CN111898908A - Production line scheduling system and method based on multiple wisdom bodies - Google Patents
Production line scheduling system and method based on multiple wisdom bodies Download PDFInfo
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Abstract
The invention belongs to the technical field related to production scheduling and discloses a production line scheduling system and method based on multiple wisdom. The production scheduling system comprises a global management module, a controller intelligent body and a sub-controller intelligent body, wherein the global management module is a manager at the highest level in the production scheduling system and has the highest management authority; the controller intelligence is the core of the production scheduling system and comprises a resource module, a planning calculation module and a task management module, wherein the resource module is used for storing resource information of the sub-controller intelligence; the planning calculation module decomposes the tasks and sends the decomposed tasks to the task management module; the task management module sorts the decomposition tasks according to the priority order and then distributes the decomposition tasks to the intelligent entity of the sub-controller; and the sub-controller intelligence is used for executing the tasks sent by the task management module. By the method and the system, the independent operation capability of the intelligence is ensured, and the working efficiency and the management capability of the dispatching system are improved.
Description
Technical Field
The invention belongs to the technical field related to production scheduling, and particularly relates to a production line scheduling system and method based on multiple wisdom.
Background
With continuous proposition of scientific technology, concepts such as intelligent manufacturing, distributed computing, internet of things, artificial intelligence and the like are produced, people continuously propose own requirements on commodities, individuation and diversification become more and more the first choice of people, and accordingly higher requirements are put forward to manufacturers, and the concepts are mainly reflected in the following aspects: (1) the conventional production mode of small variety and large batch is not applicable any more, and instead, the production mode is small batch and multi variety or even single customized production; (2) the existing dispatching system is mainly matched with each functional module, and once a certain module is abnormal, the system cannot normally run; (3) the system can quickly respond to sudden events and has an effective solution; (4) in the manufacturing process, due to the limitations of resource conflict, device hardware constraints and the like, conflict occurs between devices.
In recent years, a Multi-Agent System (MAS) is a research hotspot in the field of artificial intelligence and widely applied to the construction of large-scale complex systems, the MAS is a computing System which is formed by a plurality of distributed and parallel-working wisdom bodies to complete certain tasks through cooperation, the MAS has certain autonomy, interactivity and intelligence, the MAS can quickly and flexibly solve complex problems by planning the functions of the wisdom bodies in the System and coordinating the communication interaction among the wisdom bodies, and a new thought and mode is provided for a scheduling System.
Ningbo Saifu science and technology Limited company applied for a 'factory intelligent workshop real-time scheduling system' patent (patent number: 201610403522.5), the factory intelligent workshop real-time scheduling system of the patent uses intellectualization and informatization technology to manage a workshop production line, a logistics transportation system, a production control system, an alarm system and the like, can effectively improve the production management level of the scheduling system to factories, but the alarm system detects more environmental temperature, brightness, air quality, noise and the like, does not relate to the collection and analysis of fault information of production equipment and production rescheduling caused by faults, and simultaneously, the feedback of adding and removing workshop equipment is not timely enough, thereby influencing the practicability of the system; CN201611100675.9 is based on the workshop autonomous scheduling system and method of the multi-agent, the autonomous scheduling system of this patent is characterized by setting up the corresponding agent for all work pieces, apparatus and logistics tools, collect the data in the production run, the trouble is given an alarm in time, can guarantee robustness and reliability of the system to a certain extent, but the diagnosis to the trouble is simpler, does not fully consider the influence of the kind of trouble, and only consider a factory of the fixed position in the production control, do not consider the distributed manufacturing resource of enterprise, therefore it is not the scheduling system of the commonality; that is, the current multi-intelligence scheduling system has the following problems: (1) the intelligence bodies have too single function, so that the coupling degree between every two intelligence bodies is too high, the autonomous ability of each intelligence body is not enough, and the ability of independently solving the problem is not provided; (2) the conflict coordination scheme among the brains is too simple, the idle resources and the capacity of the other brains are not fully considered, and the obtained scheduling result is not excellent enough; (3) the rescheduling capability for an incident is insufficient and a responsive rescheduling scheme cannot be derived.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a production line scheduling system and a production line scheduling method based on multiple wisdom, wherein the wisdom is utilized to encapsulate the functions and structures of management, resources, equipment, monitoring and other modules involved in the production and processing process of an enterprise to obtain wisdom with independent functions, a corresponding multiple wisdom system is formed, the coupling of the wisdom between the systems is reduced, the independent operation capacity of the wisdom is ensured, and the working efficiency and the management capacity of the scheduling system are improved.
To achieve the above object, according to one aspect of the present invention, there is provided a production line scheduling system and method based on multiple intelligence, the production line scheduling system comprising a global management module, a controller intelligence and a sub-controller intelligence, wherein:
the global management module is a manager at the highest level in the production scheduling system, has the highest management authority, is used for inputting task information and comprises receiving external task orders, task changes and task revocation, and is connected with the controller intelligence, receives task results fed back by the controller intelligence and evaluates the task results;
the controller intelligence is the core of the production scheduling system, including a resource module, a planning calculation module and a task management module, wherein:
the resource module is used for storing resource information of the intelligent entity of the sub-controller, wherein the resource information comprises the quantity, the type and the equipment state of raw materials and can be inquired and monitored; the planning computing module is connected with the resource module, decomposes the tasks according to resource information in a sub-controller intelligence in the resource module after receiving the tasks from the global management module, and sends the decomposed tasks to the task management module, and the task management module receives the decomposed tasks from the planning computing module, sorts the received decomposed tasks according to a priority order, and then distributes the decomposed tasks to the sub-controller intelligence;
and the sub-controller intelligence is used for executing the tasks sent by the task management module.
Further preferably, the controller agent further comprises a coordination module, and the coordination module is used for coordinating among the sub-control agents when the resources of the sub-control agents are lost, so that the resources of the sub-control agents meet the requirement of executing tasks.
Further preferably, the controller agent further comprises a monitoring module for monitoring the resource information state set by each sub-controller agent, and feeding back the device state to the resource module, which updates the resource information according to the current sub-controller agent.
Further preferably, the controller intelligence further comprises a sub-controller management module for registering information of sub-controllers under each controller module, including the number, communication addresses and ports, and whether the sub-controller module is available.
Further preferably, the number of controller intelligence in the production scheduling system is one or more.
Further preferably, for a production scheduling system having a plurality of control brains, the coordination module is further configured to implement communication between different control brains, so that resources between different control brains can be used in coordination.
According to another aspect of the present invention, there is provided a method for performing production scheduling by the production line scheduling system, the method comprising the following steps:
s1, the global management module receives tasks including the task of the appointed machine tool and the task of the unspecified machine tool, the global management module sends the received tasks to the task management module, and after the task management module receives the tasks, the task management module updates a task table, an equipment table and a resource table in the task management module according to the content in the tasks;
s2, for the task of the appointed machine tool, inquiring whether the state of the corresponding machine tool in the resource module is available, if the machine tool is available, selecting the machine tool, if not, selecting the available machine tool with the same or similar capacity, and adopting the sub-controller intelligence body where the selected machine tool is located to process the task of the appointed machine tool;
s3, for the task of the unspecified machine tool, the selected machine tool information is deleted in the resource table of the task management module, the planning calculation module decomposes the task of the unspecified machine tool into the realizable modes of various machine tool combinations according to the machine tool in the current resource table, the score of each combination is calculated according to the state of the sub-control intelligence body corresponding to each machine tool, the resource and the time cost for completing the task, the realizable mode with the highest score is selected as the final realization mode, the task of the unspecified machine tool is decomposed according to the final realization mode, and the task management module issues the decomposed task to the corresponding sub-controller management module, so that the task of the unspecified machine tool is processed.
Generally, compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention uses the intelligence to package the functions and structures of management, resources, equipment, monitoring and other modules involved in the production and processing process of an enterprise to obtain the intelligence with independent functions one by one, thereby forming a corresponding multi-intelligence system, reducing the coupling of the intelligence among the systems and ensuring the independent operation capability of the intelligence;
2. according to the invention, through interactive cooperation among the intelligence bodies, decomposition, allocation and calculation planning of tasks can be rapidly completed, the calculation planning capability in the existing scheduling system is improved, and meanwhile, mutual coordination of resources and the like can be maintained among the intelligence bodies, so that the continuous stability capability of the system is enhanced;
3. according to the invention, information such as product resources and the like is hierarchically managed according to the product structure and is stored by using corresponding formal data, so that the increase, deletion, check and modification of product information can be responded in time, and the informatization management level of the system is improved;
4. the invention selects the corresponding rescheduling method according to the type of the equipment fault and the influence caused by the fault, redistributes the original scheduling scheme to meet the task requirement and ensures the self-adaptability and the robustness of the system.
Drawings
FIG. 1 is a block diagram of a scheduling system constructed in accordance with a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a controller agent and its sub-controller agents constructed in accordance with a preferred embodiment of the present invention;
FIG. 3 is a flow chart of the task delivery of a controller agent and its sub-controller agents constructed in accordance with the preferred embodiment of the present invention;
FIG. 4 is a data flow diagram of task delivery of a controller intelligence constructed in accordance with a preferred embodiment of the present invention;
FIG. 5 is a flow diagram of a decomposition of a controller intelligence task, constructed in accordance with a preferred embodiment of the present invention;
fig. 6 is a flow chart of peer controller inter-modality coordination, constructed in accordance with a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention relates to a production line scheduling system and method based on multiple wisdom, wherein the corresponding dynamic scheduling system mainly comprises a global management module and a controller wisdom of each hierarchy, and the controller wisdom comprises a task management module, a resource management module, a planning module, a coordination module, a sub-controller management module, a monitoring module and a coordination module.
The global management module is used as a system manager of system virtualization, is a message processing station of all brains, is used as information input of the whole system, and the request information of all brains on the lower layer is fed back to the global management module in time and forwarded after decision making is carried out by the global management module, and comprises equipment fault information, scheduling and rescheduling request information and the like.
The task management module in the controller intelligence manages tasks of the intelligence, and the task management module comprises the steps of detecting resources and requirements of the tasks, sequencing task priorities, issuing the tasks, updating task states, issuing emergency tasks, canceling the tasks and the like, and the accuracy of task information in the intelligence is guaranteed.
The resource management module represents a manufacturing resource structure of the controller intelligence body and comprises resource information, equipment information and some key information, wherein the resource information comprises basic information such as types, quantity and positions of raw materials, the basic information, state information, working progress and the like of the equipment are provided for the equipment in the controller intelligence body to perform simulation on the working progress of the equipment corresponding to the equipment, and the key information corresponds to map position information or process information and the like.
The controller management module is responsible for the task allocation and management of the sub-controller intelligence bodies, when the task is issued, the task is reasonably allocated according to the resource condition of the sub-controller, the planning results returned by the sub-controller intelligence bodies are summarized and calculated to obtain the optimal scheduling result, the coordination request returned by the sub-controller intelligence bodies is received, and the coordinatable intelligence bodies or resources are allocated to meet the requirements.
The planning module of the controller intelligence encapsulates the logic method of intelligence scheduling, and can flexibly adjust the corresponding algorithm logic according to the system requirements. In the module, at least two methods are needed, namely a task planning method and a rescheduling method, wherein the task planning method is a task decomposition method under a normal condition, and the rescheduling method is a rescheduling method when equipment has an abnormality such as a fault, an order change and the like, and comprises a method model, parameter setting and the like.
The coordination module of controller wisdom is responsible for the coordination with other wisdom, when self resource is not enough, can send the assistance request to other controller wisdom at the same level, simultaneously, after the coordination module received the coordination information of other wisdom, under the resource requirement condition that satisfies self task, allows the resource assistance request of other wisdom, feeds back the request result to seeking help wisdom and upper controller wisdom simultaneously.
The monitoring module of the controller intelligence is responsible for collecting reading data of mechanical state monitoring equipment, a mechanical fault diagnosis instrument and the like, and obtaining work progress information of the equipment and fault information of analysis equipment from the reading data.
In order to realize the flexibility and the high efficiency of the system, each intelligence needs to be provided with a communication management module, and the communication mode among the intelligence is appointed. The communication interface types between each device and the intelligence are mainly Redis, OPC UA and the like, and the specific communication mode can be flexibly replaced, so that the coordination working mode among systems is realized.
As shown in FIG. 2, when the scheduling system receives a task, the following operations are performed:
step 1: and starting the global management module. The management intelligence runs in the central hub of the whole scheduling system and is equivalent to a central server. After initializing the communication module, the registration from other brains is monitored. And starting the subsequent wisdom, and showing the working progress of each wisdom and the equipment to a system administrator through an interface.
Step 2: and after receiving the task, the global management module firstly judges the legality of the task, judges whether the task is in a required format, sends the task to the lower-layer wisdom after the judgment is passed, and then turns to the step3, otherwise, returns to the state that the task cannot be executed, and returns error information.
Step3: and (4) after receiving the tasks, the task management module of the controller intelligence carries out resource detection on the resource management module according to information such as resources and equipment required by the tasks, and after the resource detection is met, the current tasks of the system are sequenced and issued according to task priorities, and the step4 is carried out, otherwise, the tasks cannot be executed and error information is returned.
Step 4: after receiving the tasks, the planning calculation module of the controller intelligence decomposes the tasks according to the capacity and resources required by the tasks, issues a plurality of subtasks to the corresponding sub-controller intelligence to perform planning calculation, and after receiving the planning results of each sub-controller intelligence, the planning module performs summary calculation to obtain an optimal result and feeds the optimal result back to the global management module to perform final decision making.
Step 5: and the global management module receives the planning result and then carries out final decision judgment, and the task is issued and executed after the decision judgment is passed.
As shown in fig. 3 and 4, the planning process of the controller intelligence and the sub-controller intelligence in step4 above is further described:
step4.1: after receiving the task, the task management module of the controller intelligence judges whether the task designates equipment, if the equipment is designated, the controller intelligence queries the state of the designated equipment in the resource library according to the designated equipment, if the state is available, the equipment is selected, and the step4.2 is carried out; if the state is not available, inquiring the state of the equipment with the same or similar capability, selecting the equipment with the available state, and turning to the step 4.2;
step4.2: adding the selected equipment into an equipment list issued by the task, and deleting the resources owned by the selected equipment in a task resource demand list;
and Step4.3, the planning calculation module decomposes the task according to the remaining resources and the equipment capacity required by the task, decomposes the task into subtask combinations with different abilities and stores the subtask combinations in a task issuing list of the brains, each subtask is completed by the brains (groups) with the same or similar abilities, and the conflicts in the process can be solved by coordinating the resources or the constraints among the brains (groups).
Step4.4: for each subtask, the controller agent can perform weight value distribution according to different agent resource conditions and task completion cost so as to ensure that each subtask can obtain a task distribution scheme with the minimum time cost. For a resource condition that a certain intelligence cannot meet, the resource condition is coordinated with other mentality to call the resource to complete a subtask. And storing the allocated wisdom combination in a wisdom sequence corresponding to each subtask.
Step4.5: and the decomposed subtasks are issued to the corresponding sub-controller intelligence planning calculation specific implementation schemes according to the respectively distributed intelligence sequences, and wait for receiving the returned planning results.
Step4.6: and after receiving the planning scheme and the coordination scheme of each sub-controller intelligence, the planning calculation module compares certain weight values, coordinates possible conflicts among the intelligence schemes, combines overlapped operations, obtains the final overall scheme and feeds back the final scheme upwards to wait for final confirmation.
As shown in fig. 5, the task decomposition process of the planning and calculating module in the controller intelligence is as follows:
step 1: after receiving the task, the planning calculation module judges whether the equipment required by the task belongs to the same type of controller intelligence group, and if so, the planning calculation module goes to the step 3; if not, go to step 2.
Step 2: and decomposing the tasks according to the intelligence capability of the controller, decomposing the tasks into subtasks with different intelligence sets, and issuing the subtasks to intelligence groups with corresponding capabilities. And allocating the intelligence groups with completely different capabilities according to the capabilities of the intelligence groups, and allocating the intelligence groups with the covered capabilities according to the efficiency of completing the task and preferentially allocating the intelligence groups with high efficiency.
And Step3, if the subtask can be completed by the single sub-controller intelligence, the subtask does not need to be decomposed, the Step4 is switched to, if the subtask cannot be completed by the single sub-controller intelligence, weight scoring is carried out according to the consumption time and the resource cost of the sub-controller intelligence, and task allocation is carried out according to the optimal result of the weight scoring.
Step 4: after receiving the subtasks, the intelligent entity of the sub-controller carries out planning calculation according to own resources and scores corresponding cost weights of planned results to obtain a planning score a; and when the shared resources of the intelligent entity of the sub-controller are not empty, the intelligent entity performs planning calculation on the tasks according to the resources and the shared resources owned by the intelligent entity, and performs corresponding weight scoring on the obtained planning result to obtain a planning score b. And after the intelligence obtains the planning score a (or a and b), feeding the planning result back to the controller intelligence, and performing summary calculation by the controller intelligence.
Step 5: and after the controller intelligence obtains the planning result, the final summary calculation is carried out to obtain the optimal coordination planning result, and the optimal coordination planning result is fed back to the global management module to wait for the final result to be confirmed.
As shown in fig. 6, the task coordination process between the controller avatars is as follows:
step 1: when the controller intelligence resource is lack of assistance, whether the shared resource is empty or not is firstly inquired, and if the shared resource is empty, assistance information is returned to the father controller intelligence; if not, sending assistance information to the controller intelligence body to which the shared resource belongs, and waiting for an assistance result;
step 2: when other controller brains receive the assistance information, whether the shared resource is needed or not is judged, and if the task in the brains does not need the resource, the assistance information is returned; if the task in the brain needs the resource, non-assistance information is returned.
Step3, the controller agent receives the assistance information returned by other agents, if the assistance information is the assistance information, the controller agent uses the resource to perform planning calculation and returns the planning result; and if the information is the non-assistance information, returning the assistance information to the parent controller intelligence.
Step 4: and the parent controller intelligence specifies a corresponding coordination strategy according to the received return information.
When the equipment fails, the rescheduling process executed by the scheduling system comprises the following steps:
step 1: the monitoring module of the intelligent controller returns to receive the monitoring data of the corresponding monitoring equipment, analyzes the working state of the equipment, and when the equipment is in operation due to the fact that the working efficiency is reduced or interrupted due to abrasion, aging and breakage of parts or temperature and air pressure abnormity, the monitoring module needs to package fault information, notes fault types, consequences caused by faults and the like, and finally sends the message to the intelligent parent controller.
Step 2: after the father controller intelligence receives the fault information fed back by the monitoring module, the exception handling module analyzes the fault, a rescheduling task and an intelligence set are generated again, rescheduling decomposition of the task is carried out, and meanwhile, the fault information is fed back upwards to the global management module to inform a user;
step3: and after receiving the rescheduling request, the planning calculation module receives the rescheduling tasks and the required resource capacity requirement, acquires the simulation data of the fault equipment, eliminates the completed part of tasks and redistributes the rest tasks.
Step 4: and according to the fault information returned by the monitoring module and the predicted repair time, performing task allocation to different degrees on the fault equipment. If the equipment has major faults, the estimated repair time exceeds the completion time required by the task, and the equipment is not considered during redistribution; if the equipment failure degree is light and the predicted repair time does not exceed the completion time required by the task, adding the equipment into schedulable equipment and adding the predicted repair time on the basis of the original predicted processing time.
Step 5: and the father controller intelligence re-scheduling task is issued to the sub-controller intelligence for planning calculation, the planning results returned by each intelligence are collected and sorted, a new re-scheduling scheme is obtained and fed back to the global management intelligence, and the new re-scheduling task is issued and executed.
Step 6: when the equipment enters the system again after the fault of the equipment is repaired, the information of the sub-controller intelligence body equipment is updated, coordination is carried out again between the intelligence bodies, the optimized scheme after the equipment is updated is obtained, the optimized scheme is fed back to the upper intelligence body, and the execution is carried out after the equipment is confirmed.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. The utility model provides a production line dispatch system based on many wisdom, its characterized in that, this production dispatch system includes global management module, controller wisdom and sub-controller wisdom, wherein:
the global management module is a manager at the highest level in the production scheduling system, has the highest management authority, is used for inputting task information and comprises receiving external task orders, task changes and task revocation, and is connected with the controller intelligence, receives task results fed back by the controller intelligence and evaluates the task results;
the controller intelligence is the core of the production scheduling system, including a resource module, a planning calculation module and a task management module, wherein:
the resource module is used for storing resource information of the intelligent entity of the sub-controller, wherein the resource information comprises the quantity, the type and the equipment state of raw materials and is used for inquiring and monitoring; the planning computing module is connected with the resource module, decomposes the tasks according to resource information in a sub-controller intelligence in the resource module after receiving the tasks from the global management module, and sends the decomposed tasks to the task management module, and the task management module receives the decomposed tasks from the planning computing module, sorts the received decomposed tasks according to a priority order, and then distributes the decomposed tasks to the sub-controller intelligence;
and the sub-controller intelligence is used for executing the tasks sent by the task management module.
2. A multi-intelligence based production line scheduling system as recited in claim 1 wherein the controller intelligence further comprises a coordination module for coordinating among a plurality of the child control intelligence when the resources of the child control intelligence are missing so that the resources of the child control intelligence meet the requirements for performing tasks.
3. The method as claimed in claim 1, wherein the controller agent further comprises a monitoring module for monitoring the status of the resource information set by each sub-controller agent and feeding back the status of the equipment to the resource module, and the resource module is updated according to the resource information of the current sub-controller agent.
4. A multi-intelligence based production line scheduling system as claimed in claim 1 wherein said controller intelligence further comprises a sub-controller management module for registering information of sub-controllers under each controller module including number, communication address and port, and whether the sub-controller module is available.
5. A multi-intelligence based production line scheduling system as claimed in claim 2 wherein the number of controller intelligence in the production scheduling system is one or more.
6. A multi-agent based production line scheduling system according to claim 5 wherein, for a production scheduling system with multiple control agents, the coordination module is further configured to implement communication between different control agents so that resources between different control agents are used in coordination.
7. A method for production scheduling by a production line scheduling system according to any one of claims 1 to 6, comprising the steps of:
s1, the global management module receives tasks including the task of the appointed machine tool and the task of the unspecified machine tool, the global management module sends the received tasks to the task management module, and after the task management module receives the tasks, the task management module updates a task table, an equipment table and a resource table in the task management module according to the content in the tasks;
s2, for the task of the appointed machine tool, inquiring whether the state of the corresponding machine tool in the resource module is available, if the machine tool is available, selecting the machine tool, if not, selecting the available machine tool with the same or similar capacity, and adopting the sub-controller intelligence body where the selected machine tool is located to process the task of the appointed machine tool;
s3, for the task of the unspecified machine tool, the selected machine tool information is deleted in the resource table of the task management module, the planning calculation module decomposes the task of the unspecified machine tool into the realizable modes of various machine tool combinations according to the machine tool in the current resource table, the score of each combination is calculated according to the state of the sub-control intelligence body corresponding to each machine tool, the resource and the time cost for completing the task, the realizable mode with the highest score is selected as the final realization mode, the task of the unspecified machine tool is decomposed according to the final realization mode, and the task management module issues the decomposed task to the corresponding sub-controller management module, so that the task of the unspecified machine tool is processed.
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