Workshop scheduling method and workshop scheduling system based on equipment dynamic interaction model

Abstract

The present invention discloses a workshop scheduling method based on a dynamic equipment interaction model, which includes the following steps: setting up a workshop scheduling system; inputting data of production orders; performing priority ranking processing by MES; establishing a workshop scheduling model; and coordinating production by multiple workshop production line systems. The present invention also discloses a workshop scheduling system, which includes an ERP system, an MES system, a scheduling system, and multiple workshop production line systems. The scheduling system includes a database cluster module, a state machine module, a database storage module, a data dictionary module, an abnormality alarm module, and an upstream and downstream collaboration module. By establishing a workshop scheduling model, the present invention can autonomously perceive and analyze the status of multiple workshop production line systems, and by setting up a status module for adaptive online decision-making to dynamically regulate the mapping relationship between production plans and scheduling rules, it can prevent inaccurate production task allocation or material shortages due to changes in production status, thereby ensuring production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop production scheduling, and in particular to a workshop scheduling method and a workshop scheduling system based on a dynamic equipment interaction model. Background Art

[0002] Production scheduling bridges the gap between production planning and production control execution. It serves as both the executor of production plans and the monitor of production control execution. It is a closed-loop, dynamic management process. The research and application of effective scheduling methods are the foundation and key to achieving intelligent manufacturing, significantly improving production efficiency and resource utilization.

[0003] Most workshops rely heavily on manual experience when it comes to production scheduling. The planning level delegates production tasks to the workshop level, where schedulers must first confirm whether production resources meet requirements before allocating production plans to specific machines and personnel. In the actual production process, task allocation mechanisms are often frequently adjusted due to issues like equipment failures and personnel changes, inevitably leading to low production efficiency. Currently, some companies are applying information technology to workshop scheduling. This primarily involves leveraging automated control technologies combined with the experience of scheduling experts to manage and analyze production scheduling issues. Furthermore, they are modeling and solving the current state of workshop scheduling using algorithms such as neural networks, genetic algorithms, and simulated annealing. However, these approaches are difficult to implement on a large scale. Production scheduling is inherently an NP-hard problem. When faced with non-polynomial determinism, it's difficult to find a fixed formula that precisely determines the optimal scheduling solution. When building scheduling algorithm models, constructive methods often suffer from incomplete optimization, lack flexibility, and primarily focus on small-scale, static scheduling problems on the shop floor. However, they are insufficient for large-scale, dynamic production processes, particularly the current trend toward "smart unmanned factories." Furthermore, these approaches face challenges such as high development difficulty, high cost, and instability, hindering the stability and coordination of subsequent workshop production, and thus, hindering efficiency. Equipment interoperability is crucial in "smart unmanned factories." Without access to the internal operating status of equipment, relying solely on external automated control and scheduling algorithms, even if they can proactively issue task instructions to equipment, cannot drive the equipment to express its own state to match the production task instructions. This means they cannot proactively establish a correlation between current equipment status and production instructions, thus failing to achieve true equipment interoperability. Furthermore, existing workshop production adjustments often rely on manual adjustments to separate production tasks, resulting in inefficiencies and impacting subsequent production efficiency. Summary of the Invention

[0004] This invention aims to address the current technical deficiencies and provides a workshop scheduling method and system based on a dynamic equipment interaction model.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] A workshop scheduling method based on a dynamic equipment interaction model comprises the following steps:

[0007] (1) Setting of workshop scheduling system: The workshop scheduling system includes an ERP system, an MES system, a scheduling system and multiple workshop production line systems. The ERP system is linked to the MES system, the MES system is linked to the scheduling system, and the scheduling system is linked to the workshop production line systems respectively. The multiple workshop production line systems are all provided with a control module and a shared link module. The control module is connected to the shared link module, and the shared link modules are linked to each other. The multiple workshop production line systems include workshop production line system 1, workshop production line system 2, workshop production line system 3, workshop production line system 4 and workshop production line system 5;

[0008] The ERP system is used to place production orders;

[0009] The MES system is used to issue production plans;

[0010] The scheduling system is used to establish a scheduling model, allocate production tasks and issue and control execution instructions based on the actual production capacity of each workshop production line system;

[0011] The control module is used to process the action instructions issued by the upper system, and to set the sharing mode and degree of internal data with the external system;

[0012] (2) Production order data input: The production order data is input into the ERP system. The ERP system processes the production order data and obtains the order instructions which are then sent to the MES system.

[0013] (3) MES performs priority sorting: The MES system is equipped with a planning and scheduling module. After receiving the order instructions from the ERP system, the MES system prioritizes the orders based on the received instructions and data through the planning and scheduling module, calculates the specific start and end time of the order task, and transmits it to the scheduling system;

[0014] (4) Establishment of workshop scheduling model: After receiving the task, the scheduling system establishes the workshop scheduling model, which includes mapping and defining the multiple workshop production line systems, configuring the scheduling status and action transition rules, data collection and storage, exception handling and coordination, and assisting in adjustment processing. The workshop scheduling model is established;

[0015] (5) Coordinated production of multiple workshop production line systems: After the workshop scheduling model is established, the control data is transmitted to the control modules of the multiple workshop production line systems for control. The multiple workshop production line systems respectively perform relevant mapping definitions to perform action production, and the production process scheduling system monitors the status of the multiple workshop production line systems in real time and assists in adjustment processing.

[0016] As a further improvement, the scheduling system includes a database cluster module, a state machine module, a database storage module, a data dictionary module, an abnormality alarm module and an upstream and downstream collaboration module;

[0017] The database cluster module is used to classify and cluster data. The database cluster module includes a Redis data cluster and a MySQL database cluster. The Redis data cluster is used as a cache database to store real-time data from the industrial site, and the MySQL database cluster is used to store processed historical data in a cluster.

[0018] The state machine module is used to configure the scheduling state and action transition rules in a data table manner;

[0019] The database storage module is used to store cluster data obtained by the database cluster module, thereby facilitating subsequent massive data storage and processing capabilities and supporting the reuse of heterogeneous data sources;

[0020] The data dictionary module is used to describe the components of each module in the scheduling system. In the database of the scheduling system, the data dictionary describes the structure definition of a set of tables, views and indexes, and describes the attributes, entities, record types, data items, user identifiers, passwords and other information of all objects in the scheduling system. Through code design, the data dictionary of each object involved in scheduling is compiled, and the data elements and data structures contained in the data storage module are encoded to provide a standard for the operation of the scheduling system.

[0021] The abnormal alarm module is used to issue alarm information according to the preset alarm mechanism. The alarm information includes state abnormality, event abnormality and communication abnormality. The abnormal alarm module is equipped with an alarm information notification module. The notification module can be a simple log output or an upstream and downstream collaborative message interface.

[0022] The upstream and downstream collaboration module is used to receive or send upstream and downstream collaboration messages, and whether the upstream and downstream collaboration messages are smooth is a condition for determining whether a state migration occurs.

[0023] As a further improvement, the step (4) uses a data table to configure the scheduling state and action transition rules. The configuration of the scheduling state and action transition rules includes the scheduling state list configuration, queue information configuration, and scheduling object state list configuration. The step (4) includes the following steps:

[0024] (4.1) Status Information Configuration: The scheduling system configures and predefines status information, using a data table to configure and describe the actual equipment status information of multiple workshop production line systems, including equipment busy and idle status, start and stop status, and the satisfied and unsatisfied status of auxiliary tools;

[0025] (4.2) Queue Information Configuration: Based on the status information configuration, queue information is configured, including information such as the order processing process file, the processing task queue in the buffer area, the transportation queue of the logistics equipment, and the inbound and outbound task queue. The queue information configuration also configures the task number, process number, current location, and target location.

[0026] (4.3) Scheduling object status list configuration: The scheduling system configures the object status list participating in the scheduling, and defines the state transition function based on the configuration of the object status list participating in the scheduling. The scheduling object status list configuration and the state transition function definition are used to promote the change of the status information of the equipment, thereby controlling the automatic operation of the mobilization system and issuing instructions.

[0027] As a further improvement, the state machine module in the scheduling system of step (5) is further provided with a timer, an execution program and a state machine tool, and step (5) further includes the following steps:

[0028] (5.1) Setting and polling of timer period: The timer period is set to 100ms, and the timer detects the trigger signals fed back from the production line systems of multiple workshops every 100ms;

[0029] (5.2) Confirm whether the trigger signal is abnormal: The state machine module performs data judgment on the trigger signal and confirms whether the trigger signal is abnormal. If there is no abnormality, the workshop scheduling system works normally. If there is an abnormality, proceed to step (5.3);

[0030] (5.3) Drive the state machine tool action: The state machine module also changes the state of the objects involved in the scheduling, and performs counting processing by setting a counter. When the count reaches the set value, the execution program in the state machine module can be driven to execute, and the state machine tool is controlled to perform the material replenishment action on the workshop production line system. After the workshop production line system completes the material replenishment, it returns to steps (5.1) and (5.2).

[0031] As a further improvement, the step (5.2) further includes the following steps:

[0032] (5.2.1) The state machine module determines the process state and makes logical choices by calling functions;

[0033] (5.2.2) The state machine module determines the state to enter and confirms whether the state is consistent with the initial conditions;

[0034] (5.2.3) The state machine module confirms the exit status according to the above steps and feeds back to the state machine module for corresponding processing drive and execution.

[0035] As a further improvement, a workshop scheduling system that implements a workshop scheduling method based on a dynamic interaction model of equipment, wherein the upstream and downstream collaboration module is one of the Ethernet communication means such as Pub / Sub, registration service, master-slave connection, etc.

[0036] As a further improvement, the workshop production line system 1 is provided with processing objects, the workshop production line system 2 is provided with logistics objects, the workshop production line system 3 is provided with detection objects, the workshop production line system 4 is provided with warehousing objects, and the workshop production line system 5 is provided with auxiliary objects. The processing objects, logistics objects, detection objects, warehousing objects and auxiliary objects are respectively linked to the corresponding control modules.

[0037] As a further improvement, the shared link modules are linked to each other in communication.

[0038] Beneficial effects of the present invention: The present invention establishes a workshop scheduling model by setting up a scheduling system, so that the status of multiple workshop production line systems can be autonomously perceived and analyzed, and the mapping relationship between Dongtai's production plan and scheduling rules is adjusted by setting a status module for adaptive online decision-making, thereby preventing inaccurate production task allocation or material shortages caused by changes in production status, and ensuring production efficiency; compared with traditional workshop scheduling modeling technology and workshop scheduling algorithm research, the workshop scheduling model established by setting up a scheduling system can avoid the development of difficult scheduling algorithms, ensure the efficiency of workshop scheduling, and reduce development costs and workshop scheduling production costs; by setting up a state machine module, the scheduling system has flexibility Configurability can flexibly respond to complex and changeable workshop production environments, improve applicability and stability; by setting up a database cluster module, it is convenient to classify clusters, thereby improving the speed and response speed of subsequent data retrieval, improving the efficiency of subsequent scheduling, and improving the efficiency of workshop production; by setting up a state machine module with a timer, execution program and state machine tool to participate in the real-time dynamic information of each object in the workshop scheduling, it is convenient for the scheduling system to adjust the redistribution of production tasks online, thereby improving production efficiency; through the state machine, the scheduling rules are clearly expressed by establishing a direct mapping mechanism between software units and physical states, and the state of the scheduling object is driven by the change of the state machine matrix function based on event triggering, thereby independently completing the scheduling task.

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the steps of the workshop scheduling method based on the equipment dynamic interaction model of this embodiment;

[0041] Figure 2 This is a schematic diagram of the workshop scheduling system architecture module of this embodiment;

[0042] Figure 3 Schematic diagram of the modules of the scheduling system of this embodiment;

[0043] Figure 4 This is a schematic diagram of the state machine module scheduling program execution flow in this embodiment. DETAILED DESCRIPTION

[0044] The following description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention.

[0045] For example, see the attached Figures 1 to 4 , a workshop scheduling method based on a dynamic equipment interaction model, comprising the following steps:

[0046] (1) Setting of workshop scheduling system: The workshop scheduling system includes an ERP system 1, an MES system 2, a scheduling system 3 and multiple workshop production line systems 4. The ERP system 1 is linked to the MES system 2, the MES system 2 is linked to the scheduling system 3, and the scheduling system 3 is linked to the workshop production line system 4 respectively. The multiple workshop production line systems 4 are all provided with a control module 40 and a shared link module 41. The control module 40 is connected to the shared link module 41, and the shared link module 41 is linked to the shared link module 41. The multiple workshop production line systems include workshop production line system 1, workshop production line system 2, workshop production line system 3, workshop production line system 4 and workshop production line system 5;

[0047] The ERP system 1 is used to place production orders;

[0048] The MES system 2 is used to issue production plans;

[0049] The scheduling system 3 is used to establish a scheduling model, allocate production tasks, and issue and control execution instructions based on the actual production capacity of each workshop production line system 4. The scheduling system 3 implements adaptive online decision-making and dynamically adjusts the mapping relationship between production plans and scheduling rules, avoiding the efficiency loss caused by manual adjustment of production task allocation due to changes in production status;

[0050] The control module 40 is used to process the action instructions issued by the upper system, and to set the sharing mode and degree of internal data with the external system;

[0051] (2) Production order data input: The production order data is input into ERP system 1. ERP system 1 processes the production order data and obtains the order instructions, which are then sent to MES system 2.

[0052] (3) MES performs priority sorting: The MES system 2 is provided with a planning and scheduling module. After receiving the order instructions from the ERP system 1, the MES system 2 prioritizes the orders based on the received instructions and data through the planning and scheduling module, calculates the specific start and end time of the order task, and transmits it to the scheduling system 3;

[0053] (4) Establishment of workshop scheduling model: After receiving the task, the scheduling system 3 establishes the workshop scheduling model, which includes mapping and defining the multiple workshop production line systems, configuring the scheduling status and action transition rules, data collection and storage, exception handling and coordination, and assisting in adjustment processing. The workshop scheduling model is established;

[0054] (5) Multiple workshop production line systems coordinate production: After the workshop scheduling model is established, the control data is transmitted to the control modules 40 of the multiple workshop production line systems for control. The multiple workshop production line systems respectively perform relevant mapping definitions to perform action production, and the production process scheduling system 3 monitors the status of the multiple workshop production line systems in real time and assists in adjustment processing.

[0055] The scheduling system 3 includes a database cluster module 30, a state machine module 31, a database storage module 32, a data dictionary module 33, an abnormality alarm module 34 and an upstream and downstream collaboration module 35;

[0056] The database cluster module 30 is used to classify and cluster data. The database cluster module 32 includes a Redis data cluster 320 and a MySQL database cluster 321. The Redis data cluster 320 is used as a cache database to store real-time data from the industrial site, and the MySQL database cluster 321 is used to store processed historical data in a cluster. By setting up the database cluster module 30, classification and clustering are facilitated, thereby improving the speed and response speed of subsequent data retrieval, improving the efficiency of subsequent scheduling, and improving the efficiency of workshop production;

[0057] The state machine module 31 is used to configure the scheduling state and action transition rules in a data table manner;

[0058] The database storage module 32 is used to store the cluster data obtained by the database cluster module 30, thereby facilitating subsequent massive data storage and processing capabilities and supporting the reuse of heterogeneous data sources;

[0059] The data dictionary module 33 is used to describe the components of each module in the scheduling system 3. In the database of the scheduling system 3, the data dictionary describes the structure definition of a set of tables, views and indexes, and describes the attributes, entities, record types, data items, user identifiers, passwords and other information of all objects in the scheduling system 3. The data dictionary of each object involved in the scheduling is compiled through code design, and the data elements and data structures contained in the data storage module are encoded to provide a standard for the operation of the scheduling system 3.

[0060] The abnormal alarm module 34 is used to issue alarm information according to the preset alarm mechanism. The alarm information includes state abnormality, event abnormality and communication abnormality. The abnormal alarm module 34 is provided with an alarm information notification module. The notification module can be a simple log output or an upstream and downstream collaborative message interface.

[0061] The upstream and downstream collaboration module 35 is used to receive or send upstream and downstream collaboration messages, and whether the upstream and downstream collaboration messages are smooth is a condition for judging whether the state has been migrated, thereby ensuring the stability and coordination of information transmission.

[0062] The step (4) uses a data table to configure the scheduling state and action transition rules. The configuration of the scheduling state and action transition rules includes scheduling state list configuration, queue information configuration, and scheduling object state list configuration. The step (4) includes the following steps:

[0063] (4.1) Status Information Configuration: The scheduling system 3 configures and predefines status information by using a data table to configure and describe the actual status information of multiple equipment in the workshop production line system, including the equipment busy and idle status, start and stop status, and the satisfied and unsatisfied status of auxiliary tools;

[0064] (4.2) Queue Information Configuration: Based on the status information configuration, queue information is configured, including information such as the order processing process file, the processing task queue in the buffer area, the transportation queue of the logistics equipment, and the inbound and outbound task queue. The queue information configuration also configures the task number, process number, current location, and target location.

[0065] (4.3) Scheduling object status list configuration: The scheduling system 3 configures the object status list participating in the scheduling, and defines the state transition function based on the configuration of the object status list participating in the scheduling. The scheduling object status list configuration and the state transition function definition are used to promote the change of the status information of the equipment, thereby controlling the automatic operation of the mobilization system and issuing instructions.

[0066] The state machine module 31 in the scheduling system 3 of step (5) is further provided with a timer 310, an execution program 311 and a state machine tool 312. Step (5) further includes the following steps:

[0067] (5.1) Setting and polling the time period of the timer 310: The time period of the timer 310 is set to 100 ms. The timer 310 detects the trigger signals fed back by the production line systems of multiple workshops every 100 ms;

[0068] (5.2) Confirming whether the trigger signal is abnormal: The state machine module 31 performs data judgment on the trigger signal and confirms whether the trigger signal is abnormal. If there is no abnormality, the workshop scheduling system 3 operates normally. If there is an abnormality, proceed to step (5.3);

[0069] (5.3) Drive the state machine tool 312 to act: The state machine module 31 also changes the state of the objects involved in the scheduling, and performs counting processing by setting a counter. When the count reaches the set value, the execution program 311 in the state machine module 31 can be driven to execute, and the state machine tool 312 is controlled to perform the material replenishment action on the workshop production line system. After the workshop production line system completes the material replenishment, it returns to steps (5.1) and (5.2).

[0070] The step (5.2) further comprises the following steps:

[0071] (5.2.1) The state machine module 31 determines the process state and makes logical choices by calling functions;

[0072] (5.2.2) The state machine module 31 determines the state to be entered and confirms whether the state is consistent with the initial condition;

[0073] (5.2.3) The state machine module 31 confirms the exit status according to the above steps and feeds back to the state machine module 31 for corresponding processing drive and execution.

[0074] A workshop scheduling system 3 that implements a workshop scheduling method based on a dynamic equipment interaction model, wherein the upstream and downstream collaboration module 35 is one of Ethernet communication means such as Pub / Sub, registration service, and master-slave connection.

[0075] The workshop production line system 41 is provided with processing objects, the workshop production line system 42 is provided with logistics objects, the workshop production line system 3 is provided with detection objects, the workshop production line system 4 is provided with warehousing objects, and the workshop production line system 5 is provided with auxiliary objects. The processing objects, logistics objects, detection objects, warehousing objects and auxiliary objects are respectively linked to the corresponding control modules 40.

[0076] The shared link modules 41 communicate with each other.

[0077] The present invention establishes a workshop scheduling model by setting up a scheduling system, so that the status of multiple workshop production line systems can be autonomously perceived and analyzed. By setting up a state module for adaptive online decision-making, Dongtai regulates the mapping relationship between production plans and scheduling rules, thereby preventing inaccurate production task allocation or material shortages caused by changes in production status, and ensuring production efficiency. Compared with traditional workshop scheduling modeling technology and workshop scheduling algorithm research, the workshop scheduling model established by setting up a scheduling system can avoid the development of difficult scheduling algorithms, ensure the efficiency of workshop scheduling, and reduce development costs and workshop scheduling production costs. By setting up a state machine module, the scheduling system is made flexible and configurable. , can flexibly respond to the complex and changeable workshop production environment, improve applicability and stability; by setting up a database cluster module, it is convenient to classify clusters, thereby improving the speed and response speed of subsequent data retrieval, improving the efficiency of subsequent scheduling, and improving the efficiency of workshop production; by setting up a state machine module with a timer, an execution program and a state machine tool to participate in the real-time dynamic information of each object in the workshop scheduling, it is convenient for the scheduling system to adjust the redistribution of production tasks online, thereby improving production efficiency; through the state machine, the scheduling rules are clearly expressed by establishing a direct mapping mechanism between software units and physical states, and the state of the scheduling object is driven by the state machine matrix function change based on event triggering, thereby independently completing the scheduling task.

[0078] The present invention is not limited to the above-mentioned embodiments. Other workshop scheduling methods and systems based on the equipment dynamic interaction model obtained by using the same or similar structures, devices, processes or methods as the above-mentioned embodiments of the present invention are within the scope of protection of the present invention.

Claims

1. A workshop scheduling method based on a dynamic equipment interaction model, characterized in that: The following steps are involved: (1) Setting of workshop scheduling system: The workshop scheduling system includes an ERP system, an MES system, a scheduling system and multiple workshop production line systems. The ERP system is linked to the MES system, the MES system is linked to the scheduling system, and the scheduling system is linked to the workshop production line systems respectively. The multiple workshop production line systems are all provided with a control module and a shared link module. The control module is connected to the shared link module, and the shared link modules are linked to each other. The multiple workshop production line systems include workshop production line system 1, workshop production line system 2, workshop production line system 3, workshop production line system 4 and workshop production line system 5; The ERP system is used to place production orders; The MES system is used to issue production plans; The scheduling system is used to establish a scheduling model, allocate production tasks and issue and control execution instructions based on the actual production capacity of each workshop production line system; The control module is used to process the action instructions issued by the upper system, and to set the sharing mode and degree of internal data with the external system; (2) Production order data input: The production order data is input into the ERP system. The ERP system processes the production order data and obtains the order instructions which are then sent to the MES system. (3) MES performs priority sorting: The MES system is equipped with a planning and scheduling module. After receiving the order instructions from the ERP system, the MES system prioritizes the orders based on the instructions and data received through the planning and scheduling module, calculates the specific start and end time of the order task, and passes it to the scheduling system; (4) Establishment of workshop scheduling model: After receiving the task, the scheduling system establishes the workshop scheduling model, which includes mapping and defining the multiple workshop production line systems, configuring the scheduling status and action transition rules, data collection and storage, exception handling and coordination, and assisting in adjustment processing. The workshop scheduling model is established; (5) Multiple workshop production line systems coordinate production: After the workshop scheduling model is established, the control data is transmitted to the control modules of the multiple workshop production line systems for control. The multiple workshop production line systems respectively perform relevant mapping definitions to perform action production, and the production process scheduling system monitors the status of the multiple workshop production line systems in real time and assists in adjustment and processing.

2. The workshop scheduling method based on the equipment dynamic interaction model according to claim 1 is characterized in that: The scheduling system includes a database cluster module, a state machine module, a database storage module, a data dictionary module, an abnormality alarm module and an upstream and downstream collaboration module; The database cluster module is used to classify and cluster data. The database cluster module includes a Redis data cluster and a MySQL database cluster. The Redis data cluster is used as a cache database to store real-time data from the industrial site, and the MySQL database cluster is used to store processed historical data in a cluster. The state machine module is used to configure the scheduling state and action transition rules in a data table manner; The database storage module is used to store cluster data obtained by the database cluster module, thereby facilitating subsequent massive data storage and processing capabilities and supporting the reuse of heterogeneous data sources; The data dictionary module is used to describe the components of each module in the scheduling system. In the database of the scheduling system, the data dictionary describes the structure definition of a set of tables, views and indexes, and describes the attributes, entities, record types, data items, user identifiers, and password information of all objects in the scheduling system. Through code design, the data dictionary of each object involved in scheduling is compiled, and the data elements and data structures contained in the data storage module are encoded to provide a standard for the operation of the scheduling system. The abnormal alarm module is used to issue alarm information according to the preset alarm mechanism. The alarm information includes state abnormality, event abnormality and communication abnormality. The abnormal alarm module is equipped with an alarm information notification module. The notification module can be a simple log output or an upstream and downstream collaborative message interface. The upstream and downstream collaboration module is used to receive or send upstream and downstream collaboration messages, and whether the upstream and downstream collaboration messages are smooth is a condition for determining whether a state migration occurs.

3. The workshop scheduling method based on the equipment dynamic interaction model according to claim 2 is characterized in that: The step (4) uses a data table to configure the scheduling state and action transition rules. The configuration of the scheduling state and action transition rules includes scheduling state list configuration, queue information configuration, and scheduling object state list configuration. The step (4) includes the following steps: (4.1) Status Information Configuration: The scheduling system configures and predefines status information, using data tables to configure and describe the actual equipment status information of multiple workshop production line systems, including equipment busy and idle status, start and stop status, and the satisfied and unsatisfied status of auxiliary tools; (4.2) Queue information configuration: Based on the status information configuration, queue information is configured, including order processing documents, processing task queues in the buffer area, transportation queues for logistics equipment, and inbound and outbound task queues. The queue information configuration also configures the task number, process number, current location, and target location. (4.3) Scheduling object status list configuration: The scheduling system configures the object status list participating in the scheduling, and defines the state transition function based on the configuration of the object status list participating in the scheduling. The scheduling object status list configuration and the state transition function definition are used to promote the change of the status information of the equipment, thereby controlling the automatic operation of the mobilization system and issuing instructions.

4. The workshop scheduling method based on the equipment dynamic interaction model according to claim 2 is characterized in that: The state machine module in the scheduling system of step (5) is further provided with a timer, an execution program and a state machine tool. Step (5) further comprises the following steps: (5.1) Setting and polling of timer period: The timer period is set to 100ms, and the timer detects the trigger signals fed back from the production line systems of multiple workshops every 100ms; (5.2) Confirm whether the trigger signal is abnormal: The state machine module judges the data of the trigger signal and confirms whether the trigger signal is abnormal. If there is no abnormality, the workshop scheduling system works normally. If there is an abnormality, proceed to step (5.3); (5.3) Drive the state machine tool action: The state machine module also changes the state of the objects involved in the scheduling, and counts by setting a counter. When the count reaches the set value, the execution program in the state machine module can be driven to execute, and the state machine tool is controlled to perform the material replenishment action on the workshop production line system. After the workshop production line system completes the material replenishment, it returns to steps (5.1) and (5.2).

5. The workshop scheduling method based on the equipment dynamic interaction model according to claim 4 is characterized in that: The step (5.2) further includes the following steps: (5.2.1) The state machine module determines the process state and makes logical choices by calling functions; (5.2.2) The state machine module determines the state to enter and confirms whether the state is consistent with the initial conditions; (5.2.3) The state machine module confirms the exit status according to the above steps and feeds back to the state machine module for corresponding processing drive and execution.

6. A workshop scheduling system implementing the workshop scheduling method based on a dynamic equipment interaction model according to any one of claims 2 to 5, characterized in that: The upstream and downstream collaboration module is one of Pub / Sub, registration service, and master-slave connection Ethernet communication means.

7. The workshop scheduling system according to claim 6, characterized in that: The workshop production line system 1 is provided with processing objects, the workshop production line system 2 is provided with logistics objects, the workshop production line system 3 is provided with detection objects, the workshop production line system 4 is provided with warehousing objects, and the workshop production line system 5 is provided with auxiliary objects. The processing objects, logistics objects, detection objects, warehousing objects and auxiliary objects are respectively linked to corresponding control modules.

8. The workshop scheduling system according to claim 7, characterized in that: The shared link modules are linked to each other for communication.

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