CN110081993B - Space temperature visual monitoring system and construction method thereof - Google Patents
Space temperature visual monitoring system and construction method thereof Download PDFInfo
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- CN110081993B CN110081993B CN201910329022.5A CN201910329022A CN110081993B CN 110081993 B CN110081993 B CN 110081993B CN 201910329022 A CN201910329022 A CN 201910329022A CN 110081993 B CN110081993 B CN 110081993B
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Abstract
The invention discloses a visual space temperature monitoring system which comprises a plurality of temperature acquisition terminal nodes, wherein the temperature acquisition terminal nodes are wirelessly connected with a Zigbee wireless receiving terminal, the Zigbee wireless receiving terminal is connected with a computer system, and the computer system is connected with a data sharing module; a construction method of the space temperature visual monitoring system is also disclosed. The method based on the combination of various spatial interpolation algorithms is used for spatial interpolation, IDW interpolation of an X-Y plane and cubic spline interpolation in the Z-axis direction, so that the interpolation accuracy is improved, the interpolation difficulty is reduced, and the interpolation efficiency is improved. The algorithm of the sensor optimization distribution model is characterized in that the temperature data are selected, the selected characteristic values are used for expressing the temperature information of the whole space on the premise of ensuring the accuracy of monitoring results, the using number of the sensors is reduced, the advantages of visual real-time dynamic display, data sharing and the like are integrated, and the algorithm is simple to operate, low in power consumption, high in stability and reliable in results.
Description
Technical Field
The invention belongs to the technical field of measurement, relates to a visual space temperature monitoring system and further relates to a construction method of the visual space temperature monitoring system.
Background
Temperature is used as an important parameter for environmental monitoring, and high-precision monitoring on temperature is required in various industries such as food and medicine, life science, aerospace chemical engineering and the like. The temperature monitoring plays a vital role in ensuring the product quality, improving the production efficiency, saving energy, realizing safe production, promoting the national economic development and the like. Various large-scale production enterprises implement informatization and intelligent reformation on the traditional production mode, continuously enlarge the automatic production scale, and put higher requirements on indexes such as precision, integration level, temperature visualization, reliability and the like of a temperature monitoring system in production under the situation. Therefore, the method and the device for measuring the temperature have important significance.
Most of the temperature monitoring systems which are disclosed at present in China at present are combined with a micro-processing system, so that high-precision intelligent detection is realized. The invention discloses a ZigBee-based warehouse temperature monitoring system, which is provided with the application number of CN201611098544.1 and is named as a ZigBee-based warehouse temperature monitoring system, and mainly aims to provide the ZigBee-based warehouse temperature monitoring system which is simple to manufacture and convenient to use. The invention discloses an intelligent wireless monitoring system, which is provided with the application number of CN200610096518.5 and is named as an intelligent wireless temperature monitoring system, and mainly aims to effectively monitor and regulate the temperature change conditions of all-weather and real-time connection parts of power equipment, particularly high-voltage power equipment, realize automatic monitoring of the operation of a power grid and guarantee the safety of the operation of the power grid. The patent with the application number of CN201810416849.5 and the name of 'an intelligent temperature measurement and visualization system' discloses a sole skin temperature monitoring system, which carries out two-dimensional visual display on collected temperature data in a wired connection mode.
The temperature monitoring devices provided by the three patent documents have one or two functional advantages, and the first invention patent has the characteristics of wireless acquisition and real-time monitoring; the second invention patent has the characteristics of high precision and wireless acquisition; the third invention patent mainly realizes the two-dimensional visual display of the temperature data. The existing temperature monitoring systems do not relate to three-dimensional temperature visualization, data network sharing and how temperature sensors are arranged. Therefore, how to design a set of temperature monitoring system integrating functions of wireless data transmission, high-precision measurement, three-dimensional visual real-time dynamic display of temperature data, data sharing and the like is very necessary nowadays. Meanwhile, the research on the sensor optimization distribution model can reliably reduce the use number of the temperature sensors on the premise of ensuring the measurement precision, save the experiment cost and be beneficial to the subsequent maintenance of the system.
Disclosure of Invention
The invention aims to provide a visual space temperature monitoring system, which realizes the functions of realizing the visualization of discrete data and three-dimensional dynamic monitoring of the temperature change of a detected environment while realizing high-precision real-time remote monitoring.
The invention further aims to provide a construction method of the space temperature visual monitoring system.
The first technical scheme adopted by the invention is that the visual space temperature monitoring system comprises a plurality of temperature acquisition terminal nodes, wherein the temperature acquisition terminal nodes are wirelessly connected with a Zigbee wireless receiving terminal, the Zigbee wireless receiving terminal is connected with a computer system through a USB/RS485 patch cord, and the computer system is connected with a data sharing terminal;
the temperature acquisition terminal node comprises a Pt100 temperature sensor, a temperature acquisition module and a Zigbee wireless transmitting end which are connected in sequence and is used for acquiring temperature;
the Zigbee wireless receiving end is used for receiving data of the temperature acquisition terminal node;
the computer system consists of a sensor optimization distribution model algorithm, a discrete data visualization processing module and a visualization terminal interface; the sensor optimization distribution model algorithm is used for initializing the temperature monitoring system, the discrete data visualization processing module is used for reasonably calculating the temperature data of an unknown area according to the known temperature data, and the visualization of the discrete temperature data is realized by the visualization terminal interface;
the data sharing end is used for different users to access data and is used for remote control to monitor the temperature in real time.
The invention is characterized in that:
the temperature acquisition module converts input analog quantity into digital quantity through a 24-bit A/D converter and controls acquisition of temperature data through an STM32 controller.
The discrete data visualization processing module performs spatial interpolation by utilizing a combination of various spatial interpolation algorithms, and relates to an IDW interpolation algorithm and a cubic spline interpolation algorithm, wherein the specific algorithms are as follows:
firstly, carrying out X-Y plane interpolation, dividing the space into n layers, and carrying out interpolation on each layer by adopting an IDW interpolation algorithm, wherein the method comprises the following steps:
suppose there are S sensor measurement points in the test model domain to collect data (x)ij，yij，zij，vij) Wherein i represents a point in each layer, and the value range is i ═ 1,2, … N; j represents a layer in space, and the value range is j ═ 1,2, … n; wherein (x)ij，yij，zij) Spatial coordinates, v, representing the ith discrete data point of layer jijRepresenting the ith discrete measurement point temperature value of the jth layer. (x, y, z) represents the coordinates of the point to be interpolated;
if (x, y, z) ≠ xij,yij,zij) (i ═ 1,2, …, N), then the inverse distance weighted interpolation formula is:
where m is the number of interpolated discrete points, ui′jThe temperature value representing the ith' pre-interpolation point in the jth layer, a is the weight in the weighting method, DijThe distance of the selected ith discrete point from the interpolation unit node;
when the function value f (x, y, z) is ui′jIf (x, y, z) ═ xij,yij,zij) (i is 1,2, …, N), and the function value is f (x, y, z) which is the measured value f (x, y, z) of the coordinate position knownij,yij,zij) (i is 1,2, …, N), mapping interpolation of other spatial grid unit nodes and the like, and completing mapping interpolation of N layers of grid unit nodes in the space;
interpolating the Z-axis direction by adopting a cubic spline interpolation method according to the obtained temperature values of the n layers of X-Y planes in the space;
the involved algorithm is as follows: is provided with [ a, b]There are interpolation nodes, a ═ ui1<ui2<…<uinB, wherein uijRepresenting a temperature value at an ith point in the jth layer; s (u)ij) Is a polynomial of not more than three degrees and is in [ a, b ]]Has a second order continuous derivative set as:
S(uij)＝aiuij 3+biuij 2+ciuij+di(i＝1,2…m,j＝1,2,…,n)
wherein a isi，bi，ci，diPending and satisfying:
S(uij)，S(uij-0)＝S(uij+0),(j＝2,…,n-1)
S'(uij-0)＝S'(uij+0),S”(uij-0)＝S”(uij+0),(j＝2,…,n-1)
depending on the 4n-6 conditions, 4(n-1) coefficients,determining a cubic interpolation function; coefficient a obtained by a third type of boundary conditioni，bi，ci，diFurther, a cubic spline interpolation function S (u) is obtainedij) Acquiring a temperature value to be interpolated in the vertical direction; obtaining temperature values of all coordinate positions in the space by space interpolation in the measurement region; each temperature data is converted from red to blue from large to small, so that the temperature change condition in the space is represented by colors, and the visualization of discrete data is realized.
The invention adopts another technical scheme that a construction method of a space temperature visual monitoring system is implemented according to the following steps:
step 1: acquiring a simulation result of the temperature field of the detected environment through temperature simulation software;
step 2: obtaining the distributed coordinate position of the space sensor by the simulation result obtained in the step 1 through a sensor optimization distribution model algorithm;
and step 3: arranging sensors in the environment to be measured according to the coordinate positions of the sensors obtained in the step (2), wherein each sensor is used as a temperature acquisition terminal node;
and 4, step 4: starting a temperature measuring system, establishing wireless communication connection between the temperature acquisition terminal node in the step 3 and a Zigbee wireless receiving terminal, and acquiring temperature to obtain temperature data;
and 5: and (4) processing the temperature data obtained in the step (4) by a discrete data visualization processing module, and monitoring and controlling the temperature distribution condition of the detected environment through a visualization terminal interface.
The invention is also characterized in that:
the temperature simulation software in the step 1 is ANSYS.
The sensor optimization distribution model algorithm of step 2 selects the characteristic values of the redundancy existing in the temperature data according to a data compression method, and expresses the temperature information of the whole space by using the selected characteristic values on the premise of ensuring the accuracy of the monitoring result; the sensor optimization distribution model algorithm is to obtain coordinate position parameters of spatial sensor distribution, and the related specific algorithm is as follows:
Feture values1＝max(T)∪min(T)
wherein T is a set of temperature data arrays, and max (T) and min (T) represent the maximum value and the minimum value of the set of temperature data, respectively. The feature values1 and 2 respectively represent a set of sets of feature values;
let TCIs the maximum value, T, in the set of temperature data(qi)The maximum step length is 1, and the temperature values at the upper, lower, left and right positions are respectively: q1, q2, q3, q 4; k is a set threshold value, namely the precision requirement; if above T(qi)If present, if:
|T(qi)-TC|>k,i＝1,2,3,4
then get T(qi)Data for the eigenvalue set 2:
Feture values2＝T(qi),i＝1,2,3,4
and further obtaining a set of all characteristic values:
Feture values＝Feture values1∪Feture values2
according to the one-to-one mapping relationship between the temperature data in the space and the coordinate positions where the temperature data are located, namely:
F(x,y,z)＝t，t∈Feture values
f (x, y, z) represents a coordinate parameter corresponding to the characteristic value; and carrying out error analysis on an interpolation result obtained by substituting the characteristic value and the coordinate parameter thereof into a plurality of spatial interpolation algorithm combinations and an actually measured temperature data result, wherein the characteristic value and the coordinate parameter thereof are a sensor optimal distribution model result if the accuracy requirement is met, and continuously carrying out characteristic value selection until the accuracy requirement is met if the accuracy requirement is not met.
The discrete data visualization processing module in the step 5 is developed based on an MFC dialog box in a Microsoft Visual Studio 2010 software platform, and has the functions of serial port/TCP communication, historical data storage, real-time display of temperature data of each path, and display and input of the coordinate position of a sensor; and the discrete data visualization is realized by calling MATLAB software for operation in real time and performing color rendering on the temperature data.
The invention has the beneficial effects that:
(1) the visual timeliness is higher. The spatial interpolation is carried out in dimensionality by adopting a spatial combination interpolation algorithm, so that the interpolation difficulty is reduced, the operation speed is increased, and the three-dimensional spatial temperature distribution diagram is dynamically displayed. And the interpolation precision is improved by combining the advantages of each interpolation algorithm.
(2) The number of sensors used is reduced. The sensor optimization distribution model can reduce the use number of the sensors on the premise of ensuring the accuracy, save the economic cost and be beneficial to system maintenance.
(3) And (4) sharing data. The system realizes real-time monitoring at any time and any place, and breaks the limit of objective factors such as the traditional distance measurement.
(4) The whole system is simple to operate, good in stability and low in power consumption.
Drawings
FIG. 1 is a schematic structural diagram of a visual space temperature monitoring system according to the present invention;
fig. 2 is a visual terminal interface of a visual space temperature monitoring system according to the invention.
In the figure, 1, a Pt100 temperature sensor, 2, a temperature acquisition module, 3, a ZigBee wireless transmitting end, 4, a temperature acquisition terminal node, 5, a ZigBee wireless receiving end, 6, a USB/RS485 patch cord, 7, a computer system, 8, a data sharing end, 9, a user A, 10, a user B, 11 and a user C are arranged.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a visual space temperature monitoring system, which comprises a plurality of temperature acquisition terminal nodes 4, a Zigbee wireless receiving end 5, a USB/RS485 patch cord 6, a computer system 7 and a data sharing end 8, as shown in figure 1. The temperature acquisition terminal node 4 is wirelessly connected with the Zigbee wireless receiving terminal 5, and the Zigbee wireless receiving terminal 5 is connected with the computer system 6 through a USB/RS485 patch cord 6; the temperature acquisition terminal node 4 consists of a Pt100 temperature sensor 1, a temperature acquisition module 2 and a Zigbee wireless transmitting end 3 which are connected in sequence and is used for acquiring temperature; a Zigbee wireless receiving terminal 5 receives data of the temperature acquisition terminal node 4; the USB/RS485 patch cord 6 transmits the temperature data received by the Zigbee wireless receiving end 5 to the computer system 7; the computer system 7 consists of a sensor optimized distribution model algorithm, a discrete data visualization processing module and a visualization terminal interface, wherein the sensor optimized distribution model algorithm is used for initializing the temperature monitoring system, the discrete data visualization processing module is used for reasonably deducing the temperature data of an unknown area according to the known temperature data, and the visualization of the discrete temperature data is realized by the visualization terminal interface; the data sharing end 8 is used for accessing data, and the user A9 can remotely control and monitor the temperature in real time through a computer, a user B10 through a mobile phone and a user C11 through a Pad.
The temperature acquisition module 2 converts input analog quantity into digital quantity through a 24-bit A/D converter and controls acquisition of temperature data through an STM32 controller.
The discrete data visualization processing module is realized by combining a plurality of spatial interpolation algorithms (IDW (inverse distance weighted) interpolation and cubic spline interpolation), and the specific interpolation algorithm is as follows:
after the system initialization is completed, firstly, X-Y plane interpolation is carried out, the space is divided into n layers, and interpolation is carried out on each layer by adopting an IDW interpolation algorithm. The specific interpolation process is as follows:
suppose there are S sensor measurement points in the test model domain to collect data (x)ij，yij，zij，vij) Wherein i represents a point in each layer, and the value range is i ═ 1,2, … N; j represents a layer in space, and the value range is j ═ 1,2, … n; wherein (x)ij，yij，zij) Spatial coordinates, v, representing the ith discrete data point of layer jijRepresenting the ith discrete measurement point temperature value of the jth layer. (x, y, z) represents the coordinates of the point to be interpolated;
if (x, y, z) ≠ xij,yij,zij) (i ═ 1,2, …, N), then the inverse distance weighted interpolation formula is:
where m is the number of interpolated discrete points, ui′jThe temperature value representing the ith' pre-interpolation point in the jth layer, a is the weight in the weighting method, DijThe distance of the selected ith discrete point from the interpolation unit node;
when the function value f (x, y, z) is ui′jIf (x, y, z) ═ xij,yij,zij) (i is 1,2, …, N), and the function value is f (x, y, z) which is the measured value f (x, y, z) of the coordinate position knownij,yij,zij) (i is 1,2, …, N), mapping interpolation of other spatial grid unit nodes and the like, and completing mapping interpolation of N layers of grid unit nodes in the space;
and on the basis of the obtained temperature values of the n layers of X-Y planes in the space, performing interpolation in the Z-axis direction by adopting a cubic spline interpolation method. The specific interpolation process is as follows:
is provided with [ a, b]There are interpolation nodes, a ═ ui1<ui2<…<uinB, wherein uijRepresenting a temperature value at an ith point in the jth layer; s (u)ij) Is a polynomial of not more than three degrees and is in [ a, b ]]Has a second order continuous derivative set as:
S(uij)＝aiuij 3+biuij 2+ciuij+di(i＝1,2…m,j＝1,2,…,n)
wherein a isi，bi，ci，diPending and satisfying:
S(uij)，S(uij-0)＝S(uij+0),(j＝2,…,n-1)
S'(uij-0)＝S'(uij+0),S”(uij-0)＝S”(uij+0),(j＝2,…,n-1)
a total of 4(n-1) coefficients are given above for a total of 4n-6 conditions, n +3 (n-2). Thus, in addition to the 2 boundary conditions, the cubic interpolation function can be uniquely determined. The system uses a first type of boundary condition and a second type of boundary condition.
The coefficient a in the formula can be obtained according to the abovei，bi，ci，diFurther, a cubic spline interpolation function S (u) is obtainedij) Then passing a set of temperature values u corresponding to each layerijAnd obtaining the temperature value to be interpolated in the vertical direction. In the system, interpolation is carried out on three planes in the vertical direction, namely n is 3.
At this time, the spatial interpolation in the measurement area is completed, and the temperature values of all coordinate positions in the space are obtained. The default step size is one and the coordinates are integers. Each temperature data is converted from red to blue from large to small, so that the temperature change condition in the space is represented by colors, and the visualization of discrete data is realized.
The invention adopts another technical scheme that a construction method of a space temperature visual monitoring system is implemented according to the following steps:
step 1: acquiring a simulation result of the temperature field of the detected environment through temperature simulation software;
step 2: obtaining the distributed coordinate position of the space sensor by the simulation result obtained in the step 1 through a sensor optimization distribution model algorithm;
and step 3: arranging sensors in the environment to be measured according to the coordinate positions of the sensors obtained in the step (2), wherein each sensor is used as a temperature acquisition terminal node 4;
and 4, step 4: starting a temperature measuring system, establishing wireless communication connection between the temperature acquisition terminal node 4 in the step 3 and a Zigbee wireless receiving terminal 5, and acquiring temperature to obtain temperature data;
and 5: and (4) processing the temperature data obtained in the step (4) by a discrete data visualization processing module, and monitoring and controlling the temperature distribution condition of the detected environment through a visualization terminal interface.
The working principle of the system is as follows: after the system is started, the microcontroller STM32 in the temperature acquisition module 2 controls the temperature sensor 1 to acquire temperature, and then performs signal conversion in the temperature acquisition module 2. In order to obtain high-precision temperature measurement, the system adopts the PT100 analog quantity temperature sensor 1, so that differential signals need to be input into a 24-bit A/D converter in the temperature acquisition module 2 for analog-to-digital conversion, and then a microcontroller STM32 inputs the converted digital signals and sends the digital signals to a ZigBee wireless receiving end 5 through a wireless sending end 3. The ZigBee wireless receiving end 5 is in communication connection with a computer system 7 through a USB/RS485 patch cord 6, transmits the received temperature signal to the computer system 7, and performs discrete data visualization processing and display on the received temperature data. Different users can monitor the temperature in real time through the data sharing terminal 8.
The sensor optimization distribution model algorithm is mainly based on the idea that data redundancy exists in data compression, characteristic values of the data are screened, and a small number of characteristic values are used for expressing integral data information. The sensor optimal distribution model algorithm mainly requires finding out space coordinate parameters of optimal distribution of space sensors, and the related specific algorithm is as follows:
Feture values1＝max(T)∪min(T)
wherein T is a set of temperature data arrays, max (T) and min (T) respectively represent the maximum value and the minimum value in the set of temperature data, Feture values1 represent a set of characteristic values, T is a set of characteristic valuesCIs the maximum value, T, in the set of temperature data(qi)The maximum step length is 1, and the temperature values at the upper, lower, left and right positions are respectively: q1, q2, q3, q 4. k is a set threshold value, namely the precision requirement. If above T(qi)If present, if:
|T(qi)-TC|>k,i＝1,2,3,4
then get T(qi)Data for the eigenvalue set 2:
Feture values2＝T(qi),i＝1,2,3,4
and further obtaining a set of all characteristic values:
Feture values＝Feture values1∪Feture values2
according to the one-to-one mapping relationship between the temperature data in the space and the coordinate positions where the temperature data are located, namely:
F(x,y,z)＝t，t∈Feture values
thereby obtaining the coordinate parameters corresponding to the characteristic values. And carrying out error analysis on an interpolation result obtained by substituting the characteristic value and the coordinate parameter thereof into a plurality of spatial interpolation algorithm combinations and an actually measured temperature data result, wherein the characteristic value and the coordinate parameter thereof are a sensor optimal distribution model result if the accuracy requirement is met, and continuously carrying out characteristic value selection until the accuracy requirement is met if the accuracy requirement is not met.
The present invention is a terminal operation interface developed based on the MFC dialog in the Microsoft Visual Studio 2010 software platform, as shown in FIG. 2. And the real-time communication with a lower computer is realized by utilizing an RS485 serial port communication protocol. The discrete data visualization processing module is provided with a human-computer friendly interface and mainly comprises a communication module, a data acquisition module, a data processing module, a data storage module and a sensor position display module. The communication module comprises two communication modes of serial communication and TCP communication, and the invention uses serial communication. The data acquisition module continuously sends a temperature reading instruction to the lower computer according to a defined serial port communication protocol, so that the lower computer sends the temperature acquired in real time to the upper computer. The data processing module is mainly used for operating the above-mentioned various spatial interpolation algorithm combinations and the sensor optimization distribution model algorithm in MATLAB by calling MATLAB software, and performing color rendering of temperature data so as to realize visualization of discrete data. The data storage module can store the collected data as txt document through self-defining storage positions. The sensor position display module can display the result of the sensor optimization distribution model, and three-dimensionally displays the specific position of the sensor in spatial distribution. The data sharing is realized through a remote access technology, the result displayed on a human-computer friendly interface can be monitored and controlled at any time, and the real-time remote temperature monitoring is realized.
The real-time space temperature distribution monitoring and visualization system is verified to be high in stability, long-time measurement can be achieved, the temperature measurement precision can reach 0.1 ℃, and the system is suitable for environments with small temperature changes, such as a metering room, a precision experiment center and the like. Compared with the traditional temperature monitoring system, the three-dimensional temperature real-time dynamic display system can realize the real-time dynamic display of the three-dimensional space temperature, reliably reduce the using number of the sensors, save the cost, be beneficial to the subsequent maintenance of the system and realize the high-precision measurement at the same time.
Claims (2)
1. A visual space temperature monitoring system is characterized by comprising a plurality of temperature acquisition terminal nodes, wherein the temperature acquisition terminal nodes are wirelessly connected with a Zigbee wireless receiving terminal, the Zigbee wireless receiving terminal is connected with a computer system through a USB/RS485 patch cord, and the computer system is connected with a data sharing terminal;
the temperature acquisition terminal node (4) comprises a Pt100 temperature sensor (1), a temperature acquisition module (2) and a Zigbee wireless transmitting end (3) which are sequentially connected and is used for acquiring temperature;
the Zigbee wireless receiving end (5) receives data of the temperature acquisition terminal node (4);
the computer system (7) is composed of a sensor optimized distribution model algorithm, a discrete data visualization processing module and a visualization terminal interface, the sensor optimized distribution model algorithm is used for initializing the temperature monitoring system, the discrete data visualization processing module is used for reasonably calculating the temperature data of an unknown area according to the known temperature data, and the visualization terminal interface realizes the visualization of the discrete temperature data;
the data sharing end (8) is used for different users to access data and is used for remotely controlling and monitoring the temperature in real time;
the discrete data visualization processing module of the computer system (7) performs interpolation by using a spatial combination interpolation algorithm, wherein the specific algorithm is as follows:
firstly, carrying out X-Y plane interpolation, dividing the space into n layers, and carrying out interpolation on each layer by adopting an IDW interpolation algorithm, wherein the method comprises the following steps:
suppose there are S sensor measurement points in the test model domain to collect data (x)ij，yij，zij，vij) Wherein i represents a point in each layer, and the value range is i ═ 1,2, … N; j represents a layer in space, and the value range is j ═ 1,2, … n; wherein (x)ij，yij，zij) Space representing ith discrete data point of jth layerCoordinates, vijRepresenting the temperature value of the ith discrete measuring point of the jth layer, (x, y, z) representing the coordinate of the point to be interpolated;
if (x, y, z) ≠ xij,yij,zij) (i ═ 1,2, …, N), then the inverse distance weighted interpolation formula is:
where m is the number of interpolated discrete points, ui′jThe temperature value representing the ith' pre-interpolation point in the jth layer, a is the weight in the weighting method, DijThe distance of the selected ith discrete point from the interpolation unit node;
when the function value f (x, y, z) is ui′jIf (x, y, z) ═ xij,yij,zij) (i is 1,2, …, N), and the function value is f (x, y, z) which is the measured value f (x, y, z) of the coordinate position knownij,yij,zij) (i is 1,2, …, N), mapping interpolation of other spatial grid unit nodes and the like, and completing mapping interpolation of N layers of grid unit nodes in the space;
interpolating the Z-axis direction by adopting a cubic spline interpolation method according to the obtained temperature values of the n layers of X-Y planes in the space;
the involved algorithm is as follows: is provided with [ a, b]There are interpolation nodes, a ═ ui1<ui2<…<uinB, wherein uijRepresenting a temperature value at an ith point in the jth layer; s (u)ij) Is a polynomial of not more than three degrees and is in [ a, b ]]Has a second order continuous derivative set as:
S(uij)＝aiuij 3+biuij 2+ciuij+di(i＝1,2…m,j＝1,2,…,n)
wherein a isi，bi，ci，diPending and satisfying:
S(uij)，S(uij-0)＝S(uij+0),(j＝2,…,n-1)
S'(uij-0)＝S'(uij+0),S”(uij-0)＝S”(uij+0),(j＝2,…,n-1)
determining a cubic interpolation function according to 4(n-1) coefficients and 4n-6 conditions (n +3 (n-2)); coefficient a obtained by a third type of boundary conditioni，bi，ci，diFurther, a cubic spline interpolation function S (u) is obtainedij) Acquiring a temperature value to be interpolated in the vertical direction; obtaining temperature values of all coordinate positions in the space by space interpolation in the measurement region; each temperature data is converted from red to blue from large to small, so that the temperature change condition in the space is represented by colors, and the visualization of discrete data is realized.
2. The construction method of the visual space temperature monitoring system as claimed in claim 1 is implemented by the following steps:
step 1: acquiring a simulation result of the temperature field of the detected environment through temperature simulation software;
step 2: obtaining the distributed coordinate position of the space sensor by the simulation result obtained in the step 1 through a sensor optimization distribution model algorithm;
and step 3: arranging sensors in the environment to be measured according to the coordinate positions of the sensors obtained in the step (2), wherein each sensor is used as a temperature acquisition terminal node (4);
and 4, step 4: starting a temperature measuring system, establishing wireless communication connection between the temperature acquisition terminal node (4) in the step (3) and a Zigbee wireless receiving end (5), and acquiring temperature to obtain temperature data;
and 5: the discrete data visualization processing module processes the temperature data obtained in the step 4, and monitors and controls the temperature distribution condition of the detected environment through a visualization terminal interface;
the sensor optimization distribution model algorithm in the step 2 selects the characteristic values of the redundancy in the temperature data according to a data compression method, and expresses the temperature information of the whole space by using the selected characteristic values on the premise of ensuring the accuracy of the monitoring result; the sensor optimization distribution model algorithm is to obtain coordinate position parameters of spatial sensor distribution, and the related specific algorithm is as follows:
Feture values1＝max(T)∪min(T)
wherein T is a set of temperature data arrays, and max (T) and min (T) respectively represent the maximum value and the minimum value in the set of temperature data; the feature values1 and 2 respectively represent a set of sets of feature values; t isCIs the maximum value, T, in the set of temperature data(qi)The maximum step length is 1, and the temperature values at the upper, lower, left and right positions are respectively: q1, q2, q3, q 4; k is a set threshold value, namely the precision requirement; if above T(qi)If present, if:
|T(qi)-TC|>k,i＝1,2,3,4
Feture values2＝T(qi),i＝1,2,3,4
Feture values＝Feture values1∪Feture values2
according to the one-to-one mapping relationship between the temperature data in the space and the coordinate positions where the temperature data are located, namely:
F(x,y,z)＝t，t∈Feture values
f (x, y, z) represents a coordinate parameter corresponding to the characteristic value; and carrying out error analysis on an interpolation result obtained by substituting the characteristic value and the coordinate parameter thereof into a plurality of spatial interpolation algorithm combinations and an actually measured temperature data result, wherein the characteristic value and the coordinate parameter thereof are a sensor optimal distribution model result if the accuracy requirement is met, and continuously carrying out characteristic value selection until the accuracy requirement is met if the accuracy requirement is not met.
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| CN112512267B (en) * | 2020-11-11 | 2022-12-23 | 中国建设银行股份有限公司 | Temperature monitoring method, system, equipment and storage medium based on mobile robot |
| CN112612305B (en) * | 2020-12-04 | 2022-04-08 | 格力电器（武汉）有限公司 | Temperature adjusting method, device, equipment, storage medium and air conditioning system |
| CN112855410B (en) * | 2021-01-20 | 2021-11-30 | 昆明理工大学 | Hydraulic generator temperature field visual monitoring system and construction method thereof |
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