Method for data interaction between ERP and MES

Abstract

The invention relates to the technical field of data interaction, and particularly discloses a method for data interaction between ERP and MES, comprising an ERP module and an ERP database, an MES module and an MES database, an intermediate storage module, a monitoring module, a server and a data verification module. The ERP module is used for generating first data and storing the first data to an ERP database, and the MES module is used for generating second data and storing the second data to an MES database; the intermediate storage module is connected with the ERP database and the MES database through a TCP / IP protocol, and is used for receiving the first data and the second data in real time; and the monitoring module monitors the data change of the intermediate storage module based on a polling mechanism, and triggers the server to execute an analysis operation when detecting that the data is updated. According to the method, a verification formula in which a secret key participates is adopted, and the secret key is dynamically updated in combination with a Diffie-Hellman protocol, so that the data tampering detection rate is greatly improved; and an integrated OPC UA protocol and a RESTful API cloud are adopted, so that two-way communication of a heterogeneous system is supported, and the compatibility is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data interaction, and particularly relates to a method for data interaction between ERP and MES. Background Art

[0002] In the previous enterprise informatization process, ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) were generally used for resource configuration and management; the ERP system mainly optimizes and manages the upper layer of enterprise management, while the MES system mainly manages the lower layer of the enterprise, such as monitoring on-site equipment and process parameters.

[0003] The traditional ERP and MES data interaction technology has the following core problems:

[0004] Traditional solutions mostly adopt CRC check or fixed-period polling mechanism, lack the participation of dynamic keys, and are vulnerable to data tampering attacks;

[0005] Most systems rely on a single communication protocol (such as HTTP or OPC UA), making it difficult to adapt to heterogeneous devices and cloud platforms, resulting in high cross-system integration costs;

[0006] Full-volume data synchronization occupies a large amount of bandwidth and depends on manual configuration of field mapping tables, resulting in a resource utilization rate of less than 50% within the production beat cycle;

[0007] Traditional backup strategies lack chronological design, incremental data is not encrypted and compressed, and the cloud backup cycle is too long (≥7 days), unable to meet compliance requirements such as GMP.

[0008] Therefore, it is urgent to design a method for data interaction between ERP and MES to solve the above problems. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for data interaction between ERP and MES to solve the above deficiencies in the prior art.

[0010] To achieve the above purpose, the present invention provides the following technical solutions:

[0011] A method for data interaction between ERP and MES, including an ERP module, an ERP database, an MES module, an MES database, an intermediate storage module, a monitoring module, a server, and a data verification module;

[0012] The ERP module is used to generate first data and store it in the ERP database, and the MES module is used to generate second data and store it in the MES database;

[0013] The intermediate storage module is connected to the ERP database and the MES database respectively through the TCP / IP protocol, and is used to receive the first data and the second data in real time;

[0014] The monitoring module monitors the data changes of the intermediate storage module based on the polling mechanism, and triggers the server to execute the parsing operation when data updates are detected;

[0015] The server has a built-in data parsing engine, and uses the following formula to verify the changed data:

[0016]

[0017] where D i is the data segment, K i is the preset key, and n is the total number of data segments;

[0018] After the verification passes, the server synchronizes the parsed data to the target database;

[0019] The data verification module is connected to the server and is used to compare the data consistency before and after parsing and generate logs.

[0020] Preferably, the intermediate storage module uses a partitioned storage structure, including an ERP data partition and an MES data partition, and read-write separation is achieved through logical isolation between the partitions.

[0021] Preferably, the triggering conditions of the monitoring module include one of the data increment exceeding the threshold Δ and the update timestamp difference exceeding the preset period T, where Δ ∈ [1KB, 10MB] and T ∈ [1s, 60s].

[0022] Preferably, the data parsing engine supports JSON / XML format conversion, and matches keyword fields through regular expressions. The extraction rule is:

[0023] Field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1")

[0024] where D raw is the original data stream.

[0025] Preferably, the system realizes two-way communication with the underlying PLC device through the OPC UA protocol and the HTTP RESTful API.

[0026] Preferably, the data verification module uses the AES-256 algorithm to encrypt the transmitted data, and the encryption key is dynamically negotiated through the Diffie-Hellman protocol.

[0027] Preferably, an exception handling unit is built into the server. When the verification fails three times in a row, a data rollback mechanism is triggered and an alarm signal is sent to the operation and maintenance terminal.

[0028] Preferably, the system integrates a timing synchronization unit, and triggers a full - volume data synchronization according to the production beat cycle T cycle automatically, where T cycle is associated with the execution progress of the MES work order.

[0029] Preferably, the data parsing engine is associated with a data model mapping table. The table structure includes ERP field names, MES field names, and conversion rules. The mapping relationship is dynamically configured through a visual interface;

[0030] The intermediate storage module is connected to a cloud backup node. The backup policy follows the following rules: Incremental data is compressed and uploaded every hour, full - volume data is synchronized at midnight every day, and the retention period is ≥90 days.

[0031] Preferably, it includes the following steps:

[0032] Step 1: Data generation and storage

[0033] ERP module: Generate structured data such as orders, inventory, and finance in JSON / XML format and store it in the ERP database;

[0034] MES module: Collect real - time data such as equipment status, production progress, and process parameters and store it in the MES database;

[0035] Data standardization: Unify the field naming rules through the mapping table, such as ERP field name → MES field name, to eliminate the format differences of heterogeneous systems;

[0036] Step 2: Intermediate storage and listening

[0037] Partitioned storage: The intermediate storage module is divided into an ERP data partition and an MES data partition, and read - write separation is achieved through logical isolation;

[0038] Trigger mechanism: The listening module is based on a polling mechanism, with an interval of 1 - 60 seconds, to detect data increments, with a threshold Δ = 1KB - 10MB, and timestamp differences. Either one of them triggers the server to parse;

[0039] Dynamic encryption: Use the AES - 256 algorithm to encrypt the transmitted data, and the key is dynamically negotiated through the Diffie - Hellman protocol;

[0040] Step 3: Data parsing and verification

[0041] Parsing engine: A regular expression matcher is built into the server, using field value = Regex.Match(D raw, extract keyword fields using @"\<(\w+)([<]+)\<\ / \1")

[0042] Verification formula: Ensure data integrity by calculating the verification value:

[0043] Among them, D i is the data segment, K i is the preset key, and n is the total number of data segments;

[0044] Exception handling: Trigger data rollback when the verification fails 3 times consecutively, and send an alarm to the operation and maintenance terminal;

[0045] Step 4, Data synchronization and feedback

[0046] Protocol selection: Use the OPC UA protocol for two-way communication with the underlying PLC device, or achieve cross-platform docking through the HTTP RESTful API;

[0047] Timed synchronization: Perform full synchronization according to the production beat cycle T cycle Execute full synchronization, such as at midnight every day, and compress and upload incremental data to the cloud every hour;

[0048] Consistency verification: The data verification module compares the hash values before and after parsing and generates a log file with a timestamp.

[0049] In the above technical solution, a method for data interaction between ERP and MES provided by the present invention: (1) Adopt a verification formula involving a key, and dynamically update the key in combination with the Diffie-Hellman protocol, greatly improving the data tampering detection rate; (2) Adopt an integrated OPC UA protocol and RESTful API cloud, support two-way communication between heterogeneous systems, and greatly improve compatibility. At the same time, cooperate with the production beat cycle T cycle Dynamically adjust the synchronization frequency to improve resource utilization; (3) Dynamically configure the data model mapping table through a drag-and-drop interface, replace the traditional SQL statement modification, shorten the adaptation time of new material types from 3 days to 1 hour, and zero-code visual configuration can effectively reduce the implementation cost; (4) Compress and upload incremental data every hour, using the Zstandard algorithm with a compression ratio of 3:1, perform full synchronization of data every day, and the cloud retention period ≥ 90 days, achieving sequential backup and disaster tolerance enhancement. At the same time, an internal exception handling unit triggers a data rollback mechanism and automatically alarms when the verification fails 3 times consecutively, enabling the maintenance terminal to respond quickly. Description of the drawings

[0050] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a schematic flowchart provided for an embodiment of a method for data interaction between an ERP and an MES according to the present invention.

[0052] Figure 2 It is a schematic diagram of steps provided for an embodiment of a method for data interaction between an ERP and an MES according to the present invention. Detailed implementation manners

[0053] To enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail in conjunction with the accompanying drawings.

[0054] As Figure 1-2 shown, a method for data interaction between an ERP and an MES provided by an embodiment of the present invention includes an ERP module 101, an ERP database 201, an MES module 102, an MES database 202, an intermediate storage module 103, a monitoring module 104, a server 105, and a data verification module 106;

[0055] The ERP module 101 is used to generate first data and store it in the ERP database 201, and the MES module 102 is used to generate second data and store it in the MES database 202;

[0056] The intermediate storage module 103 is connected to the ERP database 201 and the MES database 202 respectively through the TCP / IP protocol, and is used to receive the first data and the second data in real time;

[0057] The monitoring module 104 monitors the data changes of the intermediate storage module 103 based on a polling mechanism, and triggers the server 105 to perform a parsing operation when data updates are detected;

[0058] The server 105 has a built-in data parsing engine and uses the following formula to verify the changed data:

[0059]

[0060] where D i is the data segment, K i is the preset key, and n is the total number of data segments;

[0061] After passing the verification, the server 105 synchronizes the parsed data to the target database;

[0062] The data verification module 106 is connected to the server 105 and is used to compare the data consistency before and after parsing and generate logs.

[0063] In the embodiments provided by the present invention, the intermediate storage module 103 adopts a partitioned storage structure, including an ERP data partition 103a and an MES data partition 103b. Read-write separation is achieved through logical isolation between partitions. The triggering conditions of the monitoring module 104 include one of the data increment exceeding the threshold Δ and the update timestamp difference exceeding the preset period T, where Δ ∈ [1KB, 10MB] and T ∈ [1s, 60s]. The data parsing engine supports JSON / XML format conversion and matches keyword fields through regular expressions. The extraction rule is:

[0064] Field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1")

[0065] where D raw is the original data stream.

[0066] In the embodiments provided by the present invention, the system realizes two-way communication with the underlying PLC devices through the OPC UA protocol and the HTTP RESTful API. The data verification module 106 encrypts the transmitted data using the AES-256 algorithm, and the encryption key is dynamically negotiated through the Diffie-Hellman protocol. The server 105 has a built-in exception handling unit. When the verification fails continuously three times, the data rollback mechanism is triggered and an alarm signal is sent to the operation and maintenance terminal. The system integrates a timing synchronization unit, which automatically triggers full-scale data synchronization according to the production beat cycle T cycle where T cycle is associated with the execution progress of the MES work order. The data parsing engine is associated with a data model mapping table. The table structure includes the ERP field name, the MES field name, and the conversion rule. The mapping relationship is dynamically configured through a visual interface;

[0067] The intermediate storage module 103 is connected to a cloud backup node 301. The backup strategy follows the following rules: Incremental data is compressed and uploaded every hour, full-scale data is synchronized at midnight every day, and the retention period ≥ 90 days.

[0068] A method for data interaction between ERP and MES includes the following steps:

[0069] Step 1, data generation and storage

[0070] The ERP module: generates structured data such as orders, inventory, and finance in JSON / XML format and stores it in the ERP database;

[0071] MES module: Collect real-time data such as device status, production progress, and process parameters, and store them in the MES database;

[0072] Data standardization: Unify the field naming rules through a mapping table, such as ERP field name → MES field name, to eliminate format differences in heterogeneous systems;

[0073] Step 2, Intermediate storage and monitoring

[0074] Partitioned storage: The intermediate storage module is divided into an ERP data partition 103a and an MES data partition 103b, and read-write separation is achieved through logical isolation;

[0075] Trigger mechanism: The monitoring module is based on a polling mechanism, with an interval of 1 - 60 seconds, detecting data increments, with a threshold Δ = 1KB - 10MB, and timestamp differences, either one of them, to trigger server parsing;

[0076] Dynamic encryption: Use the AES - 256 algorithm to encrypt the transmitted data, and the key is dynamically negotiated through the Diffie - Hellman protocol;

[0077] Step 3, Data parsing and verification

[0078] Parsing engine: The server has a built - in regular expression matcher, and uses the field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1") to extract key fields;

[0079] Verification formula: Ensure data integrity through verification value calculation:

[0080] Among them, D i is the data segment, K i is the preset key, and n is the total number of data segments;

[0081] Exception handling: Trigger data rollback when the verification fails 3 times consecutively, and send an alarm to the operation and maintenance terminal;

[0082] Step 4, Data synchronization and feedback

[0083] Protocol selection: Use the OPC UA protocol for two - way communication with the underlying PLC devices, or achieve cross - platform docking through the HTTP RESTful API;

[0084] Timed synchronization: Perform full - volume synchronization according to the production beat cycle T cycle , such as at midnight every day, and compress and upload incremental data to the cloud every hour;

[0085] Consistency verification: The data verification module compares the hash values before and after parsing and generates a log file with a timestamp.

[0086] Example 1: Dynamic Verification Integration Solution for Automobile Parts Manufacturing Enterprises

[0087] Technical Background: An automobile parts enterprise faced the problem of disconnection between ERP production plans and MES equipment data. The response delay for order changes reached 2 hours, and the inventory turnover rate was 30% lower than the industry standard;

[0088] Implementation Steps:

[0089] Step 1, Intermediate Storage Architecture: Deploy the Apache Kafka middleware, divide the ERP data area (storing orders and BOM) and the MES data area (storing equipment status and process parameters), and logical isolation is achieved through VLAN.

[0090] Step 2, Dynamic Verification Mechanism:

[0091] Data Verification Formula:

[0092] Among them, the key Ki is dynamically updated every hour through the Diffie-Hellman protocol (the preset master key length is 2048 bits).

[0093] Exception Handling: When the verification fails continuously 3 times, trigger data rollback and encrypt the warning log (AES-256 algorithm) and send it to the operation and maintenance terminal.

[0094] Step 3, Dual-Protocol Compatibility:

[0095] The underlying PLC device transmits real-time processing data through the OPC UA protocol (sampling period 50ms);

[0096] The cloud ERP system obtains the production progress through the RESTful API (JSON format, compression rate 85%).

[0097] Step 4, Visual Mapping Configuration:

[0098] Establish a mapping relationship table between ERP fields (such as "OrderID") and MES fields (such as "JobNo"), and support drag-and-drop interface adjustment of the date format (ERP: YYYY-MM-DD → MES: DD / MM / YYYY)

[0099] Conclusion:

[0100] The order change response time is shortened to 15 minutes, and the inventory turnover rate is increased to 120% of the industry average level;

[0101] The risk of data tampering is reduced by 92% (compared with traditional CRC verification), and the risk of key leakage is reduced by 75% due to the dynamic update mechanism;

[0102] Example 2: Cloud Backup Optimization Plan for Electronic Manufacturing Enterprises

[0103] Technical Background: Due to a single point of failure, a consumer electronics enterprise lost MES data, resulting in a production line downtime loss of up to 2 million yuan per time;

[0104] Implementation Steps:

[0105] Step 1: Intermediate Storage and Cloud Backup:

[0106] The intermediate storage module adopts a Redis Cluster partition architecture (ERP partition read-write separation TPS≥10,000); Incremental data is compressed and uploaded to Alibaba Cloud OSS every hour (Zstandard algorithm, compression ratio 3:1), and full-volume data is synchronized at midnight every day (bandwidth occupancy optimized by 40%);

[0107] Step 2: Data Model Mapping:

[0108] Configure the conversion rules between ERP material codes (such as "IC-2025A") and MES process codes ("PROC_SMT_001"), and extract key fields through regular expressions: Field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1") to achieve zero-code adaptation to new material types (response time < 1 hour)

[0109] Step 3: Timing Synchronization:

[0110] According to the production beat cycle T cycle = yield rate / standard working hours

[0111] Dynamically adjust the synchronization frequency (range 1 - 60 seconds), and predict the equipment load peak through the Kalman filter algorithm;

[0112] Conclusion: The data recovery time is shortened from 8 hours to 30 minutes, and the RTO (Recovery Time Objective) achievement rate is 99.9%;

[0113] The adaptation efficiency of new material types is increased by 90%, and the production line changeover time is reduced by 50%;

[0114] Comparative Example 1: Traditional Polling Mechanism and Static Verification Scheme

[0115] Technical Solution:

[0116] Adopt a fixed-period polling (interval 5 minutes), without an incremental detection threshold;

[0117] The verification method is CRC-32 static verification, and the key is replaced manually every month.

[0118] Defects:

[0119] The order data synchronization delay is up to 45 minutes, and the utilization rate of production line equipment is only 65%;

[0120] When attacked by a man-in-the-middle, the data tampering detection rate is only 68%, and the risk of key leakage is 3 times higher than that of the embodiment.

[0121] Comparative Example 2: Full-volume synchronization and manual mapping scheme

[0122] Technical solution:

[0123] Execute full-volume data synchronization every day at midnight (consuming 1 Gbps of bandwidth and taking 4 hours);

[0124] Data mapping depends on manual modification of SQL statements (average response time: 3 days per time).

[0125] Defects:

[0126] During synchronization, the database lock phenomenon causes the ERP system response delay ≥500 ms;

[0127] When deploying a new production line, due to field mapping errors, the material mismatch rate is 12%, and the rework cost increases by 1.5 million yuan.

[0128] Conclusion comparison

[0129]

[0130] Adopting the interaction method of this application document, during use, the data synchronization delay is reduced, the system compatibility is greatly improved by adopting the multi-cloud adaptation and dual-protocol methods, and at the same time, the security protection level is high.

[0131] Only some exemplary embodiments of the present invention have been described by way of illustration above. Without doubt, for those of ordinary skill in the art, various different ways can be used to modify the described embodiments without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A method for data interaction between ERP and MES, characterized in that, It includes an ERP module (101) and an ERP database (201), an MES module (102) and an MES database (202), an intermediate storage module (103), a monitoring module (104), a server (105), and a data verification module (106); The ERP module (101) is used to generate first data and store it in the ERP database (201), and the MES module (102) is used to generate second data and store it in the MES database (202); The intermediate storage module (103) is connected to the ERP database (201) and the MES database (202) respectively through the TCP / IP protocol, and is used to receive the first data and the second data in real time; The monitoring module (104) monitors the data changes of the intermediate storage module (103) based on a polling mechanism, and triggers the server (105) to perform a parsing operation when data updates are detected; The server (105) has a built-in data parsing engine, and uses the following formula to verify the changed data: Among them, D i is the data segment, K i is the preset key, and n is the total number of data segments; After passing the verification, the server (105) synchronizes the parsed data to the target database; The data verification module (106) is connected to the server (105), and is used to compare the data consistency before and after parsing and generate a log.

2. A method for data interaction between ERP and MES according to claim 1, characterized in that, The intermediate storage module (103) adopts a partitioned storage structure, including an ERP data partition (103a) and an MES data partition (103b), and read-write separation is achieved through logical isolation between the partitions.

3. A method for data interaction between ERP and MES according to claim 1, characterized in that, The triggering conditions of the monitoring module (104) include one of the data increment exceeding the threshold Δ and the update timestamp difference exceeding the preset period T, where Δ ∈ [1KB, 10MB] and T ∈ [1s, 60s].

4. A method for data interaction between ERP and MES according to claim 1, characterized in that The data parsing engine supports JSON / XML format conversion, and matches keyword fields through regular expressions. The extraction rules are: Field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1") Among which D raw is the original data stream.

5. A method for data interaction between ERP and MES according to claim 1, characterized in that The system realizes two-way communication with the underlying PLC device through the OPC UA protocol and the HTTP RESTful API.

6. A method for data interaction between ERP and MES according to claim 1, characterized in that, The data verification module (106) encrypts the transmitted data using the AES-256 algorithm, and the encryption key is dynamically negotiated through the Diffie-Hellman protocol.

7. A method for data interaction between ERP and MES according to claim 1, characterized in that, The server (105) has a built-in exception handling unit. When the verification fails continuously for 3 times, it triggers a data rollback mechanism and sends an alarm signal to the operation and maintenance terminal.

8. A method for data interaction between ERP and MES according to claim 1, characterized in that, The system integrates a timing synchronization unit, and triggers full - volume data synchronization automatically according to the production beat cycle T cycle where T cycle is associated with the execution progress of the MES work order.

9. A method for data interaction between ERP and MES according to claim 1, characterized in that, The data parsing engine is associated with a data model mapping table. The table structure includes ERP field names, MES field names, and conversion rules, and the mapping relationship is dynamically configured through a visual interface; The intermediate storage module (103) is connected to a cloud backup node (301), and the backup strategy follows the following rules: Incremental data is compressed and uploaded every hour, full data is synchronized at midnight every day, and the retention period ≥ 90 days.

10. A method for data interaction between ERP and MES according to claim 1, characterized in that, It includes the following steps: Step 1, Data generation and storage ERP module: Generate structured data such as orders, inventory, and finance in JSON / XML format and store it in the ERP database; MES module: Collect real-time data such as equipment status, production progress, and process parameters and store it in the MES database; Data standardization: Unify the field naming rules through a mapping table, such as ERP field name → MES field name, to eliminate format differences in heterogeneous systems; Step 2, Intermediate storage and monitoring Partitioned storage: The intermediate storage module is divided into an ERP data partition (103a) and an MES data partition (103b), and read-write separation is achieved through logical isolation; Trigger mechanism: The monitoring module, based on a polling mechanism, detects data increments at intervals of 1 - 60 seconds, with a threshold Δ = 1KB - 10MB, and timestamp differences, and triggers server parsing based on either of the two; Dynamic encryption: Use the AES-256 algorithm to encrypt the transmitted data, and the key is dynamically negotiated through the Diffie-Hellman protocol; Step 3, Data parsing and verification Parsing Engine: The server has a built-in regular expression matcher that uses the field value = Regex.Match(D raw , @"\<(\w+)([<]+)\<\ / \1") to extract the key fields; Verification formula: Ensure data integrity by calculating the verification value: Among them, D i is the data segment, K i is the preset key, and n is the total number of data segments; Exception handling: Trigger data rollback when the verification fails continuously three times, and send an alarm to the operation and maintenance terminal; Step 4, Data synchronization and feedback Protocol selection: Use the OPC UA protocol for two-way communication with the underlying PLC device, or achieve cross-platform docking through the HTTP RESTful API; Timing synchronization: according to the production beat cycle T cycle Perform full synchronization, such as at midnight every day, and compress and upload incremental data to the cloud every hour; Consistency verification: The data verification module compares the hash values before and after parsing and generates a log file with a timestamp.

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