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1
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[Simulation logic]
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2
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3
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0.common
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4
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1) simulation input data unit
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5
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a) 5 minutes : get from DB
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6
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b) 1 record (current time) : manual input
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7
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ex) 2024-10-01 00:00:00
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8
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2024-10-01 00:05:00
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9
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2024-10-01 00:10:00
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10
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11
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1.process
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12
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1) get {EC_SIMULATION_INPUT} data of selected simulation
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13
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a) if {FILE_YN} = 'N'
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14
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- get {INPUT_DATA} (JSON) data (could be multiple)
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15
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b) if {FILE_YN} = 'Y'
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16
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- load {INPUT_FILE} file data
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17
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18
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2) run simulation MODEL logic with {EC_SIMULATION.PERIOD_TYPE} unit
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19
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a) if {EC_SIMULATION.PERIOD_TYPE} is NULL
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20
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- run selected Simulation MODEL logic with simulation input data unit
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21
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b) if {EC_SIMULATION.PERIOD_TYPE} = 'PT01' (5분: 5 minutes)
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22
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- run selected Simulation MODEL logic with simulation input data (5 minutes unit)
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23
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c) if {EC_SIMULATION.PERIOD_TYPE} = 'PT02' (시간: 1 hour)
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24
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- run selected Simulation MODEL logic with simulation input data (1 hour unit)
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25
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- use average input value
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26
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(ex: if target input data count is 12, SUM(value)/12)
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27
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d) if {EC_SIMULATION.PERIOD_TYPE} = 'PT03' (일: 1 day)
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28
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- run selected Simulation MODEL logic with simulation input data (1 day unit)
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29
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- use average input value
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30
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(ex: if target input data count is 288, SUM(value)/288)
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31
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32
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*) if run simulation MODEL with multiple input data, mutlple result of the simulation MODEL are returned
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33
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34
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35
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3) INSERT {EC_SIMULATION_RESULT} after selected Simulation MODEL run
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36
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a) RESULT_SEQ : seq no per simulation
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37
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b) RESULT_DT
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38
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- if {EC_SIMULATION.PERIOD_TYPE} is NULL, set by INPUT_DT
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39
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- else, set per period unit of simulation input data
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40
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(ex: if 5 minutes unit, 2024-10-01 00:05:00
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41
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if 1 hour unit, 2024-10-01 01:00:00
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42
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if 1 day unit, 2024-10-01 00:00:00)
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43
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c) MODEL_CNT : {selected simulation MODEL count}
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44
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d) MODEL_NO_1 /.../ MODEL_NO_5 : selected simulation MODEL NO
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45
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- if multiple MODEL were selected, set multiple value
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46
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e) RESULT_VALUE_1 /.../ RESULT_VALUE_5 : result value of Simulation MODEL logic
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47
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- if multiple MODEL were selected, set multiple value
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48
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49
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4) if simulation run is finished normally
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50
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UPDATE EC_SIMULATION (of current SIMUL_SEQ)
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51
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- SIMUL_STATUS = 'SS02', UP_DT/UP_USER_ID
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52
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53
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54
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2.Simulation MODEL logic
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55
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1) M10 (Tier1)
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56
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= ((1.5/525600x5)x{BREEDING_NUMBER}x1000) / 41.7907536 x 24.45 / 16.04
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57
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58
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2) M20 (Reg1)
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59
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MET = (-12.68) + (0.0001×{AVERAGE_WEIGHT}) + (0.0098×{FEED_WEIGHT}) + (0.0235×{CO2}) + (0.6316×{NH3}) − (1.3241×{PH_P})
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60
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- if any factor value is NULL, skip (NULL is set into EC_SIMULATION_RESULT.RESULT_VALUE_)
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61
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62
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3) M21 (Reg2)
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63
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MET = (125.51) + (0.0002×{AVERAGE_WEIGHT}) + (0.0148×{FEED_WEIGHT}) + (0.0192×{CO2}) + (0.4256×{NH3}) + (3.2498×{PH_P}) − (2.5183×{Temp_P}) − (0.0694×{RH_Out}) − (3.5052×{Temp})
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64
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- if any factor value is NULL, skip (NULL is set into EC_SIMULATION_RESULT.RESULT_VALUE_)
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65
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66
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4) M22 (Reg3)
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67
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MET = (98.90) + (0.0919×{Temp_Out}) − (0.0080×{RH_Out}) − (0.8907×{Temp}) − (0.2418×{RH}) + (0.0136×{CO2}) + (0.0324×{NH3}) − (1.5973×{Temp_P}) − (4.2335×{PH_P}) − (0.0765×{EC_P}) − (28.2513×{Ventilation Rate}) + (0.0002×{AVERAGE_WEIGHT}) + (0.0162×{FEED_WEIGHT})
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68
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- if any factor value is NULL, skip (NULL is set into EC_SIMULATION_RESULT.RESULT_VALUE_)
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69
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70
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5) M30 (DeepL1) - AI: Deep Learning
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71
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http://218.232.78.85:9005/apidocs/#/
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72
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API: POST /model/PredictMetModel_DeepL_1
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73
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74
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6) M31 (DeepL2) - AI: Deep Learning
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75
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http://218.232.78.85:9005/apidocs/#/
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76
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API: POST /model/PredictMetModel_DeepL_2
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