ERP operation method for realizing cross-platform collaboration

Abstract

The invention relates to the technical field of cross-platform collaboration of an ERP (Enterprise Resource Planning) system, in particular to an ERP operation method for realizing cross-platform collaboration, which comprises a data conversion module, a protocol adaptation module, a task scheduling module and a resource allocation module. The data conversion module unifies heterogeneous data formats, the protocol adaptation module analyzes communication protocols, the task scheduling module dynamically adjusts task priorities, and the resource allocation module coordinates calculation and storage resources. According to the method, through improvements of a weight distribution algorithm, dynamic mapping updating, security verification, load balancing and the like, data integration and task collaboration among multiple platforms are optimized. The flexibility and adaptability of the system can be improved, the technical threshold is reduced, and the efficient and intelligent operation requirement of enterprises is met.

Description

Technical Field

[0001] This invention belongs to the field of information technology and enterprise management software technology, specifically an ERP operation method for achieving cross-platform collaboration. Background Technology

[0002] With the advancement of enterprise informatization and digital transformation, ERP (Enterprise Resource Planning) systems are playing an increasingly important role in enterprise management. However, existing ERP operation methods exhibit certain limitations in cross-platform collaboration, data integration, and flexible response to business needs, failing to adequately support the demands of modern enterprises for efficient and intelligent management.

[0003] A search revealed a method for integrating discrete digital workshop information systems. This method integrates product lifecycle management (PLM), enterprise resource planning (ERP), and manufacturing execution systems (MES) through data dictionary interfaces that merge different functional datasets, thus solving the "information silo" problem and improving the intelligence level of discrete manufacturing. However, this solution primarily focuses on the integration of information systems within the workshop, offering limited support for real-time data interaction and unified operation management across multiple systems. Furthermore, its integration method exhibits weak compatibility with heterogeneous systems, potentially affecting its adaptability to complex business scenarios.

[0004] Further research revealed a DevOps-based pipeline orchestration method that achieves flexible orchestration and efficient expansion in the ERP R&D and operations field through the orchestration of custom list files and core pipeline scripts. However, this technical solution primarily focuses on automating pipeline orchestration tasks and does not fully consider the actual needs of cross-platform ERP operations. Its support for inter-platform collaboration mechanisms is insufficient, particularly lacking comprehensive solutions for data synchronization, task allocation, and resource scheduling between different ERP systems. Furthermore, this solution is not very user-friendly for non-technical users, potentially increasing training costs and technical barriers for enterprises during implementation.

[0005] The above indicates that existing ERP operation methods still have room for improvement in areas such as cross-platform collaboration, heterogeneous system compatibility, and real-time data interaction. Therefore, this invention proposes an ERP operation method that achieves cross-platform collaboration, aiming to optimize data integration and task coordination across multiple platforms, enhance system flexibility and adaptability, and thus better meet the needs of modern enterprises for efficient and intelligent ERP operations. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide an ERP operation method that enables cross-platform collaboration, optimizes data integration and task collaboration between multiple platforms, improves the flexibility and adaptability of the system, and lowers the technical threshold to meet the needs of modern enterprises for efficient and intelligent ERP operation.

[0007] To address the aforementioned technical problems, this invention provides an ERP operation method for cross-platform collaboration, enabling data interaction and task coordination among multiple ERP systems. The method includes a data conversion module, a protocol adaptation module, a task scheduling module, and a resource allocation module. The data conversion module converts heterogeneous data formats from different ERP systems into a standardized data format. The protocol adaptation module parses and adapts communication protocols between different ERP systems. The task scheduling module dynamically allocates task priorities based on business needs. The resource allocation module coordinates computing and storage resources among the various ERP systems.

[0008] The data conversion module converts the original data format into an intermediate data format using preset data mapping rules. This intermediate data format is then further converted into the target system's data format. The data conversion module is connected to the protocol adaptation module, which performs protocol parsing based on the intermediate data format and generates adapted communication commands.

[0009] The task scheduling module is connected to the protocol adaptation module. The task scheduling module receives the adapted communication instructions and dynamically adjusts the task execution order according to the status of the task queue. The task scheduling module determines the priority of each task through a weight allocation algorithm, which comprehensively considers the urgency of the task, resource utilization, and business relevance.

[0010] As an improvement to the above solution, the data conversion module includes a mapping unit and a conversion unit. The mapping unit stores a mapping relationship table between various data formats. The mapping relationship table records the correspondence between the original fields and the target fields in the form of key-value pairs. The conversion unit performs field reorganization and format conversion on the original data according to the mapping relationship table.

[0011] The mapping table supports dynamic updates. The mapping unit analyzes the characteristics of newly added data formats using an incremental learning algorithm and automatically generates new mapping entries. After completing field reorganization, the conversion unit caches the result data in a temporary storage area, pending invocation by the protocol adaptation module.

[0012] As an improvement to the above solution, the protocol adaptation module includes a parsing unit and a generation unit. The parsing unit is used to identify and parse the communication protocol types between different ERP systems, and the generation unit generates adapted communication instructions based on the parsing results. The parsing unit matches the header information of the communication protocol through a protocol feature library, which stores identifiers of various protocols and their corresponding parsing rules.

[0013] The generation unit calls the template engine based on the parsing results to generate adapted communication instructions. The template engine has multiple pre-set communication instruction templates, each corresponding to a specific protocol type. The adapted communication instructions are then transmitted to the task scheduling module via a message queue.

[0014] As an improvement to the above scheme, the task scheduling module includes a priority calculation unit and a queue management unit. The priority calculation unit calculates the priority value of each task using a weight allocation algorithm, and the queue management unit sorts the task queue according to the priority values. The input parameters of the weight allocation algorithm include the estimated execution time of the task, resource consumption, and dependencies on other tasks.

[0015] The queue management unit supports dynamic adjustment of the task queue. When the priority of a task changes, the queue management unit recalculates the order of all tasks and updates the status of the task queue. The task scheduling module triggers task execution through an event-driven mechanism, which monitors changes in the status of the task queue and responds in real time.

[0016] As an improvement to the above solution, the resource allocation module includes a resource monitoring unit and an allocation strategy unit. The resource monitoring unit monitors the real-time usage of computing and storage resources in each ERP system, and the allocation strategy unit formulates a resource allocation plan based on the resource usage. The resource monitoring unit collects resource usage data from each ERP system using probe technology, which is implemented based on a lightweight agent program.

[0017] The allocation strategy unit employs a hierarchical allocation mechanism. This mechanism first allocates basic resources based on task priority, and then dynamically adjusts resource quotas according to the expanded needs of the tasks. The resource allocation module optimizes the resource allocation plan through a feedback loop, which predicts future resource demands based on historical resource usage data.

[0018] As an improvement to the above solution, the task scheduling module further includes a conflict detection unit, which is used to detect whether there are resource competitions or dependency conflicts in the task queue. The conflict detection unit analyzes the dependencies between tasks using a topology sorting algorithm and marks potential conflict nodes.

[0019] Upon detecting a conflict node, the conflict detection unit sends an adjustment request to the priority calculation unit. The priority calculation unit then recalculates the priority value of the conflict node and notifies the queue management unit to update the task queue. The conflict detection unit records key information during the conflict detection process through a log recording module, which supports multi-dimensional querying and analysis.

[0020] As an improvement to the above solution, the protocol adaptation module further includes a security verification unit, which is used to verify the legitimacy of the adapted communication commands. The security verification unit performs integrity verification on the communication commands using digital signature technology and verifies the identity and permissions of both communicating parties using access control lists.

[0021] The security verification unit triggers an exception handling process when verification fails. This process includes interrupting the execution of the current communication command, recording the exception log, and notifying the administrator for manual intervention. The security verification unit transmits the verification result through an encrypted channel to ensure the security of the verification process.

[0022] As an improvement to the above solution, the resource allocation module further includes a load balancing unit, which is used to distribute computing tasks among multiple ERP systems to avoid single-point overload. The load balancing unit selects the target system using a round-robin algorithm and dynamically adjusts the task allocation ratio according to the current load of the target system.

[0023] When the load balancing unit detects that the load of a certain system exceeds a preset threshold, it initiates the resource allocation process for the backup system. The resource allocation process for the backup system includes initializing the resource pool, allocating initial tasks, and gradually increasing the amount of tasks until a stable state is reached.

[0024] As an improvement to the above solution, the data transformation module further includes a data cleaning unit, which preprocesses the raw data to remove noisy data and redundant fields. The data cleaning unit matches the features of noisy data using regular expressions and removes redundant fields using field filters.

[0025] After preprocessing, the data cleaning unit passes the cleaned data to the mapping unit for field reorganization. The field filter supports user-defined rules, allowing users to specify which fields to retain or remove through a configuration file.

[0026] As an improvement to the above solution, the task scheduling module further includes a task tracking unit, which records the execution process of each task and generates an execution report. The task tracking unit records key nodes of the task using timestamps, including the task start time, task end time, and task status change time.

[0027] The execution report displays the task's execution trajectory through a visual interface, which supports timeline and Gantt chart views. The task tracking unit exports the execution report to multiple formats, including PDF, Excel, and JSON, via a data export interface.

[0028] The present invention also provides an ERP operation system, including the ERP operation method for achieving cross-platform collaboration as described above. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the module structure of the ERP operation method for cross-platform collaboration according to the present invention;

[0030] Figure 2 This is a flowchart of the data conversion module in this invention;

[0031] Figure 3 This is a flowchart of the dynamic adjustment of task priority in the task scheduling module of this invention.

[0032] The attached diagram is labeled as follows: 1. Data conversion module; 2. Protocol adaptation module; 3. Task scheduling module; 4. Resource allocation module; 5. Mapping unit; 6. Conversion unit; 7. Priority calculation unit; 8. Queue management unit; 9. Conflict detection unit; 10. Security verification unit. Detailed Implementation

[0033] This invention provides a method for achieving cross-platform collaborative ERP operation, as detailed below. Figure 1 To be continued Figure 3 The specific embodiments of the present invention will be described in detail below. In this embodiment, the method includes a data conversion module 1, a protocol adaptation module 2, a task scheduling module 3, and a resource allocation module 4. These modules work together through specific connection relationships and operating mechanisms to complete cross-platform collaborative ERP operation tasks.

[0034] First, the data conversion module 1 is one of the core components of the entire system. Its main function is to convert heterogeneous data formats from different ERP systems into a standardized data format. For example... Figure 2As shown, the data conversion module 1 includes a mapping unit 5 and a conversion unit 6. The mapping unit 5 stores a mapping table between various data formats, recording the correspondence between original fields and target fields in key-value pairs. For example, in practical applications, when the data field "Customer Number" from ERP system A needs to be converted to "Customer ID" in ERP system B, the mapping table will clearly define the correspondence rules between the two. The conversion unit 6 performs field reorganization and format conversion on the original data according to the mapping table. After completing the field reorganization, the conversion unit 6 caches the result data in a temporary storage area, waiting for the protocol adaptation module 2 to call it. To meet the needs of new data formats, the mapping unit 5 also supports dynamic updating, analyzing the characteristics of new data formats through an incremental learning algorithm and automatically generating new mapping entries. In addition, the data conversion module 1 also includes a data cleaning unit for preprocessing the original data to remove noisy data and redundant fields. The data cleaning unit matches the characteristics of noisy data using regular expressions and removes redundant fields through field filters. The field filters support user-defined rules; users can specify which fields to retain or remove through configuration files. After completing the preprocessing, the data cleaning unit transfers the cleaned data to the mapping unit 5 for field reorganization.

[0035] Data conversion module 1 is connected to protocol adaptation module 2. Protocol adaptation module 2 parses the protocol based on the intermediate data format and generates adapted communication commands. For example... Figure 1 As shown, the protocol adaptation module 2 includes a parsing unit and a generation unit. The parsing unit matches the header information of communication protocols using a protocol feature library, which stores identifiers for various protocols and their corresponding parsing rules. For example, in a scenario where ERP system A uses the HTTP protocol while ERP system B uses the SOAP protocol, the parsing unit will identify the header information of these two protocols and extract key features. The generation unit calls a template engine based on the parsing results to generate adapted communication instructions. The template engine has multiple pre-set communication instruction templates, each corresponding to a specific protocol type. The adapted communication instructions are then transmitted to the task scheduling module 3 via a message queue. To ensure communication security, the protocol adaptation module 2 also includes a security verification unit 10. The security verification unit 10 uses digital signature technology to verify the integrity of the communication instructions and verifies the identity and permissions of both communicating parties using an access control list. If verification fails, the security verification unit 10 triggers an exception handling process, including interrupting the execution of the current communication instruction, recording an exception log, and notifying the administrator for manual intervention. The security verification unit 10 transmits the verification results through an encrypted channel to ensure the security of the verification process.

[0036] Task scheduling module 3 is connected to protocol adaptation module 2, receives adapted communication commands, and dynamically adjusts the task execution order according to the status of the task queue. For example... Figure 3 As shown, the task scheduling module 3 includes a priority calculation unit 7, a queue management unit 8, and a conflict detection unit 9. The priority calculation unit 7 calculates the priority value of each task using a weighted allocation algorithm. The input parameters of the weighted allocation algorithm include the task's estimated execution time, resource consumption, and dependencies with other tasks. For example, in a scenario where task A has an estimated execution time of 10 minutes, a resource consumption rate of 50%, and a strong dependency on task B, the priority calculation unit 7 will comprehensively consider these factors to calculate the priority value of task A. The queue management unit 8 sorts the task queue according to the priority values ​​and supports dynamic adjustment of the task queue. When the priority of a task changes, the queue management unit 8 recalculates the order of all tasks and updates the status of the task queue. The task scheduling module 3 triggers task execution through an event-driven mechanism, which listens to changes in the status of the task queue and responds in real time. The conflict detection unit 9 is used to detect whether there is resource contention or dependency conflict in the task queue. The conflict detection unit 9 analyzes the dependencies between tasks using a topology sorting algorithm and marks potential conflict nodes. For example, when tasks C and D both require the same computing resources, the conflict detection unit 9 marks these two tasks as conflict nodes and sends an adjustment request to the priority calculation unit 7. The priority calculation unit 7 recalculates the priority value of the conflict nodes and notifies the queue management unit 8 to update the task queue. The conflict detection unit 9 records key information during the conflict detection process through a logging module, which supports multi-dimensional querying and analysis. Furthermore, the task scheduling module 3 includes a task tracking unit, which records the execution process of each task and generates an execution report. The task tracking unit records key nodes of the task using timestamps, including task start time, task end time, and task status change time. The execution report displays the task's execution trajectory through a visual interface, supporting timeline and Gantt chart views. The task tracking unit exports the execution report to various formats, including PDF, Excel, and JSON, through a data export interface.

[0037] Resource allocation module 4 is connected to task scheduling module 3 and is used to coordinate computing and storage resources among various ERP systems. Resource allocation module 4 includes a resource monitoring unit and an allocation strategy unit. The resource monitoring unit collects resource usage data from each ERP system using probe technology, which is implemented based on a lightweight agent program. For example, in a scenario, the resource monitoring unit collects CPU utilization, memory usage, and disk space usage of ERP system A using probe technology. The allocation strategy unit formulates a resource allocation plan based on resource usage, employing a hierarchical allocation mechanism. This mechanism first allocates basic resources based on task priority, and then dynamically adjusts resource quotas based on the task's expansion needs. For example, for high-priority tasks, the allocation strategy unit will prioritize allocating more computing and storage resources. Resource allocation module 4 optimizes the resource allocation plan through a feedback loop, which predicts future resource demands based on historical resource usage data. Furthermore, resource allocation module 4 also includes a load balancing unit, used to distribute computing tasks among multiple ERP systems to avoid single-point overload. The load balancing unit selects the target system using a round-robin algorithm and dynamically adjusts the task allocation ratio based on the target system's current load. For example, when the load on ERP system A exceeds a preset threshold, the load balancing unit will initiate the resource allocation process for the backup system. The resource allocation process for the backup system includes initializing the resource pool, allocating initial tasks, and gradually increasing the task load until a stable state is reached.

[0038] In a practical application scenario, suppose an enterprise needs to achieve data interaction and task collaboration between ERP system A and ERP system B. First, the data conversion module 1 converts the heterogeneous data format in ERP system A into an intermediate data format, and completes field reorganization and format conversion through a mapping table. Then, the protocol adaptation module 2 parses the communication protocol type between ERP system A and ERP system B and generates adapted communication instructions. The task scheduling module 3 receives the adapted communication instructions and dynamically adjusts the task execution order according to the status of the task queue. The resource allocation module 4 monitors the resource usage of ERP system A and ERP system B in real time and allocates computing and storage resources according to task priority. Throughout the process, the conflict detection unit 9 detects whether there are resource competitions or dependency conflicts in the task queue and recalculates the priority values ​​of conflicting nodes through the priority calculation unit 7. The security verification unit 10 verifies the legality of the adapted communication instructions to ensure the security of the communication process. Finally, the task tracking unit records the execution process of each task and generates an execution report, providing the enterprise with a visualized task execution trajectory and data analysis support.

[0039] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0040] In practical applications, a company needs to achieve data interaction and task collaboration between ERP system A and ERP system B. The following is based on the attached... Figure 1 To be continued Figure 3 A detailed explanation of the specific operating steps and the principles behind their implementation.

[0041] First, when business data from ERP System A needs to be transferred to ERP System B, Data Conversion Module 1 starts running. Mapping Unit 5 converts the original data field "Customer Number" in ERP System A into the target field "Customer ID" in ERP System B by calling the stored mapping relationship table. This mapping relationship table records the correspondence rules between different fields in the form of key-value pairs, ensuring the accuracy of field conversion. Subsequently, Conversion Unit 6 reorganizes and converts the original data according to the mapping relationship table and caches the result data in a temporary storage area. During this process, Data Cleaning Unit preprocesses the original data, matching the characteristics of noisy data using regular expressions and removing redundant fields using field filters. This preprocessing mechanism effectively reduces the error rate in subsequent data processing, thereby improving the efficiency and accuracy of data conversion.

[0042] Next, the protocol adaptation module 2 receives the intermediate data format generated by the data conversion module 1 and parses the communication protocol type between ERP system A and ERP system B. The parsing unit identifies the HTTP protocol used by ERP system A and the SOAP protocol used by ERP system B through a protocol feature library and extracts key feature information. The generation unit calls the template engine based on the parsing results to generate adapted communication instructions. Multiple pre-built communication instruction templates in the template engine can quickly generate adapted instructions for different protocol types. The security verification unit 10 verifies the legality of the generated communication instructions, checks the integrity of the instructions using digital signature technology, and confirms the identity and permissions of both communicating parties using an access control list. If verification fails, the security verification unit 10 triggers an exception handling process, including interrupting the execution of the current instruction, recording an exception log, and notifying the administrator for manual intervention. This series of operations ensures the security and reliability of cross-platform communication.

[0043] Subsequently, the task scheduling module 3 receives the adapted communication instructions and dynamically adjusts the task execution order according to the status of the task queue. The priority calculation unit 7 calculates the priority value of each task by comprehensively considering the estimated execution time, resource consumption, and dependencies with other tasks through a weighted allocation algorithm. For example, for task A, which has an estimated execution time of 10 minutes and a resource consumption rate of 50%, the priority calculation unit 7 will assign it a higher priority value. The queue management unit 8 sorts the task queue according to the priority values ​​and supports dynamic adjustment of the task queue. When the priority of a task changes, the queue management unit 8 recalculates the order of all tasks and updates the task queue status. The conflict detection unit 9 analyzes the dependencies between tasks using a topology sorting algorithm and marks potential conflict nodes. For example, when tasks C and D both need to use the same computing resources, the conflict detection unit 9 marks these two tasks as conflict nodes and sends an adjustment request to the priority calculation unit 7. The priority calculation unit 7 recalculates the priority values ​​of the conflict nodes and notifies the queue management unit 8 to update the task queue. This mechanism avoids resource contention and dependency conflicts, ensuring the efficiency of task scheduling.

[0044] Meanwhile, resource allocation module 4 monitors the resource usage of ERP systems A and B in real time. The resource monitoring unit collects data such as CPU utilization, memory usage, and disk space usage for each system using probe technology. The allocation strategy unit formulates a resource allocation plan based on resource usage, employing a tiered allocation mechanism to prioritize the resource needs of high-priority tasks. For example, for high-priority tasks, the allocation strategy unit will allocate more computing and storage resources. The load balancing unit selects target systems using a round-robin algorithm and dynamically adjusts the task allocation ratio based on the current load of the target systems. For example, when the load of ERP system A exceeds a preset threshold, the load balancing unit will initiate the resource allocation process for backup systems, gradually increasing the task load until a stable state is reached. This process continuously optimizes the resource allocation plan through a feedback loop to ensure maximum resource utilization.

[0045] Throughout the operation, the task tracking unit records the execution process of each task and marks key milestones with timestamps, including task start time, task end time, and task status change times. The execution report displays the task's execution trajectory through a visual interface, supporting timeline and Gantt chart views, providing enterprises with intuitive task execution data analysis support. Furthermore, the task tracking unit exports execution reports to multiple formats, including PDF, Excel, and JSON, via a data export interface, facilitating further data analysis and archiving for enterprises.

[0046] In summary, this invention achieves data interaction and task collaboration across platforms in an ERP system through the collaborative work of data conversion module 1, protocol adaptation module 2, task scheduling module 3, and resource allocation module 4. Data conversion module 1 ensures standardized processing of heterogeneous data formats, protocol adaptation module 2 guarantees the compatibility and security of communication protocols, task scheduling module 3 optimizes task execution order, and resource allocation module 4 coordinates the allocation of computing and storage resources. This method not only solves the problem of insufficient cross-platform collaboration capabilities in existing technologies but also significantly improves the system's flexibility and adaptability, meeting the needs of modern enterprises for efficient and intelligent ERP operations.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An ERP operation method for realizing cross-platform collaboration, used for realizing data interaction and task collaboration between multiple ERP systems, characterized in that, The system comprises a data conversion module (1), a protocol adaptation module (2), a task scheduling module (3) and a resource allocation module (4); the data conversion module (1) is used for uniformly converting heterogeneous data formats in different ERP systems into standardized data formats; the protocol adaptation module (2) is used for parsing and adapting communication protocols between different ERP systems; the task scheduling module (3) is used for dynamically allocating task priorities according to business requirements; and the resource allocation module (4) is used for coordinating computing resources and storage resources between different ERP systems.

2. The ERP operation method for realizing cross-platform cooperation according to claim 1, characterized in that, The data conversion module (1) comprises a mapping unit (5) and a conversion unit (6); the mapping unit (5) stores a mapping relationship table between multiple data formats; the mapping relationship table records the corresponding relationship between original fields and target fields in the form of key-value pairs; and the conversion unit (6) performs field reorganization and format conversion on original data according to the mapping relationship table. 3.The ERP operation method of enabling cross-platform collaboration according to claim 2, wherein, The mapping relationship table supports dynamic updating; the mapping unit (5) analyzes the characteristics of newly added data formats through an incremental learning algorithm and automatically generates new mapping relationship entries; and the conversion unit (6) caches the result data to a temporary storage area after completing field reorganization, and waits to be called by the protocol adaptation module (2).

4. The ERP operation method for realizing cross-platform cooperation according to claim 1, characterized in that, The protocol adaptation module (2) comprises a parsing unit and a generating unit; the parsing unit is used for identifying and parsing the communication protocol types between different ERP systems; and the generating unit generates adapted communication instructions according to the parsing results; the parsing unit matches the header information of the communication protocol through a protocol feature library; and the protocol feature library stores identifiers of multiple protocols and corresponding parsing rules.

5. The ERP operation method for realizing cross-platform cooperation according to claim 4, characterized in that, The generating unit calls a template engine to generate adapted communication instructions based on the parsing results; the template engine has multiple sets of communication instruction templates preset therein, and each set of template corresponds to a specific protocol type; and the adapted communication instructions are delivered to the task scheduling module (3) through a message queue.

6. The ERP operation method for realizing cross-platform cooperation according to claim 1, characterized in that, The task scheduling module (3) comprises a priority calculation unit (7) and a queue management unit (8); the priority calculation unit (7) determines the priority values of tasks through a weight allocation algorithm; the weight allocation algorithm considers the predicted execution time, resource occupation and dependency relationship with other tasks of the task; and the queue management unit (8) sorts the task queue according to the priority values.

7. The ERP operation method for realizing cross-platform cooperation according to claim 6, characterized in that, The task scheduling module (3) further comprises a conflict detection unit (9); the conflict detection unit (9) is used for detecting whether there is resource competition or dependency conflict in the task queue; the conflict detection unit (9) analyzes the dependency relationship between tasks through a topological sorting algorithm and marks potential conflict nodes; and the conflict detection unit (9) sends an adjustment request to the priority calculation unit (7) after finding a conflict node, recalculates the priority value of the conflict node and notifies the queue management unit (8) to update the task queue.

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