CN107826027A - Refrigerator car temprature control method and system based on big data analysis - Google Patents
Refrigerator car temprature control method and system based on big data analysis Download PDFInfo
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- CN107826027A CN107826027A CN201710860576.9A CN201710860576A CN107826027A CN 107826027 A CN107826027 A CN 107826027A CN 201710860576 A CN201710860576 A CN 201710860576A CN 107826027 A CN107826027 A CN 107826027A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60P—VEHICLES ADAPTED FOR LOAD TRANSPORTATION OR TO TRANSPORT, TO CARRY, OR TO COMPRISE SPECIAL LOADS OR OBJECTS
- B60P3/00—Vehicles adapted to transport, to carry or to comprise special loads or objects
- B60P3/20—Refrigerated goods vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
- B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/32—Cooling devices
- B60H1/3204—Cooling devices using compression
- B60H1/3205—Control means therefor
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- Physics & Mathematics (AREA)
- Thermal Sciences (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Transportation (AREA)
- Devices That Are Associated With Refrigeration Equipment (AREA)
Abstract
The invention discloses a kind of refrigerator car temprature control method and system based on big data analysis, the described method comprises the following steps：By the measured data for testing the rotating speed for obtaining the temperature of monitoring point in refrigerator car, refrigerator car outer surface each test point temperature, environment temperature and speed, refrigeration unit compressor and blower fan；Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters；Using the multiple cluster cluster as training sample, ellipsoidal unit neutral net is trained as output variable using compressor rotary speed and rotation speed of fan respectively；Compressor rotary speed and rotation speed of fan are controlled in real time based on the ellipsoidal unit neutral net.The present invention can realize that quantitative control, the more traditional rule of thumb regulation and control in real time of refrigerator car temperature are more reasonable.
Description
Technical field
The present invention relates to refrigeration control field, more particularly to a kind of refrigerator car temprature control method based on big data analysis
And system.
Background technology
Biological agent occupies highly important position in people live, and most of biological agent needs low in the process of circulation
Temperature refrigeration, biological agent Cold Chain Logistics market are huge.The raising required with people medical security is, it is necessary to which the biology of refrigeration is made
The safety problem of agent encounters higher requirement and challenge.The unqualified event of biological agent recurred in recent years allows people
Improve vigilant, the Cold Chain Logistics of biological agent are also increasingly taken seriously.Refrigerator car possesses the function of insulation and refrigeration, energy
Enough meet the requirement in the short-distance Refrigerated Transport of biological agent, be the key link of its Cold Chain Logistics.Maintain in refrigerator carriage body
Portion's temperature is constant, keeps carriage body various places inside to have the less temperature difference, for ensureing that the quality of biological agent in transportation rises
Important effect.
In refrigerator car, air enters refrigerator car through evaporator, if the selection of speed of the compressor of refrigeration unit and blower fan
It is improper, it can cause to flow through the air velocity of evaporator, temperature can not make the Temperature Distribution in refrigerator car meet to require, influence in-car
The quality of biological agent.Therefore, the rotating speed of refrigeration unit compressor and blower fan how is rationally controlled, ensures the temperature in refrigerator car
It is the technical problem that those skilled in the art urgently solve at present within the scope of being distributed in reasonably.
The content of the invention
In order to solve the above problems, the present invention provides a kind of refrigerator car temprature control method and system, by each monitoring point temperature
Difference, refrigerator car outer surface each point temperature, environment temperature and speed carry out cluster point to a large amount of measured datas as input variable
Analysis, it is divided into three kinds of worst hot case, high and cold operating mode and normal temperature operating mode cluster clusters, then using three kinds of cluster clusters as ellipsoidal unit
The input of neutral net, using the rotating speed of compressor of air conditioner and blower fan as output variable, ellipsoidal unit neutral net is trained, is obtained
The Controlling model of compressor and rotation speed of fan, with this rational control evaporator outlet temperature and wind speed.
To achieve these goals, the present invention adopts the following technical scheme that：
A kind of refrigerator car temprature control method, comprises the following steps：
Step 1：The temperature of monitoring point, each test point temperature in refrigerator car outer surface, environment in refrigerator car are obtained by testing
The measured data of the rotating speed of temperature and speed, refrigeration unit compressor and blower fan；
Step 2：Cluster analysis is carried out to the data of acquisition, obtains n cluster cluster；
Step 3：Using n cluster cluster as training sample, become respectively using compressor rotary speed and rotation speed of fan as output
Amount training ellipsoidal unit neutral net；
Step 4：Compressor rotary speed and rotation speed of fan are controlled in real time based on the ellipsoidal unit neutral net.
Further, the step 2 is clustered using c-means clustering algorithms, obtain worst hot case, high and cold operating mode and
Three kinds of cluster clusters of normal temperature operating mode.
Further, the step 4 includes：With the temperature of monitoring point, each monitoring point temperature difference, refrigerator car appearance in refrigerator car
Face each point temperature, environment temperature and speed are real-time input variable, based on the ellipsoidal unit neutral net, calculate compressor and
The rotating speed of blower fan is controlled refrigerator car temperature in real time.
Further, the ellipsoidal unit neutral net is made up of an input layer, a hidden layer and an output layer.
Input layer is determined according to the number of input parameter, and hidden layer has 7 implicit nodes, and output layer has 1 output node.Input
Node and 7 implicit nodes are all attached and connection weight is 1, deviation 0, imply node also by the side connected entirely
Formula is connected with output node.
Further, any one input variable x ∈ RdGaussian probability-density function form be expressed asWherein πkFor hybrid weight, and meet πk=1 and πk
>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…πk,
c1,…ck,Σ1,…,Σk}。
According to another object of the present invention, present invention also offers a kind of refrigerator car temperature control system, including：Data are adopted
Acquisition means, computing device and controller, the computing device include memory, processor and storage on a memory and can be
The computer program run on processor, it is characterised in that realize following steps during the computing device described program：
Receive temperature, each test point temperature in refrigerator car outer surface, the environment temperature of monitoring point in the refrigerator car that experiment obtains
With the measured data of the rotating speed of speed, refrigeration unit compressor and blower fan；
Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters；
Using the multiple cluster cluster as training sample, output variable is used as using compressor rotary speed and rotation speed of fan respectively
Train ellipsoidal unit neutral net；
Receive outside the temperature, each monitoring point temperature difference, refrigerator car of monitoring point in the refrigerator car that data acquisition device obtains in real time
Surface each point temperature, environment temperature and speed, as real-time input variable, based on the ellipsoidal unit neutral net, calculate pressure
The rotating speed of contracting machine and blower fan is simultaneously sent to controller；
The controller is controlled refrigerator car temperature in real time based on the compressor and the rotating speed of blower fan received.
Further, the data acquisition device includes being laid in multiple temperature sensors inside and outside refrigerator car.
The step 2 is clustered using c-means clustering algorithms, obtains worst hot case, high and cold operating mode and normal temperature operating mode
Three kinds of cluster clusters.
Further, the ellipsoidal unit neutral net is made up of an input layer, a hidden layer and an output layer；
Input layer is determined according to the number of input parameter, and hidden layer has 7 implicit nodes, and output layer has 1 output node.Input
Node and 7 implicit nodes are all attached and connection weight is 1, deviation 0, imply node also by the side connected entirely
Formula is connected with output node.
Further, any one input variable x ∈ RdGaussian probability-density function form be expressed asWherein πkFor hybrid weight, and meet πk=1 and πk
>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…πk,
c1,…ck,Σ1,…,Σk}。
Beneficial effects of the present invention：
1st, the present invention is from compressor and rotation speed of fan control refrigerator car temperature, by it with refrigerator car surfaces externally and internally temperature,
Environment temperature and speed founding mathematical models, can be according to data such as the refrigerator car internal and external temperature obtained in real time and speeds, to temperature
Degree carries out quantitative real-time control, and on the one hand more traditional rule of thumb regulation and control are more reasonable, reduce and cause because lacking experience
Improperly situation, another aspect, real-time auto-control save manual labor for control.
2nd, the present invention is in the temperature acquisition to refrigerator car, in addition to the temperature inside and outside collection refrigerator car, also goods in the car
The diverse location arrangement temperature sensor of thing, to the deployment scenarios of goods in reaction cabin so that the control of temperature is more reasonable.
3rd, the present invention neural network model training before carried out cluster analysis first, using the result of cluster analysis as
The training data of neural network model, improve neural metwork training efficiency.
Brief description of the drawings
Fig. 1 is the flow chart of refrigerator car temprature control method of the present invention；
Fig. 2 arranges for observation station of the present invention, wherein point layout when 2 (a) is empty wagons；On goods when 2 (b) is loading
Point layout；
Fig. 3 is the schematic diagram that the present invention trains ellipsoidal unit neutral net using compressor rotary speed as output variable；
Fig. 4 is the schematic diagram that the present invention trains ellipsoidal unit neutral net using rotation speed of fan as output variable.
Embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provides further instruction to the application.It is unless another
Indicate, all technologies used herein and scientific terminology are with usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
In the case where not conflicting, the feature in embodiment and embodiment in the application can be mutually combined.
Embodiment one
The purpose of the present embodiment is to provide a kind of refrigerator car temprature control method.
Based on above-mentioned purpose, a kind of refrigerator car temprature control method is present embodiments provided, as shown in figure 1, including following
Step：
Step 1：Obtain and the temperature of monitoring point in refrigerator car, refrigerator car outer surface each point temperature, environment are obtained by experiment
A large amount of measured datas of the rotating speed of temperature and speed, refrigeration unit compressor and blower fan.
The temperature of monitoring point, refrigerator car outer surface each point temperature and environment temperature are gathered by temperature sensor in refrigerator car
Obtained data, and upload in deposit portable computer.
The arrangement of temperature sensor is as shown in Fig. 2 wherein, such as Fig. 2 (a) is arranged in the inside and outside monitoring point of refrigerator car, cold
When hiding arrangement of goods on car, on goods shown in arrangement such as Fig. 2 (b) of monitoring point, by entering trip temperature to each position of goods
Monitoring, to react the deployment scenarios of goods.Each monitoring point temperature difference is refrigerator car inner sensor maximum temperature point and minimum temperature point
The temperature difference；Environment temperature is measured by car temperature outside sensor.
Step 2：Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters.
Cluster analysis is a kind of by physics or abstract object, the side for making a distinction and classifying according to the similitude between object
Method.People can determine " core " (i.e. cluster centre) first, then arrive the object " attraction " of surrounding when implementing cluster behavior
Should be around " core ", the characteristics of so as to complete cluster process.
The present embodiment is using c-means clustering algorithms to each monitoring point temperature, each monitoring point temperature difference, refrigerator car in refrigerator car
Outer surface each point temperature, environment temperature and speed are clustered, and obtain three kinds of worst hot case, high and cold operating mode and normal temperature operating mode clusters
Cluster.
Before cluster, because the unit of use is different, differs greatly, first had to at data between the order of magnitude
Reason, makes its standardization, specific standardized method is：Subtract the minimum value of codomain, then divided by codomain section.
Determine data setC cluster centre ck(k=1,2 ..., K), passes throughCalculate c-th of cluster DkCovariance matrix, this control strategy c=3.
Step 3：Using the multiple cluster cluster as training sample, become using compressor rotary speed and rotation speed of fan as output
Amount training ellipsoidal unit neutral net.
By the measured datas of worst hot case, high and cold operating mode and three kinds of normal temperature operating mode cluster cluster, as neutral net
Training sample.That is, with the temperature of monitoring point, each monitoring point temperature difference, refrigerator car outer surface each point temperature, environment temperature in refrigerator car
Degree and speed are input variable, are respectively trained using compressor rotary speed and rotation speed of fan and are used as the ellipsoidal unit nerve net of output variable
Network model.
Temperature Distribution in refrigerator car is mainly influenceed by the temperature and speed of the cold air for entering refrigerator car, and is entered cold
The temperature and speed for hiding the cold air of car are determined that refrigeration unit includes refrigeration compressor, cold by the rotating speed of refrigeration unit and blower fan
The parts such as condenser, choke valve (or expansion valve) and evaporator, therefore the present embodiment is using the rotating speed of compressor and blower fan as output
Variable, by controlling the rotating speed of compressor and blower fan to control refrigeration vehicle interior temperature.Fig. 3 is to be used as output variable using compressor rotary speed
Train the schematic diagram of ellipsoidal unit neutral net；Fig. 4 is that ellipsoidal unit neutral net is trained using rotation speed of fan as output variable
Schematic diagram.
Ellipsoidal unit network mode is a kind of feedforward neural network, and the three-layer network of ellipsoidal unit is used by hidden node
Network is formed, and each input node to each implicit node is connected by two weights, represents super ellipsoids unit respectively in the input space
Center and semi-major axis in each dimension, but each hidden node is not shared for all output nodes, and only with corresponding pattern class
Other output node connection.If pattern to be sorted similar to singlet Gaussian Profile or approximate Gaussian distribution, network can only by
Input layer and output layer are formed, and output layer uses ellipsoidal unit.
The ellipsoidal unit neutral net of the present embodiment is made up of an input layer, a hidden layer and an output layer.It is defeated
Enter node layer number to be determined according to the number of input parameter, hidden layer there are 7 implicit nodes, and output layer has 1 output section
Point.Input node is all attached with 7 implicit nodes and connection weight is 1, deviation 0, implies node also by complete
The mode of connection is connected with output node.
Using kernel function of the Gaussian probability-density function as ellipsoidal unit neutral net.Any one input variable x ∈
RdGaussian probability-density function form be expressed as
Wherein πkFor hybrid weight, and meet πk=1 and πk>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and
Full covariance matrix, parameter set θ={ π1,…πk,c1,…ck,Σ1,…,Σk}。
Step 4：Compressor rotary speed and rotation speed of fan are controlled in real time based on the ellipsoidal unit neutral net.
With the temperature of monitoring point, each monitoring point temperature difference, refrigerator car outer surface each point temperature, environment temperature and car in refrigerator car
Speed is real-time input variable, by the ellipsoidal unit neuron models of training, calculates the rotating speed of compressor and blower fan, realizes cold
Hide the temperature control of car.
Embodiment two
The purpose of the present embodiment is to provide a kind of refrigerator car temperature control system.
Based on object above, a kind of refrigerator car temperature control system is present embodiments provided, including：Data acquisition device,
Computing device and controller, the computing device include memory, processor and storage on a memory and can be in processors
The computer program of upper operation, it is characterised in that realize following steps during the computing device described program：
Receive temperature, each test point temperature in refrigerator car outer surface, the environment temperature of monitoring point in the refrigerator car that experiment obtains
With the measured data of the rotating speed of speed, refrigeration unit compressor and blower fan；
Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters；
Using the multiple cluster cluster as training sample, output variable is used as using compressor rotary speed and rotation speed of fan respectively
Train ellipsoidal unit neutral net；
Receive outside the temperature, each monitoring point temperature difference, refrigerator car of monitoring point in the refrigerator car that data acquisition device obtains in real time
Surface each point temperature, environment temperature and speed, as real-time input variable, based on the ellipsoidal unit neutral net, calculate pressure
The rotating speed of contracting machine and blower fan is simultaneously sent to controller；
The controller is controlled refrigerator car temperature in real time based on the compressor and the rotating speed of blower fan received.
Wherein, the data acquisition device includes being laid in multiple temperature sensors inside and outside refrigerator car.
The step 2 is clustered using c-means clustering algorithms, obtains worst hot case, high and cold operating mode and normal temperature operating mode
Three kinds of cluster clusters.
The ellipsoidal unit neutral net is made up of an input layer, a hidden layer and an output layer；Input layer root
It is determined according to the number of input parameter, hidden layer there are 7 implicit nodes, and output layer has 1 output node.Input node and 7
Individual implicit node is all attached and connection weight is 1, deviation 0, and implicit node is also by the mode that connects entirely and defeated
Egress is connected.
Any one input variable x ∈ RdGaussian probability-density function form be expressed asWherein πkFor hybrid weight, and meet πk=1 and πk
>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…πk,
c1,…ck,Σ1,…,Σk}。
The present invention is from compressor and rotation speed of fan control refrigerator car temperature, by itself and refrigerator car surfaces externally and internally temperature, ring
Border temperature and speed founding mathematical models, can be according to data such as the refrigerator car internal and external temperature obtained in real time and speeds, to temperature
Quantitative real-time control is carried out, on the one hand more traditional rule of thumb regulation and control are more reasonable, reduce and cause control because lacking experience
Improperly situation, another aspect, real-time auto-control save manual labor to system.
It will be understood by those skilled in the art that above-mentioned each module of the invention or each step can use general computer
Device realizes that alternatively, they can be realized with the program code that computing device can perform, it is thus possible to they are deposited
Storage performed in the storage device by computing device, either they are fabricated to respectively each integrated circuit modules or by it
In multiple modules or step be fabricated to single integrated circuit module to realize.The present invention is not restricted to any specific hardware
With the combination of software.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.
Claims (10)
1. a kind of refrigerator car temprature control method, it is characterised in that comprise the following steps：
Step 1：Temperature, each test point temperature in refrigerator car outer surface, the environment temperature of monitoring point in refrigerator car are obtained by testing
With the measured data of the rotating speed of speed, refrigeration unit compressor and blower fan；
Step 2：Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters；
Step 3：Using the multiple cluster cluster as training sample, become respectively using compressor rotary speed and rotation speed of fan as output
Amount training ellipsoidal unit neutral net；
Step 4：Compressor rotary speed and rotation speed of fan are controlled in real time based on the ellipsoidal unit neutral net.
2. a kind of refrigerator car temprature control method as claimed in claim 1, it is characterised in that the step 2 uses c-means
Clustering algorithm is clustered, and obtains three kinds of worst hot case, high and cold operating mode and normal temperature operating mode cluster clusters.
3. a kind of refrigerator car temprature control method as claimed in claim 1, it is characterised in that the step 4 includes：With refrigeration
The temperature of in-car monitoring point, each monitoring point temperature difference, refrigerator car outer surface each point temperature, environment temperature and speed become for input in real time
Amount, based on the ellipsoidal unit neutral net, the rotating speed for calculating compressor and blower fan is controlled refrigerator car temperature in real time.
A kind of 4. refrigerator car temprature control method as claimed in claim 1, it is characterised in that the ellipsoidal unit neutral net
It is made up of an input layer, a hidden layer and an output layer；Input layer is determined according to the number of input parameter, is implied
Layer has 7 implicit nodes, and output layer has 1 output node；Input node is all attached with 7 implicit nodes and connection weight
It is 1 again, deviation 0, implicit node is connected also by the mode connected entirely with output node.
A kind of 5. refrigerator car temprature control method as claimed in claim 1, it is characterised in that any one input variable x ∈ Rd
Gaussian probability-density function form be expressed asIts
Middle πkFor hybrid weight, and meet πk=1 and πk>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and complete
Covariance matrix, parameter set θ={ π1,…πk,c1,…ck,Σ1,…,Σk}。
A kind of 6. refrigerator car temperature control system, it is characterised in that including：Data acquisition device, computing device and controller, institute
The computer program that computing device includes memory, processor and storage on a memory and can run on a processor is stated,
Characterized in that, realize following steps during the computing device described program：
Receive temperature, refrigerator car outer surface each test point temperature, environment temperature and the car of monitoring point in the refrigerator car that experiment obtains
The measured data of the rotating speed of speed, refrigeration unit compressor and blower fan；
Cluster analysis is carried out to the data of acquisition, obtains multiple cluster clusters；
Using the multiple cluster cluster as training sample, trained respectively using compressor rotary speed and rotation speed of fan as output variable
Ellipsoidal unit neutral net；
Receive the temperature, each monitoring point temperature difference, refrigerator car outer surface of monitoring point in the refrigerator car that data acquisition device obtains in real time
Each point temperature, environment temperature and speed, as real-time input variable, based on the ellipsoidal unit neutral net, calculate compressor
With the rotating speed of blower fan and send to controller；
The controller is controlled refrigerator car temperature in real time based on the compressor and the rotating speed of blower fan received.
7. a kind of refrigerator car temperature control system as claimed in claim 5, it is characterised in that the data acquisition device includes
The multiple temperature sensors being laid in inside and outside refrigerator car.
8. a kind of refrigerator car temprature control method as claimed in claim 5, it is characterised in that the step 2 uses c-means
Clustering algorithm is clustered, and obtains three kinds of worst hot case, high and cold operating mode and normal temperature operating mode cluster clusters.
A kind of 9. refrigerator car temprature control method as claimed in claim 5, it is characterised in that the ellipsoidal unit neutral net
It is made up of an input layer, a hidden layer and an output layer；Input layer is determined according to the number of input parameter, is implied
Layer has 7 implicit nodes, and output layer has 1 output node；Input node is all attached with 7 implicit nodes and connection weight
It is 1 again, deviation 0, implicit node is connected also by the mode connected entirely with output node.
A kind of 10. refrigerator car temprature control method as claimed in claim 5, it is characterised in that any one input variable x ∈
RdGaussian probability-density function form be expressed as
Wherein πkFor hybrid weight, and meet πk=1 and πk>=0, ckAnd ΣkRespectively d dimension gaussian densities p (x | θk) center vector and
Full covariance matrix, parameter set θ={ π1,…πk,c1,…ck,Σ1,…,Σk}。
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| CN108773315A (en) * | 2018-05-25 | 2018-11-09 | 天津商业大学 | Intelligent temperature control refrigerator car based on real-time weather information |
| CN109050200A (en) * | 2018-08-22 | 2018-12-21 | 吉林大学 | A kind of control method of automobile heat pump air conditioner |
| CN110481412A (en) * | 2019-08-26 | 2019-11-22 | 山东大学 | A kind of refrigerator carriage and refrigerator car with temperature field dynamic regulation function |
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| CN111207498A (en) * | 2019-12-27 | 2020-05-29 | 广东美的白色家电技术创新中心有限公司 | Air conditioner, control method thereof, server, and computer-readable storage medium |
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| CN114996661A (en) * | 2022-08-04 | 2022-09-02 | 山东佳力通汽车有限公司 | Refrigerator car temperature monitoring method and system |
| KR102471478B1 (en) * | 2022-07-27 | 2022-11-28 | 주식회사 로드맵 | How to reserve the operation of the freezer of the delivery vehicle for cold chain |
| US12030366B2 (en) | 2019-08-26 | 2024-07-09 | Shandong University | Refrigerated compartments and refrigerated vehicles with function of dynamic control of temperature field |
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| CN109050200A (en) * | 2018-08-22 | 2018-12-21 | 吉林大学 | A kind of control method of automobile heat pump air conditioner |
| CN109050200B (en) * | 2018-08-22 | 2020-02-18 | 吉林大学 | Control method of automobile heat pump air conditioner |
| CN111196119A (en) * | 2018-11-16 | 2020-05-26 | 比亚迪股份有限公司 | Method and device for controlling air conditioner on vehicle |
| CN110481412A (en) * | 2019-08-26 | 2019-11-22 | 山东大学 | A kind of refrigerator carriage and refrigerator car with temperature field dynamic regulation function |
| US12030366B2 (en) | 2019-08-26 | 2024-07-09 | Shandong University | Refrigerated compartments and refrigerated vehicles with function of dynamic control of temperature field |
| CN111660755A (en) * | 2019-12-04 | 2020-09-15 | 摩登汽车有限公司 | Control method and device of automobile air conditioner, vehicle control unit and automobile |
| CN111207498A (en) * | 2019-12-27 | 2020-05-29 | 广东美的白色家电技术创新中心有限公司 | Air conditioner, control method thereof, server, and computer-readable storage medium |
| KR102471478B1 (en) * | 2022-07-27 | 2022-11-28 | 주식회사 로드맵 | How to reserve the operation of the freezer of the delivery vehicle for cold chain |
| CN114996661A (en) * | 2022-08-04 | 2022-09-02 | 山东佳力通汽车有限公司 | Refrigerator car temperature monitoring method and system |
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