CN107826027B - 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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- CN107826027B CN107826027B CN201710860576.9A CN201710860576A CN107826027B CN 107826027 B CN107826027 B CN 107826027B CN 201710860576 A CN201710860576 A CN 201710860576A CN 107826027 B CN107826027 B CN 107826027B
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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
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- 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
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- 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: the temperature, the measured data of the revolving speed of each test point temperature in refrigerator car outer surface, environment temperature and speed, refrigeration unit compressor and blower of monitoring point in refrigerator car are obtained by test；Clustering is carried out to the data of acquisition, obtains multiple cluster clusters；Using the multiple cluster cluster as training sample, ellipsoidal unit neural network is trained using compressor rotary speed and rotation speed of fan as output variable respectively；Real-time control is carried out to compressor rotary speed and rotation speed of fan based on the ellipsoidal unit neural network.The present invention can be realized the quantitative real-time control of refrigerator car temperature, and more traditional rule of thumb regulation is more reasonable.
Description
Technical field
The present invention relates to refrigeration control field more particularly to a kind of refrigerator car temprature control methods based on big data analysis
And system.
Background technique
Biological agent occupies highly important position in people's lives, and most of biological agent needs low in the circulation process
Temperature refrigeration, biological agent Cold Chain Logistics market are huge.With the raising that people require medical security, the biology for needing to refrigerate is made
The safety problem of agent encounters higher requirement and challenge.The unqualified event of the biological agent recurred in recent years allows people
Improve vigilant, the Cold Chain Logistics of biological agent are also increasingly taken seriously.Refrigerator car has the function of heat preservation and refrigeration, energy
Enough meet the requirement in the short-distance Refrigerated Transport of biological agent, is the key link of its Cold Chain Logistics.It maintains in refrigerator carriage body
Portion's temperature is constant, and carriage body various places inside is kept to have the lesser temperature difference, for guaranteeing that the quality of biological agent in transportational process rises
Important role.
In refrigerator car, air enters refrigerator car through evaporator, if the selection of speed of the compressor of refrigeration unit and blower
Improper, will lead to the air velocity for flowing through evaporator, temperature cannot be such that the Temperature Distribution in refrigerator car meets the requirements, and influence car
The quality of biological agent.Therefore, the revolving speed of refrigeration unit compressor and blower how is rationally controlled, guarantees the temperature in refrigerator car
It is the technical issues of those skilled in the art urgently solve at present within the scope of being distributed in reasonably.
Summary of the invention
To solve the above-mentioned 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 as input variable, to a large amount of measured datas
Analysis, is divided into three kinds of worst hot case, high and cold operating condition and room temperature operating condition cluster clusters, then using three kinds of cluster clusters as ellipsoidal unit
The input of neural network, using the revolving speed of compressor of air conditioner and blower as output variable, training ellipsoidal unit neural network is obtained
The Controlling model of compressor and rotation speed of fan, with this reasonable control evaporator outlet temperature and wind speed.
To achieve the goals above, the present invention adopts the following technical scheme:
A kind of refrigerator car temprature control method, comprising the following steps:
Step 1: temperature, each test point temperature in refrigerator car outer surface, environment of monitoring point in refrigerator car are obtained by test
The measured data of the revolving speed of temperature and speed, refrigeration unit compressor and blower；
Step 2: clustering being carried out to the data of acquisition, obtains n cluster cluster；
Step 3: using n cluster cluster as training sample, becoming respectively using compressor rotary speed and rotation speed of fan as output
Measure training ellipsoidal unit neural network；
Step 4: real-time control is carried out to compressor rotary speed and rotation speed of fan based on the ellipsoidal unit neural network.
Further, the step 2 is clustered using c-means clustering algorithm, obtain worst hot case, high and cold operating condition and
Three kinds of cluster clusters of room temperature operating condition.
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 be real-time input variable, be based on the ellipsoidal unit neural network, calculate compressor and
The revolving speed of blower carries out real-time control to refrigerator car temperature.
Further, the ellipsoidal unit neural network is made 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 density p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…
πk,c1,…ck,Σ1,…,Σk}。
Another object according to the present invention, the present invention also provides a kind of refrigerator car temperature control systems, comprising: data are adopted
Acquisition means, computing device and controller, include in the computing device memory, processor and storage on a memory and can be
The computer program run on processor, which is characterized in that the processor performs the steps of when executing described program
Receive temperature, each test point temperature in refrigerator car outer surface, environment temperature of monitoring point in the refrigerator car that test obtains
With the measured data of the revolving speed of speed, refrigeration unit compressor and blower；
Clustering is carried out to the data of acquisition, obtains multiple cluster clusters；
Using the multiple cluster cluster as training sample, respectively using compressor rotary speed and rotation speed of fan as output variable
Training ellipsoidal unit neural network；
Receive the temperature, each monitoring point temperature difference of monitoring point in the refrigerator car that data acquisition device obtains in real time, outside refrigerator car
Surface each point temperature, environment temperature and speed are based on the ellipsoidal unit neural network as real-time input variable, calculate pressure
The revolving speed of contracting machine and blower is simultaneously sent to controller；
Revolving speed of the controller based on the compressor and blower that receive carries out real-time control to refrigerator car temperature.
Further, the data acquisition device includes the multiple temperature sensors being laid in inside and outside refrigerator car.
The step 2 is clustered using c-means clustering algorithm, obtains worst hot case, high and cold operating condition and room temperature operating condition
Three kinds of cluster clusters.
Further, the ellipsoidal unit neural network is made 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 density p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…πk,
c1,…ck,Σ1,…,Σk}。
Beneficial effects of the present invention:
1, the present invention selects compressor and rotation speed of fan to 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 refrigeration inside and outside 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 is more reasonable, reduces because experience deficiency causes
Improper situation is controlled, on the other hand, real-time auto-control saves manual labor.
2, the present invention is in the temperature acquisition to refrigerator car, other than the temperature inside and outside acquisition refrigerator car, also goods in the car
The different location of object arranges temperature sensor, to the deployment scenarios of cargo in reaction cabin, so that the control of temperature is more reasonable.
3, the present invention neural network model training before carried out clustering first, using the result of clustering as
The training data of neural network model improves neural metwork training efficiency.
Detailed description of the invention
Fig. 1 is the flow chart of refrigerator car temprature control method of the present invention；
Fig. 2 is observation point of the present invention arrangement, point layout when wherein Fig. 2 (a) is empty wagons；Goods when Fig. 2 (b) is loading
Point layout on object；
Fig. 3 is the schematic diagram of present invention training ellipsoidal unit neural network using compressor rotary speed as output variable；
Fig. 4 is the schematic diagram of present invention training ellipsoidal unit neural network using rotation speed of fan as output variable.
Specific embodiment
The invention will be further described with embodiment with reference to the accompanying drawing.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has 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 specific 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 singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
In the absence of conflict, the features in the embodiments and the embodiments of the present application can be combined with each other.
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: obtaining and the temperature of monitoring point in refrigerator car, refrigerator car outer surface each point temperature, environment are obtained by test
A large amount of measured datas of the revolving speed of temperature and speed, refrigeration unit compressor and blower.
The temperature of monitoring point, refrigerator car outer surface each point temperature and environment temperature are acquired by temperature sensor in refrigerator car
Obtained data, and upload in deposit portable computer.
The arrangement of temperature sensor is as shown in Figure 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 vehicle, on cargo shown in arrangement such as Fig. 2 (b) of monitoring point, temperature is carried out by each position to cargo
Monitoring, the deployment scenarios of Lai Fanying cargo.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 vehicle temperature outside sensor.
Step 2: clustering being carried out to the data of acquisition, obtains multiple cluster clusters.
Clustering is a kind of by physics or abstract object, the side for distinguishing and classifying according to the similitude between object
Method.People can determine " core " (i.e. cluster centre) first, then arrive the object of surrounding " attraction " when implementing cluster behavior
It is somebody's turn to do around " core ", thus the characteristics of completing cluster process.
The present embodiment is using c-means clustering algorithm to monitoring point each in refrigerator car temperature, each monitoring point temperature difference, refrigerator car
Outer surface each point temperature, environment temperature and speed are clustered, and worst hot case, high and cold operating condition and three kinds of room temperature operating condition clusters are obtained
Cluster.
Before cluster, since 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 are as follows: 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, becoming using compressor rotary speed and rotation speed of fan as output
Measure training ellipsoidal unit neural network.
By worst hot case, the measured data of high and cold operating condition and three kinds of room temperature operating condition cluster clusters, as neural network
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 as the ellipsoidal unit nerve net of output variable
Network model.
Temperature Distribution in refrigerator car is mainly influenced 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 vehicle are determined that refrigeration unit includes refrigeration compressor, cold by the revolving speed of refrigeration unit and blower
The components such as condenser, throttle valve (or expansion valve) and evaporator, therefore the present embodiment is using the revolving speed of compressor and blower as output
Variable passes through the revolving speed of control compressor and blower control refrigeration vehicle interior temperature.Fig. 3 is using compressor rotary speed as output variable
The schematic diagram of training ellipsoidal unit neural network；Fig. 4 is training ellipsoidal unit neural network 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 constituted, and each input node to each implicit node is connected by two weights, respectively represents super ellipsoids unit in the input space
Center and semi-major axis in each dimension, but each hidden node is not shared by all output nodes, and only with corresponding mode class
Other output node connection.If mode to be sorted similar to singlet Gaussian Profile or approximate Gaussian distribution, network can only by
Input layer and output layer are constituted, and output layer uses ellipsoidal unit.
The ellipsoidal unit neural network of the present embodiment is made 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 and 7 implicit nodes are all attached and connection weight is 1, deviation 0, imply node also by complete
The mode of connection is connected with output node.
Kernel function using Gaussian probability-density function as ellipsoidal unit neural network.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 density p (x | θk) center vector and
Full covariance matrix, parameter set θ={ π1,…πk,c1,…ck,Σ1,…,Σk}。
Step 4: real-time control is carried out to compressor rotary speed and rotation speed of fan based on the ellipsoidal unit neural network.
With the temperature of monitoring point, each monitoring point temperature difference, refrigerator car outer surface each point temperature, environment temperature and vehicle in refrigerator car
Speed is real-time input variable, by trained ellipsoidal unit neuron models, calculates the revolving speed of compressor and blower, realizes cold
Hide the temperature control of vehicle.
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, comprising: data acquisition device,
Computing device and controller, include in the computing device memory, processor and storage on a memory and can be in processor
The computer program of upper operation, which is characterized in that the processor performs the steps of when executing described program
Receive temperature, each test point temperature in refrigerator car outer surface, environment temperature of monitoring point in the refrigerator car that test obtains
With the measured data of the revolving speed of speed, refrigeration unit compressor and blower；
Clustering is carried out to the data of acquisition, obtains multiple cluster clusters；
Using the multiple cluster cluster as training sample, respectively using compressor rotary speed and rotation speed of fan as output variable
Training ellipsoidal unit neural network；
Receive the temperature, each monitoring point temperature difference of monitoring point in the refrigerator car that data acquisition device obtains in real time, outside refrigerator car
Surface each point temperature, environment temperature and speed are based on the ellipsoidal unit neural network as real-time input variable, calculate pressure
The revolving speed of contracting machine and blower is simultaneously sent to controller；
Revolving speed of the controller based on the compressor and blower that receive carries out real-time control to refrigerator car temperature.
Wherein, the data acquisition device includes the multiple temperature sensors being laid in inside and outside refrigerator car.
The step 2 is clustered using c-means clustering algorithm, obtains worst hot case, high and cold operating condition and room temperature operating condition
Three kinds of cluster clusters.
The ellipsoidal unit neural network is made 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 has 7 implicit nodes, and output layer has 1 output node.Input node and 7
A 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 density p (x | θk) center vector and full covariance matrix, parameter set θ={ π1,…
πk,c1,…ck,Σ1,…,Σk}。
The present invention selects compressor and rotation speed of fan to 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 refrigeration inside and outside 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 is more reasonable, reduces because experience deficiency causes to control
Improper situation is made, on the other hand, real-time auto-control saves manual labor.
It will be understood by those skilled in the art that each module of the above invention or each step can use general computer
Device realizes that optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are deposited
Storage be performed by computing device in the storage device, perhaps they are fabricated to 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 limited to any specific hardware
With the combination of software.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (9)
1. a kind of refrigerator car temprature control method, which comprises the following steps:
Step 1: temperature, each test point temperature in refrigerator car outer surface, environment temperature of monitoring point in refrigerator car are obtained by test
With the measured data of the revolving speed of speed, refrigeration unit compressor and blower；
Step 2: clustering being carried out to the data of acquisition, obtains multiple cluster clusters；
Step 3: using the multiple cluster cluster as training sample, becoming respectively using compressor rotary speed and rotation speed of fan as output
Measure training ellipsoidal unit neural network；
Step 4: real-time control is carried out to compressor rotary speed and rotation speed of fan based on the ellipsoidal unit neural network.
2. a kind of refrigerator car temprature control method as described in claim 1, which is characterized in that the step 2 uses c-means
Clustering algorithm is clustered, and three kinds of worst hot case, high and cold operating condition and room temperature operating condition cluster clusters are obtained.
3. a kind of refrigerator car temprature control method as described in claim 1, which is characterized in that the step 4 includes: to refrigerate
The temperature of interior monitoring point, each monitoring point temperature difference, refrigerator car outer surface each point temperature, environment temperature and speed are that input becomes in real time
Amount is based on the ellipsoidal unit neural network, and the revolving speed for calculating compressor and blower carries out real-time control to refrigerator car temperature.
4. a kind of refrigerator car temprature control method as described in claim 1, which is characterized in that the ellipsoidal unit neural network
It is made 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, and input node and 7 implicit nodes are all attached and connection weight
It is again 1, deviation 0, implicit node is connected also by the mode connected entirely with output node.
5. a kind of refrigerator car temprature control method as described in claim 1, which is characterized 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 density p (x | θk) center vector and complete
Covariance matrix, parameter set θ={ π1,…πk,c1,…ck,Σ1,…,Σk}。
6. a kind of refrigerator car temprature control method as claimed in claim 5, which is characterized in that the step 2 uses c-means
Clustering algorithm is clustered, and three kinds of worst hot case, high and cold operating condition and room temperature operating condition cluster clusters are obtained.
7. a kind of refrigerator car temprature control method as claimed in claim 5, which is characterized in that the ellipsoidal unit neural network
It is made 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, and input node and 7 implicit nodes are all attached and connection weight
It is again 1, deviation 0, implicit node is connected also by the mode connected entirely with output node.
8. a kind of refrigerator car temperature control system characterized by comprising data acquisition device, computing device and controller, institute
State include in computing device memory, processor and storage on a memory and the computer program that can run on a processor,
It is characterized in that, the processor performs the steps of when executing described program
Receive temperature, each test point temperature in refrigerator car outer surface, environment temperature and the vehicle of monitoring point in the refrigerator car that test obtains
The measured data of the revolving speed of speed, refrigeration unit compressor and blower；
Clustering 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 neural network；
Receive 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 are based on the ellipsoidal unit neural network as real-time input variable, calculate compressor
With the revolving speed of blower and be sent to controller；
Revolving speed of the controller based on the compressor and blower that receive carries out real-time control to refrigerator car temperature.
9. a kind of refrigerator car temperature control system as claimed in claim 8, which is characterized in that the data acquisition device includes
The multiple temperature sensors being laid in inside and outside refrigerator car.
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Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN109050200B (en) * | 2018-08-22 | 2020-02-18 | 吉林大学 | Control method of automobile heat pump air conditioner |
| CN111196119B (en) * | 2018-11-16 | 2022-04-15 | 比亚迪股份有限公司 | Method and device for controlling air conditioner on vehicle |
| CN110481412B (en) * | 2019-08-26 | 2021-02-09 | 山东大学 | Refrigerator carriage and refrigerator car with temperature field dynamic regulation and control function |
| 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 |
| CN111207498B (en) * | 2019-12-27 | 2021-11-09 | 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 |
| CN114996661B (en) * | 2022-08-04 | 2022-11-01 | 山东佳力通汽车有限公司 | Refrigerator car temperature monitoring method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11294923A (en) * | 1998-04-08 | 1999-10-29 | Mitsubishi Heavy Ind Ltd | Refrigeration vehicle operation control system and refrigeration vehicle |
| CN103197538A (en) * | 2013-04-24 | 2013-07-10 | 中南林业科技大学 | Brooder temperature control method based on process neural network |
| CN104374053A (en) * | 2014-11-25 | 2015-02-25 | 珠海格力电器股份有限公司 | Intelligent control method, device and system |
| CN105787271A (en) * | 2016-02-26 | 2016-07-20 | 江苏方天电力技术有限公司 | Heat supply unit adjustable power output range evaluation method based on big data analysis technology |
| CN205853810U (en) * | 2016-06-24 | 2017-01-04 | 中原工学院 | A kind of refrigerator car control system utilizing phase-change material |
-
2017
- 2017-09-21 CN CN201710860576.9A patent/CN107826027B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11294923A (en) * | 1998-04-08 | 1999-10-29 | Mitsubishi Heavy Ind Ltd | Refrigeration vehicle operation control system and refrigeration vehicle |
| CN103197538A (en) * | 2013-04-24 | 2013-07-10 | 中南林业科技大学 | Brooder temperature control method based on process neural network |
| CN104374053A (en) * | 2014-11-25 | 2015-02-25 | 珠海格力电器股份有限公司 | Intelligent control method, device and system |
| CN105787271A (en) * | 2016-02-26 | 2016-07-20 | 江苏方天电力技术有限公司 | Heat supply unit adjustable power output range evaluation method based on big data analysis technology |
| CN205853810U (en) * | 2016-06-24 | 2017-01-04 | 中原工学院 | A kind of refrigerator car control system utilizing phase-change material |
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