An interface method for solving the intelligent data bridging between ERP-WMS

Abstract

The present invention discloses an interface method for solving the intelligent data bridging between ERP and WMS, which relates to the cross - technical field of information management systems and intelligent logistics technologies. The interface method is implemented through a bridging module. The bridging module is provided with a dynamic ERP interface and a dynamic WMS interface. The data interaction between the ERP system and the WMS system is realized by using the bridging module, and the bridging module uploads the interaction data between the ERP system and the WMS system to the cloud server. The cloud server, according to the received data information, uses the cloud inventory prediction algorithm and the dynamic task distribution algorithm to optimize the inventory allocation rules, and triggers a replenishment plan when the predicted inventory level is lower than the preset safety inventory threshold. The present invention enables the bridging module to be integrated with different ERP systems and WMS systems, improving the adaptability of the method; the data of the ERP system and the data of the WMS system can be synchronously updated, improving the warehousing efficiency.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of information management systems and intelligent logistics technologies, and specifically to an interface method for solving the intelligent data bridging between ERP - WMS. Background Art

[0002] With the continuous advancement of the informatization process of modern enterprises, the Enterprise Resource Planning (ERP) system and the Warehouse Management System (WMS) play a crucial role in enterprise operations. The ERP system is mainly used for the overall planning and scheduling of internal enterprise resources, while the WMS focuses on the material management and logistics operations in the warehouse. However, due to the significant differences in the functional positioning and data structures of the two systems, enterprises often need to develop interfaces to achieve data interaction in actual applications. However, existing interfaces usually adopt static development methods and cannot meet the rapid docking requirements between multiple ERP and WMS systems. In addition, traditional interface designs are also difficult to support real - time data transmission and dynamic task allocation, and only rely on simple data synchronization, resulting in particularly prominent problems of data delay and fragmentation. In the medical device field, warehouse management also needs to meet strict regulatory requirements, posing higher standards for the accuracy, traceability, and security of inbound and outbound data. Therefore, how to construct an intelligent, real - time, and scalable ERP - WMS data bridging method has become a technical problem that the industry urgently needs to solve:

[0003] 1. Complex interface development and lack of generality: Existing ERP - WMS interfaces need to be customized according to specific systems, lacking general standards and being difficult to adapt to systems of different brands, increasing the development and maintenance costs.

[0004] 2. Serious problems of data fragmentation and delay: Traditional interfaces cannot achieve real - time data interaction, resulting in the inability to synchronously update the inbound and outbound data of ERP and WMS, affecting warehouse efficiency.

[0005] 3. Single - function cloud services: Existing cloud solutions usually only provide storage and backup functions, lacking intelligent analysis capabilities for inventory forecasting, dynamic replenishment suggestions, and task allocation.

[0006] 4. Insufficient linkage of automated equipment: The task allocation of automated equipment such as AGV is difficult to be dynamically adjusted according to the data of ERP and WMS, resulting in a decline in logistics efficiency.

[0007] 5. Insufficient data security and compliance: The medical device industry has high requirements for data security and traceability, while existing technologies generally lack encryption transmission and logging functions and are difficult to meet regulatory requirements. Summary of the Invention

[0008] The present invention provides an interface method for solving the intelligent data bridging between ERP and WMS, aiming to solve the problems of difficult linkage and low intelligent data bridging efficiency between ERP and WMS in the prior art, and the lack of intelligence in cloud services.

[0009] The present invention provides the following technical solution: An interface method for solving the intelligent data bridging between ERP and WMS, which is implemented through a bridging module. A dynamic ERP interface and a dynamic WMS interface are set on the bridging module;

[0010] The interface method includes the following steps:

[0011] Step 1: The bridging module docks with the ERP system through the dynamic ERP interface and realizes data interaction with the ERP system by using the dynamic ERP interface; the bridging module docks with the WMS system through the dynamic WMS interface and realizes data interaction with the WMS system by using the dynamic WMS interface;

[0012] Step 2: The bridging module uses a data bridging algorithm to convert the inbound and outbound form information obtained from the ERP system in real time into ERP data information recognizable by the dynamic WMS interface. The converted ERP data information is transmitted to the WMS system through the dynamic WMS interface, and the converted ERP data information is uploaded to the cloud server; the bridging module uses a data bridging algorithm to convert the inventory data obtained from the WMS system in real time into WMS data information recognizable by the dynamic ERP interface. The converted WMS data information is transmitted to the ERP system through the dynamic ERP interface to realize data interaction between the ERP system and the WMS system, and the converted WMS data information is uploaded to the cloud server; and in this process, the bridging module uses an encryption transmission algorithm to realize encrypted data transmission;

[0013] The data bridging algorithm is based on dynamic data mapping and format conversion algorithms to realize data bridging between the ERP system and the WMS system; the data bridging algorithm is based on a timestamp and a unique transaction identifier to realize two-way real-time synchronization of data between the ERP system and the WMS system;

[0014] Step 3: According to the received data information, the cloud server uses cloud inventory prediction algorithms and dynamic task distribution algorithms to optimize the inventory allocation rules, and when the predicted inventory level is lower than the preset safety inventory threshold, triggers a replenishment plan;

[0015] Step 4: The cloud server converts the inventory allocation rules into control instructions for the AGV automation equipment. The cloud server uses an encryption transmission algorithm to send the control instructions to the AGV automation equipment. The AGV automation equipment completes tasks according to the control instructions and gives feedback to realize data interaction between the cloud server and the AGV automation equipment;

[0016] Step 5: The cloud server uses blockchain traceability technology to record data interaction logs.

[0017] Preferably, assume that the ERP data format is: , and the WMS data format is: , and the mapping relationship is , then the dynamic data mapping algorithm is as follows:

[0018] ;

[0019] Where: ; is the mapping parameter table, which includes data field correspondence rules and data format conversion rules.

[0020] Preferably, the operation of the data bridging algorithm to achieve data synchronization is: ;

[0021] .

[0022] Preferably, the cloud inventory prediction algorithm predicts inventory demand based on the time series analysis algorithm and optimizes the replenishment plan. The specific operation is:

[0023] Assume that the historical inventory demand is the time series , and use the ARIMA model to predict future inventory demand:

[0024] ;

[0025] Where: is the autoregressive coefficient, is the random error;

[0026] When the predicted inventory level is lower than the preset safety inventory threshold , trigger the replenishment plan:

[0027] .

[0028] Preferably, the principle of the dynamic task distribution algorithm is:

[0029] Define the task priority ,

[0030] ;

[0031] Where: represents the material importance, represents the current inventory level, represents the urgency of the material demand time, , , is the weight factor;

[0032] AGV task allocation rules:

[0033] ;

[0034] Wherein: represents the comprehensive cost for the AGV to complete the task, including but not limited to path length and time consumption.

[0035] Preferably, the encryption transmission algorithm adopts a hybrid encryption scheme combining asymmetric encryption and symmetric encryption. The specific process is as follows:

[0036] Use the RSA algorithm for key exchange:

[0037] , ;

[0038] Wherein: , , are the RSA public and private keys, is the plaintext, is the ciphertext;

[0039] Data transmission adopts the AES algorithm:

[0040] , ;

[0041] Wherein: is the symmetric key, and are the encryption and decryption functions.

[0042] Preferably, the specific operation of the blockchain traceability technology is as follows:

[0043] Define the block data structure:

[0044] ;

[0045] Wherein: is the timestamp, is the hash value of the previous block, is the current block data, is the hash value of the current block, and ;

[0046] Verification process:

[0047] .

[0048] Preferably, multiple types of ERP interfaces and multiple types of WMS interfaces are configured in the management background of the bridging module.

[0049] Preferably, the configuration operation of the dynamic ERP interface or the dynamic WMS interface is as follows:

[0050] Enter the interface type that you hope to use in the management background of the bridging module;

[0051] After the management background of the bridging module receives a call request for the interface, it parses the path and performs verification;

[0052] After successful verification, save the settings.

[0053] Preferably, the bridging module and the cloud server perform data interaction through a standardized communication protocol.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The interface method for solving the intelligent data bridging between ERP and WMS and the cloud intelligent service, combined with specific algorithms, communication encryption technology and data security design, improves the efficiency, security and intelligence of the system through multi-layer optimization.

[0056] 2. The interface method for solving the intelligent data bridging between ERP and WMS and the cloud intelligent service adopts the dynamic port technology, enabling the bridging module to be integrated with different ERP systems and WMS systems, improving the adaptability of the method; using the intelligent data bridging algorithm to realize real-time interaction between ERP system data and WMS system data, and the ERP system data and WMS system data can be synchronized and updated, improving the warehousing efficiency.

[0057] 3. The interface method for solving the intelligent data bridging between ERP and WMS and the cloud intelligent service, the cloud intelligent server uses the cloud inventory prediction algorithm and the dynamic task distribution algorithm to optimize the inventory allocation rules, reduce the situation of inventory backlog or shortage, and realize refined inventory management; the cloud server sends control instructions to the AGV automation equipment, enabling the AGV automation equipment to dynamically adjust according to the data of ERP and WMS, improving the logistics efficiency.

[0058] 4. The interface method for solving the intelligent data bridging between ERP and WMS and the cloud intelligent service has the functions of data encrypted transmission and log recording, improving data security and meeting regulatory requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is the communication logic diagram of the bridging module of the present invention;

[0060] Figure 2Inventory forecasting and replenishment logic diagram of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention;

[0061] Figure 3 Dynamic task distribution block diagram of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention;

[0062] Figure 4 Data encryption transmission flowchart of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention;

[0063] Figure 5 Blockchain technology flowchart of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0065] The present invention provides an interface method for solving intelligent data bridging between ERP and WMS, aiming to solve the problems of difficult linkage between ERP and WMS, low efficiency of intelligent data bridging, and insufficiently intelligent cloud services in the prior art. This interface method is implemented through a bridging module. The ERP system and the WMS system are linked through the bridging module, and a dynamic ERP interface and a dynamic WMS interface are set on the bridging module.

[0066] Multiple types of ERP interfaces and multiple types of WMS interfaces are configured in the management background of the bridging module. The bridging module is docked with the ERP system through the dynamic ERP interface, and data interaction is realized with the ERP system using the dynamic ERP interface. The configuration operation of the dynamic ERP interface is as follows:

[0067] 1. Enter the interface type to be used in the management background of the bridging module;

[0068] 2. After the management background of the bridging module receives a call request for the call interface, it parses the path and performs verification;

[0069] 3. After the verification is successful, save the settings.

[0070] The bridging module is docked with the WMS system through the dynamic WMS interface, and data interaction with the WMS system is realized by using the dynamic WMS interface; the configuration operation of the dynamic WMS interface is as follows:

[0071] 1. Enter the interface type to be used in the management background of the bridging module;

[0072] 2. After the management background of the bridging module receives the call request for the calling interface, it parses the path and performs verification;

[0073] 3. After the verification is successful, save the settings.

[0074] As can be seen from the above description, by adopting the dynamic interface, the bridging module can be docked with ERP systems and WMS systems of different architectures, improving the adaptability of this method.

[0075] The bridging module and the cloud server perform data interaction through a standardized communication protocol. The bridging module can upload the interaction data between the ERP system and the WMS system to the cloud server. The cloud server has a logging function to ensure the immutability of data. And based on the intelligent scheduling algorithm, the cloud server can generate an optimal task list by combining inventory data and requirements, automatically convert it into an AGV control instruction. Automated devices such as AGV realize intelligent handling according to the control instruction and give feedback in a timely manner, enabling data interaction between the cloud server and automated devices such as AGV. Through the cloud server, automated devices such as AGV are linked, and the task allocation of automated devices such as AGV can be dynamically adjusted according to the data of ERP and WMS, improving the logistics efficiency.

[0076] In addition, communication encryption technology is adopted for data transmission between the bridging module and the ERP system and the WMS system, between the bridging module and the cloud server, and between the cloud server and automated devices such as AGV, ensuring the security of data transmission and the reliability of the use of this method.

[0077] In the present invention, the communication encryption technology is implemented by using an encryption transmission algorithm. The encryption transmission algorithm adopts a hybrid encryption scheme combining asymmetric encryption and symmetric encryption. The specific process is as follows:

[0078] Use the RSA algorithm for key exchange:

[0079] , ;

[0080] Among them: , , are the RSA public and private keys, is the plaintext, is the ciphertext;

[0081] The data transmission adopts the AES algorithm:

[0082] , ;

[0083] Among them: is the symmetric key, and are the encryption and decryption functions.

[0084] An interface method for solving the intelligent data bridging between ERP-WMS provided by the present invention specifically includes the following steps:

[0085] Step 1: The bridging module automatically extracts the inbound and outbound form data from the ERP system in real time through the dynamic ERP interface; the bridging module obtains the inventory data from the WMS system in real time through the dynamic WMS interface;

[0086] Step 2: The bridging module uses the data bridging algorithm to convert the inbound and outbound form information obtained from the ERP system in real time into ERP data information recognizable by the dynamic WMS interface. The converted ERP data information is transmitted to the WMS system through the dynamic WMS interface. The WMS system obtains the inbound and outbound form data of the ERP system, and the bridging module synchronously uploads the converted ERP data information to the cloud server; the bridging module uses the data bridging algorithm to convert the inventory data obtained from the WMS system in real time into WMS data information recognizable by the dynamic ERP interface. The converted WMS data information is transmitted to the ERP system through the dynamic ERP interface to realize the data interaction between the ERP system and the WMS system, and the converted WMS data information is uploaded to the cloud server;

[0087] The data bridging algorithm is based on the dynamic data mapping and format conversion algorithm to realize the data bridging between the ERP system and the WMS system;

[0088] Description of the data bridging algorithm:

[0089] Suppose the ERP data format is: , and the WMS data format is: , and the mapping relationship is , then the dynamic data mapping algorithm is as follows:

[0090] ;

[0091] Among them: ; is the mapping parameter table, which contains the corresponding rules of data fields and the rules of data format conversion;

[0092] The data bridging algorithm is based on the timestamp and the unique transaction identifier Realize the two-way real-time synchronization of data between the ERP system and the WMS system;

[0093] The operation of realizing data synchronization by the data bridging algorithm is as follows: ;

[0094] ;

[0095] Step 3: According to the received data information, the cloud server uses the cloud inventory prediction algorithm and the dynamic task distribution algorithm to optimize the inventory allocation rules, and triggers a replenishment plan when the predicted inventory level is lower than the preset safety inventory threshold;

[0096] The cloud inventory prediction algorithm predicts inventory demand based on the time series analysis algorithm and optimizes the replenishment plan. The specific operation is as follows:

[0097] Assume that the historical inventory demand is a time series , and use the ARIMA model to predict future inventory demand:

[0098] ;

[0099] Among them: is the autoregressive coefficient, is the random error;

[0100] When the predicted inventory level is lower than the preset safety inventory threshold , trigger the replenishment plan:

[0101] ;

[0102] Step 4: The cloud server converts the inventory allocation rules into control instructions for the AGV automation equipment. The cloud server uses an encrypted transmission algorithm to send the control instructions to the AGV automation equipment. The AGV automation equipment completes the tasks according to the control instructions and gives feedback to realize the data interaction between the cloud server and the AGV automation equipment;

[0103] The principle of the dynamic task distribution algorithm is as follows:

[0104] Define the task priority ,

[0105] ;

[0106] Among them: represents the material importance, represents the current inventory level, represents the urgency of the material demand time, , , is the weight factor;

[0107] AGV task allocation rules:

[0108] ;

[0109] Among them: represents the comprehensive cost for the AGV to complete the task, including but not limited to the path length and time consumption;

[0110] Step Five: The cloud server uses blockchain traceability technology to record the data interaction log;

[0111] The specific operation of the blockchain traceability technology is as follows:

[0112] Define the block data structure:

[0113] ;

[0114] Among them: is the timestamp, is the hash value of the previous block, is the current block data, is the hash value of the current block, and ;

[0115] Verification process:

[0116] .

[0117] The usage effect of the present invention

[0118] Experimental data and comparison

[0119] 1. Bridging module efficiency experiment

[0120] A. Traditional interface: Data latency is about 5 seconds, and the error rate is 2%;

[0121] B. The present invention: Data latency is about 0.8 seconds, and the error rate is reduced to 0.2%.

[0122] 2. Inventory prediction accuracy

[0123] After using the ARIMA model, the inventory prediction accuracy rate is increased to 95%, and the replenishment plan generation efficiency is improved by 40%.

[0124] 3. Data security test

[0125] Data transmission encryption performance: The average time consumption of the RSA-2048+AES-256 encryption scheme is 1.2 ms, and the decryption time consumption is 1.0 ms, meeting the real-time requirements of medical device warehousing.

[0126] ​As can be seen from the above description, when the method of the present invention is used, in combination with specific algorithms, communication encryption technologies, and data security designs, the efficiency, security, and intelligence level of the system are improved through multi-layer optimization. By combining automation and intelligent scheduling, the manual operation time is reduced, the material management and logistics execution efficiency are improved, the error rate is reduced, the operation cost is saved, and the overall production efficiency is enhanced.

[0127] Next, the present application will be further described with reference to the accompanying drawings.

[0128] As Figure 1 shown, Figure 1 is the communication logic diagram of the bridging module: The bridging module and the ERP system perform two-way communication using a dynamic ERP interface; the bridging module and the WMS system perform two-way communication using a dynamic WMS interface; the bridging module and the cloud server perform two-way communication through a standardized communication protocol, and the cloud server and automated devices such as AGVs perform two-way communication through a standardized communication protocol.

[0129] Figure 2 is the inventory forecasting and replenishment logic diagram of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention: Historical inventory demand data and preset safety inventory threshold data are both uploaded to the cloud server. The cloud server preprocesses the historical inventory demand data to obtain the time series of the historical inventory demand data. The cloud server uses the ARIMA model to predict future inventory demand; and compares the predicted future inventory demand with the preset safety inventory threshold data. If the predicted future inventory demand is less than the preset safety inventory threshold data, a replenishment plan is triggered, otherwise a dynamic task distribution algorithm is started to optimize the inventory allocation rules.

[0130] Figure 3 is the dynamic task distribution block diagram of the interface method for solving intelligent data bridging between ERP and WMS and cloud intelligent services proposed by the present invention: The cloud server defines the task priorities of the tasks and arranges the tasks in descending order of priority. The cloud server calculates the comprehensive cost of AGVs to complete the tasks in turn and distributes the tasks to the AGV with the lowest comprehensive cost.

[0131] Figure 4The data encryption transmission flowchart of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention: The information sender generates a pair of public key and private key, and makes the public key public to the information receiver; after receiving the public key, the information receiver encrypts the symmetric encryption key with the public key, and then sends the encrypted key to the information sender; after receiving the encrypted key, the information sender decrypts it with the private key to obtain the key required for symmetric encryption; the information sender encrypts the data to be transmitted with the decrypted symmetric key; the encrypted data is transmitted to the signal receiver through a secure channel; after receiving the encrypted data, the signal receiver decrypts it with the symmetric encryption key to obtain the transmitted data.

[0132] Figure 5 The blockchain technology flowchart of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention: The cloud server defines the block data structure, generates a hash value, encrypts the generated hash value with the private key to obtain a digital signature, and writes the hash value and the digital signature into the blockchain. Take out the block data to be verified from the blockchain, recalculate the hash value of the block data using the same hash algorithm, and decrypt the digital signature with the public key to obtain the hash value, and compare the two calculated hash values. If the two are the same, it means that the block data is valid and has not been tampered with, and the verification passes.

[0133] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art. Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An interface method for solving intelligent data bridging between ERP and WMS, characterized in that: The interface method is implemented through a bridge module, on which a dynamic ERP interface and a dynamic WMS interface are provided; The interface method includes the following steps: Step 1: The bridge module is connected to the ERP system through the dynamic ERP interface, and the dynamic ERP interface is used to realize data interaction with the ERP system; the bridge module is connected to the WMS system through the dynamic WMS interface, and the dynamic WMS interface is used to realize data interaction with the WMS system; Step 2: The bridging module uses a data bridging algorithm to convert the in-and-out warehouse form information obtained from the ERP system in real time into ERP data information that can be identified by the dynamic WMS interface. The converted ERP data information is transmitted to the WMS system through the dynamic WMS interface, and the converted ERP data information is uploaded to the cloud server. The bridging module uses a data bridging algorithm to convert the inventory data obtained from the WMS system in real time into WMS data information that can be identified by the dynamic ERP interface. The converted WMS data information is transmitted to the ERP system through the dynamic ERP interface to realize data interaction between the ERP system and the WMS system, and the converted WMS data information is uploaded to the cloud server. In this process, the bridging module uses an encrypted transmission algorithm to realize data encrypted transmission. The data bridging algorithm is based on dynamic data mapping and format conversion algorithms to achieve data bridging between the ERP system and the WMS system; the data bridging algorithm is based on timestamp T and unique transaction identifier UID to achieve two-way real-time synchronization of data between the ERP system and the WMS system; ERP data format is: D e ={d e1 , d e2 , ..., d en }, WMS data format is: D w ={d w1 , d w2 , ..., d wm }, the mapping relationship is M, then the dynamic data mapping algorithm is as follows: d wi =M(d ei )=f(d ei ,P i ); in: P i It is a mapping parameter table, which contains data field correspondence rules and data format conversion rules; The data bridging algorithm implements the following operations to achieve data synchronization: sync =max(T erp , T wms ); Step 3: The cloud server optimizes the inventory allocation rules based on the received data information using the cloud inventory forecasting algorithm and the dynamic task distribution algorithm, and triggers the replenishment plan when the predicted inventory level is lower than the preset safety stock threshold; Step 4: The cloud server converts the inventory allocation rules into control instructions for the AGV automation equipment. The cloud server uses an encrypted transmission algorithm to send the control instructions to the AGV automation equipment. The AGV automation equipment completes the task according to the control instructions and provides feedback, thus realizing data interaction between the cloud server and the AGV automation equipment. Step 5: The cloud server uses blockchain traceability technology to record data interaction logs.

2. According to claim 1, an interface method for solving intelligent data bridging between ERP and WMS is characterized in that: The cloud inventory forecasting algorithm predicts inventory demand based on the time series analysis algorithm and optimizes the replenishment plan. The specific operations are as follows: Historical inventory demand is a time series X t ={x1, x2, ..., x t }, use the ARIMA model to predict future inventory demand: X t+1 =φ1X t +φ2X t-1 +…+φ p X t-p +e t ; Where: φ i is the autoregressive coefficient, ε t is a random error; When the forecast inventory is lower than the preset safety stock threshold S safe When the replenishment plan is triggered: if X t+1 <S safe ,then initiate replenishm ent。 3. According to claim 1, an interface method for solving intelligent data bridging between ERP and WMS is characterized in that: The principle of the dynamic task distribution algorithm is: Define the task priority P i , P i =w1L i +w2Q i +w3D i ; Where: L i Indicates the importance of materials, Q i Indicates the current inventory, D i Indicates the urgency of material demand time, w1, w2, w3 are weight factors; AGV task allocation rules: Where: C j，i It represents the comprehensive cost of AGV to complete task i, including but not limited to path length and time consumption.

4. According to claim 1, the interface method for solving the intelligent data bridging between ERP and WMS is characterized in that: The encryption transmission algorithm adopts a hybrid encryption scheme combining asymmetric encryption with symmetric encryption. The specific process is as follows: Use the RSA algorithm for key exchange: C=M e mod n,M=C d mod n; Among them: e, d, n are RSA public and private keys, M is plain text, and C is cipher text; Data transmission uses AES algorithm: C=E k (M),M=D k (C); Where: k is the symmetric key, E k and D k For encryption and decryption functions.

5. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 1 is characterized in that: The specific operations of the blockchain tracing technology are as follows: Define the block data structure: B t =(T t ,H t-1 ,D t ,H t ); Where: T t is the timestamp, H t-1 is the hash value of the previous block, D t is the current block data, H t is the hash value of the current block, and iH t =Hash(T t ||H t-1 ||D t ); Verification process: if H t =Hash(T t ||H t-1 ||D t ),then block is valid。 6. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 1 is characterized in that: Various types of ERP interfaces and various types of WMS interfaces are configured in the management background of the bridge module.

7. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 6 is characterized in that: The configuration operation of the dynamic ERP interface or dynamic WMS interface is: Enter the interface type you want to use in the management backend of the bridge module; After receiving the call request of the calling interface, the management background of the bridge module parses the path and performs verification; After successful verification, save the settings.

8. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 1 is characterized in that: The bridge module and the cloud server exchange data via a standardized communication protocol.

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