WO2021249461A1 - Method and apparatus for controlling refrigeration device, computer device, and computer readable medium - Google Patents
Method and apparatus for controlling refrigeration device, computer device, and computer readable medium Download PDFInfo
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- WO2021249461A1 WO2021249461A1 PCT/CN2021/099313 CN2021099313W WO2021249461A1 WO 2021249461 A1 WO2021249461 A1 WO 2021249461A1 CN 2021099313 W CN2021099313 W CN 2021099313W WO 2021249461 A1 WO2021249461 A1 WO 2021249461A1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Definitions
- the present disclosure relates to the field of automatic control technology, and in particular to a refrigeration equipment control method, device, computer equipment and computer readable medium.
- the present disclosure provides a refrigeration equipment control method, which includes: determining the current outdoor temperature; and inputting historical sample data of the refrigeration equipment load and preset influence factors as the first input parameters into a first neural network model to obtain the forecast of the refrigeration equipment on the day Load; the historical sample data of the outdoor temperature and the load predicted on the day of the refrigeration equipment as the second input parameters are input into the second neural network to obtain the predicted indoor temperature of the day; the predicted indoor temperature of the day and the preset cooling
- the efficiency factor is input into the third neural network as the third input parameter to obtain the optimal control parameter of the refrigeration equipment of the day; and the operation of the refrigeration equipment is controlled according to the optimal control parameter.
- the present disclosure also provides a refrigeration equipment control device, including: a first processing module, a second processing module, and a control module, the first processing module is used to determine the current outdoor temperature; the second processing module is used to: The historical sample data of the equipment load and the preset influencing factors are input into the first neural network model as the first input parameters to obtain the load predicted on the day of the refrigeration equipment; the historical sample data of the outdoor temperature and the load predicted on the day of the refrigeration equipment are combined Input the second neural network as the second input parameter to obtain the predicted indoor temperature of the day; input the predicted indoor temperature of the day and the preset cooling efficiency factor as the third input parameter into the third neural network to obtain the refrigeration equipment on the day
- the optimal control parameter the control module is used to control the operation of the refrigeration equipment according to the optimal control parameter.
- the present disclosure also provides a computer device including: one or more processors; and a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors At this time, the one or more processors are caused to implement the refrigeration device control method according to the present disclosure.
- the present disclosure also provides a computer-readable medium on which a computer program is stored, wherein, when the program is executed by a processor, the processor enables the processor to implement the refrigeration device control method according to the present disclosure.
- Figure 1 is a schematic diagram of a refrigeration equipment control system provided by the present disclosure
- Figures 2 to 4 are schematic diagrams of the procedures for establishing the first, second, and third neural network models provided by the present disclosure
- FIG. 5 is a schematic diagram of the control flow of the refrigeration equipment provided by the present disclosure.
- FIGS. 6A to 6C are schematic diagrams of the first, second, and third neural network models provided by the present disclosure.
- FIG. 7 is a schematic diagram of the air-conditioning control process provided by the present disclosure.
- Figure 8 is a schematic diagram of the control flow of the heat exchange equipment provided by the present disclosure.
- Fig. 9 is a schematic diagram of the process of re-determining and updating the optimal control parameters of the refrigeration equipment on the day provided by the present disclosure.
- FIG 10 and 11 are schematic diagrams of the structure of the refrigeration equipment control device provided by the present disclosure.
- the conventional linkage control algorithm is a traditional temperature control start-stop method.
- the ambient temperature is used as the main basis for the linkage control of heat exchange equipment and air conditioners.
- the algorithm is simple but difficult to improve.
- the conventional linkage control process is as follows: real-time detection of indoor and outdoor temperature, if the indoor temperature exceeds the upper limit of the equipment operation temperature, start the heat exchange equipment or air conditioning refrigeration: when the heat exchange equipment startup conditions are met (such as the indoor and outdoor temperature difference reaches the threshold), priority is started Heat exchange equipment, otherwise start the air conditioner. Air conditioning and heat exchange equipment should not be switched frequently, with an interval of more than half an hour.
- the start and stop condition parameters of the heat exchange equipment and the air conditioner separately.
- the start and stop temperature of the heat exchange equipment can be 35/25°C, and the temperature difference is 8°C.
- the temperature difference between indoor and outdoor exceeds 8°C, it is allowed to start the heat exchange equipment.
- the start-stop condition parameters are not fixed. If a fixed start-stop condition parameter is set, it will cause Frequent activation of air conditioners increases energy consumption.
- the conventional linkage control algorithm only considers the external factor of the ambient temperature. The start time and number of starts of the air conditioner are unpredictable, and the control accuracy is low, so it is very difficult to improve.
- the air conditioner needs to be turned on, but if the high temperature time above 40°C can be predicted in advance, it will be very short and will not affect the safe operation of the equipment (the working range of some base stations/transmission equipment can reach 40°C for a long time, and 50°C for a short time) In fact, there is no need to turn on the air conditioner. In this way, while ensuring the safety of the equipment, it is possible to avoid turning on the air conditioner once, and to achieve a certain degree of energy saving.
- the present disclosure provides a method for controlling refrigeration equipment, which can control the operation of refrigeration equipment in a computer room.
- the method can be applied to the refrigeration control system shown in FIG. 1.
- the refrigeration control system provided by the present disclosure includes a refrigeration equipment control device, a field supervision unit (FSU), and a refrigeration equipment.
- FSU is a field device, set in the computer room where the refrigeration equipment is located, and includes a collection unit and an execution unit.
- the collection unit is used to collect real-time data such as outdoor temperature and humidity, indoor temperature, equipment load, etc., and upload it to the refrigeration control device.
- the execution unit is used to control the operation of the refrigeration equipment according to the instruction of the refrigeration control device.
- the refrigeration equipment control device can be a cloud device, and a Unified Management Expert (UME) can be selected, which is configured with a first neural network (NN) model, a second neural network model, and a third neural network model , Historical sample database and control strategy of refrigeration equipment (for example, refrigeration control algorithm).
- UME can obtain the predictive control plan of the refrigeration equipment according to the data reported by the FSU and the first neural network model, the second neural network model, and the third neural network model, and issue the predictive control plan to the FSU.
- the refrigeration equipment can include air conditioning and heat exchange equipment, and can operate according to the issued control plan.
- the following first threshold to tenth threshold and various durations can be preset in the refrigeration equipment control device.
- the first threshold VHT may be 45°C.
- the second threshold VLT for example, may be 15° C., when the indoor temperature is lower than VLT, the air conditioner is unconditionally turned off, and the second threshold VLT is less than the first threshold VHT.
- the third threshold HT AC may be, for example, 40°C.
- the fourth threshold HT HEE for example, may be 35°C.
- the fifth threshold is used to determine whether the second high temperature pre-start condition of the indirect heat exchange equipment is met.
- the sixth threshold LT may be 25°C.
- the sixth threshold LT is less than the fourth threshold HT HEE and the third threshold HT AC .
- the seventh threshold is used to determine how long the refrigeration equipment is down.
- the eighth threshold is used to determine whether the indoor and outdoor temperature difference in the second high temperature pre-start condition of the direct heat exchange equipment is met.
- the ninth threshold is used to determine whether the humidity in the second high temperature pre-start condition of the direct heat exchange equipment is met.
- the tenth threshold is used to judge the error between the actual operating parameters of the air conditioner and the optimal control parameters of the air conditioner.
- the maximum air conditioner on time MAXCOT and the air conditioner's shortest off time MINCST is Generally, the air conditioner's maximum on time MAXCOT is 12 hours, and the air conditioner's shortest off time MINCST is 0.5 hours.
- the first neural network model, the second neural network model and the third neural network model are established.
- the flow of establishing the first neural network model, the second neural network model, and the third neural network model will be described in detail below in conjunction with FIG. 2.
- the establishment of the first neural network model, the second neural network model, and the third neural network model includes the following steps S21 to S23.
- step S21 historical sample data is acquired.
- Sample data can include outdoor temperature, indoor temperature, and refrigeration equipment load.
- the refrigeration equipment control device may obtain historical sample data from the historical database.
- the historical database can store a large amount of historical sample data such as daily outdoor temperature T Rout , indoor temperature T Rin , refrigeration equipment load L R and so on. Can vary the degree of urgency determine the sampling period, e.g., the outdoor temperature T Rout sampling period may be 10 minutes, the sampling period T Rin room temperature and refrigeration load L R five minutes may be based on these parameters.
- the sample data may include analog data and sampling data.
- the simulated data is data obtained by simulating the operation of the refrigeration equipment when the indoor temperature is greater than the third threshold HT AC.
- the sampled data is the data sampled when the indoor temperature is less than the sixth threshold LT and the actual shutdown duration of the refrigeration equipment is greater than the seventh threshold. That is to say, when the indoor temperature T Rin is high and the refrigeration equipment needs to be operated, the dummy load can be used to simulate the real refrigeration equipment, and data such as T Rout , T Rin , L R and so on can be recorded.
- T Rin is low and the refrigeration equipment is out of service for a long time (such as the season or night when the outdoor temperature T Rout is low)
- a large amount of existing historical sample data can be directly used to speed up the collection of historical sample data.
- step S22 the historical sample data is simulated and simulated, and the daily optimal control parameters of the refrigeration equipment are calculated.
- step S22 through computer simulation training, establish the heat distribution map of the computer room environment, heating equipment and refrigeration equipment, simulate and calculate the historical sample data, and output the optimal solution vector of the refrigeration equipment control of the day (that is, the daily optimal refrigeration equipment Control parameters), and save the daily optimal control parameters of the refrigeration equipment as sample data tags.
- the air conditioner should not be turned on frequently.
- the air conditioner can be turned on at most 12 times a day, and the heat exchange equipment may be turned on at most 12 times a day.
- T moment /T hours T moment /T hours
- the optimal control parameter of the air conditioner for that day is: the air conditioner is turned on and run twice every day. T moment is turned on at the turn-on time, and the running time is the value of the corresponding turn-on time T hours .
- step S23 a first neural network model, a second neural network model, and a third neural network model are established based on historical sample data and daily optimal control parameters of the refrigeration equipment.
- step S23 the first neural network model, the second neural network model and the third neural network model are established in sequence.
- the refrigeration equipment control method of the present disclosure may further include steps S22' to S23'.
- step S22' the historical sample data and the daily optimal control parameters of the refrigeration equipment are normalized.
- the historical sample data and the daily optimal control parameters of the refrigeration equipment can be normalized according to the following formula, so that the data is in the range (0, 1):
- X real is the true value of the actual sample
- X * is the normalized data
- X max is the maximum or upper limit of the corresponding type of data sample
- X min is the minimum or lower limit of the corresponding type of data sample value.
- X max is the upper limit may be 100 °C
- X min may be a lower limit -40 °C
- a training sample data set is established based on the normalized data.
- the training sample data set includes a training set, a verification set, and a test set.
- step S23' a training set, a verification set, and a test set can be established according to a sample ratio of 6:2:2.
- step S23 the step of establishing the first neural network model, the second neural network model, and the third neural network model according to the historical sample data and the daily optimal control parameters of the refrigeration equipment (ie, step S23) may include: according to the training sample The data set establishes the first neural network model, the second neural network model and the third neural network model.
- the steps of establishing the first neural network model, the second neural network model, and the third neural network model may include steps S231 to S233.
- step S231 the historical sample data of the load of the refrigeration equipment and the preset influence factor are used as the first input parameters, and the historical sample data of the load of the refrigeration equipment of the day is used as the first output parameter to establish a first neural network model.
- the impact factor can include one or any combination of the following: holiday impact factor F holiday , tide impact factor F tide , and regional event factor F event .
- holiday impact factor F holiday The value ranges of holiday influencing factor F holiday , tide influencing factor F tide and regional event factor F event are all (0, 1), and can be determined based on manual experience.
- the holiday impact factor F holiday on normal working days can be 0, the holiday impact factor F holiday on weekends can be 0.1, and the holiday impact factor F holiday on the Spring Festival holiday can be 0.25, etc.; for industrial parks , The tidal impact factor F tide during working hours can be 0.5, the tide impact factor F tide during overtime can be 0.7, and the tide impact factor F tide during the late night period can be 0.3, etc.; for some areas, normal areas
- the event factor F event can be 0, the regional event factor F event for commercial marketing activities can be 0.1, the regional event factor F event for gatherings can be 0.2, the regional event factor F event for concerts can be 0.3, and so on.
- step S232 the historical sample data of the same period of outdoor temperature and the historical sample data of the load on the day of the refrigeration equipment are used as the second input parameter, and the historical sample data of the indoor temperature of the day is used as the second output parameter to establish a second neural network model.
- step S233 the historical sample data of the indoor temperature of the day and the preset cooling efficiency factor are used as the third input parameter, and the historical sample data of the optimal control parameter of the refrigeration equipment of the day is used as the third output parameter to establish a third neural network model .
- the optimal control parameters may include the opening time T moment and the opening time T hours , that is, the air conditioner opening time T moment-AC , the heat exchange device opening time T moment-TEE , the air conditioner opening time T hours-AC and the heat exchange device opening time T hours hours-TEE .
- the refrigeration efficiency factor may include a heat exchange refrigeration efficiency factor F eff1 and an air conditioning refrigeration efficiency factor F eff2 .
- F eff1 and F eff2 are both constant. If the environment of the computer room changes (for example, the cooling equipment is replaced or the space position is moved, etc.), the heat exchange and cooling efficiency factor must be changed F eff1 and the air-conditioning refrigeration efficiency factor F eff2 are adjusted to new constants.
- T moment1 is 0.45
- T hours1 is 0.05
- T moment2 is 0.60
- T hours2 is 0.10
- the first neural network model, the second neural network model, and the third neural network model are trained and optimized, they can be deployed according to the actual operating environment.
- the three neural network models can all be deployed on UME to make full use of the powerful computing resources of the cloud to achieve real-time or online training. If necessary, the three neural network models can also be deployed on the edge side by adding computing sticks, for example, on the FSU.
- Fig. 5 is a schematic diagram of the control flow of the refrigeration equipment provided by the present disclosure.
- the refrigeration equipment control method provided by the present disclosure can be used to control the operation of the refrigeration equipment, and includes steps S11 to S15.
- step S11 the current outdoor temperature is determined.
- the current outdoor temperature T Rout can be calculated by weighting according to the predicted temperature and the detected outdoor temperature, that is, the outdoor temperature within the preset time period before the current time is first determined, and then the outdoor temperature within the preset time period before the current time and the predicted temperature of the day are determined. And the preset first weight and second weight to determine the current outdoor temperature T Rout .
- the preset duration may be 1 hour
- FSU can collect indoor and outdoor temperature, humidity, refrigeration equipment load and other data and upload it to UME.
- step S12 the historical sample data of the load of the refrigeration equipment and the preset influence factors are input as the first input parameters into the first neural network model to obtain the load predicted on the day of the refrigeration equipment.
- the same period refers to the same period in history. For example, the same moment of today last year and the same moment of today the year before can be the same period of today.
- step S12 as shown in FIG. 6A, input the historical sample data L N of the load of the refrigeration equipment, the holiday influencing factor F holiday , the tide influencing factor F tide and the regional event factor F event into the first neural network model to obtain the refrigeration equipment
- the load L R predicted on the day is used as the output value of the first neural network model.
- step S13 the historical sample data of the outdoor temperature and the load predicted on the day of the refrigeration equipment are input as the second input parameters into the second neural network to obtain the predicted indoor temperature on the day.
- step S13 as shown in FIG. 6B, input the outdoor temperature historical sample data T Rout and the load L R (ie, the output value of the first neural network) predicted on the day of the refrigeration equipment into the second neural network model to obtain The predicted indoor temperature T Rin on the day is used as the output value of the second neural network model.
- step S14 the predicted indoor temperature of the day and the preset refrigeration efficiency factor are input into the third neural network as the third input parameters to obtain the optimal control parameters of the refrigeration equipment on the day.
- step S14 as shown in FIG. 6C, the predicted indoor temperature T Rin (that is, the output value of the second neural network), the heat exchange and refrigeration efficiency factor F eff1, and the air conditioning and refrigeration efficiency factor F eff2 of the day are input to the third neural network.
- the model obtains the optimal control parameters of the air conditioner on the day (ie, the air conditioner on time T moment-AC and the air conditioner on time T hours-AC ) and the optimal control parameters of the heat exchange equipment on the day (ie, the heat exchange device on time T moment- TEE and heat exchange equipment open time T hours-TEE ).
- the opening time T moment can be rotated to the hh:mm:ss format, and the opening time T hours can be rotated to the standard time length (for example, xx hours).
- the UME runs the first neural network model, the second neural network model, and the third neural network model in sequence to output the optimal control parameters of the refrigeration equipment of the day.
- the optimal control parameters of the air conditioner may include the air conditioner on time T moment-AC and the air conditioner on time T hours-AC
- the optimal control parameters of the heat exchange equipment may include the heat exchange equipment on time T moment-TEE and the heat exchange equipment on time T hours-TEE .
- the optimal control parameters can include up to 12 sets of air conditioner on time T moment-AC and air conditioner on time T hours-AC every day , and up to 24 sets of heat exchange equipment on time T moment-TEE and heat exchange equipment on time T hours-TEE .
- step S15 the operation of the refrigeration equipment is controlled according to the optimal control parameters.
- step S15 the operation of the air conditioner is controlled according to the optimal control parameter of the air conditioner, and the operation of the heat exchange device is controlled according to the optimal control parameter of the heat exchange device.
- the neural network model is used to combine current outdoor temperature, refrigeration equipment load historical sample data, influence factors and refrigeration efficiency factors and other parameters to realize the prediction and linkage of control schemes for air conditioning and heat exchange equipment Control
- the predicted control scheme has high accuracy, overcomes the defects of traditional algorithms that are difficult to improve, realizes active control of air conditioning and heat exchange equipment, optimizes operating efficiency, and reduces energy consumption
- the chronological data is compared with the current actual measurement
- the combination of data and taking into account the influencing factors of special events and the influencing factors of the refrigeration efficiency of refrigeration equipment makes the predicted control scheme more accurate, adaptable to changes in the environment of the computer room, and enhance the scope of application.
- Fig. 7 is a schematic diagram of the air-conditioning control process provided by the present disclosure.
- the air conditioning control process provided by the present disclosure includes steps S31 to S39.
- step S31 if the current indoor temperature is greater than the first threshold VHT, step S36 is executed; otherwise, step S32 is executed.
- step S31 if the current indoor temperature is greater than VHT, indicating that the current indoor temperature is too high, it can be further judged whether the air conditioner is running overtime (ie, step S36); if the current indoor temperature is less than or equal to VHT, it can be further judged Whether the temperature is too low (ie, execute step S32).
- step S32 if the current indoor temperature is less than the second threshold VLT, step S39 is executed; otherwise, step S33 is executed.
- step S32 if the current indoor temperature is less than the second threshold VLT, indicating that the current indoor temperature is too low, the air conditioner can be shut down due to the abnormal low temperature (ie, step S39); if the current indoor temperature is greater than or equal to the second threshold VLT, It means that the current indoor temperature will not be shut down due to abnormal high temperature and will not shut down due to abnormal low temperature, and it can be further determined whether the first high temperature pre-start condition is met (ie, step S33 is executed).
- step S33 if the first high temperature pre-start condition is met, step S34 is executed; otherwise, step S31 is returned.
- step S33 the current indoor temperature is less than or equal to the first threshold VHT and greater than or equal to the second threshold VLT, and if the first high temperature pre-start condition is met, the air conditioner is controlled to operate according to the optimal control parameters of the day (ie, execute step S34); if the first high temperature pre-start condition is not met, return to step S31.
- the first high-temperature pre-start condition may include: reaching the air conditioner on time T moment-AC , the current indoor temperature is greater than the third threshold HT AC , and the actual air conditioner shutdown duration is greater than the air conditioner's shortest shutdown duration MINCST.
- step S34 the maximum air conditioner operating time T on-max is set to the minimum value of the air conditioner on time T hours-AC and the air conditioner maximum on time MAXCOT.
- step S34 the minimum value of the air conditioner on time T hours-AC and the air conditioner’s maximum on time MAXCOT may be taken as the control parameter for actually controlling the operation of the air conditioner, so as to ensure the reliability and safety of the air conditioner operation.
- Step S35 start the air conditioner, and execute step S38.
- step S35 after the air conditioner is controlled to start, start to record the actual on-time duration of the air conditioner T on-AC , reset the actual off-time duration of the air conditioner T off-AC to zero, and execute step S38.
- step S36 if the air conditioner's actual shutdown time T off-AC is greater than the air conditioner's shortest shutdown time MINCST, step S37 is executed; otherwise, step S31 is returned.
- step S36 the current indoor temperature is greater than the first threshold VHT, if the current air conditioner actual shutdown duration T off-AC is greater than the air conditioner minimum shutdown duration MINCST, indicating that the high temperature abnormal start condition is met, then the air conditioner high temperature abnormal start operation is performed (ie, execute step 37); if the current actual air conditioner shutdown time T off-AC is less than or equal to the air conditioner's shortest shutdown time MINCST, return to step S31.
- step S37 the maximum air conditioner operating time Ton-max is set to the maximum air conditioner on time MAXCOT, and step S35 is executed.
- step S37 when the air conditioner is started abnormally at a high temperature, the operating time of the air conditioner is directly controlled according to the preset maximum on time MAXCOT of the air conditioner.
- step S38 if the actual on-time of the air conditioner Ton-AC is greater than or equal to the maximum operating time of the air conditioner Ton-max , step S39 is executed; otherwise, the current state of the air conditioner is maintained.
- the air conditioner After the air conditioner is started, it starts to record the actual air conditioner on time Ton-AC . If the air conditioner’s actual on time Ton-AC is greater than or equal to the maximum air conditioner operating time Toon-max , the air conditioner is turned off; otherwise, the current state of the air conditioner is maintained.
- step S39 the air conditioner is turned off, and the process returns to step S31.
- step S39 after controlling the air conditioner to turn off, start recording the actual off time T off-AC of the air conditioner, and clear the actual on time T on-AC of the air conditioner to zero, and then return to step S31 to continue detecting the indoor temperature.
- the air-conditioning control process may further include: if the current indoor temperature is less than the sixth threshold LT, turning off the air-conditioning.
- the air conditioner When the actual room temperature exceeds the first threshold VHT, the air conditioner can be started due to abnormal high temperature; when the actual room temperature is lower than the second threshold VLT, the air conditioner can be shut down due to the abnormal low temperature; when the air conditioner is turned on T moment-AC and the actual room temperature exceeds
- the third threshold HT AC meets the requirement that the operation interval exceeds the shortest shutdown duration MINCST, the air conditioner will operate according to the prediction scheme output by the third neural network model, that is, the air conditioner will start to operate when T moment-AC arrives at the time when the air conditioner is turned on, and the operation duration is the air conditioner. Turn on time T hours-AC .
- Figure 8 is a schematic diagram of the control flow of the heat exchange equipment provided by the present disclosure.
- the heat exchange equipment control process provided by the present disclosure includes steps S41 to S44.
- step S41 if the second high temperature pre-start condition is met, step S42 is executed; otherwise, the current state of the heat exchange equipment is maintained.
- the heat exchange equipment may include direct heat exchange equipment and indirect heat exchange equipment
- the direct heat exchange equipment may include a fresh air system
- the indirect heat exchange equipment may include heat pipe equipment (HPE).
- the second high-temperature pre-start condition may include: reaching the turning-on time T moment-TEE of the heat exchange equipment, and the current indoor temperature is greater than the fourth threshold HT HEE , and the current indoor temperature and outdoor temperature The difference is greater than the fifth threshold.
- the second high temperature pre-start condition includes one of the following:
- the eighth threshold may be greater than the fifth threshold, that is, in the second high-temperature pre-start condition, the indoor and outdoor temperature difference requirements of the direct heat exchange equipment are higher than the indoor and outdoor temperature difference requirements of the indirect heat exchange equipment.
- the fifth threshold may be 6°C
- the eighth threshold can be 10°C.
- the second high temperature pre-start condition of the direct heat exchange device may include temperature conditions and humidity conditions, for example, the ninth threshold may be 90%.
- step S42 the heat exchange equipment is started.
- step S42 after the heat exchange equipment is controlled to start, it starts to record the actual turn-on time T on-HEE of the heat exchange equipment, and clears the actual shutdown time T off-HEE of the heat exchange equipment to zero.
- step S43 if the actual opening time of the heat exchange equipment Ton-HEE is greater than or equal to the opening time T hours-HEE of the heat exchange equipment, step S44 is executed; otherwise, the current state of the heat exchange equipment is maintained.
- step S44 the heat exchange equipment is turned off.
- step S44 after controlling the heat exchange equipment to turn off, start recording the actual shutdown time T off-HEE of the heat exchange equipment, and clear the actual turn-on time T on-HEE of the heat exchange equipment to zero.
- the control process of the heat exchange device may further include: if the current indoor temperature is less than the sixth threshold LT, turning off the heat exchange device.
- the air conditioner and the indirect heat exchange equipment can operate at the same time, but the operation of the air conditioner and the direct heat exchange equipment are mutually exclusive, that is, the air conditioner and the direct heat exchange equipment cannot operate at the same time.
- the air conditioner and the direct heat exchange equipment cannot operate at the same time.
- the refrigeration device control method of the present disclosure may further include: if the air conditioner is turned on, the heat exchange device is turned off; if the heat exchange is turned on Equipment, turn off the air conditioner.
- the air conditioning and heat exchange equipment control algorithm can be run on the UME cloud. If necessary, the air conditioning and heat exchange equipment control algorithm can also be copied to the FSU for local execution. In this case, the UME must first The refrigeration control plan predicted by the three neural network is issued to the FSU in advance.
- Steps S11 to S14 shown in FIG. 5 are executed once a day before zero o'clock to output the optimal control parameters of the refrigeration equipment on the day.
- FIG. 9 is a schematic diagram of the process of re-determining and updating the optimal control parameters of the refrigeration equipment on the day provided by the present disclosure.
- the refrigeration equipment control method of the present disclosure may further include steps S51 to S53.
- step S51 if the error between the actual operating parameters of the air conditioner on that day and the optimal control parameters of the air conditioner on that day exceeds the tenth threshold, step S52 is executed; otherwise, the process ends.
- step S52 the optimal control parameters of the air conditioner for the day are determined again.
- step S52 is the same as that of steps 12 to S14, and will not be repeated here.
- step S53 the training sample data set is updated according to the re-determined optimal control parameters of the air conditioner on the day.
- the parameter updates the training sample data set to improve the timely adaptability of the refrigeration control strategy and the real-time performance and accuracy of the predictive control.
- Neural network models can be deployed and run in the cloud. When external parameters are constantly changing, these models can also be continuously trained in real time or online to continuously improve prediction accuracy, and can be trained and adjusted to adapt to abnormal conditions such as changes in the computer room environment.
- the refrigeration equipment control method of the present disclosure may further include: if one type of refrigeration equipment currently running fails and the other type of refrigeration equipment is normal, turning off the failed refrigeration equipment and turning it on The normal refrigeration equipment; and if both of the two currently running refrigeration equipment are faulty, when the fault is eliminated, the refrigeration equipment with the elimination of the fault is activated. That is to say, if the currently opened refrigeration equipment fails, the faulty refrigeration equipment is turned off and the normal refrigeration equipment is turned on. When the fault is eliminated, the refrigeration equipment is turned on and the other refrigeration equipment is turned off. Through the mutual backup startup operation of the air conditioner and the heat exchanger in the event of failure, the danger of abnormally high temperature in the computer room can be avoided.
- the refrigeration equipment control method of the present disclosure may further include: if the current indoor temperature is less than the sixth threshold LT and the refrigeration equipment actually shuts down When the duration is greater than the seventh threshold, the second neural network model is trained according to the currently acquired sample data, the sample data including outdoor temperature, indoor temperature and refrigeration equipment load. That is to say, in the case of good environmental conditions (for example, there is a fast Ethernet interconnection between FSU and cloud UME, and cloud UME computing resources are sufficient), real-time or online model training can be supported.
- the refrigeration equipment control method of the present disclosure may further include: combining the acquired sample data of the day and the actual operation parameters of the refrigeration equipment on the day
- the training sample data set is added to train the first neural network model and the third neural network model according to the training sample data set.
- FSU In the case of communication network interruption, FSU cannot communicate with UME. In order to realize the control of refrigeration equipment, FSU can automatically run the built-in temperature start-stop control algorithm, and can also receive and save the cooling control plan issued by UME in advance, and run it locally Refrigeration linkage control algorithm copied from UME.
- the refrigeration equipment control method of the present disclosure may further include: combining the first neural network model and the second neural network model And the third neural network model is deployed on the FSU, so that when the FSU and the UME fail to communicate, the optimal control parameters of the refrigeration equipment of the day are determined, and the operation of the refrigeration equipment is controlled according to the optimal control parameters.
- An application scenario of the present disclosure is: a base station type computer room where the heat generated by the communication equipment in the computer room is less than 10 KW, which is usually a data, transmission, and switching type base station computer room of an operator.
- the original computer room refrigeration equipment only had one air conditioner.
- an indirect heat exchanger such as intelligent heat pipe equipment (HPE) was installed to pass the air conditioner.
- HPE intelligent heat pipe equipment
- the use of heat pipe technology does not require mechanical cooling, and the temperature difference between indoor and outdoor is basically maintained at about 6 degrees, so it can be applied to more than 90% of the year.
- the energy consumption of its components is much lower than that of the traditional compressor air conditioner, and the energy consumption is only about 1/5 of the original air conditioning system, so it can greatly save the power consumption of the air conditioner.
- the refrigeration equipment control scheme can be based on big data technology and neural network technology, fully taking into account the current indoor and outdoor temperature and humidity, system load and other data, combined with load prediction, weather forecast, historical sample data of the same period, etc., through neural network Calculating, predicting the load and indoor temperature of the refrigeration equipment in advance, and outputting the optimal plan for the linkage control of the refrigeration equipment on the day, combined with the traditional control rule strategy, can realize the predictable active control of the air conditioning and heat exchange equipment in the computer room to achieve optimization The purpose of control, energy saving and consumption reduction.
- the air conditioner running time and the number of starts are significantly reduced; at the same time, the working temperature of the equipment in the computer room can be increased to a controllable safety range of 30-40°C, further reducing The energy consumption of refrigeration equipment is improved.
- the active predictive joint control scheme of air-conditioning and heat exchange equipment can save nearly 10,000 kWh of power consumption for communication base stations each year, and the average power consumption is reduced by 40%. Calculated with a 10% ratio of base stations, annual electricity bills will be reduced by 5 billion yuan and 1.35 million tons of carbon emissions will be reduced, with significant economic and social benefits.
- the present disclosure also provides a refrigeration equipment control device.
- FIG 10 and 11 are schematic diagrams of the structure of the refrigeration equipment control device provided by the present disclosure.
- the refrigeration equipment control device includes a first processing module 101, a second processing module 102, and a control module 103.
- the first processing module 101 is used to determine the current outdoor temperature.
- the second processing module 102 is used to: input the historical sample data of the refrigeration equipment load and the preset influence factors as the first input parameters into the first neural network model to obtain the load predicted on the day of the refrigeration equipment; The data and the load predicted by the refrigeration equipment on the day are input as the second input parameters to the second neural network to obtain the predicted indoor temperature on the day; and the predicted indoor temperature on the day and the preset cooling efficiency factor are input as the third input parameters
- the third neural network obtains the optimal control parameters of the refrigeration equipment of the day.
- the control module 103 is configured to control the operation of the refrigeration equipment according to the optimal control parameter.
- the refrigeration equipment control device of the present disclosure may further include a model establishment module 104.
- the model establishment module 104 is configured to establish the first neural network model, the second neural network model, and the third neural network model in the initialization phase.
- the model building module 104 may be used to: obtain historical sample data, the sample data including outdoor temperature, indoor temperature, and refrigeration equipment load; simulate and simulate the historical sample data to calculate the daily optimal control parameters of the refrigeration equipment; and According to the historical sample data and the daily optimal control parameters of the refrigeration equipment, a first neural network model, a second neural network model, and a third neural network model are established.
- the model building module 104 may also be used to: after simulating the historical sample data to calculate the daily optimal control parameters of the refrigeration equipment, and after calculating the daily optimal control parameters of the refrigeration equipment based on the historical sample data and the refrigeration equipment Control parameters, before establishing the first neural network model, the second neural network model, and the third neural network model, normalize the historical sample data and the daily optimal control parameters of the refrigeration equipment; and
- the transformed data establishes a training sample data set, and the training sample data set includes a training set, a verification set, and a test set.
- the model establishment module 104 may be used to establish a first neural network model, a second neural network model, and a third neural network model according to the training sample data set.
- the model building module 104 may be used to: use the historical sample data of the refrigeration equipment load and the preset influence factor as the first input parameter, and use the historical sample data of the refrigeration equipment load of the day as the first output parameter to establish The first neural network model; the historical sample data of the same period of outdoor temperature and the historical sample data of the day load of the refrigeration equipment are used as the second input parameter, and the historical sample data of the indoor temperature of the day is used as the second output parameter to establish the second nerve Network model; and the historical sample data of the indoor temperature of the day and the preset cooling efficiency factor are used as the third input parameter, and the historical sample data of the optimal control parameter of the refrigeration equipment of the day is used as the third output parameter to establish the first Three neural network model.
- the sample data may include analog data and sampling data.
- the simulated data is data obtained by simulating the operation of the refrigeration equipment when the indoor temperature is greater than the preset third threshold.
- the sampled data is data sampled when the indoor temperature is less than the preset sixth threshold and the actual shutdown duration of the refrigeration equipment is greater than the preset seventh threshold.
- the first processing module 101 may be used to determine the outdoor temperature within a preset period of time before the current moment; and according to the outdoor temperature within the preset period of time before the current moment, the predicted temperature of the day, and preset first and second weights Determine the current outdoor temperature.
- the optimal control parameters may include the turn-on time and the turn-on duration.
- the impact factor may include one or any combination of the following: holiday impact factor, tide impact factor, and regional event factor.
- the control module 103 may be used to: if the current indoor temperature is less than or equal to a preset first threshold and greater than or equal to a preset second threshold, and meets the first high-temperature pre-start condition, set the maximum operating time of the air conditioner to the air conditioner on And start the air conditioner, the second threshold is less than the first threshold; and if the actual air conditioner on time is greater than or equal to the maximum operating time of the air conditioner, the air conditioner is turned off.
- the first high-temperature pre-start condition may include: reaching the time when the air conditioner is turned on, the current indoor temperature is greater than a preset third threshold, and the actual shutdown duration of the air conditioner is greater than the preset minimum shutdown duration of the air conditioner.
- the control module 103 may also be used to: in the process of controlling the operation of the refrigeration equipment according to the optimal control parameters, if the current indoor temperature is greater than the first threshold and the actual shutdown duration of the air conditioner is greater than the shortest shutdown duration of the air conditioner, then The maximum operating time of the air conditioner is set to the maximum on time of the air conditioner, and the air conditioner is started; and/or, if the current indoor temperature is less than the second threshold, the air conditioner is turned off.
- the control module 103 may also be used to: if the second high-temperature pre-start condition is met, start the heat exchange device; and if the actual opening time of the heat exchange device is greater than or equal to the opening time of the heat exchange device, turn off the heat exchange equipment.
- the second high-temperature pre-start condition may include: reaching the time when the heat exchange equipment is turned on, and the current indoor temperature is greater than a preset fourth threshold, and the current indoor temperature and outdoor temperature The difference between is greater than the preset fifth threshold.
- the second high-temperature pre-start condition may include one of the following: the time when the heat exchange device is turned on, and the current indoor temperature is greater than the preset fourth threshold, and the current indoor temperature The difference with the outdoor temperature is greater than the preset eighth threshold, the eighth threshold is greater than the fifth threshold; and the time when the heat exchange device is turned on, and the current indoor temperature is greater than the preset fourth threshold, and the current The difference between the indoor temperature and the outdoor temperature is greater than the preset eighth threshold, and the current indoor humidity is less than or equal to the preset ninth threshold.
- the control module 103 may also be used to: if the air conditioner is turned on, the heat exchange equipment is turned off; If the heat exchange device is turned on, the air conditioner is turned off.
- the control module 103 may also be used to: after controlling the operation of the refrigeration equipment according to the optimal control parameters, if the error between the actual operating parameters of the air conditioner on that day and the optimal control parameters of the air conditioner on that day exceeds the preset tenth threshold , The second processing module 102 is instructed to re-determine the optimal control parameters of the day of the air conditioner; and update the training sample data set according to the re-determined optimal control parameters of the day of the air conditioner.
- the control module 103 can also be used to: if one type of refrigeration equipment currently running is faulty and the other type of refrigeration equipment is normal, turn off the failed refrigeration equipment and turn on the normal refrigeration equipment; and if the two currently running refrigeration equipment is normal If the refrigeration equipment is faulty, when the fault is eliminated, the refrigeration equipment with the fault eliminated will be activated.
- the second processing module 102 may also be used to train the second nerve according to the currently acquired sample data if the current indoor temperature is less than the preset sixth threshold and the actual shutdown duration of the refrigeration equipment is greater than the preset seventh threshold.
- the network model, the sample data includes outdoor temperature, indoor temperature and refrigeration equipment load.
- the present disclosure also provides a computer device that includes one or more processors and a storage device.
- the storage device stores one or more programs. When the one or more programs are processed by the one or more When the device is executed, the one or more processors implement the refrigeration device control method provided in the present disclosure.
- the present disclosure also provides a computer-readable medium on which a computer program is stored.
- the processor realizes the refrigeration device control method provided in the present disclosure.
- Such software may be distributed on a computer-readable medium
- the computer-readable medium may include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium).
- the term computer storage medium includes volatile and non-volatile data implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data).
- Information such as computer-readable instructions, data structures, program modules, or other data.
- Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or Any other medium used to store desired information and that can be accessed by a computer.
- a communication medium usually contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium. .
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Abstract
Description
Claims (19)
- 一种制冷设备控制方法,包括:A method for controlling refrigeration equipment includes:确定当前室外温度;Determine the current outdoor temperature;将制冷设备负荷的同期历史样本数据和预设的影响因子作为第一输入参数输入第一神经网络模型,得到制冷设备当日预测的负荷;Input the historical sample data of the refrigeration equipment load and the preset influence factors as the first input parameters into the first neural network model to obtain the predicted load of the refrigeration equipment on the day;将室外温度的同期历史样本数据和所述制冷设备当日预测的负荷作为第二输入参数输入第二神经网络,得到当日预测的室内温度;Input the same-day historical sample data of outdoor temperature and the load predicted by the refrigeration equipment on the day as the second input parameters into the second neural network to obtain the predicted indoor temperature on the day;将所述当日预测的室内温度和预设的制冷效率因子作为第三输入参数输入第三神经网络,得到所述制冷设备当日的最优控制参数;以及Input the predicted indoor temperature and the preset refrigeration efficiency factor of the day as the third input parameters into the third neural network to obtain the optimal control parameters of the refrigeration equipment on the day; and根据所述最优控制参数控制所述制冷设备运行。The operation of the refrigeration equipment is controlled according to the optimal control parameter.
- 如权利要求1所述的方法,其中,在确定当前室外温度的步骤之前,所述方法还包括:The method of claim 1, wherein, before the step of determining the current outdoor temperature, the method further comprises:获取历史样本数据,所述样本数据包括室外温度、室内温度和制冷设备负荷;Acquiring historical sample data, the sample data including outdoor temperature, indoor temperature, and refrigeration equipment load;对所述历史样本数据仿真模拟,计算得到制冷设备每日的最优控制参数;以及Simulate and simulate the historical sample data to calculate the optimal daily control parameters of the refrigeration equipment; and根据所述历史样本数据和所述制冷设备每日的最优控制参数,建立所述第一神经网络模型、第二神经网络模型和第三神经网络模型。According to the historical sample data and the daily optimal control parameters of the refrigeration equipment, the first neural network model, the second neural network model, and the third neural network model are established.
- 如权利要求2所述的方法,其中,在对所述历史样本数据仿真模拟,计算得到制冷设备每日的最优控制参数的步骤之后,并且在根据所述历史样本数据和所述制冷设备每日的最优控制参数,建立所述第一神经网络模型、第二神经网络模型和第三神经网络模型的步骤之前,所述方法还包括:The method according to claim 2, wherein after the step of simulating the historical sample data to calculate the optimal daily control parameters of the refrigeration equipment, and according to the historical sample data and each refrigeration equipment Before the steps of establishing the first neural network model, the second neural network model, and the third neural network model, the method further includes:对所述历史样本数据和所述制冷设备每日的最优控制参数进行归一化处理;以及Normalize the historical sample data and the daily optimal control parameters of the refrigeration equipment; and根据归一化处理后的数据建立训练样本数据集,所述训练样本 数据集包括训练集、验证集和测试集,A training sample data set is established according to the normalized data, and the training sample data set includes a training set, a verification set, and a test set,根据所述历史样本数据和所述制冷设备每日的最优控制参数,建立所述第一神经网络模型、第二神经网络模型和第三神经网络模型的步骤包括:The steps of establishing the first neural network model, the second neural network model, and the third neural network model according to the historical sample data and the daily optimal control parameters of the refrigeration equipment include:根据所述训练样本数据集建立第一神经网络模型、第二神经网络模型、第三神经网络模型。The first neural network model, the second neural network model, and the third neural network model are established according to the training sample data set.
- 如权利要求2所述的方法,其中,根据所述历史样本数据和所述制冷设备每日的最优控制参数,建立所述第一神经网络模型、第二神经网络模型和第三神经网络模型的步骤包括:The method of claim 2, wherein the first neural network model, the second neural network model, and the third neural network model are established based on the historical sample data and the daily optimal control parameters of the refrigeration equipment The steps include:将制冷设备负荷的同期历史样本数据和所述预设的影响因子作为第一输入参数,并将所述制冷设备当日负荷的历史样本数据作为第一输出参数,建立第一神经网络模型;Taking the historical sample data of the load of the refrigeration equipment and the preset influence factor as the first input parameter, and taking the historical sample data of the load of the refrigeration equipment of the day as the first output parameter to establish a first neural network model;将室外温度的同期历史样本数据和所述制冷设备当日负荷的历史样本数据作为第二输入参数,并将当日室内温度的历史样本数据作为第二输出参数,建立第二神经网络模型;以及Use the historical sample data of the outdoor temperature of the same period and the historical sample data of the load of the refrigeration equipment as the second input parameter, and use the historical sample data of the indoor temperature of the day as the second output parameter to establish a second neural network model; and将所述当日室内温度的历史样本数据和所述预设的制冷效率因子作为第三输入参数,并将制冷设备当日最优控制参数的历史样本数据作为第三输出参数,建立第三神经网络模型。Use the historical sample data of the indoor temperature of the day and the preset refrigeration efficiency factor as the third input parameter, and use the historical sample data of the optimal control parameter of the refrigeration equipment of the day as the third output parameter to establish a third neural network model .
- 如权利要求2所述的方法,其中,所述样本数据包括模拟数据和采样数据,The method of claim 2, wherein the sample data includes analog data and sample data,所述模拟数据是当室内温度大于预设的第三阈值时,经过模拟制冷设备的运行得到的数据,The simulated data is data obtained by simulating the operation of the refrigeration equipment when the indoor temperature is greater than the preset third threshold,所述采样数据是当室内温度小于预设的第六阈值且所述制冷设备实际停机时长大于预设的第七阈值时采样得到的数据。The sampled data is data sampled when the indoor temperature is less than the preset sixth threshold and the actual shutdown duration of the refrigeration equipment is greater than the preset seventh threshold.
- 如权利要求1所述的方法,其中,确定当前室外温度的步骤包括:The method of claim 1, wherein the step of determining the current outdoor temperature comprises:确定当前时刻前预设时长内的室外温度;以及Determine the outdoor temperature within a preset period of time before the current time; and根据所述当前时刻前预设时长内的室外温度、当天预测温度和预设的第一权重和第二权重确定当前室外温度。The current outdoor temperature is determined according to the outdoor temperature within the preset time period before the current time, the current day's predicted temperature, and the preset first weight and second weight.
- 如权利要求1至6中任一项所述的方法,其中,所述制冷设备包括空调和换热设备,所述最优控制参数包括空调开启时刻、空调开启时长、换热设备开启时刻和换热设备开启时长,所述影响因子包括以下之一或任意组合:节假日影响因子、潮汐影响因子、区域事件因子。The method according to any one of claims 1 to 6, wherein the refrigeration equipment includes air conditioners and heat exchange equipment, and the optimal control parameters include air conditioner on time, air conditioner on time, heat exchange equipment on time, and heat exchange equipment. The thermal equipment is turned on for the length of time, and the impact factors include one or any combination of the following: holiday impact factors, tide impact factors, and regional event factors.
- 如权利要求7所述的方法,其中,根据所述最优控制参数控制所述制冷设备运行的步骤包括:The method according to claim 7, wherein the step of controlling the operation of the refrigeration equipment according to the optimal control parameter comprises:响应于当前室内温度小于或等于预设的第一阈值且大于或等于预设的第二阈值,并且满足第一高温预启动条件,将空调最大运行时长设置为所述空调开启时长和预设的空调最大开启时长中的最小值,并启动所述空调,所述第二阈值小于所述第一阈值;以及In response to the current indoor temperature being less than or equal to the preset first threshold and greater than or equal to the preset second threshold, and meeting the first high temperature pre-start condition, the maximum operating time of the air conditioner is set to the air conditioner on time and the preset The minimum value of the maximum opening time of the air conditioner, and start the air conditioner, the second threshold value is less than the first threshold value; and响应于空调实际开启时长大于或等于所述空调最大运行时长,关闭所述空调。In response to the actual on-time of the air conditioner being greater than or equal to the maximum operating time of the air conditioner, the air conditioner is turned off.
- 如权利要求8所述的方法,其中,所述第一高温预启动条件包括:The method of claim 8, wherein the first high temperature pre-start condition comprises:到达所述空调开启时刻,且当前室内温度大于预设的第三阈值,且空调实际停机时长大于预设的空调最短停机时长。The time when the air conditioner is turned on is reached, the current indoor temperature is greater than the preset third threshold, and the actual shutdown duration of the air conditioner is greater than the preset minimum shutdown duration of the air conditioner.
- 如权利要求8所述的方法,其中,在根据所述最优控制参数控制所述制冷设备运行过程中,所述方法还包括:The method according to claim 8, wherein, in the process of controlling the operation of the refrigeration equipment according to the optimal control parameter, the method further comprises:响应于当前室内温度大于所述第一阈值且空调实际停机时长大于所述空调最短停机时长,将所述空调最大运行时长设置为所述空调最大开启时长,并启动所述空调;和/或In response to the current indoor temperature being greater than the first threshold and the actual shutdown duration of the air conditioner is greater than the shortest shutdown duration of the air conditioner, the maximum operating duration of the air conditioner is set to the maximum on duration of the air conditioner, and the air conditioner is started; and/or响应于当前室内温度小于所述第二阈值,关闭所述空调。In response to the current indoor temperature being less than the second threshold, the air conditioner is turned off.
- 如权利要求7所述的方法,其中,根据所述最优控制参数控制所述制冷设备运行的步骤包括:The method according to claim 7, wherein the step of controlling the operation of the refrigeration equipment according to the optimal control parameter comprises:响应于满足第二高温预启动条件,启动所述换热设备;以及In response to meeting the second high temperature pre-start condition, start the heat exchange device; and响应于换热设备实际开启时长大于或等于所述换热设备开启时长,关闭所述换热设备。In response to the actual opening time of the heat exchange device being greater than or equal to the opening time of the heat exchange device, the heat exchange device is turned off.
- 如权利要求11所述的方法,其中,所述换热设备为间接换热设备,所述第二高温预启动条件包括:The method according to claim 11, wherein the heat exchange equipment is an indirect heat exchange equipment, and the second high temperature pre-start condition comprises:到达所述换热设备开启时刻,且当前室内温度大于预设的第四阈值,且当前室内温度与室外温度的差值大于预设的第五阈值;或者When the heat exchange device is turned on, the current indoor temperature is greater than the preset fourth threshold, and the difference between the current indoor temperature and the outdoor temperature is greater than the preset fifth threshold; or所述换热设备为直接换热设备,所述第二高温预启动条件包括以下之一:The heat exchange equipment is a direct heat exchange equipment, and the second high temperature pre-start condition includes one of the following:到达所述换热设备开启时刻,且当前室内温度大于预设的第四阈值,且当前室内温度与室外温度的差值大于预设的第八阈值,所述第八阈值大于所述第五阈值;以及When the heat exchange device is turned on, the current indoor temperature is greater than the preset fourth threshold, and the difference between the current indoor temperature and the outdoor temperature is greater than the preset eighth threshold, and the eighth threshold is greater than the fifth threshold ;as well as到达所述换热设备开启时刻,且当前室内温度大于预设的第四阈值,且当前室内温度与室外温度的差值大于预设的第八阈值,且当前室内湿度小于或等于预设的第九阈值。When the heat exchange equipment is turned on, the current indoor temperature is greater than the preset fourth threshold, the difference between the current indoor temperature and the outdoor temperature is greater than the preset eighth threshold, and the current indoor humidity is less than or equal to the preset first Nine thresholds.
- 如权利要求7所述的方法,其中,所述换热设备为直接换热设备,所述空调开启时刻与所述换热设备开启时刻不同,并且所述方法还包括:The method according to claim 7, wherein the heat exchange equipment is a direct heat exchange equipment, the time when the air conditioner is turned on is different from the time when the heat exchange equipment is turned on, and the method further comprises:响应于开启所述空调,关闭所述换热设备;以及In response to turning on the air conditioner, turning off the heat exchange device; and响应于开启所述换热设备,关闭所述空调。In response to turning on the heat exchange device, the air conditioner is turned off.
- 如权利要求3所述的方法,其中,所述制冷设备包括空调,并且在根据所述最优控制参数控制所述制冷设备运行的步骤之后,所述方法还包括:The method according to claim 3, wherein the refrigeration equipment includes an air conditioner, and after the step of controlling the operation of the refrigeration equipment according to the optimal control parameter, the method further comprises:响应于所述空调当日的实际运行参数与所述空调当日的最优控制参数之间的误差超过预设的第十阈值,再次确定所述空调当日的最 优控制参数;以及In response to the error between the actual operating parameters of the air conditioner on that day and the optimal control parameters of the air conditioner on that day exceeds the preset tenth threshold, the optimal control parameters of the air conditioner on that day are determined again; and根据再次确定的所述空调当日的最优控制参数更新训练样本数据集。The training sample data set is updated according to the re-determined optimal control parameters of the air conditioner that day.
- 如权利要求1所述的方法,还包括:The method of claim 1, further comprising:响应于当前运行的一种制冷设备故障且另一种制冷设备正常,关闭所述故障的制冷设备,并开启所述正常的制冷设备;以及In response to a failure of one type of refrigeration equipment currently running and another normal type of refrigeration equipment, turn off the failed refrigeration equipment and turn on the normal refrigeration equipment; and响应于当前运行的两种制冷设备均故障,在故障消除时,启动故障消除的制冷设备。In response to the failure of the two currently operating refrigeration equipment, when the failure is eliminated, the refrigeration equipment with the elimination of the failure is activated.
- 如权利要求1所述的方法,其中,在根据所述最优控制参数控制所述制冷设备运行的过程中,所述方法还包括:The method according to claim 1, wherein, in the process of controlling the operation of the refrigeration equipment according to the optimal control parameter, the method further comprises:响应于当前室内温度小于预设的第六阈值且所述制冷设备实际停机时长大于预设的第七阈值,根据当前获取的样本数据训练所述第二神经网络模型,所述样本数据包括室外温度、室内温度和制冷设备负荷。In response to the current indoor temperature being less than the preset sixth threshold and the actual downtime of the refrigeration equipment is greater than the preset seventh threshold, training the second neural network model according to the currently acquired sample data, the sample data including the outdoor temperature , Indoor temperature and refrigeration equipment load.
- 一种制冷设备控制装置,包括:第一处理模块、第二处理模块和控制模块,A refrigeration equipment control device, comprising: a first processing module, a second processing module, and a control module,所述第一处理模块用于确定当前室外温度;The first processing module is used to determine the current outdoor temperature;所述第二处理模块用于:The second processing module is used for:将制冷设备负荷的同期历史样本数据和预设的影响因子作为第一输入参数输入第一神经网络模型,得到制冷设备当日预测的负荷;Input the historical sample data of the refrigeration equipment load and the preset influence factors as the first input parameters into the first neural network model to obtain the predicted load of the refrigeration equipment on the day;将室外温度的同期历史样本数据和所述制冷设备当日预测的负荷作为第二输入参数输入第二神经网络,得到当日预测的室内温度;以及Input the same period historical sample data of the outdoor temperature and the load predicted by the refrigeration equipment on the day as the second input parameters into the second neural network to obtain the predicted indoor temperature on the day; and将所述当日预测的室内温度和预设的制冷效率因子作为第三输入参数输入第三神经网络,得到所述制冷设备当日的最优控制参数;Input the predicted indoor temperature of the day and the preset refrigeration efficiency factor as the third input parameters into the third neural network to obtain the optimal control parameters of the refrigeration equipment on the day;所述控制模块用于根据所述最优控制参数控制所述制冷设备运行。The control module is configured to control the operation of the refrigeration equipment according to the optimal control parameter.
- 一种计算机设备,包括:A computer equipment including:一个或多个处理器;One or more processors;存储装置,其上存储有一个或多个程序;A storage device on which one or more programs are stored;当所述一个或多个程序被所述一个或多个处理器执行时,使得所述一个或多个处理器实现如权利要求1至16中任一项所述的制冷设备控制方法。When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the refrigeration device control method according to any one of claims 1 to 16.
- 一种计算机可读介质,其上存储有计算机程序,其中,所述程序被处理器执行时,使得所述处理器实现如权利要求1至16中任一项所述的制冷设备控制方法。A computer-readable medium having a computer program stored thereon, wherein, when the program is executed by a processor, the processor realizes the refrigeration equipment control method according to any one of claims 1 to 16.
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