CN-122018612-A - AI prediction-based prefabricated cabin self-adaptive environment adjustment method and system

CN122018612ACN 122018612 ACN122018612 ACN 122018612ACN-122018612-A

Abstract

The invention relates to the technical field of environmental control, and discloses a prefabricated cabin self-adaptive environment adjusting method and system based on AI prediction, wherein the method comprises the steps of calculating equivalent temperature and equivalent humidity in a prefabricated cabin; the method comprises the steps of obtaining a predicted temperature sequence and a predicted humidity sequence within a time t range, determining the start-stop time of equipment according to the comparison result of the predicted temperature sequence and the predicted humidity sequence and the environmental parameter limit value, and continuously obtaining predicted data after the equipment finishes a start-stop instruction. The method and the device realize predictive active regulation and control, avoid the breakthrough of the safety threshold of the environmental parameters in the prefabricated cabin, match the double working modes, flexibly switch the control strategy according to the type of the equipment in the prefabricated cabin, effectively reduce the environmental fluctuation, and inhibit the generation of condensation in the cabin, thereby reducing the occurrence rate of equipment operation faults.

Inventors

SONG PENG
LIU XUEBAO
Ren Mengshuo
WANG HAOZHUO
CHEN HONGNAN
LIU JIAHUA
YU LONGTAO
GE JINXIN
Liu Zecai

Assignees

青岛特锐德电气股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (8)

1. The prefabricated cabin self-adaptive environment adjusting method based on AI prediction is characterized by comprising the following steps: S1, calculating the equivalent temperature and the equivalent humidity in the prefabricated cabin according to the setting position of a temperature and humidity sensor in the prefabricated cabin and the acquired temperature and humidity data; s2, acquiring a predicted temperature sequence and a predicted humidity sequence within a time t range; s3, determining the start-stop time of equipment according to the comparison result of the predicted temperature sequence, the predicted humidity sequence and the environmental parameter limit value, wherein the environmental parameter limit value comprises a temperature limit value, a humidity limit value and a dew point temperature limit value, the equipment comprises an air conditioner, an electric heater and a dehumidifier, and the determination process comprises the following steps: s31, comparing the predicted temperature sequence with a temperature limit value, and outputting the start-stop time of the air conditioner or the electric heater according to a comparison result; S32, determining the moment when the humidity limit value is met for the first time in the predicted humidity sequence, calculating the dehumidification time required by the predicted humidity corresponding to the moment when the predicted humidity reaches the set humidity, and recording the dehumidification time as the first dehumidification time; Calculating a predicted dew point temperature sequence, determining the moment when the predicted dew point temperature sequence meets the dew point temperature limit value for the first time, calculating the dehumidification time required by the predicted humidity corresponding to the moment when the predicted humidity reaches the target humidity, and recording the dehumidification time as second dehumidification time; if the time exists, the larger value of the first dehumidification time and the second dehumidification time is selected as the running time of the dehumidifier, otherwise, the shutdown time of the dehumidifier is determined according to the surface temperature and the dew point temperature of the prefabricated cabin; S4, returning to the step S2 after the equipment completes the start-stop instruction.
2. The AI-prediction-based prefabricated cabin adaptive environment adjustment method according to claim 1, wherein the calculation process of the first dehumidification time is: determining the time when the humidity limit value is met for the first time in the predicted humidity sequence, and marking the time as a first time; Calculating the dehumidification amount required by the first predicted humidity to reach the set humidity, and recording the dehumidification amount as a first dehumidification amount; And calculating a first dehumidification time according to the number of the dehumidifiers and the rated dehumidification amount, wherein a calculation formula is as follows: First dehumidification time=first dehumidification amount/(number of dehumidifiers×rated dehumidification amount).
3. The AI-prediction-based prefabricated cabin adaptive environment adjustment method according to claim 1, wherein the calculation process of the second dehumidification time is: Calculating a predicted dew point temperature sequence; determining the moment when the predicted dew point temperature sequence meets the dew point temperature limit value for the first time, and marking the moment as a second moment, wherein the predicted humidity corresponding to the second moment in the predicted humidity sequence is marked as a second predicted humidity, and the predicted temperature corresponding to the second moment in the predicted temperature sequence is marked as a second predicted temperature; Calculating a target humidity according to the second predicted temperature and the set dew point temperature; Calculating the dehumidification amount required by the second predicted humidity to reach the target humidity, and recording the dehumidification amount as a second dehumidification amount; And calculating a second dehumidification time according to the number of the dehumidifiers and the rated dehumidification amount, wherein a calculation formula is as follows: Second dehumidification time=second dehumidification amount/(number of dehumidifiers×rated dehumidification amount).
4. The method for adaptive environmental conditioning of a prefabricated cabin based on AI prediction according to claim 1, wherein the step S3 further includes starting a timer synchronously when the dehumidifier is started, and starting a start-stop judging program of an air conditioner if the dehumidifier is still in an on state when the timer is ended.
5. The method for adjusting the self-adaptive environment of the prefabricated cabin based on AI prediction as set forth in claim 4, wherein the start-stop judging procedure of the air conditioner comprises judging whether the equivalent temperature satisfies T max -15℃<T<T max -10℃,T max as the upper temperature limit value, if so, starting the air conditioner for refrigeration and setting a refrigeration value T set , otherwise, ending the judging procedure; during the running period of the air conditioner, judging whether the equivalent temperature meets T < T max -15 ℃, if so, closing the air conditioner, and ending the judging program; Otherwise, judging whether the dew point temperature T d and the surface temperature T b of the prefabricated cabin meet the temperature T d <T b -5 ℃ and T d <T dmax -5℃,T dmax is the dew point temperature limit value, if so, closing the air conditioner, ending the judging program, otherwise, returning to the previous judging step.
6. The AI-prediction-based adaptive environmental conditioning method of a prefabricated cabin according to claim 4, wherein determining the shutdown timing of the dehumidifier according to the surface temperature and the dew point temperature of the prefabricated cabin in step S32 includes: When the dew point temperature and the surface temperature of the prefabricated compartment meet the dew point temperature < surface temperature-5 ℃, the dehumidifier is shut down and the timer is reset.
7. The method for adaptive environmental conditioning of a prefabricated cabin based on AI prediction according to claim 1, wherein the parameters required for the process of obtaining the predicted temperature sequence and the predicted humidity sequence in step S2 include air conditioning power P L , electric heater power P d , dehumidifier dehumidification capacity C, equivalent temperature T in the prefabricated cabin, equivalent humidity H, external temperature T w outside the prefabricated cabin, external humidity H w , and open/close status of the prefabricated cabin door.
8. The prefabricated cabin adaptive environment adjustment system based on AI prediction according to any one of claims 1 to 7, comprising: The data acquisition module is used for acquiring cabin body data, real-time environment data and real-time operation data of the prefabricated cabin and calculating equivalent temperature and equivalent humidity in the prefabricated cabin; the prediction module is used for outputting a predicted temperature sequence and a predicted humidity sequence according to the data in the data acquisition module; The data processing module is used for determining the start-stop time of the equipment according to the comparison result of the predicted temperature sequence and the predicted humidity sequence and the environmental parameter limit value; And the equipment executing module is used for controlling the equipment to complete parameter adjustment and start-stop actions according to the start-stop time.

Description

AI prediction-based prefabricated cabin self-adaptive environment adjustment method and system Technical Field The invention relates to the technical field of environmental control, in particular to a prefabricated cabin self-adaptive environment adjusting method and system based on AI prediction Background The application scene of the prefabricated cabin transformer substation covers various places of the country and covers multiple complicated and severe working conditions such as high salt fog, high damp and hot, extremely cold and the like. The stability of the internal environment of the prefabricated cabin is directly related to the reliability and safety of the operation of the internal electrical equipment. The severe change of the external environment often causes the environmental fluctuation in the cabin, so that the problems of damping, condensation and the like of an insulating medium are caused, the partial discharge probability is further increased, the electrochemical corrosion is induced, the operation and maintenance cost is increased, and the operation safety of a power grid is influenced. The current prefabricated cabin transformer substation only depends on configured heating and ventilation equipment, basic regulation and control are realized through simple threshold triggering, the control strategy is single and lacks necessary cooperation, and control hysteresis and regulation hysteresis are often caused. In addition, the response of dynamic disturbance such as cabin heat transfer, equipment start-stop and the like is slow, so that the risks of severe environmental fluctuation, surface condensation and the like in the prefabricated cabin are still caused. In summary, there is a need to design a prefabricated cabin adaptive environment adjustment method and system based on AI prediction to solve the above-mentioned problems in the prior art. Disclosure of Invention The invention provides a prefabricated cabin self-adaptive environment adjusting method and system based on AI prediction, which solve the problems of obvious environment fluctuation, easy condensation, isolated operation of the conventional heating and ventilation equipment, single control strategy, lag control effect and the like in the conventional prefabricated cabin. In order to achieve the above purpose, the invention adopts the following technical scheme: A prefabricated cabin self-adaptive environment adjusting method based on AI prediction comprises the following steps: S1, calculating the equivalent temperature and the equivalent humidity in the prefabricated cabin according to the setting position of a temperature and humidity sensor in the prefabricated cabin and the acquired temperature and humidity data; s2, acquiring a predicted temperature sequence and a predicted humidity sequence within a time t range; s3, determining the start-stop time of equipment according to the comparison result of the predicted temperature sequence, the predicted humidity sequence and the environmental parameter limit value, wherein the environmental parameter limit value comprises a temperature limit value, a humidity limit value and a dew point temperature limit value, the equipment comprises an air conditioner, an electric heater and a dehumidifier, and the determination process comprises the following steps: s31, comparing the predicted temperature sequence with a temperature limit value, and outputting the start-stop time of the air conditioner or the electric heater according to a comparison result; S32, determining the moment when the humidity limit value is met for the first time in the predicted humidity sequence, calculating the dehumidification time required by the predicted humidity corresponding to the moment when the predicted humidity reaches the set humidity, and recording the dehumidification time as the first dehumidification time; Calculating a predicted dew point temperature sequence, determining the moment when the predicted dew point temperature sequence meets the dew point temperature limit value for the first time, calculating the dehumidification time required by the predicted humidity corresponding to the moment when the predicted humidity reaches the target humidity, and recording the dehumidification time as second dehumidification time; if the time exists, the larger value of the first dehumidification time and the second dehumidification time is selected as the running time of the dehumidifier, otherwise, the shutdown time of the dehumidifier is determined according to the surface temperature and the dew point temperature of the prefabricated cabin; S4, returning to the step S2 after the equipment completes the start-stop instruction. In some embodiments of the present invention, the calculating process of the first dehumidification time is: determining the time when the humidity limit value is met for the first time in the predicted humidity sequence, and marking the time as a first time; Calculating t