient is the rate of change of the data over adjacent time steps.

[0019] The consistency is used to validate and adjust the data based on its different sources.

[0020] Secondly, embodiments of this application also provide a method for determining transit flights, applied to a second node, including:

[0021] Receive the target relay plan sent by the first node;

[0022] The target transfer plan is verified based on the obtained real-time flight dynamic data;

[0023] If the target transit scheme is verified, a non-fungible token is generated based on the target transit scheme;

[0024] The non-fungible tokens are adjusted in the event of changes in the real-time flight data.

[0025] Optionally, generating a non-fungible token based on the target transit scheme includes:

[0026] The first non-fungible token is minted according to the target transit scheme; the first non-fungible token includes a global identifier for the flight itinerary, flight dynamics, and a non-fungible token index;

[0027] A second non-fungible token is generated based on the flight segments in the target transit scheme;

[0028] The second non-fungible token is bound to the first non-fungible token to obtain the non-fungible token.

[0029] Thirdly, embodiments of this application provide a transit flight determination device, applied to a first node, comprising:

[0030] A construction module is used to build a target model corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data; the target model is constructed based on a quantum-enhanced multi-objective recommendation algorithm.

[0031] The calculation module is used to take a three-dimensional dataset including time, airport status data, and flight dynamic data as input to the target model and obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan;

[0032] The execution module is used to perform a multi-path search algorithm on the multiple calculation results to obtain at least one target transit solution;

[0033] The sending module is used to send the target relay scheme to the second node;

[0034] The first node is a node not on the blockchain, and the second node is a node on the blockchain.

[0035] Fourthly, embodiments of this application provide a transit flight determination device, applied to a second node, comprising:

[0036] The receiving module is used to receive the target relay plan sent by the first node;

[0037] The verification module is used to verify the target transfer plan based on the acquired real-time flight dynamic data;

[0038] The generation module is used to generate non-fungible tokens based on the target transit scheme if the target transit scheme is verified.

[0039] The adjustment module is used to adjust the non-fungible tokens when the real-time flight dynamic data changes.

[0040] Fifthly, embodiments of this application provide a network device, including: a transceiver, a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the method as described in either the first or second aspect.

[0041] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the method described in either the first or second aspect.

[0042] In a seventh aspect, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method as described in either the first or second aspect.

[0043] Compared with existing technologies, the transfer flight determination method, apparatus, network equipment, and storage medium provided in this application, by constructing a target model for a quantum-enhanced multi-objective recommendation algorithm at a first off-chain node, and using a three-dimensional dataset including time, airport status data, and flight dynamic data as input to the target model, can more accurately calculate a flight transfer scheme that comprehensively considers time, price, and reliability. Then, through a multi-path search algorithm, the final target transfer scheme can be selected, and the target transfer scheme is sent to a second on-chain node for feasibility verification. This solution solves the significant limitations of existing centralized matching systems in terms of computational efficiency and data reliability, while pure blockchain solutions suffer from computational performance bottlenecks. Attached Figure Description

[0044] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0045] Figure 1 This is a flowchart illustrating the transit flight determination method of this application when applied to the first node;

[0046] Figure 2 This is a schematic diagram illustrating the calculation of a target model using a 3D dataset as input for an embodiment of this application.

[0047] Figure 3 This application illustrates the correspondence between weather data and time in determining the weight values ​​of the attenuation effect coefficient.

[0048] Figure 4This application illustrates the correspondence between flight delay time and the weight value of the stability sensitivity coefficient in determining flight delay time in an embodiment of the present application.

[0049] Figure 5 This is a data diagram illustrating the consistency verification of baggage carousel status monitoring data in an embodiment of this application.

[0050] Figure 6 This is a schematic diagram illustrating the verification of the quantum-enhanced multi-objective recommendation algorithm according to an embodiment of this application.

[0051] Figure 7 This is a flowchart illustrating the process of determining transit flights according to an embodiment of this application when applied to the second node.

[0052] Figure 8 This is a logical diagram illustrating the verification of the target transit scheme according to an embodiment of this application;

[0053] Figure 9 This is a schematic diagram of the structure of a transit flight determination device according to an embodiment of this application;

[0054] Figure 10 This is a schematic diagram of the transit flight determination device according to another embodiment of this application;

[0055] Figure 11 This is a schematic diagram of the structure of a network device according to an embodiment of this application. Detailed Implementation

[0056] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, "A or B" covers three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0057] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc.; an indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0058] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used in the systems and radio technologies mentioned above, as well as in other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th Generation (6G) communication systems.

[0059] In the actual process of flight matching, due to the diversity and complexity, it is difficult to accurately evaluate the cost-effectiveness of various resources. In addition, abnormal reasons such as weather make it difficult to meet the needs of all users, that is, it is impossible to obtain the relative optimal option in terms of user satisfaction and cost optimization. In view of this, we propose a connecting flight matching method based on off-chain computation and on-chain verification.

[0060] To enable those skilled in the art to better understand the embodiments of this application, the following description is provided first:

[0061] The first node is a node not on the blockchain, and the second node is a node on the blockchain.

[0062] As described in the background section, in the actual process of flight matching, due to the diversity and complexity, accurately assessing the cost-effectiveness ratio of various resources is a challenge. Furthermore, unforeseen circumstances such as weather make it difficult to meet the needs of all users, meaning it's impossible to achieve a relatively optimal balance between user satisfaction and cost. Therefore, we propose a connecting flight matching method based on off-chain computation and on-chain verification. This severely impacts user experience. To address these issues, embodiments of this application provide a connecting flight determination method, apparatus, network device, and storage medium, which can reduce or avoid the above situations and improve user experience.

[0063] This application provides a method and apparatus for flight transfer. The method and apparatus are based on the same concept, and since the principles by which they solve the problem are similar, their implementations can be referred to interchangeably; repeated details will not be repeated.

[0064] like Figure 1 As shown in the embodiment of this application, a method for determining transit flights, when applied to the first node, includes the following steps:

[0065] Step 101: Construct a target model corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data; the target model is constructed based on the quantum-enhanced multi-objective recommendation algorithm.

[0066] Step 102: The three-dimensional dataset, including time, airport status data, and flight dynamic data, is used as input to the target model to obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan;

[0067] Step 103: Execute a multi-path search algorithm on the multiple calculation results to obtain at least one target transit solution;

[0068] Step 104: Send the target relay plan to the second node;

[0069] The first node is a node not on the blockchain, and the second node is a node on the blockchain.

[0070] The connecting flight determination method provided in this application constructs a target model for a quantum-enhanced multi-objective recommendation algorithm on a first off-chain node, and uses a three-dimensional dataset including time, airport status data, and flight dynamic data as input to the target model. This allows for a relatively accurate calculation of a connecting flight plan that considers time, price, and reliability. A multi-path search algorithm then filters out the final target connecting flight plan, which is then sent to a second on-chain node for feasibility verification. This solution addresses the limitations of existing centralized matching systems in terms of computational efficiency and data reliability, as well as the computational performance bottlenecks inherent in pure blockchain solutions.

[0071] Optionally, the target model corresponding to the flight price data is the flight price data with a penalty function set;

[0072] The target model corresponding to the transit flight time data is a function of the sum of flight transit time and flight time;

[0073] The target model corresponding to the reliability assessment data is a reliable assessment function related to weather data.

[0074] Optionally, the target model for constructing the quantum-enhanced multi-objective recommendation algorithm corresponding to the target data includes:

[0075]

[0076]

[0077] Wherein, formula (1) is the target model corresponding to the flight price data; formula (2) is the target model corresponding to the transit flight time data; formula (3) is the target model corresponding to the reliability assessment data;

[0078] Let be the penalty function;

[0079] For flight transfer time, Flight time refers to the duration of a flight from takeoff to landing.

[0080] This refers to weather data, specifically the duration of weather conditions that affect aircraft takeoff, landing, or flight.

[0081] It should be noted that the constraints of the target model of the quantum-enhanced multi-objective recommendation algorithm corresponding to the target data are as follows:

[0082] ;

[0083] Optionally, such as Figure 2 As shown, a three-dimensional dataset of time, airport status data, and flight dynamic data is used as input to the target model to obtain multiple calculation results, including:

[0084] The three-dimensional dataset is used as input to the target model. The network is divided into subgraphs according to the three dimensions of price, time, and reliability, and quantum optimization is performed in the boundary region of the subgraphs.

[0085] In quantum evolutionary search, each gene bit represents a segment, using 3-bit quantum encoding;

[0086] and ,in ;

[0087] Update the quantum rotating door:

[0088] .

[0089] Optionally, the three-dimensional dataset is obtained by fusing the weather data, the airport IoT data, and the flight dynamic data within a preset time period using a data fusion algorithm with dynamic weights.

[0090] It should be noted that the three-dimensional dataset was collected by the first node.

[0091] Optionally, the three-dimensional dataset is obtained by the first node constructing a spatiotemporal data network based on flight dynamic data, real-time weather data, and airport IoT data.

[0092] For example, the flight dynamic data includes delay data for each flight;

[0093] The weather data includes real-time meteorological information, especially meteorological information on extreme weather events;

[0094] The airport IoT data includes flight delay data and airport infrastructure data (e.g., runway and gate occupancy).

[0095] It should be noted that the solution in this application embodiment breaks through the traditional method of constructing the three-dimensional dataset using weighted average and fixed time window, and adopts a dynamic weight that is adjusted in real time to adaptively fuse multi-source data to generate the three-dimensional dataset.

[0096] For example, the three-dimensional dataset is:

[0097]

[0098] in, This represents data from three different dimensions;

[0099] This indicates dynamic weighting; for example, the weight of weather data is 0.85.

[0100] This refers to raw data obtained directly from the Application Programming Interface (API), such as a wind speed of 12 m / s.

[0101] It is a spatiotemporal convolution operator used to impose spatiotemporal constraints on data;

[0102] For example, τ = 5min (flight), and is the timestamp, and n is the number of available data sources.

[0103] The three-dimensional dataset in this application embodiment is adjusted in real time through the dynamic weights to dynamically evaluate data quality, quantify the credibility of each data source in real time, and automatically reduce the decision-making impact of abnormal data.

[0104] Optionally, the dynamic weights are adjusted based on freshness, stability, and consistency;

[0105] The freshness is used to adjust the data based on its timeliness and age; the data age is the difference between the current time and the data timestamp.

[0106] The stability is used to adjust the data based on the data gradient and data type; the data gradient is the rate of change of the data over adjacent time steps.

[0107] The consistency is used to validate and adjust the data based on its different sources.

[0108] For example, the specific formula for the dynamic weight is:

[0109]

[0110] in, For freshness parameters; For stability parameters; For consistency parameters.

[0111] Specifically, in terms of freshness, The data source has a basic credibility that is manually calibrated; λ is the attenuation coefficient (unit: min⁻¹). The core idea of ​​this model, which defines the data age (i.e., current time - data timestamp), is that the more recent the data, the higher its weight, but different data types have different decay rates.

[0112] For example, flight dynamic data (such as takeoff and landing times) is highly time-sensitive and employs a rapid decay strategy; airport infrastructure data (such as gate distances) changes more slowly and employs a slow decay strategy. When extreme weather (such as typhoons) is detected, the freshness weight of the weather data is automatically increased to ensure that the system prioritizes the use of the latest meteorological information. The solution in this application adopts a dynamic decay rate selection mechanism based on data category, avoiding the simple linear decay of existing solutions.

[0113] For example, the weather data of an airport is shown in Table 1, and the weight values ​​for a time decay effect coefficient of 0.2 are as follows: Figure 3 As shown:

[0114]

[0115] Specifically, in terms of stability, The stability sensitivity coefficient, The model idea is based on a dynamic weighting algorithm for the gradient step size. The smaller the fluctuation of the data in the spatiotemporal dimension, the higher the weight, thus avoiding noise interference.

[0116] For example, in the event of a sudden surge in airport traffic or drastic weather changes, the spatiotemporal gradient of the data source is calculated in real time. Data sources with drastic fluctuations (such as third-party flight status APIs) have reduced weights, while stable data sources (such as official data from the Civil Aviation Administration of China) maintain high weights. If a data source experiences multiple consecutive drastic fluctuations (such as three flight status changes within 10 minutes), its weight is temporarily frozen and restored once the data stabilizes.

[0117] For example, the sequence of flight delay minutes is shown in Table 2, and the weight values ​​for a stability sensitivity coefficient of 0.3 are as follows: Figure 4 As shown:

[0118]

[0119] Specifically, in consistency, To collaboratively verify weights; To support the number of other devices supporting this data source; This represents the total number of validation devices. The model is based on multi-data source cross-validation, with voting determining the final confidence level.

[0120] For example, if two out of three independent weather sources predict "sunny" and one predicts "light rain," then "sunny" will be adopted as the final result, and the weight of the consistent data source will be increased. A higher consistency threshold will be required for critical data (such as flight cancellation status) (e.g., only data from 3 / 3 of the data sources that are consistent will be adopted). If the data sources cannot reach a consensus (e.g., two APIs return different flight delay times), a manual review process will be triggered, and the conflict pattern will be recorded to optimize the model.

[0121] For example, the baggage carousel status monitoring data is shown in Table 3, and the results of the data consistency verification are as follows: Figure 5 As shown in Table 4, the input data for calculating the dynamic weight of a certain flight is as follows:

[0122]

[0123] The calculation of the dynamic weights is as follows:

[0124] Weather Bureau API: 0.6*e^(-0.1*3) + 0.3*(1 / 4.2) + 0.1*(2 / 3) = 0.44 + 0.07 + 0.07 = 0.58;

[0125] Runway sensor: 0.7*e^(-0.2*0.5) + 0.2*(1 / 1.8) + 0.1*(3 / 3) = 0.63 + 0.11 +0.10 = 0.84;

[0126] For a certain airline: 0.5*e^(-0.15*8) + 0.3*(1 / 12.5) + 0.2*(1 / 3) = 0.15 + 0.02 +0.07 = 0.24.

[0127] The time decay mechanism is configured with a time decay coefficient. Based on the interval between the data generation time and the current time, the initial weight of the data source is adjusted according to a preset decay formula. The longer the interval, the greater the weight decay.

[0128] The gradient penalty mechanism is used to impose a weight penalty on data sources whose data fluctuations exceed a preset threshold. The penalty intensity is positively correlated with the data fluctuation gradient; the greater the data fluctuation gradient, the greater the weight penalty.

[0129] The multi-source voting mechanism collects associated data from multiple sources that are from the same or different sources, evaluates the credibility of the target data source by voting, and adjusts the weight of the target data source according to the voting pass rate. The higher the voting pass rate, the greater the increase in weight.

[0130] Furthermore, the dynamic weights are adaptively adjusted using a three-dimensional Pareto front:

[0131] ;

[0132] in, This represents the user preference coefficient.

[0133] Specifically, in the spatiotemporal kernel function, t is the time deviation (current time - data timestamp), and τ is the characteristic time constant (set according to the data source type). This function adds adaptive time alignment and eliminates edge effects.

[0134] For example, the input data for calculating runway occupancy status is shown in Table 5:

[0135] Table 5

[0136]

[0137] For example, the kernel function is applied as follows (assuming τ=4s):

[0138] Tower weight:

[0139] Sensor weights:

[0140] Camera weight: (Removed after timeout)

[0141] The final fusion result is: =72% probability of occupation.

[0142] Further assess the credibility of the data:

[0143]

[0144] The transit flight determination method in this application introduces dynamic multi-dimensional scoring to adapt to complex scenarios; the data source is aviation + meteorology + IoT cross-validation, which enhances the anti-attack capability, and the addition of adaptive learning (LSTM-assisted) reduces human intervention.

[0145] It should be noted that abnormal data needs to be repaired, specifically using the following formula:

[0146]

[0147] The transit flight determination method in this application can ensure data reliability, improve system robustness, and optimize decision accuracy by repairing abnormal data.

[0148] Optionally, sending the target relay plan to the second node includes:

[0149] Send the hash digest and key parameters of the target relay scheme to the second node;

[0150] The key parameters include at least one of the following: transit time and airport code.

[0151] Optionally, the key parameters include transit time and airport code.

[0152] For example, test verification of determining transfer alternatives for flights from location A to location B:

[0153] First, determine the test environment:

[0154] Route: Location A → Location B (Stop / Transfer Option)

[0155] Data scale: Flight status: 1,243 records (including historical on-time rate); Weather data: 15-day forecast for Airport B; Airport IoT: sensors for 8 boarding gates + 3 runways.

[0156] Table 6 shows a comparison between the QMEA algorithm model and the traditional NSGA-II algorithm model. Table 7 shows the target transit schemes obtained through multi-path search (e.g., using TOP-3, three target transit schemes are obtained):

[0157]

[0158] The verification of "segmenting the network into subgraphs according to the three dimensions of price, time, and reliability and performing quantum optimization in the boundary region of the subgraphs" is shown in Table 8 (spatial-temporal segmentation effect) and Table 9 (quantum encoding optimization):

[0159]

[0160] The final convergence curve of the algorithm is as follows Figure 6 As shown.

[0161] like Figure 7 As shown in the embodiment of this application, a method for determining connecting flights is also provided, applied to the second node, including the following steps:

[0162] Step 701: Receive the target relay plan sent by the first node;

[0163] Step 702: Verify the target transfer plan based on the obtained real-time flight dynamic data;

[0164] Step 703: If the target transit scheme is verified, generate a non-fungible token based on the target transit scheme;

[0165] Step 704: If the real-time flight dynamic data changes, adjust the non-fungible token.

[0166] Optionally, the target relay scheme sent by the first node includes:

[0167] Receive the hash digest and key parameters of the target relay scheme sent by the first node;

[0168] The key parameters include at least one of the following: transit time and airport code.

[0169] It should be noted that the smart contract module of the second node receives the target relay solution.

[0170] Optionally, the target transfer plan is verified based on the acquired real-time flight dynamic data, including:

[0171] The time frame is used to verify whether the target transfer scheme meets the airline's rules based on the real-time flight dynamic data, the hash digest, and the key parameters.

[0172] like Figure 8 As shown in the embodiment of this application, the method for determining transit flights includes, in which the second node comprises: a smart contract module, a non-fungible token (NFT) contract module, and an oracle module;

[0173] After receiving the hash digest and key parameters of the target relay scheme sent by the first node (off-chain node), the smart contract module sends a verification request to the oracle module.

[0174] After verifying the target relay scheme (including time verification and reliability verification of credibility), the oracle module sends the verification signature to the smart contract module.

[0175] After receiving the verification signature, the smart contract module calls the forging function to the NFT contract module to forge the main NFT (the first NFT, which includes a global identifier and a state machine (e.g., inactive / check-in / complete)) and generate an independent sub-NFT (the second NFT) according to each flight segment. The sub-NFT is then bound to the main NFT (with embedded dynamic state fields) to generate the NFT.

[0176] The NFT ID is returned to the first node; simultaneously, the NFT contract module registers a status listener with the oracle module to monitor changes in flight dynamic data (e.g., when a change in flight status is detected (such as delay or boarding), a sub-NFT state machine transition is triggered); and when the flight dynamic data changes, the updated flight dynamic data is sent to the NFT contract module.

[0177] The NFT contract module updates the sub-NFTs based on the updated flight dynamic data and aggregates the updated sub-NFTs with the main NFTs.

[0178] The transit flight determination method in this application adopts an off-chain (first node) computing layer to handle computationally intensive tasks, and an on-chain (second node) verification layer to handle the trusted verification of key business logic; the data interaction layer realizes secure data interaction between off-chain and on-chain, which not only ensures the efficiency of scheme calculation, but also ensures the accuracy of transit scheme verification. This solves the problem that the existing centralized matching system has great limitations in terms of computing efficiency and data credibility, while the pure blockchain solution has the bottleneck of computing performance.

[0179] The various methods of the embodiments of this application have been described above. Apparatus for implementing the above methods will now be provided.

[0180] like Figure 9 As shown in the illustration, this application also provides a transit flight determination device, applied to a first node, comprising:

[0181] The construction module 901 is used to construct the target model corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data; the target model is constructed based on the quantum-enhanced multi-objective recommendation algorithm.

[0182] The calculation module 902 is used to take a three-dimensional dataset including time, airport status data, and flight dynamic data as input to the target model to obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan;

[0183] Execution module 903 is used to perform a multi-path search algorithm on the multiple calculation results to obtain at least one target transit scheme;

[0184] Sending module 904 is used to send the target relay scheme to the second node;

[0185] The first node is a node not on the blockchain, and the second node is a node on the blockchain.

[0186] The connecting flight determination device provided in this application embodiment constructs a target model for a quantum-enhanced multi-objective recommendation algorithm on a first off-chain node, and uses a three-dimensional dataset of time, airport status data, and flight dynamic data as input to the target model. This allows for relatively accurate calculation of a connecting flight plan that comprehensively considers time, price, and reliability. Then, through a multi-path search algorithm, the final target connecting flight plan is selected and sent to a second on-chain node for feasibility verification. This solution addresses the limitations of existing centralized matching systems in terms of computational efficiency and data reliability, as well as the computational performance bottleneck of pure blockchain solutions.

[0187] like Figure 10 As shown in the illustration, this application also provides a transit flight determination device, applied to a second node, comprising:

[0188] Receiver module 1001 is used to receive the target relay plan sent by the first node;

[0189] The verification module 1002 is used to verify the target transfer plan based on the acquired real-time flight dynamic data;

[0190] The generation module 1003 is used to generate non-fungible tokens according to the target transit scheme if the target transit scheme is verified.

[0191] The adjustment module 1004 is used to adjust the non-fungible tokens when the real-time flight dynamic data changes.

[0192] The transit flight determination device in this application embodiment employs an off-chain (first node) computing layer to handle computationally intensive tasks and an on-chain (second node) verification layer to perform reliable verification of key business logic. The data interaction layer enables secure data interaction between off-chain and on-chain systems, ensuring both the efficiency of the solution calculation and the accuracy of the transit solution verification. This solves the problem that the existing centralized matching system has significant limitations in terms of computational efficiency and data reliability, while the pure blockchain solution suffers from bottlenecks in computational performance.

[0193] Another embodiment of this application provides a network device, such as... Figure 11 As shown, it includes a transceiver 1110, a processor 1100, a memory 1120, and a program or instructions stored in the memory 1120 and executable on the processor 1100; when the processor 1100 executes the program or instructions, it implements the various processes of the above-described transit flight determination method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0194] The transceiver 1110 is used to receive and send data under the control of the processor 1100.

[0195] Among them, Figure 11In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 1100 and memory represented by memory 1120 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. Transceiver 1110 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different user equipment, user interface 1130 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0196] The processor 1100 is responsible for managing the bus architecture and general processing, and the memory 1120 can store the data used by the processor 1100 when performing operations.

[0197] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described transit flight determination method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0198] This application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the various processes of the above-described transit flight determination method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0199] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in this disclosed technical solution all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security and network security.

[0200] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0201] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0202] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for determining connecting flights, applied to the first node, characterized in that, include: Construct a target model corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data. The target model is constructed based on the quantum-enhanced multi-objective recommendation algorithm; A three-dimensional dataset including time, airport status data, and flight dynamic data is used as input to the target model to obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan; A multi-path search algorithm is executed on the multiple calculation results to obtain at least one target transit solution; Send the target relay plan to the second node; The first node is a node not on the blockchain, and the second node is a node on the blockchain.

2. The method according to claim 1, characterized in that, The target model corresponding to the flight price data is the flight price data with a penalty function set; The target model corresponding to the transit flight time data is a function of the sum of flight transit time and flight time; The target model corresponding to the reliability assessment data is a reliable assessment function related to weather data.

3. The method according to claim 1, characterized in that, The three-dimensional dataset is obtained by fusing the weather data, airport IoT data, and flight dynamic data within a preset time period using a data fusion algorithm with dynamic weights; The dynamic weights are adjusted based on freshness, stability, and consistency. The freshness is used to adjust the data based on its timeliness and age; the data age is the difference between the current time and the data timestamp. The stability is used to adjust the data based on the data gradient and data type; the data gradient is the rate of change of the data over adjacent time steps. The consistency is used to validate and adjust the data based on its different sources.

4. A method for determining connecting flights, applied to the second node, characterized in that, include: Receive the target relay plan sent by the first node; The target transfer plan is verified based on the obtained real-time flight dynamic data; If the target transit scheme is verified, a non-fungible token is generated based on the target transit scheme; The non-fungible tokens are adjusted in the event of changes in the real-time flight data.

5. The method according to claim 4, characterized in that, Generating non-fungible tokens based on the target transit scheme includes: The first non-fungible token is minted according to the target transit scheme; the first non-fungible token includes a global identifier for the flight itinerary, flight dynamics, and a non-fungible token index; A second non-fungible token is generated based on the flight segments in the target transit scheme; The second non-fungible token is bound to the first non-fungible token to obtain the non-fungible token.

6. A transit flight determination device, applied to a first node, characterized in that, include: A construction module is used to build a target model corresponding to the target data; the target data includes flight price data, connecting flight time data, and reliability assessment data. The target model is constructed based on the quantum-enhanced multi-objective recommendation algorithm; The calculation module is used to take a three-dimensional dataset including time, airport status data, and flight dynamic data as input to the target model and obtain multiple calculation results; the airport status data includes weather data and airport IoT data; the calculation results are the time, price, and reliability corresponding to the flight transfer plan; The execution module is used to perform a multi-path search algorithm on the multiple calculation results to obtain at least one target transit solution; The sending module is used to send the target relay scheme to the second node; The first node is a node not on the blockchain, and the second node is a node on the blockchain.

7. A transit flight determination device, applied to a second node, characterized in that, include: The receiving module is used to receive the target relay plan sent by the first node; The verification module is used to verify the target transfer plan based on the acquired real-time flight dynamic data; The generation module is used to generate non-fungible tokens based on the target transit scheme if the target transit scheme is verified. The adjustment module is used to adjust the non-fungible tokens when the real-time flight dynamic data changes.

8. A network device, characterized in that, include: Transceiver, processor, memory, and programs or instructions stored in the memory and executable on the processor; When the processor executes the program or instructions, it implements the steps of the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 5.

10. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 5.

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