Two-way information flow integration method and system for ERP (Enterprise Resource Planning) and e-commerce platform

Abstract

The invention provides an ERP (Enterprise Resource Planning) and e-commerce platform bidirectional information flow integration system and method, and relates to the technical field of information flow integration, the non-tampering and traceability of data are ensured through a block chain technology, the data processing efficiency is improved by an edge computing module, loose coupling communication is realized by an event driving module, and the data processing efficiency is improved. The digital twin module maps the state of a supply chain in real time and predicts the risk, the federated learning module performs joint modeling to generate a sales strategy, the network slicing technology guarantees the stability of data flow, the Internet of Things module enhances the real-time performance of data, and the knowledge graph module solves the problem of data isomerism. According to the method, efficient and safe bidirectional information flow integration is achieved through the steps of data recording, preprocessing, event abstraction, digital twinning modeling, federated learning, network slicing, Internet of Things integration and the like, the electronic equipment and the storage medium store programs to execute the method, and the supply chain management efficiency and the data processing real-time performance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information flow integration, and more specifically, to a method and system for bidirectional information flow integration between ERP and e-commerce platforms. Background Art

[0002] For a long time, ERP system has been the core of internal resource management of enterprises, responsible for integrating key business processes such as finance, human resources, production, and supply chain. E-commerce platform, as a bridge between enterprises and consumers, undertakes functions such as sales, marketing, and customer service. The integration of information flow between the two is of great significance to improving enterprise operational efficiency and optimizing customer experience.

[0003] With the rapid development of the e-commerce industry, enterprises have an increasingly urgent need to integrate ERP systems with e-commerce platforms. However, existing technologies face many challenges in realizing the two-way information flow integration between ERP and e-commerce platforms. First, insufficient data security and privacy protection are prominent issues. There is a risk of data being tampered with and leaked during transmission. Secondly, the existing integration method has a high degree of coupling. Once one party fails, it is easy to cause the entire business process to be interrupted. In addition, the data is highly heterogeneous, and the data formats and models of ERP systems and e-commerce platforms are quite different, resulting in complex data mapping and conversion. At the same time, the real-time performance of existing technologies is poor and cannot meet the processing requirements of high-frequency interactive data, resulting in system response delays. Existing technologies lack effective prediction and early warning mechanisms for supply chain risks, making it difficult to respond to potential problems in advance.

[0004] Therefore, the existing integration of ERP and e-commerce platforms has problems such as data security risks, performance bottlenecks, excessive coupling, strong data heterogeneity, insufficient privacy protection, poor real-time performance, and insufficient predictive capabilities. Summary of the invention

[0005] In order to overcome the above problems existing in the prior art, the present invention designs a two-way information flow integration method and system of ERP and e-commerce platform, which can effectively solve the above technical problems.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] A two-way information flow integration system between ERP and e-commerce platform, comprising:

[0008] The blockchain module is used to record the interaction data between ERP and e-commerce platforms in a distributed ledger to ensure the immutability and traceability of the interaction data;

[0009] The edge computing module is deployed on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud;

[0010] The event-driven module uses an event bus to build an asynchronous message queue, abstracts the interaction events between ERP and the e-commerce platform into independent events, and implements loosely coupled communication through a publish-subscribe model;

[0011] Digital twin module, which is used to build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms;

[0012] The federated learning module uses federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies;

[0013] The network slicing module is used to allocate independent channels for data flows between ERP and e-commerce platforms to ensure the stability of key businesses;

[0014] The IoT module integrates IoT devices to collect warehouse data in real time and links with ERP and e-commerce platforms;

[0015] The knowledge graph module builds an industry knowledge graph and transforms the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion.

[0016] Preferably, the event-driven module includes:

[0017] Event abstraction unit, used to abstract the interaction events between ERP and e-commerce platform into independent events;

[0018] Message queue unit, used to build asynchronous message queues through event bus;

[0019] The rule execution unit is used to automatically execute preset rules after an event is triggered.

[0020] Preferably, the digital twin module includes:

[0021] Data mapping unit, used to map inventory, logistics and order status data between ERP and e-commerce platforms in real time;

[0022] The simulation prediction unit is used to predict potential risk points in the supply chain through simulation algorithms;

[0023] The dynamic adjustment unit is used to dynamically adjust the purchasing plan of ERP or the promotion strategy of the e-commerce platform according to the simulation results.

[0024] Preferably, the federated learning module includes:

[0025] Joint modeling unit, used to jointly model the decentralized data of ERP and e-commerce platforms;

[0026] The cross-platform strategy generating unit is used to generate a cross-platform sales strategy.

[0027] Preferably, the network slicing module includes:

[0028] The channel allocation unit is used to allocate independent channels for the data flows of ERP and e-commerce platforms;

[0029] The priority management unit is used to assign different priorities according to data types.

[0030] Preferably, the Internet of Things module includes:

[0031] Data collection unit, used to integrate IoT devices to collect warehouse data in real time;

[0032] The linkage control unit is used to link with ERP and e-commerce platforms to automatically trigger related operations.

[0033] Preferably, the knowledge graph module includes:

[0034] The semantic mapping unit is used to transform the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion;

[0035] The data association unit is used to automatically associate the relevant data between ERP and e-commerce platforms.

[0036] A method for bidirectional information flow integration between ERP and e-commerce platform, comprising the following steps:

[0037] Record the interaction data between ERP and e-commerce platforms in the distributed ledger of blockchain;

[0038] Deploy data processing modules on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud;

[0039] Use event bus to build asynchronous message queue, abstract the interaction events between ERP and e-commerce platform into independent events, and realize loosely coupled communication through publish-subscribe mode;

[0040] Build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms;

[0041] Use federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies;

[0042] Use 5G network slicing technology to allocate independent channels for data flows between ERP and e-commerce platforms;

[0043] Integrate IoT devices to collect warehouse data in real time and link with ERP and e-commerce platforms;

[0044] Build an industry knowledge graph and unify the heterogeneous data of ERP and e-commerce platforms into a standard data model through semantic mapping.

[0045] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the above-mentioned method for bidirectional information flow integration between an ERP and an e-commerce platform.

[0046] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for bidirectional information flow integration between an ERP and an e-commerce platform.

[0047] Compared with the prior art, the present invention has the following beneficial effects: the present invention records the interactive data between ERP and e-commerce platform in a distributed ledger through a blockchain module, ensuring the data is tamper-proof and traceable; at the same time, combined with federated learning technology, it can perform joint modeling without sharing original data, further protecting data privacy; the edge computing module is deployed on the local edge node, pre-processes and caches high-frequency interactive data, and uploads key data to the cloud, effectively reducing cloud computing pressure and improving data processing efficiency; 5G network slicing technology is used to allocate independent channels for data streams to ensure the stability of key businesses; the event-driven module uses an event bus to build an asynchronous message queue, and loosely coupled communication is achieved through a publish-subscribe model, avoiding fault propagation caused by tight coupling between systems; the digital twin module maps inventory, logistics and order status data in real time, and predicts potential risk points through simulation algorithms; the knowledge graph module converts heterogeneous data into a unified data model through semantic fusion, solving the problem of strong data heterogeneity; and the distributed data is jointly modeled using federated learning technology to generate cross-platform sales strategies, thereby improving the prediction capability of the supply chain; in addition, the Internet of Things module integrates real-time warehousing data acquisition equipment, further enhancing the real-time and accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying any creative work.

[0049] Figure 1 This is a structural diagram of a two-way information flow integration system between ERP and e-commerce platform;

[0050] Figure 2This is a step-by-step diagram of a two-way information flow integration method between ERP and e-commerce platform. DETAILED DESCRIPTION

[0051] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0052] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0053] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0054] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0055] Example 1

[0056] A two-way information flow integration system between ERP and e-commerce platform, please refer to Figure 1 ,include:

[0057] The blockchain module is used to record the interaction data between ERP and e-commerce platforms in a distributed ledger to ensure the immutability and traceability of the interaction data;

[0058] Every time ERP and the e-commerce platform perform data synchronization operations, the data synchronization unit will automatically trigger the smart contract. The smart contract has preset verification rules for data format and logical consistency. By strictly checking the format of the synchronized data and verifying the logical relationship, the accuracy and completeness of the data are ensured, avoiding data conflicts or errors caused by data format errors or logical inconsistencies, and ensuring the reliability of data in the interaction process. The verified interaction data records are stored in the distributed ledger. The distributed ledger uses blockchain encryption technology and consensus algorithms to ensure that data cannot be tampered with once written. At the same time, all participating nodes can synchronize the latest ledger information in real time to achieve data traceability, which is convenient for tracing and auditing when data problems arise in the future.

[0059] The edge computing module is deployed on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud;

[0060] The edge computing module is equipped with a data preprocessing unit, a data caching unit and a data upload unit. The data preprocessing unit is deployed on the local edge node. When ERP and the e-commerce platform generate high-frequency interactive data, such as order information, inventory change data, etc., the unit performs preliminary cleaning, conversion and processing on these data. For example, the order data is formatted and standardized, and the order data from different sources are unified into a standard format for subsequent processing and analysis; the inventory change data is monitored and warned in real time, and an alarm is issued in time when the inventory is lower than the set threshold.

[0061] The data cache unit caches pre-processed data in the local edge node, which can reduce the delay of data transmission and improve the response speed of the system. The cache strategy adopts a cache elimination algorithm to automatically eliminate infrequently used data based on factors such as data access frequency and timeliness, ensuring the effective use of cache space.

[0062] The data upload unit uploads key data from the local edge node to the cloud based on the importance and real-time requirements of the data. The upload process uses a secure transmission protocol to ensure the security of the data during transmission. At the same time, through reasonable bandwidth management and data compression technology, the efficiency of data upload is optimized to avoid excessive pressure on network bandwidth.

[0063] The event-driven module uses an event bus to build an asynchronous message queue, abstracts the interaction events between ERP and the e-commerce platform into independent events, and implements loosely coupled communication through a publish-subscribe model;

[0064] The publish-subscribe model (Pub / Sub) is a messaging paradigm that allows the sender (publisher) and the receiver (subscriber) to be decoupled so that they do not need to communicate directly with each other. In the bidirectional information flow integration between ERP and e-commerce platforms, this model can be used to achieve loosely coupled communication.

[0065] The event abstraction unit abstracts the interaction events between ERP and e-commerce platforms, such as order creation, inventory update, product listing and delisting, and encapsulates them into independent event objects. Each event object contains information such as event type, occurrence time, and related data. In this way, complex business interactions are simplified into event publishing and subscription, improving the flexibility and scalability of the system.

[0066] The message queue unit uses an event bus, such as Kafka or RabbitMQ, to build an asynchronous message queue. The event bus is responsible for receiving and managing the publishing and subscription of events. When an event occurs, the message queue unit will queue and distribute the event object according to preset rules to ensure that the event can be delivered to the relevant subscribers in a timely and accurate manner. Through asynchronous communication, loosely coupled communication between ERP and e-commerce platforms is achieved, reducing the dependency between systems and improving the stability and reliability of the system.

[0067] After an event is triggered, the rule execution unit automatically performs corresponding operations according to the preset rules. For example, when an inventory update event occurs, the rule execution unit automatically adjusts the sales strategy of the product according to the inventory changes, such as adjusting the product price, modifying promotional activities, etc.; when an order creation event occurs, the order processing process is automatically triggered, including order review, delivery arrangement and other operations. These preset rules can be flexibly set and adjusted through a visual configuration interface to meet different business needs.

[0068] Digital twin module, which is used to build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms;

[0069] The data mapping unit collects inventory, logistics, order status and other data from ERP and e-commerce platforms in real time, and maps these data to the digital twin model of the supply chain. The data mapping process uses data synchronization technology and data fusion algorithms to ensure that the digital twin model can accurately and in real time reflect the operating status of the actual supply chain. For example, the temperature, humidity and other data of the storage environment can be obtained in real time through IoT devices, and combined with inventory data to provide more comprehensive and accurate information for the digital twin model.

[0070] The simulation prediction unit uses simulation algorithms to simulate and predict the operation of the supply chain. By analyzing historical data and real-time data, it establishes a simulation model of the supply chain and predicts possible supply chain bottlenecks, such as logistics delays and insufficient inventory. The simulation prediction process can take into account multiple factors, such as changes in market demand, supplier delivery times, transportation routes, etc., to provide a basis for supply chain optimization.

[0071] The dynamic adjustment unit dynamically adjusts the ERP procurement plan or the e-commerce platform's promotion strategy according to the simulation prediction results. For example, when insufficient inventory is predicted, the dynamic adjustment unit will automatically increase the number of purchase orders or adjust the timing of the procurement plan; when a decrease in market demand is predicted, the e-commerce platform's promotion strategy will be adjusted to reduce the promotion intensity or adjust the types of promotional products. In this way, dynamic optimization of the supply chain is achieved, and the responsiveness and competitiveness of the supply chain are improved.

[0072] The federated learning module uses federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies;

[0073] When the data does not leave the local system, the joint modeling unit uses federated learning technology to jointly model the scattered data of ERP and e-commerce platforms, such as user behavior data, sales trend data, inventory data, etc. The joint modeling process uses a secure encryption algorithm to ensure the privacy and security of the data during the modeling process. Through federated learning technology, the data resources of all parties can be used to establish a more accurate and comprehensive prediction model and improve the value of the data.

[0074] The cross-platform strategy generation unit generates cross-platform sales strategies based on the results of joint modeling. For example, the user portrait of the e-commerce platform and the inventory data of the ERP are used to jointly train the prediction model to generate personalized product recommendation strategies, inventory optimization strategies, etc. The cross-platform strategy can be displayed to users through a visual interface and supports users to make flexible adjustments and optimizations to meet different business needs.

[0075] The network slicing module is used to allocate independent channels for data flows between ERP and e-commerce platforms to ensure the stability of key businesses;

[0076] The channel allocation unit allocates independent channels to different data flows based on the data flow characteristics and business needs of the ERP and e-commerce platforms. For example, real-time transaction data is allocated to high-priority slices to ensure that it can obtain low-latency network services; batch report data is allocated to low-priority slices to make full use of the network's bandwidth resources. The channel allocation process uses an intelligent algorithm to dynamically adjust the channel allocation strategy based on network conditions and business needs to ensure effective use of network resources.

[0077] The priority management unit sets priorities for different data streams based on data type and business importance. The priority management unit monitors the network status and data stream transmission in real time, schedules and manages the data stream according to the priority strategy, and ensures that key business data can be transmitted first. For example, when the network is congested, the transmission of real-time transaction data is prioritized to avoid transaction failures or reduced user experience due to network delays.

[0078] The IoT module integrates IoT devices to collect warehouse data in real time and links with ERP and e-commerce platforms;

[0079] The data collection unit integrates IoT devices, such as smart shelves, RFID tags, sensors, etc. to collect warehouse data in real time. IoT devices can monitor the temperature, humidity, location and quantity of goods in the warehouse environment in real time, and transmit the data to the data collection unit through a wireless network. The data collection unit performs preliminary processing and filtering on the collected data to remove invalid data and noise data to ensure the accuracy and reliability of the data.

[0080] The linkage control unit is linked with the ERP and e-commerce platforms, and automatically triggers related operations based on the collected warehousing data. For example, when an order is generated on the e-commerce platform, the linkage control unit will automatically trigger the picking instructions of the warehouse robot. The robot will automatically go to the designated shelf location to pick up goods according to the instructions, and update the inventory status in the ERP system at the same time. Through the deep integration of Internet of Things technology with ERP and e-commerce platforms, the automation and intelligence of warehousing management has been realized, and the efficiency and accuracy of warehousing operations have been improved.

[0081] The knowledge graph module builds an industry knowledge graph and transforms the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion.

[0082] The semantic mapping unit constructs an industry knowledge graph and unifies the heterogeneous data of ERP and e-commerce platforms, such as product classification, supplier relationships, customer information, etc., into standardized terms through semantic mapping. The semantic mapping process uses natural language processing technology and knowledge graph construction technology to semantically understand and analyze heterogeneous data, establish semantic associations between data, and achieve data standardization and normalization. For example, the "supplier code" in the ERP system is semantically associated with the "supplier name" of the e-commerce platform to solve the problem of data heterogeneity and improve the comparability and availability of data.

[0083] The data association unit automatically associates the relevant data of ERP and e-commerce platforms to form a complete data link. Through the query and reasoning functions of the knowledge graph, the association relationship between related data can be quickly obtained to provide strong support for business decisions. For example, the knowledge graph can be used to query the supplier information, inventory information, sales information, etc. of a certain product, providing comprehensive data support for the purchase, sales and inventory management of the product.

[0084] The event-driven module includes:

[0085] Event abstraction unit, used to abstract the interaction events between ERP and e-commerce platform into independent events;

[0086] Message queue unit, used to build asynchronous message queues through event bus;

[0087] The rule execution unit is used to automatically execute preset rules after an event is triggered.

[0088] The digital twin module includes:

[0089] Data mapping unit, used to map inventory, logistics and order status data between ERP and e-commerce platforms in real time;

[0090] The simulation prediction unit is used to predict potential risk points in the supply chain through simulation algorithms;

[0091] The dynamic adjustment unit is used to dynamically adjust the purchasing plan of ERP or the promotion strategy of the e-commerce platform according to the simulation results.

[0092] The federated learning module includes:

[0093] Joint modeling unit, used to jointly model the decentralized data of ERP and e-commerce platforms;

[0094] The cross-platform strategy generating unit is used to generate a cross-platform sales strategy.

[0095] The network slicing module includes:

[0096] The channel allocation unit is used to allocate independent channels for the data flows of ERP and e-commerce platforms;

[0097] The priority management unit is used to assign different priorities according to data types.

[0098] The Internet of Things module includes:

[0099] Data collection unit, used to integrate IoT devices to collect warehouse data in real time;

[0100] The linkage control unit is used to link with ERP and e-commerce platforms to automatically trigger related operations.

[0101] The knowledge graph module includes:

[0102] The semantic mapping unit is used to transform the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion;

[0103] The data association unit is used to automatically associate the relevant data between ERP and e-commerce platforms.

[0104] Example 2

[0105] A method for integrating bidirectional information flow between ERP and e-commerce platform, please refer to Figure 2 , including the following steps:

[0106] Record the interaction data between ERP and e-commerce platforms in the distributed ledger of blockchain;

[0107] Deploy data processing modules on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud;

[0108] Use event bus to build asynchronous message queue, abstract the interaction events between ERP and e-commerce platform into independent events, and realize loosely coupled communication through publish-subscribe mode;

[0109] Build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms;

[0110] Use federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies;

[0111] Use 5G network slicing technology to allocate independent channels for data flows between ERP and e-commerce platforms;

[0112] Integrate IoT devices to collect warehouse data in real time and link with ERP and e-commerce platforms;

[0113] Build an industry knowledge graph and unify the heterogeneous data of ERP and e-commerce platforms into a standard data model through semantic mapping.

[0114] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the above-mentioned method for bidirectional information flow integration between an ERP and an e-commerce platform.

[0115] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for bidirectional information flow integration between an ERP and an e-commerce platform.

[0116] In the specific implementation, the interaction data between ERP and e-commerce platforms is recorded in the distributed ledger of the blockchain. During each data synchronization, the data format and logical consistency are automatically verified through smart contracts. The data verification rules are preset in the smart contract to perform strict format checks and logical checks on the synchronized data to ensure the accuracy and completeness of the data. The verified data will be permanently recorded in the distributed ledger to ensure the data’s immutability and traceability, providing a data basis for subsequent data analysis and auditing.

[0117] A lightweight data processing module is deployed on the local edge node to preprocess and cache the high-frequency interaction data between ERP and e-commerce platforms. The data processing module cleans, converts and processes the data, unifies data from different sources into a standard format, and improves data quality and availability. The preprocessed data is cached on the local edge node, and an intelligent cache elimination algorithm is used to automatically eliminate infrequently used data based on the access frequency and timeliness of the data, ensuring the effective use of cache space. Key data is uploaded from the local edge node to the cloud, and a secure transmission protocol is used during the upload process to ensure the security of data during transmission.

[0118] An event bus is used to build an asynchronous message queue, and the interaction events between ERP and e-commerce platforms are abstracted into independent events. The event bus is responsible for receiving and managing the publishing and subscription of events. When an event occurs, the message queue unit queues and distributes the event objects according to preset rules, and loosely coupled communication is achieved through the publish-subscribe model, which reduces the dependency between systems and improves the stability and reliability of the system. After the event is triggered, the system automatically executes preset rules, such as inventory threshold alarms, automatic order processing, etc., which improves the system's automation level and business response speed.

[0119] Build a digital twin model of the supply chain to map the inventory, logistics, order status and other data of ERP and e-commerce platforms in real time. The digital twin model uses simulation algorithms to simulate and predict the operation of the supply chain. By analyzing historical data and real-time data, it predicts possible supply chain bottlenecks such as logistics delays and insufficient inventory. According to the simulation results, the ERP procurement plan or the e-commerce platform's promotion strategy can be dynamically adjusted to achieve dynamic optimization of the supply chain and improve the responsiveness and competitiveness of the supply chain.

[0120] Federated learning technology is used to jointly model the scattered data of ERP and e-commerce platforms. Under the premise that the data does not leave the local system, a joint model is established through federated learning technology to make full use of the data resources of all parties and improve the value of data. Based on the results of joint modeling, cross-platform sales strategies are generated, such as personalized product recommendations and inventory optimization strategies. Cross-platform strategies are displayed to users through a visual interface, and users are supported to make flexible adjustments and optimizations to meet different business needs.

[0121] 5G network slicing technology is used to allocate independent channels for data flows between ERP and e-commerce platforms. Different channels are allocated to different data flows according to data types and business requirements. For example, real-time transaction data is allocated to high-priority slices, and batch report data is allocated to low-priority slices. Through the priority management unit, priorities are set for different data flows according to data types and business importance to ensure that critical business data can be transmitted first. 5G network slicing technology ensures the stability of critical businesses and improves the efficiency and reliability of data transmission.

[0122] Integrated IoT devices collect warehouse data in real time. IoT devices can monitor the temperature, humidity, location and quantity of goods in the warehouse environment in real time, and transmit the data to the data collection unit through the wireless network. The data collection unit performs preliminary processing and filtering on the collected data, removes invalid data and noise data, and links with ERP and e-commerce platforms to automatically trigger related operations based on the collected warehouse data, such as automatic picking and inventory updates. The deep integration of IoT technology with ERP and e-commerce platforms has realized the automation and intelligence of warehouse management and improved the efficiency and accuracy of warehouse operations.

[0123] Build an industry knowledge graph, unify the heterogeneous data of ERP and e-commerce platforms into standardized terms through semantic mapping, use natural language processing technology and knowledge graph construction technology to semantically understand and analyze heterogeneous data, establish semantic associations between data, and automatically associate related data between ERP and e-commerce platforms to form a complete data link. Through the query and reasoning functions of the knowledge graph, you can quickly obtain the association between related data, provide strong support for business decisions, and improve the comparability and availability of data.

[0124] The same or similar reference numerals correspond to the same or similar components;

[0125] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0126] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A two-way information flow integration system between ERP and e-commerce platform, characterized in that: include: The blockchain module is used to record the interaction data between ERP and e-commerce platforms in a distributed ledger to ensure the immutability and traceability of the interaction data; The edge computing module is deployed on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud; The event-driven module uses an event bus to build an asynchronous message queue, abstracts the interaction events between ERP and the e-commerce platform into independent events, and implements loosely coupled communication through a publish-subscribe model; Digital twin module, which is used to build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms; The federated learning module uses federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies; The network slicing module is used to allocate independent channels for data flows between ERP and e-commerce platforms to ensure the stability of key businesses; The IoT module integrates IoT devices to collect warehouse data in real time and links with ERP and e-commerce platforms; The knowledge graph module builds an industry knowledge graph and transforms the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion.

2. According to claim 1, a two-way information flow integration system of ERP and e-commerce platform is characterized in that: The event-driven module includes: Event abstraction unit, used to abstract the interaction events between ERP and e-commerce platform into independent events; Message queue unit, used to build asynchronous message queues through event bus; The rule execution unit is used to automatically execute preset rules after an event is triggered.

3. According to claim 1, a two-way information flow integration system of ERP and e-commerce platform is characterized in that: The digital twin module includes: Data mapping unit, used to map inventory, logistics and order status data between ERP and e-commerce platforms in real time; The simulation prediction unit is used to predict potential risk points in the supply chain through simulation algorithms; The dynamic adjustment unit is used to dynamically adjust the purchasing plan of ERP or the promotion strategy of the e-commerce platform according to the simulation results.

4. The ERP and e-commerce platform two-way information flow integration system according to claim 1 is characterized in that: The federated learning module includes: Joint modeling unit, used to jointly model the decentralized data of ERP and e-commerce platforms; The cross-platform strategy generating unit is used to generate a cross-platform sales strategy.

5. The ERP and e-commerce platform two-way information flow integration system according to claim 1 is characterized in that: The network slicing module includes: The channel allocation unit is used to allocate independent channels for the data flows of ERP and e-commerce platforms; The priority management unit is used to assign different priorities according to data types.

6. The ERP and e-commerce platform two-way information flow integration system according to claim 1 is characterized in that: The Internet of Things module includes: Data collection unit, used to integrate IoT devices to collect warehouse data in real time; The linkage control unit is used to link with ERP and e-commerce platforms to automatically trigger related operations.

7. The ERP and e-commerce platform two-way information flow integration system according to claim 1 is characterized in that: The knowledge graph module includes: The semantic mapping unit is used to transform the heterogeneous data of ERP and e-commerce platforms into a unified data model through semantic fusion; The data association unit is used to automatically associate the relevant data between ERP and e-commerce platforms.

8. A method for integrating bidirectional information flow between ERP and e-commerce platform, used to implement a bidirectional information flow integration system between ERP and e-commerce platform as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Record the interaction data between ERP and e-commerce platforms in the distributed ledger of blockchain; Deploy data processing modules on local edge nodes to pre-process and cache high-frequency interactive data between ERP and e-commerce platforms, and upload key data to the cloud; Use event bus to build asynchronous message queue, abstract the interaction events between ERP and e-commerce platform into independent events, and realize loosely coupled communication through publish-subscribe mode; Build a digital twin model of the supply chain, map the inventory, logistics and order status data of ERP and e-commerce platforms in real time, and predict potential risk points of the supply chain through simulation algorithms; Use federated learning technology to jointly model the decentralized data of ERP and e-commerce platforms and generate cross-platform sales strategies; Use 5G network slicing technology to allocate independent channels for data flows between ERP and e-commerce platforms; Integrate IoT devices to collect warehouse data in real time and link with ERP and e-commerce platforms; Build an industry knowledge graph and unify the heterogeneous data of ERP and e-commerce platforms into a standard data model through semantic mapping.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for bidirectional information flow integration between ERP and e-commerce platform described in claim 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for bidirectional information flow integration between ERP and e-commerce platform described in claim 8 are implemented.

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