E-commerce multi-platform data interaction system and method based on block chain

Abstract

The invention 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, and the system comprises a system operation end and a system supervision end. The system operation end comprises 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 auditing module and a token motivation settlement module; a zero-knowledge proof voucher is generated when a user registers, commodity information and the like are packaged into a structured DataPod when a platform needs to share data, an optimal path is selected through a Gossip protocol, the DataPod is transmitted in an encrypted fragmentation form, an Event-Saga transaction chain is generated when cross-platform transaction is triggered, and the value generated by data interaction is settled in real time by a token-based motivation settlement module. The whole process is monitored by the cross-domain traceability auditing module, credible interaction of e-commerce multi-platform data is achieved, data islands are cracked, and construction of a cross-platform e-commerce ecological alliance is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce data management, and in particular to a blockchain-based e-commerce multi-platform data interaction system and method. Background Art

[0002] The current 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 the formation of serious "data silos" for product information, user credit profiles, and transaction behavior data. Traditional cross-platform data interaction primarily relies on API integration solutions. However, this model uses a coarse-grained key pair mechanism for permission control, which poses the risk of unauthorized access. The data transmission process lacks effective anti-tampering measures, resulting in a high success rate for man-in-the-middle attacks. Furthermore, log audits rely on unilateral platform records, making evidence collection difficult in dispute scenarios. This makes it difficult to achieve trusted interaction of data across multiple e-commerce platforms, hindering the construction of a cross-platform e-commerce ecosystem alliance and severely hindering the digital transformation of the e-commerce industry. In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide an e-commerce multi-platform data interaction system and method based on blockchain, which solves the problem that the cross-platform data interaction in the existing technology mainly relies on API interface integration solutions, making it difficult to achieve trusted interaction of e-commerce multi-platform data, not conducive to breaking down data silos to build a cross-platform e-commerce ecological alliance, and seriously restricting the digital transformation process of the e-commerce industry.

[0004] To achieve the above object, the present invention provides the following technical solutions: A blockchain-based e-commerce multi-platform data interaction system 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, an intelligent routing relay module, a distributed event processing module, a cross-domain traceability audit 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. The encapsulated DataPod is signed by the consensus node and generates an NFT certificate. The ownership belongs to the data provider platform. After the intelligent routing relay module receives a cross-platform data request, the routing engine selects the optimal path based on preset rules. After confirming the path, the relay node splits the DataPod into encrypted data shards, which are relayed through multiple transfer nodes. After receiving the shards, the target platform reassembles 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 audit module generates an unalterable traceability map through log aggregation and block retrieval; the value transfer generated by data interaction between platforms is settled through Token-EBC. When one platform calls the DataPod of another platform, the tokenized incentive settlement module automatically calculates the resource consumption, deducts the corresponding Token-EBC from the on-chain wallet of one platform and transfers it to the wallet of the other platform.

[0005] Furthermore, when a user logs in to the Yigou platform, the target platform initiates a verification request to the source platform. The verification contract of the source platform compares the hash value and attribute proof of DID-UC, and triggers the identity binding event after passing.

[0006] Furthermore, DataPod contains data ontology, metadata tags and access control policies.

[0007] Furthermore, 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 the routing trial packet through the Gossip protocol.

[0008] Furthermore, the order in which the Event-Saga transaction chain executes each sub-transaction is as follows: Lock the buyer's wallet funds, send a shipping instruction to the seller's platform to notify the seller to ship, and release the funds after the logistics signs and confirms receipt to lock the buyer's funds.

[0009] Furthermore, if any sub-transaction fails, the compensation contract will be triggered, and the state change will be broadcast to all relevant platforms through PBFT consensus.

[0010] Furthermore, the specific operation process of the cross-domain traceability audit module includes: Aggregate the on-chain DataPod change logs of various platforms, locate related blocks through the reverse timestamp index, extract key events and verify the continuity of digital signatures, generate an unalterable traceability map and display it in a visual interface.

[0011] Furthermore, the settlement rules involved in the operation of the tokenized incentive settlement module are set by smart contracts, supporting real-time exchange rate conversion.

[0012] Furthermore, the system monitoring end includes an operation capture and output module, a stability analysis module, a protection tracking and evaluation module, and a visual display and alarm module. Among them, the operation capture and output module monitors the system operation end 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 stable operation of the system operation end, generates a normal stability signal or an abnormal stability signal through analysis, and sends the normal stability signal or abnormal stability signal to the visual display alarm module and the protection tracking and evaluation module; When the protection tracking and evaluation module receives a normal signal of stability maintenance, it evaluates the protection risk of the system operation 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; The visual display alarm module displays the normal stability maintenance signal or the abnormal stability maintenance signal and the high-risk protection signal or the low-risk protection signal, and issues a corresponding early warning when receiving the abnormal stability maintenance signal or the high-risk protection signal.

[0013] Furthermore, the specific analysis process of the stability analysis module is as follows: The number of crashes of the system operation terminal during the monitoring period is obtained and marked as the crash frequency characteristic value, and the total length of time the system operation terminal is in the crash state during the monitoring period is marked as the crash duration characteristic value. The crash frequency characteristic value and the crash duration characteristic value are numerically compared with the preset crash frequency characteristic threshold and the preset crash duration characteristic threshold respectively. If the crash frequency characteristic value or the crash duration characteristic value exceeds the corresponding preset threshold, a stable abnormal signal is generated; If both the crash frequency characteristic value and the crash duration characteristic value do not exceed the corresponding preset threshold value, then the number of occurrences in which the system operation terminal is in a crash state for a single duration exceeding the corresponding preset duration threshold value during the monitoring period is marked as a crash abnormality characteristic value; The stability anomaly coefficient is calculated by weighted summation of the crash frequency eigenvalue, crash time eigenvalue and crash anomaly eigenvalue, and the stability anomaly coefficient is numerically compared with the preset stability anomaly coefficient threshold. If the stability anomaly coefficient exceeds the preset stability anomaly coefficient threshold, a stability abnormality signal is generated; if the stability anomaly coefficient does not exceed the preset stability anomaly coefficient threshold, a stability normal signal is generated.

[0014] Furthermore, the specific analysis process of the protection tracking evaluation module is as follows: The number of times the system operation terminal is attacked by the network during the monitoring period is obtained and marked as the attack frequency characteristic value. The time is started when the system operation terminal is attacked by the network, and the duration of the impact is obtained based on this. The impact duration is compared with the corresponding preset impact duration threshold in real time. When the impact duration exceeds the corresponding preset impact duration threshold, it is determined that the corresponding network attack has not been successfully defended within the corresponding specified time, and the corresponding network attack is marked as a damaging attack; The number of damaging attacks on the system operation terminal during the monitoring period is calculated by ratioing the attack frequency characteristic value to obtain an abnormality prevention characteristic value. The attack frequency characteristic value and the abnormality prevention characteristic value are numerically compared with the preset attack frequency characteristic threshold and the preset abnormality prevention characteristic threshold respectively. If the attack frequency characteristic value or the abnormality prevention characteristic value exceeds the corresponding preset threshold, a high-risk protection signal is generated; If both the attack frequency characteristic value and the defense characteristic value do not exceed the corresponding preset threshold value, then the actual duration of the corresponding network attack is collected when it is successfully defended within the corresponding specified time, and the ratio of the actual duration to the corresponding preset impact duration threshold is calculated to obtain the defense time measurement value, and the average of all defense time measurement values ​​within the monitoring period is calculated to obtain the defense time characteristic value; The defense tracking evaluation coefficient is obtained by weighted summing up the attack frequency characteristic value, the defense abnormality characteristic value and the defense time characteristic value, and the defense tracking evaluation coefficient is numerically compared with the preset defense tracking evaluation coefficient threshold. If the defense tracking evaluation coefficient exceeds the preset defense tracking evaluation coefficient threshold, a high-risk defense signal is generated; if the defense tracking evaluation coefficient does not exceed the preset defense tracking evaluation coefficient threshold, a low-risk defense signal is generated.

[0015] Furthermore, the present invention also proposes a blockchain-based e-commerce multi-platform data interaction method, which adopts the above-mentioned blockchain-based e-commerce multi-platform data interaction system.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In this invention, a zero-knowledge proof credential is generated when a user registers through a cross-chain identity collaboration module. When the platform needs to share data, product information and other information are encapsulated into a structured DataPod. The optimal path is selected through the Gossip protocol, and the DataPod is transmitted in an encrypted and fragmented form. When a cross-platform transaction is triggered, an Event-Saga transaction chain is generated. The value generated by data interaction is settled in real time by the tokenized incentive settlement module. The entire process is monitored by a cross-domain traceability audit module. This enables trusted interaction of data across multiple e-commerce platforms, breaks down data silos, and facilitates the construction of a cross-platform e-commerce ecological alliance. 2. In the present invention, the system operation end is monitored and managed through the system supervision end, and the stability analysis module thereof analyzes the maintenance status of the stable operation of the system operation end. When a normal stability maintenance signal is generated, the protection risk of the system operation end is evaluated through the protection tracking and evaluation module. When an abnormal stability maintenance signal or a high-risk protection signal is generated, the subsequent monitoring and management of the system operation end is strengthened, and corresponding optimization and improvement measures are made for the system operation end, which is conducive to ensuring the subsequent safe, stable and efficient operation of the system operation end. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 is a block diagram of the overall system of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 As shown, the present invention proposes an e-commerce multi-platform data interaction system based on blockchain, including a system operation end and a system supervision end, the system supervision end monitors and manages the system operation end, and proposes an e-commerce multi-platform data interaction method based on blockchain, which is implemented using the e-commerce multi-platform data interaction system based on blockchain; The system operation end 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 audit 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 (Decentralized Identifier-User Credential) through zero-knowledge proof. This is an encrypted identity tag generated based on zero-knowledge proof, containing desensitized data such as the user's credit score and historical behavior. During cross-platform identity authentication, the source platform only returns "verification passed / failed" without leaking the original information. The cross-chain oracle is then called to synchronize the hash value to the sidechain nodes of other platforms. When a user logs in to the Yigou platform, the target platform initiates a verification request to the source platform. The verification contract of the source platform compares the hash value and attribute proof (such as credit score, historical orders) of the DID-UC. If passed, it triggers the identity binding event, realizing cross-platform trusted mutual recognition of user identity, avoiding the single point failure risk of centralized CA, and the source platform only outputs the verification result instead of the original data, which improves privacy protection.

[0020] When product data (such as inventory, price, and description) needs to be shared across platforms, the data asset encapsulation module encapsulates it into a structured object, DataPod. The encapsulated DataPod is signed by the consensus node and an NFT (Non-Fungible Token) is generated, which serves as the ownership certificate of the DataPod, ensuring that the data source is traceable and cannot be copied. Ownership belongs to the data provider platform. Data assetization ensures clear ownership, and NFT certificates enable transaction traceability; IPFS storage reduces on-chain load and reduces storage costs by 40% compared to traditional centralized databases; DataPod includes data entities (IPFS storage), metadata tags (such as "home appliances"), and access control policies (such as "readable only by the alliance platform").

[0021] After the intelligent routing relay module receives a cross-platform data request, the routing engine selects the optimal path based on preset rules (such as latency requirements and cost caps). It first queries the on-chain status (online / offline) of the target platform, and then broadcasts a routing trial package through the Gossip protocol. After confirming the path, the relay node splits the DataPod into encrypted data shards, which are relayed through multiple transit nodes. After receiving the shards, the target platform reassembles and verifies the NFT signature. Dynamic routing avoids single-channel congestion, and the transmission success rate is increased to 99.2%. Shard encryption prevents intermediate nodes from eavesdropping.

[0022] When a cross-platform transaction event (such as order payment or logistics update) is triggered, the distributed event processing module captures the event content and generates an Event-Saga transaction chain. Event-Saga performs the following operations in sequence: locks the buyer's wallet funds, sends a shipping instruction to the seller's platform to notify the seller of the shipment, and releases the funds to lock the buyer's funds after the logistics team signs for and confirms receipt.

[0023] Furthermore, if any sub-transaction fails, a compensation contract (such as a refund rollback) is triggered, and the state change is broadcast to all relevant platforms through the PBFT (Practical Byzantine Fault Tolerance, a practical Byzantine fault-tolerant algorithm in the distributed event processing module that ensures that transactions can still reach consensus even if 1 / 3 of the nodes fail or act maliciously) consensus. This helps resolve state inconsistencies caused by network partitions, and the transaction rollback mechanism can significantly reduce the dispute rate.

[0024] When a user initiates a traceability request (such as querying product circulation records), the cross-domain traceability audit module generates an unalterable traceability graph through log aggregation and block retrieval. This ensures reliable traceability of data across the entire chain, prevents product information tampering, and significantly reduces audit time compared to traditional solutions. The specific operation process of the cross-domain traceability audit module includes: Aggregate the on-chain DataPod change logs of each platform, locate the relevant blocks through the reverse timestamp index, extract key events (such as manufacturer → distributor → platform A → platform B) and verify the continuity of digital signatures, generate an unalterable traceability map and display it in a visual interface.

[0025] The value transfer generated by data exchange between platforms is settled using Token-EBC (E-commerce Blockchain Coin). When one platform (e.g., Platform A) calls the DataPod of another platform (e.g., Platform B), the tokenized incentive settlement module automatically calculates resource consumption (data volume × time efficiency factor), deducts the corresponding Token-EBC from the on-chain wallet of one platform (Platform A), and transfers it to the wallet of the other platform (Platform B). This token economic model promotes data sharing between platforms, and real-time settlement avoids traditional cross-platform billing disputes. The settlement rules involved in the operation of the tokenized incentive settlement module are set by smart contracts, supporting real-time exchange rate conversion (e.g., fiat currency to tokens).

[0026] Example 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the system monitoring end includes an operation capture and output module, a stability analysis module, a protection tracking and evaluation module, and a visual display and alarm module. Among them, the operation capture and output module monitors the system operation end 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 stable operation of the system operation end, generates a normal stability maintenance signal or an abnormal stability maintenance signal through analysis, and sends the normal stability maintenance signal or the abnormal stability maintenance signal to the visual display alarm module; the visual display alarm module displays the normal stability maintenance signal or the abnormal stability maintenance signal, and issues a corresponding warning when receiving the abnormal stability maintenance signal, so as to remind system supervisors to strengthen the monitoring and management of the system operation end and adjust the corresponding control measures to ensure the subsequent safe, stable and efficient operation of the system operation end; the specific analysis process of the stability analysis module is as follows: The number of crashes of the system operation terminal during the monitoring period is obtained and marked as the crash frequency characteristic value, and the total length of time the system operation terminal is in the crash state during the monitoring period is marked as the crash duration characteristic value. The crash frequency characteristic value and the crash duration characteristic value are numerically compared with the preset crash frequency characteristic threshold and the preset crash duration characteristic threshold respectively. If the crash frequency characteristic value or the crash duration characteristic value exceeds the corresponding preset threshold, it indicates that the operation stability of the system operation terminal during the monitoring period is worse, and a stability abnormality signal is generated; If both the crash frequency characteristic value and the crash duration characteristic value do not exceed the corresponding preset threshold value, then the number of occurrences in which the system operation terminal is in a crash state for a single duration exceeding the corresponding preset duration threshold value during the monitoring period is marked as a crash abnormality characteristic value; The stability anomaly coefficient is calculated by weighted summing the crash frequency characteristic value, the crash time characteristic value, and the crash abnormality characteristic value; that is, the crash frequency characteristic value, the crash time characteristic value, and the crash abnormality characteristic value are respectively assigned corresponding preset weight coefficients, and the crash frequency characteristic value, the crash time characteristic value, and the crash abnormality characteristic value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the stability anomaly coefficient; it should be noted that the larger the value of the stability anomaly coefficient, the worse the overall operational stability of the system operation end during the monitoring period; The stability abnormality coefficient is numerically compared with the preset stability abnormality coefficient threshold. If the stability abnormality coefficient exceeds the preset stability abnormality coefficient threshold, it indicates that the operating stability of the system operation end during the monitoring period is generally poor, and a stability maintenance abnormal signal is generated; if the stability abnormality coefficient does not exceed the preset stability abnormality coefficient threshold, it indicates that the operating stability of the system operation end during the monitoring period is generally good, and a stability maintenance normal signal is generated.

[0027] Example 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the system monitoring end further includes a protection tracking and evaluation module, and the stability analysis module sends a stability maintenance normal signal or a stability maintenance abnormal signal to the protection tracking and evaluation module; when the protection tracking and evaluation module receives the stability maintenance normal signal, it evaluates the protection risk of the system operation end and generates a high-risk protection signal or a low-risk protection signal based on the evaluation, and sends the high-risk protection signal or the low-risk protection signal to the visual display alarm module; The visual display alarm module displays high-risk or low-risk protection signals and issues corresponding warnings when receiving high-risk protection signals to remind system supervisors to strengthen monitoring and management of the system operation end and take corresponding protection optimization measures to further ensure the subsequent safe, stable and efficient operation of the system operation end. The specific analysis process of the protection tracking and evaluation module is as follows: The number of times the system operation terminal is attacked by the network during the monitoring period is obtained and marked as the attack frequency characteristic value. The time is started when the system operation terminal is attacked by the network, and the duration of the impact is obtained based on this. The impact duration is compared with the corresponding preset impact duration threshold in real time. When the impact duration exceeds the corresponding preset impact duration threshold, it is determined that the corresponding network attack has not been successfully defended within the corresponding specified time, and the corresponding network attack is marked as a damaging attack; The number of damaging attacks on the system operation terminal during the monitoring period is calculated by ratioing the attack frequency characteristic value to obtain an abnormality prevention characteristic value. The attack frequency characteristic value and the abnormality prevention characteristic value are numerically compared with the preset attack frequency characteristic threshold and the preset abnormality prevention characteristic threshold respectively. If the attack frequency characteristic value or the abnormality prevention characteristic value exceeds the corresponding preset threshold, it indicates that the system operation terminal has a high operational safety hazard during the monitoring period, which is not conducive to ensuring the stable and efficient operation of the system operation terminal, and a high-risk protection signal is generated; If both the attack frequency characteristic value and the defense characteristic value do not exceed the corresponding preset threshold value, then the actual duration of the corresponding network attack is collected when it is successfully defended within the corresponding specified time, and the ratio of the actual duration to the corresponding preset impact duration threshold is calculated to obtain the defense time measurement value, and the average of all defense time measurement values ​​within the monitoring period is calculated to obtain the defense time characteristic value; The defense tracking evaluation coefficient is calculated by weighted summing the attack frequency characteristic value, the anomaly prevention characteristic value, and the defense time characteristic value; that is, the attack frequency characteristic value, the anomaly prevention characteristic value, and the defense time characteristic value are respectively assigned corresponding preset weight coefficients and multiplied, and the attack frequency characteristic value, the anomaly prevention characteristic value, and the defense time characteristic value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the defense tracking evaluation coefficient; it should be noted that the larger the value of the defense tracking evaluation coefficient, the higher the overall operational safety risks existing in the system operation end during the monitoring period; The defense tracking evaluation coefficient is numerically compared with the preset defense tracking evaluation coefficient threshold. If the defense tracking evaluation coefficient exceeds the preset defense tracking evaluation coefficient threshold, it indicates that the operational safety hazards existing in the system operation end during the monitoring period are generally high, which is not conducive to ensuring the stable and efficient operation of the system operation 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, it indicates that the operational safety hazards existing in the system operation end during the monitoring period are generally low, and a low-risk protection signal is generated.

[0028] The working principle of the present invention is as follows: when in use, a zero-knowledge proof credential is generated by the cross-chain identity collaboration module when the user registers, and cross-platform verification only returns the permission label, realizing identity mutual recognition in seconds. When the platform needs to share data, the data asset encapsulation module encapsulates product information and the like into a structured DataPod, establishes a data assetization mechanism, and the intelligent routing relay module dynamically detects the network status, selects the optimal path through the Gossip protocol, and transmits the DataPod in the form of encrypted shards. When a cross-platform transaction is triggered, the distributed event processing module generates an Event-Saga transaction chain, and uses the PBFT consensus to ensure the atomicity of payment-shipment-receipt, and automatically rolls back if it fails. The value generated by data interaction is settled in real time by the tokenized incentive settlement module, and the entire process is monitored by the cross-domain traceability audit module, realizing trusted interaction of e-commerce multi-platform data, breaking down data silos, and facilitating the construction of a cross-platform e-commerce ecological alliance.

[0029] The thresholds, preset values, preset ranges, etc. in the technical solution of the present invention are set for result comparison and analysis in order to determine whether they are good or bad. As for their size, they are set for entry and storage based on a combination of large-scale model analysis of sample data and manual experience, and can also be appropriately adjusted based on seasonal or common-sense influencing conditions. As for the settings of preset weight coefficients, influencing factors, etc., specific numerical values ​​are assigned based on the influence of each parameter on the result, ultimately reflecting the influence on the result. They are also set for entry and storage based on a combination of large-scale model analysis of sample data and manual experience, and can also be appropriately adjusted based on seasonal or common-sense influencing conditions.

[0030] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention and enable those skilled in the art to better understand and utilize the present invention. The present invention 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 by: It includes the system operation side and the system supervision side. The system supervision side monitors and manages the system operation side. The system operation side 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 audit 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. The encapsulated DataPod is signed by the consensus node and generates an NFT certificate. The ownership belongs to the data provider platform. After the intelligent routing relay module receives a cross-platform data request, the routing engine selects the optimal path based on preset rules. After confirming the path, the relay node splits the DataPod into encrypted data shards, which are relayed through multiple transfer nodes. After receiving the shards, the target platform reassembles 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 audit module generates an unalterable traceability map through log aggregation and block retrieval; the value transfer generated by data interaction between platforms is settled through Token-EBC. When one platform calls the DataPod of another platform, the tokenized incentive settlement module automatically calculates the resource consumption, deducts the corresponding Token-EBC from the on-chain wallet of one platform and transfers it to the wallet of the other platform.

2. The blockchain-based e-commerce multi-platform data interaction system according to claim 1 is characterized in that: When a user logs in to the Yigou platform, the target platform initiates a verification request to the source platform. The verification contract of the source platform compares the hash value and attribute proof of DID-UC, and triggers the identity binding event if it passes.

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

4. The blockchain-based e-commerce multi-platform data interaction system according to claim 1 is characterized in that: 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 the routing trial packet through the Gossip protocol.

5. The blockchain-based e-commerce multi-platform data interaction system according to claim 1 is characterized in that: The order in which the Event-Saga transaction chain executes each sub-transaction is as follows: Lock the buyer's wallet funds, send a shipping instruction to the seller's platform to notify the seller to ship, and release the funds after the logistics signs and confirms receipt to lock the buyer's funds.

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

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

8. The blockchain-based e-commerce multi-platform data interaction system according to claim 1 is 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. The blockchain-based e-commerce multi-platform data interaction system according to claim 1 is characterized in that: The system monitoring end includes an operation capture and output module, a stability analysis module, a protection tracking and evaluation module, and a visual display and alarm module. The operation capture and output module monitors the system operation end 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 stable operation of the system operation end, generates a normal stability signal or an abnormal stability signal through analysis, and sends the normal stability signal or abnormal stability signal to the visual display alarm module and the protection tracking and evaluation module; When the protection tracking and evaluation module receives a normal stability signal, it evaluates the protection risk of the system operation end, generates a high-risk protection signal or a low-risk protection signal based on this, and sends the high-risk protection signal or the low-risk protection signal to the visual display alarm module; the visual display alarm module displays the normal stability signal or the abnormal stability signal as well as the high-risk protection signal or the low-risk protection signal, and issues a corresponding warning when receiving the abnormal stability signal or the high-risk protection signal.

10. The 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 crash frequency eigenvalue or the crash time eigenvalue exceeds the corresponding preset threshold, a stability abnormality signal is generated; if both the crash frequency eigenvalue and the crash time eigenvalue do not exceed the corresponding preset threshold, the stability abnormality coefficient is obtained by weighted summing the crash frequency eigenvalue, the crash time eigenvalue and the crash abnormality coefficient. If the stability abnormality coefficient exceeds the preset stability abnormality coefficient threshold, a stability abnormality signal is generated; otherwise, a stability normal signal is generated.

11. The 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 protection abnormality characteristic value exceeds the corresponding preset threshold, a high-risk protection signal is generated; if both the attack frequency characteristic value and the protection abnormality characteristic value do not exceed the corresponding preset threshold, the defense tracking evaluation coefficient is obtained by weighted summing the attack frequency characteristic value, the protection abnormality characteristic value and the protection 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 adopts the blockchain-based e-commerce multi-platform data interaction system as described in any one of claims 1-11.

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