Asset data consistency monitoring method and device, electronic equipment and storage medium

Abstract

The application relates to a travel SaaS platform asset data consistency monitoring method and device, electronic equipment and a storage medium, comprising: receiving data fingerprints of asset data uploaded by a plurality of business nodes of a travel SaaS platform; performing consensus verification on the data fingerprints uploaded by the business nodes through a consensus mechanism of a block chain network to obtain a data fingerprint consensus result; performing consistency verification on the data fingerprints uploaded by the business nodes and the data fingerprint consensus result through a preset consistency rule in a smart contract; and performing consistency monitoring on asset data of the business nodes of the travel SaaS platform based on verification results of the consistency verification. The above method can automatically perform asset data consistency verification through a smart contract of a block chain, and significantly improves the consistency level of asset data in a multi-business node environment.

Description

Technical Field

[0001] This application relates to the field of ride-hailing task processing, and in particular to a method, apparatus, electronic device, and storage medium for monitoring asset data consistency for a ride-hailing SaaS platform. Background Technology

[0002] With the rapid development of the sharing economy and digital transformation, mobility SaaS platforms have become an important part of urban transportation systems. These platforms typically integrate multiple modes of transportation, providing travelers with end-to-end, multi-modal, one-stop travel services. However, due to the involvement of multiple stakeholders and distributed business nodes in mobility platforms, data consistency issues are becoming increasingly prominent. Inconsistencies in asset data across multiple business nodes can lead to serious problems such as order errors, settlement disputes, and a decline in user experience.

[0003] Traditional ride-hailing platforms typically use centralized databases or third-party data sharing platforms to manage data. While centralized architectures are easy to manage, they are difficult to effectively verify the data consistency between distributed business nodes, and lack transparency and credibility. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, electronic device, and storage medium for monitoring asset data consistency for travel SaaS platforms, which can automatically perform asset data consistency verification through blockchain smart contracts, significantly improving the consistency level of asset data in multi-business node environments.

[0005] According to a first aspect of certain exemplary embodiments of this application, a method for monitoring the consistency of asset data for a travel SaaS platform is provided, applied to a blockchain network, comprising: receiving data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; performing consensus verification on the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain a data fingerprint consensus result; performing consistency verification on the data fingerprints uploaded by each business node and the data fingerprint consensus result through pre-defined consistency rules in a smart contract; and monitoring the consistency of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification.

[0006] According to certain exemplary embodiments of this application, the consensus mechanism of the blockchain network includes a node-level consensus mechanism, a block-level consensus mechanism, and a system-level consensus mechanism. The consensus mechanism of the blockchain network is used to verify the data fingerprints uploaded by each business node to obtain a data fingerprint consensus result, including: verifying the data fingerprints uploaded by each travel platform business node through the business node-level consensus mechanism of the blockchain network to obtain a node-level consensus result; verifying the node-level consensus result through the block-level consensus mechanism of the blockchain network to obtain a block-level consensus result; and verifying the block-level consensus result through the system-level consensus mechanism of the blockchain network to obtain a data fingerprint consensus result.

[0007] According to certain exemplary embodiments of this application, the asset data of each business node of the travel SaaS platform is monitored for consistency based on the verification results of each consistency verification, including: if the data fingerprint of one or more business nodes is found to be inconsistent with the data fingerprint consensus result based on the verification results of each consistency verification, then abnormal information is recorded through a smart contract, the abnormal information including the node information and data fingerprint of the business node with inconsistent data; if the data fingerprint of each business node is found to be consistent with the data fingerprint consensus result based on the verification results of each consistency verification, then the status information of the successful verification of the data fingerprint of each business node is recorded through a smart contract.

[0008] According to certain exemplary embodiments of this application, a method for monitoring the consistency of asset data for a travel SaaS platform further includes: when abnormal information is detected in a smart contract record, determining the abnormality level based on the number of business nodes with inconsistent data in the abnormal information; if the abnormality level is a level three alarm, sending an emergency notification to the administrator through a first notification channel so that the administrator can immediately perform abnormality handling; if the abnormality level is a level two alarm, sending a viewing notification to the administrator through a second notification channel so that the administrator is passively informed of the abnormal information; if the abnormality level is a level one alarm, sending a notification that does not require a response to the administrator through a third notification channel so that the administrator is proactively informed of the abnormal information.

[0009] According to certain exemplary embodiments of this application, an asset data consistency monitoring method for a travel SaaS platform further includes: obtaining the monitoring results of consistency monitoring, the monitoring results including abnormal information, or status information that the data fingerprints of each business node have been successfully verified; storing the monitoring results in the distributed ledger of the blockchain network and an off-chain database; wherein the off-chain database also stores the data fingerprints of the asset data uploaded by each business node.

[0010] According to certain exemplary embodiments of this application, the blockchain network includes a travel SaaS platform, service provider nodes, and regulatory agency nodes; the multiple business nodes of the travel SaaS platform include nodes of various asset data sources, and the asset data provided by the nodes of various asset data sources includes one or more of vehicle status data, trip transaction data, user credit data, and payment settlement data; wherein, any node of any asset data source performs hash processing on the asset data of any node of any asset data source through a hash algorithm, and uses the hash value obtained after hash processing as the data fingerprint of the asset data of any node of any asset data source.

[0011] According to certain exemplary embodiments of this application, a data acquisition client is deployed in any node of the asset data source. The data acquisition client collects one or more of the following by changing the data capture method, the interface call method of the application programming interface, and the log capture method, and transmits the collected asset data to the message queue in the node of the asset data source. A stream processing engine is configured in any node of the asset data source. The stream processing engine retrieves the collected asset data from the message queue, performs hash processing, and then uploads the resulting hash value as the data fingerprint of the collected asset data to the blockchain network.

[0012] According to a second aspect of certain exemplary embodiments of this application, an asset data consistency monitoring device for a travel SaaS platform is provided, applied to a blockchain network, comprising: a receiving module for receiving data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; a consensus verification module for performing consensus verification on the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain a data fingerprint consensus result; a consistency verification module for performing consistency verification on the data fingerprints uploaded by each business node and the data fingerprint consensus result through a pre-set consistency rule in a smart contract; and a consistency monitoring module for performing consistency monitoring on the asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification.

[0013] According to a third aspect of certain exemplary embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0014] According to a fourth aspect of certain exemplary embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0015] The aforementioned method, apparatus, electronic device, and storage medium for monitoring asset data consistency in a travel SaaS platform are applied to a blockchain network. The method includes: receiving data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; verifying the consensus of the data fingerprints uploaded by each business node through the blockchain network's consensus mechanism to obtain a data fingerprint consensus result; verifying the consistency between the data fingerprints uploaded by each business node and the data fingerprint consensus result through pre-defined consistency rules in a smart contract; and monitoring the consistency of asset data across all business nodes of the travel SaaS platform based on the verification results. Therefore, by automatically executing data consistency verification through blockchain smart contracts, the consistency level of asset data in a multi-business node environment is significantly improved. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an asset data consistency monitoring method for a travel SaaS platform, as shown in some exemplary embodiments of this application. Figure 2 This is an overall architecture diagram of a system for implementing an asset data consistency monitoring method for a travel SaaS platform, as shown in some exemplary embodiments of this application. Figure 3 For this application Figure 2 A schematic diagram illustrating the data consistency monitoring process implemented by the overall architecture; Figure 4 This is a structural block diagram of an asset data consistency monitoring device for a travel SaaS platform, as shown in some other exemplary embodiments of this application. Figure 5 This is a diagram of the internal structure of an electronic device in some other exemplary embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] The following detailed descriptions are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, electronic devices, storage media, and / or computer program products described herein. However, after understanding the disclosure of this application, various changes, modifications, and equivalents of the methods, apparatus, storage media, and / or computer program products described herein will become apparent. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear after understanding the disclosure of this application, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0019] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, electronic devices, and / or storage media described herein, many of which will become clear upon understanding this application.

[0020] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof. Unless otherwise stated, “ / ” means “or,” for example, A / B can mean A or B; “and / or” in the text is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can mean: A alone, A and B simultaneously, and B alone. Furthermore, in the description of embodiments of the invention, “multiple” means two or more.

[0021] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains upon understanding this application. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this application, and shall not be interpreted in an idealized or overly formalistic manner.

[0022] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in some of the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0023] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this application.

[0024] In the following description, embodiments will be described in detail with reference to the accompanying drawings. However, embodiments may be implemented in various forms and are not limited to the examples described herein.

[0025] In some exemplary embodiments of this application, a method for monitoring asset data consistency for a travel SaaS platform is provided, applied to a blockchain network. For example... Figure 1 As shown, a method for monitoring asset data consistency for a travel SaaS platform includes the following steps: Step S101: Receive data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform.

[0026] In this embodiment, the travel SaaS platform involves multiple participating entities and distributed business nodes. The nodes of the participating entities include passenger nodes, driver nodes, vehicle nodes, payment institution nodes, and traffic management department nodes. The asset data uploaded by multiple business nodes includes vehicle status data, trip transaction data, user credit data, and payment settlement data. Here, a data fingerprint refers to a fixed-length, unique, and irreversible numerical or string identifier generated by processing asset data of arbitrary length using a hash algorithm or feature extraction algorithm. The generated numerical or string identifier is the data fingerprint, used for data integrity verification, unique identification, and security verification.

[0027] In this embodiment, the multiple business nodes of the travel SaaS platform include nodes of various asset data sources. The asset data provided by the nodes of various asset data sources includes one or more of the following: vehicle status data, trip transaction data, user credit data, and payment settlement data. In this process, any node of an asset data source performs hash processing on the asset data of any node of an asset data source using a hash algorithm, and uses the hash value obtained after hash processing as the data fingerprint of the asset data of any node of an asset data source.

[0028] In this system, a data acquisition client is deployed in any node of an asset data source. The data acquisition client collects one or more of the following data by changing the data capture method, the API call method, and the log capture method: vehicle status data, trip transaction data, user credit data, and payment settlement data. The collected asset data is then transmitted to the message queue in the node of any asset data source. A stream processing engine is configured in the node of any asset data source. The stream processing engine retrieves the collected asset data from the message queue, performs hash processing on it, and uploads the resulting hash value as the data fingerprint of the collected asset data to the blockchain network.

[0029] Specifically, the mobility SaaS platform includes multiple asset data sources, including microservices such as vehicle IoT devices, orders, payments, and credit. Data collection clients are deployed across these asset data sources, responsible for collecting data in real time using methods such as CDC (Change Data Capture), log scraping, and API calls.

[0030] Specifically, data acquisition modules and clients deployed at various business nodes of the travel SaaS platform collect asset data from these nodes in real time. This asset data includes vehicle status data, trip transaction data, user credit data, and payment settlement data. The collected asset data is encrypted using a hash algorithm (such as SHA-256) to generate a unique hash value for the data fingerprint. Furthermore, a data processing and computing layer is configured for each business node of the travel SaaS platform. This layer includes message queues and a stream processing engine.

[0031] The message queue (Kafka) acts as the system's "traffic hub," decoupling the data acquisition and processing processes, buffering high-concurrency data streams, and ensuring no data loss. The stream processing engine (Flink) performs ETL operations such as cleaning, standardization, and formatting on the asset data in the consumer queue.

[0032] Step S102: The consensus mechanism of the blockchain network is used to verify the data fingerprints uploaded by each business node to obtain the data fingerprint consensus result.

[0033] In this embodiment, the blockchain network includes a travel SaaS platform, service provider nodes, and regulatory agency nodes. Therefore, the blockchain network, jointly maintained by various parties in the travel ecosystem, such as the platform, service providers, and regulatory agencies, ensures decentralization and trust. The consensus mechanism is the set of rules and algorithms by which all nodes in the blockchain network achieve data consistency. Upon receiving data fingerprints uploaded by various business nodes, the blockchain network uses the consensus mechanism to verify the data fingerprints uploaded by each business node, obtaining the data fingerprint consensus result.

[0034] In this embodiment, the consensus mechanism of the blockchain network includes a node-level consensus mechanism, a block-level consensus mechanism, and a system-level consensus mechanism. The step of verifying the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain the data fingerprint consensus result includes: verifying the data fingerprints uploaded by each travel platform business node through the business node-level consensus mechanism of the blockchain network to obtain a node-level consensus result; verifying the node-level consensus result through the block-level consensus mechanism of the blockchain network to obtain a block-level consensus result; and verifying the block-level consensus result through the system-level consensus mechanism of the blockchain network to obtain the data fingerprint consensus result.

[0035] Specifically, the consensus verification for consistency adopts a multi-level consensus mechanism, including node-level consensus verification, block-level consensus verification, and system-level consensus verification. Therefore, a three-layer consensus architecture of node-level, block-level, and system-level is used to ensure data consistency.

[0036] Step S103: Verify the consistency between the data fingerprint uploaded by each business node and the data fingerprint consensus result through the consistency rules pre-set in the smart contract.

[0037] In this embodiment, a smart contract is a programmable, automatically executed computer protocol running on the blockchain. Once deployed to the blockchain, it can automatically execute transactions or operations according to preset conditions without third-party intervention, and the execution result is immutable and fully traceable. The execution result of the smart contract must pass the consensus of all nodes in the blockchain network before it can be packaged onto the blockchain, preventing malicious nodes from tampering with the execution result. In this embodiment, the smart contract acts as the "brain" of the system, pre-setting consistency rules and automatically executing hash comparison, anomaly detection, and alarm triggering logic. Specifically, after the consensus mechanism of the blockchain network completes verification, the smart contract automatically compares the data fingerprints of each business node with the data fingerprint consensus result to detect whether there are any anomalies in the data.

[0038] Step S104: Based on the verification results of each consistency verification, perform consistency monitoring on the asset data of each business node of the travel SaaS platform.

[0039] In this embodiment, the verification result of each consistency verification refers to the result of the blockchain network verifying the consistency between the data fingerprint and the consensus result of the asset data of each business node. This can be either inconsistent between the data fingerprint and the consensus result, or consistent between the data fingerprint and the consensus result. If the data fingerprint and the consensus result of the asset data of each business node are consistent, the consistency monitoring result output is "The asset data of each business node of the travel SaaS platform is consistent." If the data fingerprint of the asset data of any business node is inconsistent with the consensus result, the consistency monitoring result output is "Inconsistent asset data exists among business nodes of the travel SaaS platform."

[0040] In this embodiment, the steps of monitoring the consistency of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification include: if the data fingerprint of one or more business nodes is found to be inconsistent with the data fingerprint consensus result based on the verification results of each consistency verification, then anomaly information is recorded through a smart contract. The anomaly information includes the node information and data fingerprint of the business node with inconsistent data; if the data fingerprint of each business node is found to be consistent with the data fingerprint consensus result based on the verification results of each consistency verification, then the status information of the successful verification of the data fingerprint of each business node is recorded through a smart contract.

[0041] This further includes: when abnormal information is detected in a smart contract record, determining the abnormality level based on the number of business nodes with inconsistent data in the abnormal information; if the abnormality level is a level three alarm, sending an emergency notification to the administrator through the first notification channel so that the administrator can immediately perform abnormality handling; if the abnormality level is a level two alarm, sending a viewing notification to the administrator through the second notification channel so that the administrator is passively informed of the abnormal information; if the abnormality level is a level one alarm, sending a notification that does not require a response to the administrator through the third notification channel so that the administrator is proactively informed of the abnormal information.

[0042] Furthermore, after the above steps of monitoring the consistency of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification, the steps also include: obtaining the monitoring results of consistency monitoring, which include abnormal information or status information that the data fingerprints of each business node have been successfully verified; storing the monitoring results in the distributed ledger of the blockchain network and the off-chain database; wherein, the off-chain database also stores the data fingerprints of the asset data uploaded by each business node.

[0043] Specifically, when the data fingerprints of one or more business nodes are identified as inconsistent with the data fingerprint consensus results based on the verification results of various consistency verifications, anomaly information is recorded. This information includes the anomaly content, the time of occurrence, and the nodes involved. In addition, a monitoring and alarm center is configured. This center listens for anomaly information issued by smart contracts and triggers multi-level alarms via email, SMS, and IM tools according to preset rules. For example, different levels of alarms are triggered based on the severity of the anomaly: Level 1 alarm: Internal platform notification; Level 2 alarm: Email / SMS alert; Level 3 alarm: Emergency notification via telephone and multiple channels.

[0044] Furthermore, the monitoring results of consistency monitoring are stored and traced. Off-chain database storage: Complete monitoring results are stored in a database such as MySQL / PostgreSQL. Blockchain distributed ledger: Monitoring results and data fingerprints are stored on the blockchain to ensure immutability and traceability. For example, if the data fingerprint is a hash value, the verification result and hash value are stored on the blockchain. Additionally, a visualization platform is configured. The visualization platform provides administrators with a visual dashboard to uniformly display monitoring status, alarm information, and on-chain and off-chain data traceability results.

[0045] Specifically, distributed ledgers can immutably store the hash values ​​and operation logs of all asset data, providing ultimate data consistency proof and audit traceability capabilities. Off-chain databases can store complete raw asset data and monitoring details, supporting complex queries from business systems and rapid display on front-end interfaces.

[0046] The aforementioned asset data consistency monitoring method for a travel SaaS platform, applied to a blockchain network, includes: receiving data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; verifying the consensus of the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain a data fingerprint consensus result; verifying the consistency between the data fingerprints uploaded by each business node and the data fingerprint consensus result through pre-defined consistency rules in a smart contract; and monitoring the consistency of asset data at each business node of the travel SaaS platform based on the verification results of each consistency verification. Therefore, by automatically executing data consistency verification through blockchain smart contracts, the consistency level of asset data in a multi-business node environment is significantly improved.

[0047] The following is a specific example illustrating one of the asset data consistency monitoring methods for travel SaaS platforms: The overall architecture diagram of the system implementing the above-mentioned asset data consistency monitoring method for travel SaaS platforms is as follows: Figure 2 As shown, the detailed descriptions of each component are shown in the table below:

[0048] in, Figure 2 The data consistency monitoring process implemented by the overall system architecture shown is as follows: Figure 3 As shown. Among them, Figure 3 The flowchart stages are explained in the table below:

[0049] Combination Figure 2 and Figure 3 As shown, a method for monitoring asset data consistency for a travel SaaS platform includes the following steps:

[0050] 1. Data Acquisition and Hash Value Generation: Through the data acquisition modules deployed at various business nodes of the travel SaaS platform, asset data (including vehicle status data, trip transaction data, user credit data, and payment settlement data) is collected in real time. The collected asset data is then encrypted using a hash algorithm (such as SHA-256) to generate a unique hash value for the data fingerprint.

[0051] 2. Data Upload and Consensus Verification: The hash values ​​generated by each business node are uploaded to the blockchain network (using a consortium blockchain architecture), and data consistency verification is automatically executed through smart contracts. The smart contracts pre-define the criteria and rules for judging data consistency.

[0052] 3. Consistency Verification and Anomaly Detection: After the blockchain network's consensus mechanism has completed verification, the smart contract automatically compares the hash values ​​of each business node with the consensus result to detect any anomalies in the asset data. Consistency verification employs a multi-level consensus mechanism, including node-level consensus, block-level consensus, and system-level consensus.

[0053] 4. Anomaly Handling and Alarms: If inconsistencies in asset data are detected, the smart contract records the anomaly information (including the inconsistent data content, the time of occurrence, and the nodes involved) and triggers a multi-level alarm mechanism to notify platform administrators.

[0054] 5. Data storage and traceability: Complete monitoring results are stored simultaneously in the blockchain distributed ledger and off-chain database, leveraging the immutability and traceability of the blockchain to ensure the reliability of the test results.

[0055] In summary, the asset data consistency monitoring method for a travel SaaS platform proposed in this application can: 1. Improve data consistency: By automatically executing data consistency verification through blockchain smart contracts, the consistency level of asset data in a multi-node environment is significantly improved.

[0056] 2. Enhanced system reliability: The decentralized architecture eliminates single points of failure, and the multi-replica storage of data improves the system's fault tolerance and availability.

[0057] 3. Improved monitoring efficiency: Multi-level consensus mechanisms and automatic smart contract verification reduce manual intervention and improve monitoring efficiency, making it particularly suitable for real-time data monitoring scenarios involving high-frequency travel.

[0058] 4. Enhanced security and privacy: The immutability of hash encryption and blockchain ensures data security, while access control mechanisms protect data privacy.

[0059] 5. Improved traceability: All data operations and consistency verification results are permanently recorded on the blockchain, providing a complete audit trail and facilitating problem tracing and accountability.

[0060] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0061] In some exemplary embodiments of this application, such as Figure 4 As shown, an asset data consistency monitoring device for a travel SaaS platform is provided, applied to a blockchain network, including a receiving module 401, a consensus verification module 402, a consistency verification module 403, and a consistency monitoring module 404. The receiving module 401 receives data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; the consensus verification module 402 verifies the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain a data fingerprint consensus result; the consistency verification module 403 verifies the consistency between the data fingerprints uploaded by each business node and the data fingerprint consensus result through pre-defined consistency rules in a smart contract; and the consistency monitoring module 404 monitors the consistency of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification.

[0062] In certain exemplary embodiments of this application, the consensus mechanism of the blockchain network includes a node-level consensus mechanism, a block-level consensus mechanism, and a system-level consensus mechanism. The aforementioned consensus verification of data fingerprints uploaded by each business node through the blockchain network's consensus mechanism to obtain a data fingerprint consensus result includes: verifying the data fingerprints uploaded by each travel platform business node through the blockchain network's business node-level consensus mechanism to obtain a node-level consensus result; verifying the node-level consensus result through the blockchain network's block-level consensus mechanism to obtain a block-level consensus result; and verifying the block-level consensus result through the blockchain network's system-level consensus mechanism to obtain a data fingerprint consensus result.

[0063] In some exemplary embodiments of this application, the above-mentioned consistency monitoring of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification includes: if the data fingerprint of one or more business nodes is identified as inconsistent with the data of the data fingerprint consensus result based on the verification results of each consistency verification, then anomaly information is recorded through a smart contract, the anomaly information including the node information and data fingerprint of the business node with inconsistent data; if the data fingerprint of each business node is identified as consistent with the data of the data fingerprint consensus result based on the verification results of each consistency verification, then the status information of the successful verification of the data fingerprint of each business node is recorded through a smart contract.

[0064] In some exemplary embodiments of this application, an asset data consistency monitoring device for a travel SaaS platform further includes an alarm module, which is used to determine the anomaly level based on the number of business nodes with inconsistent data in the anomaly information when anomaly information is detected in a smart contract record; if the anomaly level is a level three alarm, an emergency notification is sent to the administrator through a first notification channel so that the administrator can immediately perform anomaly handling; if the anomaly level is a level two alarm, a viewing notification is sent to the administrator through a second notification channel so that the administrator is passively informed of the anomaly information; if the anomaly level is a level one alarm, a notification that does not require a response is sent to the administrator through a third notification channel so that the administrator is actively informed of the anomaly information.

[0065] In some exemplary embodiments of this application, an asset data consistency monitoring device for a travel SaaS platform further includes a storage module for acquiring the monitoring results of consistency monitoring, including abnormal information or status information that the data fingerprints of each business node have been successfully verified; storing the monitoring results in the distributed ledger of the blockchain network and an off-chain database; wherein the off-chain database also stores the data fingerprints of the asset data uploaded by each business node.

[0066] In some exemplary embodiments of this application, the blockchain network includes a travel SaaS platform, service provider nodes, and regulatory agency nodes; the multiple business nodes of the travel SaaS platform include nodes of various asset data sources, and the asset data provided by the nodes of various asset data sources includes one or more of vehicle status data, trip transaction data, user credit data, and payment settlement data; wherein, any node of any asset data source performs hash processing on the asset data of any node of any asset data source through a hash algorithm, and uses the hash value obtained after hash processing as the data fingerprint of the asset data of any node of any asset data source.

[0067] In some exemplary embodiments of this application, a data acquisition client is deployed in any node of the asset data source. The data acquisition client collects one or more of the following by changing the data capture method, the interface call method of the application programming interface, and the log capture method, and transmits the collected asset data to the message queue in the node of the asset data source. A stream processing engine is configured in the node of the asset data source. After the stream processing engine obtains the collected asset data from the message queue, performs hash processing, and uploads the resulting hash value as the data fingerprint of the collected asset data to the blockchain network.

[0068] For specific limitations regarding the asset data consistency monitoring device for a travel SaaS platform, please refer to the limitations of the asset data consistency monitoring method for a travel SaaS platform described above, which will not be repeated here. Each module in the aforementioned asset data consistency monitoring device for a travel SaaS platform can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0069] In some exemplary embodiments of this application, an electronic device is provided, which may be a server in a blockchain network, and its internal structure diagram may be as follows: Figure 5As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data fingerprints and other data uploaded by various business nodes. The network interface communicates with external business nodes via a network connection. When executed by the processor, the computer program implements a method for monitoring asset data consistency for a travel SaaS platform.

[0070] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0071] In some exemplary embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of an asset data consistency monitoring method for a travel SaaS platform as described in any of the above exemplary embodiments.

[0072] In some exemplary embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of an asset data consistency monitoring method for a travel SaaS platform as described in any of the exemplary embodiments above.

[0073] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0074] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for monitoring asset data consistency in a travel SaaS platform, characterized in that, Applied to blockchain networks, the method includes: Receives data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; The consensus mechanism of the blockchain network is used to verify the data fingerprints uploaded by each business node, and the data fingerprint consensus result is obtained. The consistency of the data fingerprints uploaded by each business node with the consensus results of the data fingerprints is verified by the consensus rules pre-set in the smart contract. Based on the verification results of each consistency verification, the asset data of each business node of the travel SaaS platform is monitored for consistency.

2. The method according to claim 1, characterized in that, The consensus mechanisms of the blockchain network include node-level consensus mechanisms, block-level consensus mechanisms, and system-level consensus mechanisms. The consensus verification of data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network to obtain data fingerprint consensus results includes: The data fingerprints uploaded by each travel platform business node are verified through the business node-level consensus mechanism of the blockchain network to obtain the node-level consensus result. The block-level consensus result is obtained by verifying the node-level consensus result through the block-level consensus mechanism of the blockchain network. The data fingerprint consensus result is obtained by verifying the block-level consensus result through the system-level consensus mechanism of the blockchain network.

3. The method according to claim 1 or 2, characterized in that, The consistency monitoring of asset data at each business node of the travel SaaS platform based on the verification results of each consistency verification includes: If, based on the verification results of each consistency verification, it is found that the data fingerprints of one or more business nodes are inconsistent with the data fingerprint consensus result, then the abnormal information is recorded through the smart contract. The abnormal information includes the node information of the business nodes with inconsistent data and the data fingerprint. If the data fingerprints of each business node are identified as consistent with the data fingerprint consensus results based on the verification results of each consistency verification, then the smart contract records the status information that the data fingerprints of each business node have been successfully verified.

4. The method according to claim 3, characterized in that, The method further includes: When abnormal information is detected in the smart contract record, the abnormality level is determined based on the number of business nodes with inconsistent data in the abnormal information. If the anomaly level is a Level 3 alarm, an emergency notification will be sent to the administrator through the first notification channel so that the administrator can immediately perform anomaly handling. If the anomaly level is a level 2 alarm, a viewing notification will be sent to the administrator through a second notification channel so that the administrator is passively informed of the anomaly information; If the anomaly level is a Level 1 alarm, a notification that does not require a response will be sent to the administrator through a third notification channel so that the administrator can proactively become aware of the anomaly information.

5. The method according to claim 3, characterized in that, The method further includes: Obtain the monitoring results of the consistency monitoring, including the abnormal information, or the status information that the data fingerprints of each business node have been successfully verified; The monitoring results are stored in the distributed ledger of the blockchain network and an off-chain database; The off-chain database also stores data fingerprints of asset data uploaded by each business node.

6. The method according to claim 1, characterized in that, The blockchain network includes the travel SaaS platform, service provider nodes, and regulatory agency nodes; The multiple business nodes of the travel SaaS platform include nodes of various asset data sources. The asset data provided by the nodes of various asset data sources includes one or more of the following: vehicle status data, trip transaction data, user credit data, and payment settlement data. In this process, a node of any asset data source performs hash processing on the asset data of the node of any asset data source using a hash algorithm, and uses the hash value obtained after hash processing as the data fingerprint of the asset data of the node of any asset data source.

7. The method according to claim 6, characterized in that, A data acquisition client is deployed in any node of the asset data source. The data acquisition client collects one or more of the vehicle status data, trip transaction data, user credit data, and payment settlement data by changing the data capture method, the interface call method of the application programming interface, and the log capture method, and transmits the collected asset data to the message queue in the node of the asset data source. A stream processing engine is configured in any node of the asset data source. The stream processing engine obtains the collected asset data from the message queue, performs hash processing, and uploads the resulting hash value as the data fingerprint of the collected asset data to the blockchain network.

8. An asset data consistency monitoring device for a travel SaaS platform, characterized in that, The device, applied to a blockchain network, includes: The receiving module is used to receive data fingerprints of asset data uploaded by multiple business nodes of the travel SaaS platform; The consensus verification module is used to verify the data fingerprints uploaded by each business node through the consensus mechanism of the blockchain network, and obtain the data fingerprint consensus result. The consistency verification module is used to verify the consistency between the data fingerprint uploaded by each business node and the consensus result of the data fingerprint according to the consistency rules preset in the smart contract. The consistency monitoring module is used to monitor the consistency of asset data of each business node of the travel SaaS platform based on the verification results of each consistency verification.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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