Cross-system data transmission processing method and system

Abstract

The invention discloses a cross-system data transmission processing method and system, and relates to the technical field of data processing, and the transmission processing steps are as follows: S1, data are configured as a network sharing directory among different merchant systems, the data are written into the network sharing directory, and encryption processing is performed based on a quantum encryption technology; s2, a user inputs to-be-known commodity data in a webpage, the commodity data is published to a network sharing catalog, the network sharing catalog obtains information, and matched data is retrieved in a catalog list based on a knowledge graph enhanced NER technology; and S3, the retrieved data information is sent to a webpage, and matching values of the retrieved data are calculated. According to the method, the screening time of the user is shortened, and the data demand is obtained through opening the webpage browser under the condition of not changing and destroying the operation, the interface and the data of the original business system, so that the cross-system business run-through does not depend on the original factory any more, and the data application initiative is comprehensively mastered.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically relates to a cross-system data transmission processing method and system. Background Technique

[0002] With the deepening of the digital transformation of enterprises, the demand for data interaction and process integration between business systems is increasing day by day. There are more and more merchants providing services or goods online. With the increase in the number of online services and the corresponding number of participating users, the data on the network has also increased significantly. Due to different service providers and different users participating in different services, many merchants' online data can only be obtained in their own systems to obtain the commodity data of the merchants. However, this method requires users to continuously log in repeatedly in different systems to obtain the commodity data of the merchants, which is time-consuming and inconvenient;

[0003] In the prior art, the API interface call integration method is used to achieve cross-system data transmission. System-to-system data exchange is carried out through standard APIs such as RESTful and SOAP, which has the advantages of high standardization and low development cost. However, it is necessary for the original system to open the API interface and rely on the support of the original manufacturer; the data format needs to be predefined, and the flexibility is poor. In view of this, we propose a cross-system data transmission processing method and system. Summary of the Invention

[0004] To solve the above technical problems, a cross-system data transmission processing method and system are provided, and the present technical solution solves the above problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a cross-system data transmission processing method, and the transmission processing steps are as follows:

[0006] S1. Configure data as a network shared directory between different merchant systems, write the data into the network shared directory, and perform encryption processing based on quantum encryption technology;

[0007] S2. The user inputs the commodity data to be understood in the web page, publishes the commodity data to the network shared directory, and the network shared directory obtains the information and retrieves and matches the data in the directory list based on the NER technology enhanced by the knowledge graph;

[0008] S3. Send the retrieved data information to the web page, calculate the matching values of the retrieved data respectively, and place the data with high matching values in a prominent position for the user to view and process;

[0009] S4. Record the operations of data sending and receiving in the blockchain to trace the data operation process throughout the process.

[0010] Preferably, the specific steps for configuring data as a network shared directory between different merchant systems in step S1 are as follows:

[0011] Before configuration, clarify the requirements for data sharing and determine the type and scope of data sharing;

[0012] Use the ping command to test the network connectivity performance between the servers of different merchant systems;

[0013] Determine the sharing protocol based on the operating systems of different merchants;

[0014] For Linux systems, configure by installing the nfs-utils software through the service, create a shared directory, turn off SELinux, edit the / etc / exports to configure permissions, and start the rpcbind and nfs-server services; use the mount command to mount the shared directory on the server side;

[0015] For Windows systems, set sharing through folders, check the sharing options in advanced sharing and configure permissions; on the client side, enter the server IP in the address bar of Windows Explorer and enter the account password to access;

[0016] For Linux to mount a Windows shared directory, install the cifs-utils tool, create a mount directory, and use the mount -t cifs command, carrying parameters such as the server address, local mount point, and account password to complete the mount.

[0017] Preferably, when writing data into the network shared directory in step S1, the encryption processing steps based on quantum encryption technology are as follows:

[0018] Ensure that the shared directory is correctly mounted;

[0019] Deploy QKD hardware devices, build an optical fiber network as a quantum channel, and connect the devices of both parties for data transmission;

[0020] Execute the QKD protocol to generate the raw key. After error correction and privacy amplification processing, store the key in the HSM to ensure synchronization between both parties;

[0021] Preprocess the data in blocks, and use the one-time pad method to encrypt the data block by block using the quantum key;

[0022] Establish TLS and VPN secure channels based on the quantum key, and write the encrypted data into the shared directory;

[0023] Generate a digital signature using the quantum key to verify data integrity, and set access control based on quantum identity and dynamic permissions.

[0024] Preferably, in step S2, the user publishes the commodity data to the network shared directory through a blockchain-based storage platform. The specific steps are as follows:

[0025] Select Filecoin as the blockchain storage platform and organize the commodity data to be understood into a file form.

[0026] Use the tools provided by Filecoin to upload the file to the Filecoin network.

[0027] After the upload is completed, generate a unique content identifier CID and publish the CID to the network shared directory.

[0028] The network shared directory downloads and obtains the commodity data to be understood by the user through a client that supports the IPFS protocol.

[0029] Preferably, in step S2, for the NER technology enhanced by the knowledge graph, the specific steps for retrieving and matching data in the directory list are as follows:

[0030] Preprocess the commodity data to be understood by the user obtained by downloading, perform word segmentation and syntactic analysis processing to construct text data.

[0031] Obtain the knowledge graph data, perform entity extraction, relationship extraction and attribute extraction on the knowledge graph data to construct a structured knowledge graph.

[0032] Perform entity linking on the knowledge graph to match the vocabulary in the text with the vocabulary in the knowledge graph.

[0033] Fuse the successfully matched graph vocabulary and the vocabulary in the text based on the attention mechanism.

[0034] Based on the text content after the fusion processing, perform keyword retrieval and matching in the network shared directory.

[0035] Preferably, in the matching, the calculation method regards all the vocabulary in the text as an overall set, regards all the vocabulary in the knowledge graph as another overall set, and through the entity linking function, performs matching operations on the vocabulary of these two sets, adopts a matching method based on similarity calculation, calculates the similarity between the vocabulary in the text and the vocabulary in the knowledge graph, sets a similarity threshold, and when the calculated similarity exceeds the threshold, the vocabulary matching is successful and put into the matching set.

[0036] The attention mechanism fusion processing steps are as follows: For the successfully matched set of matches, calculate the association weights between the text vocabulary and the graph vocabulary, use the attention scoring function to calculate the scores. After obtaining the scores, through exponential operation and normalization processing, calculate the attention weights between each text vocabulary and each graph vocabulary. Based on the attention weights, perform weighted summation on the graph vocabulary to obtain the result as the fused text element. Repeat the calculation process to finally form the fused representation result.

[0037] Preferably, in step S3, calculate the matching values for the retrieved data respectively, and place the data with high matching values in a prominent position for the user to view. The processing steps are as follows:

[0038] Extract the key features from the retrieved data and the data of the product to be understood respectively. The features include: keywords, entities, and attributes.

[0039] Quantify the obtained features, calculate the similarity of the feature quantification based on the cosine similarity, and calculate the similarity value between the two sets of features.

[0040] Based on weighted calculation of the similarity values between all features, obtain the comprehensive matching value.

[0041] Compare the obtained comprehensive matching values, select the data with high matching values, and place them in a prominent position on the web page.

[0042] Preferably, the steps of recording the operations of data sending and receiving in the blockchain in step S4 are as follows:

[0043] Collect information on the identities of the sender and receiver, the data content summary, and the timestamp.

[0044] Perform data encryption processing, and broadcast the operation record to the blockchain network nodes.

[0045] The nodes verify the signature, check the content, and compare the data.

[0046] After being recognized by the majority of nodes, the operation record is packaged into a new block, and the new block is generated according to the consensus mechanism and added to the end of the blockchain.

[0047] Legal users can query and trace the operation records in the blockchain based on the retrieval conditions.

[0048] Preferably, in the signature verification step, the nodes decrypt the signature information with the public key of the sender and compare it with the original content. When the two match, it is confirmed that the information source corresponds to the sender and has not been tampered with, and the identity and source verification are completed.

[0049] A cross-system data transmission processing system, the data transmission processing system includes:

[0050] Configure an encryption module, configured to configure the data as a network shared directory and perform encryption processing on it;

[0051] A retrieval module, configured to retrieve matching data in a directory list based on knowledge graph-enhanced NER technology;

[0052] A sending module, configured to calculate a matching value for the retrieved data, place the data with a high matching value in a prominent position, and send it to a web page;

[0053] A recording module, configured to record the operations of data sending and receiving to a blockchain.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] The present invention uses quantum physical properties to resist quantum computing attacks and ensure the security of data transmission and storage; the blockchain technology records the entire process of data operations on the chain to ensure that the records cannot be tampered with, facilitating responsibility traceability and risk control. The NER technology enhanced by the knowledge graph breaks through the limitations of traditional keywords and realizes precise retrieval; dynamic matching value calculation and intelligent sorting, through multi-dimensional weighted analysis, present high-correlation data first, reducing the user's screening time. Without changing or damaging the operation, interfaces, and data of the original business system, data requirements can be obtained by opening through a web browser, enabling cross-system business integration without relying on the original manufacturer and comprehensively mastering the initiative of data application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flowchart of the transmission and processing steps of the present invention;

[0057] Figure 2 It is a framework diagram of the data transmission and processing system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0059] Referring to Figure 1 as shown, a cross-system data transmission and processing method, the transmission and processing steps are as follows:

[0060] S1. Configure data as a network shared directory between different merchant systems, write the data into the network shared directory, and perform encryption processing based on quantum encryption technology;

[0061] S2. A user inputs data of a product to be understood in a web page, publishes the product data to the network shared directory, the network shared directory obtains the information, and based on knowledge graph-enhanced NER technology, retrieves matching data in the directory list;

[0062] S3. Send the retrieved data information to the web page, calculate the matching values for the retrieved data respectively, and place the data with high matching values in prominent positions for users to view and process;

[0063] S4. Record the operations of data sending and receiving in the blockchain to trace the data operation process throughout the whole process.

[0064] The data processing flow of this application shows significant advantages in terms of security, efficiency, experience and management. In terms of security protection, quantum encryption technology uses the characteristics of quantum physics to resist quantum computing attacks and ensure the security of data transmission and storage; blockchain technology records the whole process of data operations on the chain to ensure that the records cannot be tampered with, which is convenient for responsibility tracing and risk control. In terms of retrieval efficiency, the NER technology enhanced by the knowledge graph breaks through the limitations of traditional keywords and realizes accurate retrieval by combining semantics and structured knowledge; dynamic matching value calculation and intelligent sorting present high-correlation data first through multi-dimensional weighted analysis, reducing the user's screening time. In terms of user experience, cross-platform data sharing and real-time response enable users to quickly obtain information through a unified entry; prominently display high-matching data, which conforms to the browsing habits and improves the convenience of interaction. In terms of data management, the standardized shared directory promotes cross-system data circulation and is convenient for industry collaboration; the auditing function of the blockchain meets regulatory requirements, reduces legal risks, and enhances users' trust in data use, providing an effective solution for digital data governance.

[0065] The specific steps for configuring the data as a network shared directory between different merchant systems in step S1 are as follows:

[0066] Before configuration, clarify the requirements for data sharing and determine the type and scope of data sharing;

[0067] Use the ping command to test the network connectivity performance between the servers of different merchant systems;

[0068] Determine the sharing protocol based on the operating systems of different merchants;

[0069] For configuration between Linux systems, install the nfs-utils software through the service, create a shared directory, turn off SELinux, edit the / etc / exports to configure permissions, and start the rpcbind and nfs-server services; use the mount command to mount the shared directory on the server side;

[0070] For configuration between Windows systems, set sharing through folders, check the sharing options and configure permissions in advanced sharing; on the client side, enter the server IP in the address bar of Windows Explorer and enter the account password to access;

[0071] To mount a Windows shared directory in Linux, install the cifs-utils tool, create a mount directory, and use the mount -t cifs command with the server address, local mount point, and account password parameters to complete the mount.

[0072] Before configuring this application, clarify the data sharing requirements, types, and scopes, which can avoid data redundancy and privacy leakage problems caused by blind sharing, ensure that the shared content meets the business objectives, improve the resource utilization efficiency, and reduce the subsequent data management costs.

[0073] Use the ping command to test the network connectivity between servers, which can pre-check potential network faults, ensure the smoothness of data transmission, and avoid data transmission interruption and delay problems caused by network instability, laying a solid network foundation for efficient data sharing.

[0074] Determine the sharing protocol according to the operating systems of different merchants, which can give full play to the advantages of each system, achieve seamless docking between heterogeneous systems, eliminate data sharing failure problems caused by protocol incompatibility, and expand the applicable scope of data sharing; detailed configuration and mounting methods are given for Linux and Windows systems respectively, with clear operation steps, reducing the technical threshold, facilitating technicians to get started quickly, and at the same time, the standardized configuration process can reduce human errors and improve the success rate and stability of data sharing configuration.

[0075] In step S1, write the data into the network shared directory. The encryption process based on quantum encryption technology is as follows:

[0076] Before data transmission, technicians will use various methods to verify the mounting status of the shared directory. Through the command-line tool provided by the operating system, input specific instructions to query the mount point information, check whether the shared directory is successfully associated with the local system, and whether the directory path and permission settings are accurate; conduct actual data read and write tests, try to create, modify, and delete files in the shared directory, and observe whether the operations can be executed smoothly to ensure the stable availability of the directory mount.

[0077] The deployment of quantum key distribution (QKD) hardware devices is the cornerstone of the entire security system. In the computer rooms of both data transmission parties, install high-performance QKD devices, quantum key distributors based on the BB84 protocol, which use the quantum state characteristics of photons to generate keys, and lay a dedicated optical fiber network as the quantum channel.

[0078] After the device deployment is completed, the QKD protocol is executed. The two parties of data transmission send and receive single-photon signals through the quantum channel. Using the uncertainty and non-clonable principle of quantum states, the initial raw key is generated. The two parties exchange information through the classical communication channel, correct the errors in the key to make the keys of both parties consistent, and perform privacy amplification operations. The hash function is used to compress the corrected key to eliminate potential security risks. The obtained high-strength quantum key will be securely stored in the hardware security module HSM;

[0079] To ensure the efficiency and security of data encryption, the original data is pre-blocked before encryption, divided into appropriately sized data blocks, and the one-time pad encryption method is used. This method is a theoretically perfectly secure encryption method. The quantum key is retrieved from the HSM, and each data block is encrypted block by block. Each data block corresponds to a different part of the key. The encrypted ciphertext has no relation to the original data. Even if part of the ciphertext is intercepted, the attacker cannot obtain any valid information from it, greatly enhancing the confidentiality of data during transmission and storage;

[0080] Based on the generated quantum key, a Transport Layer Security (TLS) protocol and a Virtual Private Network (VPN) secure channel are established. The TLS protocol can encrypt, authenticate the identity, and verify the integrity of data during data transmission to ensure that the data is not stolen or tampered with when transmitted over the network; the VPN hides the real network addresses of the two parties of data transmission by establishing a dedicated encrypted channel over the public network to prevent network attacks and eavesdropping, and the encrypted data is written into the shared directory through these dual secure channels;

[0081] The integrity of the data is verified. When the receiver reads the data, it judges whether the data has been tampered with during transmission and storage by verifying the digital signature.

[0082] In step S2, the user publishes the commodity data to the network shared directory through a blockchain-based storage platform. The specific steps are as follows:

[0083] Select Filecoin as the blockchain storage platform and organize the commodity data to be understood into a file form;

[0084] Use the tools provided by Filecoin to upload the file to the Filecoin network;

[0085] After the upload is completed, a unique Content Identifier (CID) is generated and the CID is published to the network shared directory;

[0086] The network shared directory downloads and obtains the commodity data to be understood by the user through a client that supports the IPFS protocol.

[0087] The distributed storage architecture of Filecoin in this application is based on IPFS, which divides commodity data into multiple encrypted fragments and stores them in multiple computer servers around the world, rather than centrally storing them in a single server. This makes it difficult for hackers to attack or tamper with the data. Even if some nodes fail or are invaded, the complete data can still be restored through other nodes, significantly improving the security and disaster resistance of data storage.

[0088] The IPFS protocol naturally supports content addressing. The same file is only stored in one copy on the network and is identified by the hash value CID. When multiple users upload the same product data, the system automatically identifies duplicate content, avoids redundant storage, saves storage space, and improves transmission efficiency.

[0089] In step S2, based on the knowledge graph-enhanced NER technology, the specific steps for retrieving matching data in the directory list are as follows:

[0090] Pre-process the downloaded product data of users to be known, perform word segmentation and syntactic analysis to construct text data;

[0091] Obtain knowledge graph data, extract entities, relationships and attributes from the knowledge graph data, and build a structured knowledge graph;

[0092] Perform entity linking on the knowledge graph to match the words in the text with the words in the knowledge graph;

[0093] The successfully matched graph vocabulary and the vocabulary in the text are fused based on the attention mechanism;

[0094] Based on the fused text content, keyword search and matching are performed in the network shared directory.

[0095] This application builds a structured knowledge graph containing entities, relationships, and attributes, and combines NER technology to accurately extract specific entities in product data, breaking through the limitations of traditional keyword surface matching and achieving deep semantic understanding. Entity linking can use multi-source vocabulary mapping and context semantic disambiguation to improve matching accuracy. The attention mechanism dynamically assigns weights to matching vocabulary to focus on key information, and can also integrate multi-dimensional features across modalities. Multi-hop relationship reasoning and attribute filtering based on the knowledge graph can support complex queries. In addition, the knowledge graph can be dynamically updated and optimized based on user feedback, allowing the retrieval model to adapt to business changes, promoting the upgrade of data applications to knowledge services, and effectively improving the accuracy, depth, and intelligence of retrieval.

[0096] The matching calculation method regards all the words in the text as a whole set, and all the words in the knowledge graph as another whole set. Through the entity linking function, it performs matching operations on the words in these two sets, adopts a matching method based on similarity calculation, calculates the similarity between the words in the text and the words in the knowledge graph, sets a similarity threshold, and when the calculated similarity exceeds the threshold, the word matching is successful and put into the matching set;

[0097] Among them, the similarity calculation formula is:

[0098]

[0099] Where a and b respectively represent the word vector in the text and the word vector in the knowledge graph;

[0100] The attention mechanism fusion processing steps are as follows: for the successfully matched matching set, calculate the association weight between the text word and the graph word, use the attention scoring function to calculate the score, and after obtaining the score, through exponential operation and normalization processing, calculate the attention weight between each text word and each graph word. Based on the attention weight, perform weighted summation on the graph words to obtain the result as the fused text element, and repeat the calculation process to finally form the fused representation result.

[0101] The specific calculation formula is:

[0102] Let the text word set be , and the graph word set be ;

[0103] The text word and the graph word The attention weight between them is , obtained through the attention scoring function and softmax normalization;

[0104] The vector representation of the graph word is , where is the vector dimension;

[0105] The weighted summation calculation formula is:

[0106] For each text word , its fused representation The weighted sum calculation formula of the graph word vector is:

[0107]

[0108] Among them, the attention weight satisfies the normalization condition: and

[0109]

[0110] Attention weight calculation process

[0111] Calculate the text vocabulary using the attention scoring function and the graph vocabulary of the correlation score :

[0112] Exponential operation and normalization: Convert the score to a probability distribution through the softmax function. The calculation formula is:

[0113] Repeat the above process for all text vocabularies Calculate their fused representations , and finally form a set of fused representations:

[0114]

[0115] where H is the result of the fused representation.

[0116] In step S3, calculate the matching values for the retrieved data respectively, and place the data with high matching values in a prominent position for the user to view. The processing steps are as follows:

[0117] Extract the key features from the retrieved data and the data of the product to be understood respectively. The features include: keywords, entities, and attributes;

[0118] Quantify the obtained features, calculate the similarity of the quantified features based on the cosine similarity, and calculate the similarity value between the two sets of features;

[0119] Calculate the comprehensive matching value based on the weighted calculation of the similarity values between all features;

[0120] Compare the obtained comprehensive matching values, select the data with high matching values, and place them in a prominent position on the web page.

[0121] This application disassembles the core elements of the data, ensures capturing the relevance between data at the semantic level, converts text features into numerical values in the vector space, calculates the similarity through mathematical methods, avoids subjective judgment biases, assigns weights to different features, conforms to the actual needs and priorities of users. Users do not need to manually screen a large amount of information, and the system automatically tops or highlights the data with the highest matching value, shortening the decision-making path.

[0122] The steps for recording the operations of data sending and receiving in step S4 into the blockchain are as follows:

[0123] Collect information on the identities of the sender and receiver, the data content summary, and the timestamp;

[0124] Perform data encryption processing and broadcast the operation records to the blockchain network nodes;

[0125] The nodes verify the signature, check the content, and compare the data;

[0126] After being recognized by the majority of nodes, the operation records are packaged into a new block. The new block is generated according to the consensus mechanism and added to the end of the blockchain;

[0127] Legal users can query and trace the operation records in the blockchain based on the retrieval conditions;

[0128] In the signature verification step, the node decrypts the signature information with the public key of the sender and compares it with the original content. When the two match, it is confirmed that the information source corresponds to the sender and has not been tampered with, completing the verification of identity and source.

[0129] The blockchain of this application records every operation detail of data sending / receiving through timestamp + chained storage, forming an irreversible audit trail. The operation records are encrypted and broadcast to the blockchain network, and the nodes ensure that the data has not been tampered with through signature verification + content verification.

[0130] Reference Figure 2 As shown, a cross-system data transmission and processing system, the data transmission and processing system includes:

[0131] A configured encryption module, configured to configure the data as a network shared directory and encrypt it;

[0132] A retrieval module, configured to retrieve and match data in the directory list based on the NER technology enhanced by the knowledge graph;

[0133] A sending module, configured to calculate the matching value for the retrieved data, place the data with a high matching value in a prominent position, and send it to the web page;

[0134] A recording module, configured to record the operation of data sending and receiving to the blockchain.

[0135] By utilizing the quantum physical properties to resist quantum computing attacks, the security of data transmission and storage is guaranteed; the blockchain technology records the entire process of data operations on the chain to ensure that the records cannot be tampered with, facilitating responsibility tracing and risk control. The NER technology enhanced by the knowledge graph breaks through the traditional keyword limitations to achieve accurate retrieval; the dynamic matching value calculation and intelligent sorting, through multi-dimensional weighted analysis, present the highly relevant data first, reducing the user's screening time. Without changing or damaging the operation, interface, and data of the original business system, the data requirements can be obtained by opening through a web browser, enabling cross-system business integration without relying on the original manufacturer and comprehensively mastering the initiative of data application.

[0136] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. A cross-system data transmission and processing method, characterized in that, The transmission processing steps are as follows: S1. Between different merchant systems, configure the data as a network shared directory, write the data into the network shared directory, and perform encryption processing based on quantum encryption technology; S2. The user enters the product data to be understood on the web page, publishes the product data to the network shared directory, the network shared directory obtains the information, and retrieves the matching data in the directory list based on the NER technology enhanced by the knowledge graph; S3. Send the retrieved data information to the web page, calculate the matching values for the retrieved data respectively, and place the data with high matching values in a prominent position for the user to view and process; S4. Record the data sending and receiving operations in the blockchain to trace the data operation process throughout the process.

2. The cross-system data transmission processing method according to claim 1, wherein The specific steps for configuring the data as a network shared directory between different merchant systems in step S1 are as follows: Before configuration, clarify the data sharing requirements and determine the type and scope of data sharing; Use the ping command to test the network connectivity performance between the servers of different merchant systems; Determine the sharing protocol based on the operating systems of different merchants; For Linux systems, configure by installing the nfs-utils software through the service, create a shared directory, turn off SELinux, edit the / etc / exports to configure permissions, and start the rpcbind and nfs-server services; use the mount command to mount the shared directory on the server side; For Windows systems, set sharing through folders, check the sharing options and configure permissions in advanced sharing; on the client side, enter the server IP in the address bar of Windows Explorer and enter the account password to access; To mount a Windows shared directory in Linux, install the cifs-utils tool, create a mount directory, and use the mount -t cifs command with parameters such as the server address, local mount point, and account password to complete the mount.

3. A cross-system data transmission and processing method according to claim 1, characterized in that The steps for writing the data into the network shared directory and performing encryption processing based on quantum encryption technology in step S1 are as follows: Ensure that the shared directory is correctly mounted; Deploy QKD hardware devices, build an optical fiber network as a quantum channel, and connect the devices of both data transmission parties; Execute the QKD protocol to generate the original key. After error correction and privacy amplification processing, store the key in the HSM to ensure synchronization between both parties; Preprocess the data in blocks, adopt the one-time pad method, and encrypt the data block by block using the quantum key; Establish TLS and VPN secure channels based on the quantum key, and write the encrypted data into the shared directory; Generate a digital signature using the quantum key to verify the data integrity, and set access control based on quantum identity and dynamic permissions.

4. A cross-system data transmission and processing method according to claim 1, characterized in that In step S2, the user publishes the product data to the network shared directory through a blockchain-based storage platform. The specific steps are as follows: Select Filecoin as the blockchain storage platform and organize the product data to be understood into a file form; Use the tools provided by Filecoin to upload the file to the Filecoin network; After the upload is completed, generate a unique content identifier CID and publish the CID to the network shared directory; The network shared directory downloads and obtains the commodity data to be understood by the user through a client that supports the IPFS protocol.

5. A cross-system data transmission processing method according to claim 1, characterized in that In step S2, the specific steps of the NER technology enhanced by the knowledge graph to retrieve and match data in the directory list are as follows: Preprocess the commodity data to be understood by the user obtained by downloading, perform word segmentation and syntactic analysis to construct text data; Obtain the knowledge graph data, perform entity extraction, relationship extraction and attribute extraction on the knowledge graph data, and construct a structured knowledge graph; Perform entity linking on the knowledge graph, and match the vocabulary in the text with the vocabulary in the knowledge graph; Fuse the successfully matched graph vocabulary and the vocabulary in the text based on the attention mechanism; Based on the text content after the fusion process, perform keyword retrieval and matching in the network shared directory.

6. The cross-system data transmission processing method according to claim 5, wherein In the matching, the calculation method regards all the vocabulary in the text as a whole set, and all the vocabulary in the knowledge graph as another whole set. Through the entity linking function, perform matching operations on the vocabulary of these two sets, adopt a matching method based on similarity calculation, calculate the similarity between the vocabulary in the text and the vocabulary in the knowledge graph, set a similarity threshold, and when the calculated similarity exceeds the threshold, the vocabulary matching is successful and put into the matching set; The attention mechanism fusion process is as follows: for the successfully matched matching set, calculate the correlation weight between the text vocabulary and the graph vocabulary, use the attention scoring function to calculate the score, after obtaining the score, through exponential operation and normalization processing, calculate the attention weight between each text vocabulary and each graph vocabulary, and based on the attention weight, perform weighted summation on the graph vocabulary to obtain the result as the fused text element, repeat the calculation process, and finally form the fused representation result.

7. A cross-system data transmission processing method according to claim 1, characterized in that In step S3, calculate the matching value for the retrieved data respectively, and place the data with a high matching value in a prominent position for the user to view. The processing steps are as follows: Extract the key features of the retrieved data and the commodity data to be understood respectively. The features include: keywords, entities and attributes; Quantify the obtained features, calculate the similarity of the feature quantification based on the cosine similarity, and calculate the similarity value between the two groups of features; Based on the weighted calculation of the similarity values between all features, obtain the comprehensive matching value; Compare the obtained comprehensive matching values, select the data with a high matching value, and place it in a prominent position on the web page.

8. A cross-system data transmission and processing method according to claim 1, characterized in that In step S4, the steps of recording the operation of data sending and receiving into the blockchain are as follows: Collect information such as the identities of the sender and receiver, the data content summary and the time stamp; Perform data encryption processing, and broadcast the operation record to the blockchain network nodes; The nodes verify the signature, check the content and compare the data; After being recognized by the majority of nodes, the operation record is packaged into a new block, and the new block is generated according to the consensus mechanism and added to the end of the blockchain; Legal users can query and trace the operation record in the blockchain based on the retrieval conditions.

9. The cross-system data transmission processing method according to claim 8, wherein, In the signature verification step, the node decrypts the signature information with the public key of the sender and compares it with the original content. When the two are consistent, it is confirmed that the information source corresponds to the sender and has not been tampered with, and the identity and source verification are completed.

10. A cross-system data transmission and processing system, characterized in that, The data transmission processing system includes: Configure an encryption module, configured to configure data as a network shared directory and perform encryption processing on it; A retrieval module, configured to retrieve matching data in a directory list based on NER technology enhanced by a knowledge graph; A sending module, configured to calculate a matching value for the retrieved data, place the data with a high matching value in a prominent position, and send it to a web page; A recording module, configured to record the operations of data sending and receiving to a blockchain.

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