CN111008812A - Intelligent refinement system and method for ternary precursor - Google Patents
Intelligent refinement system and method for ternary precursor Download PDFInfo
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- 239000003513 alkali Substances 0.000 claims description 3
- 239000008139 complexing agent Substances 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
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- PXHVJJICTQNCMI-UHFFFAOYSA-N nickel Substances [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 11
- 229910052759 nickel Inorganic materials 0.000 description 10
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Abstract
The invention provides a ternary precursor intelligent lean system, which comprises an ERP management module, an MES management module, a DCS control module, an OA module and a database, wherein the ERP management module is connected with the MES management module, the OA module and the database, the MES management module is connected with the ERP management module, the DCS control module, the OA module and the database, the DCS control module is connected with the MES management module and the OA module, wherein the ERP management module makes a purchasing plan according to the customer demand information, sends the purchasing plan to the manager terminal through the OA module for purchasing, and transfer the production data, equipment operation data and production plan data from the database to the MES management module, the MES management module sends the calculated production schedule data and production data to the DCS control module, the DCS control module controls the equipment operation, and the MES management module feeds back related data to the MES management module, and the MES management module feeds back actual production data of the equipment to the ERP management module. The invention also provides an intelligent lean method of the ternary precursor by applying the system.
Description
Technical Field
The invention relates to the technical field of process production automation, in particular to a ternary precursor intelligent lean system and a ternary precursor intelligent lean method.
Background
At present, the mainstream high-nickel ternary precursor production line mainly adopts manual operation to operate equipment, then processes such as high-nickel ternary precursor reaction, aging, filtering and drying are carried out, most of materials in the production process are also manually sampled and manually detected, and then the materials are analyzed by a process or a product maintainer to judge whether a precursor semi-finished product enters the next working section or whether a precursor finished product is sold and which customers the precursor finished product is sold to.
The above process has the following disadvantages: the production process of the precursor is not automatic enough, so that the labor intensity of operation, detection and process personnel is overlarge, the state in the production process of the product cannot be timely and effectively confirmed by manual sampling and detection, unqualified products are easy to enter the next working section for operation, so that unqualified products and qualified products are mixed, the unqualified products are further increased, or the unqualified products enter the next working section for unapproved production operation, so that the waste of production cost is caused, the control of the product process is not facilitated, and lean production cannot be realized; the whole product production flow is not intelligent enough, raw material purchasing, production plan making, raw material purchasing, material production, data detection, data summarization and product warehousing and selling are all carried out manually, a large amount of manual work participates in the processes, on one hand, the production cost is increased, on the other hand, flow delay and error probability rising are easily caused, the problem of production line cannot be rapidly and efficiently solved, and lean management cannot be realized.
Disclosure of Invention
In order to overcome the defect that the production process of the ternary precursor in the prior art cannot timely control the production state of the product, the invention provides an intelligent lean system and an intelligent lean method for the ternary precursor, and the automatic operation and real-time control of the production process of the ternary precursor are realized by combining an ERP management module, an MES management module, a DCS control module and an OA module.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the utility model provides a ternary precursor intelligence lean system, includes ERP management module, MES management module, DCS control module, OA module, database, and the ERP management module is connected with MES management module, OA module, database, and the MES management module is connected with ERP management module, DCS control module, OA module, and the DCS control module is connected with MES management module, OA module, database, wherein:
the ERP management module is used for making a purchasing plan according to the input customer demand information, sending the purchasing plan to the manager terminal through the OA module for purchasing, and calling production data, equipment operation data and production plan data from the database and transmitting the production data, the equipment operation data and the production plan data to the MES management module;
the MES management module is used for calculating production scheduling data according to the received data, sending the production data and the production scheduling data to the DCS control module and feeding back actual production data of equipment to the ERP management module;
and the DCS control module is used for controlling the operation of the ternary precursor production workshop equipment according to the received production data and production scheduling data, monitoring whether process detection data are normal in the production process, and feeding back equipment operation and energy consumption data, process detection data and finished product detection data to the MES management module.
In the technical scheme, the automation and the intellectualization of the processes of raw material purchase, production plan process management, production process monitoring and feedback, workshop equipment control, capacity calculation, energy consumption accounting, finished product sale plan and the like of high-nickel ternary precursor production are realized through the synergistic effect of the ERP management module, the MES management module, the DCS control module, the OA module and the database.
Preferably, the customer demand information includes, but is not limited to, ternary precursor product model number, product quantity, delivery time;
production data includes, but is not limited to, finished product indicators, finished product quantity, finished process plant equipment parameters, PFMEA, and control plans;
equipment operating data includes, but is not limited to, MFMEA, plant equipment operating data, plant equipment ultimate capacity, and standard capacity;
production plan data includes, but is not limited to, daily energy plans, production schedules for the product;
production scheduling data includes, but is not limited to, daily product batch and quantity of production line equipment in each workshop section;
the actual production data of the equipment includes, but is not limited to, actual capacity of the plant equipment, unit energy consumption data, batch product index, batch yield data and finished product qualification rate.
Preferably, the ERP management module comprises a sales management unit, a manufacturing management unit and a purchasing management unit, wherein the sales management unit is respectively connected with the database, the MES management module and the OA module; the manufacturing management unit is respectively connected with the database, the purchasing management unit, the MES management module and the OA module; the purchase management unit is respectively connected with the production management unit and the OA module. In the using process, the sales management unit calls product finished product indexes and quantity data from the database, performs matching calculation according to input customer demand information, determines the delivery quantity, the target manufacturer and the delivery time of the product, and then sends the product quantity, the target manufacturer and the delivery time to the manager terminal through the OA module; the manufacturing management unit calls production data and equipment operation data of related product models from a database according to the input customer demand information, calculates production plan data and then sends the production plan data to the purchasing management unit to manufacture a purchasing plan; and the purchase management unit calculates the quantity and price of the raw materials required by production according to the production plan data to be used as a purchase plan, and then sends the purchase plan to the administrator terminal through the OA module.
Preferably, the MES management module comprises an equipment data management unit, a production schedule management unit and a finished product quality management unit, wherein the equipment data management unit is respectively connected with the database, the production schedule management unit, the DCS control module and the OA module; the production scheduling management unit is respectively connected with the DCS control module and the OA module; the finished product quality management unit is respectively connected with the sales management unit in the database, the DCS control module, the OA module and the ERP management module. In the using process, the equipment data management unit calculates the real-time capacity and unit energy consumption data of the equipment according to the equipment operation data received from the DCS control module and then sends the data to the production scheduling management unit; the production scheduling management unit calculates production scheduling data according to the received production data, equipment operation data and production plan data, and then transmits the production scheduling data to the DCS control module; and the finished product quality management unit judges whether the finished product is qualified or not according to the equipment operation and energy consumption data and finished product detection data fed back by the DCS control module, and if not, a finished product warning signal is sent to the manager terminal through the OA module.
The invention also provides an intelligent refining method of the ternary precursor, and the intelligent refining system of the ternary precursor is applied, and comprises the following steps:
s1: the management personnel inputs the customer demand information into the ERP management module, the ERP management module calls production data and equipment operation data from the database, the production plan data are obtained through calculation, then a purchase plan is formulated according to the customer demand information and then sent to the management personnel terminal through the OA module for purchase, and meanwhile the called production data, equipment operation data and production plan data are transmitted to the MES management module;
s2: a production schedule management unit in the MES management module calculates production schedule data of production line equipment of each workshop section according to the received production data, equipment operation data and production plan data, and then sends the production data and the production schedule data to the DCS control module;
s3: the DCS control module controls workshop equipment to produce according to the received production data and production schedule data, judges whether process detection data meet a preset qualified threshold value or not in the production process, and sends process warning information to a manager terminal through the OA module and controls the equipment to transfer materials to a re-dissolution area if the process detection data do not meet the preset qualified threshold value; if yes, feeding back equipment operation and energy consumption data, process detection data and finished product detection data information to the MES management module;
s4: the MES management module calculates actual production data of the equipment according to the received equipment operation and energy consumption data, process detection data and finished product detection data information, then judges whether the actual production data of the equipment meets a preset equipment qualified threshold and a preset finished product qualified threshold, if so, the actual production data of the equipment is sent to the ERP management module, and the ERP management module determines the delivery quantity, a target manufacturer and delivery time of the product according to the actual production data of the equipment and customer demand information and then sends the delivery quantity, the target manufacturer and the delivery time to a manager terminal through the OA module; if not, the MES management module sends equipment warning information and finished product warning information to the manager terminal through the OA module.
Preferably, in the step S1, the specific steps include:
s1.1: the method comprises the following steps that a manager inputs customer demand information into an ERP management module, a manufacturing management unit in the ERP management module calls production data of corresponding product models from a database according to the product models, the product quantity and the delivery time in the customer demand information, a daily production energy plan and a production schedule of ordered products are calculated to serve as production plan data, and then whether the production plan data meet a preset production allowance threshold value or not is judged: if not, sending production alarm information to a manager through the OA module; if yes, the production data and the production plan data are sent to a production scheduling management unit of an MES module for production scheduling processing;
s1.2: a purchase management unit in the ERP management module calculates the types, the quantities and the prices of raw materials required by production according to the customer demand information, and then sends the raw materials to a manager terminal for purchase through an OA module by combining characters and diagrams as a purchase plan; and when the purchasing plan cannot be carried out or is wrong, the purchasing management unit sends purchasing warning information to the management personnel terminal through the OA module.
Preferably, in step S3, the DCS control module controls the plant equipment to perform production according to the received production data and production schedule data as follows: the DCS control module sets parameters of a reaction workshop section, a filter-pressing workshop section and a drying workshop section according to production data and production schedule data, wherein the parameters of the reaction workshop section comprise flow rates of salt liquid, alkali liquor and a complexing agent of a reaction kettle, a reaction pH value, a reaction rotating speed, a reaction temperature and granularity and a radial distance of reaction slurry, the parameters of the filter-pressing workshop section comprise washing liquid conductivity, filter-pressing time and filter cake water content of a filter press, and the parameters of the drying workshop section comprise drying temperature and powder water content; in the production process, when the DCS control module judges that the process detection data do not meet the preset threshold value, process warning information is sent to a manager through the OA module, and the material is transferred to the re-dissolving area.
Preferably, in the step S4, the specific steps include:
s4.1: an equipment data management unit in the MES management module calculates real-time capacity of equipment, unit energy consumption data, batch product data and product qualification rate as actual production data of the equipment according to equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module;
s4.2: the equipment data management unit judges whether the actual production data of the equipment meet a preset equipment qualified threshold value, and if so, the S4.3 step is executed; if not, sending equipment warning information to a manager terminal through the OA module, carrying out maintenance processing on the equipment by the manager, and updating equipment operation data in the database;
s4.3: a finished product quality management unit in the MES management module judges whether a finished product meets a preset finished product qualified threshold value according to equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module, if so, actual production data of the equipment is sent to a sales management unit in the ERP management module, and the sales management unit determines the delivery quantity, a target manufacturer and delivery time of the product according to the actual production data of the equipment and customer demand information and then sends the delivery quantity, the target manufacturer and the delivery time to a manager terminal through the OA module; if not, the finished product quality management unit sends finished product warning information to a manager through the OA module, and the manager checks the production process and updates the production data in the database.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: under the synergistic effect of the ERP management module, the MES management module, the DCS control module, the OA module and the database, the automatic management, control and detection of the production of the ternary precursor are realized, the management cost is effectively reduced, the forward feedback of production data of a production line can be realized, and the pollution to qualified materials and the unapproved processing caused by the fact that unqualified products enter the next process are avoided.
Drawings
Fig. 1 is a schematic structural diagram of a ternary precursor intelligent lean system of embodiment 1.
Fig. 2 is a flowchart of a ternary precursor intelligent refinement method of embodiment 2.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a ternary precursor intelligent lean system, which is a schematic structural diagram of the ternary precursor intelligent lean system of the embodiment, as shown in fig. 1.
The ternary precursor intelligent lean system comprises an ERP management module 1, an MES management module 2, a DCS control module 3, an OA module 4 and a database 5, wherein the ERP management module 1 is connected with the MES management module 2, the OA module 4 and the database 5, the MES management module 2 is connected with the ERP management module 1, the DCS control module 3, the OA module 4 and the database 5, and the DCS control module 3 is connected with the MES management module 2 and the OA module 4.
In this embodiment, the ERP management module 1 includes a purchase management unit 11, a production management unit 12, and a sales management unit 13, and the MES management module 2 includes an equipment data management unit 21, a production schedule management unit 22, and a finished product quality management unit 23.
In the ERP management module 1, a sales management unit 13 is connected with a database 5 and an OA module 4, and the input end of the sales management unit 13 is connected with the output end of a finished product quality management unit 23; the production management unit 12 is connected with the database 5 and the OA module 4, and the output end of the production management unit 12 is respectively connected with the input end of the procurement management unit 11 and the input end of the production schedule management unit 22; procurement management unit 11 is connected with OA module 4.
In the MES management module 2, the equipment data management unit 21 is connected to the database 5 and the OA module 4, the input end of the equipment data management unit 21 is connected to the output end of the DCS control module 3, and the output end of the equipment data management unit 21 is connected to the input end of the production schedule management unit 22; the production schedule management unit 22 is connected to the OA module 4, an input end of the production schedule management unit 22 is connected to an output end of the manufacturing management unit 12, and an output end of the production schedule management unit 22 is connected to an input end of the DCS control module 3; the finished product quality management unit 23 is connected with the OA module 4 and the database 5, and an input end of the finished product quality management unit 23 is connected with an output end of the DCS control module 3.
In this embodiment, the customer requirement information includes, but is not limited to, a ternary precursor product model number, a product quantity, and a delivery time; production data includes, but is not limited to, finished product indicators, finished product quantity, finished process plant equipment parameters, PFMEA, and control plans; equipment operating data includes, but is not limited to, MFMEA, plant equipment operating data, plant equipment ultimate capacity, and standard capacity; production plan data includes, but is not limited to, daily energy plans, production schedules for the product; production scheduling data includes, but is not limited to, daily product batch and quantity of production line equipment in each workshop section; the actual production data of the equipment includes, but is not limited to, actual capacity of the plant equipment, unit energy consumption data, batch product index, batch yield data, and finished product qualification rate.
In the concrete implementation process, the production management unit 12 in the ERP management module 1, according to the customer requirement information input by the system, including the product model and quantity information of the high nickel ternary precursor needed by the customer, the production data and equipment operation data of different product models are called from the database 5, wherein the production data comprises finished product indexes, finished product quantity, workshop equipment parameters, PFMEA and control plan in the production process, the equipment operation data comprises MFMEA, workshop equipment operation data, workshop equipment ultimate capacity, standard capacity and the like, used for calculating production plan data such as daily energy plan and production schedule of the customer order products, then, the data are sent to a purchasing management unit 11, the purchasing management unit 11 calculates the quantity and price of raw materials required by production according to the production plan data to be used as a purchasing plan, and then the raw materials are sent to a manager terminal through an OA module 4 to perform purchasing work;
meanwhile, the manufacturing management unit 12 sends the data to a production schedule management unit 22 in the MES management module, the production schedule management unit 22 calculates production schedule data of production lines of each workshop section, that is, plans daily production batches and quantities of equipment of the production lines of each workshop section, and then sends the production schedule data calculated by the production schedule management unit 22 and the received production data to the DCS control module 3, and the DCS control module 3 controls production line equipment to produce a high-nickel ternary precursor of a target product according to the received production schedule data, and control plan, workshop equipment operation data, product process workshop equipment parameters, PFMEA and the like.
Specifically, after the DCS control module 3 sets equipment parameters and process parameters of each section according to data such as a control plan, product process parameters, a production schedule, a control plan and product process parameters, the DCS control module controls each feed flow valve to flow into a reaction kettle with a reaction rotating speed of 400rpm according to the data, adjusts the flow of steam and a cooling water electromagnetic valve to stabilize the reaction temperature at 55.0 ℃, controls the pH value of the reaction at 12.5 by adjusting the flow of alkaline liquor, opens an overflow pipeline electromagnetic valve when the reaction slurry tested by an online particle sizer grows to D50 ═ 10.3 μm and the radius distance is 1.1, overflows the slurry into a transfer tank, closes a pipeline electromagnetic valve of a return solution area, opens an electromagnetic valve of a pipeline of a two-in-one pressure filtration equipment, pumps a high-nickel precursor three-element slurry into the two-in-one pressure filtration equipment to carry out filtration, Washing, then, introducing compressed air, performing filter pressing until the water content of a filter cake is 10% in an online moisture meter test, then, closing the discharge port to a back dissolution area, then, discharging to a disc type drying device through a qualified material discharge port, drying at the drying temperature of 150 ℃ until the water content of high-nickel ternary precursor powder is 0.5% in the online moisture meter test, then, closing solenoid valves of pipelines of a bin of the back dissolution area and a bin of the back drying device, opening the solenoid valve of the pipeline of the bin of the qualified material, conveying the dried high-nickel ternary precursor powder to the qualified material bin through negative pressure conveying equipment, and testing a high-nickel ternary precursor finished product through desk type detection equipment to obtain finished product detection data.
In the process of controlling the production line equipment to produce the target product by the DCS control module 3, the DCS control module 3 monitors whether the process detection data meets a preset threshold value or not, if not, process warning information is sent to the manager terminal through the OA module 4, and equipment operation and energy consumption data are fed back to the equipment data management unit 21 in the MES management module 2, and sends the equipment operation and energy consumption data, the process detection data, the finished product detection data to the finished product quality management unit 23, wherein the device data management unit 21 obtains device operation and energy consumption data from the database 5, and calculating actual production data of the equipment such as real-time capacity, unit energy consumption data and the like according to the equipment operation and energy consumption data obtained by feedback, when the real-time capacity and the unit energy consumption data of the equipment are judged not to meet the preset equipment qualified threshold value, the device data management unit 21 sends device alert information to the manager terminal through the OA module 4;
the finished product quality management unit 23 judges the quality of the finished product according to the finished product detection data obtained by feedback, and when the quality of the finished product is judged not to meet the preset finished product qualified threshold, the finished product quality management unit 23 sends finished product warning information to a manager through the OA module 4; when the finished product quality management unit 23 judges that the quality of the current finished product meets the preset finished product qualified threshold value, the finished product quality management unit 23 sends actual equipment production data to the sales management unit 13 in the ERP management module 1, wherein the actual equipment production data comprises actual workshop equipment capacity, unit energy consumption data, batch product indexes, batch yield data and the like, the sales management unit 13 calculates and draws the batch product indexes and the product quantity into a chart according to the fed back actual equipment production data, determines the delivery quantity, the target manufacturer, delivery time and the like of the product according to the customer demand information, and sends sales warning information to a manager for processing through the OA module 4, and when the existing product quantity cannot meet the customer demand information, sends the sales warning information to the manager for processing through the OA module 4.
The intelligent lean system for the ternary precursor in the embodiment realizes automation and intellectualization of processes of raw material purchase, production plan process management, production process monitoring and feedback, workshop equipment control, capacity calculation, cost accounting, finished product sale plan and the like in high-nickel ternary precursor production through the synergistic effect of the ERP management module, the MES management module, the DCS control module, the OA module and the database.
Example 2
In this embodiment, the intelligent refining system for a ternary precursor in embodiment 1 is applied to provide an intelligent refining method for a ternary precursor, as shown in fig. 2, which is a flowchart of the intelligent refining method for a ternary precursor in this embodiment.
The intelligent refinement method for the ternary precursor comprises the following steps:
s1: the management personnel inputs the customer requirement information into the ERP management module 1, the ERP management module 1 calls production data and equipment operation data from the database 5, production plan data are obtained through calculation, then a purchase plan is formulated according to the customer requirement information and then sent to the management personnel terminal through the OA module 4 for purchase, and meanwhile the called production data, equipment operation data and production plan data are transmitted to the MES management module 2.
The method comprises the following specific steps:
s1.1: the manager inputs the customer demand information into the ERP management module 1, the production management unit 12 in the ERP management module 1 calls production data of corresponding product models from the database 5 according to the product models, the product quantity and the delivery time in the customer demand information, calculates a daily production energy plan and a production schedule of ordered products as production plan data, and then the production management unit 12 judges whether the production plan data meets a preset production allowance threshold value: if not, the manufacturing management unit 12 sends production alarm information to the administrator terminal through the OA module 4; if yes, the manufacturing management unit 12 sends the production data, the equipment operation data, and the production plan data to the production schedule management unit 22 of the MES module 2 for production schedule processing;
s1.2: a purchasing management unit 11 in the ERP management module 1 calculates the types, the quantities and the prices of raw materials required by production according to the customer demand information, and then sends the raw materials to a manager terminal for purchasing through an OA module 4 by combining characters and diagrams as a purchasing plan; when the purchase plan cannot be made or there is an error, the purchase management unit 11 sends purchase warning information to the manager terminal through the OA module 4.
S2: the production schedule management unit 22 in the MES management module 2 calculates the production schedule data of the production line equipment of each section according to the received production data, equipment operation data and production plan data, and then sends the production data and the production schedule data to the DCS control module 3.
S3: the DCS control module 3 controls workshop equipment to produce according to the received production data and production schedule data, and the DCS control module 3 judges whether the process detection data meet a preset qualified threshold value in the production process, if not, the process warning information is sent to a manager terminal through the OA module 4, and the equipment is controlled to transfer the materials to a re-melting area; if yes, equipment operation and energy consumption data, process detection data and finished product detection data information are fed back to the MES management module 2.
In the step, the DCS control module 3 sets parameters of a reaction workshop section, a filter-pressing workshop section and a drying workshop section according to production data and production schedule data, wherein the parameters of the reaction workshop section comprise the flow of salt solution, alkali liquor and a complexing agent of a reaction kettle, a reaction pH value, a reaction rotating speed, a reaction temperature and the granularity and the radial distance of reaction slurry, the parameters of the filter-pressing workshop section comprise the washing liquid conductivity, the filter-pressing time and the water content of a filter cake of a filter press, and the parameters of the drying workshop section comprise the drying temperature and the water content of powder; in the production process, when the DCS control module 3 judges that the process detection data do not accord with the preset threshold value, the OA module 4 sends process warning information to the manager terminal, and the materials are transferred to the re-melting area.
S4: the MES management module 2 calculates actual production data of the equipment according to the received equipment operation and energy consumption data, process detection data and finished product detection data information, then judges whether the actual production data of the equipment meets a preset equipment qualified threshold and a preset finished product qualified threshold, if so, the actual production data of the equipment is sent to the ERP management module 1, and the ERP management module 1 determines the delivery quantity, a target manufacturer and delivery time of the product according to the customer requirement information and then sends the delivery quantity, the target manufacturer and the delivery time to a manager terminal through the OA module 4; if not, the MES management module 2 sends equipment warning information and finished product warning information to the manager terminal through the OA module 4; the method comprises the following specific steps:
s4.1: the equipment data management unit 21 in the MES management module 2 calculates real-time capacity of equipment, unit energy consumption data, batch product data and product qualification rate as actual production data of the equipment according to the equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module 3;
s4.2: the device data management unit 21 determines whether the actual production data of the device meets a preset device qualification threshold, and if yes, executes step S4.3; if not, sending equipment warning information to a manager terminal through the OA module 4, carrying out maintenance processing on the equipment by the manager, and updating equipment operation data in the database 5;
s4.3: a finished product quality management unit 23 in the MES management module 2 judges whether a finished product meets a preset finished product qualification threshold according to equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module 3, if so, actual production data of the equipment is sent to a sales management unit 13 in the ERP management module 1, and the sales management unit 13 determines the delivery quantity, target manufacturer and delivery time of the product according to the actual production data of the equipment and customer demand information, and then sends the delivery quantity, target manufacturer and delivery time of the product to a manager terminal through the OA module 4; if not, the finished product quality management unit 23 sends finished product warning information to the manager through the OA module 4, and the manager checks the production process and updates the production data in the database 5.
In this embodiment, the customer requirement information includes, but is not limited to, a ternary precursor product model number, a product quantity, and a delivery time; production data includes, but is not limited to, finished product indicators, finished product quantity, finished process plant equipment parameters, PFMEA, and control plans; equipment operating data includes, but is not limited to, MFMEA, plant equipment operating data, plant equipment ultimate capacity, and standard capacity; production plan data includes, but is not limited to, daily energy plans, production schedules for the product; production scheduling data includes, but is not limited to, daily product batch and quantity of production line equipment in each workshop section; the actual production data of the equipment includes, but is not limited to, actual capacity of the plant equipment, unit energy consumption data, batch product index, batch yield data and finished product qualification rate.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. An intelligent ternary precursor refining system is characterized by comprising an ERP management module, an MES management module, a DCS control module, an OA module and a database, wherein the ERP management module is connected with the MES management module, the OA module and the database, the MES management module is connected with the ERP management module, the DCS control module, the OA module and the database, and the DCS control module is connected with the MES management module and the OA module, wherein:
the ERP management module is used for making a purchasing plan according to input customer demand information, sending the purchasing plan to a manager terminal through the OA module for purchasing, and calling production data, equipment operation data and production plan data from a database and transmitting the production data, the equipment operation data and the production plan data to the MES management module;
the MES management module is used for calculating production scheduling data according to the received data, sending the production data and the production scheduling data to the DCS control module and feeding back actual production data of equipment to the ERP management module;
and the DCS control module is used for controlling the operation of the ternary precursor production workshop equipment and monitoring process detection data in the production process according to the received production schedule data, and feeding back equipment operation and energy consumption data, process detection data and finished product detection data to the MES management module.
2. The ternary precursor intelligent lean system according to claim 1, wherein:
the customer requirement information includes but is not limited to a ternary precursor product model number, a product quantity and a delivery time;
the production data includes, but is not limited to, finished product indicators, finished product quantity, finished process plant equipment parameters, PFMEA, and control plans;
the plant operational data includes, but is not limited to, MFMEA, plant operational data, plant ultimate capacity, and standard capacity;
the production plan data includes, but is not limited to, daily energy production plans, production schedules for the product;
the production scheduling data includes but is not limited to daily product batch and quantity of production line equipment in each workshop section;
the actual production data of the equipment includes, but is not limited to, actual capacity of workshop equipment, unit energy consumption data, batch product index, batch yield data, and finished product qualification rate.
3. The ternary precursor intelligent lean system of claim 2, wherein: the ERP management module comprises a sales management unit, a manufacturing management unit and a purchasing management unit, wherein: the sales management unit is respectively connected with the database, the MES management module and the OA module; the manufacturing management unit is respectively connected with the database, the purchasing management unit, the MES management module and the OA module; the purchase management unit is respectively connected with the manufacture management unit and the OA module.
4. The ternary precursor intelligent lean system of claim 3, wherein: the MES management module comprises an equipment data management unit, a production schedule management unit and a finished product quality management unit, wherein: the equipment data management unit is respectively connected with the database, the production schedule management unit, the DCS control module and the OA module; the production scheduling management unit is respectively connected with a manufacturing management unit, a DCS control module and an OA module in the ERP management module; and the finished product quality management unit is respectively connected with the sales management unit in the database, the DCS control module, the OA module and the ERP management module.
5. An intelligent refining method of a ternary precursor is characterized by comprising the following steps:
s1: the management personnel inputs the customer demand information into the ERP management module, the ERP management module calls production data and equipment operation data from the database, the production plan data are obtained through calculation, then a purchase plan is formulated according to the customer demand information and then sent to the management personnel terminal through the OA module for purchase, and meanwhile the called production data, equipment operation data and production plan data are transmitted to the MES management module;
s2: a production schedule management unit in the MES management module calculates production schedule data of production line equipment of each workshop section according to the received production data, equipment operation data and production plan data, and then sends the production data and the production schedule data to the DCS control module;
s3: the DCS control module controls workshop equipment to produce according to the received production data and production schedule data, judges whether the process detection data meet a preset qualified threshold value or not in the production process, and sends process warning information to a manager terminal through the OA module and controls the equipment to transfer the materials to a re-dissolution area if the process detection data do not meet the preset qualified threshold value; if yes, feeding back equipment operation and energy consumption data, process detection data and finished product detection data information to the MES management module;
s4: the MES management module calculates actual production data of the equipment according to the received equipment operation and energy consumption data, process detection data and finished product detection data information, then judges whether the actual production data of the equipment meets a preset equipment qualified threshold and a preset finished product qualified threshold, if so, the actual production data of the equipment is sent to the ERP management module, and the ERP management module determines the delivery quantity, a target manufacturer and delivery time of the product according to the actual production data of the equipment and customer demand information and then sends the delivery quantity, the target manufacturer and the delivery time to a manager terminal through the OA module; if not, the MES management module sends equipment warning information and finished product warning information to the manager terminal through the OA module.
6. The intelligent refinement method of ternary precursors according to claim 5, characterized in that: in the step S1, the specific steps include:
s1.1: the method comprises the following steps that a manager inputs customer demand information into an ERP management module, a manufacturing management unit in the ERP management module calls production data of corresponding product models from a database according to the product models, the product quantity and the delivery time in the customer demand information, a daily production energy plan and a production schedule of ordered products are calculated to serve as production plan data, and then whether the production plan data meet a preset production allowance threshold value or not is judged: if not, sending production alarm information to a manager through the OA module; if yes, the production data and the production plan data are sent to a production scheduling management unit of an MES module for production scheduling processing;
s1.2: a purchase management unit in the ERP management module calculates the types, the quantities and the prices of raw materials required by production according to the customer demand information, and then sends the raw materials to a manager terminal for purchase through an OA module by combining characters and diagrams as a purchase plan; and when the purchasing plan cannot be carried out or is wrong, the purchasing management unit sends purchasing warning information to the management personnel terminal through the OA module.
7. The intelligent refinement method of ternary precursors according to claim 5, characterized in that: in the step S3, the DCS control module controls the plant equipment to perform production according to the received production data and production schedule data, and includes the following specific steps: the DCS control module sets parameters of a reaction workshop section, a filter-pressing workshop section and a drying workshop section according to production data and production schedule data, wherein the parameters of the reaction workshop section comprise flow of salt liquid, alkali liquor and a complexing agent of a reaction kettle, a reaction pH value, a reaction rotating speed, a reaction temperature and reaction slurry granularity and a radial distance, the parameters of the filter-pressing workshop section comprise washing liquid conductivity, filter-pressing time and filter cake water content of a filter press, and the parameters of the drying workshop section comprise drying temperature and powder water content.
8. The intelligent refinement method of ternary precursors according to claim 5, characterized in that: in the step S4, the specific steps include:
s4.1: an equipment data management unit in the MES management module calculates real-time capacity of equipment, unit energy consumption data, batch product data and product qualification rate as actual production data of the equipment according to equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module;
s4.2: the equipment data management unit judges whether the actual production data of the equipment meet a preset equipment qualified threshold value, and if so, the S4.3 step is executed; if not, sending equipment warning information to a manager terminal through the OA module, carrying out maintenance processing on the equipment by the manager, and updating equipment operation data in the database;
s4.3: a finished product quality management unit in the MES management module judges whether a finished product meets a preset finished product qualified threshold value according to equipment operation and energy consumption data, process detection data and finished product detection data information fed back by the DCS control module, if so, actual production data of the equipment is sent to a sales management unit in the ERP management module, and the sales management unit determines the delivery quantity, a target manufacturer and delivery time of the product according to the actual production data of the equipment and customer demand information and then sends the delivery quantity, the target manufacturer and the delivery time to a manager terminal through the OA module; if not, the finished product quality management unit sends finished product warning information to a manager through the OA module and updates production data in the database.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111399453A (en) * | 2020-03-23 | 2020-07-10 | 无锡威孚环保催化剂有限公司 | Intelligent catalyst manufacturing method based on MES big data |
| CN112150099A (en) * | 2020-08-18 | 2020-12-29 | 新丰电器(深圳)有限公司 | Interface for connecting ERP and MES in enterprise production scheduling |
| CN112163727A (en) * | 2020-08-19 | 2021-01-01 | 新丰电器(深圳)有限公司 | Early warning reporting system based on MES (manufacturing execution system) parts shortage |
| CN113977337A (en) * | 2021-12-28 | 2022-01-28 | 广州市腾嘉自动化仪表有限公司 | Material control system based on automatic production line |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101329747A (en) * | 2008-06-25 | 2008-12-24 | 北汽福田汽车股份有限公司潍坊模具厂 | Die enterprise informatization management system |
| CN101408769A (en) * | 2008-11-21 | 2009-04-15 | 冶金自动化研究设计院 | On-line energy forecasting system and method based on product ARIMA model |
| CN102176154A (en) * | 2011-03-17 | 2011-09-07 | 南京迪奈特自控科技有限公司 | Integrated and intelligent flow enterprise information management and control system |
| CN105467946A (en) * | 2015-02-05 | 2016-04-06 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolytic MES system based on accurate perception and intelligent decision |
| CN107545358A (en) * | 2017-07-21 | 2018-01-05 | 合肥未来计算机技术开发有限公司 | A kind of wisdom garden comprehensive management platform system |
| CN107831750A (en) * | 2017-12-04 | 2018-03-23 | 青岛鹏海软件有限公司 | IMES Intelligent Manufacturing Execution Systems |
| CN108229782A (en) * | 2017-10-26 | 2018-06-29 | 北京航天智造科技发展有限公司 | A kind of visual production management platform based on cloud |
| CN108320069A (en) * | 2017-12-20 | 2018-07-24 | 甘肃银光化学工业集团有限公司 | A kind of manufacturing execution system |
| CN108596422A (en) * | 2018-02-27 | 2018-09-28 | 南京林业大学 | A kind of MES platforms of large-scale customization furniture |
| CN109165851A (en) * | 2018-08-28 | 2019-01-08 | 武汉易普优科技有限公司 | A kind of rolling planning scheduling method for intelligence manufacture |
| CN109190944A (en) * | 2018-08-20 | 2019-01-11 | 国药集团重庆医药设计院有限公司 | Medicine enterprise manufactures execution system |
| CN109284941A (en) * | 2018-10-29 | 2019-01-29 | 天津恒兴机械设备有限公司 | A kind of intelligence process for cold stamping shaping |
| CN109740943A (en) * | 2019-01-07 | 2019-05-10 | 广州市酷匠信息科技有限公司 | A kind of furniture lean production planning system |
| CN109754227A (en) * | 2019-01-04 | 2019-05-14 | 江苏省（扬州）数控机床研究院 | Intelligent precise forging operation management system |
| CN110045693A (en) * | 2019-03-25 | 2019-07-23 | 日昌升集团有限公司 | A kind of grouping of the world economy aggregate production managing and control system |
| CN110334950A (en) * | 2019-07-05 | 2019-10-15 | 福州国电远控科技开发有限公司 | Steel tower intelligence production and group management and control system |
-
2019
- 2019-10-31 CN CN201911054832.0A patent/CN111008812A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101329747A (en) * | 2008-06-25 | 2008-12-24 | 北汽福田汽车股份有限公司潍坊模具厂 | Die enterprise informatization management system |
| CN101408769A (en) * | 2008-11-21 | 2009-04-15 | 冶金自动化研究设计院 | On-line energy forecasting system and method based on product ARIMA model |
| CN102176154A (en) * | 2011-03-17 | 2011-09-07 | 南京迪奈特自控科技有限公司 | Integrated and intelligent flow enterprise information management and control system |
| CN105467946A (en) * | 2015-02-05 | 2016-04-06 | 贵阳铝镁设计研究院有限公司 | Aluminum electrolytic MES system based on accurate perception and intelligent decision |
| CN107545358A (en) * | 2017-07-21 | 2018-01-05 | 合肥未来计算机技术开发有限公司 | A kind of wisdom garden comprehensive management platform system |
| CN108229782A (en) * | 2017-10-26 | 2018-06-29 | 北京航天智造科技发展有限公司 | A kind of visual production management platform based on cloud |
| CN107831750A (en) * | 2017-12-04 | 2018-03-23 | 青岛鹏海软件有限公司 | IMES Intelligent Manufacturing Execution Systems |
| CN108320069A (en) * | 2017-12-20 | 2018-07-24 | 甘肃银光化学工业集团有限公司 | A kind of manufacturing execution system |
| CN108596422A (en) * | 2018-02-27 | 2018-09-28 | 南京林业大学 | A kind of MES platforms of large-scale customization furniture |
| CN109190944A (en) * | 2018-08-20 | 2019-01-11 | 国药集团重庆医药设计院有限公司 | Medicine enterprise manufactures execution system |
| CN109165851A (en) * | 2018-08-28 | 2019-01-08 | 武汉易普优科技有限公司 | A kind of rolling planning scheduling method for intelligence manufacture |
| CN109284941A (en) * | 2018-10-29 | 2019-01-29 | 天津恒兴机械设备有限公司 | A kind of intelligence process for cold stamping shaping |
| CN109754227A (en) * | 2019-01-04 | 2019-05-14 | 江苏省（扬州）数控机床研究院 | Intelligent precise forging operation management system |
| CN109740943A (en) * | 2019-01-07 | 2019-05-10 | 广州市酷匠信息科技有限公司 | A kind of furniture lean production planning system |
| CN110045693A (en) * | 2019-03-25 | 2019-07-23 | 日昌升集团有限公司 | A kind of grouping of the world economy aggregate production managing and control system |
| CN110334950A (en) * | 2019-07-05 | 2019-10-15 | 福州国电远控科技开发有限公司 | Steel tower intelligence production and group management and control system |
Non-Patent Citations (1)
| Title |
|---|
| 高远飙 等: "ERP与MES集成技术及其应用研究", 计算机应用与软件, vol. 26, no. 9, 30 September 2009 (2009-09-30), pages 69 - 71 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111399453A (en) * | 2020-03-23 | 2020-07-10 | 无锡威孚环保催化剂有限公司 | Intelligent catalyst manufacturing method based on MES big data |
| CN112150099A (en) * | 2020-08-18 | 2020-12-29 | 新丰电器(深圳)有限公司 | Interface for connecting ERP and MES in enterprise production scheduling |
| CN112163727A (en) * | 2020-08-19 | 2021-01-01 | 新丰电器(深圳)有限公司 | Early warning reporting system based on MES (manufacturing execution system) parts shortage |
| CN113977337A (en) * | 2021-12-28 | 2022-01-28 | 广州市腾嘉自动化仪表有限公司 | Material control system based on automatic production line |
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