A flight fare multi-channel synchronous publishing system and method

Abstract

The application discloses a kind of flight fare multi-channel synchronous release system and method, comprising: API gateway, for receiving fare release request, and after completing authentication, fare release request is forwarded to release strategy engine;Release strategy engine is used when receiving fare release request, according to the priority weight of each release channel, predicted push delay, probability of effect by time and risk index, the scheduling score of each release channel is calculated, and concurrent scheduling instruction is generated based on the scheduling score;Channel interface adaptation cluster, for executing the concurrent scheduling instruction, the fare data package containing version token, uniform effective time and global consistency hash value is parallelly issued to each release channel, and the confirmation receipt returned by channel and echo hash value are collected. The system and method of the application effectively solve the channel tearing problem, significantly improve the release success rate and system robustness.

Description

Technical Field

[0001] This invention relates to the field of airfare management technology, and in particular to a system and method for simultaneous multi-channel release of flight fares. Background Technology

[0002] Airlines' revenue management systems need to frequently and promptly adjust flight fares based on factors such as market supply and demand, competitive landscape, and sales cycles. The adjusted fares must be quickly and accurately synchronized to multiple heterogeneous external channels, including global distribution systems, online travel agents, the airline's own website, mini-programs, and connecting flight platforms, to ensure the effective execution of marketing strategies and consistency in user experience.

[0003] However, existing fare publication schemes generally face significant technical challenges. First, most airlines rely on multiple, fragmented, and independent tools, often requiring substantial manual intervention to update fares across different channels. This model is not only inefficient but also highly susceptible to price inconsistencies between channels due to operational errors or delays.

[0004] The more fundamental technical challenge lies in the significant differences in the interface protocols, performance, and rate limiting strategies of the various external channels mentioned above, and the fact that the vast majority of these channels inherently lack database-like transactional characteristics. Specifically, systems such as GDS's black-screen system, OTA's caching interface, and official website CDN nodes cannot support strong transactional semantics like two-phase commit, transaction rollback, or row-level locking. This "non-transactional channel condition" directly leads to the following core problems that have remained unresolved for a long time:

[0005] Channel Disruption Window: During the release process, unpredictable and random delays in the effective time of different channels can lead to discrepancies in the displayed fares within a significant time window. Users may see different prices for the same flight at the same time, severely damaging the airline's pricing credibility and brand image, and potentially causing transaction disputes.

[0006] Failure and Overload Risks: Failure of external interface calls is unpredictable. A lack of intelligent retry mechanisms may cause overload to downstream channels or the system itself due to inappropriate and frequent retries when failures occur, and may even trigger a cascading avalanche effect, affecting the overall availability of the system.

[0007] Rigid release strategies: Existing canary release or channel priority strategies are mostly statically configured and cannot be adaptively adjusted according to the real-time health status, load capacity and historical success rate of the channel, making it difficult to effectively control risk exposure while ensuring release speed.

[0008] Lack of consistency verification: The lack of an efficient and reliable strong consistency verification mechanism makes it difficult to detect and locate whether there are deviations between the freight rate data of one or more channels and the benchmark version in a timely and automatic manner, and it is even more impossible to achieve rapid automatic remediation. Summary of the Invention

[0009] To address the technical problems existing in the background art, this invention proposes a system and method for simultaneous multi-channel release of flight fares.

[0010] This invention proposes a multi-channel simultaneous release system for flight fares, comprising:

[0011] The API gateway is used to receive fare publication requests and forward them to the publication strategy engine after authentication.

[0012] The release strategy engine is used to calculate the scheduling score of each release channel based on the priority weight of each release channel, the predicted push delay, the probability of on-time effectiveness and risk indicators when a fare release request is received, and to generate concurrent scheduling instructions based on the scheduling score.

[0013] The channel interface adaptation cluster is used to execute the concurrent scheduling instructions, which distributes the price data packets containing version tokens, unified effective time and globally consistent hash values ​​to each publishing channel in parallel, and collects the confirmation receipts and echo hash values ​​returned by the channels.

[0014] The monitoring and rollback module is used to monitor the confirmation receipt and compare the echo hash value of each publishing channel with the global consistency hash value within a threshold time window. Based on the comparison result, it triggers a submission command, adaptive retry, or global rollback operation.

[0015] Preferably, in the release strategy engine, the formula for calculating the scheduling score is as follows:

[0016] ;

[0017] in, The scheduling score for channel c; The priority weight for channel C; Predicted push latency for channel C; This represents the probability that channel c will successfully take effect on time before the scheduled effective date. For channel C, it is a comprehensive risk indicator; , , These are the weighting coefficients for the corresponding feature terms, used to adjust the impact of priority weights, predicted push delays, on-time activation probability, and risk indicators on the scheduling score.

[0018] Preferably, in the release strategy engine, the generation of concurrent scheduling instructions based on the scheduling score specifically includes:

[0019] Sort the release channels in descending order according to their scheduling scores;

[0020] Based on the sorting order, and provided that the concurrency limit and rate limit of each publishing channel are met, the publishing channels are sequentially loaded into the micro-batch concurrent batch to generate concurrent scheduling instructions.

[0021] Preferably, the fare data packet issued by the channel interface adaptation cluster is a shadow fare data packet, which is used to request each publishing channel to switch fares at the unified effective time.

[0022] Preferably, the determination of the unified effective time satisfies the following: the unified effective time is not earlier than the sum of the global commitment time and the safety margin, and the global commitment time is determined based on the historical release delay quantiles of all release channels.

[0023] Preferably, in the monitoring and rollback module, the step of triggering a submission command, adaptive retry, or global rollback operation based on the comparison result specifically includes:

[0024] If the number of publishing channels whose echoed hash value matches the global consistent hash value and successfully responds reaches a preset threshold, a commit command is triggered; otherwise, an adaptive retry or global rollback operation is triggered.

[0025] Preferably, it further includes: a version and verification component, connected to the release strategy engine and the monitoring and rollback module, for storing and managing fare version data, generating the globally consistent hash value and idempotent key, and calculating the difference data between versions.

[0026] Preferably, the release strategy engine is also used to perform adaptive quota allocation for canary releases, dynamically adjusting the canary release quotas for each release channel or user group through a multi-armed gambling machine model.

[0027] Preferably, the adaptive retry operation performed by the monitoring and rollback module has a retry interval that is dynamically calculated based on the channel's instant success probability model and risk model to obtain the optimal interval.

[0028] This invention proposes a method for simultaneous multi-channel release of flight fares, comprising the following steps:

[0029] Receive and authenticate freight rate publication requests;

[0030] The scheduling score is calculated based on the priority weight of each release channel, the predicted push latency, the probability of on-time effectiveness, and risk indicators.

[0031] Concurrent scheduling instructions are generated based on the scheduling score;

[0032] Execute the concurrent scheduling instruction to distribute the fare data packet containing the version token, unified effective time, and globally consistent hash value to each release channel in parallel;

[0033] Collect confirmation receipts and echo hash values ​​returned from various publishing channels;

[0034] Within the threshold time window, compare the echo hash values ​​of each release channel with the globally consistent hash value;

[0035] Based on the comparison results, a commit command, adaptive retry, or global rollback operation is triggered.

[0036] The proposed multi-channel synchronous release system and method for flight fares in this invention effectively solves the channel fragmentation and consistency problems in fare release under multi-external channel environments where transaction characteristics cannot be guaranteed by introducing original concurrent scheduling, shadow release, adaptive grayscale, and risk-aware retry mechanisms. It achieves near-atomic consistent switching visible to users. While ensuring strong consistency of fare data, the system significantly improves the overall efficiency, success rate, and robustness of multi-channel release, and also has adaptive optimization and intelligent risk control capabilities, providing efficient and reliable technical support for airline revenue management in terms of fare synchronization. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the system architecture of a multi-channel synchronous release system for flight fares proposed in this invention;

[0038] Figure 2 This is a schematic diagram of the main publishing process of a multi-channel synchronous publishing system for flight fares proposed in this invention;

[0039] Figure 3 This is a schematic diagram illustrating the workflow of a method for simultaneous multi-channel release of flight fares proposed in this invention. Detailed Implementation

[0040] Reference Figures 1-3 The present invention proposes a multi-channel simultaneous release system for flight fares, comprising:

[0041] The API gateway is used to receive fare publication requests and, after authentication, forward the fare publication requests to the publication strategy engine.

[0042] Specifically, the API gateway, acting as the system's unified access point, is responsible for receiving fare publishing requests from front-end clients or automated systems and executing identity authentication and permission verification processes. Only authenticated requests are forwarded to the publishing policy engine to prevent data corruption or security risks caused by unauthorized operations. This module can integrate standard authentication protocols such as OAuth2.0 and JWT, and can also be customized to suit the enterprise's internal permission system. Its deployment supports a highly available cluster architecture, with load balancing and circuit breaker capabilities, ensuring stable access under high concurrency scenarios.

[0043] The release strategy engine is used to calculate the scheduling score of each release channel when a fare release request is received, based on the priority weight of each release channel, the predicted push latency, the probability of on-time effectiveness, and risk indicators, and to generate concurrent scheduling instructions based on the scheduling score.

[0044] In this embodiment, the formula for calculating the scheduling score in the release strategy engine is as follows:

[0045] ;

[0046] in, The scheduling score for channel c; The priority weight for channel C; Predicted push latency for channel C; This represents the probability that channel c will successfully take effect on time before the scheduled effective date. For channel C, it is a comprehensive risk indicator; , , These are the weighting coefficients for the corresponding feature terms, used to adjust the impact of priority weights, predicted push delays, on-time activation probability, and risk indicators on the scheduling score.

[0047] It should be noted that, , , They are non-negative real numbers, and are mutually independent. , , It can be set based on experience according to business needs, or obtained through training based on historical release performance data; in one optional implementation, it can also be... , , Perform normalization processing (e.g., constraints) , , However, this is not a requirement for this application.

[0048] Specifically, priority weight This indicates the importance level of a particular distribution channel at the business level, pre-set by the airline's operational strategy. For example, the official website and mini-programs, as direct sales channels, typically have the highest priority (e.g., set to 1.0), while some secondary OTA platforms might be set to 0.6. This weight reflects the differentiated needs of enterprises in terms of market coverage, user reach, and brand control. Predicted push latency refers to the estimated time required from when the system sends out the fare data packet to when the target channel confirms receipt, usually in seconds. This value can be obtained through historical data modeling, such as using a sliding window to calculate the average response time of the interface over the past 24 hours, and dynamically adjusting it based on current network conditions and load. Lower latency means higher transmission efficiency, which helps shorten the overall release cycle. On-time effectiveness probability. This indicates the probability that the channel, upon receiving a rate update request, can complete internal processing and make it publicly visible before the specified effective time. This metric can be predicted using a machine learning model, with input features including but not limited to: historical success / failure records, time-period load trends, and interface stability scores. Risk Metric It is a comprehensive measure of channel operational instability, covering multiple dimensions such as the frequency of technical failures, the number of times interface rate limiting is triggered, and the occurrence rate of data inconsistency events. This indicator can be processed into a value in the range [0, 1] using a normalization method, with higher values ​​indicating greater risk.

[0049] In this embodiment, the concurrent scheduling instructions are generated based on the scheduling score in the release policy engine, specifically including:

[0050] Sort the release channels in descending order according to their scheduling scores;

[0051] Based on the sorting order, and provided that the concurrency limit and rate limit of each publishing channel are met, the publishing channels are sequentially loaded into the micro-batch concurrent batch to generate concurrent scheduling instructions.

[0052] Specifically, by organizing each distribution channel according to its scheduling score and considering the technical capabilities of each channel (i.e., the concurrency limit and API call rate limit), the channels are orchestrated in batches to form feasible micro-batch concurrent batches. This generates concurrent scheduling instructions that can be actually executed by the channel interface-adapted cluster. This mechanism ensures priority response for high-priority, low-risk, and high-success-probability channels while avoiding overloading external services or triggering rate-limiting policies due to blind concurrency, thus achieving synergistic optimization of distribution throughput and system stability.

[0053] In this embodiment, the release strategy engine is also used to perform adaptive quota allocation for canary releases, dynamically adjusting the canary release quotas for each release channel or user group through a multi-armed gambling machine model.

[0054] Specifically, for the release strategy engine, upon receiving an authenticated fare release request, the engine first obtains the fare version information to be released and then calls the prediction model to retrieve historical performance parameters for each release channel, including priority weights, predicted push latency, on-time effectiveness probability, and risk indicators. These parameters can be derived from an offline-trained risk profiling model or a real-time channel health scoring system. Subsequently, the engine calculates the scheduling score for each channel according to a preset algorithm, sorts them by score, and generates concurrent scheduling instructions that meet resource constraints by combining the concurrency limits and rate constraints of each channel. This process maximizes throughput efficiency while ensuring release reliability.

[0055] Specifically, the release strategy engine is also used to perform adaptive quota allocation for canary releases. The release strategy engine models different release channels or user groups as independent arms in a multi-armed gambling machine model, and dynamically evaluates the expected performance of each arm through an online learning algorithm. The algorithm uses the timely and successful activation of the fare package in the channel as positive feedback reward, and release timeout, failure, or triggering rollback as negative feedback, continuously updating the success rate distribution parameters of each arm. Based on this dynamic estimation result, the release strategy engine adaptively allocates the canary release quota ratio and release time window for each channel or user group in each release cycle, thereby achieving a balance between exploration and utilization, and systematically reducing the global release risk while ensuring release efficiency.

[0056] The channel interface is adapted to the cluster to execute concurrent scheduling instructions, which distributes the price data packet containing the version token, unified effective time and globally consistent hash value to each publishing channel in parallel, and collects the confirmation receipts and echo hash values ​​returned by the channels.

[0057] In this embodiment, the fare data packet issued by the channel interface adaptation cluster is a shadow fare data packet, which is used to request each publishing channel to switch fares at the unified effective time.

[0058] In this embodiment, the determination of the unified effective time satisfies the following conditions: the unified effective time is not earlier than the sum of the global commitment time and the safety margin, and the global commitment time is determined based on the historical release latency quantile of all release channels.

[0059] Specifically, the unified effective time refers to the absolute point in time when all publishing channels should display the new fare, i.e., the moment when the price switch is perceptible to users. The unified effective time is not simply set to the current time plus a fixed delay, but rather is the result of algorithmic optimization, which must satisfy a minimum requirement of the sum of the globally committed time and a safety margin. Its purpose is to coordinate the differences in the actual processing capabilities of various channels, achieving near-atomic visible switching while ensuring eventual consistency.

[0060] The global commitment time is the consistency threshold time set by the system to complete the shadow preparation phase. Its value is the sum of the maximum value among the highest percentile values ​​(such as the 90th, 95th, or 99th percentile) of historical release latency across all release channels, plus a basic safety margin. To obtain, that is ,in, This represents the q-quantile of the channel latency distribution. Safety margin refers to the additional time buffer added on top of the globally committed timeframe to cope with uncertainties such as sudden network jitter, transient congestion, or prediction errors. Safety margin can be configured differently based on channel type, time period load characteristics, or service priority.

[0061] Specifically, the channel interface adaptation cluster consists of multiple independently deployed adapter instances, which respectively connect to heterogeneous publishing terminals such as the GDS black screen system, mainstream OTA platforms, airline's own official website, WeChat mini-program, and inter-trip cooperation platform. Each adapter encapsulates the communication protocol (such as SOAP, RESTful API, FTP file push, etc.), message format, and rate limiting rules specific to the corresponding channel.

[0062] In this embodiment, the fare data packet issued by the channel interface adaptation cluster is a shadow fare data packet, which is used to request each publishing channel to switch fares at the unified effective time.

[0063] The shadow freight rate data package is a data structure with version control and delayed implementation capabilities. Its core lies in decoupling the processes of "data transmission" and "user visibility." The shadow freight rate data package contains three key pieces of information: a version token, a unified effective time, and a globally consistent hash value. The version token uniquely identifies the release, preventing confusion between different versions; the unified effective time defines the point in time when all channels should simultaneously display the new price; and the globally consistent hash value serves as the basis for verifying the integrity of the freight rate content, ensuring that the content received by each channel is consistent with the source system. By distributing this type of shadow freight rate data package to various publishing channels, the system can achieve precise time alignment of price update operations across multiple heterogeneous external channels.

[0064] The channel interface adaptation cluster is responsible for connecting to publishing channels with different protocol types, such as GDS (Global Distribution System), OTA (Online Travel Agency), airline websites, mini-programs, and connecting travel platforms. Due to significant differences in the technical architecture of these channels, some support pre-loading or shadow mode (e.g., some GDS systems allow submission of prices awaiting effect), while others only provide simple API interfaces and lack transaction support (e.g., most third-party OTA platforms). Therefore, the shadow fare data package needs to be adapted and encapsulated according to the capabilities of the target channel. For channels supporting shadow mode, the adaptation module parses it into a "fare instruction awaiting effect" conforming to the channel's specifications, and the channel side completes the internal cache update and promises to switch at a specified time. For channels that do not support shadow mode, logical delays in exposure are achieved by adding control instructions, combined with the visibility gate mechanism on the API gateway side (e.g., dynamic cache refresh, reverse proxy TTL adjustment, front-end route blocking, etc.), thus achieving an equivalent shadow effect.

[0065] The unified effective time is a key coordination parameter for achieving consistent switching across multiple channels. Its setting must comprehensively consider factors such as historical push latency, network fluctuations, and safety margins for each channel. Although the current claims do not specify the exact calculation method for this time, it adheres to the basic principle of not being less than the longest predicted processing time among all channels plus a safety buffer, ensuring that all channels have sufficient time to complete data reception and internal preparation. Under this premise, the role of the shadow freight rate data packet is to complete the data pre-configuration across the entire link before this time arrives, making the actual switching action as close to atomic as possible.

[0066] The globally consistent hash value is typically generated based on a normalized fare data object. For example, the HMAC-SHA256 algorithm is used to calculate the digest of fare entries in standardized JSON format, forming an irreversible and highly distinguishable fingerprint identifier. After receiving the shadow fare, each channel should also perform the same hash operation on its local parsing results and return the echoed hash value to the monitoring and rollback module for subsequent consistency comparison. If the echoed hash value from a channel does not match the global value, it indicates a deviation in its reception or parsing process, which may affect the final price consistency and requires triggering a remedial procedure.

[0067] Furthermore, there are various implementation options for shadow freight rate data packets to adapt to different deployment environments. For example, in scenarios with high security requirements, a digital signature mechanism can be introduced into the data packet, using asymmetric encryption technology to verify the authenticity of the publishing source and prevent intermediate tampering. Another example is to improve transmission efficiency by compressing and packaging differential change items, transmitting only the changed fields between versions instead of the complete freight rate record, while also including idempotent keys to avoid redundant processing. Finally, for applications requiring extremely low latency, shadow packets can be distributed in batches via UDP broadcast or multicast, combined with an ACK / NACK feedback mechanism to achieve efficient and reliable transmission.

[0068] The monitoring and rollback module is used to monitor confirmation receipts and compare the echo hash values ​​of each release channel with the global consistency hash value within a threshold time window. Based on the comparison results, it triggers a commit command, adaptive retry, or global rollback operation.

[0069] In this embodiment, the monitoring and rollback module triggers a commit command, adaptive retry, or global rollback operation based on the comparison results, specifically including:

[0070] If the number of publishing channels whose echoed hash value matches the global consistent hash value and successfully responds reaches a preset threshold, a commit command is triggered; otherwise, an adaptive retry or global rollback operation is triggered.

[0071] In this embodiment, the adaptive retry operation performed by the monitoring and rollback module has a retry interval that is dynamically calculated based on the channel's instant success probability model and risk model to obtain the optimal interval.

[0072] Specifically, the formula for calculating the retry interval is as follows:

[0073] ;

[0074] in, This represents the optimal value for the retry interval; is the candidate value for the retry interval (independent variable); t is the current calculation time; Let t be the time interval after the current time. Then, predict the probability of successful push for channel C; The time penalty factor (which can be estimated using the time cost weighting coefficient) is used to quantify the "time cost" or penalty level incurred by delaying by one unit of time during the retry interval optimization process. The larger the value, the more sensitive the system is to waiting time and the more likely it is to choose a shorter retry interval; The smaller the value, the longer the wait can be tolerated in exchange for a higher probability of successful push. It is a positive real number used to balance the trade-off between increasing the probability of success and increasing the waiting time.

[0075] In this embodiment, it also includes: a version and verification component, connected to the release strategy engine and the monitoring and rollback module, used to store and manage fare version data, generate globally consistent hash values ​​and idempotent keys, and calculate the difference data between versions.

[0076] Specifically, the version and verification components are also used to generate globally consistent hash values ​​and idempotent keys. The globally consistent hash value (FareHash) is calculated based on the normalized fare object content. Its generation process first involves sorting the original fare data according to preset rules, removing spaces, standardizing numerical precision, and normalizing enumeration values ​​to form a standard canonical representation. Then, the cryptographically secure HMAC-SHA256 algorithm is used to digest this canonical text, outputting a fixed-length hash string as the unique fingerprint for this release. This hash value is distributed to various channels along with the shadow fare package. Recipients must also perform the same normalization process and display their local calculation results for the monitoring module to compare and verify. If any channel displays a hash value that is inconsistent with the globally consistent hash value, it is determined that there is data distortion or parsing deviation, triggering an automatic remediation process. The idempotent key (idemp_key) is an anti-duplicate token designed to prevent duplicate submissions from causing state inconsistencies. It is typically generated by hashing a combination of "version ID + channel code + operation type," ensuring that multiple push requests for the same version on the same channel are processed only once. When the channel interface adaptation module sends an update command to a specific channel, it includes the idempotent key. Upon receiving the request, the target system first checks if a processing record with the same idempotent key already exists locally. If it does, it directly returns a success response without performing the actual update action, thus avoiding duplicate price modifications or abnormal inventory deductions caused by network jitter, timeout retries, etc. The validity period of the idempotent key is bound to the lifecycle of the corresponding version, and it automatically becomes invalid after the version is revoked or expires.

[0077] Specifically, the versioning and verification components are also used to calculate the difference data between versions, that is, to extract only the set of fields that have changed between the old and new versions, forming a minimal update package. The difference calculation uses a tree-structured comparison algorithm, which parses the fare data of the two versions into a nested key-value pair structure, traverses layer by layer to compare the changes in the values ​​of each node, and finally outputs a change list containing "modified path, original value, and new value". This difference package can significantly reduce network transmission load, and is especially suitable for large-scale channel synchronization in high-frequency fare adjustment scenarios. At the same time, the difference package itself also carries the aforementioned idempotent keys and local hash values, supporting fine-grained idempotent control and consistency verification.

[0078] In this embodiment, Figure 2The main deployment process of this system is demonstrated as follows: First, the system obtains the approved target fare version; then, the deployment strategy engine uses the WSECT-TS algorithm to intelligently schedule and control the concurrency of multiple channels, generating parallel push batches; after entering the A2P-Shadow stage, each channel receives and prepares a shadow fare package containing a version token, a unified effective time, and a globally consistent hash; the monitoring module collects channel receipts in real time and performs consistent hash verification; based on the verification results, the system makes a global atomic commit or rollback decision within the consistency threshold time; finally, all channels complete the fare visibility switch at a unified effective time, thus completing a complete and closed-loop manageable deployment process.

[0079] It's worth noting that the API gateway handles request admission control, the release strategy engine intelligently determines the optimal release order and concurrency strategy based on multi-dimensional metrics, the channel interface adaptation cluster enables cross-protocol parallel distribution, and the monitoring and rollback module provides final consistency arbitration and fault tolerance guarantees. The entire process does not rely on external channel transaction support, completing multi-terminal synchronization preparation within seconds and achieving price consistency at the user's perceived level through a unified effective time.

[0080] Reference Figures 1-3 The present invention proposes a method for simultaneous multi-channel release of flight fares, comprising the following steps:

[0081] Receive and authenticate freight rate publication requests;

[0082] The scheduling score is calculated based on the priority weight of each release channel, the predicted push latency, the probability of on-time effectiveness, and risk indicators.

[0083] Generate concurrent scheduling instructions based on scheduling scores;

[0084] Execute concurrent scheduling instructions to distribute freight data packets containing version tokens, unified effective times, and globally consistent hash values ​​to various release channels in parallel;

[0085] Collect confirmation receipts and echo hash values ​​returned from various publishing channels;

[0086] Within the threshold time window, compare the echo hash values ​​of each release channel with the globally consistent hash value;

[0087] Based on the comparison results, a commit command, adaptive retry, or global rollback operation is triggered.

[0088] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A system for simultaneous multi-channel release of flight fares, characterized in that, include: The API gateway is used to receive fare publication requests and forward them to the publication strategy engine after authentication. The release strategy engine is used to calculate the scheduling score of each release channel based on the priority weight of each release channel, the predicted push delay, the probability of on-time effectiveness and risk indicators when a fare release request is received, and to generate concurrent scheduling instructions based on the scheduling score. The channel interface adaptation cluster is used to execute the concurrent scheduling instructions, which distributes the price data packets containing version tokens, unified effective time and globally consistent hash values ​​to each publishing channel in parallel, and collects the confirmation receipts and echo hash values ​​returned by the channels. The monitoring and rollback module is used to monitor the confirmation receipt and compare the echo hash value of each publishing channel with the global consistency hash value within a threshold time window. Based on the comparison result, it triggers a submission command, adaptive retry, or global rollback operation.

2. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, In the release strategy engine, the formula for calculating the scheduling score is as follows: ; in, The scheduling score for channel c; The priority weight for channel C; Predicted push latency for channel C; This represents the probability that channel c will successfully take effect on time before the scheduled effective date. For channel C, it is a comprehensive risk indicator; , , These are the weighting coefficients for the corresponding feature terms, used to adjust the impact of priority weights, predicted push delays, on-time activation probability, and risk indicators on the scheduling score.

3. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, In the release strategy engine, generating concurrent scheduling instructions based on the scheduling score specifically includes: Sort the release channels in descending order according to their scheduling scores; Based on the sorting order, and provided that the concurrency limit and rate limit of each publishing channel are met, the publishing channels are sequentially loaded into the micro-batch concurrent batch to generate concurrent scheduling instructions.

4. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, The fare data packets issued by the channel interface adaptation cluster are shadow fare data packets, which are used to request each publishing channel to switch fares at the unified effective time.

5. The multi-channel simultaneous release system for flight fares according to claim 4, characterized in that, The determination of the unified effective time satisfies the following condition: the unified effective time is not earlier than the sum of the global commitment time and the safety margin, and the global commitment time is determined based on the historical release delay quantiles of all release channels.

6. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, In the monitoring and rollback module, the step of triggering a submission command, adaptive retry, or global rollback operation based on the comparison results specifically includes: If the number of publishing channels whose echoed hash value matches the global consistent hash value and successfully responds reaches a preset threshold, a commit command is triggered; otherwise, an adaptive retry or global rollback operation is triggered.

7. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, Also includes: The version and verification component, connected to the release strategy engine and the monitoring and rollback module, is used to store and manage fare version data, generate the globally consistent hash value and idempotent key, and calculate the difference data between versions.

8. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, The release strategy engine is also used to perform adaptive quota allocation for canary releases, dynamically adjusting the canary release quotas for each release channel or user group through a multi-armed gambling machine model.

9. The multi-channel simultaneous release system for flight fares according to claim 1, characterized in that, The adaptive retry operation performed by the monitoring and rollback module has an optimal retry interval dynamically calculated based on the channel's instant success probability model and risk model.

10. A method for simultaneous multi-channel release of flight fares, characterized in that, Includes the following steps: Receive and authenticate freight rate publication requests; The scheduling score is calculated based on the priority weight of each release channel, the predicted push latency, the probability of on-time effectiveness, and risk indicators. Concurrent scheduling instructions are generated based on the scheduling score; Execute the concurrent scheduling instruction to distribute the fare data packet containing the version token, unified effective time, and globally consistent hash value to each release channel in parallel; Collect confirmation receipts and echo hash values ​​returned from various publishing channels; Within the threshold time window, compare the echo hash values ​​of each release channel with the globally consistent hash value; Based on the comparison results, a commit command, adaptive retry, or global rollback operation is triggered.

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