Intelligent manufacturing multi-source system process integration platform and method based on discrete manufacturing enterprise service bus

Abstract

The invention provides an intelligent manufacturing multi-source system process integration platform and method based on a discrete manufacturing enterprise service bus. The platform comprises an enterprise service bus ESB core module, a multi-source system access layer, a data processing and conversion module and a self-adaptive fault switching routing module. According to the invention, each node and connection state in the enterprise service bus are monitored in real time through the self-adaptive failover routing module, and self-adaptive failover routing is carried out in time when a fault occurs and the performance is reduced, so that data transmission interruption caused by network and node faults can be effectively avoided; according to the technical scheme of the invention, the system can quickly adopt corresponding switching strategies to ensure that data can be continuously transmitted regardless of various emergencies such as node crash, connection interruption or network congestion, so that the continuity and reliability of data transmission are ensured, and the potential loss of enterprises caused by interruption of a service process is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of information system integration technology for discrete manufacturing enterprises, and in particular to an intelligent manufacturing multi-source system process integration platform and method based on a discrete manufacturing enterprise service bus. Background Technology

[0002] In today's rapidly developing digital era, the operation and management of discrete manufacturing enterprises heavily rely on various advanced information systems. Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and Office Automation (OA) systems, each with different functions, perform their respective duties within the enterprise, undertaking important tasks such as resource allocation, customer maintenance, and daily office process management. As enterprise business continues to expand and become more complex, data interaction and collaborative work between these systems become increasingly crucial. However, achieving effective collaboration between multiple systems is not easy. To address this, the Enterprise Service Bus (ESB) architecture has emerged. As a middleware technology, it aims to provide a unified integration platform. By defining standard interfaces and protocols, it can, to some extent, shield the heterogeneity between different systems, enabling relatively convenient data interaction between business systems. Many enterprises have begun to adopt ESB to build integrated environments for multiple systems, hoping to achieve more efficient and flexible inter-system collaboration.

[0003] Despite the numerous conveniences brought by the ESB architecture, some problems still need to be addressed in practical applications. As enterprise business scales further expand and data traffic continues to rise, the network environment becomes increasingly complex, and network failures, node failures, and path performance degradation occur frequently. Existing multi-source system process integration solutions based on ESB are inadequate in the face of these emergencies. When network congestion occurs or a node fails, existing solutions cannot detect the occurrence of the failure in a timely and accurate manner, and it is even more difficult to take effective measures to ensure the continuity and reliability of data transmission. This often leads to data transmission interruptions and business process stagnation during actual business operations, resulting in serious economic losses and operational risks for enterprises.

[0004] Therefore, it is necessary to propose an intelligent manufacturing multi-source system process integration platform and method based on discrete manufacturing enterprise service bus to solve the problems in the existing technology. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent manufacturing multi-source system process integration platform and method based on discrete manufacturing enterprise service bus, so as to solve the problems existing in the prior art.

[0006] The technical solution adopted to achieve the purpose of this invention is as follows: an intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus, including an enterprise service bus (ESB) core module, a multi-source system access layer, a data processing and conversion module, and an adaptive fault switching routing module.

[0007] The multi-source system access layer connects various heterogeneous business systems within the enterprise, enabling data format conversion, protocol adaptation, and connection management. After converting the raw data from each heterogeneous business system into a standard format, the multi-source system access layer sends it to the Enterprise Service Bus (ESB) core module. The multi-source system access layer also provides real-time feedback on the connection status with each heterogeneous business system to the ESB core module.

[0008] The Enterprise Service Bus (ESB) core module is used to uniformly receive, forward, and process data requests and messages from various business systems, and provides standard interfaces and protocols.

[0009] The data processing and conversion module is used to clean, encrypt, decrypt, and convert the format of the transmitted data. The Enterprise Service Bus (ESB) core module forwards the data to the processing module for cleaning, encryption, and format conversion, and then returns the processed data to the ESB core module.

[0010] The adaptive failover routing module monitors the system's health status in real time and intelligently switches transmission paths to ensure continuity in the event of a fault or performance degradation. The Enterprise Service Bus (ESB) core module delivers data to the adaptive failover routing module for monitoring and routing. Based on the status decision, the adaptive failover routing module controls the ESB core module to switch transmission paths.

[0011] Furthermore, the Enterprise Service Bus (ESB) core module includes a data caching mechanism. This caching mechanism dynamically adjusts the cache capacity based on the real-time data traffic load and data transmission priority of each node using a specific algorithm. The algorithm specifically considers the node data traffic load value Q. i Priority coefficient P for each type of data transmission j Calculate the comprehensive impact factor F = ∑ i ∑ j (Q i ×P j The cache capacity is adjusted based on the relationship between F and the preset threshold T.

[0012] Furthermore, the multi-source system access layer data format conversion is based on an intelligent format matching algorithm, which extracts key feature vectors V by analyzing the data structure features of the business system. k , and the feature vector V of the preset standard data format s Calculate similarity S ksConvert the data format based on similarity.

[0013] Furthermore, the data processing and transformation module constructs a data association graph G by analyzing the relationships between data items, and then determines the data item connectivity C based on the graph. d and average correlation strength I d Identify and remove noisy data.

[0014] Furthermore, the adaptive fault-switching routing module includes a status monitoring submodule, a fault and performance degradation judgment submodule, a switching strategy selection submodule, and a path switching execution submodule.

[0015] The status monitoring submodule employs a hybrid monitoring algorithm. This algorithm integrates network heartbeat detection, node performance metric acquisition, and connection packet loss rate and latency parameter monitoring. Specifically, it periodically sends a heartbeat signal H. s Receive response heartbeat signal H r Calculate the heartbeat signal loss rate Where m represents the number of monitoring cycles, and U represents the CPU utilization of the real-time data acquisition node. c Memory usage U m Network bandwidth utilization U h and connection packet loss rate L p The delay D is used to combine these monitoring data into a state monitoring vector V. m =(L h U c U m U b ,L p (D).

[0016] The fault and performance degradation judgment submodule is based on a dynamic threshold judgment algorithm. The dynamic threshold judgment algorithm is based on the state monitoring vector V provided by the state monitoring submodule. m Dynamic threshold ranges are set for different monitoring indicators, specifically: for heartbeat signal loss rate L... h The threshold range is set to [L] hmin ,L hmax When L h Exceeding this range is considered a risk of connection failure, and the CPU utilization rate U c, Set the threshold range to [U cmin U cmax ], when U c When the value exceeds this range, it is considered a risk of node performance degradation. c Exceeding the separately set severity threshold range [U cseveremin U cseveremaxWhen a node is identified as having a serious risk of failure, corresponding dynamic threshold ranges are also set for other monitoring indicators, including memory usage, network bandwidth utilization, packet loss rate, and latency. The presence of failures and performance degradation is determined based on whether each monitoring indicator exceeds its corresponding threshold range.

[0017] The switching strategy selection submodule predefines multiple switching strategies based on fault type and severity classification. Through an intelligent matching algorithm, it selects the appropriate switching strategy based on fault type T. f and severity S f The feature vector V corresponding to each switching strategy s Calculate the matching degree M fs Matching degree Choose the switching strategy with the highest matching degree.

[0018] The path switching execution submodule performs the path switching operation according to the selected switching strategy. During switching, based on a data protection algorithm, the transmitted data is divided into data blocks B. i Determine the importance weight W for each data block. i The data is cached, and transmission is resumed according to the cache order after the switch is completed. If interrupted, the transmission is resumed using a breakpoint resume algorithm based on data block index.

[0019] Furthermore, the dynamic threshold judgment algorithm of the fault and performance degradation judgment submodule of the adaptive fault switching routing module fully considers the different situations of peak and off-peak business periods when setting the threshold range. During peak business periods, the system load is large, and the normal fluctuation range of various monitoring indicators will be larger. The algorithm will correspondingly increase the threshold range of some monitoring indicators, raising the threshold range of CPU utilization from the usual [U cmin U cmax Adjusted to in Greater than U cmin , Greater than U cmax During periods of low business activity, when the system load is lighter, the algorithm lowers the threshold range of some monitoring indicators.

[0020] Furthermore, the breakpoint resume algorithm based on data block index in the path switching execution submodule of the adaptive fault-failure routing module records the data block index I. i At this time, a hierarchical indexing method is used. Specifically, the data blocks are divided into m levels according to their size, and each level corresponds to an index range. Smaller data blocks are divided into the first level, and their index range is... For medium-sized data blocks, a second level is defined, with an index range of [missing information]. Larger data blocks are divided into a third level, with an index range of [missing information].

[0021] Furthermore, the intelligent matching algorithm of the switching strategy selection submodule of the adaptive fault-handling routing module calculates the matching degree M. fs In this process, dynamic adjustments are made based on the company's past experience in handling faults. Specifically, as the company's business expands, it continuously accumulates information on different fault types (T). f and severity S f Based on the actual performance data of each switching strategy, the algorithm periodically updates the feature vector V corresponding to each predefined switching strategy. s Adjustments will be made.

[0022] This invention also discloses a method for integrating processes in a multi-source intelligent manufacturing system based on a discrete manufacturing enterprise service bus according to the above-mentioned platform, comprising the following steps:

[0023] S1. Multi-source system access and data initialization: Each business system accesses the enterprise service bus through the multi-source system access layer, initializes its own data according to the format specified by the access layer, and uses an adaptive encoding algorithm to calculate the encoding priority P based on data characteristics and target business process requirements. e And divide into coding groups G e Different encoding methods are used to encode data from different encoding groups, while the access layer establishes a stable and reliable connection and feeds back connection status information.

[0024] S2. Data Processing and Transmission Preparation: After initialization, the data enters the data processing and transformation module. First, data cleaning is performed, based on the similarity of data item content S... c The system identifies and removes valid duplicate data, then encrypts the data using an encryption algorithm based on data encryption layers according to the data characteristics, and finally converts the data format to meet the requirements of transmission and business processes.

[0025] S3. Adaptive Fault Switching Route Monitoring and Processing: During data transmission, the adaptive fault switching route module continuously monitors the data. The status monitoring submodule uses a hybrid monitoring algorithm to obtain status information. The fault and performance degradation judgment submodule uses a dynamic threshold judgment algorithm to analyze and judge the data. In case of faults or performance degradation, the switching strategy selection submodule uses an intelligent matching algorithm to select a suitable strategy. The path switching execution submodule uses a data protection algorithm and a breakpoint resume algorithm to execute the path switching operation.

[0026] S4. Data Reception and Subsequent Processing: Data transmitted via the Enterprise Service Bus is received by the target business system. The target business system performs subsequent processing on the received data according to its own business needs and provides feedback on relevant information.

[0027] Furthermore, in step S1, the adaptive coding algorithm calculates the coding priority P. eIn this process, the algorithm closely monitors the update frequency and data changes of the enterprise's business systems. When there are significant changes in the business system updates or data structure, the algorithm will promptly recalculate the coding priority P. e Specifically, when a business system adds important new business functions or undergoes significant changes in data types, the algorithm will reassess the importance, source, and relevance of the data to the target business process, thereby re-determining the coding priority P. e .

[0028] The technical effects of this invention are undeniable: by monitoring the status of each node and connection in the enterprise service bus in real time through the adaptive failover routing module, and promptly performing adaptive failover routing when faults and performance degradation occur, data transmission interruptions caused by network and node failures can be effectively avoided. Regardless of various emergencies such as node crashes, connection interruptions, or network congestion, the system can quickly adopt corresponding switching strategies to ensure that data can continue to be transmitted, thereby guaranteeing the continuity and reliability of data transmission and effectively reducing the potential losses to the enterprise caused by business process interruptions. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of a smart manufacturing multi-source system process integration platform based on a discrete manufacturing enterprise service bus;

[0030] Figure 2 This is a flowchart of a method for integrating processes in a multi-source intelligent manufacturing system based on a discrete manufacturing enterprise service bus. Detailed Implementation

[0031] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0032] Example 1:

[0033] See Figure 1 This embodiment provides an intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus, including an enterprise service bus (ESB) core module, a multi-source system access layer, a data processing and conversion module, and an adaptive fault switching routing module.

[0034] The multi-source system access layer connects various heterogeneous business systems within the enterprise, enabling data format conversion, protocol adaptation, and connection management. After converting the raw data from each heterogeneous business system into a standard format, the multi-source system access layer sends it to the Enterprise Service Bus (ESB) core module. The multi-source system access layer also provides real-time feedback on the connection status with each heterogeneous business system to the ESB core module.

[0035] The Enterprise Service Bus (ESB) core module is used to uniformly receive, forward, and process data requests and messages from various business systems, and provides standard interfaces and protocols.

[0036] The data processing and conversion module is used to clean, encrypt, decrypt, and convert the format of the transmitted data. The Enterprise Service Bus (ESB) core module forwards the data to the processing module for cleaning, encryption, and format conversion, and then returns the processed data to the ESB core module.

[0037] The adaptive failover routing module monitors the system's health status in real time and intelligently switches transmission paths to ensure continuity in the event of a fault or performance degradation. The Enterprise Service Bus (ESB) core module delivers data to the adaptive failover routing module for monitoring and routing. Based on the status decision, the adaptive failover routing module controls the ESB core module to switch transmission paths.

[0038] By innovatively introducing adaptive failover routing functionality, the system's ability to cope with various faults and performance degradation is comprehensively enhanced, thereby effectively ensuring the continuity and reliability of data transmission and guaranteeing the smooth operation of enterprise business processes.

[0039] Example 2:

[0040] This embodiment is largely the same as embodiment 1, except that the Enterprise Service Bus (ESB) core module includes a data caching mechanism. The caching mechanism dynamically adjusts the cache capacity based on the real-time data traffic load and data transmission priority of each node using a specific algorithm. The specific algorithm is as follows: it comprehensively considers the node data traffic load value Q. i Priority coefficient P for each type of data transmission j Calculate the comprehensive impact factor F = ∑ i ∑ j (Q i ×P jThe cache capacity is adjusted based on the relationship between F and the preset threshold T. The standard interfaces and protocols of the Enterprise Service Bus (ESB) core module cover common enterprise application integration specifications, including SOAP and REST. In its data caching mechanism, the preset threshold T and the capacity adjustment coefficient k are flexibly set according to the specific business needs and network environment characteristics of the enterprise. For business scenarios with large data traffic fluctuations and high real-time requirements, the threshold T is lowered and the coefficient k is increased to respond promptly to changes in data traffic.

[0041] Example 3:

[0042] The main content of this embodiment is the same as that of Embodiment 1 or 2, wherein the multi-source system access layer data format conversion is based on an intelligent format matching algorithm, and key feature vector V is extracted by analyzing the data structure features of the business system. k , and the feature vector V of the preset standard data format s Calculate similarity S ks The data format is converted based on similarity. The intelligent format matching algorithm of the multi-source system access layer calculates the similarity S. ks At the same time, the algorithm will fully consider the update frequency and data changes of the enterprise's business system. When the business system is updated frequently and the data structure changes significantly, the algorithm will periodically re-extract the key feature vector V of the data structure. k And recalculate the feature vector V in the standard data format s The similarity.

[0043] Example 4:

[0044] The main content of this embodiment is the same as any one of embodiments 1 to 3. The data processing and conversion module constructs a data association graph G by analyzing the relationships between data items, and then determines the data connection degree C based on the data items in the graph. d and average correlation strength I d The data processing and transformation module identifies and removes noisy data. When constructing the data association graph G, the data association algorithm based on data correlation considers not only the conventional relationships between data items but also the timestamp information of the data. For data in different time periods, the algorithm constructs separate data association graphs to analyze the changes in the correlation of data items at different points in time. For data with obvious periodicity, data association graphs are constructed quarterly.

[0045] Example 5:

[0046] The main content of this embodiment is the same as any one of embodiments 1 to 4. The adaptive fault switching routing module includes a status monitoring submodule, a fault and performance degradation judgment submodule, a switching strategy selection submodule, and a path switching execution submodule.

[0047] The status monitoring submodule employs a hybrid monitoring algorithm. This algorithm integrates network heartbeat detection, node performance metric acquisition, and connection packet loss rate and latency parameter monitoring. Specifically, it periodically sends a heartbeat signal H. s Receive response heartbeat signal H r Calculate the heartbeat signal loss rate Where m represents the number of monitoring cycles, and U represents the CPU utilization of the real-time data acquisition node. c Memory usage U m Network bandwidth utilization U h and connection packet loss rate L p The delay D is used to combine these monitoring data into a state monitoring vector V. m =(L h U c U m U b ,L p (D).

[0048] The fault and performance degradation judgment submodule is based on a dynamic threshold judgment algorithm. The dynamic threshold judgment algorithm is based on the state monitoring vector V provided by the state monitoring submodule. m Dynamic threshold ranges are set for different monitoring indicators, specifically: for heartbeat signal loss rate L... h The threshold range is set to [L] hmin ,L hmax When L h Exceeding this range is considered a risk of connection failure, and the CPU utilization rate U c, Set the threshold range to [U cmin U cmax ], when U c When the value exceeds this range, it is considered a risk of node performance degradation. c Exceeding the separately set severity threshold range [U cseveremin U cseveremax When a node is identified as having a serious risk of failure, corresponding dynamic threshold ranges are also set for other monitoring indicators, including memory usage, network bandwidth utilization, packet loss rate, and latency. The presence of failures and performance degradation is determined based on whether each monitoring indicator exceeds its corresponding threshold range.

[0049] The switching strategy selection submodule predefines multiple switching strategies based on fault type and severity classification. Through an intelligent matching algorithm, it selects the appropriate switching strategy based on fault type T. f and severity S f The feature vector V corresponding to each switching strategy s Calculate the matching degree M fs Matching degree Choose the switching strategy with the highest matching degree.

[0050] The path switching execution submodule performs the path switching operation according to the selected switching strategy. During switching, based on a data protection algorithm, the transmitted data is divided into data blocks B. i Determine the importance weight W for each data block. i The data is cached, and transmission is resumed according to the cache order after the switch is completed. If interrupted, the transmission is resumed using a breakpoint resume algorithm based on data block index.

[0051] The hybrid monitoring algorithm of the state monitoring submodule of the adaptive fault-switching routing module collects node performance indicators in a differentiated manner according to the importance level of the node. For core nodes, the collection frequency is set to once every n1 seconds, and for ordinary nodes, the collection frequency is set to once every n2 seconds, where n2 is greater than n1. This allows for more focused monitoring of the state changes of key nodes and timely detection of potential problems that may affect business processes.

[0052] The dynamic threshold judgment algorithm of the fault and performance degradation judgment submodule of the adaptive fault switching routing module fully considers the different situations of peak and off-peak business periods when setting the threshold range. During peak business periods, the system load is large, and the normal fluctuation range of various monitoring indicators will be larger. The algorithm will correspondingly increase the threshold range of some monitoring indicators, raising the threshold range of CPU utilization from the usual [U cmin U cmax Adjusted to in Greater than U cmin , Greater than U cmax During periods of low business activity, when the system load is lighter, the algorithm lowers the threshold range of some monitoring indicators.

[0053] The path switching execution submodule of the adaptive fault-failure routing module uses a breakpoint resume algorithm based on data block indexes to record data block indexes I. i At this time, a hierarchical indexing method is used. Specifically, the data blocks are divided into m levels according to their size, and each level corresponds to an index range. Smaller data blocks are divided into the first level, and their index range is... For medium-sized data blocks, a second level is defined, with an index range of [missing information]. Larger data blocks are divided into a third level, with an index range of [missing information].

[0054] The intelligent matching algorithm of the switching strategy selection submodule of the adaptive fault-switching routing module calculates the matching degree M. fsIn this process, dynamic adjustments are made based on the company's past experience in handling faults. Specifically, as the company's business expands, it continuously accumulates information on different fault types (T). f and severity S f Based on the actual performance data of each switching strategy, the algorithm periodically updates the feature vector V corresponding to each predefined switching strategy. s Adjustments will be made.

[0055] Example 6:

[0056] See Figure 2 This embodiment provides a method for integrating processes in a multi-source intelligent manufacturing system based on a discrete manufacturing enterprise service bus according to any one of embodiments 1 to 5, including the following steps:

[0057] S1. Multi-source system access and data initialization: Each business system accesses the enterprise service bus through the multi-source system access layer, initializes its own data according to the format specified by the access layer, and uses an adaptive encoding algorithm to calculate the encoding priority P based on data characteristics and target business process requirements. e And divide into coding groups G e Different encoding methods are used for data from different encoding groups, while the access layer establishes a stable and reliable connection and feeds back connection status information. The adaptive encoding algorithm calculates the encoding priority P. e In this process, the algorithm closely monitors the update frequency and data changes of the enterprise's business systems. When there are significant changes in the business system updates or data structure, the algorithm will promptly recalculate the coding priority P. e Specifically, when a business system adds important new business functions or undergoes significant changes in data types, the algorithm will reassess the importance, source, and relevance of the data to the target business process, thereby re-determining the coding priority P. e .

[0058] S2. Data Processing and Transmission Preparation: After initialization, the data enters the data processing and transformation module. First, data cleaning is performed, based on the similarity of data item content S... c The system identifies and removes valid duplicate data, then encrypts the data using an encryption algorithm based on data encryption layers according to the data characteristics, and finally converts the data format to meet the requirements of transmission and business processes.

[0059] S3. Adaptive Fault Switching Route Monitoring and Processing: During data transmission, the adaptive fault switching route module continuously monitors the data. The status monitoring submodule uses a hybrid monitoring algorithm to obtain status information. The fault and performance degradation judgment submodule uses a dynamic threshold judgment algorithm to analyze and judge the data. In case of faults or performance degradation, the switching strategy selection submodule uses an intelligent matching algorithm to select a suitable strategy. The path switching execution submodule uses a data protection algorithm and a breakpoint resume algorithm to execute the path switching operation.

[0060] S4. Data Reception and Subsequent Processing: Data transmitted via the Enterprise Service Bus is received by the target business system. The target business system performs subsequent processing on the received data according to its own business needs and provides feedback on relevant information.

[0061] Example 7:

[0062] This embodiment provides an application of the method described in Embodiment 6. It addresses a large manufacturing enterprise with multiple business systems, including an order processing system (for receiving customer orders and processing order-related business processes), an inventory management system (for real-time monitoring and management of enterprise inventory levels), and other related auxiliary business systems. These systems originally operated relatively independently, but with the development of the enterprise's business, efficient process integration is required to ensure that inventory information can be obtained in real-time during order processing. Simultaneously, the inventory management system must update inventory promptly based on order status, and the continuity and reliability of data transmission must be guaranteed throughout the entire data interaction process. Even in the event of network failures or system node failures, the normal operation of business processes should not be affected. Therefore, the enterprise adopted the multi-source system process integration platform and method based on Enterprise Service Bus provided by this invention.

[0063] As important business systems within an enterprise, the order processing system and inventory management system are first connected to the enterprise service bus through a multi-source system access layer.

[0064] During the integration process, the order processing system initializes order-related data (such as order number, customer information, product quantity requirements, etc.) according to the format specified by the integration layer. For example, an adaptive encoding algorithm is used to calculate the encoding priority P of the data based on the data type (basic order information, product details, etc.), source (orders from different sales channels), and the data requirements of the target business process (such as the format required for subsequent interaction with the inventory management system). ∈ Assuming that critical data such as order numbers and customer information have a high coding priority, they are classified into a high coding priority group G. e1 A more efficient and secure encoding method is used; while relatively minor data such as product demand quantities are classified into a low-priority encoding group G. e2 It uses conventional encoding methods and establishes a stable and reliable connection at the access layer, and promptly feeds back the connection status information to the upper-layer module.

[0065] The inventory management system also performs similar initialization processing on inventory-related data (such as product inventory quantity, inventory location, product inbound and outbound records, etc.) to ensure that the data can enter subsequent processes in a format that meets the requirements of enterprise service bus transmission.

[0066] The data from the initialized order processing system and inventory management system enter the data processing and conversion module.

[0067] First, data cleaning is performed. In the order processing system data, a noise reduction algorithm based on data correlation is used. This involves constructing a data correlation graph G by analyzing the relationships between data items in the dataset. For example, for customer address information in the order data, if certain address data items are found to have a connectivity C in graph G... d Less than the preset connectivity threshold C t And the average correlation strength I with other data items d Less than the preset correlation strength threshold I t These address data items are identified as potentially noisy (such as incorrect or incomplete address information) and removed to improve the quality of order data.

[0068] For data in the inventory management system, perform similar data cleaning operations to remove any erroneous inventory records or redundant information.

[0069] Next, encryption is performed based on the sensitivity of the data and the company's security policy. For critical data involving customer privacy in the order processing system (such as customer credit card information), encryption algorithms based on data encryption layers are used, and the data is classified into high encryption layers L according to factors such as the importance of the data and the transmission path. el High-strength encryption algorithms (such as AES Advanced Encryption Standard) are used for encryption, while general order information and inventory data are classified as low-encryption level L. e2 It employs a relatively weak encryption algorithm that still ensures basic security.

[0070] Finally, the data undergoes further format conversion. The data from the order processing system is converted from its original format to a format more suitable for transmission in the enterprise service bus and for interaction with the inventory management system (such as converting a specific database format to a common XML format). Similarly, the data from the inventory management system is also converted accordingly so that both parties can exchange data smoothly.

[0071] During data transmission, the adaptive failover routing module continuously monitors its status.

[0072] The status monitoring submodule employs a hybrid monitoring algorithm to periodically send a heartbeat signal H. s It provides responses and heartbeat signals to various nodes in the order processing system, inventory management system, and enterprise service bus. r Calculate the heartbeat signal loss rate Where m represents the number of monitoring cycles, and U represents the CPU utilization of the real-time acquisition node. cMemory usage U m Network bandwidth utilization U h and connection packet loss rate L p The delay D is used to combine these monitoring data into a state monitoring vector V. m =(L h U c U m U b ,L p For example, when monitoring the node connecting the order processing system to the enterprise service bus, it is found that the CPU utilization U of that node at a certain moment is... c It reached 85%, and the memory usage rate U m The network bandwidth utilization rate reached 70%, U h The packet loss rate L for the connection is 60%. p With a value of 5% and a delay of D of 10 milliseconds, the state monitoring vector of this node at this time is formed.

[0073] The fault and performance degradation judgment submodule is based on a dynamic threshold judgment algorithm, according to the state monitoring vector V provided by the state monitoring submodule. m Analysis and judgment were conducted, and dynamic threshold ranges were set for different monitoring indicators, such as CPU utilization U. c The threshold range is set to [U cmin U cmax Assume U cmin =70%, U cmax =90%, when the U of this node is detected c At 85%, although it has not yet exceeded the severe threshold, it is close to U. cmax This indicates a risk of node performance degradation.

[0074] Since the risk of node performance degradation has been identified, the switching strategy selection submodule uses an intelligent matching algorithm to encode the fault type and severity information into a feature vector V based on the fault type (node ​​performance degradation) and severity (minor). f = (Slight node performance degradation), and compared with the feature vector V corresponding to each predefined switching strategy. s Perform a matching degree calculation; matching degree (where n represents the dimension of the feature vector). Suppose that through calculation, a switching strategy for adjusting the data transmission priority is selected, that is, to prioritize the transmission of key data (such as order number, customer information, etc.) and increase its transmission priority, while appropriately reducing the transmission priority of some non-key data (such as order notes, etc.).

[0075] The path switching execution submodule performs path switching operations according to the selected switching strategy. During path switching, based on the data protection algorithm, the data being transmitted is first divided into data blocks B.i Divide the data into blocks (i represents different data blocks), and calculate the importance weight W for each data block. i For example, for a data block containing order numbers, its importance weight W i High; for data blocks containing order notes, their importance weight W is relatively high. i The data blocks are first cached according to their importance weight from highest to lowest. After the switch is complete, the transmission of data blocks is restored sequentially according to the cached order. If the transmission is interrupted, the transmission is restarted using a breakpoint resumption algorithm based on the data block index, according to the interruption location and the status of the transmitted data blocks. For example, if an interruption occurs during the transmission of order data, the transmission can be resumed using the index I of each recorded data block. i It finds the data blocks that have not been completely transmitted and continues to transmit them, ensuring the continuity of data transmission.

[0076] Data from the order processing system, after being transmitted and processed by the enterprise service bus, is received by the inventory management system, and similarly, data from the inventory management system can also be received by the order processing system.

[0077] After receiving order-related data, the inventory management system performs subsequent processing according to its own business needs. For example, it updates the inventory quantity in real time based on the product demand quantity in the order, checks whether the inventory is sufficient to meet the order demand, and if the inventory is insufficient, it promptly triggers the replenishment process or issues an inventory warning to the relevant departments.

[0078] After receiving inventory-related data, the order processing system can adjust the order processing progress in a timely manner based on the inventory situation. For example, when the inventory is insufficient, further processing of the order can be suspended and resumed after the inventory is replenished, or the system can communicate and negotiate with the customer in a timely manner to adjust the order content.

[0079] The effects of this embodiment are as follows: Through the integration platform and method of the present invention, the order processing system and the inventory management system are efficiently integrated, enabling the real-time acquisition of accurate inventory information during the order processing process. The inventory management system can also make timely adjustments according to the order situation. The adaptive fault switching routing module plays an important role in the data transmission process. When faced with situations such as node performance degradation, it ensures the continuity of data transmission by real-time monitoring, accurately judging fault risks, and taking appropriate switching strategies in a timely manner.

[0080] Overall, by achieving efficient multi-source system process integration and ensuring the reliability of data transmission and the efficiency of business processes, enterprises can better meet customer needs and provide better services. For example, they can process orders in a timely and accurate manner and ensure inventory supply, thereby improving customer satisfaction, gaining a more advantageous position in market competition, and enhancing their competitiveness.

[0081] Example 8:

[0082] This embodiment provides an application of the method described in Embodiment 6. A large manufacturing enterprise's Human Resources Management System (HRMS) is responsible for a series of human resources-related business processes, including employee recruitment, onboarding, attendance, and payroll calculation. Meanwhile, its Financial Management System (FMS) focuses on financial transactions such as revenue and expenditure records, budget preparation, and cost accounting. With the expansion of the enterprise and the increasing demands for refined management, it is necessary to effectively integrate these two key systems to enable real-time interaction with financial data during human resources-related operations (such as employee salary payments and training expense reimbursements). This ensures accurate updates of financial information and rational allocation of funds, while also guaranteeing the continuity and reliability of data transmission throughout the data interaction process. Even in the event of system failures or other unforeseen circumstances, the normal operation of business should not be affected. Therefore, the enterprise decided to adopt the multi-source system process integration platform and method based on Enterprise Service Bus provided by this invention to achieve this goal.

[0083] The human resources management system and the financial management system are connected to the enterprise service bus through a multi-source system access layer.

[0084] During the integration process, the human resources management system initializes various types of data. For example, for employee payroll data, including basic salary, performance bonus, attendance deductions, etc., an adaptive coding algorithm is used to calculate the data coding priority P based on the data type (fixed salary, variable salary, etc.), source (payroll data from different departments), and the data requirements of the target business process (such as the format required for interaction with the financial management system). ∈ Assuming that core salary data such as base salary and performance bonuses have a higher coding priority, they are classified into a high coding priority group G. e1 A more rigorous and secure coding method is adopted, while relatively minor data such as attendance and deductions are divided into a low-priority coding group G. e2 It uses conventional encoding methods and establishes a stable and reliable connection at the access layer, and promptly feeds back the connection status information to the upper-layer module.

[0085] The financial management system also performs initialization processing on financial data. For example, financial income and expenditure records, including income sources, expenditure items, and amounts, are converted and encoded according to the format specified by the access layer to ensure that they can be successfully entered into the enterprise service bus for subsequent processing.

[0086] The data from the initialized human resources management system and financial management system enter the data processing and conversion module.

[0087] First, data cleaning is performed. In the human resources management system, for employee information data (such as employee name, contact information, and job title), a noise reduction algorithm based on data correlation is used. A data correlation graph G is constructed by analyzing the relationships between data items in the dataset. If certain employee information data items are found to have a connectivity C in graph G... d Less than the preset connectivity threshold C t And the average correlation strength I with other data items d Less than the preset correlation strength threshold I t These data items are identified as potentially noisy (such as incorrect inputs or outdated contact information) and removed to improve the quality of employee information data.

[0088] When cleaning data in a financial management system, for example, identifying and removing duplicate or abnormal records in financial income and expenditure records, and analyzing the relationships between income and expenditure records, if some records are found to have extremely low correlation with other normal records and do not conform to financial logic, they are identified as abnormal records and removed to ensure the accuracy of financial data.

[0089] Next, encryption is performed based on the sensitivity of the data and the company's security policy. For sensitive information such as salary data involving employee privacy in the human resources management system, encryption algorithms based on data encryption layers are used, and the data is classified into high encryption layers L according to factors such as the importance of the data and the transmission path. el High-strength encryption algorithms (such as AES Advanced Encryption Standard) are used for encryption, while general employee information and some financial data are classified as low-encryption level L. e2 It employs a relatively weak encryption algorithm that still ensures basic security.

[0090] Finally, the data undergoes further format conversion. The data from the human resources management system is converted from its original format to a format more suitable for transmission in the enterprise service bus and for interaction with the financial management system (such as converting a specific human resources database format to a common XML format). Similarly, the data from the financial management system is also converted accordingly so that both parties can exchange data smoothly.

[0091] During data transmission, the adaptive failover routing module continuously monitors its status.

[0092] The status monitoring submodule employs a hybrid monitoring algorithm to periodically send a heartbeat signal H. s It provides responses and heartbeat signals to various nodes in the human resource management system, financial management system, and enterprise service bus. r Calculate the heartbeat signal loss rate Where m represents the number of monitoring cycles, and U represents the CPU utilization of the real-time data acquisition node. c Memory usage U m Network bandwidth utilization U h and connection packet loss rate L p The delay D is used to combine these monitoring data into a state monitoring vector V. m =(L h U c U m U b ,L p For example, when monitoring the node connecting the human resource management system to the enterprise service bus, it is found that the CPU utilization U of that node at a certain moment is... c It reached 80%, and the memory usage rate U m The network bandwidth utilization rate reached 65%, U h The connection blockage rate is 55%, L. p With a value of 3% and a delay of 8 milliseconds (D), the state monitoring vector of this node at this moment is formed.

[0093] The fault and performance degradation judgment submodule is based on a dynamic threshold judgment algorithm, according to the state monitoring vector V provided by the state monitoring submodule. m Analysis and judgment were conducted, and dynamic threshold ranges were set for different monitoring indicators, such as CPU utilization U. c The threshold range is set to [U cmin U cmax Assume U cmin =70%, U cmax =90%, when the U of this node is detected c At 80%, although it has not yet exceeded the severe threshold, it is close to U. cmax This indicates a risk of node performance degradation.

[0094] Since the risk of node performance degradation has been identified, the switching strategy selection submodule uses an intelligent matching algorithm to encode the fault type and severity information into a feature vector V based on the fault type (node ​​performance degradation) and severity (minor). f = (Slight node performance degradation), and compared with the feature vector V corresponding to each predefined switching strategy. s Perform a matching degree calculation; matching degree (where n represents the dimension of the feature vector). Suppose that through calculation, a switching strategy for adjusting the data transmission priority is selected, that is, to prioritize the transmission of key data (such as employee basic salaries, major financial income and expenditure items, etc.) and increase their transmission priority, while appropriately reducing the transmission priority of some non-key data (such as attendance details, minor financial income and expenditure items, etc.).

[0095] The path switching execution submodule performs path switching operations according to the selected switching strategy. During path switching, based on the data protection algorithm, the data being transmitted is first divided into data blocks B. i Divide the data into blocks (i represents different data blocks), and calculate the importance weight W for each data block. i For example, for a data block containing employees' basic salaries, its importance weight W i The importance weight W is relatively high for data blocks containing attendance details. i The data blocks are cached in descending order of importance. After the switchover, the data blocks are restored sequentially according to the cached order. If a transmission is interrupted, the transmission is restarted using a breakpoint resumption algorithm based on the interruption location and the already transmitted data blocks. For example, if an interruption occurs during the transmission of employee salary data, the transmission can be resumed using the index |I| of each recorded data block. i It finds the data blocks that have not been completely transmitted and continues to transmit them, ensuring the continuity of data transmission.

[0096] Data from the human resources management system, after being processed by the enterprise service bus, is received by the financial management system, and similarly, data from the financial management system can also be received by the human resources management system.

[0097] After receiving human resources-related data, the financial management system performs subsequent processing according to its own business needs. For example, after receiving employee payroll data, it allocates funds according to the company's financial system and relevant budget to ensure that employee salaries are paid on time and accurately. At the same time, it performs corresponding financial recording and accounting processing for financial matters such as employee training expense reimbursement.

[0098] After receiving financial data, the human resource management system can adjust human resource-related operations based on the financial situation. For example, it can rationally arrange employee training plans based on the company's financial budget to avoid exceeding the budget, or optimize and adjust the employee salary structure based on financial data feedback to better align with the company's cost control and development strategy.

[0099] The benefits of this embodiment are as follows: Through the integration platform and method of this invention, efficient integration of the human resource management system and the financial management system is achieved, enabling real-time and accurate data exchange between human resource-related business processes and financial operations. This avoids problems such as delayed salary payments and uncontrolled financial budgets caused by untimely or inaccurate data transmission between systems in the past, greatly improving the efficiency of internal business collaboration. By achieving efficient integration of the human resource management system and the financial management system and ensuring the reliability of data transmission and the efficiency of business collaboration, enterprises can more accurately allocate human and financial resources and achieve refined management. For example, they can rationally arrange employee training and salary adjustments based on financial conditions and rationally plan financial budgets based on human resource needs, thereby improving the enterprise's management level and operational efficiency and enhancing its competitiveness.

Claims

1. A smart manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus, characterized in that: It includes the Enterprise Service Bus (ESB) core module, the multi-source system access layer, the data processing and conversion module, and the adaptive fault-switching routing module; The multi-source system access layer is used to connect various heterogeneous business systems within the enterprise, realizing data format conversion, protocol adaptation and connection management; the multi-source system access layer converts the raw data of each heterogeneous business system into a standard format and then sends it to the Enterprise Service Bus (ESB) core module. The multi-source system access layer provides real-time feedback on the connection status of each heterogeneous business system to the Enterprise Service Bus (ESB) core module. The Enterprise Service Bus (ESB) core module is used to uniformly receive, forward, and process data requests and messages from various business systems, and provides standard interfaces and protocols. The data processing and conversion module is used to clean, encrypt, decrypt, and convert the format of the transmitted data; the Enterprise Service Bus (ESB) core module forwards the data to the processing module for cleaning, encryption, and format conversion, and returns the processed data to the Enterprise Service Bus (ESB) core module. The adaptive fault-switching routing module is used to monitor the system health status in real time and intelligently switch transmission paths when there is a fault or performance degradation to ensure continuity. The Enterprise Service Bus (ESB) core module hands over the data to the adaptive failover routing module for monitoring and routing. After making a decision based on the status, the adaptive failover routing module controls the Enterprise Service Bus (ESB) core module to switch transmission paths.

2. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The Enterprise Service Bus (ESB) core module is equipped with a data caching mechanism. This mechanism dynamically adjusts the cache capacity based on the real-time data traffic load and data transmission priority of each node using a specific algorithm. The algorithm comprehensively considers the node data traffic load value Q. i Priority coefficient P for each type of data transmission j Calculate the comprehensive impact factor F = ∑ i ∑ j (Q i ×P j The cache capacity is adjusted based on the relationship between F and the preset threshold T.

3. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The multi-source system access layer data format conversion is based on an intelligent format matching algorithm, which extracts key feature vectors V by analyzing the data structure features of the business system. k , and the feature vector V of the preset standard data format s Calculate similarity S ks Convert the data format based on similarity.

4. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The data processing and transformation module constructs a data association graph G by analyzing the relationships between data items, and then determines the connection degree C of the data items in the graph. d and average correlation strength I d Identify and remove noisy data.

5. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The adaptive fault switching routing module includes a status monitoring submodule, a fault and performance degradation judgment submodule, a switching strategy selection submodule, and a path switching execution submodule. The status monitoring submodule employs a hybrid monitoring algorithm. This algorithm integrates network heartbeat detection, node performance metric collection, and connection packet loss rate and latency parameter monitoring. Specifically, it periodically sends a heartbeat signal H. s Receive response heartbeat signal H r Calculate the heartbeat signal loss rate Where m represents the number of monitoring cycles, and U represents the CPU utilization of the real-time data acquisition node. c Memory usage U m Network bandwidth utilization U h and connection packet loss rate L p The delay D is used to combine these monitoring data into a state monitoring vector V. m =(L h U c U m U b ,L p ,D); The fault and performance degradation judgment submodule is based on a dynamic threshold judgment algorithm; the dynamic threshold judgment algorithm is based on the state monitoring vector V provided by the state monitoring submodule. m Dynamic threshold ranges are set for different monitoring indicators, specifically: for heartbeat signal loss rate L... h The threshold range is set to [L] hmin ,L hmax When L h Exceeding this range is considered a risk of connection failure, and the CPU utilization rate U c, Set the threshold range to [U cmin U cmax ], when U c When the value exceeds this range, it is considered a risk of node performance degradation. c Exceeding the separately set severity threshold range [U cseveremin U cseveremax When a node is identified as having a serious risk of failure, corresponding dynamic threshold ranges are also set for other monitoring indicators, including memory usage, network bandwidth utilization, packet loss rate and latency. The occurrence of failure and performance degradation is determined based on whether each monitoring indicator exceeds its corresponding threshold range. The switching strategy selection submodule predefines multiple switching strategies based on fault type and severity classification. Through an intelligent matching algorithm, it selects the appropriate switching strategy based on fault type T. f and severity S f The feature vector V corresponding to each switching strategy s Calculate the matching degree M fs Matching degree Select the switching strategy with the highest matching degree; The path switching execution submodule performs path switching operations according to the selected switching strategy; during switching, based on a data protection algorithm, the transmitted data is divided into data blocks B. i Determine the importance weight W of each data block. i The data is cached, and transmission is resumed according to the cache order after the switch is completed. If interrupted, the transmission is resumed using a breakpoint resume algorithm based on data block index.

6. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The dynamic threshold judgment algorithm of the fault and performance degradation judgment submodule of the adaptive fault switching routing module fully considers the different situations of peak and off-peak business periods when setting the threshold range. During peak business periods, the system load is large, and the normal fluctuation range of various monitoring indicators will be larger. The algorithm will correspondingly increase the threshold range of some monitoring indicators, raising the threshold range of CPU utilization from the usual [U cmin U cmax Adjusted to in Greater than U cmin , Greater than U cmax During periods of low business activity, when the system load is lighter, the algorithm lowers the threshold range of some monitoring indicators.

7. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The path switching execution submodule of the adaptive fault-failure routing module uses a breakpoint resume algorithm based on data block indexes to record data block indexes I. i At this time, a hierarchical indexing method is used. Specifically, the data blocks are divided into m levels according to their size, and each level corresponds to an index range. Smaller data blocks are divided into the first level, and their index range is... For medium-sized data blocks, a second level is defined, with an index range of [missing information]. Larger data blocks are divided into a third level, with an index range of [missing information].

8. The intelligent manufacturing multi-source system process integration platform based on discrete manufacturing enterprise service bus according to claim 1, characterized in that: The intelligent matching algorithm of the switching strategy selection submodule of the adaptive fault-switching routing module calculates the matching degree M. fs In this process, dynamic adjustments are made based on the company's past experience in handling faults. Specifically, as the company's business expands, it continuously accumulates information on different fault types (T). f and severity S f Based on the actual performance data of each switching strategy, the algorithm periodically updates the feature vector V corresponding to each predefined switching strategy. s Adjustments will be made.

9. A method for integrating multi-source intelligent manufacturing systems based on a discrete manufacturing enterprise service bus according to any one of claims 1 to 8, characterized in that, Includes the following steps: S1. Multi-source system access and data initialization: Each business system accesses the enterprise service bus through the multi-source system access layer, initializes its own data according to the format specified by the access layer, and uses an adaptive encoding algorithm to calculate the encoding priority P based on data characteristics and target business process requirements. e And divide into coding groups G e Different encoding methods are used to encode data from different encoding groups, while the access layer establishes a stable and reliable connection and feeds back connection status information. S2. Data Processing and Transmission Preparation: After initialization, the data enters the data processing and transformation module. First, data cleaning is performed, based on the similarity of data item content S... c Identify and remove valid duplicate data, then encrypt the data using an encryption algorithm based on data encryption layers according to the data characteristics, and finally convert the data format to meet the requirements of transmission and business processes. S3. Adaptive Fault Switching Route Monitoring and Processing: During data transmission, the adaptive fault switching route module continuously monitors the data. The status monitoring submodule uses a hybrid monitoring algorithm to obtain status information. The fault and performance degradation judgment submodule uses a dynamic threshold judgment algorithm to analyze and judge the data. In case of fault and performance degradation, the switching strategy selection submodule uses an intelligent matching algorithm to select a suitable strategy. The path switching execution submodule uses a data protection algorithm and a breakpoint resume algorithm to execute the path switching operation. S4. Data Reception and Subsequent Processing: Data transmitted via the Enterprise Service Bus is received by the target business system. The target business system performs subsequent processing on the received data according to its own business needs and provides feedback on relevant information.

10. A method for integrating processes in a multi-source intelligent manufacturing system based on a discrete manufacturing enterprise service bus according to claim 9, characterized in that: In step S1, the adaptive coding algorithm calculates the coding priority P. e In this process, the algorithm closely monitors the update frequency and data changes of the enterprise's business systems. When there are significant changes in the business system updates or data structure, the algorithm will promptly recalculate the coding priority P. e Specifically, when a business system adds important new business functions or undergoes significant changes in data types, the algorithm will reassess the importance, source, and relevance of the data to the target business process, thereby re-determining the coding priority P. e .

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