CN116414096A - Intelligent chemical plant production management and control system - Google Patents
Intelligent chemical plant production management and control system Download PDFInfo
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Abstract
The invention discloses a production management and control system of an intelligent chemical plant, which comprises the following components: the production line is provided with a plurality of equipment nodes; the collector is arranged on each equipment node and used for acquiring at least one monitoring data of the equipment node; the upper computer is provided with a plurality of independent data acquisition channels, each collector is connected with one data acquisition channel, a configuration table between the data acquisition channels and the collectors is set, and the upper computer acquires monitoring data of equipment nodes acquired by the corresponding collectors through the independent data acquisition channels; the simulation model is provided with a task monitoring unit, a task distributor and a simulation matrix, and the production line management and control model is used for judging the running condition of each equipment node based on the simulation data of the running state of each equipment node and managing and controlling the production line based on the judgment of the running condition of the equipment node.
Description
Technical Field
The invention relates to the technical field of factory informatization, in particular to a production management and control system of an intelligent factory.
Background
The traditional chemical plant basically needs manual control, in particular, in the control of a production line, automatic monitoring cannot be realized, after digital and informatization are introduced, the plant control can form a flexible system, the system can self-optimize the performance of the whole network, self-adapt, learn new environmental conditions in real time or near real time, and automatically operate the whole production process. Each feature helps the management layer make informed decisions and helps the enterprise improve its production flow. When an intelligent factory is built, the existing means are to monitor the running state of equipment by collecting the running parameters of various equipment on a production line, and the running state of single equipment can be regulated and controlled only, and after the running of the previous equipment is in a problem on the production line, the connected downstream equipment is often caused to be in failure.
Disclosure of Invention
Accordingly, the present invention is directed to a system for controlling intelligent industrial production.
In order to achieve the above object, the present invention provides a production control system for an intelligent chemical plant, comprising:
the production line is provided with a plurality of equipment nodes;
the collector is arranged on each equipment node and used for acquiring at least one monitoring data of the equipment node;
the upper computer is provided with a plurality of independent data acquisition channels, each collector is connected with one data acquisition channel, a configuration table between the data acquisition channels and the collectors is set, and the upper computer acquires monitoring data of equipment nodes acquired by the corresponding collectors through the independent data acquisition channels;
a simulation model having a task monitoring unit, a task allocator, and a simulation matrix, wherein,
the task monitoring unit is connected with the upper computer and is used for monitoring the acquisition action of the data acquisition channel in the upper computer;
the task distributor is connected with the task monitoring unit, and a simulation matrix is correspondingly started based on the monitoring result of the task monitoring unit on the acquisition action of the data acquisition channel in the upper computer;
the simulation matrix is provided with a plurality of simulation units, and the simulation units are in one-to-one correspondence with the data acquisition channels based on the configuration table;
the task monitoring unit sets an enabling state between the data acquisition channel and the simulation units through an on-off program according to the monitoring result, and each simulation unit is used for obtaining simulation data of the running state of the equipment node based on the corresponding monitoring data;
and the production line control model judges the operation condition of the equipment nodes based on the simulation data of the operation state of each equipment node and controls the production line based on the judgment of the operation condition of the equipment nodes.
Further, the production line management model has a model combining section and a control combining section:
the model combining part comprises a model link part and a model part;
the control combination part comprises a control module and a linkage management and control module, wherein the control module and the linkage management and control module are configured to a production line model and are applied to the production line model to form the production line management and control model;
the control module is provided with a plurality of control units corresponding to the simulation units, each control unit compares the simulation data of the corresponding simulation unit with a set threshold value to check the running state of the corresponding equipment node, and judges whether to regulate and control the running parameters of the equipment node based on the running state;
the linkage management and control module is internally provided with a logic control program, the association relation between the control units is set through the logic control program, the influence condition of other control units associated with the control unit is judged based on the regulation and control of at least one control unit on the operation parameters of the equipment nodes, and whether linkage management and control is started or not is determined based on the influence condition; or, based on the simulation data acquired by at least one control unit, judging the influence condition of other control units associated with the control unit, and deciding whether to enable linkage management or not based on the influence condition.
Further, the model part includes an equipment model, a pipeline model, a mounting accessory model, an electrical device model, a control equipment model, a collector model, and a communication device model, and the equipment model, the pipeline model, the mounting accessory model, the electrical device model, the control equipment model, the collector model, and the communication device model are combined into a production line model through the model link part.
Further, the logic control program sets an association relation between control units based on the model link part, and sets an influence coefficient between the associated control units according to the operation parameters of the equipment nodes corresponding to the control units;
the influence condition is set based on the influence coefficient.
Further, the model link section includes:
connection relation between production equipment set on the basis of assembly files of a build production line;
an electrical control relationship between production equipment set based on the electrical topology map;
a monitoring control relationship between production equipment set based on the monitoring topological graph;
the control relation between the upper computer and the production equipment is set based on the control topological graph;
and the communication control relationship among the upper computer, the communication device and the production equipment is set based on the communication topological graph.
Further, the simulation matrix is composed of M rows and N columns of simulation units, wherein M is more than or equal to 2, N is more than or equal to 2, and M, N is an integer.
Further, the control module is composed of A row and B column control units, wherein A is more than or equal to 2, B is more than or equal to 2, and A, B is an integer.
Further, a recording module and an artificial intelligence system are arranged in the control combination part;
the recording module is used for recording the operation state data of the equipment node obtained by each control unit according to the time sequence and regulating and controlling instructions of the operation parameters of the equipment node based on the operation state data;
the artificial intelligence system is connected with the linkage management and control module, invokes a logic control program arranged in the linkage management and control module, acquires the operation state data of the equipment nodes according to the recording time sequence through the logic control program and comprehensively analyzes the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
Further, the method for predicting the equipment failure of the equipment nodes on the production line by the artificial intelligence system comprises the following steps:
a1 Acquiring historical equipment node basic data, operation state data of the historical equipment nodes and a regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data, and confirming life cycle stages of the equipment nodes on a production line after manual expert labeling;
a2 Inputting the operation state data of the historical equipment nodes marked by the artificial expert and the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data into a neural network model in the artificial intelligent system for iterative training to obtain a life cycle prediction model of the equipment nodes on the production line;
a3 Based on life cycle prediction model, acquiring the running state data of the equipment nodes according to the recording time sequence by a logic control program and comprehensively analyzing the regulation and control instructions of the running parameters of the equipment nodes based on the running state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
Further, the equipment node basic data comprises basic information of equipment, equipment parameters, maintenance record files, running state data, history fault record files and history regulation record files.
According to the production management and control model, when the operation state of a single equipment node is monitored, the associated equipment node can be subjected to linkage analysis and management and control, so that the production line is ensured to be in the optimal operation state when in operation, meanwhile, equipment fault prediction can be performed on equipment on the production line through the artificial intelligence system, and the safe and stable operation of the production line equipment is ensured.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of a method for predicting equipment failure of equipment nodes on a production line by an artificial intelligence system;
fig. 3 is a schematic diagram of the frame principle of the system in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, in order to realize omnibearing monitoring of a production line, including implementation of equipment operation state, operation data monitoring, operation regulation and control and preset fault prediction, a control model of the production line is constructed based on the production line so as to simulate the operation condition of the production line in reality. That is to say, the control model of the production line is a simulation of the real production line, and for this purpose, the application provides an intelligent factory production control method, which comprises the following steps:
1) Sequentially constructing an equipment model, a pipeline model, an installation accessory model, an electric device model, a control equipment model, a collector model and a communication device model of production equipment in the BIM, and temporarily storing the equipment model, the pipeline model, the installation accessory model, the electric device model, the control equipment model, the collector model and the communication device model in a storage library after constructing;
2) Sequentially constructing a production line assembly file, an electrical topological graph, a monitoring topological graph, a control topological graph and a communication topological graph, sequentially loading the production line assembly file, the electrical topological graph, the monitoring topological graph, the control topological graph and the communication topological graph in the BIM, sequentially calling an equipment model, a pipeline model, an installation accessory model, an electrical device model, a control equipment model, a collector model and a communication device model in a storage library, and correspondingly combining to form a production line management model;
3) Setting a plurality of independent collectors to be connected with equipment nodes on a production line, configuring a plurality of independent data acquisition channels in an upper computer, connecting each collector with one data acquisition channel, setting a configuration table between the data acquisition channels and the collectors, and acquiring monitoring data of the equipment nodes acquired by the corresponding collectors through the independent data acquisition channels by the upper computer;
4) Configuring a simulation model in the upper computer, wherein the simulation model enables a simulation matrix correspondingly based on a monitoring result of the actions of the data acquisition channels in the upper computer by monitoring the actions of the data acquisition channels in the upper computer; the simulation matrix is composed of a plurality of simulation units, and an enabling state is set by an on-off program according to the data acquisition channel in the upper computer and the simulation units corresponding to the monitoring result; each simulation unit is used for obtaining simulation data of the running state of the equipment node based on the corresponding monitoring data;
5) And correspondingly configuring the simulation matrix to a production line control model, judging the operation condition of the equipment nodes based on the simulation data of the operation state of each equipment node by the production line control model, and carrying out production control on the production line based on the judgment of the operation condition of the equipment nodes.
Further, the simulation model has:
the task monitoring unit is connected with the upper computer and used for monitoring the acquisition action of the data acquisition channel in the upper computer;
the task distributor is connected with the task monitoring unit and used for enabling the simulation matrix correspondingly based on the monitoring result of the task monitoring unit on the acquisition action of the data acquisition channel in the upper computer;
the simulation matrix is composed of M rows and N columns of simulation units, wherein M is more than or equal to 2, N is more than or equal to 2, M, N are integers, and the task monitoring unit sets an enabling state according to the data acquisition channels in the upper computer and the simulation units corresponding to the monitoring result through an on-off program.
Further, the production control includes:
the control module is provided with a plurality of control units corresponding to the simulation units, each control unit compares the simulation data of the corresponding simulation unit with a set threshold value to check the running state of the corresponding equipment node, and judges whether to regulate and control the running parameters of the equipment node based on the running state;
the linkage management and control module is internally provided with a logic control program, the association relation between the control units is set through the logic control program, the influence condition of other control units associated with the control unit is judged based on the regulation and control of at least one control unit on the operation parameters of the equipment nodes, and whether linkage management and control is started or not is determined based on the influence condition; or, based on the simulation data acquired by at least one control unit, judging the influence condition of other control units associated with the control unit, and deciding whether to enable linkage management or not based on the influence condition.
The artificial intelligence system is connected with the linkage management and control module, invokes a logic control program arranged in the linkage management and control module, acquires the operation state data of the equipment nodes according to the recording time sequence through the logic control program and comprehensively analyzes the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
Referring to fig. 2, the method for predicting equipment failure of equipment nodes on a production line by the artificial intelligence system is as follows:
a1 Acquiring historical equipment node basic data, operation state data of the historical equipment nodes and a regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data, and confirming life cycle stages of the equipment nodes on a production line after manual expert labeling;
a2 Inputting the operation state data of the historical equipment nodes marked by the artificial expert and the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data into a neural network model in the artificial intelligent system for iterative training to obtain a life cycle prediction model of the equipment nodes on the production line;
a3 Based on life cycle prediction model, acquiring the running state data of the equipment nodes according to the recording time sequence by a logic control program and comprehensively analyzing the regulation and control instructions of the running parameters of the equipment nodes based on the running state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
Further, the equipment node basic data comprises basic information of equipment, equipment parameters, maintenance record files, running state data, history fault record files and history regulation record files.
Example 2
Referring to fig. 3 and fig. 2, the present embodiment is a control system formed on the basis of a production line control model provided in embodiment 1, and the specific scheme includes:
an intelligent factory production management and control system, comprising:
the production line is provided with a plurality of equipment nodes;
the collector is arranged on each equipment node and used for acquiring at least one monitoring data of the equipment node;
the upper computer is provided with a plurality of independent data acquisition channels, each collector is connected with one data acquisition channel, a configuration table between the data acquisition channels and the collectors is set, and the upper computer acquires monitoring data of equipment nodes acquired by the corresponding collectors through the independent data acquisition channels;
a simulation model having a task monitoring unit, a task allocator, and a simulation matrix, wherein,
the task monitoring unit is connected with the upper computer and is used for monitoring the acquisition action of the data acquisition channel in the upper computer;
the task distributor is connected with the task monitoring unit, and a simulation matrix is correspondingly started based on the monitoring result of the task monitoring unit on the acquisition action of the data acquisition channel in the upper computer;
the simulation matrix is provided with a plurality of simulation units, and the simulation units are in one-to-one correspondence with the data acquisition channels based on the configuration table;
the task monitoring unit sets an enabling state between the data acquisition channel and the simulation units through an on-off program according to the monitoring result, and each simulation unit is used for obtaining simulation data of the running state of the equipment node based on the corresponding monitoring data;
and the production line control model judges the operation condition of the equipment nodes based on the simulation data of the operation state of each equipment node and controls the production line based on the judgment of the operation condition of the equipment nodes.
In the above, the production line control model has a model combining section and a control combining section:
the model combining part comprises a model link part and a model part;
the model part comprises an equipment model, a pipeline model, a mounting accessory model, an electric device model, a control equipment model, a collector model and a communication device model, and the equipment model, the pipeline model, the mounting accessory model, the electric device model, the control equipment model, the collector model and the communication device model are combined into a production line model through the model link part;
the control combination part comprises a control module, a linkage management and control module, a recording module and an artificial intelligent system, wherein the control module, the linkage management and control module, the recording module and the artificial intelligent system are configured to a production line model and are applied to the production line model to form the production line management and control model;
the control module is provided with a plurality of control units corresponding to the simulation units, each control unit compares the simulation data of the corresponding simulation unit with a set threshold value to check the running state of the corresponding equipment node, and judges whether to regulate and control the running parameters of the equipment node based on the running state;
the linkage management and control module is internally provided with a logic control program, the association relation between the control units is set through the logic control program, the influence condition of other control units associated with the control unit is judged based on the regulation and control of at least one control unit on the operation parameters of the equipment nodes, and whether linkage management and control is started or not is determined based on the influence condition; or, judging the influence condition of other control units associated with the control unit based on the simulation data acquired by at least one control unit, and deciding whether to enable linkage management based on the influence condition;
the recording module is used for recording the operation state data of the equipment node obtained by each control unit according to the time sequence and regulating and controlling instructions of the operation parameters of the equipment node based on the operation state data;
the artificial intelligence system is connected with the linkage management and control module, invokes a logic control program arranged in the linkage management and control module, acquires the operation state data of the equipment nodes according to the recording time sequence through the logic control program and comprehensively analyzes the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
In the above, the method for predicting the equipment failure of the equipment node on the production line by the artificial intelligence system is as follows:
a1 Acquiring historical equipment node basic data, operation state data of the historical equipment nodes and a regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data, and confirming life cycle stages of the equipment nodes on a production line after manual expert labeling;
a2 Inputting the operation state data of the historical equipment nodes marked by the artificial expert and the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data into a neural network model in the artificial intelligent system for iterative training to obtain a life cycle prediction model of the equipment nodes on the production line;
a3 Based on life cycle prediction model, acquiring the running state data of the equipment nodes according to the recording time sequence by a logic control program and comprehensively analyzing the regulation and control instructions of the running parameters of the equipment nodes based on the running state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
Further, the equipment node basic data comprises basic information of equipment, equipment parameters, maintenance record files, running state data, history fault record files and history regulation record files.
Further, the logic control program sets an association relation between control units based on the model link part, and sets an influence coefficient between the associated control units according to the operation parameters of the equipment nodes corresponding to the control units;
the influence condition is set based on the influence coefficient.
Further, the model link section includes:
connection relation between production equipment set on the basis of assembly files of a build production line;
an electrical control relationship between production equipment set based on the electrical topology map;
a monitoring control relationship between production equipment set based on the monitoring topological graph;
the control relation between the upper computer and the production equipment is set based on the control topological graph;
and the communication control relationship among the upper computer, the communication device and the production equipment is set based on the communication topological graph.
Further, the simulation matrix is composed of M rows and N columns of simulation units, wherein M is more than or equal to 2, N is more than or equal to 2, and M, N is an integer.
Further, the control module is composed of A row and B column control units, wherein A is more than or equal to 2, B is more than or equal to 2, and A, B is an integer.
According to the production management and control model, when the operation state of a single equipment node is monitored, the associated equipment node can be subjected to linkage analysis and management and control, so that the production line is ensured to be in the optimal operation state when in operation, meanwhile, equipment fault prediction can be performed on equipment on the production line through the artificial intelligence system, and the safe and stable operation of the production line equipment is ensured.
Example 3
This example is based on examples 1 and 2, and the present application will be further explained.
The application can monitor, manage and control, adjust, link and manage the equipment node on the production line and equipment fault predicts, for example in embodiment 2, control combination part includes control module, link and manage the accuse module, record module and artificial intelligence system, control module, link and manage the accuse module, record module and artificial intelligence system configuration to the production line model and use and form in the production line model the production line management and control model.
When single equipment node control is performed, the control unit compares the simulation data of the corresponding simulation unit with a set threshold value to check the operation state of the corresponding equipment node, and judges whether to regulate and control the operation parameters of the equipment node based on the operation state.
When linkage control is performed, setting an association relation between control units through a logic control program, judging influence conditions of other control units associated with the control units based on the regulation and control of at least one control unit on the operation parameters of the equipment nodes, and determining whether linkage control is started or not based on the influence conditions; or, judging the influence condition of other control units associated with the control unit based on the simulation data acquired by at least one control unit, and deciding whether to enable linkage management based on the influence condition;
the logic control program sets the association relation between the control units based on the model link part, and sets the influence coefficient between the associated control units according to the operation parameters of the equipment nodes corresponding to the control units; the influence condition is set based on the influence coefficient.
When the artificial intelligence system predicts equipment faults of equipment nodes on a production line: a1 Acquiring historical equipment node basic data, operation state data of the historical equipment nodes and a regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data, and confirming life cycle stages of the equipment nodes on a production line after manual expert labeling; a2 Inputting the operation state data of the historical equipment nodes marked by the artificial expert and the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data into a neural network model in the artificial intelligent system for iterative training to obtain a life cycle prediction model of the equipment nodes on the production line; a3 Based on life cycle prediction model, acquiring the running state data of the equipment nodes according to the recording time sequence by a logic control program and comprehensively analyzing the regulation and control instructions of the running parameters of the equipment nodes based on the running state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
In addition to the above, the present application can also realize the following functions: the production line is assumed to be composed of equipment nodes A, equipment nodes B and equipment nodes C, when the equipment nodes A input a set amount of initial materials, chemical products are obtained through processing of the equipment nodes B and the equipment nodes C, when the equipment nodes A input initial materials are insufficient (or smaller than the set amount), and at the moment, when the equipment nodes A input materials are monitored to be smaller than the set amount, on one hand, the equipment nodes A input the materials to be controlled to increase the materials, and on the other hand, in order to ensure the yield of the products, the equipment nodes B and the equipment nodes C also need to be controlled in a linkage mode, in the embodiment, the equipment nodes B and the equipment nodes C are controlled in an equal proportion mode according to the input materials, and in the process of the input materials reaching the set amount, the processing efficiency is gradually improved in the synchronous control of the running states of the equipment nodes B and the equipment nodes C.
In addition to the above, the present application can also realize the following functions: when the electric parts of the equipment nodes are monitored to be in faults, such as too small voltage and too large current, and when the voltage and the current of one of the equipment nodes are regulated, the voltage and the current are also required to be checked through linkage control, and whether the voltage and the current are influenced between linkage equipment is judged.
The foregoing analysis and description of only some of the common problems does not represent that the present application is practiced according to the specific examples disclosed, but also includes the regulation and control of other aspects that can utilize the present application.
It should be noted that the above-described various models may be built by BIM software or three-dimensional design software embedded in the BIM software, which is built by a three-dimensional design drawing of the apparatus or a three-dimensional drawing or model provided by the manufacturer. The construction of various models is only for forming the construction of a management and control model, and the construction of the model belongs to the prior technical means.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. Wisdom chemical plant production management and control system, its characterized in that includes:
the production line is provided with a plurality of equipment nodes;
the collector is arranged on each equipment node and used for acquiring at least one monitoring data of the equipment node;
the upper computer is provided with a plurality of independent data acquisition channels, each collector is connected with one data acquisition channel, a configuration table between the data acquisition channels and the collectors is set, and the upper computer acquires monitoring data of equipment nodes acquired by the corresponding collectors through the independent data acquisition channels;
a simulation model having a task monitoring unit, a task allocator, and a simulation matrix, wherein,
the task monitoring unit is connected with the upper computer and is used for monitoring the acquisition action of the data acquisition channel in the upper computer;
the task distributor is connected with the task monitoring unit, and a simulation matrix is correspondingly started based on the monitoring result of the task monitoring unit on the acquisition action of the data acquisition channel in the upper computer;
the simulation matrix is provided with a plurality of simulation units, and the simulation units are in one-to-one correspondence with the data acquisition channels based on the configuration table;
the task monitoring unit sets an enabling state between the data acquisition channel and the simulation units through an on-off program according to the monitoring result, and each simulation unit is used for obtaining simulation data of the running state of the equipment node based on the corresponding monitoring data;
and the production line control model judges the operation condition of the equipment nodes based on the simulation data of the operation state of each equipment node and controls the production line based on the judgment of the operation condition of the equipment nodes.
2. The intelligent plant production control system of claim 1, wherein the production control model has a model combining section and a control combining section:
the model combining part comprises a model link part and a model part;
the control combination part comprises a control module and a linkage management and control module, wherein the control module and the linkage management and control module are configured to a production line model and are applied to the production line model to form the production line management and control model;
the control module is provided with a plurality of control units corresponding to the simulation units, each control unit compares the simulation data of the corresponding simulation unit with a set threshold value to check the running state of the corresponding equipment node, and judges whether to regulate and control the running parameters of the equipment node based on the running state;
the linkage management and control module is internally provided with a logic control program, the association relation between the control units is set through the logic control program, the influence condition of other control units associated with the control unit is judged based on the regulation and control of at least one control unit on the operation parameters of the equipment nodes, and whether linkage management and control is started or not is determined based on the influence condition; or, based on the simulation data acquired by at least one control unit, judging the influence condition of other control units associated with the control unit, and deciding whether to enable linkage management or not based on the influence condition.
3. The smart factory production control system according to claim 2, wherein the model section includes an equipment model, a pipeline model, a mounting accessory model, an electrical device model, a control equipment model, a collector model, and a communication device model, and the equipment model, the pipeline model, the mounting accessory model, the electrical device model, the control equipment model, the collector model, and the communication device model are combined into a production line model through the model link section.
4. The intelligent factory production control system according to claim 2, wherein the logic control program sets an association relationship between control units based on the model link section, and sets an influence coefficient between the associated control units according to an operation parameter of an equipment node to which the control unit corresponds;
the influence condition is set based on the influence coefficient.
5. The intelligent plant production control system according to claim 2, 3 or 4, wherein the model link section includes:
connection relation between production equipment set on the basis of assembly files of a build production line;
an electrical control relationship between production equipment set based on the electrical topology map;
a monitoring control relationship between production equipment set based on the monitoring topological graph;
the control relation between the upper computer and the production equipment is set based on the control topological graph;
and the communication control relationship among the upper computer, the communication device and the production equipment is set based on the communication topological graph.
6. The intelligent factory production control system according to claim 1, wherein the simulation matrix is composed of M rows and N columns of simulation units, wherein M is equal to or greater than 2, N is equal to or greater than 2, and M, N is an integer.
7. The intelligent factory production control system of claim 2, wherein the control module is comprised of a row a and a column B control units, wherein a is greater than or equal to 2, B is greater than or equal to 2, and A, B are integers.
8. The intelligent factory production control system according to claim 2, wherein a recording module and an artificial intelligence system are further provided in the control combination section;
the recording module is used for recording the operation state data of the equipment node obtained by each control unit according to the time sequence and regulating and controlling instructions of the operation parameters of the equipment node based on the operation state data;
the artificial intelligence system is connected with the linkage management and control module, invokes a logic control program arranged in the linkage management and control module, acquires the operation state data of the equipment nodes according to the recording time sequence through the logic control program and comprehensively analyzes the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
9. The intelligent factory production control system according to claim 8, wherein the method for predicting equipment failure of an equipment node on a production line by the artificial intelligence system is as follows:
a1 Acquiring historical equipment node basic data, operation state data of the historical equipment nodes and a regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data, and confirming life cycle stages of the equipment nodes on a production line after manual expert labeling;
a2 Inputting the operation state data of the historical equipment nodes marked by the artificial expert and the regulation and control instruction of the operation parameters of the equipment nodes based on the operation state data into a neural network model in the artificial intelligent system for iterative training to obtain a life cycle prediction model of the equipment nodes on the production line;
a3 Based on life cycle prediction model, acquiring the running state data of the equipment nodes according to the recording time sequence by a logic control program and comprehensively analyzing the regulation and control instructions of the running parameters of the equipment nodes based on the running state data to obtain equipment failure prediction of the equipment nodes on the production line in a set period.
10. The intelligent factory production control system of claim 9, wherein the equipment node base data comprises equipment base information, equipment parameters, maintenance records, and operational status data, historical fault records, historical regulation records.
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