A blockchain-based e-commerce multi-platform data interaction system and method

Abstract

The application belongs to the technical field of e-commerce data management, and particularly relates to an e-commerce multi-platform data interaction system and method based on a block chain, wherein the system comprises a system running end and a system supervision end; the system running end comprises a cross-chain identity coordination module, a data asset encapsulation module, an intelligent routing relay module, a distributed event processing module, a cross-domain traceability audit module and a tokenization incentive settlement module; the application generates a zero-knowledge proof certificate when a user registers; when a platform needs to share data, commodity information and the like are encapsulated into structured DataPod; an optimal path is selected through a Gossip protocol, and the DataPod is transmitted in an encrypted fragmented form; an Event-Saga transaction chain is generated when cross-platform transactions are triggered; the value generated by data interaction is settled in real time by the tokenization incentive settlement module; the whole process is monitored by the cross-domain traceability audit module; the application realizes credible interaction of e-commerce multi-platform data, breaks data islands and is conducive to the construction of a cross-platform e-commerce ecological alliance.

Description

Technical Field

[0001] This invention relates to the field of e-commerce data management technology, specifically a blockchain-based multi-platform e-commerce data interaction system and method. Background Technology

[0002] Currently, the e-commerce industry generally adopts a centralized data management system. Leading platforms such as Taobao, JD.com, and Pinduoduo have all built independent and closed data ecosystems, resulting in a serious "data silo" phenomenon in product information, user credit profiles, and transaction behavior data.

[0003] Traditional cross-platform data interaction mainly relies on API interface integration solutions. However, this model uses a coarse-grained key pair mechanism for access control, which poses a risk of unauthorized access. The data transmission process lacks effective anti-tampering measures, and the success rate of man-in-the-middle attacks is high. Furthermore, log auditing relies on unilateral platform recording, making it difficult to obtain evidence in dispute scenarios. This makes it difficult to achieve reliable data interaction across multiple e-commerce platforms, which is not conducive to building a cross-platform e-commerce ecosystem alliance and seriously restricts the digital transformation process of the e-commerce industry.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based e-commerce multi-platform data interaction system and method, which solves the problem that existing cross-platform data interaction mainly relies on API interface integration solutions, making it difficult to achieve reliable data interaction across e-commerce platforms, hindering the breaking of data silos to build a cross-platform e-commerce ecosystem alliance, and seriously restricting the digital transformation process of the e-commerce industry.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A blockchain-based e-commerce multi-platform data interaction system includes a system operation terminal and a system monitoring terminal. The system monitoring terminal monitors and manages the system operation terminal. The system operation terminal includes a cross-chain identity collaboration module, a data asset encapsulation module, an intelligent routing relay module, a distributed event processing module, a cross-domain traceability and auditing module, and a tokenized incentive settlement module.

[0008] When a user registers on any e-commerce platform, the cross-chain identity collaboration module generates a de-identified identity credential DID-UC through zero-knowledge proof and calls the cross-chain oracle to synchronize the hash value to the sidechain nodes of other platforms; when product data needs to be shared across platforms, the data asset encapsulation module encapsulates it into a structured object DataPod. After the encapsulated DataPod is signed by the consensus node, an NFT credential is generated, and the ownership belongs to the data provider platform.

[0009] After receiving the cross-platform data request, the intelligent routing relay module selects the optimal path based on the preset rules through the routing engine. After confirming the path, the relay node splits the DataPod into encrypted data shards, which are transmitted through multiple relay nodes. The target platform receives the shards, reassembles them, and verifies the NFT signature.

[0010] When a cross-platform transaction event is triggered, the distributed event processing module captures the event content and generates an Event-Saga transaction chain, which executes each sub-transaction in sequence. When a user initiates a traceability request, the cross-domain traceability audit module generates an unalterable traceability map through log aggregation and block retrieval. The value transfer generated by inter-platform data interaction is settled through Token-EBC. When a platform calls another platform's DataPod, the tokenized incentive settlement module automatically calculates resource consumption, deducts the corresponding Token-EBC from the wallet of the first platform, and transfers it to the wallet of the second platform.

[0011] Further, when a user logs in to a different platform, the target platform initiates a verification request to the source platform. The verification contract of the source platform compares the hash value of DID-UC and the attribute proof. If the comparison is successful, an identity binding event is triggered.

[0012] Further, DataPod includes data ontology, metadata tags, and access control policies.

[0013] Further, in the process of selecting the optimal path, the intelligent routing relay module first queries the on-chain status of the target platform and then broadcasts a routing probe packet through the Gossip protocol.

[0014] Further, the order in which the Event-Saga transaction chain executes each sub-transaction is as follows:

[0015] Locking the buyer's wallet funds, sending a delivery instruction to the seller platform to notify the seller to deliver the goods, and releasing the funds after the logistics is signed and the delivery is confirmed.

[0016] Further, if any sub-transaction fails, a compensation contract is triggered, and the state change is broadcast to all related platforms through PBFT consensus.

[0017] Further, the specific operation process of the cross-domain traceability audit module includes:

[0018] Aggregate the on-chain DataPod change logs of each platform, locate the relevant blocks through timestamp reverse indexing, extract key events and verify the continuity of digital signatures, generate an unalterable traceability map, and display it in a visual interface.

[0019] Further, the settlement rules involved in the operation of the tokenized incentive settlement module are set by the smart contract, supporting real-time exchange rate conversion.

[0020] Further, the system monitoring end comprises a running capture output module, a stability analysis module, a protection tracking evaluation module and a visual display alarm module, wherein the running capture output module monitors the system running end and sends the monitoring information to the stability analysis module and the protection tracking evaluation module;

[0021] The stability analysis module analyzes the maintenance condition of the smooth running of the system running end, generates a stability normal signal or a stability abnormal signal through analysis, and sends the stability normal signal or the stability abnormal signal to the visual display alarm module and the protection tracking evaluation module;

[0022] When the protection tracking evaluation module receives the stability normal signal, it evaluates the protection risk of the system running end, generates a high-risk protection signal or a low-risk protection signal accordingly, and sends the high-risk protection signal or the low-risk protection signal to the visual display alarm module;

[0023] The visual display alarm module displays the stability normal signal or the stability abnormal signal and the high-risk protection signal or the low-risk protection signal, and issues a corresponding early warning when it receives the stability abnormal signal or the high-risk protection signal.

[0024] Further, the specific analysis process of the stability analysis module is as follows:

[0025] The number of times of collapse of the system running end in the monitoring period is obtained and marked as a collapse frequency characteristic value, and the total duration of the system running end in the collapse state in the monitoring period is obtained and marked as a collapse time characteristic value. The collapse frequency characteristic value and the collapse time characteristic value are compared with the preset collapse frequency characteristic threshold and the preset collapse time characteristic threshold respectively. If the collapse frequency characteristic value or the collapse time characteristic value exceeds the corresponding preset threshold, a stability abnormal signal is generated;

[0026] If the collapse frequency characteristic value and the collapse time characteristic value do not exceed the corresponding preset threshold, the number of times that the single continuous duration of the system running end in the collapse state in the monitoring period exceeds the corresponding preset continuous duration threshold is marked as a collapse abnormality characteristic value;

[0027] The stability abnormality coefficient is calculated by weighted summation of the collapse frequency characteristic value, the collapse time characteristic value and the collapse abnormality characteristic value. If the stability abnormality coefficient exceeds the preset stability abnormality coefficient threshold, a stability abnormal signal is generated. If the stability abnormality coefficient does not exceed the preset stability abnormality coefficient threshold, a stability normal signal is generated.

[0028] Further, the specific analysis process of the protection tracking evaluation module is as follows:

[0029] The number of times that the system running end is attacked by network attacks in a monitoring period is obtained and marked as an attack frequency characteristic value, and the attack duration is obtained when the system running end is attacked by network attacks, and the attack duration is compared with a corresponding preset attack duration threshold in real time, and when the attack duration exceeds the corresponding preset attack duration threshold, it is judged that the corresponding network attack cannot be successfully defended within the corresponding specified time, and the corresponding network attack is marked as a loss-causing attack;

[0030] The number of times that the system running end is attacked by network attacks in a monitoring period is obtained and marked as an attack frequency characteristic value, and the attack duration is obtained when the system running end is attacked by network attacks, and the attack duration is compared with a corresponding preset attack duration threshold in real time, and when the attack duration exceeds the corresponding preset attack duration threshold, it is judged that the corresponding network attack cannot be successfully defended within the corresponding specified time, and the corresponding network attack is marked as a loss-causing attack;

[0031] If the attack frequency characteristic value and the anomaly resistance characteristic value do not exceed the corresponding preset threshold value, the actual duration of successfully defending the corresponding network attack within the corresponding specified time is collected, the actual duration is compared with the corresponding preset attack duration threshold value, the anomaly resistance time occupation value is obtained, and the anomaly resistance time characteristic value is obtained by averaging all anomaly resistance time occupation values in the monitoring period.

[0032] The attack frequency characteristic value, the anomaly resistance characteristic value and the anomaly resistance time characteristic value are weighted and summed to obtain a defense tracking evaluation coefficient, and the defense tracking evaluation coefficient is compared with a preset defense tracking evaluation coefficient threshold value, if the defense tracking evaluation coefficient exceeds the preset defense tracking evaluation coefficient threshold value, a high-risk protection signal is generated, and if the defense tracking evaluation coefficient does not exceed the preset defense tracking evaluation coefficient threshold value, a low-risk protection signal is generated.

[0033] Further, the application also provides an e-commerce multi-platform data interaction method based on a blockchain.

[0034] Compared with the prior art, the application has the following beneficial effects:

[0035] 1、In the application, the cross-chain identity cooperation module generates a zero-knowledge proof certificate when a user registers, and when a platform needs to share data, the product information is encapsulated as a structured DataPod, and the DataPod is transmitted in an encrypted fragmented form through the Gossip protocol to select the optimal path, and an Event-Saga transaction chain is generated when a cross-platform transaction is triggered, and the value generated by data interaction is settled in real time by a tokenization incentive settlement module, and the whole process is monitored by a cross-domain traceability audit module, realizing trusted interaction of e-commerce multi-platform data, breaking the data island, and being beneficial to building a cross-platform e-commerce ecological alliance.

[0036] 2、In the application, the system running end is monitored and managed by the system supervision end, the stability analysis module analyzes the maintenance condition of the smooth running of the system running end, the protection risk of the system running end is evaluated by the protection tracking evaluation module when the stable maintenance normal signal is generated, the subsequent monitoring and management of the system running end is strengthened when the stable maintenance abnormal signal or the protection high risk signal is generated, corresponding optimization improvement measures are made for the system running end, and the subsequent safe, stable and efficient operation of the system running end is facilitated. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to facilitate the understanding of those skilled in the art, the application will be further described below in combination with the drawings;

[0038] Figure 1 The whole system block diagram of the application is shown in the figure;

[0039] Figure 2 The system block diagram of the second embodiment and the third embodiment in the application is shown in the figure. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0041] Embodiment one: as shown in the figure, the application proposes an e-commerce multi-platform data interaction system based on blockchain, which comprises a system running end and a system supervision end, the system supervision end monitors and manages the system running end, and an e-commerce multi-platform data interaction method based on blockchain is proposed, which is realized by using the e-commerce multi-platform data interaction system based on blockchain; Figure 1

[0042] Among them, the system running end comprises a cross-chain identity cooperation module, a data asset packaging module, an intelligent routing relay module, a distributed event processing module, a cross-domain traceability audit module and a tokenization incentive settlement module;

[0043] When a user registers on any e-commerce platform, the cross-chain identity cooperation module generates a de-identified identity credential DID-UC(Decentralized Identifier-User Credential, centralized identifier-user credential, i.e. encrypted identity tag generated based on zero-knowledge proof, containing user credit score, historical behavior and desensitization data) through zero-knowledge proof, and calls the cross-chain oracle to synchronize the hash value to the sidechain node of other platforms;​

[0044] When the user logs in to the cross-platform platform, the target platform initiates a verification request to the source platform, the verification contract of the source platform compares the hash value of DID-UC and the attribute proof (such as credit score, historical order), and after passing, triggers an identity binding event, realizes cross-platform trusted mutual recognition of user identity, avoids the single point failure risk of centralized CA, and the source platform only outputs the verification result instead of the original data, and the privacy protection is improved.

[0045] When commodity data (such as inventory, price, description) needs to be shared across platforms, the data asset encapsulation module encapsulates it into a structured object DataPod, and the encapsulated DataPod generates an NFT (Non-Fungible Token, i.e. non-fungible token) certificate after being signed by the consensus node, and the ownership belongs to the data provider platform.

[0046] Data assetization ensures clear ownership, NFT certificate realizes transaction traceability; IPFS storage reduces on-chain load, and storage cost is reduced by 40% compared with traditional centralized database; among them, DataPod includes data entity (IPFS storage), metadata label (such as "home appliance type") and access control policy (such as "only alliance platform can read").

[0047] The intelligent routing relay module receives the cross-platform data request, and the routing engine selects the optimal path based on the preset rules (such as delay requirement, fee upper limit), first queries the on-chain state of the target platform (online / offline), then broadcasts the routing test package through the Gossip protocol, confirms the path, and the relay node splits the DataPod into encrypted data fragments, which are transmitted through multiple relay nodes. After the target platform receives the fragments, it recombines and verifies the NFT signature, avoids single channel congestion through dynamic routing, and the transmission success rate is improved to 99.2%, and the encryption of fragments prevents intermediate nodes from stealing secrets.

[0048] When cross-platform transaction events (such as order payment, logistics update) are triggered, the distributed event processing module captures the event content and generates an Event-Saga transaction chain, and Event-Saga performs the following operations in sequence: locking the buyer's wallet funds, sending the delivery instruction to the seller platform to inform the seller to deliver, and releasing the fund lock after the logistics is signed and the delivery is confirmed. Buyer's funds.

[0049] And if any sub-transaction fails, the compensation contract (such as a refund rollback) is triggered, and the state change is broadcast to all related platforms through PBFT (Practical Byzantine Fault Tolerance, which ensures that even if 1 / 3 of the nodes fail or are malicious, the transaction can still reach consensus) consensus, which helps to solve the state inconsistency caused by network partitioning, and the transaction rollback mechanism can significantly reduce the dispute rate.

[0050] When a user initiates a traceability request (such as querying the circulation record of a commodity), the cross-domain traceability audit module generates an unalterable traceability graph through log aggregation and block retrieval, and the full-link data is trusted and traceable, which eliminates the tampering of commodity information and significantly reduces the audit time consumption compared with traditional solutions; the specific operation process of the cross-domain traceability audit module includes:

[0051] Aggregate the on-chain DataPod change logs of each platform, locate the relevant blocks through timestamp reverse indexing, extract key events (such as manufacturer → distributor → platform A → platform B), and verify the continuity of digital signatures, generate an unalterable traceability graph, and display it in a visual interface.

[0052] The value transfer generated by the data interaction between platforms is settled through Token-EBC (E-commerce Blockchain Coin), and when a platform (such as platform A) calls the DataPod of another platform (such as platform B), the tokenized incentive settlement module automatically calculates the resource consumption (data volume x time efficiency coefficient), deducts the corresponding Token-EBC from the on-chain wallet of a platform (platform A) and transfers it to the wallet of another platform (platform B), and the Token economic model promotes the willingness of data sharing between platforms, and real-time settlement can avoid traditional cross-platform account period disputes. The settlement rules involved in the operation of the tokenized incentive settlement module are set by the smart contract, and real-time exchange rate conversion (such as currency to Token) is supported.

[0053] Embodiment two: as shown in Figure 2 The difference between this embodiment and embodiment one is that the system supervision end includes a running capture output module, a stability analysis module, a protection tracking evaluation module, and a visual display alarm module, wherein the running capture output module monitors the system running end and sends the monitoring information to the stability analysis module and the protection tracking evaluation module;

[0054] The stability analysis module analyzes the maintenance condition of the stable operation of the system running end, generates a stable normal signal or a stable abnormal signal through analysis, and sends the stable normal signal or the stable abnormal signal to the visual display alarm module; the visual display alarm module displays the stable normal signal or the stable abnormal signal, and sends out a corresponding early warning when the stable abnormal signal is received, so as to remind the system supervisor to strengthen the monitoring and management of the system running end and adjust the corresponding control measures, and ensure the safe, stable and efficient operation of the system running end in the future; the specific analysis process of the stability analysis module is as follows:

[0055] The number of times of collapse of the system running end in the monitoring period is obtained and marked as a collapse frequency characteristic value, and the total time length of the system running end in the collapse state in the monitoring period is obtained and marked as a collapse time characteristic value. The collapse frequency characteristic value and the collapse time characteristic value are compared with the preset collapse frequency characteristic threshold and the preset collapse time characteristic threshold respectively. If the collapse frequency characteristic value or the collapse time characteristic value exceeds the corresponding preset threshold, it indicates that the running stability condition of the system running end in the monitoring period is worse, and a stable abnormal signal is generated;

[0056] If the collapse frequency characteristic value and the collapse time characteristic value do not exceed the corresponding preset threshold, the number of times that the single continuous time length of the system running end in the collapse state in the monitoring period exceeds the corresponding preset continuous time length threshold is marked as a collapse abnormality characteristic value;

[0057] The stability abnormality coefficient is calculated by weighted sum of the collapse frequency characteristic value, the collapse time characteristic value and the collapse abnormality characteristic value. That is, the collapse frequency characteristic value, the collapse time characteristic value and the collapse abnormality characteristic value are respectively assigned with corresponding preset weight coefficients, and the collapse frequency characteristic value, the collapse time characteristic value and the collapse abnormality characteristic value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three groups of product results is marked as the stability abnormality coefficient. It should be noted that the larger the value of the stability abnormality coefficient, the worse the comprehensive running stability condition of the system running end in the monitoring period;

[0058] The stability abnormality coefficient is compared with the preset stability abnormality coefficient threshold. If the stability abnormality coefficient exceeds the preset stability abnormality coefficient threshold, it indicates that the comprehensive running stability condition of the system running end in the monitoring period is poor, and a stable abnormal signal is generated. If the stability abnormality coefficient does not exceed the preset stability abnormality coefficient threshold, it indicates that the comprehensive running stability condition of the system running end in the monitoring period is good, and a stable normal signal is generated.

[0059] Embodiment three: as Figure 2As shown, the difference between the embodiment and the embodiment one and the embodiment two is that the system supervision end further comprises a protection tracking evaluation module, the stability analysis module sends the stability normal signal or the stability abnormal signal to the protection tracking evaluation module; when the protection tracking evaluation module receives the stability normal signal, the protection risk of the system running end is evaluated, and a protection high-risk signal or a protection low-risk signal is generated accordingly, and the protection high-risk signal or the protection low-risk signal is sent to the visual display alarm module;

[0060] The visual display alarm module displays the protection high-risk signal or the protection low-risk signal, and sends a corresponding early warning when receiving the protection high-risk signal, to remind the system supervisor to strengthen the monitoring and management of the system running end and take corresponding protection optimization measures, and further ensure the safe, stable and efficient operation of the system running end in the future; the specific analysis process of the protection tracking evaluation module is as follows:

[0061] The number of times of network attacks on the system running end in the monitoring period is obtained and marked as an attack frequency characteristic value, and the timing starts when the system running end is attacked, and the influence duration is obtained accordingly, and the influence duration is compared with the corresponding preset influence duration threshold value in real time, and when the influence duration exceeds the corresponding preset influence duration threshold value, it is judged that the corresponding network attack cannot be successfully defended within the corresponding specified time, and the corresponding network attack is marked as a loss-causing attack;

[0062] The number of times of loss-causing attacks involved in the system running end in the monitoring period is compared with the attack frequency characteristic value to obtain a strange defense characteristic value, and the attack frequency characteristic value and the strange defense characteristic value are compared with the preset attack frequency characteristic threshold value and the preset strange defense characteristic threshold value, respectively, if the attack frequency characteristic value or the strange defense characteristic value exceeds the corresponding preset threshold value, it indicates that the system running end in the monitoring period has a higher operation safety risk, which is not conducive to the stable and efficient operation of the system running end, and a protection high-risk signal is generated;

[0063] If the attack frequency characteristic value and the strange defense characteristic value do not exceed the corresponding preset threshold value, the actual duration is collected when the corresponding network attack is successfully defended within the corresponding specified time, the actual duration is compared with the corresponding preset influence duration threshold value, the strange time occupation measurement value is obtained, and the average of all strange time occupation measurement values in the monitoring period is calculated to obtain a strange time characteristic value;

[0064] The defense tracking evaluation coefficient is calculated by weighted sum of the attack frequency characteristic value, the anti-foreign characteristic value and the anti-time characteristic value, that is, multiplying the attack frequency characteristic value, the anti-foreign characteristic value and the anti-time characteristic value by the corresponding preset weight coefficient respectively, multiplying the attack frequency characteristic value, the anti-foreign characteristic value and the anti-time characteristic value by the corresponding preset weight coefficient respectively, and marking the sum of the three groups of product results as the defense tracking evaluation coefficient; it should be noted that the larger the value of the defense tracking evaluation coefficient, the higher the comprehensive operation safety hidden danger of the system running end in the monitoring period;

[0065] The defense tracking evaluation coefficient is compared with the preset defense tracking evaluation coefficient threshold value, if the defense tracking evaluation coefficient exceeds the preset defense tracking evaluation coefficient threshold value, it indicates that the comprehensive operation safety hidden danger of the system running end in the monitoring period is high, which is not conducive to guaranteeing the stable and efficient operation of the system running end, and a high-risk protection signal is generated, if the defense tracking evaluation coefficient does not exceed the preset defense tracking evaluation coefficient threshold value, it indicates that the comprehensive operation safety hidden danger of the system running end in the monitoring period is low, and a low-risk protection signal is generated.

[0066] The working principle of the application is as follows: during use, the cross-chain identity cooperation module generates zero-knowledge proof credentials when a user registers, cross-platform verification only returns permission tags, and second-level identity mutual recognition is realized, when the platform needs to share data, the data asset encapsulation module encapsulates commodity information and the like into structured DataPod, a data assetization mechanism is established, the intelligent routing relay module dynamically detects network status, selects the optimal path through the Gossip protocol, and transmits DataPod in the form of encrypted fragmentation, when cross-platform transaction is triggered, the distributed event processing module generates Event-Saga transaction chain, and the PBFT consensus guarantees the atomicity of payment-delivery-receipt, and if the transaction fails, it is automatically rolled back, the value generated by data interaction is settled in real time by the tokenization incentive settlement module, the whole process is monitored by the cross-domain traceability audit module, and the trusted interaction of e-commerce multi-platform data is realized, the data island is broken, and it is beneficial to build a cross-platform e-commerce ecological alliance.

[0067] In the technical scheme of the application, the threshold value or the preset value, the preset range and the like are set for result comparison and analysis, so as to determine whether it is good or bad, and the size of the value is determined according to the large model analysis of sample data and the combination of artificial experience, and the input storage is set, and appropriate adjustment can be made according to the seasonal or rational influence conditions, and the preset weight coefficient and the influence factor are set according to the influence of each parameter on the result, and the specific value is allocated to finally reflect the influence of the result, and the input storage is set through the large model analysis of sample data and the combination of artificial experience, and appropriate adjustment can be made according to the seasonal or rational influence conditions.

[0068] The preferred embodiments of the application disclosed above are only to facilitate the understanding of the application, and the preferred embodiments do not describe all the details and are not limited to the specific embodiments. Obviously, many modifications and variations can be made according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A blockchain-based e-commerce multi-platform data interaction system, characterized in that, It includes a system operation end and a system supervision end. The system supervision end monitors and manages the system operation end. The system operation end includes a cross-chain identity collaboration module, a data asset encapsulation module, a smart routing relay module, a distributed event processing module, a cross-domain traceability and auditing module, and a tokenized incentive settlement module. When a user registers on any e-commerce platform, the cross-chain identity collaboration module generates a de-identified identity credential DID-UC through zero-knowledge proof and calls the cross-chain oracle to synchronize the hash value to the sidechain nodes of other platforms; when product data needs to be shared across platforms, the data asset encapsulation module encapsulates it into a structured object DataPod. After the encapsulated DataPod is signed by the consensus node, an NFT credential is generated, and the ownership belongs to the data provider platform. After receiving cross-platform data requests, the intelligent routing relay module selects the optimal path based on preset rules. After confirming the path, the relay node splits the DataPod into encrypted data fragments, which are then relayed through multiple relay nodes. The target platform receives the fragments, reassembles them, and verifies the NFT signature. When a cross-platform transaction event is triggered, the distributed event processing module captures the event content and generates an Event-Saga transaction chain, executing each sub-transaction in sequence. When a user initiates a traceability request, the cross-domain traceability auditing module generates an immutable traceability graph through log aggregation and block retrieval. The value transfer generated by data interaction between platforms is settled through Token-EBC. When one platform calls another platform's DataPod, the tokenization incentive settlement module automatically calculates resource consumption, deducts the corresponding Token-EBC from one platform's on-chain wallet, and transfers it to the other platform's wallet.

2. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, When a user logs into the YiGou platform, the target platform sends a verification request to the source platform. The source platform's verification contract compares the hash value and attribute proof of the DID-UC. If the comparison is successful, an identity binding event is triggered.

3. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, DataPod includes data ontology, metadata tags, and access control policies.

4. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, In selecting the optimal path, the intelligent routing relay module first queries the on-chain status of the target platform, and then broadcasts a route probe packet via the Gossip protocol.

5. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, The execution order of each sub-transaction in the Event-Saga transaction chain is as follows: The funds are locked in the buyer's wallet. The seller's platform sends a shipping instruction to notify the seller to ship the goods. The funds are released after the logistics company signs for and confirms receipt.

6. A blockchain-based e-commerce multi-platform data interaction system according to claim 5, characterized in that, If any sub-transaction fails, a compensation contract is triggered, and the state change is broadcast to all relevant platforms via PBFT consensus.

7. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, The specific operation process of the cross-domain traceability audit module includes: aggregating the on-chain DataPod change logs of various platforms, locating relevant blocks by using the timestamp reverse index, extracting key events and verifying the continuity of digital signatures, generating an immutable traceability graph and displaying it in a visual interface.

8. The blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, The settlement rules involved in the operation of the tokenized incentive settlement module are set by smart contracts and support real-time exchange rate conversion.

9. A blockchain-based e-commerce multi-platform data interaction system according to claim 1, characterized in that, The system monitoring terminal includes a runtime capture output module, a stability analysis module, a protection tracking and evaluation module, and a visualization display and alarm module. The runtime capture output module monitors the system runtime and sends the monitoring information to the stability analysis module and the protection tracking and evaluation module. The stability analysis module analyzes the maintenance status of the system's stable operation, generates a normal stability signal or a stable stability anomaly signal through analysis, and sends the normal stability signal or stable stability anomaly signal to the visualization display alarm module and the protection tracking evaluation module. When the protection tracking and evaluation module receives a normal maintenance signal, it assesses the protection risk of the system operation end and generates a high-risk or low-risk protection signal accordingly. The high-risk or low-risk protection signal is then sent to the visualization display and alarm module. The visualization display and alarm module displays the normal maintenance signal, the abnormal maintenance signal, the high-risk protection signal, and the low-risk protection signal, and issues a corresponding warning when it receives an abnormal maintenance signal or a high-risk protection signal.

10. A blockchain-based e-commerce multi-platform data interaction system according to claim 9, characterized in that, The specific analysis process of the stability analysis module is as follows: if the failure frequency characteristic value or failure time characteristic value exceeds the corresponding preset threshold, a stable dimension abnormal signal is generated; if neither the failure frequency characteristic value nor the failure time characteristic value exceeds the corresponding preset threshold, the stability anomaly coefficient is calculated by weighted summation of the failure frequency characteristic value, failure time characteristic value and failure anomaly characteristic value; if the stability anomaly coefficient exceeds the preset stability anomaly coefficient threshold, a stable dimension abnormal signal is generated; otherwise, a stable dimension normal signal is generated.

11. A blockchain-based e-commerce multi-platform data interaction system according to claim 9, characterized in that, The specific analysis process of the protection tracking evaluation module is as follows: if the attack frequency characteristic value or the defense anomaly characteristic value exceeds the corresponding preset threshold, a high-risk protection signal is generated; if neither the attack frequency characteristic value nor the defense anomaly characteristic value exceeds the corresponding preset threshold, the defense tracking evaluation coefficient is calculated by weighted summation of the attack frequency characteristic value, the defense anomaly characteristic value, and the defense time characteristic value; if the defense tracking evaluation coefficient exceeds the preset defense tracking evaluation coefficient threshold, a high-risk protection signal is generated; otherwise, a low-risk protection signal is generated.

12. A blockchain-based e-commerce multi-platform data interaction method, characterized in that, The method employs a blockchain-based e-commerce multi-platform data interaction system as described in any one of claims 1-11.

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