CN106774499B - A kind of air pollution monitoring temperature control system - Google Patents
A kind of air pollution monitoring temperature control system Download PDFInfo
- Publication number
- CN106774499B CN106774499B CN201710113711.3A CN201710113711A CN106774499B CN 106774499 B CN106774499 B CN 106774499B CN 201710113711 A CN201710113711 A CN 201710113711A CN 106774499 B CN106774499 B CN 106774499B
- Authority
- CN
- China
- Prior art keywords
- temperature
- control
- moment
- pollution monitoring
- pid
- 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.)
- Active
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 present invention provides a kind of air pollution monitoring temperature control systems, belong to traffic information intelligent integration technical field.This temperature control system is embedded in traffic pollution monitoring instrument, including temperature sensor, control unit, heating/heat dissipation executing agency and data memory module.Control unit receives the temperature of temperature sensor measurement, and historical temperature is read from data memory module, and controlling program according to markov PID temperature makes decisions, control heating/heat dissipation executing agency work.The present invention has carried out intelligent integration to temperature prediction and temperature control, reduces the temperature-responsive delay of managed object so that variation early response of the instrument to temperature improves temperature control effect.
Description
Technical field
The invention belongs to traffic information intelligent integration technical fields, and in particular to a kind of intelligentized temperature control system
System, for realizing the temperature control of 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,
Since the social equity problem that regional pollution unevenness is brought also constantly causes concern.Just because of the health of air pollution and people, society
Can be fair the problems such as, is indivisible, while the influence research in relation to traffic pollution to pedestrian and resident is even more to be 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 monitors
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 for being used as information collection is often sensitive to temperature change.The sensors such as CO, SO2, NO2, O3 in instrument
Identification is affected by temperature just fairly obvious.If cannot temperature in controller unit in time, by the dynamic monitoring to traffic pollution
Cause bigger influence.Mostly based on PID (proportional-integral-differential) controls, this method has traditional temprature control method
The advantages that response quickly, versatile engineering;However instrument is to the process that the response of temperature is a gradual change, when pure PID is controlled
System according to the deviation of Current Temperatures and target temperature respond and decision systems need heat or radiate when, due to physics because
Heating caused by element/heat dissipation delay effect makes Traditional control be difficult to reach ideal effect, has especially for temperature range
The sensor of considered critical, if deviation chooses too small meeting and system robustness is made to be deteriorated, and can if deviation selection is excessive
It is limited by equipment heating/heat-sinking capability itself, ideal effect is not achieved.
Invention content
The present invention be directed to presently, there are traffic pollution monitoring process in brought due to physical heating/heat dissipation delay effect
Temperature control response retardation problem provides a kind of air pollution monitoring temperature control system.The present invention is from traffic information intelligence
The integrated thinking of energyization is set out, and instrument is risen to Information Level control by data Layer control so that instrument proposes the variation of temperature
Preceding response, to promote temperature control effect.
Air pollution monitoring temperature control system provided by the invention, is embedded in traffic pollution monitoring instrument, this is
System includes temperature sensor, control unit, heating/heat dissipation executing agency and data memory module.
The 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/heat dissipation executing agency work.
The markov-PID temperature controls program, and realization process is:
(1) temperature and historical temperature composition sequence { x at k-th of the control moment currently measured are utilized0(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 the observed temperature x at k-th of control moment0(k) and predicted temperature
Preconditioning parameter lambda is calculated, then determines and it is expected 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, integral, differential adjustment parameter.
Advantages of the present invention is with good effect:
(1) air pollution monitoring temperature control system of the invention has carried out intelligence to temperature prediction and temperature control
Change and integrate, compared with traditional temperature control system, reduces the temperature-responsive delay of managed object, compensate for conventional temperature PID
Control method when applied to slow response system due to response time lag, response speed caused by physical factor slowly not
Foot.
(2) air pollution monitoring temperature control system of the invention is by PID control and Grey -- Markov prediction technique
It is combined and is used for temperature control, belongs to the control of anticipation formula temperature, is a kind of new temprature control method.
(3) it air pollution monitoring temperature control system of the invention and the temperature of script is controlled considered deviation selects
It selects and control parameter problem of tuning, is converted into the validity and data volume for considering the problems of temperature history periodic samples data;And
The Information Level that instrument is realized in temperature control loop section controls, and to the multi-region of legacy Markov chain in specific implementation process
Between state demarcation method improved, more traditional data Layer control it is more intelligent.
Description of the drawings
Fig. 1 is the structural schematic diagram of the temperature control system of the present invention;
Fig. 2 is chip schematic diagram used by the power module of the present invention;(a) it is chip LM2596, is (b) chip
LM1117;
Fig. 3 is the workflow schematic diagram of the temperature control system of the present invention.
Specific implementation mode
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/heat dissipation 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 to control unit.Temperature data by
Control unit focuses on, and makes a policy and controls heating or the work of heat dissipation executing agency, while temperature data can be also stored in
Data memory module, in 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 fully considered its computing capability
And logical operation capability;SD card memory module is devised during system building to support historical data to access, and is directed to
The characteristics of system components, devises power module, as shown in Figure 2.As shown in Fig. 2, using the switching voltage tune of chip LM2596
The low difference voltage regulator for saving device and chip LM1117 forms power module.
In the embodiment of the present invention, the acquisition selection DHT22 of environment temperature, a kind of high-sensitivity digital formula temperature sensor,
It can convert the temperature value analog quantity in external environment to digital quantity by DHT22.
In the embodiment of the present invention, radiate executing agency, i.e. radiator use 12V/0.6A temperature control PWM speed-regulating fans.Heating
Executing agency, i.e. heater, select peak power be 20W controllable temperature heating cushion, and Surface mulch silica gel thermally conductive sheet with
Ensure that heat distributes uniformly.
The control unit of the present invention realizes that markov-PID temperature control algolithm program, Computing Principle are:
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)
x0(k) indicate that the temperature acquired at current time, k indicate k-th of control moment, X0Indicate the sequence of k known temperature
Row.
One-accumulate sequence (AGO) is constructed, sequence X is obtained1:
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 return to obtain by historical data, x1(k+1) grey derivative, i.e. x0(k+1) be GM (1,1) model under
One control moment, i.e. the tentative prediction result at+1 control moment of kth
Then, Markov process is built.
It is worth to residual error relative value according to current temperature value and Current Temperatures prediction:
X0 (k) is the temperature value currently surveyed.Prediction for 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 conditions
aij(k) indicate that matrix is arranged since there is no transfers for same state in the frequency for becoming state j from state i at current time
Middle aii(k) it is 0.
According to matrix F (k), the Probability p that state i is transferred to state j can getij(k)=Fij(k)/Fi(k), wherein Fij
(k)=aij(k), Fi(k)=ai1(k)+ai2(k)+…+ain(k)。
And then the state transition probability matrix P (k) that the moment is controlled at current k-th can be obtained, 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 the predicted value at+1 control moment of kth is obtained
The principle that control unit carries out PID control is as follows:
First, it in conjunction with heat transfer theory, obtains:
Wherein, φ is desired 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 of control moment, Δ t=x0(k)-T, T are set temperature.
Secondly, it is modeled, is obtained in conjunction with PID according to formula (6):
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, integral, differential adjustment parameter.Final temperature control
Parameter is [λ, KP,KI,KD]。
λ is markov dynamically-adjusting parameter, according to the observed temperature x at current k-th control moment0(k) and pre- thermometric
DegreeIt is calculated,
The temperature control system of the present invention is embedded into as subsystem in traffic pollution monitoring instrument, and managed object is in instrument
Temperature-sensitive components, heating implements and heat dissipation executing agency be arranged in suitable position in instrument.According to the state of practical outer temperature
And the temperature change state of prediction, control unit control operation heating implements and heat dissipation executing agency have an effect, most
Eventually so that instrument is maintained at target temperature.
As shown in figure 3, being with the method that temperature control system carries out monitoring temperature using the air pollution monitoring of the present invention:
Environment Current Temperatures t is obtained as last look x by DHT22 first0(k), the embeded processor of control unit is by data x0(k)
It is stored in data memory module, forms data sequence X 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 obtained by the markoff process
FruitThe temperature prediction value for controlling moment calculating according to upper oneWith current temperature value x0(k) it 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 calculate and it is expected heat, start heating and holds
Row mechanism/heat dissipation executing agency.When temperature is up to standard, keep PID- lambda parameters to [λ, KP,KI,KD].When temperature is not up to standard, sentence
Whether disconnected heat gain is more than 0, when being more than 0, continues to heat, otherwise continues to radiate.This 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.Such as when having had been started up heating implements, if prediction error, program can certainly detect at next control moment
Heat gain is less than 0, therefore can start heat dissipation executing agency and eliminate error in time.It is adjacent control the moment between time interval compared with
Small, the temperature error as caused by heating implements or heat dissipation executing agency can be ignored.And long-term Accurate Prediction result is then
It can achieve the effect that control temperature in advance.
Claims (5)
1. a kind of air pollution monitoring temperature control system, is embedded in traffic pollution monitoring instrument, which is characterized in that this is
System includes temperature sensor, control unit, heating/heat dissipation executing agency and data memory module;
The control unit receives the temperature of temperature sensor measurement, and historical temperature is read from data memory module, root
It makes decisions according to markov-PID temperature control program, control heating/heat dissipation executing agency work;
The markov-PID temperature control program is realized and includes:(1) temperature at k-th of the control moment currently measured is utilized
Degree and historical temperature composition sequence { x0(1),x0(2),…x0(k) } 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 the actual measurement at k-th of control moment
Temperature x0(k) and predicted temperaturePreconditioning parameter lambda is calculated, then determines and it is expected 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, integral, differential adjustment parameter.
2. air pollution monitoring temperature control system according to claim 1, which is characterized in that the control list
Member is calculating predicted valueWhen, realization process includes:
(1) to sequence { x0(1),x0(2),…x0(k) } one-accumulate sequence is constructed, { x is obtained1(1),x1(2),x1(3)…x1
(k)};
(2) according to GM (1,1) rules ,+1 control moment of kth corresponding value in one-accumulate sequence is obtained
x1(k+1)=(x0(1)-b/a)·exp(-ak)+(b/a);Parameter a, b return to obtain by historical data, x1(k+1)
Grey derivative x0(k+1) it is the tentative prediction of the control moment temperature of kth+1 as a result, being set as
(3) Markov process is built, including:The residual error relative value of observed temperature and predicted temperature is subjected to state demarcation, if
It is divided into n state interval, the intermediate value of each residual error state interval is expressed as ε1,ε2,…εn;
The transition frequency between different conditions is calculated, the state transition probability matrix P (k) at k-th of control moment is obtained, in matrix
Element pij(k) indicate that the state i at k-th of control moment is transferred to the probability of state j;I=1,2 ..., n;J=1,2 ..., n;
Obtain the residual prediction value at+1 control moment of kth
Finally obtain the temperature prediction value at+1 control moment of kth
3. air pollution monitoring temperature control system according to claim 1, which is characterized in that the control list
Member is obtaining PID- lambda parameters to [λ, KP,KI,KD] after, it calculates and it is expected heat, start heating/heat dissipation executing agency, when temperature reaches
When mark, PID- lambda parameters pair are kept;When temperature is not up to standard, judge whether heat gain is more than 0, when being more than 0, continues to heat, it is no
Then continue to radiate.
4. air pollution monitoring temperature control system according to claim 1 or 3, which is characterized in that the control
Unit is provided with self-check program in 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 reversed executing agency to eliminate error.
5. air pollution monitoring temperature control system according to claim 1, which is characterized in that the control unit
Select 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 CN106774499A (en) | 2017-05-31 |
CN106774499B true 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) |
Families Citing this family (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 |
Family Cites Families (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 |
CN104317193B (en) * | 2014-09-19 | 2017-04-05 | 北京空间机电研究所 | A kind of pid control parameter off-line setting calculation method based on HMM |
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 |
CN105937823B (en) * | 2016-03-31 | 2019-03-29 | 中国农业大学 | A kind of earth source heat pump control method and system |
-
2017
- 2017-02-28 CN CN201710113711.3A patent/CN106774499B/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN106774499A (en) | 2017-05-31 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106774499B (en) | A kind of air pollution monitoring temperature control system | |
Zhang et al. | Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system | |
CN101435727B (en) | Temperature predicting method and apparatus | |
Gao et al. | Optimal personal comfort management using SPOT+ | |
CN105699775B (en) | Discrete square wave extraction method and device for IGBT coupling thermal impedance | |
US7187053B2 (en) | Thermal sensing method and system | |
US20190243396A1 (en) | Water heater energy management controller | |
CN103412590B (en) | Method of high-precision temperature control suitable for space remote sensing camera | |
EP3997545B1 (en) | Method and system for controlling the temperature of a room | |
CN102411660A (en) | Heat supply process modeling method based on typical test signals and least squares | |
Chen et al. | Investigation of practical issues in building thermal parameter estimation | |
de Arruda et al. | Relay-based gain and phase margins PI controller design | |
JPH0518592A (en) | Method and apparatus for calculating warm heat feeling, method and apparatus for calculating predicted mean warm feeling | |
KR20220146158A (en) | Apparatus for predicting solar radiation and method thereof | |
Basak et al. | Performance evaluation of material and comparison of different temperature control strategies of a Guarded Hot Box U-value Test Facility | |
Xia et al. | A method to measure thermal conductivity of vacuum insulation panel using enhanced extreme learning machine model | |
CN103869694A (en) | Regulator and operation quantity output method | |
CN113465020A (en) | Intelligent constant-temperature control system, control method and bathroom heater | |
Long et al. | The design of temperature and humidity Control System for Incubation based on data fusion and fuzzy decoupling | |
Saikia et al. | An ANN model to estimate the impact of tea process parameters on tea quality | |
US20220236117A1 (en) | Thermal Diode Sensor Self-Correction | |
Burger et al. | Piecewise linear thermal model and recursive parameter estimation of a residential heating system | |
Zhao et al. | WSN based thermal modeling: a new indoor energy efficient solution | |
Barauskas et al. | Detection and correction of the controlled object transfer function in the smart home control system | |
Liang et al. | Research on temperature control system of desktop 3D printer based on fuzzy adaptive |
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 |