An interface method to solve the intelligent data bridge between ERP and WMS

Abstract

The present invention discloses an interface method for solving the intelligent data bridging between ERP and WMS, and relates to the cross-technical field of information management system and intelligent logistics technology. The interface method is implemented by a bridging module, and a dynamic ERP interface and a dynamic WMS interface are provided on the bridging module. The data interaction between the ERP system and the WMS system is realized by the bridging module, and the bridging module uploads the interactive data between the ERP system and the WMS system to a cloud server. The cloud server optimizes the inventory allocation rules according to the received data information using the cloud inventory prediction algorithm and the dynamic task distribution algorithm, and triggers the replenishment plan when the predicted inventory 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, thereby improving the adaptability of the method; the ERP system data and the WMS system data can be updated synchronously, thereby improving the warehousing efficiency.

Description

Technical Field

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

[0002] As the informatization process of modern enterprises continues to advance, enterprise resource planning (ERP) systems and warehouse management systems (WMS) play a vital role in enterprise operations. The ERP system is mainly used for the overall planning and scheduling of internal resources of the enterprise, while the WMS focuses on material management and logistics operations of the warehouse. However, due to the significant differences in the functional positioning and data structure of the two systems, enterprises often need to develop interfaces to realize data interaction in actual applications. However, the existing interfaces usually adopt a static development method and cannot meet the needs of rapid docking between multiple ERP and WMS systems. In addition, traditional interface design is also difficult to support real-time data transmission and dynamic task allocation, and only relies on simple data synchronization, resulting in data delay and fragmentation problems. In the field of medical devices, warehouse management must also meet strict regulatory requirements, and put forward higher standards for the accuracy, traceability and security of inbound and outbound data. Therefore, how to build an intelligent, real-time and scalable ERP-WMS data bridging method has become a technical problem that the industry urgently needs to solve: 1. Interface development is complex and lacks universality: Existing ERP and WMS interfaces need to be customized and developed according to specific systems. They lack universal standards and are difficult to adapt to systems of different brands, increasing development and maintenance costs.

[0003] 2. Data fragmentation and delay problems are serious: Traditional interfaces cannot achieve real-time data interaction, resulting in the inability to synchronize the inbound and outbound data of ERP and WMS, affecting warehousing efficiency.

[0004] 3. Single cloud service function: Existing cloud solutions usually only provide storage and backup functions, and lack intelligent analysis capabilities for inventory forecasting, dynamic replenishment suggestions and task allocation.

[0005] 4. Insufficient linkage of automated equipment: The task allocation of automated equipment such as AGV is difficult to dynamically adjust based on ERP and WMS data, resulting in reduced logistics efficiency.

[0006] 5. Insufficient data security and compliance: The medical device industry has high requirements for data security and traceability, but existing technologies generally lack encrypted transmission and logging functions, making it difficult to meet regulatory requirements. Summary of the invention

[0007] The present invention provides an interface method for solving the intelligent data bridging between ERP and WMS, aiming to solve the problems of difficulty in linkage between ERP and WMS, low efficiency of intelligent data bridging, and insufficient intelligence of cloud services in the prior art.

[0008] The present invention provides the following technical solutions: an interface method for solving the intelligent data bridging between ERP and WMS, the interface method is implemented through a bridging module, and a dynamic ERP interface and a dynamic WMS interface are provided on the bridging module; 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 timestamps and unique transaction identifier Realize two-way real-time synchronization of data between ERP system and WMS system; 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.

[0009] Preferably, it is assumed that the ERP data format is: , the WMS data format is: , the mapping relationship is , then the dynamic data mapping algorithm is as follows: ; in: ; It is a mapping parameter table, which contains data field correspondence rules and data format conversion rules.

[0010] Preferably, the data bridging algorithm implements the following operations to synchronize data: ; .

[0011] Preferably, the cloud inventory forecasting algorithm predicts inventory demand based on a time series analysis algorithm and optimizes replenishment plans, and the specific operations are: Assume that historical inventory demand is a time series , using the ARIMA model to predict future inventory demand: ; in: is the autoregression coefficient, is a random error; When the forecast inventory level is lower than the preset safety stock threshold When the replenishment plan is triggered: .

[0012] Preferably, the principle of the dynamic task distribution algorithm is: Defining task priorities , ; in: Indicates the importance of the material. Indicates the current inventory quantity. Indicates the urgency of material demand time. , , is the weight factor; AGV task allocation rules: ; in: Indicates that AGV completes the task The comprehensive cost includes but is not limited to path length and time consumption.

[0013] Preferably, the encryption transmission algorithm adopts a hybrid encryption scheme combining asymmetric encryption with symmetric encryption, and the specific process is as follows: Use the RSA algorithm for key exchange: , ; in: , , are RSA public and private keys, For plain text, is the ciphertext; Data transmission uses AES algorithm: , ; in: is a symmetric key, and For encryption and decryption functions.

[0014] Preferably, the specific operations of the blockchain tracing technology are: Define the block data structure: ; in: 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 ; Verification process: .

[0015] Preferably, multiple types of ERP interfaces and multiple types of WMS interfaces are configured in the management background of the bridge module.

[0016] Preferably, the configuration operation of the dynamic ERP interface or the 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.

[0017] Preferably, the bridge module and the cloud server exchange data via a standardized communication protocol.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This method solves the interface of intelligent data bridging between ERP-WMS and cloud intelligent services, combines specific algorithms, communication encryption technology and data security design, and improves the efficiency, security and intelligence of the system through multi-layer optimization.

[0019] 2. This method for solving the intelligent data bridging between ERP and WMS and the interface method for cloud-based intelligent services adopts dynamic port technology, so that the bridging module can be integrated with different ERP systems and WMS systems, thereby improving the adaptability of the method; the intelligent data bridging algorithm is used to enable real-time interaction between ERP system data and WMS system data, and the ERP system data and WMS system data can be updated synchronously, thereby improving warehousing efficiency.

[0020] 3. This method solves the interface between ERP-WMS intelligent data bridging and cloud intelligent services. The cloud intelligent server uses cloud inventory forecasting algorithm and dynamic task distribution algorithm to optimize inventory allocation rules, reduce inventory squeeze or shortage, and realize refined inventory management; the cloud server sends control instructions to AGV automation equipment, so that the AGV automation equipment can be dynamically adjusted according to ERP and WMS data to improve logistics efficiency.

[0021] 4. This method of interfacing with intelligent data bridging between ERP and WMS and intelligent cloud services has data encryption transmission and log recording functions, which improves data security and meets regulatory requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the communication logic diagram of the bridge module of the present invention; Figure 2 The inventory forecasting and replenishment logic diagram of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention; Figure 3 A dynamic task distribution block diagram of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention; Figure 4 This is a data encryption transmission flow chart of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention; Figure 5 This is a flowchart of the blockchain technology for the method of interfacing between ERP-WMS and cloud-based intelligent services proposed by the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0024] The present invention provides an interface method for solving the intelligent data bridge between ERP and WMS, aiming to solve the problems of difficult linkage between ERP and WMS, low efficiency of intelligent data bridge, and insufficient intelligence of cloud service in the prior art. The interface method is implemented through a bridge module, and the ERP system and the WMS system are linked through the bridge module, and a dynamic ERP interface and a dynamic WMS interface are provided on the bridge module.

[0025] Various types of ERP interfaces and various types of WMS interfaces are configured in the management background of the bridge module. The bridge module connects with the ERP system through the dynamic ERP interface and uses the dynamic ERP interface to realize data interaction with the ERP system. The configuration operation of the dynamic ERP interface is as follows: 1. Enter the interface type you want to use in the management background of the bridge module; 2. After receiving the call request of the calling interface, the management background of the bridge module parses the path and performs verification; 3. After successful verification, save the settings.

[0026] The bridge module connects to the WMS system through the dynamic WMS interface and uses the dynamic WMS interface to achieve data interaction with the WMS system; the configuration operations of the dynamic WMS interface are: 1. Enter the interface type you want to use in the management background of the bridge module; 2. After receiving the call request of the calling interface, the management background of the bridge module parses the path and performs verification; 3. After successful verification, save the settings.

[0027] From the above description, it can be seen that by adopting a dynamic interface, the bridge module can be connected with ERP systems and WMS systems of different architectures, thereby improving the adaptability of the method.

[0028] The bridge module and the cloud server exchange data through standardized communication protocols. The bridge module can upload the interaction data between the ERP system and the WMS system to the cloud server. The cloud server has a log recording function to ensure that the data cannot be tampered with. In addition, the cloud server is based on an intelligent scheduling algorithm and can generate an optimal task list based on inventory data and demand, which is automatically converted into AGV control instructions. AGV and other automated equipment can realize intelligent transportation according to the control instructions and provide timely feedback, so that the cloud server and AGV and other automated equipment can realize data interaction. Through the cloud server, AGV and other automated equipment are linked, and the task allocation of AGV and other automated equipment can be dynamically adjusted according to the data of ERP and WMS to improve logistics efficiency.

[0029] In addition, communication encryption technology is used to transmit data between the bridge module and the ERP system and WMS system, between the bridge module and the cloud server, and between the cloud server and automation equipment such as AGV to ensure the security of data transmission and the reliability of the method.

[0030] In the present invention, the communication encryption technology is implemented by an encryption transmission algorithm, and the encryption transmission algorithm adopts a hybrid encryption scheme of asymmetric encryption combined with symmetric encryption. The specific process is as follows: Use the RSA algorithm for key exchange: , ; in: , , are RSA public and private keys, For plain text, is the ciphertext; Data transmission uses AES algorithm: , ; in: is a symmetric key, and For encryption and decryption functions.

[0031] The present invention provides an interface method for solving the intelligent data bridging between ERP and WMS, which specifically includes the following steps: Step 1: The bridge module automatically captures the inventory form data from the ERP system in real time through the dynamic ERP interface; the bridge module obtains the inventory data from the WMS system in real time through the dynamic WMS interface; 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. The WMS system obtains the in-and-out warehouse 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 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; 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; Data bridging algorithm description: Assume that the ERP data format is: , the WMS data format is: , the mapping relationship is , then the dynamic data mapping algorithm is as follows: ; in: ; It is a mapping parameter table, which contains data field correspondence rules and data format conversion rules; Data bridging algorithm based on timestamp and unique transaction identifier Realize two-way real-time synchronization of data between ERP system and WMS system; The data bridging algorithm implements data synchronization operations as follows: ; ; 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; 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: Assume that historical inventory demand is a time series , using the ARIMA model to predict future inventory demand: ; in: is the autoregression coefficient, is a random error; When the forecast inventory level is lower than the preset safety stock threshold When the replenishment plan is triggered: ; 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. The principle of the dynamic task distribution algorithm is: Defining task priorities , ; in: Indicates the importance of the material. Indicates the current inventory quantity. Indicates the urgency of material demand time. , , is the weight factor; AGV task allocation rules: ; in: Indicates that AGV completes the task The comprehensive cost, including but not limited to the path length and time consumption; Step 5: The cloud server uses blockchain traceability technology to record data interaction logs; The specific operations of the blockchain tracing technology are as follows: Define the block data structure: ; in: 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 ; Verification process: .

[0032] Effect of the present invention Experimental data and comparison 1. Bridge module efficiency experiment A. Traditional interface: data delay is about 5 seconds, error rate is 2%; B. The present invention: data delay is about 0.8 seconds, and the error rate is reduced to 0.2%.

[0033] 2. Inventory forecast accuracy After using the ARIMA model, the accuracy of inventory forecasting increased to 95%, and the efficiency of replenishment plan generation increased by 40%.

[0034] 3. Data security testing Data transmission encryption performance: The RSA-2048+AES-256 encryption solution takes an average of 1.2ms, and decryption takes 1.0ms, meeting the real-time requirements of medical device warehousing.

[0035] From the above description, it can be seen that when the method of the present invention is used, it combines specific algorithms, communication encryption technology and data security design to improve the efficiency, security and intelligence of the system through multi-layer optimization. Through the combination of automation and intelligent scheduling, it reduces manual operation time, improves material management and logistics execution efficiency, reduces error rates, saves operating costs, and improves overall production efficiency.

[0036] Below, the present application is further described with reference to the accompanying drawings.

[0037] like Figure 1As shown, Figure 1 This is the communication logic diagram of the bridge module: the bridge module and the ERP system use the dynamic ERP interface for two-way communication; the bridge module and the WMS system use the dynamic WMS interface for two-way communication; the bridge module and the cloud server use the standardized communication protocol for two-way communication, and the cloud server and automation equipment such as AGV use the standardized communication protocol for two-way communication.

[0038] Figure 2 The inventory forecasting and replenishment logic diagram of the interface method for solving the intelligent data bridging between ERP-WMS and cloud intelligent services proposed by the present invention: the historical inventory demand data and the preset safety stock threshold data are uploaded to the cloud server, the cloud server pre-processes 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 stock threshold data. If the predicted future inventory demand is less than the preset safety stock threshold data, the replenishment plan is triggered, otherwise the dynamic task distribution algorithm is started to optimize the inventory allocation rules.

[0039] Figure 3 The dynamic task distribution block diagram of the interface method for solving the intelligent data bridging between ERP-WMS and cloud-based intelligent services proposed by the present invention is as follows: the cloud server defines the task priority and arranges the tasks in order of priority from high to low. The cloud server calculates the comprehensive cost of the AGV to complete the task in turn and distributes the task to the AGV with the lowest comprehensive cost.

[0040] Figure 4 The data encryption transmission flow chart of the interface method for solving the intelligent data bridging between ERP-WMS and cloud-based intelligent services proposed by the present invention is as follows: the information sending end generates a pair of public keys and private keys, and discloses the public key to the information receiving end; after receiving the public key, the information receiving end uses the public key to encrypt the symmetric encryption key, and then sends the encrypted key to the information sending end; after receiving the encrypted key, the information sending end uses the private key to decrypt it to obtain the key required for symmetric encryption; the information sending end uses the decrypted symmetric key to encrypt the data to be transmitted; the encrypted data is transmitted to the signal receiving end through a secure channel; after receiving the encrypted data, the signal receiving end uses the symmetric encryption key to decrypt it to obtain the sent data.

[0041] Figure 5This is a blockchain technology flow chart of the interface method for solving the intelligent data bridging between ERP-WMS and cloud-based intelligent services proposed in the present invention: the cloud server defines the block data structure, generates a hash value, uses a private key to encrypt the generated hash value to obtain a digital signature, and writes the hash value and the digital signature into the blockchain, takes out the block data to be verified from the blockchain, uses the same hash algorithm to recalculate the hash value of the block data, and uses the public key to decrypt the digital signature to obtain the hash value, and compares the two calculated hash values. If the two are consistent, it means that the block data is valid and has not been tampered with, and the verification is passed.

[0042] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the embodiments of the present invention have been shown and described, it is understood by ordinary technicians in the field 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 attached 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 timestamps and unique transaction identifier Realize two-way real-time synchronization of data between ERP system and WMS system; 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: Assume that the ERP data format is: , the WMS data format is: , the mapping relationship is , then the dynamic data mapping algorithm is as follows: ； in: ; It is a mapping parameter table, which contains data field correspondence rules and data format conversion rules.

3. According to claim 1, an interface method for solving intelligent data bridging between ERP and WMS is characterized in that: The data bridging algorithm implements the following operations to achieve data synchronization: ; 。 4. According to claim 1, the interface method for solving the 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: Assume that historical inventory demand is a time series , using the ARIMA model to predict future inventory demand: ； in: is the autoregression coefficient, is a random error; When the forecast inventory level is lower than the preset safety stock threshold When the replenishment plan is triggered: 。 5. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 1 is characterized in that: The principle of the dynamic task distribution algorithm is: Defining task priorities , ； in: Indicates the importance of the material. Indicates the current inventory quantity. Indicates the urgency of material demand time. , , is the weight factor; AGV task allocation rules: ； in: Indicates that AGV completes the task The comprehensive cost includes but is not limited to path length and time consumption.

6. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 1 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: ， ； in: , , are RSA public and private keys, For plain text, is the ciphertext; Data transmission uses AES algorithm: ， ； in: is a symmetric key, and For encryption and decryption functions.

7. 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: ； in: 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 ; Verification process: 。 8. 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.

9. The interface method for solving the intelligent data bridging between ERP and WMS according to claim 8 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.

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

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