CN116540584A - Intelligent management and control system of unmanned production line of fusion-cast charging - Google Patents
Intelligent management and control system of unmanned production line of fusion-cast charging Download PDFInfo
- Publication number
- CN116540584A CN116540584A CN202310236001.5A CN202310236001A CN116540584A CN 116540584 A CN116540584 A CN 116540584A CN 202310236001 A CN202310236001 A CN 202310236001A CN 116540584 A CN116540584 A CN 116540584A
- Authority
- CN
- China
- Prior art keywords
- production
- casting
- unmanned
- production line
- equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 452
- 238000005266 casting Methods 0.000 claims abstract description 199
- 238000000034 method Methods 0.000 claims description 226
- 230000008569 process Effects 0.000 claims description 170
- 238000007726 management method Methods 0.000 claims description 95
- 239000000463 material Substances 0.000 claims description 77
- 230000004927 fusion Effects 0.000 claims description 67
- 238000012544 monitoring process Methods 0.000 claims description 53
- 238000003860 storage Methods 0.000 claims description 30
- 238000004422 calculation algorithm Methods 0.000 claims description 29
- 239000003016 pheromone Substances 0.000 claims description 28
- 239000002994 raw material Substances 0.000 claims description 27
- 238000013439 planning Methods 0.000 claims description 24
- 238000004088 simulation Methods 0.000 claims description 24
- 238000013461 design Methods 0.000 claims description 19
- 239000000843 powder Substances 0.000 claims description 19
- 238000011068 loading method Methods 0.000 claims description 18
- 230000007613 environmental effect Effects 0.000 claims description 16
- 238000010839 reverse transcription Methods 0.000 claims description 15
- 230000000007 visual effect Effects 0.000 claims description 15
- 239000003814 drug Substances 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 14
- 239000007788 liquid Substances 0.000 claims description 13
- 238000012423 maintenance Methods 0.000 claims description 13
- 238000007781 pre-processing Methods 0.000 claims description 13
- 238000011156 evaluation Methods 0.000 claims description 12
- 239000002360 explosive Substances 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 7
- 238000013500 data storage Methods 0.000 claims description 7
- 230000008447 perception Effects 0.000 claims description 7
- 230000003449 preventive effect Effects 0.000 claims description 7
- 238000012384 transportation and delivery Methods 0.000 claims description 7
- 241000257303 Hymenoptera Species 0.000 claims description 6
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000002955 isolation Methods 0.000 claims description 6
- 239000004973 liquid crystal related substance Substances 0.000 claims description 6
- 238000007711 solidification Methods 0.000 claims description 6
- 230000008023 solidification Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000001816 cooling Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 238000002360 preparation method Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 239000000428 dust Substances 0.000 claims description 4
- 238000005265 energy consumption Methods 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 4
- 238000010248 power generation Methods 0.000 claims description 4
- 239000000015 trinitrotoluene Substances 0.000 claims description 4
- 239000002699 waste material Substances 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000005065 mining Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 239000007790 solid phase Substances 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000010924 continuous production Methods 0.000 claims description 2
- 239000013538 functional additive Substances 0.000 claims description 2
- 238000003062 neural network model Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 description 13
- 230000006399 behavior Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000002156 mixing Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000009776 industrial production Methods 0.000 description 3
- 239000000155 melt Substances 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000003542 behavioural effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000004880 explosion Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000003756 stirring Methods 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 241000238413 Octopus Species 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- -1 density Substances 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011049 filling Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 230000000474 nursing effect Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000036314 physical performance Effects 0.000 description 1
- 238000011176 pooling Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses an intelligent control system of an unmanned fused cast charge production line, which is characterized in that the intelligent control system is designed for the overall architecture of the system, namely, the intelligent control system is constructed for the overall intelligent control mode of the unmanned fused cast charge production line from four layers of a physical equipment layer, a data sensing layer, a twin model layer and an application service layer, so that the problem that the intelligent autonomous decision can not be made based on the full-flow full-element information due to low intelligent control level of the existing unmanned fused cast charge production line is solved; by the intelligent control system of the unmanned casting and charging production line, the intelligent and refined control level of the unmanned casting and charging production line on the casting process, logistics transportation and production scheduling under the condition of ensuring production safety and product forming quality requirements can be improved, and the safe and efficient production target of the unmanned casting and charging production line is realized.
Description
Technical Field
The invention belongs to the technical field of explosive fusion casting, and particularly relates to an intelligent management and control system of an unmanned production line for fusion casting charging.
Background
The traditional casting charging production line has low automation degree, and the following procedures need to be manually participated after the casting charging raw materials such as Otto gold, aluminum powder and the like are manually transported to the production line, and the casting charging production process is controlled by means of manual experience. However, the fusion-cast explosive has the characteristic of easy burning or explosion after being stimulated by the outside, and the safety risk factors involved in the production process are large, once the explosion accident of the production line is caused by improper operation of operators or equipment failure and the like, serious casualties and economic losses are caused. Along with the rapid development of industrial automatic production technology, the field of initiating explosive devices also needs to gradually transition from a production mode mainly comprising manpower to an automatic direction, wherein an unmanned production line for the fused and cast charge is an important research direction in the future, the unmanned production line can realize man-machine isolation operation and automatic production, and the intrinsic safety level of the fused and cast charge production process is improved.
The industrial production line needs to improve the controllability of the production process through an intelligent control system so as to ensure the smooth production process of the production line. There are some studies and applications on intelligent management and control systems of production lines. The Chinese patent with the patent number of CN201910311030.7 discloses an intelligent control system of a large-scale structural member automatic production line, which constructs an overall intelligent control mode of a workshop automatic production line manufacturing site, so that the problem of manufacturing resource guarantee of the large-scale structural member automatic production line is solved. However, the system is mainly used for intelligently controlling warehouse logistics of production resources such as cutters, materials and the like, and cannot be applied to intelligent control of production elements such as equipment states, workshop environments and the like which influence production safety on a high-risk product preparation production line. The Chinese patent application with the application number of CN202111128080.5 discloses a three-dimensional visual intelligent management and control system and a production system of a nuclear industrial production line, and the intelligent management and control system can comprehensively and dynamically display the actual conditions, the operation process, the storage process and the like of on-site equipment to monitoring commanders, so that the production efficiency and the management and control safety are improved. However, the system is mainly used for three-dimensional visual monitoring of various production resources and processes in a nuclear industrial production line, and cannot early warn and actively regulate production information such as equipment running states, dangerous material transportation states and the like. In summary, the current intelligent control systems of production lines, although being studied and applied in other fields, are not suitable for the high-risk production process of the fused and cast charges.
The existing unmanned production line for casting and charging adopts automatic equipment, AGV transport trolleys and other substitute operators to finish production operation, and realizes robot replacement through the automation of each procedure and each equipment, thereby reducing the manual intervention in the production process. However, the existing unmanned production line for casting and charging only realizes unmanned operation of casting equipment and unmanned conveying of raw materials in the casting and charging process, but the intelligent management level is not enough, a set of intelligent management and control system facing to the unmanned production line is not formed, the unmanned management and control system is still mainly based on artificial management and guarantee, and intelligent management and control can not be realized in aspects of casting and charging process, material and die logistics transportation, production and scheduling.
In terms of process management, the existing unmanned production line for casting and charging is mainly controlled by working experience of operators in the key processes of loading and unloading and the like, a large amount of process information is expressed in two-dimensional modes such as drawings, reports, documents and the like, manual writing is relied on, and the automation degree of process design is low. Because the prior fusion casting process knowledge is discrete and does not form an intelligent process management system, the process knowledge such as the manual experience of key procedures, process rules and the like is difficult to be converted into the inference rules which are easy to identify by a computer, the historical fusion casting process instance data cannot be searched under the new working condition of different batches of fusion casting charging products, and the process parameters are required to be reset by depending on the manual experience and product trial production, so that the process design efficiency is low and intelligent process decisions cannot be quickly generated.
In the aspect of logistics management, unmanned production lines for fusion casting charging need to utilize AGV trolley and other transportation materials and moulds in different operation areas, and because objects of logistics transportation are dangerous explosive products, the requirements on timeliness, stability and the like of the transportation process are extremely high. The traditional informatization management system is mainly used for only managing static information such as storage, purchase, inventory and the like of raw materials, and cannot be used for carrying out real-time monitoring and dynamic management on logistics transportation of an unmanned fused cast charge production line. When the dead time of the die and the material transportation is too long or the material flow paths collide, dynamic material flow scheduling is difficult to be performed in time, so that the liquid medicine generates larger temperature drop or the materials collide, and the molding quality and the production safety of the product are seriously affected.
In terms of production scheduling, unmanned production lines for fusion-cast charging are mainly scheduled in advance, and management staff issues a production plan to the production lines for production in advance according to productivity and process requirements. Because the production resource information such as material storage, equipment state, logistics transportation and the like cannot be monitored in a centralized manner, the production progress of the fused cast charge product is difficult to master in real time, and the production beats of each process cannot be controlled accurately. When dynamic disturbance events such as shortage of materials such as octopus and aluminum powder, faults of fusion casting charging equipment or overlong cooling time of liquid medicine are generated in the production process, deviation between the actual plan execution process and the production plan is caused, so that problems such as plan delay and resource waste are generated.
In summary, since the existing unmanned production line for the molten and cast charge lacks an intelligent management and control system, the data flow and the information flow of production resources such as equipment information, material information and environmental information generated in different working areas along with the progress of the process are difficult to sense in real time, and the complete intelligent management system cannot be used for centralized management of production scheduling under the influence of logistic transportation of the molten and cast process, raw materials and a mould and disturbance factors, so that the unmanned production line for the molten and cast charge is low in management and control efficiency, intelligent autonomous decision making cannot be made based on the whole-flow full-factor information of the unmanned production line for the molten and cast charge, and therefore the intelligent and refined management and control of the unmanned production line level is difficult to realize.
Disclosure of Invention
In view of the above, the invention aims to solve the defects in the prior art and provide an intelligent control system of an unmanned production line for casting and charging.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent control system of a fusion-cast-charge unmanned production line comprises a physical equipment layer, a data perception layer, a twin model layer and an application service layer;
the physical equipment layer comprises intelligent casting charging equipment, an intelligent logistics transport vehicle and intelligent sensing equipment which are used for forming a casting charging unmanned production line, and the casting charging unmanned production line can automatically complete various working procedures of casting charging production according to a set program, so that man-machine isolation and automatic production are realized;
The data sensing layer comprises a data acquisition module, a data preprocessing module and a data storage module, wherein the data acquisition module is used for acquiring multi-source heterogeneous data of an unmanned production line of the casting powder charge in real time, and the multi-source heterogeneous data comprises production resource information which is acquired by a casting equipment control system, a sensor sensing and enterprise information management system and comprises casting equipment operation data, material storage data and production environment index data; the data preprocessing module is used for preprocessing the multi-source heterogeneous data acquired by the data acquisition module in sequence, wherein the data comprises cleaning and dimension reduction data, so that empty values, missing values or repeatedly recorded data can be removed, and the data which can be directly used for analysis and calculation can be obtained; the data storage module is used for classifying and sorting the multi-source heterogeneous data preprocessed by the data preprocessing module into time sequence data and relation data, and storing the time sequence data and the relation data in a time sequence database and a relation database respectively;
the twin model layer comprises a virtual model module and a virtual-real mapping module, wherein the virtual model module comprises a geometric model, a physical model, a behavior model and a rule model and is used for constructing, storing and managing a virtual information model of a physical entity of the fusion-cast-charge unmanned production line; the virtual-real mapping module utilizes real-time acquired multi-source heterogeneous real data to drive a virtual information model of an unmanned fusion-cast charging production line, restores the production condition of the production line site and ensures that each production element in the virtual information model is synchronous with the state in a physical production line;
The application service layer comprises a visual monitoring module, a casting process management module, a logistics transportation management module, a production scheduling management module and a production line fault alarm management module, and is used for analyzing and pre-judging the overall operation state and the variation trend of the unmanned production line of the casting powder charge and carrying out collaborative intelligent management and control on multiple elements.
Further, the visual monitoring module comprises a production plan monitoring board, an equipment state monitoring board, a material storage monitoring board and a production environment monitoring board;
the production plan monitoring board is used for monitoring production information including the planned starting time, the planned ending time, the product quantity and the procedure content of the batch of the fused cast charge products so as to check the production resources of the whole production process and the plan information of the scheme;
the equipment state monitoring board is used for carrying out centralized monitoring on the operation states of the casting equipment, including different operation and maintenance information of the casting equipment, the starting and stopping states of the equipment, the operation time length of the equipment, the current technological parameters and the energy consumption value of the equipment, and carrying out automatic early warning when the equipment state is abnormal, so as to remind a manager of carrying out preventive maintenance on the equipment;
The material storage monitoring board is used for carrying out centralized monitoring on material information including material names, storage positions, storage quantity and batches of the prepared casting charges so as to update material in-out storage and stock information in real time, track the circulation condition of raw materials between a warehouse and a working procedure and automatically early warn the stock quantity of the materials;
the production environment monitoring board is used for monitoring environmental parameters including environmental temperature, humidity and dust in different operation areas of the unmanned production line of the fused and cast charging, and carrying out early warning on abnormal environmental parameters so as to ensure that the production environment of the fused and cast charging is safe and controllable.
Further, the casting process management module comprises a process knowledge base unit, a process case reasoning unit and a process intelligent decision unit;
the process knowledge base unit comprises an explosive raw material parameter base, a casting equipment parameter base, a process rule base, a product model base and a process quality problem base, and is used for uniformly classifying and encoding the process knowledge including traditional manual experience knowledge, a process specification and a process management system formed by enterprises for a long time, a process standard published by national publications and a process knowledge of a manual in the production process of casting charges so as to update and maintain the experience of process experts, process data and casting production examples in real time;
The process case reasoning unit is used for matching the current new working condition with the typical casting process and the historical casting process in the process knowledge base aiming at the new working condition comprising different charging materials, grain composition and solid phase content, if the matching degree is higher, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition; if the typical process cannot be matched, determining the design of a casting process flow and the selection of process parameters under a new working condition by utilizing process rules and process reasoning;
the intelligent process decision unit performs simulation on the casting process flow design and process parameter selection of the new working condition obtained by the process case reasoning unit, verifies the feasibility of the new process scheme, and finally generates a casting charge preparation process scheme through intelligent decision.
Further, in the process case reasoning unit, the method for designing the casting process flow and selecting the process parameters under the new working condition comprises the following steps:
firstly, matching the current new working condition with a typical casting process and a historical casting process in a process knowledge base, and expressing a case base in the process knowledge base as:
Case＝{case i |case i (F i ,R i ),i＝1,2,...,N}
wherein Case is a Case library, case i Representing an ith case of the case library; f (F) i Representing a set of features describing the problem in the i-th case; r is R i Representing a set of solution features described in the ith case; n represents the number of cases contained in the case library;
secondly, case organization searching, namely comparing a target case with a case of a case library by using a nearest neighbor method:
wherein Sim (x i ) Representing the total similarity of the new working condition and the cases in the process knowledge base, omega k Weights, x, representing attributes k ik Representing cases with attribute k, sim ak (x ik ) Representing case similarity under attribute k; m represents genusNumber of sexes;
if Sim (x) i ) If the value of the formula (I) is greater than or equal to the set threshold value, the matching degree between the new working condition and the typical casting process and the historical casting process in the process knowledge base is higher, and at the moment, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition;
if Sim (x) i ) If the value of the formula (I) is smaller than or equal to the set threshold value, the matching degree between the new working condition and the typical casting process and the historical casting process in the process knowledge base is lower, and a casting process scheme under the new working condition is generated by reasoning through a fuzzy rule reasoning algorithm, wherein the method comprises the following steps:
Rule1:Ifx 1 isA 1,1 andx 2 isA 2,1 and,···,andx n isA n,1 B 1 ThenyisB 1
wherein x is i As regular front piece, A i,j As a condition variable x i Is a rule back part, B j Fuzzy sets for conclusion variable y; according to condition x i And fuzzy rule A i,j And (5) reasoning to obtain a casting process scheme under the new working condition.
Further, the logistics transportation management module comprises a path planning unit, a path conflict resolution unit, a material buffer area scheduling unit and a die loading and unloading area scheduling unit;
the path planning unit is used for forming a logistics transportation plan of raw materials and moulds comprising aluminum powder, TNT and functional additives, optimizing a transportation path according to constraint conditions comprising material storage time, logistics transportation efficiency and liquid medicine cooling temperature, and driving the AGV transportation trolley to complete timely delivery and transportation of the raw materials and the moulds among a warehouse, a product area, a finished product area and a line side warehouse;
the path conflict resolution unit is used for ensuring that the logistics transportation paths of the raw materials and the dies cannot collide, when two or more conveying tasks are opposite to each other on the same path, if at least two conveying tasks collide, the collision or potential collision is resolved in a mode that the conveying tasks sequentially pass through according to the order of the priority from high to low, so that the safety accidents caused by collision are avoided;
The material buffer area scheduling unit is used for connecting each material buffer area positioned on the path between the previous working procedure and the next working procedure, ensuring continuous production of the casting charging equipment and reducing the path congestion before the parallel equipment; when the casting charging equipment in a certain procedure fails and stops production or the materials stored in the buffer areas are excessive, the material buffer area scheduling unit can dynamically adjust the capacity of each buffer area on the unmanned production line, so that the phenomena of material accumulation and resource waste are avoided;
the die loading and unloading area scheduling unit is used for managing storage and transportation of the die in the loading and unloading areas in the whole process from unloading to clamping to solidification of the carrier, intelligently scheduling the assembly and disassembly tasks of the die according to the emergency degree of the tasks and the execution conditions of all tasks of the solidification stations, forming a die assembly and disassembly task work sequence, and completing clamping and disassembly of the die according to the work sequence by all the stations in each loading and unloading area.
Further, in the path planning unit, an ant colony algorithm is adopted to solve and obtain an optimal transportation path under the influence of multiple factors, and in order to avoid the algorithm from entering precocity, a suboptimal solution obtained by an algorithm A is used as a priori knowledge; the optimized pheromone updating formula is as follows:
Wherein τ (i) is the pheromone concentration of grid node i, c 1 A constant less than 0; d (P, i) is expressed as the Euclidean distance from node P to node i; p is a grid node through which an A-algorithm passes; i is a grid node around the node p; count (i) represents the sum of the grid numbers from the current node P to the surrounding feasible nodes i in the a-algorithm;
after the routing of m ants is completed, the pheromones are required to be updated; the number of turns of AGV is considered, and the concentration of pheromone is increasedIs corrected to make the turnConsidering the balance of number and path length, the pheromone concentration increase amount +.>The method comprises the following steps:
wherein c 2 Is a path length influencing factor; c 3 Is a turning factor; q represents a constant; l (L) k Expressed as the length of the path travelled by the kth ant; t (T) k Is the sum of the number of turns from the start point to the end point; c (C) i Representing a set of feasible nodes around the current node i;
the pheromone concentration updating mode from node i to node j in t+1 iterations is as follows:
wherein ρ is a pheromone volatilization factor; m is the total number of ants in the t-th iteration; τ ij (t) is the pheromone concentration from node i to node j in the t-th iteration;is the increase in pheromone concentration of ant k from node i to node j in the t-th iteration.
Further, the production scheduling management module comprises a planning production scheduling unit, a production scheduling simulation unit and a production disturbance rescheduling unit;
the planning and scheduling unit is used for managing the production tasks of the fusion-cast-charge products in different batches, and calculating the optimal scheduling schemes under different optimization targets by using a genetic algorithm according to the influencing factors including the station state, the task delivery time, the equipment productivity and the process requirements of the fusion-cast-charge unmanned production line;
the production scheduling simulation unit is used for performing simulation on different production tasks so as to check production running conditions and Gantt charts, verify rationality and operability of a planned production scheduling scheme and display an optimal production scheduling result to a user;
when a dynamic disturbance event which causes deviation of production plan execution and comprises fusion casting process constraint change, production resource constraint change and procedure execution time error occurs in the fusion casting charging production process, the production disturbance rescheduling unit makes a rescheduling response mechanism for the dynamic event and obtains a new scheduling scheme; and simulating and judging whether the scheduling scheme meets the requirement through the scheduling simulation unit, and executing a new production scheduling scheme if the scheme meets the requirement, so as to realize intelligent adjustment and decision of production scheduling.
Further, in the planning and scheduling unit, a production scheduling model is established to express the purpose and constraint of the molten and cast charge product scheduling, and the scheduling model is as follows:
f＝max(min{RT 1 -(T 1k +b 1k ),RT 2 -(T 2k +b 2k ),···,RT j -(T jk +b jk )})
j is the production task number; k is the number of working procedures under the task; RT (reverse transcription) method j The required completion time for task j; t (T) jk B is the start time of the kth process in task j jk The execution time of the kth procedure in the task j;
the method adopts a population production strategy, uses procedures and machine variables to realize fixed-point intersection in the intersection and variation, so as to avoid the generation of non-feasible solutions, save the correction time of the non-feasible solutions, and use a local search strategy to avoid sinking into a local optimal solution, and finally obtains an optimal production scheduling scheme of the fusion casting charge production task under the multi-constraint condition through the scheduling model solution.
Further, the production disturbance rescheduling unit adopts the production time deviation tolerance to evaluate the deviation degree of the production plan under the influence of disturbance factors, so as to realize quantitative control of the production time fluctuation of each working procedure of the unmanned production line for the fused cast charge; the tolerance delta of the production time deviation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,and max (T) i ) Respectively representing the predicted production completion time of the planned production scheduling unit and the completion time in the actual production scheduling process;
Tolerance delta for a given maximum production time deviation max When delta exceeds the maximum production time deviation tolerance delta due to the occurrence of production dynamic disturbance event max When the production disturbance processing mechanism is used, rescheduling the production plan is performed by using the schedule based on the production disturbance processing mechanism.
Further, the production line fault alarm management module comprises a safety evaluation early warning unit and an equipment fault evaluation early warning unit;
the safety evaluation early warning unit is used for carrying out safety risk evaluation on dangerous sources comprising human factors, environmental factors and equipment factors existing in the unmanned production line of the fused cast charge, predicting possible dangerous situations and related probabilities, and reminding a manager to take corresponding safety control measures in a grading early warning mode so as to avoid safety accidents;
the equipment fault early warning unit is used for carrying out centralized monitoring on the casting equipment in all operation areas of the unmanned casting powder charging production line, carrying out deep mining on data including vibration, rotation speed, temperature and power generation time length of key parts, and utilizing a neural network model to pre-judge the running state of the equipment, so as to realize intelligent early warning of equipment fault and remind a manager of carrying out preventive maintenance on the equipment.
The invention has the beneficial effects that:
According to the intelligent control system of the unmanned fused cast charge production line, through designing the overall system architecture, the overall intelligent control mode of the unmanned fused cast charge production line is constructed from four layers of a physical equipment layer, a data sensing layer, a twin model layer and an application service layer, and the problems that the existing unmanned fused cast charge production line is low in intelligent control level and cannot make intelligent independent decisions based on full-flow full-element information are solved; by the intelligent control system of the unmanned casting and charging production line, the intelligent and refined control level of the unmanned casting and charging production line on the casting process, logistics transportation and production scheduling under the condition of ensuring production safety and product forming quality requirements can be improved, and the safe and efficient production target of the unmanned casting and charging production line is realized. Specifically, the intelligent control system of the unmanned production line for the fusion-cast charging has the following advantages:
1) By collecting multi-source heterogeneous production line data, the whole running state and the change trend of the production line are intelligently evaluated, and active early warning and regulation measures are adopted according to the evaluation result, so that the intelligent management and control level of unmanned production of the fused cast charge is improved, and the safe and efficient production target of the unmanned production line of the fused cast charge is realized;
2) The integrated management and control platform of full-flow full-element information is provided for unmanned production of the fusion and cast charging, the blank of an intelligent management and control method of an unmanned production line of the fusion and cast charging in China is filled, and the safety level and the production efficiency of the unmanned production line of the fusion and cast charging are improved;
3) The visual monitoring module can realize the remote visual monitoring of production behaviors and production resource data on the unmanned production line of the fused cast charges, so that a manager can remotely grasp the running state of the production line in real time under the requirement of man-machine isolation and timely discover the abnormal condition of the charging process, and compared with the traditional video monitoring system, the visual monitoring module can monitor the full-flow full-element information of the unmanned production of the fused cast charges more comprehensively and accurately;
4) The fusion casting process management module can summarize artificial experience parameters, process specifications, process flow problem libraries and the like formed by manual operation links in the production process of the fusion casting explosive for a long time to form a process knowledge base, and then quickly and accurately generate a process route of the fusion casting explosive loading product under a new working condition through intelligent process decision, so that process resource guarantee is provided for an unmanned production line of the fusion casting explosive loading, the process management level is improved, and the labor intensity of process designers is reduced;
5) The logistics transportation management module can form a transportation path planning and scheduling scheme of the raw materials, the liquid medicine and the mould of the fused cast charge through an artificial intelligent algorithm, and drives a material AGV trolley, a transportation forklift and the like to complete timely distribution of materials of an unmanned fused cast charge production line in different operation areas, so that potential conflict of transportation paths is eliminated, the problems of overlong transportation time or conflict of logistics paths are avoided, and the whole unmanned safe circulation of dangerous articles is realized;
6) The production scheduling management module is responsible for planning production scheduling, production scheduling simulation and production disturbance rescheduling of the unmanned production line of the fused cast charge, carrying out overall management on production tasks of the production line, and calculating the optimal production scheduling scheme under different constraint conditions; when dynamic disturbance such as constraint change of casting process, constraint change of production resources and the like occurs, the production scheduling scheme is automatically updated, intelligent adjustment and decision of production scheduling are realized, and the daily work of production planning personnel is greatly facilitated;
7) The production line fault alarm management module can intelligently manage production safety and equipment state of the unmanned production line of the molten cast charge, comprehensively evaluate safety risks of materials, equipment, environment and other dangerous sources of the unmanned production line of the molten cast charge, and remind management personnel of timely disposal through a grading early warning mechanism; and the fault of the casting equipment is intelligently early-warned, so that a manager is reminded to carry out preventive maintenance on the equipment, and the safe operation of the production process is ensured.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a block diagram of an embodiment of an intelligent control system for an unmanned melt-cast charge production line of the present invention;
fig. 2 is a schematic diagram of the main interface of the intelligent control system of the unmanned production line of the fused and cast charges developed in this example.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to limit the invention, so that those skilled in the art may better understand the invention and practice it.
As shown in fig. 1, the intelligent control system of the unmanned production line for the fused and cast charges of the embodiment comprises a physical equipment layer, a data perception layer, a twin model layer and an application service layer. Next, detailed descriptions of specific embodiments of the physical device layer, the data aware layer, the twin model layer, and the application service layer are respectively described in this embodiment.
1. Physical device layer
The physical equipment layer is a physical entity of an unmanned production line for the casting charge, and comprises automatic casting equipment, casting charge raw materials, an automatic guiding transport vehicle and the like in the casting process, intelligent sensing equipment for data acquisition and communication and the like, and the unmanned production task of the whole flow of the casting charge is realized through organic cooperation among the physical equipment. Namely, in the embodiment, the physical equipment layer comprises intelligent casting charging equipment, an intelligent logistics transport vehicle and intelligent sensing equipment which are used for forming a casting charging unmanned production line, and the casting charging unmanned production line can automatically complete various working procedures of casting charging production according to a set program, so that man-machine isolation and automatic production are realized, and the intrinsic safety level of a casting technological process is improved.
2. Data perception layer
The data sensing layer is used for acquiring real-time state data and operation information of production elements such as equipment, materials, environment and energy consumption of the unmanned production line of the fused and cast charge by using intelligent sensing equipment and an industrial communication protocol and by means of big data cleaning and data fusion technology and intelligent sensing and classification, preprocessing various acquired data, and finally storing the data in a database in a classified manner for later calling so as to realize intelligent sensing of the full-flow full-element data of the unmanned production line of the fused and cast charge. In this embodiment, the data sensing layer includes data acquisition module, data preprocessing module and data storage module for gather the multisource heterogeneous data of unmanned production line of fusion cast charging in real time, then carry out the preliminary treatment to the data of gathering, finally store to the database, provide data support for unmanned production line intelligent management and control system of fusion cast charging.
2.1 data acquisition module
The data acquisition module is used for acquiring multi-source heterogeneous data of the unmanned production line of the fusion casting powder charge in real time, wherein the multi-source heterogeneous data comprises production resource information which is acquired by a fusion casting equipment control system, a sensor perception and an enterprise information management system and comprises fusion casting equipment operation data, material storage data and production environment index data.
The data of the unmanned production line of the fused and cast charges mainly comprise multi-source heterogeneous data such as production plan data, equipment system operation data, material storage data, sensor detection data and the like. Aiming at the data of different sources of the unmanned production line of the fused cast charge, the multi-source heterogeneous data is acquired by adopting a fused cast equipment control system acquisition mode, a sensor perception acquisition mode and an enterprise information management system acquisition mode.
(1) The acquisition mode of the casting equipment control system is as follows: and a built-in control system of the casting equipment on the unmanned production line of the casting powder charge utilizes the industrial information communication protocols such as OPC UA, MTConnect and the like to collect equipment system operation data in real time from a data interface provided by the control system of the casting equipment. The equipment system operation data comprise equipment on-off state, equipment process parameters, equipment operation time, single-process processing time and other data;
(2) the sensor perceives the acquisition mode: for state information which cannot be directly acquired from equipment on a unmanned production line of the fused cast charge, a corresponding sensor can be installed at a position where information needs to be acquired, and data can be acquired through a data acquisition system. Measuring the temperature of the liquid medicine in a melt mixing pot by a thermometer; the infrared thermal imager is arranged outside the die to measure the temperature of the wall surface of the die; and (5) measuring environmental information such as temperature, humidity and the like by using a hygrothermograph at a fused cast charge production site.
(3) Enterprise information management system: the production plan information of each batch of products, the physical parameters of raw materials, the component proportion, the mould model and other data on the unmanned production line of the fused and cast charges are used as input variables of an intelligent control system of the unmanned production line of the fused and cast charges, and the input variables need to be acquired in advance. The data can be derived from the ERP, MES and other information management systems of the enterprise, and the accuracy of system simulation is improved.
2.2 data preprocessing module
The data preprocessing module sequentially preprocesses the multi-source heterogeneous data acquired by the data acquisition module, including cleaning and dimension reduction, so as to remove null values, missing values or repeatedly recorded data, and obtain data which can be directly used for analysis and calculation. In particular, data collected from unmanned melt-cast charge production lines often cannot be used directly for analytical calculations and requires pre-processing of the data. First, data cleaning is performed, and the embodiment uses a hash table-based data cleaning algorithm to perform inspection and verification on collected data, so as to clean empty values, missing values or repeatedly recorded data. The attribute characteristics of various data can be associated, so that the final analysis result is affected, and therefore, the data is subjected to dimension reduction by adopting a principal component analysis method, and the maximum similarity between the low-dimensional data and the original data characteristics is ensured. And cleaning and dimension reduction of the data sample library are completed through the steps, and data which can be directly used for analysis and calculation are obtained.
2.3 data storage Module
The data storage module classifies and sorts the preprocessed multi-source heterogeneous data of the unmanned fusion and cast charge production line into time sequence data and relational data, wherein the time sequence data is high-frequency big data acquired by a sensor, and the relational data is equipment start-stop state information data. And classifying and storing the data of different types to form a corresponding time sequence type database and a corresponding relation type database for the intelligent management and control system of the fusion casting charging unmanned production line to call. In this embodiment, the preprocessed multi-source heterogeneous data of the unmanned production line for the molten cast charges is classified and sorted by attribute identifiers, and is divided into time sequence data and relational data, wherein the time sequence data is high-frequency big data collected by a sensor, and the relational data is equipment start-stop state information data. And then, packaging the two types of data in a JSON format, wherein the JSON file format of the time sequence type data comprises the necessary attributes such as equipment identification, equipment information, real-time data, time stamp and the like, and the JSON file of the relational data comprises the attributes such as equipment identification, equipment working state, task execution condition and the like. The time line classification is stored in a time sequence type database and a relational database for the intelligent management and control system of the unmanned production line of the fused and cast charge to call.
3. Twin model layer
The twin model layer is the reconstruction and digital mapping of the unmanned production line of the fused and cast charge in the physical equipment layer in the virtual space, and the real-time mapping of the physical production line and the virtual production line is realized by organically combining the virtual model and the production perception data. By establishing the geometric, physical, behavioral, rule and other characteristic models of the physical production line, the fusion-cast charge production process is presented in real time in a virtual digital space, so that the transparentization production is realized. In this embodiment, the twin model layer includes a virtual model module and a virtual-real mapping module, so as to realize real-time mapping of a physical production line to a virtual production line, that is, synchronous mapping of unmanned production processes of the fused and cast charge, so that a manager can intuitively and rapidly master the production condition of the unmanned production line of the fused and cast charge under man-machine isolation, discover abnormality in production in time, and take corresponding disposal measures.
3.1 virtual model Module
The virtual model module is used for constructing, storing and managing virtual information models of fusion casting equipment, AGV (automatic guided vehicle), loading and unloading robots, materials and the like which are related to the physical entity of the fusion casting charging unmanned production line, and the virtual information models comprise a geometric model, a physical model, a behavior model and a rule model. In order to complete the complete mapping from physical production line entities to virtual space, a geometric model of the physical entities needs to be established, wherein the geometric model comprises an appearance, a geometric dimension, an initial position, a positioning relation with other equipment or tools and the like; and building a physical model, a behavior model and a rule model according to the actual motion mode, the constraint and the experience summary.
In this embodiment, the virtual model is a digital reconstruction of the unmanned production line of the fused and cast charge, and is a virtual mapping of the geometric, physical, behavioral, and regular characteristics of the physical entities such as the fused and cast equipment, the AGV, the loading and unloading robot, and the materials. The unmanned production line for the fusion casting charging consists of a material fusion mixing system, a pouring system and a forming system, wherein for fusion casting equipment, the geometric model comprises equipment structure and shape and size information; the physical model comprises linkage relation information of physical materials, volumes, weights and motion mechanisms; the behavior model comprises an action gesture in the running process of the equipment; the rule model includes equipment calibration time, maintenance period, and equipment failure rate information. For materials, the physical model comprises material properties, appearance color, volume and weight information of the materials; the rule model comprises material numbers, material uses and warehouse in and out inspection requirement information.
Specifically, the virtual model construction steps of the unmanned fusion-cast-charge production line are as follows: firstly, carrying out parameterized modeling on physical entities on a fusion-cast charging unmanned production line by using SolidWorks; then, mapping the mapping texture of the model by using the 3D MAX, and improving the simulation degree of the virtual model; and finally, carrying out model structural design, light ray rendering design and the like of the unmanned fusion cast charging production line in Unity3D software, thereby constructing a virtual scene of the unmanned fusion cast charging production line. The process flow rules of the physical production line are converted into the logic of the simulation operation of the twin model, so that the equipment on the unmanned production line of the fused and cast charge is ensured to be connected according to the established logic rules, and the production process of the unmanned production line of the fused and cast charge is displayed with high fidelity.
3.2, virtual-real mapping module
The virtual-real mapping module utilizes real-time acquired multi-source heterogeneous real data to drive a virtual information model of an unmanned fusion casting powder charging production line to carry out virtual model, restores the production condition of the production line site, ensures the synchronization of each production element in the virtual scene and the state in the physical production line, such as the production data and the posture information of fusion casting equipment, the running state and the position information of an AGV trolley and the like, and realizes the transparent production of the unmanned fusion casting powder charging production process.
In the virtual scene of the unmanned fusion casting powder charging production line, the virtual model of the unmanned fusion casting powder charging production line is driven by utilizing multi-source heterogeneous real data acquired in real time, the production condition of the production line site is restored, and the synchronization of each production element in the virtual scene and the state of the physical production line, such as the production data and the gesture of fusion casting equipment, the running state and the position information of an AGV trolley and the like, is ensured.
Editing a model motion script in an intelligent control system, calling an Update function, and updating position, rotation and scale attribute parameters under a virtual model Transform component in real time; in the motion script, the script is bound to a corresponding casting equipment virtual model through a Gameobject.component function, so that the real-time position of the casting equipment is updated once per frame in the running process of the intelligent control system, and an object can translate, rotate and zoom. The production behaviors of the unmanned production line for the molten and cast drug filling are displayed in real time in the above mode, such as the behavior of stirring and preparing the liquid medicine in the melt mixing stage, the behavior of pouring the liquid medicine into a mold in the pouring stage and the behavior of carrying out heat preservation and nursing on the liquid medicine in the mold in the forming stage.
4. Application service layer
The application service layer is used for carrying out centralized integrated intelligent management on production elements, fusion casting technology, logistics transportation, production plans and other data flows and information flows of the fusion casting powder charging unmanned production line by means of industrial big data and an artificial intelligent algorithm, and making intelligent autonomous decision based on the whole-flow full-element information of the fusion casting powder charging production, so as to form a lean production management and control center and realize intelligent and fine management and control on the fusion casting powder charging unmanned production line. In this embodiment, the application service layer includes a visual monitoring module, a fusion casting process management module, a logistics transportation management module, a production scheduling management module, and a production line fault alarm management module, which are configured to analyze and pre-judge the overall operation state and the variation trend of the unmanned production line of the fusion casting powder charge, and perform collaborative intelligent management and control on multiple elements, so as to implement key technologies such as intelligent process, intelligent logistics, intelligent scheduling, and the like.
The system comprises a fusion casting process management module, a logistics transportation management module, a production scheduling management module and a production line fault alarm management module in an application service layer, wherein the fusion casting process management module, the logistics transportation management module, the production scheduling management module and the production line fault alarm management module are based on a data interaction bus platform, a fusion casting charging unmanned production line full-flow full-element intelligent management and control system taking a fusion casting charging production state intelligent management and control center as a core is constructed, and the system has the capability of integrating and sharing data with an ERP system, an MES system, a PDM system and other information systems of a fusion casting charging workshop, and is communicated with and uses equipment information, product information, process information, inventory information, logistics information and other data flows and information flows in a fusion casting charging unmanned production line microsystem.
As shown in fig. 2, a schematic diagram of a main interface of an intelligent control system of an unmanned production line for molten and cast charges developed in this embodiment is shown. The application service layer can monitor production resource information such as production plans, material storage, equipment states, production environments and the like of the unmanned production line of the molten cast charges in real time through the production resource visual bulletin board, and the centralized intelligent management and control of the molten cast process, production scheduling, logistics transportation and fault alarm of the unmanned production line of the molten cast charges is realized through the application service function module.
4.1 visual monitoring module
The visual monitoring module comprises a production plan monitoring board, an equipment state monitoring board, a material storage monitoring board and a production environment monitoring board and is used for monitoring production resource information of the unmanned production line of the fused cast charge in real time.
The production plan monitoring board can monitor production information such as the planned starting time, the planned ending time, the product quantity, the process content and the like of the batch of the fused cast charge products, and view the production resources and the plan information of the scheme in the whole production process. The equipment state monitoring board can carry out centralized monitoring on the operation states of the casting equipment in all operation areas of the unmanned production line for casting and charging, and comprises operation and maintenance information of different casting equipment, equipment start-stop states, equipment operation time length, current process parameters, equipment energy consumption values and the like, and when the equipment state is abnormal, automatic early warning is carried out to remind a manager to carry out preventive maintenance on the equipment. The material storage monitoring board can perform centralized monitoring on materials such as raw materials, functional auxiliaries and the like for preparing the fused and cast charges, comprises information such as material names, storage positions, storage quantity, belonging batches and the like, updates material in-out and in-storage and inventory information in real time, tracks the circulation condition of the raw materials between a warehouse and a working procedure, and can automatically early warn the stock quantity of the materials. The production environment monitoring board can monitor environmental parameters such as environmental temperature, humidity, dust and the like of different operation areas of the unmanned production line of the fused cast charge, and early warning is carried out aiming at abnormal environmental parameters, so that the production environment of the fused cast charge is ensured to be safe and controllable.
And transmitting the multi-source heterogeneous data acquired in real time by the unmanned fusion cast charging production line to an intelligent management and control system, performing visual processing on the data, and establishing a UI visual interface by utilizing a UGUI system. Wherein, the text component displays the production plan data such as the type, the component proportion, the dosage and the like of the raw materials of the casting charge; displaying equipment state parameters such as the starting state, the program running state and the like of the casting charging equipment through the slide bar assembly; and displaying the change trend of technological parameters such as stirring rotation speed, heating temperature and the like in the process of casting and charging through a graph component, and finally realizing the real-time update of the data through an On GUI function.
4.2, fusion casting process management module
The fusion casting process management module comprises a process knowledge base unit, a process case reasoning unit and a process intelligent decision unit and is used for intelligently managing the production process of the fusion casting charging unmanned production line.
The technological knowledge base unit comprises an explosive raw material parameter base, a casting equipment parameter base, a technological rule base, a product model base and a technological quality problem base, and can uniformly classify and encode traditional artificial experience knowledge, a technological specification and a technological management system formed by enterprises for a long time, a technological standard and a manual published by China and the like in the casting charge production process by using artificial intelligence technologies such as machine learning, natural language understanding and the like, store the technological knowledge into the casting technological knowledge base, update and maintain a large amount of technological expert experience, technological data, casting production examples and the like in real time. Wherein, the explosive raw material parameter library stores physical performance parameters of materials, such as TNT, density, particle size, melting point, sensitivity and other data of aluminum powder. The fusion casting equipment parameter library stores the working performance parameter information of equipment, including equipment type, technological parameters, operation and maintenance information and the like; the process rule base stores data such as rules, reasoning methods and the like for designing the process route of the fusion-cast charging. The product model stores a mold model including data such as inner diameter, outer diameter, wall thickness, etc. The method for constructing the process knowledge base is as follows: firstly, classifying the technology knowledge of the fusion and casting charging, and dividing the technology knowledge into objectivity knowledge and subjectivity knowledge according to the application of the technology knowledge in the production process, wherein the objectivity knowledge mainly refers to technology specifications, technology management systems and the like formed by enterprises for a long time, and the subjectivity knowledge refers to traditional artificial experience knowledge in the production process of the fusion and casting charging; then, carrying out conceptual design, wherein the main method is entity-contact method, and the influence factors of process decisions and the contact among different factors in the production process of the fused and cast charge are shown in the form of an E-R diagram; and finally, storing the process knowledge data in a relational database in a form of a table, and updating and maintaining the fusion casting process expert experience, the process data, the fusion casting production example and the like in real time.
The process case reasoning unit is used for matching the current new working condition with the typical casting process and the historical casting process in the process knowledge base aiming at the new working condition comprising different charge materials, grain composition and solid phase content, if the matching degree is higher, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition; if the typical process cannot be matched, the process rules and process reasoning are utilized to determine the design of the casting process flow and the selection of the process parameters under the new working condition. Specifically, in the process case reasoning unit of the embodiment, for the fact knowledge in the casting process, such as the casting resource knowledge, the casting defect knowledge, the process instance knowledge, and the like, a knowledge-based matching technology is adopted to generate the case knowledge of the casting process in a matching manner. Firstly, case structure design, namely, the case structure in a process knowledge base is expressed as: case (F, R), where F is the set describing the problem features and R is the set describing the solution features. Thus, the case library is expressed as:
Case＝{case i |case i (F i ,R i ),i＝1,2,...,N}
wherein Case is a Case library, case i Representing an ith case of the case library; f (F) i Representing a set of features describing the problem in the i-th case; r is R i Representing a set of solution features described in the ith case; n represents the number of cases contained in the case library. Through the case structure design, the target case can be conveniently compared with the case of the case library in the follow-up process. Secondly, case organization searching, namely comparing a target case with a case of a case library by using a nearest neighbor method, wherein the specific formula is as follows:
wherein Sim (x i ) Indicating the total similarity of the new working condition and the cases in the process knowledge base,ω k weights, x, representing attributes k ik Representing cases with attribute k, sim ak (x ik ) Representing case similarity under attribute k; m represents the number of attributes.
If Sim (x) i ) If the value of the formula (I) is greater than or equal to the set threshold value, the matching degree between the new working condition and the typical casting process and the historical casting process in the process knowledge base is higher, and at the moment, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition.
Aiming at the reasoning type knowledge in the casting process, such as casting process parameters, casting defect knowledge, process instance knowledge and the like, a welding process generation method based on fuzzy rule reasoning is adopted. Firstly, fuzzy pretreatment is carried out on the input new working condition casting process conditions by adopting a triangle membership function, wherein the process conditions comprise a casting explosive formula, grain composition, casting charging equipment and the like. And then carrying out fuzzy reasoning operation by adopting a Mamdani reasoning machine, wherein the rule reasoning form is as follows:
Rule1:Ifx 1 isA 1,1 andx 2 isA 2,1 and,···,andx n isA n,1 B 1 ThebyisB 1
Wherein x is i As regular front piece, A i,j As a condition variable x i Is a rule back part, B j Fuzzy sets for conclusion variable y; according to condition x i And fuzzy rule A i,j And (5) reasoning to obtain a casting process scheme under the new working condition.
The intelligent process decision unit performs simulation on the casting process flow design and process parameter selection of the new working condition obtained by the process case reasoning unit, verifies the feasibility of the new process scheme, and finally generates a casting charge preparation process scheme through intelligent decision. The technological decision is mainly to express the knowledge of the preparation technology of the fused and cast charge, and the fused and cast charge technological route is produced reasonably under the new working condition.
4.3 Logistics transportation management module
The logistics transportation management module comprises a path planning unit, a path conflict resolution unit, a material buffer area scheduling unit and a die loading and unloading area scheduling unit and is used for intelligently managing raw materials and die transportation of the unmanned fused cast charge production line.
4.3.1 Path planning Unit
The path planning unit is used for forming a logistics transportation plan of raw materials and moulds comprising aluminum powder, TNT and functional auxiliaries, optimizing a transportation path according to constraint conditions comprising material storage time, logistics transportation efficiency and liquid medicine cooling temperature, and driving the AGV transportation trolley to complete timely delivery and transportation of the raw materials and the moulds among a storehouse, a product production area, a finished product area and a line side warehouse. The factors such as strict precedence relation, material storage time, liquid medicine cooling temperature, transportation turning times and the like among the casting procedures have important influences on logistics scheduling, and the problems of large blindness, overlong searching path, too slow convergence speed and the like of the traditional ant colony algorithm in solving are solved.
In the path planning unit of the embodiment, an improved ant colony algorithm is adopted to solve and obtain an optimal transportation path under the influence of multiple factors on an unmanned production line of the fused and cast charge, and in order to avoid the algorithm from entering early maturity, a suboptimal solution obtained by an A algorithm is used as a priori knowledge; the optimized pheromone updating formula is as follows:
wherein τ (i) is the pheromone concentration of grid node i, c 1 A constant less than 0; d (P, i) is expressed as the Euclidean distance from node P to node i; p is a grid node through which an A-algorithm passes; i is a grid node around the node p; count (i) represents the sum of the grid numbers from the current node P to the surrounding feasible nodes i in the a-algorithm;
after the m ants complete the route searching, the pheromones are required to be updated, the traditional ant colony algorithm only considers the cost problem caused by the path length, but the embodiment considers the influence of the turning times of the AGV on the pheromones and the concentration increment of the pheromonesIs corrected to take the number of turns and the path length into account, the pheromone concentration increaseThe method comprises the following steps:
wherein c 2 Is a path length influencing factor; c 3 Is a turning factor; q represents a constant; l (L) k Expressed as the length of the path travelled by the kth ant; t (T) k Is the sum of the number of turns from the start point to the end point; c (C) i Representing a set of feasible nodes around the current node i. Therefore, the pheromone concentration updating mode from node i to node j in t+1 iterations is as follows:
wherein ρ is a pheromone volatilization factor; m is the total number of ants in the t-th iteration; τ ij (t) is the pheromone concentration from node i to node j in the t-th iteration;is the increase in pheromone concentration of ant k from node i to node j in the t-th iteration. And the AGV transportation trolley is driven by the path planning unit to complete timely delivery and transportation of the raw materials and the molds among the warehouse, the product producing area, the finished product area and the line side warehouse.
4.3.2 Path Conflict resolution Unit
The path conflict resolution unit is used for ensuring that the logistics transportation paths of the raw materials and the dies cannot collide, calculating the possibility of collision or potential collision when two or more conveying tasks run in opposite directions on the same path, and re-planning the transportation paths by using an optimization algorithm if the possibility of collision or potential collision exceeds a threshold value, so that the collision or potential collision is resolved, and safety accidents caused by collision are avoided. In this embodiment, when two or more conveying tasks are opposite to each other on the same path, if at least two conveying tasks have collision, the collision or potential collision is resolved in a manner that each conveying task sequentially passes through according to the order of priority from high to low, so as to avoid a safety accident caused by collision.
Specifically, the path conflict resolution unit is used for ensuring that the logistics transportation of each material and each die cannot generate conflict, and resolving potential conflicts existing on the same transport path of a plurality of AGVs. In order to determine the AGV priority moving sequence of the fusion-cast charge unmanned production line, a conflict resolution strategy algorithm based on dynamic priority is provided, and an AGV trolley R is assumed a Priority of (c) may be expressed as PRI a When a path collision occurs, R a Distance from the transport end is l a Another AGV trolley R b Distance from the transport end is l b When l a ＞l b When then PRI a Ratio PRI b Low, i.e. PRI a ＜PRI b Where a+.b. The AGVs of the unmanned production line for the molten cast charging are monitored in real time, the current carrying task information, the real-time position information of the AGVs, the fault information of the AGVs and the like are obtained by utilizing the RFID read-write and transmitting device, when potential conflict occurs among a plurality of AGVs, the AGVs are calculated by utilizing a conflict resolution strategy algorithm, for example, if l of each AGV is calculated 1 ＞l 2 ＞l 3 ＞l 4 The corresponding priority sequence is PRI 1 ＞PRI 2 ＞PRI 3 ＞PRI 4 . And enabling the AGVs with lower priorities to pause to advance outside the conflict area, waiting for the AGVs with higher priorities to pass through the conflict area and then starting to travel, so that the same path resources are not occupied at the same time. Thus, potential collision existing in the transportation of a plurality of AGVs is resolved, and safety accidents caused by collision are avoided.
4.3.3 Material buffer scheduling Unit
Because the production beats of the unmanned production line for the casting powder charge are different, if the operation time of the liquid medicine pouring process is short and the operation time of the liquid medicine solidifying process is long, the materials may need to be temporarily stored in a buffer area on a buffer path when being transported from the previous process to the next process. The material buffer area scheduling unit is connected with each material buffer area on the path between the previous process and the next process and is used for ensuring continuous supply of equipment production, reducing path congestion before parallel equipment, ensuring that the buffer areas have equal capacity, ensuring timely conveying and ensuring equipment load balance. When the casting equipment in a certain procedure causes production stoppage, too much materials are stored in the buffer areas, so that the capacity of each buffer area on the unmanned production line can be dynamically adjusted, and the phenomena of material accumulation and resource waste are avoided.
4.3.4 and die loading and unloading area scheduling unit
The die loading and unloading area scheduling unit is used for managing storage and transportation of the die in the loading and unloading areas in the whole process from unloading to clamping to solidification of the carrier, intelligently scheduling the assembly and disassembly tasks of the die according to the emergency degree of the tasks and the execution conditions of the tasks of the solidification stations, forming a die assembly and disassembly task work sequence, and completing clamping and disassembly of the die according to the work sequence by each station in each loading and unloading area.
4.4 production scheduling management Module
The production scheduling management module comprises a planning production scheduling unit, a production scheduling simulation unit and a production disturbance rescheduling unit and is used for intelligently managing the production scheduling of the unmanned production line for the fused and cast charging.
4.4.1 planned scheduling units
The planning production scheduling unit is used for managing the production tasks of the fusion-cast-charge products in different batches, calculating the optimal production scheduling schemes under different optimization targets by using a genetic algorithm according to the station state, the task delivery time, the equipment productivity, the process requirements and other influencing factors of the fusion-cast-charge unmanned production line, and the calculation result is accurate to specific fusion casting procedures, personnel, starting and ending time and used equipment. In the planning and scheduling unit of the embodiment, a production scheduling model is established to express the purpose and constraint of the molten and cast charge product scheduling, and the scheduling model is as follows:
f＝max(min{RT 1 -(T 1k +b 1k ),RT 2 -(T 2k +b 2k ),···,RT j -(T jk +b jk )})
j is the production task number; k isThe number of steps under the task; RT (reverse transcription) method j The required completion time for task j; t (T) jk B is the start time of the kth process in task j jk The execution time of the kth process in task j. The scheduling model is used for enabling tasks on a fusion and cast charge unmanned production line to be completed as early as possible, solving the difference value between the predicted completion time of the fusion and cast charge tasks under different working conditions and the required completion time of the tasks, obtaining the minimum value, and enabling the minimum value to be as large as possible under the condition that constraint conditions are met.
The method adopts a population production strategy, uses procedures and machine variables to realize fixed-point intersection in the intersection and variation, so as to avoid the generation of non-feasible solutions, save the correction time of the non-feasible solutions, and use a local search strategy to avoid sinking into a local optimal solution, and finally obtains an optimal production scheduling scheme of the fusion casting charge production task under the multi-constraint condition through the scheduling model solution.
4.4.2 production-scheduling simulation unit
The production scheduling simulation unit is used for performing simulation on different production tasks, checking production running conditions and Gantt charts, verifying rationality and operability of a planned production scheduling scheme, and displaying an optimal production scheduling result to a user. In the embodiment, various resources of the unmanned fused and cast charge production line are analyzed by adopting unified modeling language UML, and production orders, process information and corresponding model simulation parameters are loaded according to a production scheduling scheme. After the model simulation operation, simulation indexes are analyzed through Gantt charts, equipment efficiency, production batch change trend charts and the like, and if the optimization indexes are not met, the process is repeated after the production scheduling scheme is adjusted.
4.4.3 production disturbance rescheduling Unit
When a dynamic disturbance event occurs in the production process of the casting powder charge, deviation occurs in the execution of the production plan caused by the constraint change of the casting technology, the constraint change of the production resource, the execution time error of the working procedure and the like, and the production disturbance rescheduling unit can make a rescheduling response mechanism for the dynamic event and obtain a new scheduling scheme. Judging whether the scheduling scheme meets the requirements through simulation, and executing a new production scheduling scheme if the scheme meets the requirements, so as to realize intelligent adjustment and decision of production scheduling. The disturbance events for changing the process constraint comprise the advance of task requirement completion time, emergency bill insertion, production task cancellation and the like, the disturbance events for changing the production resource elements comprise the faults of casting equipment, material loss, exceeding of environmental parameters and the like, and the disturbance events for process execution time errors comprise the material distribution delay, daily operation plan execution time errors, equipment problem troubleshooting and the like. The production disturbance rescheduling unit adopts the production time deviation tolerance to evaluate the deviation degree of the production plan under the influence of disturbance factors, so as to realize quantitative control of the production time fluctuation of each working procedure of the unmanned production line for the fused cast charge; the tolerance delta of the production time deviation is:
Wherein, the liquid crystal display device comprises a liquid crystal display device,and max (T) i ) Respectively representing the predicted production completion time of the planned production scheduling unit and the completion time in the actual production scheduling process;
tolerance delta for a given maximum production time deviation max When delta exceeds the maximum production time deviation tolerance delta due to the occurrence of production dynamic disturbance event max When the production disturbance processing mechanism is used, rescheduling the production plan is performed by using the schedule based on the production disturbance processing mechanism. Judging whether the scheduling scheme meets the requirements through simulation, and executing a new production scheduling scheme if the scheme meets the requirements, so as to realize intelligent adjustment and decision of production scheduling.
4.5, production line fault alarm management module
The production line fault alarm management module comprises a safety evaluation early warning unit and an equipment fault evaluation early warning unit and is used for intelligently managing the production safety and the equipment state of the unmanned production line for the molten and cast charging.
The safety evaluation early warning unit can evaluate safety risks of a large number of dangerous sources existing in the unmanned production line of the fused cast charge, including human factors, environmental factors, equipment factors and the like, forecast possible dangerous situations and relevant probabilities, and remind management personnel to take corresponding safety control measures in a grading early warning mode so as to avoid safety accidents. Wherein, human factors mean personnel mistakenly enter an unmanned production area or perform error management operation and the like; the environmental factors refer to factors such as working temperature, humidity and dust of a production workshop; the device factor refers to whether the operation state of the device is normal, whether the device can fail, and the like.
The equipment fault early warning unit performs centralized monitoring on the casting equipment in all operation areas of the unmanned casting and charging production line, performs deep mining on data such as vibration, rotation speed, temperature, power generation time length and the like of key parts, prejudges the running state of the equipment by using a neural network algorithm, realizes intelligent early warning on equipment faults, and reminds management personnel to perform preventive maintenance on the equipment. In the embodiment, the intelligent early warning of the equipment fault is realized by constructing a CNN-LSTM hybrid neural network to pre-judge the running state of the equipment. The CNN model comprises a convolution layer, a pooling layer and a full connection layer, the LSTM model comprises an input layer, a double hidden layer and an output layer, and the extracted multi-dimensional time sequence key characteristic variables such as vibration, rotation speed, temperature, power generation time length and the like are used as the input of the CNN-LSTM network, and the output is the equipment fault type. The LSTM model is input into the characteristic parameters extracted by the CNN, and is output into the running state parameters of the equipment at the current moment, such as the temperature of a bearing of the melt mixing pot, and the characteristic parameters of the running data at the previous moment and the current moment can be extracted through the CNN. And judging whether the target equipment can normally operate in the next stage by combining the state monitoring result and the fault early warning result, thereby reminding a manager to make a reasonable predictive maintenance plan.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. Intelligent management and control system of unmanned production line of fusion cast powder charge, its characterized in that: the system comprises a physical equipment layer, a data perception layer, a twin model layer and an application service layer;
the physical equipment layer comprises intelligent casting charging equipment, an intelligent logistics transport vehicle and intelligent sensing equipment which are used for forming a casting charging unmanned production line, and the casting charging unmanned production line can automatically complete various working procedures of casting charging production according to a set program, so that man-machine isolation and automatic production are realized;
the data sensing layer comprises a data acquisition module, a data preprocessing module and a data storage module, wherein the data acquisition module is used for acquiring multi-source heterogeneous data of an unmanned production line of the casting powder charge in real time, and the multi-source heterogeneous data comprises production resource information which is acquired by a casting equipment control system, a sensor sensing and enterprise information management system and comprises casting equipment operation data, material storage data and production environment index data; the data preprocessing module is used for preprocessing the multi-source heterogeneous data acquired by the data acquisition module in sequence, wherein the data comprises cleaning and dimension reduction data, so that empty values, missing values or repeatedly recorded data can be removed, and the data which can be directly used for analysis and calculation can be obtained; the data storage module is used for classifying and sorting the multi-source heterogeneous data preprocessed by the data preprocessing module into time sequence data and relation data, and storing the time sequence data and the relation data in a time sequence database and a relation database respectively;
The twin model layer comprises a virtual model module and a virtual-real mapping module, wherein the virtual model module comprises a geometric model, a physical model, a behavior model and a rule model and is used for constructing, storing and managing a virtual information model of a physical entity of the fusion-cast-charge unmanned production line; the virtual-real mapping module utilizes real-time acquired multi-source heterogeneous real data to drive a virtual information model of an unmanned fusion-cast charging production line, restores the production condition of the production line site and ensures that each production element in the virtual information model is synchronous with the state in a physical production line;
the application service layer comprises a visual monitoring module, a casting process management module, a logistics transportation management module, a production scheduling management module and a production line fault alarm management module, and is used for analyzing and pre-judging the overall operation state and the variation trend of the unmanned production line of the casting powder charge and carrying out collaborative intelligent management and control on multiple elements.
2. The intelligent control system of an unmanned melt-cast-charge production line of claim 1, wherein: the visual monitoring module comprises a production plan monitoring board, an equipment state monitoring board, a material storage monitoring board and a production environment monitoring board;
The production plan monitoring board is used for monitoring production information including the planned starting time, the planned ending time, the product quantity and the procedure content of the batch of the fused cast charge products so as to check the production resources of the whole production process and the plan information of the scheme;
the equipment state monitoring board is used for carrying out centralized monitoring on the operation states of the casting equipment, including different operation and maintenance information of the casting equipment, the starting and stopping states of the equipment, the operation time length of the equipment, the current technological parameters and the energy consumption value of the equipment, and carrying out automatic early warning when the equipment state is abnormal, so as to remind a manager of carrying out preventive maintenance on the equipment;
the material storage monitoring board is used for carrying out centralized monitoring on material information including material names, storage positions, storage quantity and batches of the prepared casting charges so as to update material in-out storage and stock information in real time, track the circulation condition of raw materials between a warehouse and a working procedure and automatically early warn the stock quantity of the materials;
the production environment monitoring board is used for monitoring environmental parameters including environmental temperature, humidity and dust in different operation areas of the unmanned production line of the fused and cast charging, and carrying out early warning on abnormal environmental parameters so as to ensure that the production environment of the fused and cast charging is safe and controllable.
3. The intelligent control system of an unmanned melt-cast-charge production line of claim 1, wherein: the casting process management module comprises a process knowledge base unit, a process case reasoning unit and a process intelligent decision unit;
the process knowledge base unit comprises an explosive raw material parameter base, a casting equipment parameter base, a process rule base, a product model base and a process quality problem base, and is used for uniformly classifying and encoding the process knowledge including traditional manual experience knowledge, a process specification and a process management system formed by enterprises for a long time, a process standard published by national publications and a process knowledge of a manual in the production process of casting charges so as to update and maintain the experience of process experts, process data and casting production examples in real time;
the process case reasoning unit is used for matching the current new working condition with the typical casting process and the historical casting process in the process knowledge base aiming at the new working condition comprising different charging materials, grain composition and solid phase content, if the matching degree is higher, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition; if the typical process cannot be matched, determining the design of a casting process flow and the selection of process parameters under a new working condition by utilizing process rules and process reasoning;
The intelligent process decision unit performs simulation on the casting process flow design and process parameter selection of the new working condition obtained by the process case reasoning unit, verifies the feasibility of the new process scheme, and finally generates a casting charge preparation process scheme through intelligent decision.
4. An intelligent control system for an unmanned melt-cast-charge production line according to claim 3, wherein: in the process case reasoning unit, the method for designing the casting process flow and selecting the process parameters under the new working condition comprises the following steps:
firstly, matching the current new working condition with a typical casting process and a historical casting process in a process knowledge base, and expressing a case base in the process knowledge base as:
Case＝{case i |case i (F i ,R i ),i＝1,2,...,N}
wherein Case is a Case library, case i Representing an ith case of the case library; f (F) i Representing a set of features describing the problem in the i-th case; r is R i Representing a set of solution features described in the ith case; n represents the number of cases contained in the case library;
secondly, case organization searching, namely comparing a target case with a case of a case library by using a nearest neighbor method:
wherein Sim (x i ) Representing the total similarity of the new working condition and the cases in the process knowledge base, omega k Weights, x, representing attributes k ik Representing cases with attribute k, sim ak (x ik ) Representing case similarity under attribute k; m represents the number of attributes;
if Sim (x) i ) If the value of the formula (I) is greater than or equal to the set threshold value, the matching degree between the new working condition and the typical casting process and the historical casting process in the process knowledge base is higher, and at the moment, the similar process in the process knowledge base can be used for modifying and realizing the casting process flow design and process parameter selection under the new working condition;
if Sim (x) i ) If the value of the formula (I) is smaller than or equal to the set threshold value, the matching degree between the new working condition and the typical casting process and the historical casting process in the process knowledge base is lower, and a casting process scheme under the new working condition is generated by reasoning through a fuzzy rule reasoning algorithm, wherein the method comprises the following steps:
Rule1:Ifx 1 isA 1,1 andx 2 isA 2,1 and,···,andx n isA n,1 B 1 ThenyisB 1
wherein x is i As regular front piece, A i,j As a condition variable x i Is a rule back part, B j Fuzzy sets for conclusion variable y; according to condition x i And fuzzy rule A i,j And (5) reasoning to obtain a casting process scheme under the new working condition.
5. The intelligent control system of an unmanned melt-cast-charge production line of claim 1, wherein: the logistics transportation management module comprises a path planning unit, a path conflict resolution unit, a material buffer area scheduling unit and a die loading and unloading area scheduling unit;
The path planning unit is used for forming a logistics transportation plan of raw materials and moulds comprising aluminum powder, TNT and functional additives, optimizing a transportation path according to constraint conditions comprising material storage time, logistics transportation efficiency and liquid medicine cooling temperature, and driving the AGV transportation trolley to complete timely delivery and transportation of the raw materials and the moulds among a warehouse, a product area, a finished product area and a line side warehouse;
the path conflict resolution unit is used for ensuring that the logistics transportation paths of the raw materials and the dies cannot collide, when two or more conveying tasks are opposite to each other on the same path, if at least two conveying tasks collide, the collision or potential collision is resolved in a mode that the conveying tasks sequentially pass through according to the order of the priority from high to low, so that the safety accidents caused by collision are avoided;
the material buffer area scheduling unit is used for connecting each material buffer area positioned on the path between the previous working procedure and the next working procedure, ensuring continuous production of the casting charging equipment and reducing the path congestion before the parallel equipment; when the casting charging equipment in a certain procedure fails and stops production or the materials stored in the buffer areas are excessive, the material buffer area scheduling unit can dynamically adjust the capacity of each buffer area on the unmanned production line, so that the phenomena of material accumulation and resource waste are avoided;
The die loading and unloading area scheduling unit is used for managing storage and transportation of the die in the loading and unloading areas in the whole process from unloading to clamping to solidification of the carrier, intelligently scheduling the assembly and disassembly tasks of the die according to the emergency degree of the tasks and the execution conditions of all tasks of the solidification stations, forming a die assembly and disassembly task work sequence, and completing clamping and disassembly of the die according to the work sequence by all the stations in each loading and unloading area.
6. The intelligent control system of an unmanned melt-cast-charge production line of claim 5, wherein: in the path planning unit, an ant colony algorithm is adopted to solve and obtain an optimal transportation path under the influence of multiple factors, and in order to avoid the algorithm from entering precocity, a suboptimal solution obtained by an algorithm A is used as a priori knowledge; the optimized pheromone updating formula is as follows:
wherein τ (i) is the pheromone concentration of grid node i, c 1 A constant less than 0; d (P, i) is expressed as the Euclidean distance from node P to node i; p is a grid node through which an A-algorithm passes; i is a grid node around the node p; count (i) represents the sum of the grid numbers from the current node P to the surrounding feasible nodes i in the a-algorithm;
after the routing of m ants is completed, the pheromones are required to be updated; the number of turns of AGV is considered, and the concentration of pheromone is increased Is corrected so that the number of turns and the path length are balanced, the pheromone concentration increase amount +.>The method comprises the following steps:
wherein c 2 Is a path length influencing factor; c 3 Is a turning factor; q represents a constant; l (L) k Expressed as the length of the path travelled by the kth ant; t (T) k Is the sum of the number of turns from the start point to the end point; c (C) i Representing a set of feasible nodes around the current node i;
the pheromone concentration updating mode from node i to node j in t+1 iterations is as follows:
wherein ρ is a pheromone volatilization factor; m is the total number of ants in the t-th iteration; τ ij (t) is the pheromone concentration from node i to node j in the t-th iteration;is the increase in pheromone concentration of ant k from node i to node j in the t-th iteration.
7. The intelligent control system of an unmanned melt-cast-charge production line of claim 1, wherein: the production scheduling management module comprises a planning production scheduling unit, a production scheduling simulation unit and a production disturbance rescheduling unit;
the planning and scheduling unit is used for managing the production tasks of the fusion-cast-charge products in different batches, and calculating the optimal scheduling schemes under different optimization targets by using a genetic algorithm according to the influencing factors including the station state, the task delivery time, the equipment productivity and the process requirements of the fusion-cast-charge unmanned production line;
The production scheduling simulation unit is used for performing simulation on different production tasks so as to check production running conditions and Gantt charts, verify rationality and operability of a planned production scheduling scheme and display an optimal production scheduling result to a user;
when a dynamic disturbance event which causes deviation of production plan execution and comprises fusion casting process constraint change, production resource constraint change and procedure execution time error occurs in the fusion casting charging production process, the production disturbance rescheduling unit makes a rescheduling response mechanism for the dynamic event and obtains a new scheduling scheme; and simulating and judging whether the scheduling scheme meets the requirement through the scheduling simulation unit, and executing a new production scheduling scheme if the scheme meets the requirement, so as to realize intelligent adjustment and decision of production scheduling.
8. The intelligent control system of an unmanned melt-cast-charge production line of claim 7, wherein: in the planning and production scheduling unit, a production scheduling model is established to express the purpose and constraint of molten and cast charge product scheduling, and the scheduling model is as follows:
f＝max(min{RT 1 -(T 1k +b 1k ),RT 2 -(T 2k +b 2k ),···,RT j -(T jk +b jk )})
j is the production task number; k is the number of working procedures under the task; RT (reverse transcription) method j The required completion time for task j; t (T) jk B is the start time of the kth process in task j jk The execution time of the kth procedure in the task j;
the method adopts a population production strategy, uses procedures and machine variables to realize fixed-point intersection in the intersection and variation, so as to avoid the generation of non-feasible solutions, save the correction time of the non-feasible solutions, and use a local search strategy to avoid sinking into a local optimal solution, and finally obtains an optimal production scheduling scheme of the fusion casting charge production task under the multi-constraint condition through the scheduling model solution.
9. The intelligent control system of an unmanned melt-cast-charge production line of claim 7, wherein: the production disturbance rescheduling unit adopts the production time deviation tolerance to evaluate the deviation degree of the production plan under the influence of disturbance factors, so as to realize quantitative control of the production time fluctuation of each working procedure of the unmanned fused cast charge production line; the tolerance delta of the production time deviation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,and max (T) i ) Respectively representing the predicted production completion time of the planned production scheduling unit and the completion time in the actual production scheduling process;
tolerance delta for a given maximum production time deviation max When delta exceeds the maximum production time deviation tolerance due to the occurrence of a production dynamic disturbance eventδ max When the production disturbance processing mechanism is used, rescheduling the production plan is performed by using the schedule based on the production disturbance processing mechanism.
10. The intelligent control system of an unmanned melt-cast-charge production line of claim 1, wherein: the production line fault alarm management module comprises a safety evaluation early warning unit and an equipment fault evaluation early warning unit;
the safety evaluation early warning unit is used for carrying out safety risk evaluation on dangerous sources comprising human factors, environmental factors and equipment factors existing in the unmanned production line of the fused cast charge, predicting possible dangerous situations and related probabilities, and reminding a manager to take corresponding safety control measures in a grading early warning mode so as to avoid safety accidents;
the equipment fault early warning unit is used for carrying out centralized monitoring on the casting equipment in all operation areas of the unmanned casting powder charging production line, carrying out deep mining on data including vibration, rotation speed, temperature and power generation time length of key parts, and utilizing a neural network model to pre-judge the running state of the equipment, so as to realize intelligent early warning of equipment fault and remind a manager of carrying out preventive maintenance on the equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310236001.5A CN116540584A (en) | 2023-03-13 | 2023-03-13 | Intelligent management and control system of unmanned production line of fusion-cast charging |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310236001.5A CN116540584A (en) | 2023-03-13 | 2023-03-13 | Intelligent management and control system of unmanned production line of fusion-cast charging |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116540584A true CN116540584A (en) | 2023-08-04 |
Family
ID=87449483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310236001.5A Pending CN116540584A (en) | 2023-03-13 | 2023-03-13 | Intelligent management and control system of unmanned production line of fusion-cast charging |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116540584A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117270479A (en) * | 2023-11-21 | 2023-12-22 | 清远欧派集成家居有限公司 | Method and system for monitoring multi-working-procedure production line of molding plate |
| CN117350492A (en) * | 2023-10-13 | 2024-01-05 | 宿迁云瑞国科信息技术有限公司 | MES operation management system capable of intelligently controlling comparison historical data |
| CN117523503A (en) * | 2024-01-08 | 2024-02-06 | 威科电子模块（深圳）有限公司 | Preparation equipment safety monitoring method and system based on thick film circuit board |
-
2023
- 2023-03-13 CN CN202310236001.5A patent/CN116540584A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350492A (en) * | 2023-10-13 | 2024-01-05 | 宿迁云瑞国科信息技术有限公司 | MES operation management system capable of intelligently controlling comparison historical data |
| CN117270479A (en) * | 2023-11-21 | 2023-12-22 | 清远欧派集成家居有限公司 | Method and system for monitoring multi-working-procedure production line of molding plate |
| CN117270479B (en) * | 2023-11-21 | 2024-02-06 | 清远欧派集成家居有限公司 | Method and system for monitoring multi-working-procedure production line of molding plate |
| CN117523503A (en) * | 2024-01-08 | 2024-02-06 | 威科电子模块（深圳）有限公司 | Preparation equipment safety monitoring method and system based on thick film circuit board |
| CN117523503B (en) * | 2024-01-08 | 2024-05-03 | 威科电子模块（深圳）有限公司 | Preparation equipment safety monitoring method and system based on thick film circuit board |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108009739B (en) | Intelligent machining workshop production planning and scheduling integrated method | |
| Zhuang et al. | The connotation of digital twin, and the construction and application method of shop-floor digital twin | |
| Wang et al. | Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits | |
| Lim et al. | A digital twin-enhanced system for engineering product family design and optimization | |
| Wang et al. | A proactive material handling method for CPS enabled shop-floor | |
| CN116540584A (en) | Intelligent management and control system of unmanned production line of fusion-cast charging | |
| Lee | Industrial ai | |
| Han et al. | An overall review of research on prefabricated construction supply chain management | |
| CN112085261B (en) | Enterprise production status diagnosis method based on cloud fusion and digital twin technology | |
| CN106527373B (en) | Workshop Autonomous Scheduling system and method based on multiple agent | |
| CN111915410B (en) | Intelligent management and control system for high-dynamic production logistics process | |
| Fengque et al. | Research on design of the smart factory for forging enterprise in the industry 4.0 environment | |
| CN112800668A (en) | Digital twin-based complex product operation, maintenance and manufacturing integrated overall model | |
| CN106292580A (en) | A kind of intelligent integrated formula semiconductor manufacturing performs system | |
| Jia et al. | From simple digital twin to complex digital twin part II: multi-scenario applications of digital twin shop floor | |
| CN115097788A (en) | Intelligent management and control platform based on digital twin factory | |
| CN116485576A (en) | Intelligent manufacturing management platform for brain data with known source in aviation manufacturing industry | |
| CN111144632A (en) | Prediction management and control model for power storage materials | |
| Lin et al. | Integrated cyber physical simulation modelling environment for manufacturing 4.0 | |
| CN116401951A (en) | Integrated multi-AGV path planning and multi-row equipment layout optimization method | |
| CN111915167A (en) | Intelligent manufacturing virtual shipyard information model construction method | |
| CN115544775A (en) | Digital twin workshop multi-dimensional multi-level model construction and dynamic configuration method | |
| CN116703303A (en) | Warehouse visual supervision system and method based on multi-layer perceptron and RBF | |
| CN115946132A (en) | Intelligent dense warehouse and logistics robot system and fault monitoring method thereof | |
| CN111650912A (en) | Intelligent manufacturing production management platform for intelligent factory/workshop |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |