CN117515785B - Temperature centralized control method, device, central control equipment and readable storage medium - Google Patents
Temperature centralized control method, device, central control equipment and readable storage medium Download PDFInfo
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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
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
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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
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied 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
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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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
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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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
- F24F2120/14—Activity of occupants
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Abstract
The application discloses a temperature centralized control method, a device, central control equipment and a readable storage medium, and relates to the technical field of central control. According to the central control equipment, the heat exchange rate between the target indoor place and the outdoor is estimated according to the dynamic variable of the target indoor place and the preset depth neural network model, and then the temperature adjusting equipment is controlled based on parameters such as the heat exchange rate.
Description
Technical Field
The present disclosure relates to the field of central control technologies, and in particular, to a method and an apparatus for controlling temperature in a centralized manner, a central control device, and a readable storage medium.
Background
At present, a central air conditioner is usually installed in some indoor public places, such as a mall, a commercial center, an underground commercial street and the like, so as to regulate and control the indoor temperature. However, in the existing solutions, the temperature is usually adjusted by controlling the operation of the air conditioner based on the temperature change detected by the temperature sensor, or the air conditioner is controlled by using a PID (proportio integral differential, proportional-integral-derivative) control algorithm. However, the indoor space in the above-mentioned place is relatively large, and the flow of people is large and the real-time flow of people is also changed greatly, that is, the indoor place environment in which temperature control is required is complex, so that the problem that the actual indoor temperature is too high or too low is easily caused, and the actual temperature adjustment requirement cannot be satisfied.
Disclosure of Invention
The main purpose of the application is to provide a centralized temperature control method, which aims to solve the technical problem that the traditional temperature control method in large public places is difficult to meet the actual temperature regulation requirement.
In order to achieve the above object, the present application provides a temperature centralized control method, which is applied to a central control device for temperature control of a target indoor location, the temperature centralized control method including:
acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise temperature difference between the target indoor place and the outdoor, people flow of the target indoor place, gas exchange quantity between the target indoor place and the outdoor and power consumption data of the target indoor place;
the dynamic variable is input into a preset deep neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
a temperature regulating device in the target indoor location is controlled based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature.
Optionally, the dynamic variable further includes the number of people in the room, and the step of obtaining the dynamic variable of the target indoor location includes:
Determining the temperature difference based on temperature sensors disposed inside and outside the target indoor location;
determining the people flow through an image sensor arranged at an entrance and an exit of the target indoor place, and calculating the number of indoor people through the people flow;
determining the gas exchange amount through the working power of ventilation equipment in the target indoor place and the communication area between the target indoor place and the outside;
and extracting power consumption data from the power consumption information of the target indoor place.
Optionally, the step of controlling the temperature regulating device in the target indoor location based on the heat exchange rate, the actual temperature of the target indoor location, and the target temperature includes:
determining an output coefficient curve according to the difference between the actual temperature of the target indoor place and the target temperature;
constructing an output curve of the temperature regulating device based on the heat exchange rate and the output coefficient curve;
and controlling the output of the temperature regulating device based on the output curve.
Optionally, the step of controlling the output of the temperature regulating device based on the device output curve comprises:
extracting real-time output corresponding to the current moment from the output curve;
Extracting a predicted output corresponding to the current moment from a historical output curve of the temperature regulating equipment;
and calculating a target output of the temperature adjusting device based on the real-time output and the predicted output, and controlling the output of the temperature adjusting device based on the target output.
Optionally, before the step of inputting the dynamic variable to a preset deep neural network model to estimate the heat exchange rate between the indoor location and the outdoor location of the target, the method includes:
generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
and training the preset deep neural network model based on the training sample set.
Optionally, the step of generating training samples in the training sample set based on the historical operation and maintenance record of the target indoor location includes:
extracting dynamic variables of the target indoor place in any constant temperature time period from the operation and maintenance records as sample characteristics for the operation and maintenance records in any constant temperature time period in the historical operation and maintenance records;
extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
And constructing training samples in the training sample set based on the sample characteristics and the sample labels.
Optionally, the step of training the preset deep neural network model based on the training sample set includes:
for any training sample in the training sample set, inputting the training sample into the preset deep neural network model for estimation to obtain an estimation result of the training sample;
calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and updating model parameters of the preset deep neural network model based on the model loss.
In order to achieve the above object, the present application further provides a temperature centralized control device, the temperature centralized control device is built in a central control apparatus, the central control apparatus is used for controlling the temperature of a target indoor location, the temperature centralized control device includes:
the acquisition module is used for acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise a temperature difference between the target indoor place and the outdoor, a human flow of the target indoor place, a gas exchange amount between the target indoor place and the outdoor and power consumption data of the target indoor place;
The estimation module is used for inputting the dynamic variable to a preset depth neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
a control module for controlling the temperature regulating device in the target indoor location based on the heat exchange rate, the actual temperature of the target indoor location, and a target temperature.
To achieve the above object, the present application further provides a central control device, including: the temperature centralized control system comprises a memory, a processor and a temperature centralized control program which is stored in the memory and can run on the processor, wherein the temperature centralized control program realizes the steps of the temperature centralized control method when being executed by the processor.
In order to achieve the above object, the present application further provides a readable storage medium, where the readable storage medium is a computer readable storage medium, and a temperature centralized control program is stored on the readable storage medium, and the temperature centralized control program when executed by a processor implements the steps of the temperature centralized control method described above.
The embodiment of the application provides a temperature centralized control method, a device, central control equipment and a readable storage medium. In this embodiment, the temperature centralized control method is applied to a central control device, where the central control device is used for controlling the temperature of a target indoor location, and the central control device obtains a dynamic variable of the target indoor location, where the dynamic variable includes a temperature difference between the target indoor location and the outdoor location, a person flow rate of the target indoor location, a gas exchange amount between the target indoor location and the outdoor location, and power consumption data of the target indoor location; the dynamic variable is input into a preset deep neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target; a temperature regulating device in the target indoor location is controlled based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature. That is, the central control device of the present application estimates the heat exchange rate between the target indoor location and the outdoor according to the dynamic variable of the target indoor location and the preset depth neural network model, and controls the temperature adjusting device based on parameters such as the heat exchange rate.
Drawings
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a first embodiment of a method for centralized temperature control according to the present application;
FIG. 3 is a schematic flow chart of a second embodiment of the method for controlling temperature in a centralized manner;
FIG. 4 is a schematic flow chart of a third embodiment of a method for controlling temperature in a centralized manner according to the present application;
fig. 5 is a schematic structural diagram of a temperature centralized control device in the temperature centralized control method of the present application.
The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware running environment according to an embodiment of the present application.
The device of the embodiment of the application can be electronic terminal devices such as a central control device, a tablet personal computer and a portable computer.
As shown in fig. 1, the central control apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the central control device may further include various connection interfaces, such as COM (cluster communication port, serial communication port), IR (Infrared Radiation, infrared) interface, LAN (Local Area Network ) interface, and USB (Universal Serial Bus, serial bus standard) interface, which are not described herein.
It will be appreciated by those skilled in the art that the device structure shown in fig. 1 is not limiting of the device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a temperature centralized control program may be included in a memory 1005, which is a type of computer storage medium.
In the central control device shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a temperature centralized control program stored in the memory 1005 and perform the following operations:
acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise temperature difference between the target indoor place and the outdoor, people flow of the target indoor place, gas exchange quantity between the target indoor place and the outdoor and power consumption data of the target indoor place;
The dynamic variable is input into a preset deep neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
a temperature regulating device in the target indoor location is controlled based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
the dynamic variable further comprises the number of people in the room, and the step of acquiring the dynamic variable of the target indoor place comprises the following steps:
determining the temperature difference based on temperature sensors disposed inside and outside the target indoor location;
determining the people flow through an image sensor arranged at an entrance and an exit of the target indoor place, and calculating the number of indoor people through the people flow;
determining the gas exchange amount through the working power of ventilation equipment in the target indoor place and the communication area between the target indoor place and the outside;
and extracting power consumption data from the power consumption information of the target indoor place.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
The step of controlling the temperature adjusting device in the target indoor location based on the heat exchange rate, the actual temperature of the target indoor location, and the target temperature includes:
determining an output coefficient curve according to the difference between the actual temperature of the target indoor place and the target temperature;
constructing an output curve of the temperature regulating device based on the heat exchange rate and the output coefficient curve;
and controlling the output of the temperature regulating device based on the output curve.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
the step of controlling the output of the temperature regulating device based on the device output curve includes:
extracting real-time output corresponding to the current moment from the output curve;
extracting a predicted output corresponding to the current moment from a historical output curve of the temperature regulating equipment;
and calculating a target output of the temperature adjusting device based on the real-time output and the predicted output, and controlling the output of the temperature adjusting device based on the target output.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
Before the step of inputting the dynamic variable to a preset deep neural network model to estimate the heat exchange rate between the indoor location and the outdoor location of the target, the method comprises the following steps:
generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
and training the preset deep neural network model based on the training sample set.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
the step of generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor location comprises:
extracting dynamic variables of the target indoor place in any constant temperature time period from the operation and maintenance records as sample characteristics for the operation and maintenance records in any constant temperature time period in the historical operation and maintenance records;
extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
and constructing training samples in the training sample set based on the sample characteristics and the sample labels.
Further, the processor 1001 may call a temperature centralized control program stored in the memory 1005, and further perform the following operations:
The training the preset deep neural network model based on the training sample set comprises the following steps:
for any training sample in the training sample set, inputting the training sample into the preset deep neural network model for estimation to obtain an estimation result of the training sample;
calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and updating model parameters of the preset deep neural network model based on the model loss.
Referring to fig. 2, a first embodiment of a temperature centralized control method of the present application is applied to a central control apparatus for temperature control of a target indoor location, the temperature centralized control method including:
step S10, acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise temperature difference between the target indoor place and the outdoor, people flow of the target indoor place, gas exchange quantity between the target indoor place and the outdoor and power consumption data of the target indoor place;
it should be noted that, the above-mentioned temperature centralized control method is mainly applied in some relatively large-scale indoor public places, because public places have a lot of people's flow, the change of people's flow is great, the consumer is many so that the temperature condition environment in this kind of places is very complicated, the corresponding influence to indoor temperature is also great. In the conventional temperature regulation scheme at present, feedback regulation is basically performed on a central air conditioner by detecting the current indoor temperature in real time. Taking winter as an example, in winter, the central air conditioner will supply heat to the indoor public place, for example, the indoor temperature is lower than a preset value, and the heating amount of the central air conditioner is regulated when the indoor temperature is lower, and the heating amount of the central air conditioner is regulated when the indoor temperature is higher than the preset value, and the indoor temperature is higher, on one hand, the regulation of the central air conditioner to the indoor temperature changes with hysteresis, and on the other hand, the people flow change in the public place also affects the current indoor temperature, so the traditional regulation mode based on the temperature alone is difficult to realize stable regulation of the temperature. Aiming at the problems, the application provides a centralized temperature control method which refers to dynamic variables in indoor places to control corresponding temperature regulating equipment so as to realize stable temperature control. In addition, it should be noted that the above-mentioned temperature centralized control method is generally applied to a central control device, and the central control device may be used for temperature control of a target indoor location.
For example, the central control device may be communicatively coupled to sensors disposed inside and outside the target indoor location for acquiring dynamic variables of the target indoor location. In addition, the central control device is also in communication connection with the temperature regulating device arranged in the target indoor place so as to control the starting, stopping and outputting of the temperature regulating device, wherein the temperature regulating device can be a central air conditioner, and the central air conditioner can exchange heat by using a medium such as gas or liquid and the like, and achieves the aim of temperature regulation. The dynamic variables in this embodiment may include a temperature difference between the target indoor location and the outside, a flow rate of people at the target indoor location, an amount of gas exchange between the target indoor location and the outside, and power consumption data at the target indoor location. The temperature difference between the indoor location and the outdoor location of the target can be obtained by a temperature sensor arranged inside and outside the room. The people flow in the target indoor place refers to the number of people entering and exiting the target indoor place in real time, and can be determined by data acquired by an image sensor arranged at an entrance and an exit of the target indoor place. The amount of gas exchange between the target indoor location and the outside air can be determined by the operating parameters of the ventilation system in the target indoor location and the communication area between the target indoor location and the outside air. And the power consumption data of the target indoor place is collected from the power supply system of the target indoor place.
Step S20, inputting the dynamic variable to a preset depth neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
the collected dynamic variables are input to a preset depth neural network model to estimate and obtain heat exchange rates of the target indoor location and the outdoor location, and it is to be noted that heat exchange forms in different heat exchange rates are different according to seasons, for example, the temperature adjusting device mainly heats in winter, correspondingly, the heat exchange forms are used for transferring heat from the target indoor location to the outdoor location, the temperature adjusting device mainly supplies cold in summer, and the heat exchange forms are used for transferring heat from the outdoor location to the target indoor location. In addition, in order to improve the universality of the preset depth neural network model, the fixed characteristics of the target indoor location may also be used, for example, the fixed characteristics may include a design heat exchange coefficient of the target indoor location (generally, the fixed characteristics may be determined according to building materials and building shapes used in the design and construction of the target indoor location) and an area or a volume of the target indoor location, and the fixed characteristics and dynamic variables may be used as inputs of the preset depth neural network model. The preset deep neural network model can comprise an output layer, a plurality of hidden layers, an output layer and the like, and the specific model structure is not limited in practical application, so that a technician can set according to practical requirements. The preset deep neural network model is trained in advance through training samples, so that the model has the capacity of carrying out heat exchange rate estimation according to fixed characteristics and dynamic variables.
And step S30, controlling the temperature regulating equipment in the target indoor place based on the heat exchange rate, the actual temperature of the target indoor place and the target temperature.
Illustratively, after the heat exchange rate of the target indoor location is determined, the temperature regulating device in the target indoor location is controlled based on the heat exchange rate, the actual temperature of the target indoor location, and the target temperature control. Taking winter as an example, the heat exchange mode of the target indoor place and the outdoor is that the target indoor place transfers heat to the outdoor, if the actual temperature of the current target indoor place is consistent with the target temperature (the target temperature can be a preset temperature range, and the actual temperature can be considered to be consistent with the target temperature when the actual temperature is in the preset temperature range), the heat exchange rate (heat loss rate) can be used as an output target (namely, heat supply rate) of the temperature regulating device to control the temperature regulating device, so that the heat exchange balance of the target indoor place is realized, the stability of temperature control is ensured, wherein the heating capacity of the temperature regulating device under different powers is an inherent attribute of the temperature regulating device, and the heating capacity can be determined according to the device parameters of the temperature regulating device. Similarly, based on the above example, if the actual temperature of the current indoor location is smaller than the target temperature, the target output of the temperature adjustment device is larger than the heat exchange rate, so as to ensure that the actual temperature of the current indoor location can rise, wherein the magnitude of the heat exchange difference of the target output exceeding the heat exchange rate can be determined according to the difference between the actual temperature and the target temperature, and the target output can be specifically set by a technician. For the summer scenario, the heat exchange between the target indoor location and the outdoor location is that the outdoor heat is transferred to the target indoor location, and the temperature adjusting device is used for cooling, and the control mode of the temperature adjusting device can refer to the winter scenario example, which is not described herein.
In this embodiment, the temperature centralized control method is applied to a central control device, where the central control device is used for controlling the temperature of a target indoor location, and the central control device obtains a dynamic variable of the target indoor location, where the dynamic variable includes a temperature difference between the target indoor location and the outdoor location, a person flow rate of the target indoor location, a gas exchange amount between the target indoor location and the outdoor location, and power consumption data of the target indoor location; the dynamic variable is input into a preset deep neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target; a temperature regulating device in the target indoor location is controlled based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature. That is, the central control device of the present application estimates the heat exchange rate between the target indoor location and the outdoor according to the dynamic variable of the target indoor location and the preset depth neural network model, and controls the temperature adjusting device based on parameters such as the heat exchange rate.
In a possible implementation manner, the dynamic variable further includes the number of people in the room, and the step of obtaining the dynamic variable of the target indoor location includes:
step S11, determining the temperature difference based on temperature sensors arranged inside and outside the target indoor place;
step S12, determining the people flow through an image sensor arranged at the entrance and the exit of the target indoor place, and calculating the number of people in the room through the people flow;
step S13, determining the gas exchange amount through the working power of the ventilation equipment in the target indoor place and the communication area between the target indoor place and the outside;
and S14, extracting power consumption data from the power consumption information of the target indoor place.
The characteristics of the person include the number of persons in the room, that is, the number of persons in the target indoor place, in addition to the flow rate of the person.
For example, regarding the temperature difference of the dynamic variable, the outdoor temperature may be obtained by a temperature sensor provided inside the indoor location, and the indoor temperature may be obtained by a temperature sensor provided outside the indoor location, and the temperature difference may be calculated from the outdoor temperature and the indoor temperature. It should be noted that, to ensure accuracy of the obtained temperature, a plurality of temperature sensors may be set and distributed at different positions, and the highest temperature of the occurrence frequency is selected, or an average value of the temperatures is calculated. The flow rate of the people can be determined by image data acquired by an image sensor provided at the entrance/exit of the target indoor location. For example, the people flow is identified by technologies such as image identification or dynamic tracking, and the existing statistics algorithm of people flow can be referred to, which is not described herein. After the people flow is counted, the people flow can be calculated in an accumulated mode, and therefore the number of people in the indoor places is obtained. Regarding the gas exchange amount, on the one hand, the working power of the ventilation equipment in the target indoor place can be determined, and the rated ventilation amount exists in the ventilation equipment under the rated working power, so that a part of the gas exchange amount can be determined by the working power of the ventilation equipment, and on the other hand, the communication area is determined by the communication area between the target indoor place and the outdoor place, for example, the communication area can be an opening and closing opening of the target indoor place, and then another part of the gas exchange amount is determined according to the communication area, the preset air flow coefficient, the temperature difference and other parameters, and then the total gas exchange amount can be calculated by combining the two parts of the gas exchange amounts. The power consumption data can be extracted from the power consumption information acquired from the power supply system of the target indoor place. If a large number of electric equipment exists in the target indoor place under the general payment, the temperature of the target indoor place is affected by the heat emitted by the electric equipment.
Referring to fig. 3, in this embodiment, the same or similar parts as those of the above embodiment may be referred to the above, and will not be described herein. The step of controlling the temperature adjusting device in the target indoor location based on the heat exchange rate, the actual temperature of the target indoor location, and the target temperature includes:
step S310, determining an output coefficient curve according to the difference between the actual temperature of the target indoor place and the target temperature;
step S320, constructing an output curve of the temperature adjustment device based on the heat exchange rate and the output coefficient curve;
and step S330, controlling the output of the temperature regulating device based on the output curve.
The output coefficient curve is determined according to the difference between the actual temperature of the target indoor place and the target temperature, and the output coefficient curve is multiplied by the heat exchange rate to construct the output curve of the temperature regulating device. It should be noted that, the output coefficient curve may be preset by a technician, and is also usually tested, and the output coefficient at any position in the output coefficient curve represents the relationship between the output of the temperature adjusting device and the heat exchange rate, and in general, the output coefficient is greater than or equal to 1, and in any one output coefficient curve, the output coefficient will be stable to 1 over time. After the output curve is determined, the output of the temperature regulating equipment can be controlled according to the output curve.
In a possible embodiment, the step of controlling the output of the temperature adjustment device based on the device output curve includes:
step S331, extracting real-time output corresponding to the current moment from the output curve;
step S332, extracting a predicted output corresponding to the current moment from a historical output curve of the temperature regulating device;
step S333 calculates a target output of the temperature adjusting device based on the real-time output and the predicted output, and controls the output of the temperature adjusting device based on the target output.
Illustratively, the real-time output corresponding to the current time is extracted from an output curve, the predicted output corresponding to the current time is extracted from a historical output curve of the temperature adjusting device, the historical output curve can be the output curve generated last time, an average value of the real-time output and the predicted output is calculated to obtain a target output, and the target output is used as an output target of the temperature adjusting device to be controlled.
Referring to fig. 4, in order to propose a third embodiment of the present application based on the first embodiment and the second embodiment of the present application, in this embodiment, the same or similar parts as those of the above embodiment may be referred to the above, and will not be repeated here.
Before the step of inputting the dynamic variable to a preset deep neural network model to estimate the heat exchange rate between the indoor location and the outdoor location of the target, the method comprises the following steps:
step S110, generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
and step S120, training the preset deep neural network model based on the training sample set.
It should be noted that, in this embodiment, a training sample of a preset deep neural network model is constructed, and the preset deep neural network model is trained through the training sample, so that the preset deep neural network model has the ability of estimating the heat exchange rate of the target indoor location.
Illustratively, training samples are constructed based on historical operating records of the target indoor location. The historical operation and maintenance record mainly records dynamic variables, actual temperature and working parameters (such as output of the temperature regulating device, heating rate and refrigerating rate) of a target indoor place in the history. It should be noted that, if only the historical operation and maintenance records generated by the target indoor location are used to construct the training samples, there may be a problem that the number of samples is small in the early operation period, so that the fixed features can be added as sample features to construct the training samples on the basis of the dynamic variables, and in this case, the training samples of other indoor locations can be used to train the preset depth neural network model, and accordingly, when estimating the heat exchange rate of the target indoor location, the dynamic variables input to the preset depth neural network model in addition to the dynamic variables of the target indoor location also include the fixed features of the target indoor location. After the training sample set is constructed, training samples in the training sample set are input into a preset deep neural network model for iterative training until a set training ending condition is set.
In a possible embodiment, the step of generating training samples in the training sample set based on the historical operation and maintenance records of the target indoor location includes:
step S111, for an operation and maintenance record in any constant temperature time period in the history operation and maintenance records, extracting a dynamic variable of the target indoor location in the constant temperature time period from the operation and maintenance records as a sample feature;
step S112, extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
step S113, constructing a training sample in the training sample set based on the sample feature and the sample label.
The constant temperature time period is extracted from the historical operation and maintenance record, wherein the constant temperature time period refers to a period that the actual temperature change amplitude of the target indoor place is smaller than a preset period, the period can be determined through an actual temperature curve of the target indoor place in the historical operation and maintenance record, for example, the constant temperature time period can be determined through a temperature sliding window, that is, the actual temperature fluctuation of the actual temperature curve under the corresponding temperature sliding window does not exceed a fluctuation threshold value, and the constant temperature time period can be considered. Correspondingly, the dynamic variable in the constant temperature time period is extracted as a sample characteristic (the fixed characteristic of the indoor location of the target can be synchronously used as the sample characteristic). And extracting the output of the temperature regulating device in the constant temperature time period, wherein the time period is the constant temperature time period, so that the output of the temperature regulating device can be regarded as the heat exchange efficiency of the target indoor place and the target outdoor place, the output of the temperature regulating device is used as the sample label structure of the determined sample characteristics, one training sample is constructed and generated, and each generated training sample forms a training sample set.
In a possible implementation manner, the step of training the preset depth neural network model based on the training sample set includes:
step S121, for any one training sample in the training sample set, inputting the training sample into the preset deep neural network model to estimate to obtain an estimation result of the training sample;
step S122, calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and step S123, updating model parameters of the preset deep neural network model based on the model loss.
For any training sample in the training sample set, the training sample is input into a preset deep neural network model, and the preset deep neural network model obtains a sample estimation through the training sample to obtain an estimation result. And comparing the estimation result with a sample label of a training sample to obtain a difference between the estimation result and the sample label, and calculating the model loss of the preset depth neural network model according to the difference, wherein a loss function of the preset depth neural network model can be set by a technician. And updating model parameters of the preset deep neural network model based on model loss and back propagation by a gradient descent method. The training end condition may be that training samples in the training sample set are trained, model loss may be converged, or a set training frequency may be reached, etc.
In addition, referring to fig. 5, the embodiment of the present application further proposes a temperature centralized control apparatus 100, where the temperature centralized control apparatus 100 is built in a central control device, and the central control device is used for temperature control of a target indoor location, and the temperature centralized control apparatus 100 includes:
an obtaining module 10, configured to obtain dynamic variables of the target indoor location, where the dynamic variables include a temperature difference between the target indoor location and the outside, a flow rate of people in the target indoor location, an exchange amount of gas between the target indoor location and the outside, and power consumption data of the target indoor location;
the estimation module 20 is used for inputting the dynamic variable to a preset depth neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
a control module 30 for controlling the temperature regulating device in the target indoor location based on the heat exchange rate, the actual temperature of the target indoor location, and a target temperature.
Optionally, the dynamic variable further includes the number of people in the room, and the obtaining module 10 is further configured to:
determining the temperature difference based on temperature sensors disposed inside and outside the target indoor location;
determining the people flow through an image sensor arranged at an entrance and an exit of the target indoor place, and calculating the number of indoor people through the people flow;
Determining the gas exchange amount through the working power of ventilation equipment in the target indoor place and the communication area between the target indoor place and the outside;
and extracting power consumption data from the power consumption information of the target indoor place.
Optionally, the control module 30 is further configured to:
determining an output coefficient curve according to the difference between the actual temperature of the target indoor place and the target temperature;
constructing an output curve of the temperature regulating device based on the heat exchange rate and the output coefficient curve;
and controlling the output of the temperature regulating device based on the output curve.
Optionally, the control module 30 is further configured to:
extracting real-time output corresponding to the current moment from the output curve;
extracting a predicted output corresponding to the current moment from a historical output curve of the temperature regulating equipment;
and calculating a target output of the temperature adjusting device based on the real-time output and the predicted output, and controlling the output of the temperature adjusting device based on the target output.
Optionally, the temperature centralized control device 100 further includes a training module 40, where the training module 40 is configured to:
generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
And training the preset deep neural network model based on the training sample set.
Optionally, the training module 40 is further configured to:
extracting dynamic variables of the target indoor place in any constant temperature time period from the operation and maintenance records as sample characteristics for the operation and maintenance records in any constant temperature time period in the historical operation and maintenance records;
extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
and constructing training samples in the training sample set based on the sample characteristics and the sample labels.
Optionally, the training module 40 is further configured to:
for any training sample in the training sample set, inputting the training sample into the preset deep neural network model for estimation to obtain an estimation result of the training sample;
calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and updating model parameters of the preset deep neural network model based on the model loss. The temperature centralized control device provided by the application adopts the temperature centralized control method in the embodiment, and aims to solve the technical problem that the traditional temperature control method in a large public place is difficult to meet the actual temperature regulation requirement. Compared with the prior art, the beneficial effects of the temperature centralized control device provided by the embodiment of the present application are the same as those of the temperature centralized control method provided by the first embodiment, and other technical features of the temperature centralized control device are the same as those disclosed by the method of the first embodiment, which are not described in detail herein.
In addition, the embodiment of the application also provides a central control device, which comprises: the temperature centralized control system comprises a memory, a processor and a temperature centralized control program which is stored in the memory and can run on the processor, wherein the temperature centralized control program realizes the steps of the temperature centralized control method when being executed by the processor.
The specific implementation manner of the control device in the present application is basically the same as the above embodiments of the temperature centralized control method, and will not be repeated here.
In addition, the embodiment of the application also provides a readable storage medium, which is a computer readable storage medium, wherein a temperature centralized control program is stored on the readable storage medium, and the temperature centralized control program realizes the steps of the temperature centralized control method when being executed by a processor.
The specific implementation manner of the medium is basically the same as that of each embodiment of the above-mentioned temperature centralized control method, and is not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above, including several instructions for causing a terminal device (which may be a computer, a central control device, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (7)
1. A temperature centralized control method, characterized in that the temperature centralized control method is applied to a central control device for temperature control of a target indoor place, the temperature centralized control method comprising:
acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise temperature difference between the target indoor place and the outdoor, people flow of the target indoor place, gas exchange quantity between the target indoor place and the outdoor and power consumption data of the target indoor place;
the dynamic variable is input into a preset deep neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
controlling a temperature regulating device in the target indoor location based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature;
before the step of inputting the dynamic variable to a preset deep neural network model to estimate the heat exchange rate between the indoor location and the outdoor location of the target, the method comprises the following steps:
generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
training the preset deep neural network model based on the training sample set;
Wherein the step of generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor location comprises:
extracting dynamic variables of the target indoor place in any constant temperature time period from the operation and maintenance records as sample characteristics for the operation and maintenance records in any constant temperature time period in the historical operation and maintenance records;
extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
constructing training samples in the training sample set based on the sample features and the sample labels;
the training the preset deep neural network model based on the training sample set comprises the following steps:
for any training sample in the training sample set, inputting the training sample into the preset deep neural network model for estimation to obtain an estimation result of the training sample;
calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and updating model parameters of the preset deep neural network model based on the model loss.
2. The method of centralized temperature control as set forth in claim 1, wherein the dynamic variables further include the number of persons in the room, and the step of acquiring the dynamic variables of the target room location includes:
determining the temperature difference based on temperature sensors disposed inside and outside the target indoor location;
determining the people flow through an image sensor arranged at an entrance and an exit of the target indoor place, and calculating the number of indoor people through the people flow;
determining the gas exchange amount through the working power of ventilation equipment in the target indoor place and the communication area between the target indoor place and the outside;
and extracting power consumption data from the power consumption information of the target indoor place.
3. The temperature centralized control method as set forth in claim 1, wherein the step of controlling the temperature adjusting device in the target indoor place based on the heat exchange rate, the actual temperature of the target indoor place, and the target temperature comprises:
determining an output coefficient curve according to the difference between the actual temperature of the target indoor place and the target temperature;
constructing an output curve of the temperature regulating device based on the heat exchange rate and the output coefficient curve;
And controlling the output of the temperature regulating device based on the output curve.
4. The method of centralized temperature control as set forth in claim 3, wherein the step of controlling the output of the temperature adjustment device based on the device output curve comprises:
extracting real-time output corresponding to the current moment from the output curve;
extracting a predicted output corresponding to the current moment from a historical output curve of the temperature regulating equipment;
and calculating a target output of the temperature adjusting device based on the real-time output and the predicted output, and controlling the output of the temperature adjusting device based on the target output.
5. A temperature centralized control device, the temperature centralized control device is built in a central control device, the central control device is used for controlling the temperature of a target indoor place, and the temperature centralized control device comprises:
the acquisition module is used for acquiring dynamic variables of the target indoor place, wherein the dynamic variables comprise a temperature difference between the target indoor place and the outdoor, a human flow of the target indoor place, a gas exchange amount between the target indoor place and the outdoor and power consumption data of the target indoor place;
The estimation module is used for inputting the dynamic variable to a preset depth neural network model to estimate and obtain the heat exchange rate of the indoor place and the outdoor place of the target;
a control module for controlling a temperature regulating device in the target indoor location based on the heat exchange rate, an actual temperature of the target indoor location, and a target temperature;
before the step of inputting the dynamic variable to a preset deep neural network model to estimate the heat exchange rate between the indoor location and the outdoor location of the target, the method comprises the following steps:
generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor places;
training the preset deep neural network model based on the training sample set;
wherein the step of generating training samples in a training sample set based on the historical operation and maintenance records of the target indoor location comprises:
extracting dynamic variables of the target indoor place in any constant temperature time period from the operation and maintenance records as sample characteristics for the operation and maintenance records in any constant temperature time period in the historical operation and maintenance records;
extracting the output of the temperature regulating device in the constant temperature time period from the operation and maintenance record as a sample label;
Constructing training samples in the training sample set based on the sample features and the sample labels;
the training the preset deep neural network model based on the training sample set comprises the following steps:
for any training sample in the training sample set, inputting the training sample into the preset deep neural network model for estimation to obtain an estimation result of the training sample;
calculating model loss of the preset deep neural network model based on the difference between the estimation result and the sample label of the training sample;
and updating model parameters of the preset deep neural network model based on the model loss.
6. A central control apparatus, characterized in that the central control apparatus comprises: a memory, a processor and a temperature centralized control program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the temperature centralized control method of any one of claims 1 to 4.
7. A readable storage medium, characterized in that the readable storage medium is a computer readable storage medium, on which a temperature centralized control program is stored, which when executed by a processor, implements the steps of the temperature centralized control method according to any one of claims 1 to 4.
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