CN112783988A - Monitoring feedback and analysis method for internal environmental parameters of air-conditioning ventilation system - Google Patents
Monitoring feedback and analysis method for internal environmental parameters of air-conditioning ventilation system Download PDFInfo
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Abstract
A monitoring feedback and analysis method for internal environmental parameters of an air conditioning ventilation system is characterized in that the internal environmental parameters are obtained in a mode that an unmanned aerial vehicle carries a pollution monitoring sensor to cruise in the air conditioning ventilation system, and monitoring data are analyzed in a clustering mode. Carrying a pollution monitoring sensor by an unmanned aerial vehicle, and cruising according to a track optimally designed by a genetic algorithm in an air conditioning ventilation system; and the monitored environmental parameters are stored through a constructed database and analyzed and prompted by a k-means clustering method to the area with higher air pollution concentration in the air-conditioning ventilation system. The invention can monitor and feed back the environmental parameters of each point in the air-conditioning ventilation system, and can comprehensively cover the monitoring target. The monitored air pollution data can reflect the actual environment condition in the air-conditioning ventilation system, and the region with higher pollution concentration in the air-conditioning ventilation system can be obtained through the clustering analysis of the patrol data for many times so as to implement timing and directional cleaning on the ventilation system.
Description
Technical Field
The invention belongs to the field of air pollution monitoring of human-occupied environments, and relates to an air conditioner ventilation system internal environment parameterization method based on an unmanned aerial vehicle carried pollution monitoring sensor.
Background
The public building area in China exceeds 120 hundred million square meters, and more than 20 percent of the public building area is provided with a centralized air-conditioning ventilation system. The system mainly comprises a fresh air port, an air supply and exhaust fan, a filter, a ventilation pipeline and the like, can control and adjust the temperature and humidity of indoor air, provides fresh and clean air and removes indoor pollutants, and creates a comfortable and healthy indoor environment for people. People are in the indoor environment for more than 80% of the time on average, and the influence of the air conditioning and ventilating system on the indoor air is directly related to the physical health and the working efficiency of people. The existing human-living environmental pollution monitoring technology is mostly aimed at indoor environment, and a plurality of real-time online air quality monitoring sensors are developed and distributed at all corners in a building space to reveal the indoor air pollution degree in an approximate 'white box' manner. In contrast, the air conditioning and ventilating system lacks a corresponding monitoring means and parameterization method for the internal environment (such as PM, VOCs, and the like), and researches find that the pollution monitoring sensors arranged in the air conditioning and ventilating system in a limited number cannot effectively cover the whole system, and the real air quality in the air conditioning and ventilating system is difficult to restore. Therefore, the internal environment of the air conditioning ventilation system is greatly limited due to the current quantitative monitoring method, and can be similar to a black box in the building environment. Under the condition that COVID-19 is abused, before a mechanism that virus microorganisms are spread through a centralized air conditioning ventilation system is not disclosed, the invention provides the parameterization evaluation method of the internal environment of the air conditioning ventilation system and the related technology, which have important significance.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a method for monitoring, feeding back and analyzing internal environmental parameters of an air conditioning ventilation system, which uses an unmanned aerial vehicle carrying a pollution monitoring sensor to obtain the internal environmental parameters in a cruising manner in the air conditioning ventilation system, and analyzes the monitored data in a clustering manner to prompt where in the ventilation system the pollution is serious, so as to provide a basis for timing and directional cleaning.
In order to achieve the purpose, the invention adopts the technical scheme that:
(1) the unmanned aerial vehicle that this application plans to adopt is the small-size unmanned aerial vehicle that can purchase on the existing market, and it possesses certain load capacity (carrying weight is less than or equal to 1kg), duration (duration is less than or equal to 1 hour) to can stably march in the wind speed of 10 meters per second below, in order to satisfy its requirement of patrolling and examining during air pipe normal operating period.
(2) The sensor module who plans to carry on is fixed in unmanned aerial vehicle is on one's body, mainly contains sensors such as particulate matter, formaldehyde, humiture, and miniaturized volume production has all been realized to above sensor, can be convenient integrated on unmanned aerial vehicle through the singlechip.
The (2.1) sensor adopts a multi-type air quality integrated micro self-recording instrument (PM monitoring error is less than +/-5%, formaldehyde monitoring error is less than +/-8%, temperature and humidity monitoring error is less than +/-5%, the shortest sampling interval is 2.0s), the volume of the sensor is only 0.06m multiplied by 0.03m multiplied by 0.05m, the weight of the sensor is about 200g, and the sensor can be installed on an unmanned aerial vehicle body and is simple and feasible in implementation.
And (2.2) in the aspect of sensor precision calibration, other conventional pollution monitoring instruments can be utilized to perform data transverse comparison calibration on the sensor under the same condition before each unmanned aerial vehicle inspection. The calibration of the sensor can be conveniently realized through a built-in calibration module.
(3) A patrol path of the unmanned aerial vehicle in the ventilation system needs to be optimized through a genetic algorithm, and the specific implementation steps of the patrol path include:
(3.1) test unmanned aerial vehicle in the concentrated ventilation system of reality and patrol and examine speed when components such as straight tube section, elbow, tee bend, the influence of centralized ventilation system scale, complexity to unmanned aerial vehicle total time is patrolled and examined in the analysis, and then can patrol and examine the speed and the ventilation system concrete structure of each component of ventilation system and patrol and examine unmanned aerial vehicle total time according to the unmanned aerial vehicle who records and estimate.
And (3.2) in the cruising mileage of the unmanned aerial vehicle, aiming at the target with the largest patrol coverage area, determining the optimal patrol path of the unmanned aerial vehicle by adopting a genetic algorithm according to the patrol speed and the centralized ventilation system scale of different components of the actually measured unmanned aerial vehicle through the patrol speed of different components by taking the total patrol time less than the longest working time of the unmanned aerial vehicle and the distribution of the positions of the inspection openings of the ventilation system as constraints.
And (3.3) the genetic algorithm is realized by a genetic algorithm tool kit in Matlab, an optimization objective function is a patrol path of the unmanned aerial vehicle in the ventilation system, an effective random patrol path of the unmanned aerial vehicle can be obtained by an initial population generation function of the genetic algorithm aiming at a typical ventilation system structure, the optimization objective is to maximize a region covered by the patrol of the unmanned aerial vehicle, the fitness of each path in the initial population is calculated through two input constraint conditions, namely ventilation system maintenance port position distribution and the longest working time of the unmanned aerial vehicle, then the solving range is narrowed through selection, crossing and variation operations in the genetic algorithm, and finally an individual with the largest output fitness is the optimal patrol path of the unmanned aerial vehicle in the current ventilation system.
(4) The inside environmental data of air conditioning ventilation system who gathers is collected and is recorded through the built-in storage module of unmanned aerial vehicle carried on the sensor, and unmanned aerial vehicle's real-time position provides the record by its built-in navigation module. And after the unmanned aerial vehicle finishes the inspection and is recovered, analyzing the measured internal environmental parameters of the ventilation system.
And (4.1) according to the measured internal environment parameters of the ventilation system, adopting conventional database construction software MySQL to establish an environment parameter (PM 1, PM2.5, PM10, formaldehyde, temperature and humidity) database. The above pollutants are all parameters which can be monitored by the sensor at present, and actually, the method can also be expanded (carried with other sensors) such as monitoring total VOCs, wind speed and the like.
And (4.2) based on the environmental parameter data of the periodic inspection, disclosing the position of the air pollution concentration inside the air pipeline from a big data layer by using a k-means cluster analysis method.
(4.3) the k-means cluster analysis method is specifically realized by the following steps:
1) inputting a data set with the size of N, selecting a position with higher PM concentration and formaldehyde concentration measured by each time of routing inspection of the unmanned aerial vehicle as data to be clustered, making Y equal to X, and randomly selecting k clustering centers Ui(Y), i 1,2, j, Y is the clustering center for different iteration rounds.
2) Estimating the distance L(s) of each sample data to the cluster centerk,Ui(Y)), k ═ 1,2,3kFor unmanned aerial vehicle position when acquireing concentration data.
3) Let Y be Y +1, calculate the sum of the squares of the errors of the new cluster centers and the objective function values:
4) if Fr(Y+1)-Fr(Y) | < a set threshold or no change in data category, ending clustering; otherwise, returning to the step 2).
The steps can be conveniently realized in a corresponding function module in Matlab, and the input data set is a data matrix in the MySQL construction database.
Due to the adoption of the technical scheme, the invention has the beneficial effects that:
the unmanned aerial vehicle carrying the pollution monitoring sensor is adopted to cruise the whole ventilation system, the environment parameters (including concentration, temperature, humidity and the like) of all points in the air conditioning ventilation system can be monitored and fed back, the capability of the unmanned aerial vehicle for crossing obstacles (such as vertical air pipelines and tee joints) is strong, the unmanned aerial vehicle can smoothly advance in ventilation pipelines with complex structures, and the target monitoring ventilation system can be comprehensively covered through a patrol route which is optimized in advance. The monitored air pollution data can reflect the actual environment condition in the air-conditioning ventilation system, and the region with higher pollution concentration in the air-conditioning ventilation system can be obtained through the clustering analysis of the patrol data for many times so as to implement timing and directional cleaning on the ventilation system.
Drawings
Fig. 1 is a schematic view of an unmanned aerial vehicle to be used in the present invention.
Fig. 2 is a schematic view of a pollution monitoring sensor module carried on the body of the unmanned aerial vehicle shown in fig. 1.
Fig. 3 is a schematic diagram of the inspection speed of the unmanned aerial vehicle when the unmanned aerial vehicle passes through components such as a straight pipe section, an elbow and a tee joint by means of unit distance timing and the like.
Fig. 4 is a schematic view of unmanned aerial vehicle multi-sensor navigation monitoring.
Fig. 5 is a schematic diagram of a region of the ventilation system where the concentration of air pollution is high, which is obtained by the unmanned aerial vehicle through a plurality of patrols.
FIG. 6(a) is a graph showing the measured formaldehyde concentration in a central air conditioning ventilation system.
Fig. 6(b) is a graph of the measured PM2.5 concentration in a certain central air conditioning ventilation system.
Detailed Description
The invention is further described with reference to the following figures and examples.
Fig. 1 shows a schematic view of an unmanned aerial vehicle to be adopted by the invention, the flight attitude and the traveling route of the unmanned aerial vehicle can be edited by a flight control module, and a pollution monitoring sensor module (the size of the sensor is 0.06 mx 0.03 mx 0.05m) carried on the body of the unmanned aerial vehicle is shown in fig. 2, and the sensor can monitor parameters including PM1, PM2.5, PM10, formaldehyde concentration, temperature and humidity and the like on the premise of meeting engineering requirements, and can be conveniently integrated on the unmanned aerial vehicle through a single chip microcomputer, and the sensor realizes miniaturization and mass production.
The stability that unmanned aerial vehicle marchd in ventilation system under the different wind speeds of at first needing to test, through means such as unit distance timing obtain unmanned aerial vehicle through straight tube section, elbow, tee bend when component, the analysis is concentrated ventilation system scale, the influence of complexity to unmanned aerial vehicle total time of patrolling and examining, and then can patrol and examine the speed of each component of ventilation system and the concrete structure of ventilation system and patrol and examine unmanned aerial vehicle total time according to the unmanned aerial vehicle who records and estimate, as shown in figure 3.
Aiming at a certain practical air-conditioning ventilation system, a genetic algorithm is used for planning a patrol path of the unmanned aerial vehicle in advance, the genetic algorithm is realized by a genetic algorithm tool box in Matlab, and the specific implementation steps of the genetic algorithm are as follows:
(1) aiming at a typical ventilation system structure, an effective random unmanned aerial vehicle patrol path is obtained by an initial population generation function of a genetic algorithm, and the optimization aim is to maximize the coverage area of the unmanned aerial vehicle patrol ventilation system;
(2) with two constraints entered: the unmanned aerial vehicle who ventilates system access hole position distribution and actual measurement patrols and examines speed, calculates the fitness on each route in the initial population, fitness computational formula as follows:
Fit(x)＝fmax(x)v∈vmeasured in fact,s∈sDistribution of access holes
In the formula (f)maxCalculating a function for the patrol coverage area; x is an unmanned aerial vehicle routing inspection path randomly generated by an algorithm; v is the unmanned aerial vehicle inspection speed; s is the entrance and exit of the unmanned plane in the ventilation system.
(3) The solving range is narrowed through selection, intersection and variation operations in the genetic algorithm, and the selection operation adopts the optimal reservation selection in a Matlab genetic algorithm toolbox, namely, the individual structure with the highest fitness in the current population is completely copied to the next generation population; the crossing operation adopts uniform crossing, namely genes on two paired individuals are exchanged with the same probability to generate two new individuals; the variation operation adopts Gaussian approximate variation, namely a random number generated by normal distribution with the mean value of A and the variance of B is used for replacing the original gene during variation.
(4) And (4) generating a new population, repeating the steps (1) to (3), judging whether a preset algebra is reached, and if so, outputting the individual with the maximum fitness as the optimal patrol path of the unmanned aerial vehicle.
And (4) checking whether the time and the coverage area required by patrol of the unmanned aerial vehicle according to the designed optimized path are consistent with the algorithm prediction through experiments. Then, the sensor is started, the change rules of the concentration field and the temperature and humidity field inside the unmanned aerial vehicle under different working conditions (different air supply speeds and air port opening rates) are closed and started to operate by the unmanned aerial vehicle patrol experiment contrast ventilation system, and the unmanned aerial vehicle multi-sensor cruising monitoring schematic diagram is shown in fig. 4. In the aspect of the accuracy of the monitoring data of the sensor carried by the unmanned aerial vehicle, a relatively accurate measuring point of the particulate matter or gaseous pollution sensor can be arranged at a position of the ventilation system, the measured data of the particulate matter or gaseous pollution sensor is compared with the data acquired at the position of the unmanned aerial vehicle at the same moment, and the deviation between the measured data and the data acquired at the position of the unmanned aerial vehicle is smaller than a set threshold value so that the accuracy of the data of the unmanned aerial. In the aspect of the calibration, usable laboratory glassware and clean outdoor air calibrate the sensor that unmanned aerial vehicle carried on before unmanned aerial vehicle patrols and examines at every turn, and this process also can be through the convenient realization of the built-in calibration module of sensor.
And finally, constructing a database according to the sensor monitoring data and carrying out data clustering analysis, wherein the clustering analysis adopts a k-means clustering analysis method, and the specific implementation flow is as follows:
(1) inputting a data set with the size of N, selecting a position with higher PM concentration and formaldehyde concentration measured by each time of routing inspection of the unmanned aerial vehicle as data to be clustered, making Y equal to X, and randomly selecting k clustering centers Ui(Y), i 1,2, j, Y is the clustering center for different iteration rounds.
(2) Estimating the distance L(s) of each sample data to the cluster centerk,Ui(Y)), k ═ 1,2,3kFor unmanned aerial vehicle position when acquireing concentration data.
(3) Let Y be Y +1, calculate the sum of the squares of the errors of the new cluster centers and the objective function values:
4) if Fr(Y+1)-Fr(Y) | < a set threshold or no change in data category, ending clustering; otherwise, returning to the step 2).
The steps can be conveniently realized in a corresponding function module in Matlab, and the input data set is a data matrix required for constructing the MySQL database. The construction and implementation of the MySQL database are based on the C language code which is disclosed at present, and the MySQL database is completely feasible.
Areas with high air pollution concentration in the ventilation system are obtained through multiple patrols, as shown in fig. 5, the areas are prompted to be the key points in cleaning of the air conditioning ventilation system, and the reasons of the areas with high air pollution concentration are analyzed from the air conditioning operation angle, such as humidity rise caused by condensation and pollutant accumulation. As shown in fig. 6, the concentration distribution of formaldehyde and PM2.5 with time change obtained by monitoring formaldehyde and PM2.5 near the air outlet in a central air conditioning ventilation system by using the sensor to be adopted in the present invention shows that the sensor can accurately detect the concentration change of formaldehyde and PM2.5 in the ventilation system, and has a higher detection resolution.
Therefore, the unmanned aerial vehicle with the pollution monitoring sensor is adopted to realize the complete cruise of the whole ventilation system, the environmental parameters (including the concentration, the temperature, the humidity and the like of common pollutants) of all points in the air-conditioning ventilation system can be monitored and fed back, the unmanned aerial vehicle can smoothly move in a complex ventilation pipeline due to the obstacle crossing capability which is difficult to reach by a common robot, and the monitoring target can be comprehensively covered through a patrol route which is optimized in advance.
The foregoing description and description of the embodiments are provided to facilitate understanding and application of the invention by those skilled in the art. It will be readily apparent to those skilled in the art that various modifications can be made to these teachings and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above description and the description of the embodiments, and those skilled in the art should make improvements and modifications within the scope of the present invention based on the disclosure of the present invention.
Claims (8)
1. A monitoring feedback and analysis method for internal environmental parameters of an air conditioner ventilation system is characterized by comprising the following steps: the method comprises the steps of acquiring internal environment parameters in a cruising mode of an unmanned aerial vehicle carrying a pollution monitoring sensor in an air conditioning ventilation system, and analyzing monitoring data in a clustering mode.
2. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, wherein: (1) carrying a pollution monitoring sensor by an unmanned aerial vehicle, and cruising according to a track optimally designed by a genetic algorithm in an air conditioning ventilation system; (2) and the monitored environmental parameters are stored through a constructed database and analyzed and prompted by a k-means clustering method to the area with higher air pollution concentration in the air-conditioning ventilation system.
3. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, which comprises the following steps:
(1) selecting a suitable unmanned aerial vehicle;
(2) fixing a sensor module to be carried on the unmanned aerial vehicle body;
(3) the patrol path of the unmanned aerial vehicle in the ventilation system needs to be optimized through a genetic algorithm;
(4) the internal environmental data of air conditioning ventilation system who gathers is collected and the record through the built-in storage module of sensor, waits that unmanned aerial vehicle patrols and examines the end and retrieve the back, carries out the analysis to the ventilation system internal environment parameter who records.
4. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, wherein:
in the step (1), the carrying weight of the unmanned aerial vehicle is less than or equal to 1kg, the endurance time is less than or equal to 1 hour, and the unmanned aerial vehicle can stably travel at the wind speed of less than 10 meters per second so as to meet the inspection requirement of the unmanned aerial vehicle during the normal operation of the ventilation pipeline; and/or the presence of a gas in the gas,
in the step (2), the sensor module contains particulate matters, formaldehyde and temperature and humidity sensors, and is integrated on the unmanned aerial vehicle through the single chip microcomputer.
5. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, wherein:
in the step (2): the sensor adopts a multi-type air quality integrated micro self-recording instrument (PM monitoring error is less than or equal to 5 percent, formaldehyde monitoring error is less than or equal to 8 percent, temperature and humidity monitoring error is less than or equal to 5 percent, and the shortest sampling interval is 2.0 s); and/or the presence of a gas in the gas,
in the aspect of sensor precision calibration, other conventional pollution monitoring instruments can be utilized to perform data transverse comparison calibration on the sensor under the same condition before each unmanned aerial vehicle patrols and examines. The calibration of the sensor can be conveniently realized through a built-in calibration module.
6. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, wherein the step (3) comprises the following steps:
(3.1) testing the inspection speed of the unmanned aerial vehicle when the unmanned aerial vehicle passes through components such as a straight pipe section, an elbow, a tee joint and the like in an actual centralized ventilation system, and analyzing the influence of the scale and the complexity of the centralized ventilation system on the total inspection time of the unmanned aerial vehicle; estimating the inspection time of the unmanned aerial vehicle according to the cruising speed of the unmanned aerial vehicle and the specific structure of the ventilation system;
(3.2) in the cruising range of the unmanned aerial vehicle, facing to the target with the largest patrol coverage area, determining the optimal patrol path of the unmanned aerial vehicle by adopting a genetic algorithm according to the patrol speed of the unmanned aerial vehicle passing through different components and the scale of a centralized ventilation system by taking the total patrol time less than the longest working time of the unmanned aerial vehicle and the distribution of positions of inspection openings of the ventilation system as constraints; and/or the presence of a gas in the gas,
and (3.3) the genetic algorithm is realized by a genetic algorithm tool kit in Matlab, the optimization target is a patrol path of the unmanned aerial vehicle in the ventilation system, aiming at a typical ventilation system structure, an effective random patrol path of the unmanned aerial vehicle can be obtained by an initial population generation function of the genetic algorithm, the fitness of each path in the initial population is calculated through two input constraint conditions, namely total patrol time, the distribution of positions of inspection openings of the ventilation system and the actual inspection speed of the unmanned aerial vehicle, then the solving range is reduced through selection, crossing and variation operations in the genetic algorithm, and finally the individual with the maximum fitness is output to be the optimal patrol path of the unmanned aerial vehicle in the current ventilation system.
7. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 1, wherein the step (4) comprises the following steps:
and (4.1) according to the measured internal environmental parameters of the ventilation system, adopting conventional database construction software MySQL to establish an environmental parameter database.
And (4.2) based on the environmental parameter data of the periodic inspection, disclosing the position of the air pollution concentration inside the air pipeline from a big data layer by using a k-means cluster analysis method.
8. The method for monitoring, feeding back and analyzing the internal environmental parameters of the air conditioning and ventilating system as claimed in claim 7, wherein the k-means cluster analysis method is implemented as follows:
1) inputting a data set with the size of N, selecting a position with higher PM concentration and formaldehyde concentration measured by each time of routing inspection of the unmanned aerial vehicle as data to be clustered, making Y equal to X, and randomly selecting k clustering centers Ui(Y), i ═ 1, 2.., j, Y is the clustering center under different iteration rounds;
2) estimating the distance L(s) of each sample data to the cluster centerk,Ui(Y)), k ═ 1,2,3kThe position of the unmanned aerial vehicle when the concentration data is obtained;
3) let Y be Y +1, calculate the sum of the squares of the errors of the new cluster centers and the objective function values:
4) if Fr(Y+1)-Fr(Y) | < a set threshold or no change in data category, ending clustering; otherwise, returning to the step 2).
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