CN115808211A - Public building temperature and thermal comfort monitoring and predicting system - Google Patents
Public building temperature and thermal comfort monitoring and predicting system Download PDFInfo
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Abstract
The invention discloses a public building temperature and thermal comfort monitoring and predicting system, which comprises: the sensor arrangement strategy making module is used for making a corresponding sensor arrangement strategy according to the spatial characteristics of the building to be tested; the human factor internal disturbance correction matrix database module is used for acquiring a quantization matrix influenced by temperature; the temperature measurement space adaptability calibration module is used for dynamically calibrating the abnormal value of the temperature of the optical fiber measurement point; the empty field temperature and internal disturbance information database module is used for integrating the corrected air temperature at the optical fiber measuring point and the information of the flow and distribution of personnel in the area; the thermal comfort parameter database module is used for measuring and integrating environmental parameters and human factors related to thermal comfort; the indoor temperature prediction module is used for predicting the temperature field distribution at the height required by different types of spaces; and the thermal comfort degree prediction module is used for deducing the thermal comfort degree distribution condition of the plane at the required height.
Description
Technical Field
The invention relates to the field of monitoring and predicting indoor temperature and thermal comfort, in particular to a public building temperature and thermal comfort monitoring and predicting system based on temperature measurement of a hybrid sensor.
Background
With the acceleration of socialization process, the number of large public buildings is increased day by day, and the establishment of comfortable public environment has important social significance; the increasing appeal of the country to energy conservation and emission reduction also leads the control of building energy consumption to become an important concern for avoiding resource waste and promoting sustainable development. The individualized adjustment of the air conditioner is used as a novel indoor environment regulation and control method, and the energy utilization efficiency can be effectively improved while the requirement of personnel on differentiated heat can be met. However, this method still faces short plates: firstly, the internal space characteristics of different types of public buildings are different, and no method can be used for adaptively predicting different types of spaces at present; secondly, personnel distribution and flow in public buildings have large fluctuation, and the existing temperature prediction method is mainly based on historical time sequence data or air conditioning equipment operation parameters, lacks of consideration of real-time internal disturbance change on indoor temperature influence and lacks of real-time monitoring of indoor thermal comfort change; thirdly, the implementation of the individualized adjustment of the air conditioner depends on high-precision indoor temperature measurement, and according to the conventional temperature measurement method, a large number of point sensors are required to be arranged for fine indoor temperature field distribution, so that the cost is overhigh.
The most recent prior patents and papers to date have the following:
1) A building space temperature monitoring device (CN 213274615U) based on a digital temperature sensor comprises the digital temperature sensor, a bus cable, a temperature patrol instrument, a data processor and a heating and refrigerating unit. The digital temperature sensor is arranged in the building space, the temperature sensor is connected with the temperature polling instrument by using the bus cable, and then the output end of the temperature polling instrument is connected with the input end of the data processor, so that the measured temperature signal can be analyzed.
The technical scheme aims to solve the problems that the traditional thermocouple and thermal resistor type temperature sensors are complex to install, the data transmission distance is limited, the monitoring device is high in manufacturing cost and the like, and low cost and easiness in installation are achieved. Compared with a distributed optical fiber temperature measurement system, the device still has the defects of limited measurement sites and low spatial resolution, is difficult to obtain a refined temperature measurement result, is not beneficial to drawing a temperature field, does not consider other environmental factors influencing the thermal comfort degree of a building space, and is difficult to meet the requirement of people on the thermal comfort function of the monitoring device.
2) An indoor environment monitoring system and method (CN 107036652B) combined with building environment simulation comprises an environment monitoring system, a building environment simulation system, a server and a terminal, wherein the environment monitoring system is used for monitoring indoor and outdoor environments of a building and feeding monitoring results and positioning information back to the server in real time, and the related environments comprise a thermal environment, a light environment, a sound environment, indoor air quality and wind speed; the building environment simulation system is used for simulating indoor environment parameter distribution by taking building environment simulation software as a tool, correcting a simulation result in the server by taking the feedback content of the environment monitoring system to the server as a basis, and outputting the corrected indoor environment parameter distribution in the terminal.
The technical scheme aims to reduce the number of sensors, realize more accurate monitoring of indoor environment parameter distribution under a low-density sensing network and simultaneously effectively save energy. However, the simulation link of the patent is complex, and the simulation link cannot accurately respond to indoor heat sources and gas flow conditions, because no sensor for opening and closing the external surface is used for detecting in the existing system, the data cannot be obtained through the existing system, and therefore only the existing model simulation can be adopted during simulation. In addition, indoor personnel detection adopts fixed and mobile sensor detection modes, and the measurement error exists because the measurement cannot be accurately carried out on public buildings.
3) A data center distributed optical fiber sensing monitoring system (CN 103701898A) comprises a plurality of temperature measurement optical fibers, a distributed optical fiber temperature measurement host and a monitoring server. The temperature measurement optical fibers are arranged on the front door and the back door of the cabinet to measure temperature distribution information in real time. The distributed optical fiber temperature measurement host is connected with the monitoring server through a network, and temperature data measured by the optical fiber can be uploaded to the monitoring server through the network, so that the temperature data can be stored and analyzed.
This technical scheme aims at reducing the quantity that is applied to data center computer lab temperature monitoring electron temperature sensor, reduces the temperature monitoring system installation degree of difficulty, promotes environmental monitoring system's accuracy. However, the invention lacks the function of predicting the indoor temperature field in real time, only the optical fibers are arranged at the front door and the back door of the cabinet, the surface temperature of the server can be monitored only, the temperature distribution information of the indoor whole space cannot be obtained, the temperature of the indoor area with large internal disturbance cannot be accurately responded, and the universality and popularization of different functional scenes are difficult to realize.
In summary, no research results regarding "hybrid sensor-based monitoring and prediction method for public building temperature and thermal comfort" are found at present.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a public building temperature and thermal comfort monitoring and predicting system based on a hybrid sensor, the internal space of a public building is treated in a modularization way, and different sensor arrangement strategies are set according to the space attribute and personnel characteristic of each module; based on fluid dynamics simulation calculation, a series of quantitative matrixes of influences of different heat sources and different personnel distribution conditions on temperatures at different heights are obtained; based on the measurement result of the optical fiber temperature sensor, the system error of the optical fiber temperature measurement host installed and applied in the new space is corrected through an optical fiber temperature sensor error calibration model, and then dynamic feedback calibration is carried out through the measurement result of the combined point type temperature and humidity sensor; then, analyzing and processing image information provided by a monitoring system to obtain personnel flow distribution information, and establishing an empty field temperature and internal disturbance information database by combining temperature data measured and corrected by an optical fiber temperature sensor; measuring indoor environmental parameters influencing thermal comfort based on the sensor module group, determining human factors related to the thermal comfort according to the functions of the building space, and establishing a thermal comfort parameter database after integration; the method comprises the steps of stripping and superposing the temperature of the empty field and the internal disturbance temperature, performing different types of model training on temperature data by using a regression method of a support vector machine according to different optical fiber arrangement strategies, and realizing real-time prediction on the temperature at the required height; and finally, according to the temperature distribution prediction result at the required height, and in combination with parameter data related to the thermal comfort degree, predicting the thermal comfort degree at the height. The temperature prediction result can be applied to the fields of high-precision analysis of temperature optimization potential, supercooling and overheating detection of different temperature partitions and the like, and the thermal comfort prediction result can be applied to the operation and maintenance of air conditioners of various public buildings, so that the energy-saving and comfortable public environment can be created.
The purpose of the invention is realized by the following technical scheme:
a public building temperature and thermal comfort monitoring and predicting system comprises a sensor arrangement strategy making module, a human factor internal disturbance correction matrix database module, a temperature measurement space adaptability calibrating module, an empty field temperature and internal disturbance information database module, a thermal comfort parameter database module, an indoor temperature predicting module and a thermal comfort predicting module;
the sensor arrangement strategy making module is used for making a corresponding sensor arrangement strategy according to the spatial characteristics of the building to be tested, and finishing the installation of the distributed optical fiber temperature sensor, the point type temperature and humidity sensor, the black ball temperature sensor and the wind speed sensor in the building to be tested;
the human factor internal disturbance correction matrix database module is used for acquiring a series of quantitative matrixes of influences of different heat sources and different personnel distribution conditions on the temperature at the ceiling height and the temperature at the required height as the input of the indoor temperature prediction module;
the temperature measurement space adaptability calibration module is used for correcting a system error of temperature measurement result change caused by the change of the working environment of the optical fiber temperature sensor, dynamically calibrating an abnormal value of the temperature of the optical fiber measurement point, and taking the abnormal value as the input of the blank field temperature and internal disturbance information database module;
the empty field temperature and internal disturbance information database module is used for integrating the corrected air temperature at the optical fiber measuring point and the information of personnel flow and distribution in the area as the input of the indoor temperature prediction module;
the thermal comfort parameter database module is used for measuring and integrating a thermal comfort environment parameter and a thermal comfort human factor parameter related to the thermal comfort as the input of the thermal comfort prediction module;
the indoor temperature prediction module is used for setting a corresponding machine learning training model according to the spatial characteristics of the building to be tested based on the input data of the human factor internal disturbance correction matrix database module, the empty field temperature and internal disturbance information database module, so as to realize prediction of temperature field distribution at the required height positions of different types of spaces;
and the thermal comfort degree prediction module is used for establishing temperature according to the prediction result of the temperature field distribution at the required height by combining the environmental parameter and the human factor parameter data related to the thermal comfort degree, and deducing the thermal comfort degree distribution condition of the plane at the required height.
Further, in the strategic formulation module of sensor arrangement, the interior space of the building to be tested is subjected to modular processing, the size of each space module in the building to be tested is considered, the number of the personnel can be accommodated, and two different strategies of sensor arrangement are formulated: aiming at a space module with more personnel and large size, arranging optical fibers at the specified height of the ceiling and the peripheral side walls, and aiming at a space module with less personnel and small size, arranging the optical fibers in a square surrounding manner from the center of the ceiling to the intersection angle of the ceiling and the peripheral side walls along the specified laying interval; each space module is formed by connecting the same optical fiber temperature measuring system in series with the same optical fiber, in two arrangement strategies, the laying distance of the optical fiber is kept consistent with the spatial resolution of the optical fiber temperature sensor, and the optical fiber is used for measuring and collecting space temperature information; building a building space information model according to the collected space temperature information, and meanwhile, according to the space characteristics of a building to be measured, setting a sensor module group along each optical fiber passing through a fixed number of optical fiber measurement sites, wherein each sensor module group comprises a point type temperature and humidity sensor, a black ball temperature sensor and an air velocity sensor, the point type temperature and humidity sensor is used for measuring the relative humidity and the air temperature in the environment, and the dynamic calibration of the measurement result of the optical fiber temperature sensor is carried out subsequently; the black ball temperature sensor is used for measuring the radiation temperature in the environment; the wind speed sensor is used to measure the air flow rate in the environment.
Furthermore, in the human factor internal disturbance correction matrix database module, a building space information model is established based on the spatial attributes of the plane arrangement and the space size of the building to be tested and based on the utilization of modeling software; based on a fluid dynamics computing platform, an indoor thermal environment is simulated, a series of quantitative results of temperature influences of different heat sources and different personnel distribution conditions on different heights are obtained, a series of temperature influence quantitative matrixes corresponding to different positions and different heights are established, and a human factor internal disturbance correction matrix database is obtained after integration and is used for a subsequent indoor temperature prediction module. Further, in the temperature measurement space adaptability calibration module, building space adaptability calibration is performed on the temperature measured by the optical fiber based on an optical fiber temperature sensor error calibration model; and calculating the temperature difference of the point type temperature and humidity sensors closest to the temperature of the fiber measurement points along the fiber arrangement direction and the variation rate of the historical temperature data of the previous 5 times measured by the corresponding point type temperature and humidity sensors for all the calibrated fiber measurement points, treating the corresponding fiber measurement points as temperature abnormal points if the difference value exceeds 2 ℃, re-calibrating by calling an error calibration model of the fiber temperature sensors for the temperature abnormal points, and continuously completing the test after automatic re-calibration, wherein the dynamic calibration runs through the whole process of measuring the indoor air temperature by each sensor.
Furthermore, the optical fiber temperature sensor error calibration model is a field calibration model which is established by adopting a temperature sensor with the resolution of 0.01 ℃ and the optical fiber temperature sensor to synchronously monitor the whole indoor temperature change interval of the public building and based on an SVR algorithm and is used for reducing the indoor air temperature measurement error of the optical fiber temperature sensor.
Further, in the empty field temperature and internal disturbance information database module, the indoor air temperature is collected based on the optical fiber temperature sensor, and the temperature information of a series of optical fiber measuring points is obtained by the temperature measuring space adaptability calibration module; the method comprises the steps that indoor image information is collected regularly by using a monitoring system in a building space, a person target is detected through a Gaussian mixture modeling method, a human body is identified through a BP neural network, and moving person tracking is achieved through a Meanshift algorithm, so that information of person flowing and distribution is obtained; and after integration, establishing a temperature and internal disturbance information database as the input of a subsequent indoor temperature prediction module.
Further, in the thermal comfort parameter database module, the thermal comfort environment parameters include air temperature, air relative humidity, average radiation temperature, and air flow rate; thermal comfort parameters include: thermal resistance of clothes of indoor personnel and metabolism of indoor personnel; on one hand, according to the behavior mode and the operation temperature corresponding to the indoor space function of the building to be tested, the corresponding indoor personnel metabolism amount and the indoor personnel clothes thermal resistance are determined; on the other hand, three parameters of air relative humidity, average radiation temperature and air flow rate in the environment are measured through the sensor module group, and a thermal comfort parameter database is established after integration and is used as the input of a subsequent thermal comfort prediction module.
Furthermore, in the indoor temperature prediction module, based on the empty field temperature and the real-time personnel information provided by the empty field temperature and internal disturbance information database module, matching with a matrix provided by a human factor internal disturbance correction matrix database module, and selecting a ceiling height temperature influence quantization matrix and a required height temperature influence quantization matrix which are closest to the ceiling height in the current environment; the influence of the man-caused internal disturbance on temperature measurement is stripped by using a ceiling height temperature influence quantization matrix, and the temperature at the ceiling height without the man-caused internal disturbance is obtained; setting a corresponding training model according to an arrangement strategy of the sensors by utilizing a machine learning algorithm to realize preliminary prediction of the temperature of the unmanned aerial vehicle at the required height; and superposing the preliminary prediction result and the corresponding temperature influence quantization matrix at the required height to obtain an accurate prediction result of the temperature plane field at the required height.
Further, in the thermal comfort prediction module, accurate prediction based on the temperature plane field at the required height is carried outMeasuring results, establishing a mapping relation between thermal comfort parameters and temperature on the height plane by combining environmental parameter data which are provided by the thermal comfort parameter database module and influence thermal comfort in the corresponding environment, and calculating the distribution condition of the thermal comfort at the corresponding height according to a thermal comfort calculation formula by inputting thermal comfort environmental parameter prediction results and human factor parameter data which influence the thermal comfort; the thermal comfort calculation formula is as follows:
in the formula (I), the compound is shown in the specification,Min order to obtain the rate of metabolism,Wthe power is made for the human body,P a is the partial pressure of water vapor in the ambient air,t a it is the temperature of the air that is,f cl is the ratio of the surface area of the human body and the naked body,t s in order to obtain the average radiation temperature,t cl the average temperature of the outer surface of the human body,h c convective heat transfer coefficient.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. economic implications for reducing the number of sensors required: after the size and scale of the public building reach a certain degree, if planar distribution of temperature and thermal comfort is required to be obtained, a large number of various sensors need to be arranged, which inevitably causes economic problems of high instrument cost, high calibration and maintenance difficulty, long investment recovery period and the like caused by excessive measuring point quantity and types. The hybrid sensor adopted by the invention can accurately measure the temperature at multiple sites by only one optical fiber and a small amount of sensors, and deduces the thermal comfort degree distribution condition at the required height according to the small amount of thermal comfort environment parameter data, thereby effectively solving the sensor cost problem.
2. The application significance of improving the performance of the monitoring equipment is as follows: the hybrid sensor adopted by the invention can make different sensor arrangement strategies aiming at large or small indoor spaces with different space characteristics based on a set of sensor system, and establish different temperature prediction models, thereby reducing the complexity of system operation and improving the accuracy of temperature prediction. Meanwhile, the invention can effectively deduce and predict the thermal comfort distribution at the required height, and can provide a more direct data source for the regional intelligent operation regulation of the air conditioner. On the other hand, the optical fiber temperature sensor adopted by the invention can be cooperatively arranged in the ceiling and the side wall combined interior decoration, and integrated with the existing building, so that the functions of monitoring and predicting an indoor temperature field and drawing a temperature map are realized on the premise of not interfering the normal function of a public building and not occupying the use space. And the sensor can be integrated with a ceiling and a side wall circuit, so that the influence of the sensor on the aesthetic degree of a public space is reduced.
3. The social significance of providing a data base for reducing the building operation energy consumption is as follows: the large public building is one of main building types appearing in the process of urbanization, and the control of the energy consumption of the public building is an important way for avoiding energy waste. The currently common indoor temperature monitoring method is usually used for regulating and controlling the air conditioner based on the current indoor temperature, and has relatively obvious time lag. The invention corrects the inherent space adaptability of the equipment and the result error caused by internal disturbance of human factors, and takes the prediction result of the temperature and the thermal comfort degree as the information input of the subsequent air conditioner regulation and control, thereby providing powerful data support for reducing the energy consumption of building operation.
Drawings
FIG. 1 is a schematic flow chart of the system of the present invention.
Fig. 2a and 2b are schematic diagrams of partial temperature influence quantization matrices at the ceiling height and 0.8m height under a small space arrangement strategy respectively.
Fig. 3a and 3b are schematic diagrams of partial temperature influence quantization matrices at a ceiling height and a height of 0.8m under a large space arrangement strategy respectively.
Fig. 4a and 4b are examples of temperature clouds at 0.8m height and ceiling height, respectively, for a small space layout strategy.
Fig. 5a and 5b are examples of temperature clouds at 0.8m height and ceiling height, respectively, for a large spatial arrangement strategy.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a public building temperature and thermal comfort degree monitoring and predicting system based on a hybrid sensor, which can make up for a short board of the existing public building indoor temperature and thermal comfort degree monitoring technology, realize high-resolution real-time accurate prediction of the temperature at the required height and deduce the thermal comfort degree distribution at the height. The method can effectively reduce the number of sensors, improve the attractiveness of equipment layout, effectively solve the influence of internal disturbance of environments such as personnel flow on the prediction result, enhance the indoor thermal comfort monitoring function and improve the accuracy of indoor temperature prediction.
The public building temperature and thermal comfort monitoring and predicting system comprises a sensor arrangement strategy making module, a human factor internal disturbance correction matrix database module, a temperature measurement space adaptability calibrating module, an empty field temperature and internal disturbance information database module, a thermal comfort parameter database module, an indoor temperature predicting module and a thermal comfort predicting module.
And the sensor arrangement strategy making module is used for making a corresponding sensor arrangement strategy according to the spatial characteristics of the measured space, and finishing the installation of the distributed optical fiber temperature sensor, the point type temperature and humidity sensor, the black ball temperature sensor and the wind speed sensor in a new building. Specifically, carry out the modularization with the public building inner space that awaits measuring and handle, consider the size of its each space module and can hold personnel's quantity usually, make two kinds of different sensor arrangement strategies: for more than 50m 2 The space module with larger size arranges the optical fibers at the specified height of the ceiling and the peripheral side walls and aims at the space module with the height less than 50m 2 The space module is used for arranging the optical fibers from the center of the ceiling to the intersection angle of the ceiling and the peripheral side walls in a square surrounding mode along the specified laying interval. Each space module is connected in series by the same optical fiber temperature measurement system and the same optical fiber, in two arrangement strategies, the laying distance of the optical fiber is kept consistent with the spatial resolution of the optical fiber temperature sensor, and the optical fiber is used for measuring and collectingA volume of spatial temperature information; meanwhile, according to the spatial characteristics of the building to be detected, a building spatial information model is established for determining the spatial position coordinates of the optical fiber sensor. The method comprises the following steps that a sensor module group is arranged along an optical fiber after passing through a certain number of optical fiber measuring sites, each sensor module group comprises a point type temperature and humidity sensor, a black ball temperature sensor and a wind speed sensor, wherein the point type temperature and humidity sensors are used for measuring relative humidity and air temperature in the environment, and dynamic calibration of the measuring result of the optical fiber temperature sensors is carried out subsequently; the black ball temperature sensor is used for measuring the radiation temperature in the environment; the wind speed sensor is used to measure the air flow rate in the environment. The number of optical fiber measurement sites spaced among the sensor module groups is determined by the building space scale and the functional part.
And the human factor internal disturbance correction matrix database module is used for acquiring a series of quantitative matrices of influences of different heat sources and different personnel distribution conditions on the temperatures at the ceiling height and the required height as the input of the indoor temperature prediction module. Specifically, based on spatial attributes such as spatial plane arrangement, spatial dimension and the like of a building to be tested, based on the building spatial information model and the fluid dynamics computing platform, a large number of indoor thermal environments are simulated, quantitative results of temperature influences of a series of different heat sources and different personnel distribution conditions at different heights are obtained, a series of temperature influence quantitative matrixes corresponding to different positions and different heights are established, and a human factor internal disturbance correction matrix database is obtained after integration and is used for a subsequent indoor temperature prediction module.
The temperature measurement space adaptability calibration module is used for correcting system errors of temperature measurement result changes caused by changes of working environments of the optical fiber temperature sensor, dynamically calibrating abnormal values of the temperature of the optical fiber measurement points, and taking the abnormal values as input of the empty field temperature and internal disturbance information database module. Specifically, building space adaptability calibration is carried out on the temperature measured by the optical fiber based on an optical fiber temperature sensor error calibration model. For the temperature of all calibrated optical fiber measuring points, the temperature difference of the point type temperature and humidity sensor closest to the optical fiber measuring points along the optical fiber arrangement direction and the variation rate of the previous 5 times of historical temperature data measured by the point type temperature and humidity sensor are calculated, if the difference value exceeds 2 ℃, the difference value is taken as a temperature abnormal point to be processed, the optical fiber temperature sensor error calibration model provided by the embodiment is called to recalibrate the temperature abnormal point, the test is continuously completed after the automatic recalibration, and the dynamic calibration runs through the whole process of measuring the indoor air temperature by the hybrid sensor. In the embodiment, the optical fiber temperature sensor error calibration model is a field calibration model which is established based on an SVR algorithm and used for reducing the indoor air temperature measurement error of the optical fiber temperature sensor by adopting a high-precision temperature sensor (the resolution is 0.01 ℃) and the optical fiber temperature sensor to synchronously monitor the whole indoor temperature change interval of a public building, taking the indication temperature of the distributed optical fiber temperature sensor as an input variable, taking the temperature indication of the point type temperature and humidity sensor as an output variable.
And the empty field temperature and internal disturbance information database module is used for integrating the corrected air temperature at the optical fiber measuring point and the internal disturbance information (namely the related information of personnel flow and distribution) of the personnel in the area as the input of the indoor temperature prediction module. Specifically, the module collects the indoor air temperature through the optical fiber temperature sensor, and obtains temperature information of a series of optical fiber measuring points after the space adaptability calibration. On the other hand, indoor image information is collected regularly by using a monitoring system in an office space, a person target is detected by a Gaussian mixture modeling method, a human body is identified by using a BP neural network, and moving person tracking is realized by using a Meanshift algorithm, so that information of person distribution and flow is obtained. And after integration, establishing a temperature and internal disturbance information database as the input of a subsequent indoor temperature prediction module.
And the thermal comfort parameter database module is used for measuring and integrating the environmental parameters and the human factors related to the thermal comfort as the input of the thermal comfort prediction module. Specifically, on one hand, the module determines corresponding human metabolism and thermal resistance of clothes according to a behavior mode and an operation temperature corresponding to indoor space functions of a public building. On the other hand, three parameters of relative humidity, average radiation temperature and air flow rate in the environment are measured through the sensor module group, and a thermal comfort parameter database is established after integration and is used as the input of a subsequent thermal comfort prediction module. The thermal resistance of the clothes is related to seasons and the operation temperature corresponding to different public building functions, and the typical thermal resistance of the clothes of the personnel under the conditions can be output in the thermal comfort parameter database according to the input conditions of different public building function types and seasons. The human body metabolism quantity is related to personnel behavior modes corresponding to different public building functions, and the human body metabolism quantity of typical personnel under the condition can be output in the thermal comfort parameter database according to the functional room input condition of different public buildings.
The indoor temperature prediction module is used for stripping and superposing the influence of the air field temperature and the internal disturbance temperature, and is used for setting a corresponding machine learning training model according to the spatial characteristics of the measured space, so that the temperature field distribution at the required height of different types of spaces can be predicted. Specifically, based on the empty field temperature and the real-time personnel information provided by the empty field temperature and internal disturbance information database module, the matrix provided by the personnel internal disturbance correction matrix database is matched, and the ceiling temperature influence quantization matrix and the required ceiling temperature influence quantization matrix which are closest to the current environment are selected. And stripping the influence of the internal disturbance of the human factors on the temperature measurement by using a ceiling height temperature influence quantification matrix to obtain the ceiling height temperature without the internal disturbance of the human factors. Setting a corresponding training model according to an arrangement strategy of the sensors by utilizing a machine learning algorithm to realize preliminary prediction of the temperature of the unmanned aerial vehicle at the required height; and (3) overlapping the preliminary prediction result and the matrix by using the temperature influence quantification matrix at the required height so as to obtain an accurate prediction result of the temperature plane field at the required height.
Specifically, according to the arrangement strategy of the sensors, the following training models are set:
for more than 50m 2 Large space of (2): arranging the distributed optical fiber temperature sensors at the specified height of the ceiling and the peripheral side walls, and establishing the corrected indication temperature of the distributed optical fiber temperature sensors at the corresponding positions and the temperature set at the required height by adopting an SVR algorithmAnd the mapping model between the temperature values indicated by the temperature sensors predicts the unmanned internal disturbance temperature at the required height.
For less than 50m 2 The small space of (2): the distributed optical fiber temperature sensors are arranged along the specified laying interval in a square surrounding mode from the center of the ceiling to the intersection angle between the ceiling and the peripheral side wall, a mapping model between the indication value temperature corrected by the distributed optical fiber temperature sensors at the corresponding positions and the indication value temperature of the temperature sensors arranged at the required height is established by adopting an ANN algorithm, and the unmanned factor internal disturbance temperature at the required height is predicted.
In both placement strategies, the fiber lay spacing is kept consistent with the fiber temperature sensor spatial resolution.
And the thermal comfort degree prediction module is used for establishing a thermal comfort degree prediction model according to the prediction result of the temperature field distribution at the required height and by combining the parameter data related to the thermal comfort degree, and deducing the thermal comfort degree distribution condition of the plane at the required height.
Specifically, based on the accurate prediction result of the temperature plane field at the required height, the mapping relation between the thermal comfort parameter and the temperature on the height plane is established by combining the environmental parameter data which are provided by the thermal comfort parameter database module and influence the thermal comfort level under the corresponding environment, and then the air relative humidity field at the required height, the average radiation temperature, the air flow rate prediction result and the human factor parameter data which influence the thermal comfort level are substituted, and the thermal comfort level distribution condition at the height is calculated according to the thermal comfort level calculation formula. The thermal comfort calculation formula is as follows:
the range of PMV values is PMV ∈ [ -3,3]Wherein M is the metabolic rate, W is the power of human body, pa is the water vapor pressure in the ambient air, ta is the air temperature, fcl is the ratio of the surface area of the human body and the naked body, ts is the average radiation temperature, t cl Average temperature of the outer surface of the body, h, for dressing c Convective heat transfer coefficient.
Specifically, the thermal comfort environment parameter respectively serves as an input parameter through a temperature plane field prediction result at a required height and a single-point temperature value measured by a point type temperature and humidity sensor, a black ball temperature sensor and an air speed sensor, outputs a corresponding air relative humidity, an average radiation temperature and an air flow speed parameter through a CFD simulation result in a human factor internal disturbance correction matrix database, and respectively outputs an air relative humidity field, an average radiation temperature field and an air flow speed field at the required height based on a mapping model between the input and the output established by an ANN artificial neural network algorithm.
Proved by verification experiments, compared with the calculation result of substituting the field measured value, the average error percentage of the thermal comfort prediction result of the thermal comfort prediction module is less than 5%.
Specifically, the embodiment selects a typical library to specifically explain the technical solution. Referring to fig. 1, the working flow steps of the hybrid sensor-based public building temperature and thermal comfort monitoring and predicting system are as follows:
in the test, a single interval of data acquisition and temperature regulation is carried out on the personnel in the library for 15 minutes, and the situation that the personnel in the library are always in a sitting posture state is considered, so that a prediction flow of a temperature field and thermal comfort after 15 minutes at the height of 0.8m is given.
Firstly, laying of a hybrid temperature sensor in the internal space of a library is completed, wherein the hybrid temperature sensor comprises a distributed optical fiber temperature sensor and a sensor module group. The method comprises the following steps of adopting an arrangement strategy of arranging optical fibers at specified heights of a ceiling and peripheral side walls in areas with large space sizes, such as book borrowing areas and student self-service rooms and densely distributed personnel; for areas with compact space sizes and sparsely distributed personnel, optical fibers are arranged from the center of the ceiling to the intersection angle of the ceiling and the peripheral side walls in a square surrounding mode. The arrangement interval of the optical fibers is consistent with the spatial resolution of the optical fiber temperature sensors, the distance between every two optical fiber temperature measurement sites is 1m, every 15 measurement sites are arranged, a sensor module group is arranged near the next measurement site, and each sensor module group comprises a point type temperature and humidity sensor, a black ball temperature sensor and a wind speed sensor. And after debugging is finished, acquiring space temperature data of the optical fiber at the ceiling and the side wall and parameter data measured by each sensor.
And secondly, calling a library type building database established on the basis of the CFD simulation model establishing platform, matching the library type building database with the building space information model, obtaining temperature influence quantization matrixes matched with the building under different heat sources and different personnel distribution conditions, and establishing a special human factor internal disturbance correction matrix database of the building. See fig. 2 a-3 b.
And thirdly, based on the measurement result of the hybrid sensor, firstly, correcting the system error of the optical fiber temperature sensor through an optical fiber temperature sensor error calibration model, dynamically calibrating the calibrated optical fiber measurement temperature point result through a point type temperature and humidity sensor, eliminating temperature abnormal points and obtaining the accurate air temperature at the optical fiber measurement point. And then, image information is acquired through a monitoring system, human head and shoulder features are extracted on the basis of a Matlab platform to determine personnel targets, and personnel in the flow are tracked on the basis of an OpenCV platform, so that flow and distribution information of indoor personnel is obtained.
Fourthly, based on the measurement result of the hybrid sensor, three environment parameters of air relative humidity, average radiation temperature and air flow rate in the environment, which influence thermal comfort, are obtained, further, according to the median of the design temperature of the indoor air conditioning system of rooms with different seasons and different functions in the library (for example, the design temperature of a common reading room in winter is 18-20 ℃, and the median is 19 ℃), the thermal resistance of clothes in the corresponding environment is determined, and according to the typical personnel behavior mode corresponding to the rooms with different functions in the library (for example, the typical personnel behavior mode of the common reading room is reading on a seat, and the corresponding metabolic quantity is 55w/m 2 ) And determining the metabolic quantity of the human body in the corresponding environment.
And fifthly, matching the personnel distribution and flow information obtained in the third step with the human factor internal disturbance correction matrix database established in the second step to obtain corresponding temperature influence quantization matrixes at the ceiling height and 0.8m height. And subtracting the air temperature data at the optical fiber measuring point obtained in the third step from the ceiling height temperature influence quantization matrix to obtain the ceiling height temperature under the condition of no internal disturbance. Then, a corresponding training model is selected according to a distribution mode of the optical fiber by utilizing a support vector machine regression algorithm (SVR), a mapping model between the indication temperature corrected by the distributed optical fiber temperature sensor at the corresponding position and the indication temperature of the temperature sensor arranged at the required height is established, the unmanned factor internal disturbance temperature at the required height is predicted, a predicted value of the unmanned factor internal disturbance temperature at the height of 0.8m after 15 minutes is obtained through calculation, and then the predicted value is added with a temperature influence quantization matrix at the height of 0.8m, so that the temperature at the height of 0.8m after 15 minutes can be accurately predicted. The prediction results show that the average absolute error of the temperature in the area is 0.13 ℃, and the accuracy is good. All temperature points can present real-time temperature field distribution through visual processing. Fig. 4a and 5a are examples of temperature clouds at a height of 0.8m for small and large space deployment strategies, respectively, and fig. 4b and 5b are examples of temperature clouds at a ceiling height for small and large space deployment strategies, respectively.
And sixthly, establishing a mapping relation between the environmental parameters influencing the thermal comfort degree obtained in the fourth step and the temperature field distribution at the required height obtained in the fifth step according to the thermal comfort degree prediction model, and deducing the thermal comfort degree distribution situation at the height after 15 minutes according to the temperature field. The thermal comfort degree of the area is within 0.97 to 1.43, and the average value is 1.28. All the thermal comfort degree values can be output to the air conditioner intelligent operation control system, and data support is provided for achieving air conditioner regional intelligent operation regulation and control.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make various changes in form and details without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A public building temperature and thermal comfort monitoring and predicting system is characterized by comprising a sensor arrangement strategy making module, a human factor internal disturbance correction matrix database module, a temperature measurement space adaptability calibrating module, an empty field temperature and internal disturbance information database module, a thermal comfort parameter database module, an indoor temperature predicting module and a thermal comfort predicting module;
the sensor arrangement strategy making module is used for making a corresponding sensor arrangement strategy according to the spatial characteristics of the building to be tested, and finishing the installation of the distributed optical fiber temperature sensor, the point type temperature and humidity sensor, the black ball temperature sensor and the wind speed sensor in the building to be tested;
the human factor internal disturbance correction matrix database module is used for acquiring a quantization matrix of the influence of different heat sources and different human distribution conditions on the temperature at the ceiling height and the temperature at the required height as the input of the indoor temperature prediction module;
the temperature measurement space adaptability calibration module is used for correcting a system error of temperature measurement result change caused by the change of the working environment of the optical fiber temperature sensor, dynamically calibrating an abnormal value of the temperature of the optical fiber measurement point, and using the abnormal value as the input of the empty field temperature and internal disturbance information database module;
the empty field temperature and internal disturbance information database module is used for integrating the corrected air temperature at the optical fiber measuring point and the information of personnel flow and distribution in the area as the input of the indoor temperature prediction module;
the thermal comfort parameter database module is used for measuring and integrating a thermal comfort environment parameter and a thermal comfort human factor parameter related to the thermal comfort as the input of the thermal comfort prediction module;
the indoor temperature prediction module is used for setting a corresponding machine learning training model according to the spatial characteristics of the building to be tested based on the input data of the human factor internal disturbance correction matrix database module, the empty field temperature and internal disturbance information database module, so as to realize prediction of temperature field distribution at the required height positions of different types of spaces;
the thermal comfort degree prediction module is used for establishing temperature according to the prediction result of the temperature field distribution at the required height and combining the environmental parameter and the human factor parameter data related to the thermal comfort degree, and deducing the thermal comfort degree distribution condition of the plane at the required height.
2. The public building temperature and thermal comfort monitoring and predicting system according to claim 1, wherein in the sensor arrangement strategy making module, an internal space of a building to be tested is subjected to modularization treatment, and two different sensor arrangement strategies are made by considering the size and the number of accommodated personnel of each space module in the building to be tested: for the space module with large size, arranging the optical fibers at the specified height of the ceiling and the peripheral side walls, and for the space module with small size, arranging the optical fibers in a square surrounding manner from the center of the ceiling to the intersection angle of the ceiling and the peripheral side walls along the specified laying distance; each space module is formed by connecting the same optical fiber temperature measuring system in series with the same optical fiber, in two arrangement strategies, the laying distance of the optical fiber is kept consistent with the spatial resolution of the optical fiber temperature sensor, and the optical fiber is used for measuring and collecting space temperature information; building a building space information model according to the collected space temperature information, and meanwhile, according to the space characteristics of a building to be measured, setting a sensor module group along each optical fiber passing through a fixed number of optical fiber measurement sites, wherein each sensor module group comprises a point type temperature and humidity sensor, a black ball temperature sensor and an air velocity sensor, the point type temperature and humidity sensor is used for measuring the relative humidity and the air temperature in the environment, and the dynamic calibration of the measurement result of the optical fiber temperature sensor is carried out subsequently; the black ball temperature sensor is used for measuring the radiation temperature in the environment; the wind speed sensor is used to measure the air flow rate in the environment.
3. The public building temperature and thermal comfort monitoring and predicting system according to claim 2, wherein in the human factor internal disturbance correction matrix database module, based on the spatial attributes of the plane arrangement and the spatial dimension of a building to be detected, based on a building spatial information model and a fluid dynamics computing platform, an indoor thermal environment is simulated, quantized results of temperature influences of different heat sources and different personnel distribution conditions at different heights are obtained, temperature influence quantized matrices corresponding to different positions and different heights are established, and a human factor internal disturbance correction matrix database is obtained after integration.
4. The public building temperature and thermal comfort monitoring and predicting system according to claim 1, wherein in the temperature measurement space adaptability calibration module, building space adaptability calibration is performed on the temperature measured by the optical fiber based on an optical fiber temperature sensor error calibration model; and calculating the temperature difference of the point type temperature and humidity sensors closest to the temperature of the optical fiber measuring points along the optical fiber arrangement direction and the variation rate of the historical temperature data of the previous 5 times measured by the corresponding point type temperature and humidity sensors for all the calibrated optical fiber measuring points, treating the corresponding optical fiber measuring points as temperature abnormal points if the difference value exceeds 2 ℃, re-calibrating by calling an optical fiber temperature sensor error calibration model for the temperature abnormal points, and continuously completing the test after automatic re-calibration, wherein the dynamic calibration runs through the whole process of measuring the indoor air temperature by each sensor.
5. The public building temperature and thermal comfort monitoring and predicting system according to claim 4, wherein the optical fiber temperature sensor error calibration model is a field calibration model for monitoring indoor air temperature measurement errors of the public building synchronously with the optical fiber temperature sensor by using a temperature sensor with a resolution of 0.01 ℃ and is established based on an SVR algorithm, and is used for reducing the indoor air temperature measurement errors of the optical fiber temperature sensor.
6. A public building temperature and thermal comfort monitoring and prediction system according to claim 1,
in the empty field temperature and internal disturbance information database module, the indoor air temperature is collected based on an optical fiber temperature sensor, and the temperature information at the optical fiber measuring point is obtained by a temperature measuring space adaptability calibration module; the method comprises the steps that indoor image information is collected regularly by using a monitoring system in a building space, a person target is detected through a Gaussian mixture modeling method, a human body is identified through a BP neural network, and moving person tracking is achieved through a Meanshift algorithm, so that information of person flowing and distribution is obtained; and after integration, establishing a temperature and internal disturbance information database as the input of a subsequent indoor temperature prediction module.
7. A public building temperature and thermal comfort monitoring and prediction system according to claim 1,
in the thermal comfort parameter database module, the thermal comfort environment parameters comprise air temperature, air relative humidity, average radiation temperature and air flow rate; thermal comfort parameters include: thermal resistance of clothes of indoor personnel and metabolism of indoor personnel; on one hand, according to a behavior mode and an operation temperature corresponding to indoor space functions of a building to be tested, corresponding indoor personnel metabolism amount and indoor personnel clothes thermal resistance are determined; on the other hand, three parameters of air relative humidity, average radiation temperature and air flow rate in the environment are measured through the sensor module group, and a thermal comfort parameter database is established after integration and is used as the input of a subsequent thermal comfort prediction module.
8. A public building temperature and thermal comfort monitoring and prediction system according to claim 1,
in the indoor temperature prediction module, based on the empty field temperature and the real-time personnel information provided by the empty field temperature and internal disturbance information database module, matching with a matrix provided by a human factor internal disturbance correction matrix database module, and selecting a ceiling height temperature influence quantization matrix closest to the current environment and a required height temperature influence quantization matrix; the influence of the man-caused internal disturbance on temperature measurement is stripped by using a ceiling height temperature influence quantization matrix, and the temperature at the ceiling height without the man-caused internal disturbance is obtained; setting a corresponding training model by using a machine learning algorithm according to an arrangement strategy of the sensors to realize preliminary prediction of the temperature of the unmanned aerial vehicle at the required height; and superposing the preliminary prediction result and the corresponding temperature influence quantization matrix at the required height to obtain an accurate prediction result of the temperature plane field at the required height.
9. A public building temperature and thermal comfort monitoring and prediction system according to claim 8,
in the thermal comfort degree prediction module, based on an accurate prediction result of a temperature plane field at a required height, combining environmental parameter data which are provided by the thermal comfort parameter database module and influence thermal comfort degree under a corresponding environment, establishing a mapping relation between thermal comfort parameters and temperature on the required height plane, and calculating the distribution condition of the thermal comfort degree at the corresponding height according to a thermal comfort degree calculation formula by inputting the thermal comfort environmental parameter prediction result and human factor parameter data influencing the thermal comfort degree; the thermal comfort calculation formula is as follows:
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