CN115203973A - Simulation system and simulation method for device twin data - Google Patents
Simulation system and simulation method for device twin data Download PDFInfo
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Abstract
The invention provides a simulation system and a simulation method of equipment twin data. The simulation system of the equipment twin data comprises a storage device and a processor. The storage device stores a simulation configuration data set and a plurality of modules. The processor is coupled to the processor and is used for executing: inputting the simulation configuration data set into an initial data generation module to obtain an initial data set; carrying out statistical calculation on the initial data set through a data calculation module to obtain a statistical characteristic data set; obtaining a simulation data group according to the simulation configuration data group, the initial data group and the statistical characteristic data group through a data generation module; and executing abnormal data judgment according to the analog data set through the abnormal data detection module, and outputting corresponding output data set/warning information according to a judgment result of the abnormal data judgment.
Description
Technical Field
The invention relates to a device twin data simulation technology, in particular to a device twin data simulation system and a device twin data simulation method.
Background
In simulating each item of data of the device, since parameters/data required by the device are not a single-line data set, a plurality of simulated data sets need to be generated in the simulation process of the twin data of the device. In other words, the data needed and associated in the operation of the manufacturing/fabrication facility is a multi-dimensional set of data that is assembled into data sets/groups. Therefore, there is a need for a system and method that can provide a multidimensional simulation data set while simultaneously monitoring and simulating anomalies in multiple sets of parameters of a plant when simulating, evaluating, and optimizing production of a product in a data twinning perspective.
Disclosure of Invention
The invention aims at a simulation system of equipment twin data and a simulation method of the equipment twin data, which can correspondingly generate a simulation data group containing a plurality of parameters according to different set values.
According to an embodiment of the invention, a simulation system of twin data of an apparatus of the invention includes a storage device and a processor. The storage device stores a plurality of modules, wherein the plurality of modules comprise an initial data generation module, a data calculation module, a data generation module, an abnormal data detection module and a data output module. The processor is coupled to the storage device. The processor executes the initial data generation module to generate an initial data set from the simulated configuration data set via the initial data generation module. And the processor executes the data calculation module to perform statistical calculation on the initial data set through the data calculation module, so as to respectively generate a plurality of statistical characteristic data sets. The processor executes the data generation module to generate a simulated data set from the simulated configuration data set, the initial data set, and the statistical signature data set via the data generation module. And the data generation module inputs the simulated data set into the database. The processor executes abnormal data judgment on the analog data set according to the abnormal data detection module, and determines to continue executing or stop the data generation module to generate the next analog data set according to the judgment result of the abnormal data judgment.
According to an embodiment of the present invention, the simulation method of twin data of a device of the present invention includes the steps of: generating an initial data set according to the simulation configuration data set through an initial data generation module; carrying out statistical calculation on the initial data set through a data calculation module to respectively generate a plurality of statistical characteristic data sets; generating a simulation data set according to the simulation configuration data set, the initial data set and the plurality of statistical characteristic data sets through a data generation module, and inputting the simulation data set into a database; and executing abnormal data judgment according to the simulation data set stored in the database by the abnormal data detection module, and determining to continue executing or stop the data generation module to generate the next simulation data set according to the judgment result of the abnormal data judgment.
Based on the above, the simulation system of the device twin data and the simulation method of the device twin data according to the present invention can generate the simulation data sets correspondingly according to different setting values. The simulation data set contains a plurality of parameters/data associated with the device. Therefore, the simulation system and the simulation method of the equipment twin data achieve higher simulation degree/simulation degree when the digital factory is in simulation operation. Meanwhile, comprehensive and real-time abnormality judgment data and processing are provided, so that various conditions of the equipment in an actual operation state are more approached.
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a schematic diagram of a simulation system of twin data for a device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of simulation of twin data for a device according to an embodiment of the invention;
FIG. 3 is a schematic flow diagram of a simulation system of twin data of a device according to an embodiment of the present invention.
Description of the reference numerals
100: a simulation system of equipment twinning data;
110: a processor;
120: a storage device;
121: an initial data generation module;
122: a data calculation module;
123: a data generation module;
1231: a normal data generation unit;
1232: an abnormal data generation unit;
124: an abnormal data detection module;
125: a database;
126: a data output module;
127: an abnormal probability triggering module;
131: simulating configuration data;
132: an abnormal data pattern library;
133: a database of data patterns;
S210-S260: and (5) carrying out the following steps.
Detailed Description
Reference will now be made in detail to exemplary embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts.
FIG. 1 is a schematic diagram of a simulation system of twin data of a device according to an embodiment of the present invention. Referring to fig. 1, a simulation system 100 of device twin data includes a processor 110 and a storage 120. The processor 110 is coupled to a storage device 120. The storage device 120 may store a simulation configuration data set, a database 125, and a plurality of modules. Processor 110 may access data in memory device 120 to execute the modules. In the present embodiment, the modules include an initial data generating module 121, a data calculating module 122, a data generating module 123, an abnormal data detecting module 124 and a data outputting module 126. For example, the initial data generating module 121 may be an initializer for generating initial data according to initial settings in the simulation configuration data set.
FIG. 2 is a flow chart of a method of simulating twin data for a device according to an embodiment of the invention. Referring to fig. 1 and 2, the simulation system 100 for equipment twin data of the present embodiment may perform the following steps S210 to S260 to generate a simulation data set corresponding to the simulation configuration data set, and perform anomaly detection on each item of data. In step S210, the processor 110 inputs the simulated configuration data set to the initial data generating module 121, and generates an initial data set according to the simulated configuration data by the initial data generating module 121. For example, the processor 110 inputs the simulation configuration data set preset/selected by the user to the initial data generating module 121, so that the initial data generating module 121 generates the initial data set. In this embodiment, the initial data set is a first plurality of data in the simulation data. For example, the initial data set may include items of cooling water, tool, and screw, each numbered 1 and having values of 20, 100, and 0, respectively.
In step S220, the processor 110 may perform a statistical calculation on the initial data set through the data calculation module 122 to generate a statistical feature data set. For example, the processor 110 executes the data calculation module 122 to perform statistical calculation on the initial data set according to different item/device parameter types through the data calculation module 122 to generate a plurality of corresponding statistical characteristic data sets. In step S230, the processor 110 generates a simulation data set according to the simulation configuration data set, the initial data set and the statistical characteristic data set through the data generating module 123.
In this way, the data generating module 123 generates corresponding simulation data according to the multidimensional data, the multidimensional data types (for example, different growth trends/data trends of a plurality of items of data), the relationship between a plurality of items of data, and a plurality of setting values (for example, upper and lower bounds, an average value, a starting value, a variance, a generated data amount, an abnormality occurrence type, and an abnormality occurrence probability) corresponding to the plurality of items of data in the simulation configuration data set, and the plurality of items of simulation data form a simulation data set. For example, the multidimensional data is, for example, a plurality of data related to each other, and thus the related plurality of data can be represented by a flat data diagram or a multidimensional diagram. That is, the simulation configuration data set includes not only a plurality of items of data related to the device, but also a relationship between the plurality of items of data, so that the simulation system 100 of the device twin apparatus and the method thereof can provide a highly simulated, highly realistic simulation data set.
In step S240, the processor 110 inputs the simulation data set into the database 125 through the data generation module 123. The database 125 is, for example, a memory block of the storage device 120 for storing the initial data set and the simulation data set. In step S250, the abnormal data detection module 124 performs an abnormal data determination according to the simulation data set. In the present embodiment, the processor 110 executes the abnormal data detecting module 124, so that the abnormal data detecting module 124 performs the abnormal data determination according to the simulation data set stored in the database 125.
In step S260, a warning message/analog data set is output according to the determination result of the abnormal data determination. In the present embodiment, the processor 110 determines to continue or stop the data generating module 123 from generating the next set of simulation data according to the determination result of the abnormal data determination (i.e., the determination performed by the abnormal data detecting module 124). In an embodiment, the step of determining to continue or stop the generation of the next set of simulation data set by the data generation module 126 according to the determination result of the abnormal data determination by the processor 110 further includes: when the abnormal data is judged to have abnormal data, the abnormal data detecting module 124 outputs warning information and stops the data generating module 123 from generating the next set of simulation data, and when the abnormal data is judged to have abnormal data, the abnormal data detecting module 124 outputs the simulation data set through the data output module 126, and the processor 110 continues/executes the data generating module 123 again to generate the next set of simulation data.
That is, the simulation system 100 and the method for device twin data store the simulation data sets in the database 125 at each time, so that the abnormal data detection module 124 is connected to the database 125 to access any data in the database 125. Thus, the abnormal data detection module 124 can detect and determine whether any data value of the analog data set has a data abnormal condition (e.g., a value greater than/less than a predetermined data normal range) in real time. If so, the data generation module 123 is stopped from generating the next set of analog data sets, and alert information is output to the external system/user's device. If not, the execution data generation module 123 continues to generate the next set of simulation data.
In one embodiment, the processor 110 may be further coupled to an external database or an internal database 125 for reading/writing the simulation configuration data set in the external database or the internal database 125 and the simulation configuration data set preset by the user. In another embodiment, the simulation system 100 of device twin data may further include a transceiver to receive/transmit the data set/setting data set transmitted by the client. Thus, the twin data simulation system 100 of the present invention can generate corresponding simulation data sets according to the selected data types, the parameter settings, and the multidimensional data, and perform abnormal data determination on each simulation data set according to the abnormal data detection module 124. Thus, the simulation system 100 for the device twin data and the simulation data set generated by the method thereof have the advantages of high authenticity and have the function of judging data abnormality in real time according to different set ranges in the data.
In the present embodiment, the Processor 110 may include, for example, a Central Processing Unit (CPU), or other Programmable general purpose or special purpose Microprocessor (Microprocessor), digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), programmable Logic Device (PLD), other similar Processing Circuits, or a combination thereof. The storage 120 may include a Memory (Memory) and/or a database (database). In one embodiment, the storage device 120 may store a simulation configuration data set, a data pattern library, an exception data pattern library, and a database 125. Also, the storage 120 may be, for example, a Non-Volatile Memory (NVM). The storage device 120 may store the relevant programs, modules, systems or algorithms for implementing the embodiments of the present invention, so as to be accessed and executed by the processor 110 to implement the relevant functions and operations described in the embodiments of the present invention.
In an embodiment, the initial data generating module 121, the data calculating module 122, the data generating module 123, the abnormal data detecting module 124, the database 125, the data outputting module 126 and the abnormal probability triggering module 127 may be implemented by a program Language such as JSON (JavaScript Object notification), extensible Markup Language (XML) or YAML, but the invention is not limited thereto. In this embodiment, the simulation system 100 for device twin data may be implemented in a Personal Computer (PC), a Server (Server), or a cloud Server, but the invention is not limited thereto. In an embodiment, the simulation system 100 for device twin data may also be integrated in an Enterprise Resource Planning (ERP) system to provide device twin data (simulation data set) with high authenticity according to a predetermined data type.
In the present invention, the data type in the simulation configuration data set may be at least one of a fluctuation type, an increment level type, a decrement level type, an exponential type, or a linear rule type. And, the processor 110 conforms the simulation data of the next group in the simulation data group to the corresponding data type through the data generating module 123 and the data calculating module 122. Thus, the user can set the corresponding data type according to each parameter type of the equipment to be simulated/simulated currently, for example, the simulated production equipment data comprises tool wear data and water temperature data, so that the user selects the decreasing data type to simulate the tool wear and selects the increasing data type to simulate the gradual rise of the water temperature. In this way, a plurality of items of data in the simulation data set can be simulated and generated according to the corresponding data types, so that the simulation data set generated by the simulation system 100 for equipment twin data and the method thereof has high simulation and high applicability.
FIG. 3 is a flow chart of a simulation system of twin data of the apparatus according to an embodiment of the present invention. Referring to fig. 1 to fig. 3, in the present embodiment, the modules further include a data output module 126 and an anomaly probability triggering module 127. Further, the storage device 120 further stores an abnormal data pattern library 132 and a data pattern library 133. In the present embodiment, the data generation module 123 further includes a normal data generation unit 1231 and an abnormal data generation unit 1232.
In one embodiment, the step of generating the simulation data set by the data generating module 123 according to the simulation configuration data set (simulation configuration data 131), the initial data set and the plurality of statistical characteristic data sets includes: the normality data generation unit 1231 performs normality data generation based on the simulated configuration data set (simulated configuration data 131), the initial data set, and the plurality of statistical characteristic data sets to generate a normality-mode data set, and the simulated data set (i.e., the current simulated data set) includes the initial data set and the normality-mode data set. The normal data generation unit 1231 inputs the analog data set (i.e., the current analog data set) to the abnormal data generation unit 1232. Then, the abnormal data generating unit 1232 generates an abnormal pattern data set based on the plurality of abnormal occurrence probabilities in the simulation configuration data set (simulation configuration data 131), and when the abnormal pattern data set is generated by the abnormal data generating unit 1232, the simulation data set (i.e. the current simulation data set) further includes the abnormal pattern data set.
It should be noted that the normal mode data set mentioned in the present invention is a data set generated according to the setting of the normal mode, that is, even if the data set generated in the normal mode may be larger than the upper threshold and the lower threshold (i.e. the upper/lower limit) in the simulation configuration data 131, for example, if the production equipment to be simulated is a wear-type equipment, even if any abnormality occurrence probability is not triggered/no abnormality occurs. However, according to the actual operation situation and the normal mode data set of the present invention, the data of the equipment belonging to the wear-type production equipment still exceeds the critical value after a certain number of uses/time (for example, the number of uses of the wear-type production equipment reaches the critical value and the temperature of the equipment is too high). Thus, the system 100 for simulating device twin data and the method thereof of the present invention can be used not only to generate a device simulation data set with high realism, but also to simulate each parameter/estimated operation time of each component of a device, so that a user can obtain device twin data through the simulation system.
In one embodiment, when the abnormal data generating unit 1232 generates the abnormal pattern data set based on the plurality of abnormal occurrence probabilities, the abnormal data generating unit 1232 performs the abnormal data generation to generate the abnormal pattern data set according to the simulation configuration data set (the simulation configuration data 131), the initial data set, and the current simulation data set (i.e., the normal pattern data set and the initial data set generated by the normal data generating unit 1231 in the previous step), and the abnormal data generating unit 1232 takes the abnormal pattern data set, the initial data set, and the current simulation data set as the current simulation data set and inputs the current simulation data set to the data calculating module 122.
On the other hand, when the abnormal data generation unit 1232 does not generate the abnormal pattern data set based on the plurality of abnormal occurrence probabilities (i.e., none of the abnormal occurrence probabilities is triggered/abnormal), the abnormal data generation unit 1232 uses the initial data set and the simulation data set as the current simulation data set and inputs the current simulation data set to the data calculation module 122. Then, the data calculating module 122 performs statistical calculation according to the current simulated data set to generate a plurality of current statistical feature data sets, and the processor 110 repeatedly performs normal data generation and abnormal data generation respectively by the normal data generating unit 1231 and the abnormal data generating unit 1232 to obtain a next simulated data set until the processor 110 stops the normal data generating unit 1231 and the abnormal data generating unit 1232 by the abnormal data detecting module 124 to generate a new simulated data set.
For example, the analog configuration data 131 may store at least one of a normal data type, a start value, an average value, a maximum value, a minimum value, an abnormal data type, a data type (e.g., a normal data type), an abnormal occurrence probability, and a single data size. It is noted that the simulation configuration data set (i.e., the simulation configuration data 131) of the present invention further includes a plurality of abnormal data types of a single generated data item and corresponding abnormal probabilities. That is, the abnormal pattern data set generated by the abnormal data generating unit 1232 may include different abnormal probabilities corresponding to various abnormal occurrences/data types. For example, the abnormal state of the screw vibration in the production equipment may include various abnormal states such as position abnormality, rotational speed abnormality, or power abnormality. The simulation configuration data 131 can set different abnormal probabilities (e.g., one-thousandth or one-hundred-thousand) for different abnormal states, so that the abnormal pattern data set has high simulation performance.
The simulated configuration data 131 may be as shown in table 1 below.
TABLE 1
Next, the initial data generating module 121 generates an initial data set from the simulation configuration data set (step S210). The initial data set may be as shown in table 2 below. The initial data generating module 121 inputs the initial data set into the database 125, so that the abnormal data detecting module 124 can detect the initial data set stored in the database 125. In the present embodiment, the abnormal data of the abnormal data detecting module 124 is determined as abnormal-free data, so the processor 110 inputs the initial data set to the data calculating module 122 through the database 125. The data calculation module 122 performs statistical calculation on the initial data set to generate a corresponding statistical characteristic data set (step S220). The initial data set may be as shown in table 3 below.
| Item | Serial number | Numerical value |
| Cooling water | 1 | 20 |
| Cutting tool | 1 | 100 |
| Screw rod | 1 | 0 |
TABLE 2
TABLE 3
In this example, the processor 110 then generates the simulation data sets (i.e. the normal mode data set and the abnormal mode data set) respectively by the normal data generating unit 1231 and the abnormal data generating unit 1232 of the data generating module 123 according to the simulation configuration data set, the initial data set and the statistical characteristic data set (step S230). In the present example, the normal mode data set may be as shown in table 4 below, and the abnormal mode data set may be as shown in table 5 below. The current set of simulation data can be as shown in table 6 below.
TABLE 4
TABLE 5
| Item | Serial number | Numerical value |
| Cooling water | 1 | 20 |
| Cooling water | 2 | 23 |
| Cutting tool | 1 | 100 |
| Cutting tool | 2 | 99.9998 |
| Screw rod | 1 | 0 |
| Screw rod | 2 | 4950 |
TABLE 6
In one embodiment, the data calculation module 122 performs a statistical calculation set according to the simulation data sets stored in the database 125 and the current simulation data set to generate a statistical feature data set corresponding to each data. That is, the simulation system 100 of the device twin data and the simulation method of the device twin data repeatedly generate the normal mode data set and the abnormal mode data set a plurality of times by the normal data generation unit 1231 and the abnormal data generation unit 1232 in the data generation module 123. In addition, the normal mode data set and the abnormal mode data set generated each time use the statistical feature data of the current simulation data (i.e. the normal mode data of each time, the abnormal mode data of each time and the initial data) as the reference data, so that the simulation data set highly conforms to the set value in the simulation configuration data set.
For example, after the data generating module 123 generates the first set of simulation data sets, the first set of simulation data sets is stored in the database 125, and then the data calculating module 122 reads the first set of simulation data sets and the initial data set for statistical calculation, so as to generate statistical characteristic data sets corresponding to the first set of simulation data sets and the initial data set. Next, the data generating module 123 performs data generation again according to the statistical characteristic data, the initial data and the first set of simulation data to generate a second set of simulation data (i.e. including the initial data, the first simulation data and the second simulation data). Similarly, the data generating module 123 stores the second set of simulation data in the database 125, and repeats the statistical characteristic analysis and the data generation until the abnormal data detecting module 124 detects abnormal data, and outputs an abnormal determination result to stop the data generating module 123 from generating the next set of simulation data.
In the present example, the processor 110 inputs the normal mode data set and the abnormal mode data set into the database 125, so that the abnormal data detecting module 124 reads the data in the database 125 and generates a corresponding abnormal data determining result (step S250). In this example, the abnormal data determination result is that there is no abnormal data. Therefore, the processor 110 continues to execute the data calculation module 122 and the data generation module 123 through the abnormal data detection module 124 according to the abnormal data determination result (step S260) to repeatedly generate the next statistical feature data set, the next normal data set and the next abnormal data set until the abnormal data generated by the abnormal data detection module 124 is determined to have abnormal data. In this embodiment, the next set of statistical characteristic data sets may be shown in table 7 below, the next set of normal mode data sets may be shown in table 8 below, and the next set of abnormal mode data sets may be shown in table 9 below.
| Item | Statistical features | Numerical value |
| Cooling water | Total number of data | 2 |
| Mean value of | 21.5 | |
| Maximum value of | 23 | |
| Minimum value | 20 | |
| Cutting tool | Total number of data | 2 |
| Mean value of | 99.9999 | |
| |
100 | |
| Minimum value | 99.9998 | |
| Screw rod | Total number of data | 2 |
| Mean value of | 2475 | |
| Maximum value | 4950 | |
| Minimum value | 0 |
TABLE 7
TABLE 8
TABLE 9
In one embodiment, the data pattern library 133 stores normal data patterns. As such, the normality data generation unit 1231 generates a normality pattern data set including a plurality of device data types and corresponding sets of device normality data based on the normality data pattern. The abnormal data pattern library 132 stores abnormal data patterns. As such, the abnormal data generation unit 1232 generates an abnormal pattern data set including a plurality of device data types and corresponding sets of device abnormal data based on the abnormal data pattern.
For example, the normal data pattern is at least one of a fluctuation type, an increment level type, a decrement level type, an exponential type, and a linear rule type. The abnormal data pattern is at least one of a single point exception, a multi-point continuous exception, an alarm type exception, and a no data type exception, but the present invention is not limited thereto. That is, the normal data pattern and abnormal data pattern read from the memory device 120 by the normal data generation unit 1231 and the abnormal data generation unit 1232 are increased by the two memory blocks divided in the memory device 120, the data pattern library 133 and the abnormal data pattern library 132.
In one embodiment, the data output module 126 is configured to output the simulation data set and/or the warning message to an external electronic device, and the output simulation data set is a simulation data set specified by a user. The data output module 126 may be, for example, a data exchange interface or an output module communicatively connected to a data transceiver. Thus, the data output module 126 is used for outputting the analog data to an external electronic device. Examples of electronic devices/electronic apparatuses are servers, databases, notebook computers, desktops, and the like.
In one embodiment, the database 125 may store the initial data set, the normal mode data set, the abnormal mode data set, the current simulation data set, and the simulation data set according to a strip column. The resulting data set 125 is coupled to the data output module 126 to output the specified data through the data output module 126. For example, the user or the related person transmits the data specifying instruction through the transceiver and the data output module 126. In this manner, the processor 110 outputs the designated data to the electronic device of the user according to the data designation instruction.
In one embodiment, the abnormal probability triggering module 127 triggers the abnormal data generating unit 1232 to generate the abnormal pattern data set based on a plurality of abnormal occurrence probabilities, which include a plurality of abnormal states and a plurality of corresponding abnormal probabilities. The simulation configuration data set (i.e., the simulation configuration data 131) stores a plurality of abnormal occurrence probabilities, and respectively corresponds to a plurality of abnormal states (as shown in table 1 above). For example, the abnormal state is, but not limited to, a cooling water blockage, a power failure, and an excessive water temperature. The abnormal occurrence probability corresponding to the plurality of abnormal states may be, for example, 1 ten thousandth, one hundredth and one thousandth of 50 ten thousandth, respectively. Also, each exception state corresponds to an exception data pattern (i.e., data type). Thus, the abnormal data generation unit 1232 generated by the simulation system 100 for device twin data and the simulation method for device twin data can cover a plurality of abnormal states, and the simulation configuration data set (i.e. the simulation configuration data 131) can be set to include different abnormal occurrence probabilities according to different abnormal states. Therefore, the simulation system 100 for device twin data and the method thereof have the advantages of being applicable to different production devices and correspondingly generating abnormal data, thereby having flexibility and being comprehensively applied to different production devices.
As described above, the simulation system 100 for device twin data and the simulation method for device twin data according to the present invention can generate simulation data sets corresponding to different setting values. The simulation data set contains a plurality of parameters/data associated with the device. Therefore, the simulation system and the simulation method of the equipment twin data achieve higher simulation degree/simulation degree when the digital factory is in simulation operation. Meanwhile, the simulation system 100 for device twin data and the simulation method for device twin data of the present invention provide comprehensive and real-time abnormal data judgment and processing through the abnormal data detection module 124. In addition, the comprehensive state data of the equipment is simulated by combining and setting the multi-dimension/multi-item data through the simulation configuration data set, so that various conditions of the equipment in an actual operation state are more approximate.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (20)
1. A system for simulating twin data of a plant, comprising:
the storage device is used for storing a simulation configuration data set, a database and a plurality of modules, wherein the modules comprise an initial data generation module, a data calculation module, a data generation module, an abnormal data detection module and a data output module; and
a processor coupled to the storage device,
wherein the processor executes the initial data generation module and inputs the simulated configuration data set to the initial data generation module to produce an initial data set by the initial data generation module,
wherein the processor executes the data calculation module to perform statistical calculation on the initial data set by the data calculation module to generate a plurality of statistical characteristic data sets, respectively,
wherein the processor executes the data generation module to generate a simulated data set from the simulated configuration data set, the initial data set, and the statistical signature data set via the data generation module, and the data generation module inputs the simulated data set into the database,
the processor executes the abnormal data detection module to execute abnormal data judgment according to the simulation data groups stored in the database, and decides to continue executing or stop the data generation module from generating the next simulation data group according to the judgment result of the abnormal data judgment.
2. The system as claimed in claim 1, wherein when the abnormal data is determined as abnormal data, the abnormal data detecting module outputs a warning message and stops executing the data generating module to generate the next set of simulation data,
when the judgment result of the abnormal data judgment is abnormal data, the abnormal data detection module outputs the simulation data set through the data output module, and the processor continuously executes the data generation module to generate the next simulation data set.
3. The system as claimed in claim 1, wherein the data generating module is configured to generate the simulation data sets according to a plurality of data types in the simulation configuration data set, respectively, and the plurality of data types includes at least one of a fluctuation type, an increment level type, a decrement level type, an exponent, and a linear rule type, and the processor is configured to conform the data relationship between the data set of the next set and the data set of the previous set in the simulation data set to the corresponding plurality of data types through the data generating module and the data calculating module.
4. The simulation system of plant twin data according to claim 1, wherein the data generation module further includes a normal data generation unit and an abnormal data generation unit,
wherein the normality data generation unit performs normality data generation based on the simulated configuration data set, the initial data set, and the plurality of statistical feature data sets to produce a normality-pattern data set, and the simulated data set includes the initial data set and the normality-pattern data set,
wherein the abnormal data generation unit generates an abnormal pattern data set based on a plurality of abnormal occurrence probabilities in the simulated configuration data set, and the simulated data set further includes the abnormal pattern data set when the abnormal pattern data set is generated by the abnormal data generation unit.
5. The simulation system of the device twin data according to claim 4, wherein when the abnormal data generation unit generates the abnormal pattern data group based on the plurality of abnormality occurrence probabilities, the abnormal data generation unit performs abnormal data generation to generate the abnormal pattern data group from the simulation configuration data group, the initial data group, and the simulation data group, and the abnormal data generation unit takes the abnormal pattern data group, the initial data group, and the simulation data group as a current simulation data group and inputs them to the data calculation module,
when the abnormal data generation unit does not generate the abnormal pattern data set based on the plurality of abnormal occurrence probabilities, the abnormal data generation unit takes the initial data set and the simulation data set as the current simulation data set and inputs them to the data calculation module,
wherein the data calculation module performs the statistical calculation according to the current simulation data set to generate a plurality of current statistical characteristic data sets, and the processor repeatedly performs the normal data generation and the abnormal data generation respectively by the normal data generation unit and the abnormal data generation unit to obtain a next set of the simulation data sets until the processor stops the normal data generation unit and the abnormal data generation unit from generating the simulation data sets by the abnormal data detection module.
6. The system for simulating plant twin data as claimed in claim 5, wherein the storage device further stores a database of data patterns and a database of abnormal data patterns,
wherein the data pattern library stores normal data patterns, and the normal data generation unit generates the normal-mode data sets based on the normal data patterns, the normal-mode data sets including a plurality of device data types and corresponding sets of device normal data,
wherein the abnormal data pattern base stores abnormal data patterns, and the abnormal data generation unit generates the abnormal pattern data group based on the abnormal data patterns, the abnormal pattern data group including a plurality of device data types and a corresponding plurality of sets of device abnormal data,
wherein the database stores the initial data set, the normal data set, the abnormal data set, the current simulation data set, and the simulation data set in a columnar order.
7. The system as claimed in claim 5, wherein the modules further comprise an abnormal probability triggering module, the abnormal probability triggering module triggers the abnormal data generating unit to generate the abnormal pattern data set based on the abnormal occurrence probabilities, and the abnormal occurrence probabilities comprise abnormal states and corresponding abnormal probabilities.
8. The system for simulating twin data of a plant as claimed in claim 1, wherein the set of simulated configuration data comprises: at least one of a normal data type, a start value, an average value, a numerical maximum value, a numerical minimum value, an abnormal data type, the plurality of data types, an abnormal occurrence probability, a data generation amount, and a single data amount.
9. The system for simulating twin data of an apparatus as claimed in claim 1, wherein the data output module is configured to output the simulated data set and the warning information to an external electronic device.
10. The system as claimed in claim 5, wherein the data calculation module performs the statistical calculation set according to the simulation data set stored in the database and a current simulation data set to generate a statistical characteristic data set corresponding to each data.
11. A method of simulating twin data of a device, comprising:
generating an initial data set according to the simulation configuration data set through an initial data generation module;
performing statistical calculation on the initial data set through a data calculation module to respectively generate a plurality of statistical characteristic data sets;
generating a simulation data set according to the simulation configuration data set, the initial data set and the plurality of statistical characteristic data sets through a data generation module, and inputting the simulation data set into a database; and
and executing abnormal data judgment according to the simulation data groups stored in the database through an abnormal data detection module, and determining to continue executing or stop the data generation module to generate the next simulation data group according to the judgment result of the abnormal data judgment.
12. The method for simulating twin data of a plant according to claim 11, further comprising:
when the abnormal data is judged to be abnormal data, outputting warning information through the abnormal data detection module, and stopping executing the data generation module to generate the next group of simulation data set,
when the judgment result of the abnormal data judgment is abnormal data, the analog data set is output through the abnormal data detection module and the data output module, and the next analog data set is generated through the data generation module.
13. The simulation method of twin data of a plant according to claim 11, wherein the simulation data groups are respectively generated according to a plurality of data types in the simulation configuration data group, and the plurality of data types include at least one of a fluctuation type, an increment level type, a decrement level type, an exponent, and a linear rule type;
wherein the simulation method further comprises:
and enabling the data relation between the next group of data set and the previous group of data set in the simulation data set to accord with the corresponding multiple data types through the data generation module and the data calculation module.
14. The method for simulating twin data of a plant according to claim 11, further comprising:
performing, by the normality data generation unit, normality data generation from the simulated configuration data set, the initial data set, and the plurality of statistical feature data sets to produce a normality-mode data set, and the simulated data set includes the initial data set and the normality-mode data set,
generating, by the abnormal data generation unit, an abnormal pattern data set based on a plurality of abnormal occurrence probabilities in the simulated configuration data set, and when the abnormal pattern data set is generated by the abnormal data generation unit, the simulated data set further includes the abnormal pattern data set.
15. The method for simulating twin data of a plant according to claim 14, further comprising:
when the abnormal data generation unit generates the abnormal pattern data set based on the plurality of abnormal occurrence probabilities, performing abnormal data generation by the abnormal data generation unit to generate the abnormal pattern data set from the simulation configuration data set, the initial data set, and the simulation data set, and the abnormal data generation unit having the abnormal pattern data set, the initial data set, and the simulation data set as a current simulation data set and inputting to the data calculation module,
when the abnormal data generation unit does not generate the abnormal pattern data set based on the plurality of abnormal occurrence probabilities, the initial data set and the simulation data set are used as the current simulation data set by the abnormal data generation unit and input to the data calculation module,
wherein the statistical calculation is performed by the data calculation module according to the current simulation data set to generate a plurality of current statistical characteristic data sets, and the normal data generation and the abnormal data generation are repeatedly performed by the normal data generation unit and the abnormal data generation unit, respectively, to obtain a next set of the simulation data sets until the normal data generation unit and the abnormal data generation unit are stopped from generating the simulation data sets by the abnormal data detection module.
16. The method for simulating twin data of a plant according to claim 15, further comprising:
storing a normal data pattern by a data pattern library, and the normal data generation unit generating the normal mode data set based on the normal data pattern, the normal mode data set including a plurality of device data types and corresponding sets of device normal data,
storing an abnormal data pattern by an abnormal data pattern library, and the abnormal data generation unit generating the abnormal pattern data group based on the abnormal data pattern, the abnormal pattern data group including a plurality of device data types and a corresponding plurality of sets of device abnormal data,
wherein the database stores the initial data set, the normal data set, the abnormal data set, the current simulation data set, and the simulation data set in a columnar manner.
17. The method for simulating twin data for a device as claimed in claim 15, further comprising:
and triggering the abnormal data generation unit to generate the abnormal pattern data set based on the abnormal occurrence probabilities by an abnormal probability triggering module, wherein the abnormal occurrence probabilities comprise a plurality of abnormal states and a plurality of corresponding abnormal probabilities.
18. The method of simulating device twin data according to claim 11, wherein the simulating configuration data set includes: at least one of a normal data type, a start value, an average value, a numerical maximum value, a numerical minimum value, an abnormal data type, the plurality of data types, an abnormal occurrence probability, a data generation amount, and a single data amount.
19. The method for simulating twin data of an apparatus as claimed in claim 11, wherein the data output module is used to output the simulated data set and warning information to an external electronic device.
20. The method of simulating twin data of an apparatus as claimed in claim 15, wherein the statistical calculation set is performed by the data calculation module according to the simulation data set stored in the database and a current simulation data set to generate a statistical characteristic data set corresponding to each data.
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