CN106774499A - A kind of air pollution monitoring temperature control system - Google Patents
A kind of air pollution monitoring temperature control system Download PDFInfo
- Publication number
- CN106774499A CN106774499A CN201710113711.3A CN201710113711A CN106774499A CN 106774499 A CN106774499 A CN 106774499A CN 201710113711 A CN201710113711 A CN 201710113711A CN 106774499 A CN106774499 A CN 106774499A
- Authority
- CN
- China
- Prior art keywords
- temperature
- control
- moment
- temperature control
- pollution monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a kind of air pollution monitoring temperature control system, belong to transport information intelligent integration technical field.This temperature control system is embedded in traffic pollution monitoring instrument, including temperature sensor, control unit, heating/radiating executing agency and data memory module.Control unit receives the temperature of temperature sensor measurement, and historical temperature is read from data memory module, is made decisions according to markov PID temperature control program, control heating/radiating executing agency work.The present invention has carried out intelligent integration to temperature prediction and temperature control, and the temperature-responsive for reducing managed object postpones so that change early response of the instrument to temperature, improves temperature control effect.
Description
Technical field
The invention belongs to transport information intelligent integration technical field, and in particular to a kind of intelligentized temperature control system
System, the temperature control for realizing air pollution monitor under outdoor trackside traffic environment.
Background technology
The air pollution problems inherent of the developing countries such as China, India is increasingly taken seriously；Even if in European and American developed countries,
Because the social equity problem that regional pollution inequality is brought also constantly triggers concern.Just because of health, the society of air pollution and people
Can be fair the problems such as, is indivisible, while the influence research about traffic pollution to pedestrian and resident is even more and is faced with data acquisition
It is difficult, it is difficult to the problems such as particulate metrization.Therefore needing to research and develop a kind of outdoor-monitoring instrument -- traffic pollution is monitored
Instrument, hereinafter referred to as instrument, more effectively to monitor traffic pollution level.
Under outdoor conditions, it is a Universal Problems that equipment is easily influenced by ambient temperature.Especially for outdoor-monitoring equipment
For, the core component as information gathering is often sensitive to temperature change.The sensors such as CO, SO2, NO2, O3 in instrument
Identification temperature influence is just fairly obvious.If can not temperature in controller unit in time, by the dynamic monitoring to traffic pollution
Cause to compare large effect.Traditional temprature control method is more based on PID (proportional-integral-differential) controls, and the method has
The advantages of response quickly, engineering highly versatile；But response of the instrument to temperature is a process for gradual change, when pure PID controls
Make and responded according to the deviation of Current Temperatures and target temperature and when decision systems need to heat or radiate, due to physics because
Heating/radiating delay effect causes that Traditional control is difficult to reach ideal effect caused by plain, has especially for temperature range
The sensor of considered critical, if deviation chooses too small meeting and causes that system robustness is deteriorated, and can if deviation selection is excessive
Heated in itself by equipment/heat-sinking capability limited, and does not reach ideal effect.
The content of the invention
The present invention in the traffic pollution monitoring process that presently, there are because physical heating/radiating delay effect is brought
Temperature control responds retardation problem, there is provided a kind of air pollution monitoring temperature control system.The present invention is from transport information intelligence
The integrated thinking of energyization is set out, and instrument is risen into Information Level control by data Layer control so that change of the instrument to temperature is carried
Preceding response, to lift temperature control effect.
The air pollution monitoring temperature control system that the present invention is provided, is embedded in traffic pollution monitoring instrument, and this is
System includes temperature sensor, control unit, heating/radiating executing agency and data memory module.
Described control unit receives the temperature of temperature sensor measurement, and history temperature is read from data memory module
Degree, makes decisions according to markov-PID temperature control program, control heating/radiating executing agency work.
Described markov-PID temperature control program, implementation process is：
(1) k-th using current measurement controls the temperature and historical temperature composition sequence { x at moment0(1),x0(2),…
x0(k) }, the temperature at+1 control moment of kth is predicted by Markov model, if predicted value is
(2) PID temperature control is carried out, first according to k-th observed temperature x at control moment0(k) and predicted temperature
Preconditioning parameter lambda is calculated, it is then determined that expecting heat φ；
φ=(λ+1) * (KP*(x0(k)-x0(k-1))+KI*x0(k)+KD*(x0(k)-2x0(k-1)+x0(k-2)))
Wherein, KP,KI,KDRespectively ratio, integration, differential regulation parameter.
Advantages of the present invention is with good effect：
(1) air pollution monitoring temperature control system of the invention, intelligence has been carried out to temperature prediction and temperature control
Change is integrated, and compared with traditional temperature control system, the temperature-responsive for reducing managed object postpones, and compensate for conventional temperature PID
Control method when slow response system is applied to because the response time is delayed caused by physical factor, response speed slowly not
Foot.
(2) air pollution monitoring temperature control system of the invention is by PID control and Grey -- Markov Forecasting Methodology
It is combined and for temperature control, belongs to anticipation formula temperature control, is a kind of new temprature control method.
(3) air pollution monitoring temperature control system of the invention and the deviation choosing that the temperature control of script is considered
Select and control parameter problem of tuning, be converted into the problem of the validity and data volume that consider temperature history periodic samples data；And
The Information Level that instrument is realized in temperature control link is controlled, and to the multi-region of legacy Markov chain in specific implementation process
Between state demarcation method improved, more traditional data Layer control is more intelligent.
Brief description of the drawings
Fig. 1 is the structural representation of temperature control system of the invention；
Fig. 2 is the chip schematic diagram that power module of the invention is used；A () is chip LM2596, (b) is chip
LM1117；
Fig. 3 is the workflow schematic diagram of temperature control system of the invention.
Specific embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
The present invention devises a kind of intelligentized temperature control system, such as Fig. 1 on the basis of conventional temperature control method
Shown, primary structure includes temperature sensor, control unit, heating/radiating executing agency and data memory module, power supply mould
Block etc..The Main Function of temperature sensor is sensing external environment temperature, and data are emitted into control unit.Temperature data by
Control unit is focused on, and executing agency's work of heating or radiate is made a policy and control, while temperature data can also be stored in
Data memory module, with real-time update control unit to the dependence sample of temperature information processing method.
In the embodiment of the present invention, control unit selects STM32F107VCT6 integrated circuits, has taken into full account its computing capability
And logical operation capability；SD card memory module is devised during system building to be used to support that historical data is accessed, and is directed to
The characteristics of system components, devises power module, as shown in Figure 2.As shown in Fig. 2 being adjusted using the switching voltage of chip LM2596
The low difference voltage regulator composition power module of section device and chip LM1117.
In the embodiment of the present invention, DHT22 is selected in the acquisition of environment temperature, a kind of high-sensitivity digital formula temperature sensor,
Temperature value analog quantity in external environment can be converted into by digital quantity by DHT22.
In the embodiment of the present invention, radiate executing agency, i.e. radiator, uses 12V/0.6A temperature control PWM speed-regulating fans.Heating
Executing agency, i.e. heater, are the controllable temperature heating cushion of 20W from peak power, and in Surface mulch silica gel conducting strip with
Ensure that heat is distributed uniformly.
Control unit of the invention realizes markov-PID temperature control algolithm program, and Computing Principle is：
First, GM (1,1) model is built；
Time series is obtained from known temperature data, it is as follows：
X0={ x0(1),x0(2),x0(3)…x0(k)} (1)
x0K () represents the temperature in current time collection, k represents k-th control moment, X0Represent the k sequence of known temperature
Row.
Construction one-accumulate sequence (AGO), obtains sequence X1：
X1={ x1(1),x1(2),x1(3)…x1(k)} (2)
Wherein, x1(k)=x0(1)+x0(2)+x0(3)…x0(k)。
According to GM (1,1) rules, obtain：
x1(k+1)=(x0(1)-b/a)·exp(-ak)+(b/a) (3)
Parameter a, b are returned by historical data and obtained, x1(k+1) grey derivative, i.e. x0(k+1) for GM (1,1) model under
One control moment, i.e.+1 tentative prediction result at control moment of kth
Then, Markov process is built.
Residual error relative value is worth to according to current temperature value and Current Temperatures prediction：
X0 (k) is the temperature value of current actual measurement.It is the prediction at the current time obtained according to k-1 historical temperature
Value.
State demarcation is carried out according to residual error relative value ε, if being divided into n state { (ε '0,ε'1),(ε'1,ε'2),…(ε'n-
1,ε'n)}.The state-transition matrix at current time is obtained by the transition frequency between different conditionsaijK () represents and is changed into the frequency of state j from state i at current time, due to same
State sets a in matrix in the absence of transferiiK () is 0.
According to matrix F (k), the Probability p that state i is transferred to state j can be obtainedij(k)=Fij(k)/Fi(k), wherein Fij
(k)=aij(k), Fi(k)=ai1(k)+ai2(k)+…+ain(k)。
And then state transition probability matrix P (k) that the moment is controlled at current k-th is can obtain, it is as follows：
Obtain the residual prediction value at next control momentFor
In formula (6), ε1,ε2,…εnIt is the intermediate value of each residual error state interval, such as ε1=(ε '0+ε'1)/2。
Then+1 predicted value at control moment of kth is obtained
The principle that control unit carries out PID control is as follows：
First, with reference to heat transfer theory, obtain：
Wherein, φ is expectation heat；α is temperature transition coefficient；A is heat transfer area；δ is Heat Conduction Material thickness；Δ t is
The temperature difference at current k-th control moment, Δ t=x0K ()-T, T are design temperature.
Secondly, combine PID according to formula (6) to model, obtain：
φ=(λ+1) * (KP*(x0(k)-x0(k-1))+KI*x0(k)+KD*(x0(k)-2x0(k-1)+x0(k-2))) (8)
Wherein, λ is preconditioning parameter, KP,KI,KDRespectively ratio, integration, differential regulation parameter.Final temperature control
Parameter is [λ, KP,KI,KD]。
λ is markov dynamically-adjusting parameter, according to the current k-th observed temperature x at control moment0(k) and pre- thermometric
DegreeIt is calculated,
Temperature control system of the invention is embedded into traffic pollution monitoring instrument as subsystem, and managed object is in instrument
Temperature-sensitive components, heating implements and radiating executing agency be arranged in correct position in instrument.According to the state of actual outer temperature
And the temperature change state of prediction, control unit control operation heating implements and radiating executing agency have an effect, most
Cause that instrument is maintained at target temperature eventually.
As shown in figure 3, being with the method that temperature control system carries out monitoring temperature using air pollution monitoring of the invention：
Environment Current Temperatures t is obtained as last look x by DHT22 first0K (), the embeded processor of control unit is by data x0(k)
Data memory module is stored in, data sequence X is formed together with historical data0={ x0(1),x0(2),x0(3)…x0(k)}.So
Markov temperature prediction program starts afterwards, and the temperature prediction knot at next control moment is drawn by described markoff process
ReallyAccording to the temperature prediction value that upper one control moment calculatedWith current temperature value x0K () is used as PID temperature control
The input of system, rule of thumb chooses the corresponding parameters of λ to [λ, K in tableP,KI,KD], and then expectation heat is calculated, start heating and hold
Row mechanism/radiating executing agency.When temperature is up to standard, PID- lambda parameters are kept to [λ, KP,KI,KD].When temperature is not up to standard, sentence
Whether disconnected heat gain is more than 0, during more than 0, continues to heat, and otherwise continues to radiate.The system by program logic mistake
Journey supervision error, if predicted temperature is made a fault, the self-check program at next control moment will quickly judge, and reverse starting
Executing agency.For example when having had been started up heating implements, if prediction error, program can be at next control moment from detection
Heat gain be less than 0, therefore can start radiating executing agency eliminate error in time.It is adjacent control the moment between time interval compared with
Small, the temperature error caused by heating implements or radiating executing agency can be ignored.And long-term Accurate Prediction result is then
The effect for controlling temperature in advance can be reached.
Claims (5)
1. a kind of air pollution monitoring temperature control system, is embedded in traffic pollution monitoring instrument, it is characterised in that this is
System includes temperature sensor, control unit, heating/radiating executing agency and data memory module；
Described control unit receives the temperature of temperature sensor measurement, and historical temperature, root are read from data memory module
Made decisions according to markov-PID temperature control program, control heating/radiating executing agency work；
Described markov-PID temperature control program is realized including：(1) using k-th temperature at control moment of current measurement
Degree and historical temperature composition sequence { x0(1),x0(2),…x0(k) }, the moment is controlled for+1 to kth by Markov model
Temperature is predicted, if predicted value is(2) PID temperature control is carried out, first according to k-th actual measurement at control moment
Temperature x0(k) and predicted temperaturePreconditioning parameter lambda is calculated, it is then determined that expecting heat φ；
φ=(λ+1) * (KP*(x0(k)-x0(k-1))+KI*x0(k)+KD*(x0(k)-2x0(k-1)+x0(k-2)))
Wherein, KP,KI,KDRespectively ratio, integration, differential regulation parameter.
2. air pollution monitoring temperature control system according to claim 1, it is characterised in that described control list
Unit, is calculating predicted valueWhen, implementation process includes：
(1) to sequence { x0(1),x0(2),…x0(k) } construction one-accumulate sequence, obtain { x1(1),x1(2),x1(3)…x1
(k)}；
(2) according to GM (1,1) rules, kth+1 controls moment corresponding value in obtaining one-accumulate sequence
x1(k+1)=(x0(1)-b/a)·exp(-ak)+(b/a)；Parameter a, b are returned by historical data and obtained, x1(k+1)
Grey derivative x0(k+1) it is+1 tentative prediction result of control moment temperature of kth, is set to
(3) Markov process is built, including：The residual error relative value of observed temperature and predicted temperature is carried out into state demarcation, if
N state interval is divided into, the intermediate value of each residual error state interval is expressed as ε1,ε2,…εn；
The transition frequency between different conditions is calculated, k-th state transition probability matrix P (k) at control moment is obtained, in matrix
Element pijK () represents that the state i at k-th control moment is transferred to the probability of state j；I=1,2 ..., n；J=1,2 ..., n；
Obtain+1 residual prediction value at control moment of kth
Finally obtain+1 temperature prediction value at control moment of kth
3. air pollution monitoring temperature control system according to claim 1, it is characterised in that described control list
Unit, is obtaining PID- lambda parameters to [λ, KP,KI,KD] after, calculate and expect heat, start heating implements/radiating executing agency,
When temperature is up to standard, PID- lambda parameters pair are kept；When temperature is not up to standard, whether heat gain is judged more than 0, during more than 0, after
Continuous heating, otherwise continues to radiate.
4. the air pollution monitoring temperature control system according to claim 1 or 3, it is characterised in that described control
Unit, self-check program is provided with markov-PID temperature control program, when predicted temperature is made a fault, then next control
The self-check program at moment processed judges heat gain, and starts reverse executing agency and eliminate error.
5. air pollution monitoring temperature control system according to claim 1, it is characterised in that described control unit
From STM32F107VCT6 integrated circuits.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710113711.3A CN106774499B (en) | 2017-02-28 | 2017-02-28 | A kind of air pollution monitoring temperature control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710113711.3A CN106774499B (en) | 2017-02-28 | 2017-02-28 | A kind of air pollution monitoring temperature control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106774499A true CN106774499A (en) | 2017-05-31 |
| CN106774499B CN106774499B (en) | 2018-07-17 |
Family
ID=58960055
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710113711.3A Active CN106774499B (en) | 2017-02-28 | 2017-02-28 | A kind of air pollution monitoring temperature control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106774499B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109939322A (en) * | 2019-03-29 | 2019-06-28 | 杭州弯超医疗科技有限公司 | Breathe the temperature/humidity control method and system of humidifying equipment |
| CN115291650A (en) * | 2022-08-18 | 2022-11-04 | 皇虎测试科技(深圳)有限公司 | Temperature control system, method and equipment for semiconductor device under test |
| CN116277690A (en) * | 2023-05-23 | 2023-06-23 | 成都正西液压设备制造有限公司 | Composite material molding press electric control system based on mold parameter detection |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101392939A (en) * | 2008-11-18 | 2009-03-25 | 天津大学 | Nonlinear prediction and control method for independence energy supply temperature of buildings |
| US20130226501A1 (en) * | 2012-02-23 | 2013-08-29 | Infosys Limited | Systems and methods for predicting abnormal temperature of a server room using hidden markov model |
| CN104317193A (en) * | 2014-09-19 | 2015-01-28 | 北京空间机电研究所 | PID control parameter off-line tuning method based on hidden Markov model |
| CN104950948A (en) * | 2015-05-21 | 2015-09-30 | 淮阴工学院 | Intelligent cowshed temperature control system |
| CN105045305A (en) * | 2015-07-21 | 2015-11-11 | 国家电网公司 | Transformer oil temperature monitoring system with temperature prediction function |
| CN105937823A (en) * | 2016-03-31 | 2016-09-14 | 中国农业大学 | Control method and system for ground source heat pump |
-
2017
- 2017-02-28 CN CN201710113711.3A patent/CN106774499B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101392939A (en) * | 2008-11-18 | 2009-03-25 | 天津大学 | Nonlinear prediction and control method for independence energy supply temperature of buildings |
| US20130226501A1 (en) * | 2012-02-23 | 2013-08-29 | Infosys Limited | Systems and methods for predicting abnormal temperature of a server room using hidden markov model |
| CN104317193A (en) * | 2014-09-19 | 2015-01-28 | 北京空间机电研究所 | PID control parameter off-line tuning method based on hidden Markov model |
| CN104950948A (en) * | 2015-05-21 | 2015-09-30 | 淮阴工学院 | Intelligent cowshed temperature control system |
| CN105045305A (en) * | 2015-07-21 | 2015-11-11 | 国家电网公司 | Transformer oil temperature monitoring system with temperature prediction function |
| CN105937823A (en) * | 2016-03-31 | 2016-09-14 | 中国农业大学 | Control method and system for ground source heat pump |
Non-Patent Citations (1)
| Title |
|---|
| LANZHI TENG等: "Networked PID Control System Modeling and Simulation Using Markov Chain", 《PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON INTEGRATION TECHNOLOGY》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109939322A (en) * | 2019-03-29 | 2019-06-28 | 杭州弯超医疗科技有限公司 | Breathe the temperature/humidity control method and system of humidifying equipment |
| CN115291650A (en) * | 2022-08-18 | 2022-11-04 | 皇虎测试科技(深圳)有限公司 | Temperature control system, method and equipment for semiconductor device under test |
| CN116277690A (en) * | 2023-05-23 | 2023-06-23 | 成都正西液压设备制造有限公司 | Composite material molding press electric control system based on mold parameter detection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106774499B (en) | 2018-07-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system | |
| WO2020001523A1 (en) | Temperature control method and temperature control device | |
| CN106774499B (en) | A kind of air pollution monitoring temperature control system | |
| Paudel et al. | Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network | |
| CN101140450B (en) | Energy conservation type heat comfortable controller and control method | |
| US20160146497A1 (en) | Maintaining an attribute of a building | |
| CN103003637A (en) | Customized control of the thermal comfort of an occupant of a building | |
| CN107490202A (en) | A kind of temperature control method of water and device | |
| US20200005185A1 (en) | Automatic control artificial intelligence device and method for updating a control function | |
| CN108873966A (en) | A kind of temperature control equipment and control method | |
| Chen et al. | Investigation of practical issues in building thermal parameter estimation | |
| CN105468054B (en) | The intelligent control method of brake temperature monitoring device | |
| CN102411660A (en) | Heat supply process modeling method based on typical test signals and least squares | |
| Cai et al. | Optimizing zone temperature setpoint excitation to minimize training data for data-driven dynamic building models | |
| Zeng et al. | An adaptive model predictive control scheme for energy-efficient control of building HVAC systems | |
| Revati et al. | Facilitating energy-efficient operation of smart building using data-driven approaches | |
| EP3997545B1 (en) | Method and system for controlling the temperature of a room | |
| CN117190393A (en) | Intelligent air conditioner temperature adjusting method, device, equipment and medium | |
| Xia et al. | A method to measure thermal conductivity of vacuum insulation panel using enhanced extreme learning machine model | |
| Sanino et al. | Modeling and identification of solar energy water heating system incorporating nonlinearities | |
| Laftchiev et al. | Personalizing individual comfort in the group setting | |
| CN115062847A (en) | Carbon fiber heating energy consumption prediction method and system based on logistic regression algorithm | |
| Wen et al. | Development and validation of online parameter estimation for HVAC systems | |
| Burger et al. | Piecewise linear thermal model and recursive parameter estimation of a residential heating system | |
| CN115930448B (en) | Control method, control device, electronic equipment, water heater and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |